Table of contents

i

ii

Article No. Contents Page No.Acknowledgements xiii

List of Abbreviations xvii

Abstract xix

Chapter 1

Introduction 1

1.1 General Introduction 1

1.2 Water Quality 5

1.2.1 Climatic Factors 5

1.2.2 Physico-Chemical Parameters 6

1.2.2.1 Physical Parameters 6

1.2.2.2 Chemical Parameters 8

1.2.3 Biological Parameters 11

1.2.3.1 Planktonic Biodiversity 11

1.3 Fish Biodiversity 15

1.4 Fish Morphometry 18

1.5 Aims and Objectives 22

Chapter 2

Materials and Methods 23

2.1 Study Area 23

Figure 2.1 Location map of hill torrents of Suleman Mountain Range, Dera Ghazi Khan, Pakistan

24

Table 2.1 Names and location of sampling sites 27

Table 2.2 Vegetation and soil types of water sampling sites 28

2.2 Water Quality 29

Table 2.3 Dates and weather conditions of each sampling site 30

2.2.1 Climatic Factors 31

2.2.1. a Air Temperature (oC) 31

2.2.1. b Photoperiod (hours) 31

2.2.1. c Humidity (%) 31

Article No. Contents Page No.2.2.2 Physico-Chemical Parameters 31

2.2.2.1 Physical Parametrs 31

2.2.2.1 a Water Temperature (oC) 31

2.2.2.1 b Light Penetration (cm) 31

2.2.2.1 c Total Dissolved Solids (mgL-1) 32

2.2.2.2 Chemical Parametrs 33

2.2.2.2 a pH 33

2.2.2.2 b Electrical Conductivity (dSm-1) 33

2.2.2.2 c Dissolved Oxygen (mgL-1) 33

2.2.2.2 d Free Carbon dioxide (mgL-1) 34

Dedication

To

My Late Loving Mother and My beloved wife

My daughters and my son

iii

Acknowledgements

I would like to pay all my praises to AL-MIGHTY ALLAH, the most Merciful, the most

Beneficent and The Holly Prophet, HAZRAT MOHAMMAD (Peace BeUpon Him) who

showed the right direction and path to humanity and enabled us to recognize our

Creator.

I can not express my gratitude enough to my supervisor Prof. Dr. Muhammad Ali,

Vice Chancellor, Government College University, Faisalabad. It was an absolute

privilege and honour to work under the supervision of Prof. Dr. Muhammad Ali, who

is both exceptional teacher and caring supervisor. As with my Ph.D. the last few years

have been a roller coaster; thankfully with many many more highs than lows, mostly

due to Prof. Dr. Muhammad Ali. If it wasn’t for you I may never have entered the

world of Aquatic Biodiversity, Freshwater fish biodiversity and fish morphometry. For

the last few years you have taken me under your wing, have always shared your

knowledge freely and have allowed me for the completion of my research work. I can

safely say that I feel like I have been your apprentice in the true sense of the word.

Thank you for everything, especially letting me turns into a boomerang every time I

need to scratch my itchy feet. I can only hope you are as proud of this thesis as I am.

We got there in the end!

To Dr. Furhan Iqbal, my co-superviser, I extend my sincere thank you for your swift

comments and feedback. It was great to have someone to talk who knew the ropes so

well. I am eternally indebted to Prof. Dr. Tariq Mahmood Ansari, Dean Facuty of

Science, Bahauddin Zakariya University, Multan and to Prof. Dr. Seema Mehmood,

iv

Director, Institute of Pure and Applied Biology, Bahauddin Zakariya University,

Multan for all your help during this Ph.D. thesis.

I would like to express my sincere thanks to Dr. Muhammad Zubair Hussain,Assistant

professor of Zoology, Govt. Emerson College, Multan, for his accommodative attitude,

patience, sympathetic behaviour during writing and compilation of Ph.D. thesis.I am

also thankful to Dr.Abdul Latif, Assistant professor of Zoology, Govt. Emerson

College, Multan, for his accommodative attitude, patience, sympathetic behaviour

during formatting and proof reading of Ph.D. thesis. I would like to thank to Mrs.

Rehana Iqbal, Assistant Professor of Zoology, Bahauddin Zakariya University, Multan

for her valuable help in providing me all types of facilities and inspiring guidance,

valuable suggestions and dedicated co-operation in the development of this work. I

would like to pay my sincere thanks to Dr. Kashif Umer, Lecturer in Zoology, Govt.

Alamdar Hussain Islamia College, Multan, for his valuable guideline and his help in

analysis of data for the thesis. I would like to pay my special thanks to Dr. Qaswar Ali

Shah for his valuable guidance during thesis writing.

In no particular order, I, would like to thank Dr. Ramzan Mirza, Ex-Professor and Ex-

Head of Zoology Department, GC, University, Lahore, for all his valuable guideline in

fish identification.

I would also like to thank to Mr. Muhammad Aslam, Laboratory analyst, Water and

Soil Laboratory, Dera Ghazi Khan, Department of Agriculture, Punjab, who supported

me in providing facilities for chemical analysis of water samples.

v

My special thanks are to Mr. Abdul Rehman, Mr. Muhammad Khalid Balouch, Mr.

Asmat-Ullah, Mr. Mujahid Niaz Akhtar of Zoology Department, Govt. Emerson

College, Multan and Mr. Muhammad Latif (Ph.D. Scholar) for moral support during

thesis writing.

There are numerous people at the Zoology Department, Govt. Postgraduate College,

Dera Ghazi Khan, who have always offered their assistance both freely and with

kindness. In no particular order, I would like to thank Prof. Muhammad Hussain, Head

of Zoology Department, Mr. Imran Ali, Mr. Abrar Husssain,and Mr. Muhammad

Frooq. Prof. Dr. Amin-Ud-Din, Principal Goverenment College University of

Education, Dera Ghazi Khan. I am also grateful to Dr. Muhammad Atif, Assistant

Professor, Bahauddin Zakariya University, Multan, for providing me help in statistical

analyses of data.

In addition, there have been numerous people from outside of Zoology Department

that have given me support during my Ph.D. I would like to thank Mr. Faiz Bakhsh

Assistant Manager, Stat Life Insurance Corporation, Multan, Pakistan, Dr Muhammad

Imran, Dr Sajjad Hussain, Mr. Alamdar Husssain Bukhari, Hafiz Muhammad Saeed

and Atif Hussain Shah.

As a matter of fact, I, acknowledge all my friends for their support, love, words of

encouragement and advice throughout this particular path – thank you all so much for

your friendship.

vi

Finally I pray and pay my gratitude to late father, my mother and eldest brother and to

my beloved wife, I really could not have done this without you. Your love and support

over the past four years got me through the tough times and made the fun times even

more enjoyable. Thank you for keeping me laughing and for everything you have done

to make my life easier during the writing up stage. I cannot imagine a better person to

have shared this roller coaster with! I can not wait to start our next adventure

together…..

Sajjad Hussain

vii

Abbreviations

ANOVA: Analysis of variance

a: intercept

AFL: anal fin length

AFB: anal fin base

b: slope

BD: body depth

CL: confidence limits

°C: Degree celcius

dd: Double distilled

df: Degree of freedom

DO: Dissolved oxygen

DFL: dorsal fin length

DFB: dorsal fin base

dSm-1: Desi Siemens per meter

EC: Electrical conductivity

ED: eye diameter

EDTA: Ethylenediaminetetraacetic acid

FSS: Fish sampling site

FL: fork length

GL: girth length

Hum: Humidity

HD: head depth

HL head length

viii

K: Condition factor

L: Liter

LLR: Length length relationship

LWR: Length weight relationship

m: Meter

mgL-1: Milligram per liter

ml: Milli liter

mm: Millimeter

PreOL: pre-orbital length

PostOL: post-orbital length

PecFL: pectoral fin length

PecFB: pectoral fin base

PelFL: pelvic fin length

PelFB: pelvic fin base

ppm: Parts per million

r: Correlation coefficient

SAR: Sodium adsorption ratio

SL standard length

TA: Total Alkalinity

TH: Total hardness

TL: total length

WSS: Water sampling site

W: wet body weight

ix

ABSTRACT

Freshwater ecosystems have been playing important role in development and

maintenance of human civilization. Present study depicted the planktonic and

ichthyofaunal biodiversity of Suleman Mountain Range, Dera Ghazi Khan Region of

Pakistan. Water quality was assessed by analyzing physico-chemical parameters and

the ichthyofaunal status of the region was assessed by calculating abundance and

diversity of fish fauna. The fish health status was estimated by measuring several

morphometric characteristics and condition factor.

The analysis of TDS, pH, CO2, CO3-2, HCO3

-1, TA, TH, Na+1, Ca+2, Mg+2, Cl-1, SO4-2,

EC and SAR showed significant site variations while water temperature, light

penetration, pH, DO and CO2demonstrated the significant seasonal variations. Out of

119, 83 phytoplankton and 36 zooplankton genera were recorded wherein chlorophyta

was the most abundant with (RA= 28.80%) and xanthophyta was the least (RA =

0.87%) while Protozoa was the most occuring with (RA = 11.57%) and cladocera was

the least with (RA = 0.80%).

Twenty fish species were explored in which Tor macrolepis was the most and Labeo

calbasu was least abundant in the region. The family Cyprinidae was found to be most

abundant and dominant in Suleman Mountain Range, Dera Ghazi Khan, Region while

three fish species endemic to Pakistan namely Barilius pakistanicus, Labeo dyocheilus

pakistanicus and Salmophasia punjabensis were also observed from this experimental

area.

Total length ranged from 7.80-31.0 cm for Tor macrolepis, 8.08-17.20 for

Schizothorax plagiostomus, 9.0-20.5 for Labeo diplostomus, 9.30-23.60 cm for Labeo

dyocheilus pakistanicus, 8.00-14.30 cm for Cyprinion watsoni, 10.30- 18.90 cm for

x

Ompok pabda and 9.00-14.10 cm were calculated. Similarly, wet body weight ranged

from 5.20-301.90 g for Tor macrolepis, 7.05-67.08 g for Schizothorax plagiostomus,

5.50-90.0 g for Labeo diplostomus, 10.20-125.0 g for Labeo dyocheilus pakistanicus,

7.90-30.70 for Cyprinion watsoni, 8.50-57.0 g for Ompok pabda and 8.60-39.30 g for

Garra gotyla were recorded. New records of maximum total length for Cyprinion

watsoni (14.3 cm), Labeo dyocheilus pakistanicus (22.50 cm), Labeo diplostomus

(22.0 cm) and Tor macrolepis (29.0 cm) were obtained. Condition factors of most

fishes were optimum except Ompok pabda. In general, most of the water quality

parameters of the studied sites were within suitable range for growth of living

organism. The diversity indices value indicated moderate planktonic diversity.

However, ichthyofaunal diversity of the region was poor. The morphometric

characteristics and condition factor values indicated good fish health.

xi

Introduction

1.1 General Introduction

Fresh water is defined as “water with less than 500 parts per million (ppm) of

dissolved salts” (Dobson and Frid, 1998; Gleick, 1993). It comprises nearly 2.5-2.75%

of the total earth water; while remaining 97% is marine water. Naturally, it occurs in

two forms which are surface and ground water. Small amount (1.75-2%) of water

exists in the form of frozen surface water like glaciers, snow and ice. Only 0.01%

water is the available surface water existing in the form of rivers, lakes, swamps etc.

Freshwater habitats include both lentic systems (standing waters) such as ponds, lakes

and swamps; as well as lotic systems (running waters), such as streams and rivers

(Gleick, 1993; Dobson and Frid, 1998).

Freshwater ecosystems are studied under the science of “limnology” a term which

define asthe study of inland freshwater and saline ecosystem. It isconcerned with

structure as well as material and energy balances within these ecosystems with

emphasis on the biogenic materials balance of natural waters (Goldman and Horne,

1983; Schwoerbel, 1987). Limnological studies include three major features of fresh

water ecosystemsi.e.physical, chemical and biological. Physical characteristics include

studies of physical parameters like water temperature, light penetration, water current

etc. Chemical aspects include studies of chemical parameters like dissolved oxygen

(DO), total alkalinity (TA), total hardness (TH) etc. Biological aspects include studies

of biological parameters like diversity of planktons (Ali, 1999).

1

Primarily, freshwater habitats occur as distinct spatial units, and therefore are viewed

as well-defined ecosystems. These are not separate units, but are in continuous

interaction with their surroundings (Jeffries and Mills, 1990). Freshwater ecosystems

are a major resource for vital elements for humans particularly water and nutrients

(Baron et al., 2002; Dudgeon et al., 2006). Majority of the cities and massive

civilizations flourished across the banks of fresh water sources (Van De Meene et al.,

2001).

Biodiversity refers to organisms found within the living world including their number,

variety and variability.In biological terms, it has been defined as the “quantity, variety

and distribution across biological scales ranging through genetics and life forms of

species, populations, communities and ecosystems” (Wilcox, 1984; Mace et al., 2005).

The assessment of the status of species is helpful for monitoringthe trends and losses

of biodiversity. This is an important indicator for the status of biodiversity and in

accordance with the principles of the Convention on Biological Diversity (IUCN

2001). Identification and characterization of those species which are important for

humans is necessary for conservation process. The conservation of biodiversity has

become a core issue for scientific community (Wilson, 1988; Nielsen, 1995).

Freshwater biodiversity comprises of relatively more species richness compared to

terrestrial and marine ecosystems (Dudgeonet al.,2006). Freshwater ecosystem

nourishes 10% of the world existing species including a quarter of all vertebrates even

though, they cover less than 1% of the Earth’s surface (Strayer and Dudgeon, 2010).

Freshwater ecosystems are among the richest and most diverse ecosystems on earth

(Revenga and Mock, 2000). The quality and quantity of available freshwater is of

2

prime importance for estimating the health of ecosystems and sustainability of human

societies worldwide (Dziock et al., 2006; Brazner et al., 2007; Danz et al., 2007).

Biodiversity is important to sustain the ecosystem, maintenance of inclusive

environmental quality, and thereby, helps to understand innate value of all species on

the earth (Ehrlich and Wilson, 1991). However, the importance of freshwater

ecosystems to human culture, welfare and development has resulted in severe and

complex impacts to biodiversity and ecology (Malmqvist and Rundle, 2002).

Our world especially developing countries are facing the problem of water stress due

to rapid growth in population. The world’s hunger population is estimated to be about

923 million (FAO, 2008). This problem will be more aggravated with an expected

addition of 2 billion individuals by the year 2030 (Gany, 2006). The increasing

population will put an increasing demand of food consumed; enhanced water supply

for irrigation and drinking (Steffen et al., 2015; Gerten et al., 2013). Water shortages

due to limited amount and inefficient use of available water are one of the major

causes for lack of food (Laghari et al., 2008).

In Pakistan, aquatic resources are in the form of rivers, canals, drains, lakes, reservoirs,

village ponds, dams, waterlogged areas etc. This great variety of habitats is rightly

inhabited by diverse forms of aquatic life, the fish being a significant constituent

(Mirza and Bhatti, 1999). The largest contiguous gravity flow irrigation system in the

world, which is found in Pakistan, serves as a lifeline for sustaining agriculture and

biodiversity. Based upon pattern of the flow of its rivers and streams, Pakistan can be

divided into three major drainage systems: the Indus drainage, Balochistan coastal

drainage and landlocked drainage. Indus drainage is the largest river system of the

3

country, consisting of the main Indus River and all its associated rivers and streams.

The Balochistan coastal drainage system consists of a number of relatively small and

shallow rivers, all of which emerge from southwestern hills and independently fall into

the Arabian Sea. The landlocked drainage is constituted by a number of small and

shallow landlocked rivers and streams of central and western Balochistan (Rafiq and

Khan, 2012).

However, the mountainous/sub-mountainous areas of north eastern Balochistan,

central and Southern Khyber Pakhtoon Kha and Southwestern Punjab have a very

unique type of freshwater ecosystem, the mountainous streams, named as “Hill

Torrents, locally called Rodhkohi/Nallah”, with associated pits and ponds. The

seasonal torrential rains result in high velocity flowing water which is mostly drained

off either in the form of flood water to nearby areas or drained into nearby River Indus.

However, certain hill torrents have smooth flow of water for certain time of the year.

There have been no previous studies on assessment of water quality and exploration of

planktonic biodiversity of such type of ecosystem in Pakistan.

4

1.2 Water quality

Water quality is “any characteristics of water that influences its beneficial use. It is the

sum of all physical, chemical, biological and aesthetic characteristics of water”. Any

characteristics of water in production system that affect survival, reproduction, growth

and aquaculture species; influences management decision, causes environmental

impacts and reduces product quality and safety can be considered as water quality

variable (Boyd and Tucker, 1998).

Several of available water resources such as rivers, lakes and man-made reservoirs

have been used for water supply to domestic, agriculture, industrial, and aquaculture.

Therefore, assessment of water resources quality from any region is important aspect

for the developmental activities of the region. The thorough assessment of several

physico-chemical and biological characteristics is necessary for sustaining good

quality of water resources. These physico-chemical and biological parameters should

be in optimal range (Postel et al., 1996; Quyang et al., 2006; Gleick et al., 2011).

1.2.1 Climatic Factors

The climatic factors include humidity, air temperature, photoperiod and clouds.

Humidity usually measured as the amount of water vapors in air and the equilibrium

vapor pressure of the water that air can hold at the existing temperature. Humidity

along with temperature and light has an important role in regulating the activities of

organism and in limiting their distribution (Odum, 1971).Photoperiod is the process of

lengthening and shortening of days (Ricklefs and Miller, 2000). Seasonal variations in

day length are used by many organisms to synchronize their life cycle as opposed to

rainfall or temperature and are more reliable (Boyle and Senior, 2002).Clouds are

5

visible accumulations of water droplets or solid ice crystals that float in the earth

troposphere, the lowest part of earth atmosphere moving with the wind

(www.hopkin.dartmooth.edu).

1.2.2 Physico-Chemical Parameters

Several physical characteristics of water like temperature, light penetration, density,

viscosity and solubility of gases are the significant physical factors influencing aquatic

ecosystem. The chemical properties of water like pH, DO, carbonates (CO3-2),

bicarbonates (HCO3-1), salinity and conductivity etc comprise chemical parameters.

Such physico-chemical parameters as well as the biological characteristics

directly/indirectly affect the growth and survival of aquatic species (Boyd, 1998).

Physico-chemical characteristics and nutrients concentration in water significantly

influences the species composition and distribution of plankton (Reynolds, 1984;

Ganai and Parveen, 2014). These factors play important role for abundance and growth

of phytoplankton and consequently zooplankton and other consumer which depend on

phytoplankton for their existence. The seasonal fluctuations in environmental

parameters also significantly influence population density and distribution and of

plants and animals (Odum, 1971).

1.2.2.1 Physical Parameters

The temperature governs all other physical, chemical and biological characteristics of

water bodies and hence is considered as the most important and key factor in aquatic

habitats (Alabaster and Lloyd, 1980). There are variations in water all the year round

due to seasonal fluctuations in solar radiations, air temperature and day length. The

temperature of water depends on sunlight, climate and water depth. It influences the

6

oxygen contents of water being inversely proportional (Boyd, 1998), abundance and

diversity of autotrophs, influencing the percentage of photosynthesis and therefore

indirectly affects the abundance and diversity of heterotrophs (Barnabe, 1994). All

organisms including fish possess limits of temperature tolerance. High temperature

magnifies the impact of toxic substances and increases biodegradation process

(Barnabe, 1990).

Light penetrationis determined by the light intensity which falls on the surface of water

as well as amount of turbidity of water. It is a measure of the extent of eutrophic zone

(Boyd, 1998), and consequently influenes the primary production of water body

(Ramachandra and Solanki, 2007).Various substances present in natural waters such as

silt, clay and suspended particles affect light penetration (Rath, 1993).

Total dissolved solids (TDS) of water are composed of inorganic salts as well as

organic material in small amounts. It constitutes all the floating suspended solids in a

water sample. CO3-2, HCO3

-1, sulphates (SO4-2), chlorides (Cl-1), nitrates (NO3

-2),

potassium (K+1), sodium (Na+1), calcium (Ca+2) and magnesium (Mg+2) are the major

ions which contribute to TDS. Organic material in water may come from natural

sources, industrial discharges and sewage effluent discharges. Dissolved solids

concentration affects osmoregulation of fresh water organisms and reduces solubility

of gases (Boyd, 1998; Kumar and Kakrani, 2000).

7

1.2.2.2 Chemical Parameters

pH measurement accounts for the alkalinity or acidity of water. It is the determination

of hydrogen ion (H+1) and the hydroxyl ion (OH-1) in water. pH indicates the

magnitude of the acidic and basic nature of a solution at a given temperature (APHA,

1989). The rate of daily fluctuations in pH depends upon TA of water and rates of

photosynthesis/respiration (Boyd, 1998). During daytime, carbon dioxide (CO2)is

removed from the water resulting in increase of pH, while CO2 accumulates at night

resulting in decline of pH (Boyd, 1998).

Dissolved oxygen (DO) has a crucial role in determining the potential biological

quality of water. It facilitate degradation of organic detritus, completion of metabolic

pathways and essential for respiration, (Boyd, 1998). Therefore, it is very important

for the survival of fish and other aquatic organisms (Ramchandra and Solanki, 2007).

The concentration of DO in water depends on temperature, partial pressure of oxygen

in contact with water surface and amount of dissolved salts (Boyd, 1981).

Due to respiration of organisms and microbial activity, free carbon dioxide (CO2)

accumulates in water. By dissolving in water, it forms carbonic acid that imparts

acidity to the water. Free CO2 acts as a readily available source of carbon for high

sustainable growth of submerged macrophytes and algae. The dissolved CO2

influences water quality properties such as acidity, hardness and related characteristics

(Ekhande, 2010).

Carbonates (CO3-2) and bicarbonates (HCO3

-1) are the major components of pond

water. Their amounts are described as total alkalinity. There are usually more HCO3-1

8

in natural waters than that which result from ionization of carbonic acid in water

saturated with carbon dioxide (Rath, 1993).

One of the major environmental factors in aquaculture is total alkalinity (TA) due to its

interactions with other factors which affect the fertility of the ecosystem and health of

aquatic animals (Boyd, 1998). It measures the quantity of such ions in water, which

could react to neutralize H+1 (APHA, 1989). The dissolved minerals in water that come

from soil, atmospheric inputs, waste discharge and microbial decomposition of organic

matter provide in water the source of alkalinity. Various ionic substances contributing

to alkalinity include HCO3-1, CO3

-2, OH-1, HPO4-2, and H2PO4

-1 (Abbasi, 1998). In

natural waters TA is correlated with rates of primary production (Boyd, 1998).

Total hardness (TH) is usually expressed as Ca+2 and Mg+2 ions only and described as

equivalent CaCO3. Mostly it exists in the form of bicarbonates of Ca+2 and Mg+2 and

with lesser amount of chlorides and sulphates. TH (Ca+2 and Mg+2) is an important

parameter to detect water pollution (Abbasi, 1998; Boyd, 1979).

Sodium (Na) is found in high concentration in fresh water. Na+1 is essential to human

being and other higher animal. It regulates the composition of body fluids and is

dispensable for many bacteria and most plants. It is found as the principal cation of the

extra cellular fluid. Na+1 is involved in processes occurring outside the cell (Ohta,

2003; Borsani et al., 2003).

Calcium (Ca) is found as Ca+2 and as suspended particulates (mainly CaCO3). It is

essential for metabolism in all living organisms as well as structural/skeletal

9

substances in many animals. Calcium salts in excessive amount result in hard water

(Skipton and Dvorak, 2009). It influences the distribution of diatoms in aquatic

ecosystems. High calcium contents with high temperature cause high abundance of

diatoms (Vidya et al., 2013).

Magnesium (Mg) in water is dissolved from all rocks and soils. Moderate quantities of

Mg+2 have little effect on the usefulness of water for most purposes. Mg+2 also promote

the hardness in water and along with Ca+2 posses the problem of scale formation (EPA,

2012; Vidya et al., 2012). It has been an essential constituent of chlorophyll, without

which no ecosystem can work. Italso plays an important role in metabolism (Skipton

and Dvorak, 2009).

Chlorides (Cl-1) are present as chloride ion (Cl-1) in water and waste water (Trivedi and

Raj, 1992). Cl-1 influences osmotic and salt balance and ion exchange. However,

metabolic utilization does not cause large variations in the spatial and seasonal

distribution of Cl-1 within most lakes. High concentration of Cl-1 indicates the pollution

by sewage or industrial waste (APHA, 1998).

Sulphate (SO4-2) is deposited in water through natural means. All natural waters have

sulphates. Generally, industrial waste water has higher sulphate contents than natural

water. Main sources for sulphates in natural waters are oxidation of pyrite, dolomite

layers and industrial activities (Schippers et al., 1996; Koltuniewicz and Drioli, 2008).

SO4-2 in excess amounts exerts adverse effects on health. Excess amounts of SO4

-2 also

affect aesthetic value of water like odour and taste. It influences economy due to

corrosion of the structures (EPA, 1998; Yalcin and Guru, 2002).

10

Electrical conductivity (EC) in natural water can be measured by its ability to conduct

an electric current. Generally, it is directly proportional to the concentration of ions in

the natural water (Frank et al., 1990; Boyd, 1998). EC levels in water indicate the

amount of joinable substances like phosphate, nitrate and nitrites that are dissolved in

it (Boyd, 1998).

SAR is a more reliable and generally adopted criterion for evaluating sodium risk. It is

used to determine exchangeable value of the soil in equilibrium with irrigation water

(Jivendra, 1995).

1.2.3 Biological Parameters

Biological characteristics of an ecosystem are concerned with density and diversity of

organisms (Barnabe, 1990). There are various communities of living organisms which

survive in an aquatic ecosystem (Declince, 1992; Dolan, 2005) and play an important

role in productivity of aquatic ecosystem. The major groups of organisms inhabiting in

aquatic habitats are phytoplankton, zooplankton, fishes, macrophytes, epiphytes etc.

(Scheffer, 2004).

1.2.3.1 Planktonic Biodiversity

Planktons are “heterogeneous assemblage of pelagic organisms consisting of various

groups that are adapted to suspension in sea and freshwater” (Battish, 1992; Declince,

1992). Planktons are divided into phytoplankton (photosynthetic organisms) and

zooplanktons (heterotrophic organisms) (Battish, 1992; Richmond, 2004).

Phytoplankton consists of several prokaryotic and eukaryotic microscopic organisms.

In fresh water ecosystems major algal groups include diatoms (bacillariophyta), green

11

algae (chlorophyta) and blue green algae (cyanophyta) (Michael and Paerl 1994;

Chang et al., 1995).

Phytoplanktons act as the primary producers thereby comprising the first trophic level

in the food chain. Their total energy fixed in the form of carbon compounds i.e.

primary production forms the basis for oceanic and freshwater food webs (Boyd and

Tucker, 1998). Additionally, phytoplanktons also play a significant role in the material

circulation and energy flow in the aquatic ecosystem. They also exert influence on the

growth, reproductive capacity and population characteristics of other aquatic

organisms (Ariyadej et al., 2008).

The quantitative and qualitative characteristics of phytoplankton are good indicators of

water quality (Boyd and Tucker, 1998). Variations in the phytoplankton of freshwater

lakes act as a good indicator of the environmental quality and trophic status of the

system (Reynolds, 1996). Increase in phytoplankton biomass in surface water is often

related with nutrient enrichment (Smith, 2003). The higher abundance of chlorophyta

indicates productive water (Boyd and Tucker, 1998). Phytoplankton diversity responds

rapidly to changes in the aquatic environment in relation to nutrients (Eggs and

Aksnes, 1992; Chellappa et al., 2008). Floating algae (phytoplankton) are used as

biological indicators for benthic lakes or reservoirs (Michael and Paerl 1994; Chang et

al., 1995).

High sensitivity of planktons to variations in abiotic factors results in changes in their

dominance, abundance and diversity. Therefore, these changes in population dynamics

of planktons are useful indicator of pollution status of water bodies (Basu et al., 2010;

12

Prabhahar et al., 2011). Various phytoplankton species have been used as bioindicators

(Vareethiah and Haniffa, 1998; Bianchi et al., 2003; Tiwari and Chauhan, 2006; Hoch

et al., 2008). Increased growth of certain groups of phytoplankton especially blue

green algae can cause deoxygenation of the waterleading to fish death (Whitton and

Patts, 2000).

Zooplanktons constitute heterotrophic microscopic organisms and include protozoans,

microcrustaceans and other micro invertebrates, which are suspended in water (Omudu

and Odeh, 2006). These serve as important source of food for many other aquatic

organisms (Guy, 1992). They play key role in transferring energy between the

phytoplankton and fish populations (Martin et al, 2006; Holmborn, 2009). The

zooplankton distribution varies spatially as well as temporally within aquatic

ecosystems (Declince, 1992). They also serve as pollution indicators in aquatic

environment (Yakubu et al., 2000).

The assessment of productivity, trophic status and planktonic density of water bodies

is crucial for fisheries management (Peckham et al., 2006). The density and diversity

of planktonic organisms is variable from pond to pond within one locality and from

locality to locality within a region. Greater density and diversity of planktonic

communities indicate successful aquaculture (Bhuiyan et al., 2008).

Various physico-chemical factors like temperature, pH, light penetration, turbidity,

nutrients and salinity regulate species composition, richness of phytoplankton and

zooplankton in aquatic ecosystems (Buzzi, 1999; Veereshakumar and Hosmani, 2006).

The productivity of planktons in an aquatic ecosystem is affected by the physico-

13

chemical characteristics and nutrient level of water (Odum, 1971; Wetzel, 1983). The

optimum level of the physico-chemical factors results in maximum yield of

phytoplankton (Sinha and Srivastava, 1991; Aliet al., 2005; Peerapornpisal et al.,

2004). Variations in physico-chemical and biological characteristics influence several

factors resulting in seasonal changes (Reynold 1984). Seasonal variations in radiations,

temperature, hydraulic output and nutrient status influence composition and abundance

of phytoplankton (Reynolds 1990; Reynolds, 1998; Marinho and Huszar, 2002).

A volume of research work is available to estimate productivity and plankton analysis

in lakes and reservoirs in temperate (Mallin et al., 1994; Watson et al., 1997; Huszar

and Caraco, 1998; Trifonova, 1998), tropical (Figueredo and Giani, 2001; Nogueira,

2000; Talling, 1987) and sub-tropical (Harris and Baxter, 1996; Silva, 2005; Rajagopal

et al., 2010) region. Several studies in Pakistan have described the physico-chemical

characteristics and biodiversity of aquatic ecosystems at various times and places

(Hussainet al., 2014; Chughtai et al., 2011; Chughtai et al., 2013; Khan et al., 2013;

Ghazala and Arifa, 2011; Iqbal et al., 2004; Baloch et al., 2005; Ali et al., 2003; Ali et

al., 2005; Rafique et al., 2002). However, there are no previous studies on hill-torrent

type of ecosystems in this sub-tropical region. This is the first type of study describing

detailed investigations of planktonic diversity with reference to their spatial and

temporal distribution and changes in species composition in relation to environmental

variables.

14

1.3. Fish Biodiversity

Ichthyofaunal diversity refers to “the variety of fish species depending on context and

scale, it could refer to alleles or genotypes within fish population to species of life

forms within a fish community and to species or life forms across aqua regimes”

(Burton et al., 1992). Biodiversity influences the response of living systems to

environmental changes, facilitates functions of ecosystem and provides several

essential nutrients and utilities for human well-being like nutrient cycling and clean

water (Costanza et al., 1997; Hooper et al., 2005; Diaz et al., 2006).

Freshwater fishes constitute a significant proportion of global biodiversity (Reid et al.,

2013). This fact is reflected by the large number of freshwater living fish species. The

number of all fish species is estimated to be 32,500 (Nelson, 2006). Of these, nearly

50% is contributed by freshwater fish species and still the numbers of described

freshwater fishes are increasing (Froese and Pauly, 2010). About 305 new fish species

are described per year since 1976 (Reid et al., 2013). Even though, freshwater makes

less than 0.3% of total available water in the world (Ormerod, 2003). The fishes

demonstrate enormous diversity of morphological, behavioral and physiological

adaptations (Nelson, 1994). By virtue of these adaptations, fishes inhabit a wide

variety of habitats including springs, ponds, pools, lakes, reservoirs, streams, rivers

and sea (Moyle and Cech, 1996).

Fisheries have a significant contribution in the national economy of many countries

(Valdimarsson, 2001; Bostock et al., 2004). The total fish capture has been recorded to

be 149 million tonnes in the year 2010 (FAO, 2012). The developing countries

contribute approximately 94% of all freshwater fisheries (FAO, 2007). Fish meat is a

15

primary source of protein for approximately 1 billion people throughout the globe

(FAO, 2010). The contribution of freshwater fishes to annual animal protein source for

humans is more than 6% (FAO, 2007). Animal protein intake from fish source reaches

up to 40-50% in several developing/underdeveloped countries like Bangladesh,

Indonesia, Philippines, Thailand and Vietnam (Briones et al., 2004).

Being an intrinsic component, fish has a significant contribution in regulation of

energy flow, cycling of nutrients and maintenance of community balance of the

aquatic ecosystem (Nelson, 2006). They act as a key monitor of ecosystem quality due

to their overwhelming impact on the abundance and distribution of other aquatic

organisms (Moyle and Leidy, 1992). The life history characteristics of fishes render

them as appropriate indicators of anthropogenic stress. They are also sensitive to

various other stresses like diseases, parasites and acidification. Several factors of fish

i.e. large body sizes, high growth rates and trophic status make many fishes to

bioaccumulate toxic substances (Holmlund and Hammer, 1999).

The changes in water quality parameters including water temperature, DO contents,

amount of suspended solids, Cl-1, Ca+2, Na+1 and other chemical constituents affect

species composition and relative abundance (RA) of fish communities (Grizzle, 1981).

Therefore, the fishes are highly sensitive to both the quantitative and qualitative

changes in aquatic ecosystems (Laffaille et al., 2005; Kang et al., 2009; Sarkar et al.,

2008). This fact has made freshwater fishes as one of the most threatened taxonomic

groups (Darwall and Vie, 2005).

16

The main factors which cause loss to fish biodiversity include habitat destruction,

damming, industrialization, pollution and other global climatic changes (Prenda et al.,

2006; Collare-Pereira and Cowx, 2004; Allan and Flecker, 1993; Gibbs, 2000; Dawson

et al., 2003; Leveque et al., 2005; Mas-Marti, 2010). The decline in fish biodiversity is

a phenomenon well known from local to global levels (Duncan and Lockwood, 2001;

Cowx and Collares-Pereira, 2002).

IUCN Red List “Categories” and “Criteria” are most commonly used to determine

species status. It also help to detect the extinction risk and provides foundation to

understand status of species which are extinct, threatened, near threatened, least

concern or data deficient. It publishes the results of global assessment, taxanomy,

habitat, distributions and ecology for each species (www.iucnredlist.org).

About 20% of the world’s freshwater fish is either threatened or extinct (IUCN, 2014).

Approximately 1275 fish species have been listed as threatened in the IUCN red list of

2008, including 11 in Pakistan (Rafique and Khan, 2012). The freshwater fish fauna

from various water bodies of Pakistan has been investigated through a number of

comparatively recent studies carried out at different places and times (Mirza, 1990;

Mirza, 2003; Rafique and Qureshi, 1997; Rafique, 2000; Rafique, 2001; Rafique et al.,

2003; Hasan et al., 2013; Iqbal et al., 2013; Akhtar et al., 2014). Much taxonomic

work still remains to be done.Our study (Ali et al., 2010) has previousely described the

fish diversity of Suleman Mountain Range, Dera Ghazi Khan, Region; however,

present study was carried out to explore fish diversity in more detail.

17

1.4 Fish Morphometry

Morphometry describes variations and changes in form i.e. size and shape of organism,

thus facilitating numerical comparison between different forms and description of

complex shapes in a comprehensive manner (Webster, 2006). Different aspects of fish

growth e.g. pattern of shape, variation within and among sample of life stage,

population and species and to manipulate hypothesis about the origin of these

variations in growth patterns are the focus of scientific interest (Shearer, 1994). During

growth, body proportions change resulting in alterations of body form (Le Cren, 1951).

Morphometric characters explain various aspects of body forms and shape in fish

(Turan, 2004). Morphometric analysis utilizes a continuous data set of measurement of

size and shape variation of a population. Morphometrics and meristics are important in

fisheries science because of the extensive use to identify fish species and their habitat

specificity (Geldiay and Balik, 1998, Karatas, 2005), characterize fish stock (Turan et

al., 2004) and assessing the evolutionary adaptations of a species to environment

(Kovac et al., 1999).

Length and weight are two basic components in the biology of fish species and

relationships of such morphological characters of the fish are helpful in predicting the

growth pattern of fish species at individual as well as population level (Pauly, 1993).

Length-weight relation (LWR), Fulton’s condition factor (K) and the relative condition

factor (Kn) are some of the most widely used morphometric indices to assess fish

condition (Froese, 2006).

18

The LWR is a useful tool in fish biology, physiology, ecology and stock assessment

(Oscoz et al., 2005). Body size in fishes is the most easily measured characteristic and

is often measured in terms of body length. The relationship of body length with body

weight has been extensively documented in fisheries research (Froese, 2006; Froese et

al., 2011). Virtually size is more relevant than age as several ecological and

physiological factors are more size-dependent than age-dependent (Santos et al.,

2002). LWRs measurements along with age data can provide information on the stock

composition, age at maturity, life span, mortality, growth and production (Beyer, 1987;

Bolger and Connoly, 1989; King, 1996; Diaz et al., 2000).

LWR information are helpful for determining biomass measures by inter-converting

fish length and weight and/or growth in length to growth in weight equation as well as

prediction of weight during different growth periods (Froese, 1998; Pauly, 1993;

Anderson and Neumann, 1996; Binohlan and Pauly, 1998). The estimation of fish

biomass and information about body weight for regulation of fish catches are

necessary for fishery management and conservation (Froese, 1998; Oscoz, 2005).

Length-weight data indicates the degrees of stabilization of taxonomic characters in

fish species (Pervin and Mortuza, 2008).

The above mentioned factors regarding LWRs are applicable in population dynamics

and aquatics ecology science (Pauly, 1993; Santos et al., 2002). LWRs are useful in

fisheries research to explore life history changes and make morphological comparisons

between and among different fish species or fish populations from different habitats

and geographical areas (Goncalves et al., 1997; Moutopoulos and Stergio, 2002;

Petrakis and Stergiou, 1995). This information is necessary for stock assessment

19

models (Borges et al., 2003; Mendes et al., 2004) and is commonly used in the

ecosystem modeling approach (Pauly et al., 2000). Differences in the parameters of

LWRs show spatial variations (Sparre and Venema, 1998) and the impact of abiotic

factors and availability of food for fish growth (Mommsen, 1998).

The LWRs of a species depend on many factors i.e sex, size range, habitat, food

availability and fishing pressure and season (Froese, 2006; Karachle and Stergio, 2008;

Liousia et al., 2012). In the FishBase, up to now, LWRs of thousands of species

(number ranging to 3584) can be viewed / examined (FishBase, 2012).

In addition to LWRs, relationships between different types of lengths also termed as

length-length relationships (LLRs) are important in fisheries management for

comparative growth studies (Froese and Pauly, 1998; Moutopoulos and Stergiou,

2002). These include measurements of different lengths including: total length

(Gordon, 1978), head length (Coggan et al., 1999; Swan et al., 2003), pre-anus length

(Bergstad, 1990; Kelly et al., 1997), and pre-anal fin length (Jorgensen, 1996). In the

FishBase, LLRs records are available for most of the European and North American

freshwater and marine fishes (Froese and Pauly, 2012; Bagenal and Tesch, 1978;

Petrakis and Stergiou, 1995; Koutrakis and Tsikliras, 2003; Sinovcic et al., 2004;

Leunda et al., 2006; Miranda et al., 2006). However, this record is very poor for

tropical and subtropical fishes (Harrison, 2001; Ecoutin et al., 2005).

Condition factor (K) is extensively used in fisheries and fish biology studies. It

indicates the well-being and health of a certain fish species depending upon the weight

of sampled fish (Pauly 1983; Fafioye and Oluajo, 2005). It is calculated from the

20

relationship between the weight of a fish and its length and reflects the condition of the

individual fish (Froese, 2006). Condition factor indicates the degree of food sources

available, age and sex of same species (Williams, 2000). Therefore, it has been used as

an index of growth and feeding intensity. It decreases with increase in length (Fagade,

1979). It is specific for each species (Fafioye and Oluajo, 2005; Froese, 2006; Treer et

al., 2009).

There have been several studies in the past to describe LWRs, LLRs and condition

factor of several fish species from wild (Narejo, 2006; Narejo et al., 2008; Laghari et

al., 2009; Laghari et al., 2011; Yousaf et al., 2011; Pervaiz et al, 2012); and farmed

fishes (Chatta and Ayub, 2010; Naeem et al., 2012) from various freshwater bodies of

different regions of Pakistan. This is the first study to describe LWRs, LLRs and

condition factor of fishes from Suleman Mountain Range, Dera Ghazi Khan Region,

Pakistan.

21

1.5 Aims and Objectives

The present study explored the aquatic biodiversity of Suleman Mountain Range, Dera

Ghazi Khan Region, Pakistan, with following aims and objectives:

To characterize physico-chemical parameters of water

To evaluate planktons abundance and diversity

To assess the status of water quality of the region

To analyze seasonal effects on aquatic biodiversity

To identify fish community composition

To evaluate fish abundance and diversity

To explore the status of the ichthyofauna of major water bodies of the region

To characterize morphometrics of ichthyofauna

To estimate fisheries potential of the region for future manipulation

22

Materials and Methods

2.1 Study Area

The Suleman Mountain Range in Pakistan encompasses South Waziristan, most of the

Northern Balochistan Province and some of the South Western Punjab Province. To

the East, the mountain range enters the Rajan Pur District (in Punjab Province) and

traverses through Dera Ghazi Khan District (in Punjab Province) to Dera Ismail Khan

District (in Khyber Pakhtun khwa Province). It approaches the Indus River near

Mithan Kot in Rajan Pur District. The eastern slopes drop very quickly to the Indus

River but towards West, the mountain range drops gradually to the Sistan Basin. The

Suleman Mountain Range, Dera Ghazi Khan Region, consists of Dera Ghazi Khan

District and Rajan Pur District (28o 28/ to 31o 18/ North latitude and between 69o 20/

to 70o 55/ East longitudes) covering an area of approximately 12000 Km2. It is

bordered by Musa Khel District and Barkhan District to the West, Dera Ismail Khan

District to the North, Muzaffargarh District and Layyah District to the East and Jakob

Abad District to the South (Figure 2.1).

The Suleman Mountain Range, Dera Ghazi Khan Region, consists of 13 catchments

areas/hill torrents (Malkani, 2010). Of these, ten catchment areas were investigated for

fish biodiversity and four for water quality (Figure 2.1, Table 2.1). The region has sub-

tropical climate with four distinct seasons; the summer from May to August, the post

monsoon from September to October, the winter from November to February and the

spring from March to April (Chaudhry and Rasool, 2004). Mean annual precipitation

in the area is around 350 mm. The climate of the region is extremely dry in the

summer as well as in the winter season both in hilly and plain areas.

23

Figure 2.1: Location map of hill torrents of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan, showing fish sampling

sites (FSS) from FSS-1 to FSS-10 and water sampling sites (WSS) from WSS-1 to WSS-4.

24

The precipitation is in the form of occasional seasonal rain fall particularly during

rainy season (July-September) which turns into hill torrents locally called as

“Nallahs/Rodhkohi”. Along the water course of these hill torrents a number of

associated pits and ponds occur frequently. Besides cold water springs scattered

throughout the region and fewer hot water springs are also found in the region. The

water of these springs drains into associated pits and ponds.

The mean elevation of mountain range is around 1100 m. The minimum elevation is

less than 300 m and maximum elevation exceeds 2000 m. Approximately, 55% of the

area lies between elevation of 900 m and 1300 m. The 65% of total catchment areas of

the mountain range comprise of lime stones, shale and sand stone, 30% gravelly loamy

soils and 5% loamy silt soil cover (Hanson et al., 1995). The geomorphology of the

area is characterized by rugged terrain, large variations in land slope and sharp bends

in natural streams. The stream flow velocity is high that can carry gravels and boulder

sized stones. The high flow velocities are due to high intensity flashy rains resulting in

torrential flows. Almost all the streams are ephemeral in nature flowing during

monsoon or winter rains only. Most of the catchments drain to the Indus River (Hasan

et al., 2001; Malkani, 2010; Malkani, 2011). Around 65% of catchment areas consist

of barren mountains without vegetative cover. The vegetation type of the area is

predominantly xeric (Dasti et al., 2010) (Table 2.2).

Suleman (Middle Indus) Basin represents Mesozoic and Cenozoic strata and have

deposits of sedimentary rocks with radioactive and fuel minerals (Malkani, 2010). The

geologic reports about the presence of reserves oil, gas and minerals makes this area

economically important (Malkani, 2010; Malkani, 2011). Region is a far off area of the

25

country and the transport and communication network is poor and the advent of

modern technology in this area is lagging behind compared to other regions of the

country. There is little industrial human activity like oil, gas and mineral exploration;

including agricultural activity. Human intervention and impact on the ecosystem in the

area is less compared to other regions of the country. Therefore, biodiversity of the

region is expected to be conserved. There have been very few studies on exploration of

flora and fauna of the region (Dasti et al., 2010; Ali et al., 2010) the planktonic

diversity of the region is still unexplored.

26

Table 2.1: Names and location of sampling sites

Site number Hill torrent

name

Site name Altitude*

(Feet)

Geographic coordinates*

Fish sampling Water sampling

FSS-1 WSS-1 Gang Vehowa 1200 31°14′N 70°37′E

FSS-2 WSS-3 Sanghar Barthi 1050 30°55′N 70°36′ E

FSS-3 --- Hingloon Hingloon 3043 30°46′N 70°06′E

FSS-4 WSS-4 Sori Lund Zinda Peer 1500 30°40′N 70°48′E

FSS-5 --- Dalana Jaj 1583 30°08′N 70°34′E

FSS-6 WSS-2 Kaha Harand 1250 29°54′N 70°09′E

FSS-7 --- Siri Siri 2100 30°01′N 70°13′ E

FSS-8 --- Sori Murunj 2300 29°24′N 69°42′E

FSS-9 --- Toe Sar Chitar Wata 1800 31°23′N 70°32′ E

FSS-10 --- Rakhi Rakhi Gaj 2600 29°95′N 70°10′E

27

                                   *(www.latlong.net/c/?lat)

Table 2.2: Vegetation and soil type of water sampling sites of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan

Site Number Nallah Soil Type* Vegetation Type* Dominant species*

WSS-1 Gang (Vehowa)

Silt in texture, fragmented

rocks and more and more

gravel particles of soil

Almost xeric

Trees + woody shrubs, Acaciamodesta,

Salvodora, Oleodes, Capparis aphylla

WSS-2 Kaha (Harand)Sandy + large gravel particles

and silt also presentXeric

Capparis apylla, Salvadorsp,Prosopis

sp,Aerua persica, Withania coagulens,

Fagonia indica, Rhazya stricta, Suaeda

fructicosa, Phlomis sp, Peganum

hermala, Leptadaenia sp.

WSS-3 Sanghar (Barthi)Silty sand in texture and

almost upper piedmont area

Xeric

and samophytes

species

Prosopis sp, Tamarix aphylla, Capparis

apylla, Calotropis procera, Citrullus

colocynthes and Acacia sp

WSS-4Suri Lund

(Zinda Peer)Calcarious mostly clay

particles and sandy clay in Xeric

Calotropis sp, Peganum sp, Fagoniasp,

Caparis apylla, Acacia jacumontii,

28

textureSalvadora sp, Aeruapersica, Rhazya

stricta, Solanum sp

*(Dasti et al., 2010)

29

2.2 Water Quality

The water sampling was conducted from January 2012 to December 2012 on monthly

basis from four sites (Figure 2.1, Table 2.1). The specific sampling time, date and

weather conditions for each site are given in Table 2.3. The water samples were taken

from sub surface at a depth of 30 cm. The samples were collected from middle of the

standing water bodies. Some of the physico-chemical parameters i.e. air and water

temperature, light penetration, pH, EC and DO were measured at the spot, while other

parameters were determined in the laboratory (Water and Soil Laboratory, Agriculture

Department, Government of the Punjab, Dera Ghazi Khan). For laboratory analysis of

physico-chemical parameters, samples were taken in glass bottles of 1.5 L capacity.

The water samples for biological parameters analysis were taken in plastic bottles of 1

L capacity. The water samples for biological diversity were preserved by adding 100

ml of 4% formaldehyde (Battish, 1992). The bottles were labeled with site number,

date and time with the help of waterproof markers.

Continued availability of water throughout the year facilitated limnological studies at

these sites. The water level fluctuations were observed during study period. The

average depth of the sampling spots varied between 3 to 10 feet. The anthropogenic

activity in the form of agricultural practices was observed in the vicinity of sampling

spots. The sampling sites were selected randomly to represent the whole region. The

selected sampling sites had elevation between 1050 to 1500 feet (Figure 2.1, Table

2.1).

Various climatic and physico-chemical parameters for estimation of water quality of

these sites were determined by methods described as Boyd (1981) and APHA (1998).

30

Table 2.3: Date and weather conditions of each sampling site during study period

Month WSS-1 (12.00 noon*) WSS-2 (1.00 p.m*) WSS-3 (1.00 p.m*) WSS-4 (12.00 noon*)

Date

Cloud Hum

Date

Cloud Hum

Date

Clou

d

Hum

Date

Clou

d

Hum

Jan 10.01.2012 >25 79.50 11.01.2012 >50 76.00 13.01.2012 <50 80.50 14.01.2012 >25 76.50

Feb 10.02.2012 <25 77.00 11.02.2012 >25 73.50 13.02.2012 <25 74.00 14.02.2012 <50 70.00

Mar 10.03.2012 <25 66.50 11.03.2012 np 64.00 13.03.2012 np 64.50 14.03.2012 np 59.50

Apr 10.04.2012 Np 36.00 11.04.2012 np 34.00 13.04.2012 np 39.00 14.04.2012 np 40.00

May 11.05.2012 Np 31.50 12.05.2012 np 35.00 13.05.2012 np 36.50 14.05.2012 np 35.00

Jun 11.06.2012 Np 33.50 12.06.2012 np 38.00 13.06.2012 np 40.50 14.06.2012 np 38.00

Jul 11.07.2012 <25 59.00 12.07.2012 <25 59.00 14.07.2012 >25 58.00 15.07.2012 >25 55.50

Aug 11.08.2012 <50 63.00 12.08.2012 <50 65.00 14.08.2012 <50 66.50 15.08.2012 <50 62.50

Sep 11.09.2012 >50 66.50 12.09.2012 >50 67.50 14.09.2012 >25 67.00 15.09.2012 >25 67.50

Oct 11.10.2012 np 67.00 12.10.2012 np 75.00 14.10.2012 np 73.00 15.10.2012 np 72.00

Nov 10.11.2012 Np 72.50 11.11.2012 np 78.50 13.11.2012 np 78.00 15.11.2012 np 75.00

Dec 10.12.2012 >25 74.00 11.12.2012 <50 79.00 13.12.2012 <50 80.50 15.12.2012 >50 77.00

*Water sampling time; Hum = humidity; np = not present

31

2.2.1 Climatic factors

Following climatic parameters were recorded.

2.2.1 a Air Temperature (oC)

At the time of sampling, the air temperature (oC) was recorded by simple alcoholic

laboratory thermometer.

2.2.1 b Photoperiod (hours)

Noted the sunrise and sunset to calculate the photoperiod (hours).

2.2.1 c Humidity (%)

The data for minimum and maximum humidity (%) was obtained from metereological

department, Multan Division. The mean humidity (%) was calculated.

2.2.2 Physico-Chemical Parameters

2.2.2.1 Physical Parameters

Following physical parameters were recorded.

2.2.2.1 a Water temperature (oC)

Water temperature(oC)was recorded by simple alcoholic laboratory thermometer.

2.2.2.1 b Light Penetration (cm)

Light penetration (cm) was determined using Secchi disc, a metallic disc of 20 cm

diameter with four alternate black and white quarters on the upper surface. The disc

with centrally placed weight at the lower surface was suspended with a graduated cord

32

at the center. Light penetration was measured by gradually lowering the Secchi disc in

water. The depths at which it disappears (A) and reappears (B) in the water were

noted. The light penetration of the water was calculated as follows;

Secchi disc light penetration (cm) = A + B / 2

Where,

A = depth at which Secchi disc disappears

B = depth at which Secchi disc reappears

2.2.2.1 c Total Dissolved Solids (mgL-1)

A clean crucible was ignited in a muffle furnace (RJM 1.8-10A, China) at 550 oC for

30 minutes. Weigh the crucible after cooling it in a vacuum desiccator. Mixed the

water sample and filtered 100 ml. Measured 10 ml of filtrate and poured into crucible.

The contents of crucible were evaporated in an oven at 95 oC, and then, the

temperature of oven was increased to 103 oC for 1 hour. The crucible and residue was

cooled in desiccator and then weighed.The concentration of TDS was calculated as

follows;

Total dissolved solids (mgL-1) = R – D × 1000 sample volume (ml)

Where,

R = weight of empty crucible

D = weight of crucible + water sample

33

2.2.2.2 Chemical Parameters

2.2.2.2 a pH

The pH of water sample was measured by electronic portable pH meter (HANNA 212,

China). pH meter was calibrated with phosphate buffer of known pH. It uses electrodes

that are free from interference. At constant temperature, a pH change produces a

corresponding change in the electrical property of the solution. This change was read

by the electrode and the accuracy was the greatest in the middle pH ranges.

2.2.2.2 b Electrical Conductivity (dSm-1)

The EC (dSm-1) was determined by using EC meter (JENCO 3173 COND, China). The

instrument was standardized with 0.1 N KCl solution (0.7456 g KCl dissolved in 1L of

dd H2O), which has an EC of 1.413 dS m-1at 25 oC. When the 0.1 N KCl solution

gives an EC values different from 1.413 dS m-1, then the sample reading was

multiplied by the cell constant (K). Then the EC of water sample is calculated by the

formula;

EC of Water = Observed EC × K

Where,

K = 1.413

2.2.2.2 c Dissolved Oxygen (mgL-1)

DO (mgL-1) was measured by oxygen meter (JENCO 9250 M, China) after calibration.

5 drops of dd H2O were put into sponge located inside calibration bottle. The excess

water was drained out of the bottle by turning over the bottle. The bottle was screwed

into probe by keeping a space of at least 5 mm between the sponge and probe. The

34

instrument was turned on and waits until reading of DO and temperature become

stabilized. Then pressed the ‘Cal’ key. The local pressure in mbar appears on LCD.

Adjusted it to attain proper pressure value.Then pressed the ‘Enter’ key to check

calibration value of pressure.Again pressed the ‘Enter’ key for salinity calibration

procedure. The salinity of sampling water appears on LCD. Adjusted it to attain the

correct salinity value. The DO meter was calibrated every time after turning it off. The

DO meter was calibrated at temperature close to the temperature of sample water.

2.2.2.2 d Free Carbon Dioxide (mgL-1)

50 ml of water sample were taken in a flask by avoiding its contact with air. 3 ̶ 4 drops

of phenolphthalein indicator (0.5 gm phenolphthalein dissolved in 50 ml of 95%

C2H5OH with 50 ml distilled water) were added in sample water. If sample turns pink,

free CO2 is absent and if it remains colorless, it contains free CO2. Sample containing

CO2 was titrated against 0.045 NNa2CO3solution (2.407 g Na2CO3 dissolved in 1 L of

CO2 free dd H2O) rapidly. A faint pink color which persisted for 30 seconds marked

end point. Concentration of CO2 was calculated by following formula;

CO2 (mgL-1) = ml of titrant (N) (22) × 1000sample volume (ml)

2.2.2.2 e Carbonates and Bicarbonates (mgL-1)

50 ml of water sample was taken in a conical flask for determination of carbonates. 1̶3

drops of phenolphthalein indicator (0.5 g phenolphthalein dissolved in 50 ml of 95%

C2H5OH) added in water sample and titrated it against 0.1 N H2SO4 solution (2.77 ml

of H2SO4 dissolved in 1 L of dd H2O) until the pink color disappear. Noted the volume

of acid used.

35

1̶2 drops of methyl orange (0.1 g methyl orange in 100 ml dd H2O) as an indicator

were added to the same conical flask for the determination of bicarbonates. Titrated it

against 0.1 NH2SO4 until the appearance of light orange color which marks end point.

Noted the volume of acid used. The concentration of carbonates was calculated by

applying the following formula;

Carbonates (mgL-1) = 2R1 × Normality of H2SO4 × 1000 sample volume (ml)

Where,

R1 = Volume of 0.1 N H2SO4 used

Actual volume of 0.1 N H2SO4 used = 2R1 (This reaction is completed in two steps)

Bicarbonates (mgL-1) = R2-R1 × Normality of H 2SO4 × 1000 sample volume (ml)

Where,

R2 - R1 = Volume of 0.1 N H2SO4 (actual amount of acid used for neutralizing

                            the originally present bicarbonates)

R1 = Amount of the acid used to convert the carbonate into bicarbonates

R2 = Total amount of acid used to neutralize the bicarbonates present in the   

                     sample.

2.2.2.2 f Total Alkalinity (mgL-1)

50 ml of water sample were taken in a conical flask. 4̶5 drops of phenolphthalein

indicator (0.5 g phenolphthalein dissolved in 50 ml of 95% C2H5OH with 50 ml dd

water) were added in sample water. If the color developed pink, the sample was

36

titrated against 0.02 N HCl (1.72 ml of concentrated HCl dissolved in dd H2O with

final volume up to 1L), until the color disappeared. Noted the volume of acid used. 2 ̶3

drops of methyl orange indicator solution (0.5 g of methyl orange dissolved in 50 ml

of dd H2O) were added in the same sample. The titration was continued until end point

with change of color from orange to brick red was observed. The volume of acid

consumed was noted. Total alkalinity (TA) was calculated by using the following

formula;

Total alkalinity (mgL-1) = ml of titrant (N) (50) × 1000 sample volume (ml)

2.2.2.2 g Total Hardness (mgL-1)

EDTA titrimetric method was used for the estimation of total hardness. 1̶2 ml of buffer

solution (17.5 g of NH4Cl added to 142 ml of concentrated ammonia solution with

final volume of 250 ml by adding deionized water) and a pinch of Eriochrome Black-

T, as an indicator, were added in100 ml of water sample. After the appearance of wine

red color, the mixture was titrated against EDTA by stirring continuously till the

change of wine red color to blue which marks end point. The total hardness is

calculated using following formula;

Total Hardness as CaCO3 (mgL-1) = (Volume of EDTA) (N) (100.1) × 1000 sample volume (ml)

Where,

M = molarity of EDTA solution

37

2.2.2.2 h Chlorides (mgL-1)

The chloride contents were determined by argentometric titrimetric method.In the

same conical flask used for the determination of carbonates and bicarbonates [section

3.2.2. e.], added 3̶4 drops of 5% K2CrO4(5.0 g of K2CrO4 dissolved in 50 ml dd H2O

and added l N AgNO3 (169.87 g AgNO3 dissolved in 1 L dd H2O) drop wise until red

precipitate was produced. The solution was filtered and filtrate was diluted to 100 ml.

While stirring, titrated under bright light with 0.05 N AgNO3 (8.494 g AgNO3

dissolved in dd H2O with final volume up to 1 L, and kept in a brown bottle to avoid

photolysis) to a brick red precipitate/permanent reddish brown color.Chlorides were

calculated using following formula;

Cl (meL-1) = volume of AgNO3 used × N × 100 sample volume (ml)

The meL-1 of chlorides was converted to mgL-1 of chloride contents by multiplying

with its equivalent weight.

2.2.2.2 i Sulphates (mgL-1)

The sulphates were determined with the help of spectrophotometer (Spectronic 20D,

Japan). 5 ml of water sample was taken in a 50 ml volumetric flask. 5 ml acid mixture

reagent (125 ml HNO3 was added, 250 ml CH3COOH and 100 ml of 85% H3PO4 with

final volume of 1 L) and 1 ml acid sulphate solution [86 ml concentrated HCl and100

ml sulphate stock solution (100 ppm) withfinal volume up to 500 ml (20 ppm)] were

mixed. 0.5 g BaCl2.2H2O crystals were added in this mixture. This mixture was kept

undisturbed for 3 minutes and then mixed. Then 1 ml gum acacia reagent was added

and mixed. Total volume was made up to 50 ml. Samples and standards were run at

38

420̶450 nm between 3 to 8 minutes after final shaking. Run reagent blank. The

sulphate contents of the water sample were calculated as follows;

Sulphate concentration (ppm) = Sulphate concentration (ppm) × dilution factor

2.2.2.2 j Calcium and Magnesium (mgL-1)

Firstly, the concentration of calcium in water was determined by taking 10 ml of water

sample in a conical flask. 5 drops of 4 N NaOH (160 g of NaOH dissolved in dd H2O

up to final volume of 1 L) and 50 mg of ammonium purporate indicator (0.5 g

C8H4N5O6.NH4 thoroughly mixed with 100 g of K2CrO4) were added in sample water.

Titrated it against 0.01 N EDTA solution (2.0 g of EDTA and 0.05 g of MgCl 2.6H2O

dissolved in dd H2O with final volume of 1L and then standardized against 0.01 N

CaCl2 (0.5 g pure CaCl2 in 10 ml of 3 N 1+3HCl with final volume up to 1L) using

given titration procedure until the approach of end point which is from orange red to

lavender/purple. When closed to the endpoint, ammonium purporate (C8H4N5O6. NH4)

indicator is added drop wise (each drop after 5 − 10 second) as the color change is not

instantaneous.

Then, concentration of both calcium and magnesium together was determined by

taking 10 ml of water sample in a conical flask. 10 drops of NH4Cl/NH4OH buffer

solution (67.5 g NH4Cl dissolved in 570 ml of concentrated NH4OH with final volume

upto 1L) and 3-4 drops of Eriochrome black-T indicator (0.5 g of EBT and 4.5 g

hydroxyl amine hydrochloride was dissolved in 100 ml of 95% ethanol) were added in

sample water. Titrated it against EDTA solution until a change in color from wine red

to bluish green. Concentration of calcium and magnesium was calculated as follows;

39

Ca++ or Ca++ + Mg++ (meL-1) = R1 – R 2 × N × 1000 sample volume (ml)

Mg++ (me L-1) = Ca++ + Mg++ – Ca++ = R1 – R 2 × 0.01 × 1000 = (R1-R2) = R meL-1

10

Where,

R1 = ml of EDTA solution for sample

R2 = ml of EDTA solution for blank

All cations are expressed as meL-1. The meL-1 of calcium and magnesium were

converted to mgL-1 of calcium and magnesium by multiplying with their respective

equivalent weight.

2.2.2.2 k Sodium (mgL-1)

Sodium concentration was determined by using flame Photometer (Jenway PFP7,

China). Stock solution of Na (1000 ppm) was prepared by dissolving 2.5435 g dry

NaCl in 1 L of dd H2O and stored in a cool and dry place. Standards solutions of 10-

100 ppm were prepared from this stock solution by using the following formula;

C1V1 = C2V2

Where,

C1 = Concentration of stock solution in ppm

V1 = Volume to be taken of stock solution in ml

C2 = Concentration of Na or K to be required in ppm

V2 = Total volume to be required in ml

40

C2 (ppm required) = V1 × C 1

V2

Where, V1 = ml of known solution

C1 = ppm of known solution

V2 = total vokume to be made

The fuel supply of flame photometer was turned on and it was ignited by rotating the

fuel knob clockwise. After ignition the knob was rotated anticlockwise in such a way

that small cones were formed in flame. First aspirated the dd H2O for at least 10-15

minutes. Adjusted the required filter i.e. sodium filter by the knob provided. Adjusted

the zero on the scale by rotating the knob “Blank”. Aspirated the standard working

solution and recorded the reading in ppm. Again aspirated the distilled water and

checked whether there is zero on the scale, which indicates that instrument is

standardized according to the given sodium filter. Distilled water was aspirated before

aspirating the next sample. Distilled water was aspirated for at least 15 minutes for

cleaning after finishing the work. The concentration of sodium was determined as

follows;

Na (ppm) = ppm from calibration curve of standards.

2.2.2.2 l Sodium Adsorption Ratio (SAR)

The SAR was determined by the following calculations;

SAR = Na + [(Ca++ + Mg++)/2] 1/2

All the cations are expressed as meL-1.

2.2.3 Biological Parameters

2.2.3.1 Planktonic Biodiversity

41

The planktons were identified and counted by taking 1 ml of water sample on

Sedgewick rafter chamber (Pyser – SGI S50, UK) under compound microscope (PW-

BK 5000 China) at magnification 10 × 40x following Stephen et al., 2011. The

identification of phytoplanktons and zooplanktons upto genera level was done

following the standard taxonomic literature;

Ward and Whipple (1959); Prescott (1978); Belcher and Swale (1979); Fritsch (1979);

Tonapi (1980); Chapman and Chapman (1981); Battish (1992); Yunfang (1995) and

APHA (1998).

Relative abundance (%) and biodiversity indices (including Margalefs index, Shanon-

Weiner index, Simpson diversity index, Evenness index and Sorensom index of

similarity) were calculated by the following formulae;

RA (%) = n × 100 (Simpson, 1949) N

Margalef index (d) = (S − 1) (Margalef, 1968) Ln of N

Simpson diversity index (D) = ∑ ni (ni-1) (Simpson, 1949) N (N-1)

Shanon-Weaner index (H) = ∑ ni × Ln (ni/N) (Shannon and Weiner, 1963) N

Evenness index (E) = H (Pielou, 1966) LnS

Sorenson index of similarity (SI) = 2 E (Sorensen, 1948) A + B + C + D

Where,

A = number of species at WSS-1

B = number of species at WSS-2

C = number of species at WSS-3

42

D = number of species at WSS-4

E = number of species common between sites

n = number of individuals of one genera/phyla/division/group

ni = number of ith species

N = total number of individual in division/ phyla/group

S = number of species

Ln = natural logarithm

2.3 Fish biodiversity

Fish samples were collected from different localities of Suleman Mountain Range,

Dera Ghazi Khan (Table 2.1, Figure 2.1). The fishes were collected from standing and

running water bodies. Different gears including cast net (mesh size 2.5 cm2), scoop net

and drag net (mesh size 5 cm2) were used. Fish specimens were counted and then

transferred to laboratory in plastic containers alive. Fish specimens were preserved in

10% formaldehyde solution for identification. Fish specimens were identified

following standard taxonomic keys (Talwar and Jhingran, 1991; Mirza and Sharif,

1996).

Relative abundance (%) and various biodiversity indices [Margalef index (d), Shanon-

Weiner index (H), Simpson diversity index (D)and Evenness index] were calculated

by using formulae as described in section 2.2.3.1 The Jaccard’s index (j) of similarity

was calculated by using formula;

Sj= j (Jaccard, 1912) [(x+y) − j]

43

2.4 Fish External Morphometry

Fishes were anaesthetized and blotted dry on towels and were weighed on a balance

(CHYO 3000, Japan) to the nearest 0.1 g. Total length, standard length, fork length

and 18 other external body morphometric measurements were taken on measuring tray

(Perpex) to the nearst 0.1 mm. The condition factor (K) was calculated by the

following formula;

Condition factor (K) = W × 100 (Pauly, 1983) L3

Total length was taken flat and was taken as the length from tip of the snout to the tip

of the tail. Standard length was taken as the length from tip of the snout to the hidden

base of caudal fin. Head length was taken as the distance from the tip of the snout to

the most distant point on the opercular membrane. Inter-orbital width was taken as the

least distance between the bony rims between inner margins of eyes. Pre-orbital length

was measured from tip of the snout to the anterior hard margin of the orbit. Post-

orbital length was measured from the posterior edge of the orbit to the posterior tip of

the flashy operculum. Eye diameter was taken as the distance between the margins of

the cartilaginous eye ball across the cornea.

The length-weight relationship was calculated from W = aLb

Where,

W = total weight in g

L = total length in centimeters

a = coefficient related to body form

b = an exponent indicating isometric growth when equal to 3

44

The parameters ‘a’ and ‘b’ were estimated by linear regression on the transformed

equation:

log W = log a + b log L.

45

2.5 Statistical Analyses

Statistical software SPSS (version 20) was used for all statistical analyses. The effects

of monthly variations, seasonal variations and site variations on physico-chemical

parameters, planktonic abundance, richness and diversity were analyzed by applying

one way analysis of variance (ANOVA). The assumptions of i) normality of data and

ii) homogeneity of variance were fulfilled when and where required before applying

ANOVA. The Pearson correlations between various water quality parameters were

also calculated. The data for fish biodiversity was statistically analyzed for comparison

of fish abundance, species richness and diversity between various sites and fish

families, by applying one way ANOVA.The data on various morphometric parameters

was log transformed and coefficient of correlation (r) and regression (b) was

determined by least squared linear regression.

46

Results

3.1 Water Quality

3.1.1 Climatic factors

During sampling dates, clouds were observed occassionally at all sampling sites. The

clouds were present during January, February, July, August, September and December

at all sites on sampling dates. The clouds concentration observed was categorized as >

25%, < 25%, > 50% and < 50% (Table 2.3). The clouds were not present in March,

April, May, June, October and November at any of sites. Rain was not observed

during sampling dates at all sites. The day length recorded as photo period (hours) was

maximum (> 14 hours) in June and minimum (< 10.21 hours) in December for all sites

(Table 3.1.1 b). One way ANOVA demonstrated that there was i) no significant

difference between four sites (df = 3, F = 0.002, P > 0.05), ii) a significant effect of

seasonal variations (df = 3, F = 64.14, P < 0.001) (Table 3.1.1 a) and iii) a significant

effect of monthly variations (df = 11, F = 153.23, P < 0.001) (Table 3.1.1 a) for

photoperiod.

The maximum mean relative humidity (%) was observed in January for all four water

sampling sites (79.50% at WSS-1, 79% at WSS-2, 80.50% at WSS-3, 77% at WSS-4)

and minimum in May for three water sampling sites (31.50% at WSS-1, 36.50% at

WSS-3, 35.0% at WSS-4), and in April for WSS-2 (35. 0 %) (Figure 3.1.1 a). One way

ANOVA demonstrated that there was i) no significant difference between four sites

(df = 3, F = 0.41, P > 0.05), ii) a significant effect of seasonal variations (df = 3, F =

38.73, P < 0.001) (Table 3.1.1 a) and iii) a significant effect of monthly variations (df

= 11, F = 25.64, P < 0.001) (Table 3.1.1 b) for mean relative humidity.

47

Maximum air temperature (oC) was observed in July (41.8 oC for WSS-1, 40.6 oC for

WSS-2, 40.5 oC for WSS-3 and 40.8 oC for WSS-4), While minimum air temperature

was observed in January (12.5 oC for WSS-1, 15.4 oC for WSS-2, 18.5 oC for WSS-3

and 23.8 oC for WSS-4) (Figure 3.1.1 b). One way ANOVA demonstrated that there

was i) no significant difference between four sites (df = 3, F = 0.47, P > 0.05), ii) a

significant effect of seasonal variations (df = 3, F = 55.38, P < 0.001) (Table 3.1.1 a)

and iii) a significant effect of monthly variations (df = 11, F = 36.95, P < 0.001) for air

temperature (Table 3.1.1 b).

3.1.2 Physico-Chemical Parameters

The mean values along with standard deviations (SD) of all physico-chemical

parameters at four water sampling sites and in four seasons are presented in table 3.1.2

a and 3.1.2 b respectively. Maximum water temperature was recorded in July (32.8 °C

for WSS-1, 32.7 °C for WSS-2, 31.4 °C for WSS-3 and 34.7 °C for WSS-4)(Figure

3.1.2 a). The minimum water temperature was recorded in January (9.0 oC for WSS-1,

10.2 oC for WSS-2, in December 12.8 °C for WSS-3 and in January 18.2 °C for WSS-

4) (Figure 4.1.2 a). One way ANOVA demonstrated that there was i) no significant

difference between four sites (P >0.05, df = 3, F = 1.19) (Table 3.1.2 a) ii) a significant

effect of seasonal variations (df = 3, F = 50.71, P < 0.001) (Table 3.1.2 b) and iii) a

significant effect of monthly variations (df = 11, F = 28.30, P < 0.001) for water

temperature (Table 3.1.2 c).

Light penetration (cm) was maximum in January (32.5 cm at WSS-1), (30.8 cm at

WSS-2), in May (35.1 cm at WSS-3) and in March (33.4 cm at WSS-4). Light

penetration (cm) was minimum in September (11.8 cm at WSS-1), in August (12.5 cm

48

at WSS-2), in September (16.2 cm at WSS-3) and in August (17.4 cm at WSS-4)

(Figure 3.1.2.b). One way ANOVA demonstrated that there was i) no significant

difference between four sites (P >0.05, df = 3, F = 2.71) (Table 3.1.2 a), ii) a

significant effect of seasonal variations (df = 3, F = 61.37, P < 0.05) (Table 3.1.2 b)

and iii) a significant effect of monthly variations (df = 11, F = 5.26, P < 0.001) for

light penetration (Table 3.1.2 c).

The TDS (mgL-1) were maximum in February (1385 mgL-1 at WSS-1), in May (1430

mgL-1at WSS-2), in June (1360 mgL-1 at WSS-3 and 1795 mgL-1at WSS-4). The TDS

(mgL-1) were minimum in December (870 mgL-1at WSS-1), in August (870 mgL-1at

WSS-2), in September (941 mgL-1at WSS-3) and in January (1450 g at WSS-4)

(Figure 4.1.2 c). One way ANOVA demonstrated that there was i) a significant

difference between four water sampling sites (df = 3, F = 23.51, P < 0.001) (Table

3.1.2 a), ii) a non-significant effect of seasonal variations (df = 3, F = 0.55, P < 0.64)

(Table 3.1.2 b) and iii) a non-significant effect of monthly variations (df = 11, F =

0.46, P > 0.05) (Table 3.1.2 c) for total dissolved solids.

pH was maximum in June (7.5 at WSS-1), in May (8.3 at WSS-2), in August (8.5 at

WSS-3) and in May (8.3 at WSS-4). pH was minimum in October (7.5 at WSS-1), in

January (7.4 at WSS-2), in February (7.6 at WSS-3) and in November (7.4 at WSS-4)

(Figure 4.1.2 d). One way ANOVA demonstrated that there was i) a non-significant

difference between four water sampling sites (df = 3, F = 0.93, P > 0.05) (Table 3.1.2

a), ii) a significant effect of seasonal variations (df = 3, F = 6.47, P < 0.01) (Table

3.1.2 b) and iii) a non-significant effect of monthly variations (df = 11, F = 1.56, P >

0.5) (Table 3.1.2 c) for pH.

49

DO (mgL-1) was maximum in January (9.1 mgL-1at WSS-1), in December (8.9 mgL-1

at WSS-2), in January (8.6 mgL-1 at WSS-3 and 8.6 mgL-1 at WSS-4). DO (mgL-1)

was minimum in July (5.7 mgL-1 at WSS-1), in August (5.8 mgL-1 at WSS-2), in July

(5.8 mgL-1 at WSS-3) and (5.7 mgL-1 at WSS-4) (Figure 4.1.2 e). One way ANOVA

demonstrated that there was i) a non-significant difference between four water

sampling sites (df = 3, F = 1.15, P > 0.05) (Table 3.1.2 a), ii) a significant effect of

monthly variations (df = 11, F = 16.33, P < 0.001) (Table 3.1.2 b) and iii) a significant

effect of seasonal variations (df = 11, F = 32.53, P < 0.001) (Table 3.1.2 c) for DO.

CO2 (mgL-1) was maximum in May (9.7 mgL-1 at WSS-1), in June (8.9 mgL-1 at WSS-

2), in May (8.8 mgL-1 at WSS-3) and in June (8.5 mgL-1 at WSS-4). CO2 (mgL-1) was

minimum in December (6.1 mgL-1 at WSS-1), (5.5 mgL-1 at WSS-2), (5.6 mgL-1 at

WSS-3) and (6.4 mgL-1 at WSS-4) (Figure 4.1.2 f). One way ANOVA demonstrated

that there was i) a non-significant difference between four water sampling sites (df = 3,

F = 0.83, P > 0.05) (Table 3.1.2. a), ii) a significant effect of seasonal variations (df =

3, F = 51.68, P < 0.001) (Table 3.1.2 b) and iii) a significant effect of monthly

variations (df = 11, F = 19.06, P < 0.001) (Table 3.1.2 c) for CO2.

The CO3-2 (mgL-1) were maximum in December (19.8 mgL-1 at WSS-1), in June (24.0

mgL-1 at WSS-2), in January (20.1 mgL-1 at WSS-3) and in June (18.3 mgL-1 at WSS-

4). The CO3-2(mgL-1) were minimum in November (8.4 mgL-1 at WSS-1), in February

(8.4 mgL-1 at WSS-2), in September (9.0 mgL-1 at WSS-3) and in January (18.3 mgL-1

at WSS-4) (Figure 3.1.2 g). One way ANOVA demonstrated that there was i) a

significant difference between four water sampling sites (df = 3, F = 3.44, P < 0.05)

(Table 3.1.2 a), ii) a non-significant effect of seasonal variations (df = 3, F = 0.80, P =

50

0.49) (Table 3.1.2 b) and iii) a non-significant effect of monthly variations (df = 11, F

= 0.908, P = 0.543) (Table 3.1.2 c) for CO3-2.

HCO3-1 (mgL-1) were maximum in October (769.21 mgL-1 at WSS-1), in May (762.5

mgL-1 at WSS-2), in May (658.8 mgL-1 at WSS-3) and in June (433.1 mgL-1 at WSS-4).

HCO3-1(mgL-1) were minimum in December (286.04 mgL-1 at WSS-1), in August (305

mgL-1 at WSS-2), in September (527.65 mgL-1 at WSS-3) and in January (347.7 mgL-1

at WSS-4) (Figure 3.1.2 h). One way ANOVA demonstrated that there was i) a

significant differenceamong four water sampling sites (df = 3, F = 6.30, P = 0.001)

(Table 3.1.2 a), ii) non-significant effect of seasonal variations (df = 3, F = 0.44, P =

0.72) (Table 3.1.2 b) and iii) non-significant effect of monthly variations (df = 11, F =

0.550, P = 0.855) (Table 3.1.2 c) for HCO3-1.

TA (mgL-1) was maximum in October (125 mgL-1 at WSS-1), in May (150 mgL-1 at

WSS-2), in January (646 mgL-1 at WSS-3), in September (452 mgL-1 at WSS-4). TA

(mgL-1) was minimum in December (58 mgL-1 at WSS-1), in August (70 mgL-1 at

WSS-2), in January (538 mgL-1 at WSS-3), in August (358 mgL-1 at WSS-4) (Figure

3.1.2 i). One way ANOVA demonstrated that there was i) a significant difference

between four water sampling sites (df = 3, F = 48.09, P < 0.001) (Table 3.1.2 a.), ii)

non-significant effect of seasonal variations (df = 3, F = 0.27, P = 0.84) (Table 3.1.2 b)

and iii) non-significant effect of monthly variations (df = 11, F = 0.232, P = 0.993)

(Table 3.1.2 c) for TA.

TH (mgL-1) was maximum in April (290 mgL-1 at WSS-1), in May (310 mgL-1 at WSS-

2), in April (239 mgL-1at WSS-3), and in June (268 mgL-1at WSS-4). TH (mgL-1) was

51

minimum in November (210 mgL-1 at WSS-1), in August (230 mgL-1 at WSS-2), in

September (133 mgL-1 at WSS-3) and in January (217 mgL-1 at WSS-4) (Figure 3.1.2

j). One way ANOVA demonstrated that there was i) a significant difference between

four water sampling sites (df = 3, F = 12.05, P < 0.001) (Table 3.1.2 a), ii) no

significant effect of seasonal variations (df = 3, F = 0.70, P = 0.55) (Table 3.1.2 b) and

iii) no significant effect of monthly variations (df = 11, F = 0.855, P = 0.589) (Table

3.1.2 c) for TH.

Na+1 (mgL-1) was maximum in April (154.10 mgL-1 at WSS-1), in April (282.90 mgL-1

at WSS-2), in April and May (210 mgL-1 at WSS-3), in July and November (216.20

mgL-1 at WSS-4). Na+1 (mgL-1) was minimum in December (48.30 mgL-1 at WSS-1), in

January (71.3 mgL-1 at WSS-2), in July (69.0 mgL-1 at WSS-3) and in February (177.10

mgL-1 at WSS-4) (Figure 3.1.2 k). One way ANOVA demonstrated that there was i) a

significant difference between four water sampling sites (df = 3, F = 12.05, P < 0.001)

(Table 3.1.2 a), ii) non-significant effect of seasonal variations (df = 3, F = 0.84, P =

0.47) (Table 3.1.2 b) and iii) non-significant effect of monthly variations (df = 11, F =

0.85, P = 0.58) (Table 3.1.2 c) for Na+1.

Ca+2(mgL-1) was maximum in February (212 mgL-1 at WSS-1), in May (232 mgL-1 at

WSS-2), in April (210 mgL-1 at WSS-3) and in June (250 mgL-1 at WSS-4). Ca+2(mgL-

1) was minimum in November (150 mgL-1 at WSS-1), in August (120.40 mgL-1 at

WSS-2), in September (122 mgL-1 at WSS-3) and in August (206 mgL-1 at WSS-4)

(Figure 3.1.2 l). One way ANOVA demonstrated that there was i) a significant

difference between four water sampling sites (df = 3, F = 10.82, P < 0.001) (Table

3.1.2 a), ii) non-significant effect of seasonal variations (df = 3, F = 0.42, P = 0.73)

52

(Table 3.1.2 b) and iii) non-significant effect of monthly variations (df = 11, F = 0.811,

P = 0.629) (Table 3.1.2 b) for Ca+2.

Mg+2 (mgL-1) was maximum in April (68.40 mgL-1 at WSS-1), in July (66.0 mgL-1 at

WSS-2), in January (68.4 mgL-1 at WSS-3), and in June (74.4 mgL-1 at WSS-4). Mg+2

(mgL-1) was minimum in December (24 mgL-1 at WSS-1), in August (36 mgL-1 at

WSS-2), in September (30 mgL-1 at WSS-3) and in December (51.6 mgL-1 at WSS-4)

(Figure 3.1.2 m). One way ANOVA demonstrated that there was i) a significant

difference between four water sampling sites (df = 3, F = 3.64, P < 0.05) (Table 3.1.2

a), ii) non-significant effect of seasonal variations (df = 3, F = 0.90, P = 0.44) (Table

3.1.2 b) and iii) non-significant effect of monthly variations (df = 11, F = 1.40, P =

0.215) (Table 3.1.2 c) for Mg+2.

Cl-1 (mgL-1) was maximum in December (156.20 mgL-1 at WSS-1), in April (382.69

mgL-1 at WSS-2), in June (120.70 mgL-1 at WSS-3 and422.45 mgL-1 at WSS-4). Cl-1

(mgL-1) was minimum in April (79.87 mgL-1 at WSS-1), in February (86.97 mgL-1 at

WSS-2), in September (74.55 mgL-1 at WSS-3) and in August (323.05 mgL-1 at WSS-

4) (Figure 3.1.2 n). One way ANOVA demonstrated that there was i) a significant

difference between four water sampling sites (df = 3, F = 21.82, P < 0.001) (Table

3.1.2 a), ii) non-significant effect of seasonal variations (df = 3, F = 0.80, P = 0.50)

(Table 3.1.2 b) and iii) non-significant effect of monthly variations (df = 11, F = 0.410,

P = 0.942) (Table 3.1.2 c) for Cl-1.

SO4-2(mgL-1) were maximum in February (306.20 mgL-1 at WSS-1), in May (321.60

mgL-1 at WSS-2), in April and June (321.60 mgL-1 at WSS-3) and in June (427.20

mgL-1 at WSS-4). SO4-2(mgL-1) were minimum in December (148.80 mgL-1 at WSS-1),

53

in August (48.96 mgL-1 at WSS-2), in September (182.40 mgL-1 at WSS-3) and in

August (297.60 mgL-1 at WSS-4) (Figure 3.1.2 o). One way ANOVA demonstrated

that there was i) a significant difference between four water sampling sites (df = 3, F =

18.58, P < 0.001) (Table 3.1.2 a), ii) non-significant effect of seasonal variations (df =

3, F = 0.35, P = 0.78) (Table 3.1.2 b) and iii) non-significant effect of monthly

variations (df = 11, F = 0.696, P = 0.733) (Table 3.1.2 c) for SO4-2.

EC (dS m-1) was maximum in February (2.21 dS m-1 at WSS-1), in May (2.33 dS m-1 at

WSS-2), in January (2.8 dS m-1 at WSS-3) and in June (2.83 dS m-1 at WSS-4). EC (dS

m-1) was minimum in December (1.22 dS m-1 at WSS-1), in August (1.41 dS m-1 at

WSS-2), in September (1.48 dS m-1 at WSS-3) and in August (2.26 dS m-1 at WSS-4)

(Figure 3.1.2 p). One way ANOVA demonstrated that there was i) a significant

difference between four water sampling sites (df = 3, F = 15.40, P < 0.001) (Table

3.1.2 a), ii) non-significant effect of seasonal variations (df = 3, F = 0.46, P = 0.70)

(Table 3.1.2 b) and iii) non-significant effect of monthly variations (df = 11, F = 0.517,

P = 0.879) (Table 4.1.2 c) for EC.

SAR (mgL-1) was maximum in September (2.50 mgL-1 at WSS-1), in April (5.42 mgL-1

at WSS-2), in June (1.92 mgL-1 at WSS-3) and in July (3.90 mgL-1 at WSS-4). SAR

(mgL-1) was minimum in December (0.98 mgL-1 at site 1), in January (1.55 mgL-1 at

WSS-2), in September (1.48 mgL-1 at WSS-3) and in August (2.50 mgL-1 at WSS-4)

(Figure 3.1.2 q). One way ANOVA demonstrated that there was i) a significant

difference between four water sampling sites (df = 3, F = 21.82, P < 0.001) (Table

3.1.2 a), ii) non-significant effect of seasonal variations (df = 3, F = 0.80, P = 0.50)

54

(Table 3.1.2 b) and iii) non-significant effect of monthly variations (df = 11, F = 0.410,

P = 0.942) (Table 3.1.2 c) for SAR.

3.1.3 Planktonic Biodiversity

3.1.3.1 Species richness

A total of 119 planktonic genera including 83 of phytoplankton and 36 of zooplankton

were recorded in this study from four sampling sites of the four catchment areas of

Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan. Among phytoplankton

groups, the cyanophyta contributed 12, chlorophyta 33, chrysophyta 7, Euglenophyta

6, xanthophyta 4, bacillariophyta 12 and pyrrhophyta 9 genera at all sites during this

study (Table 3.1.3. a). Among zooplankton groups, the protozoa contributed 15, rotifer

11 and cladocera 10 genera at all sites during this study. The species richness was

variable at different sites (Table 3.1.3 a, Figure 3.1.3.1 a - Figure 3.1.3.1c), in seasons

(Table 3.1.3 b, Figure 3.1.3.1 d - Figure 3.1.3.1 n) and in months (Table 3.1.3 c, Figure

3.1.3.1o - 3.1.3.1z).

Among major phytoplankton groups, cyanophyta contributed 11 genera at WSS-1 and

WSS-2, 12 at WSS-3 and 9 at WSS-4 (Table 3.1.3 a, Figure 3.1.3.1b). Cyanophyta

were recorded 12 in winter season, 8 in spring season, 10 in summer and post-

monsoon (Table 3.1.3 b). Cyanophyta showed maximum species richness (9 genera) in

October and minimum species richness (7 genera) in several months (Table 3.1.3 c).

One way ANOVA demonstrated that there was i) no significant difference between

four water sampling sites (df = 3, F = 0.51, P = 0.67) (Table 3.1.3 a), ii) a significant

effect of seasonal variations (df = 3, F = 5.37, P = 0.003) (Table 3.1.3 b) and iii) non-

55

significant effect of monthly variations (df = 11, F = 3.40, P < 0.01), (Table 3.1.3. c)

for species richness of cyanophyta.

Chlorophyta contributed 31 genera at WSS-1, 26 at WSS-2, 24 at WSS-3 and 16 at

WSS-4 (Table 3.1.3 a). Chlorophyta were recorded 28 in winter season, 18 in spring

season, 21 in summer season and 15 in post-monsoon(Table 3.1.3 b). Chlorophyta

showed maximum species richness (14 genera) in November and minimum species

richness (9 genera) in June and September (Table 3.1.3 c). One way ANOVA

demonstrated that there was i) a significant difference between four sites (df = 3, F =

4.61, P < 0.01) (Table 3.1.3 a), ii) non-significant effect of seasonal variations (df = 3,

F = 0.47, P = 0.70) (Table 3.1.3 b) and iii) a significant effect of monthly variations (df

= 11, F = 2.25, P = 0.033) (Table 3.1.3 c) for species richness of chlorophyta.

Chrysophyta contributed 7 genera at WSS-1, 5 at WSS-2 and WSS-3 and 3 at WSS-4

(Table 3.1.3 a).Chrysophyta were 5 in winter and 4 in spring, summer, post-monson

season each (Table 3.1.3 b). Chrysophyta showed maximum species richness (5

genera)in April and minimum species richness (2 genera) in November (Table 3.1.3 a,

Table 3.1.3 b). One way ANOVA demonstrated that there was i) no significant

difference between four sites (df = 3, F = 0.59, P = 0.62) (Table 3.1.3 a), ii) no

significant effect of seasonal variations (df = 3, F = 1.55, P = 0.21) (Table 3.1.3 b.) and

iii) non-significant effect of monthly variations (df = 11, F = 0.83, P = 0.60) (Table

3.1.3 c) for species richness of chrysophyta.

Euglenophyta contributed 5 genera at WSS-1, 4 at WSS-2, WSS-3 and WSS-4 (Table

3.1.3 a). Euglenophyta showed 5 genera in winter, 4 in spring, 3 in summer and 4 in

56

post-monson (Table 3.1.3 b). Euglenophyta showed maximum species richness in

January (6 genera) and minimum species richness (2 genera) in July (Table 3.1.3 c).

One way ANOVA demonstrated that there was i) a significant difference between four

sites (df = 3, F = 1.23, P = 0.31) (Table 3.1.3 a), ii) non-significant effect of seasonal

variations (df = 3, F = 1.20, P = 0.32) (Table 3.1.3 b) and iii) non-significant effect of

monthly variations (df = 11, F = 1.65, P = 0.12) (Table 3.1.3 c) for species richness of

euglenophyta.

Xanthophyta contributed 4 genera at WSS-1, 3 at WSS-2 and WSS-3 and 2 at WSS-4

(Table 3.1.3 a). Xanthophyta showed 3 genera in winter, 1 in spring, 3 in summer and

2 in post-monson season (Table 3.1.3 b). Xanthophyta showed maximum species

richness in January (3 genera) while no xanthophyta was observed in September at all

sites (Table 3.1.3 c). One way ANOVA was not applicable for statistical analysis of

species richness of xanthophyta.

Bacillariophyta contributed 11 genera at WSS-1, 12 at WSS-2, 11 at WSS-3 and 9 at

WSS-4 (Table 3.1.3 a). Bacillariophyta were recorded 11 in winter season, and 9 in

spring, summer and post-monsoon season (Table 3.1.3 b). Bacillariophyta showed

maximum species richness (14 genera) in November and minimum species richness (9

genera) in June and September (Table 3.1.3 c). One way ANOVA demonstrated that

there was i) no significant difference between four sites (df = 3, F = 0.88, P = 0.45)

(Table 3.1.3 a), ii) a significant effect of seasonal variations (df = 3, F = 9.38, P

<0.001) (Table 3.1.3 b) and iii) a significant effect of monthly variations (df = 11, F =

7.62, P < 0.001) (Table 3.1.3 c) for species richness of bacillariophyta.

57

Pyrrhophyta contributed 5 genera at WSS-1, 6 at WSS-2, 7 at WSS-3 and 5 at WSS-4

(Table 3.1.3 a). Pyrrhophyta were recorded 4 in winter, spring, post-monson each and

6 in summer (Table 3.1.3 c). Pyrrhophyta showed maximum species richness (4

genera) in April, August and October and minimum (2 genera) in February, July and

September (Table 3.1.3 c). One way ANOVA demonstrated that there was i) no

significant difference between four sites (df = 3, F = 2.04, P = 0.12) (Table 3.1.3 a), ii)

non-significant effect of seasonal variations (df = 3, F = 1.79, P = 0.16) (Table 3.1.3 c)

and iii) non-significant effect of monthly variations (df = 11, F = 1.23, P = 0.30)

(Table 3.1.3 c) for species richness of pyrrhophyta.

Among major zooplankton groups, protozoa contributed 9 genera at WSS-1, 13 at

WSS-2, 10 at WSS-3 and WSS-4 (Table 3.1.3 a). Protozoa were recorded 10 in winter

season, 8 in spring season, 11 in summer season and 7 in post-monsoon (Table 3.1.3

b). Protozoa has maximum species richness (8 genera) in April, July, October and

November, while minimum (5 genera) in June, August and September (Table 3.1.3 c).

One way ANOVA demonstrated that there was i) a significant difference between four

sites (df = 3, F = 3.46, P = 0.024) (Table 3.1.3 a), ii) non-significant effect of seasonal

variations (df = 3, F = 0.94, P = 0.42) (Table 3.1.3 b) and iii) a significant effect of

monthly variations (df = 11, F = 2.80, P = 0.01) (Table 3.1.3 c) for species richness of

protozoa.

Rotifera contributed 11 genera at WSS-1, 8 at WSS-2 & WSS-3 and 7 at WSS-4

(Table 3.1.3 a). Rotifera were recorded 8 in winter and spring season, 6 in summer

season and 5 in post-monsoon season (Table 3.1.3 b). Rotifera has maximum species

richness (7 genera) in March and September while minimum (3 genera) in June and

July (Table 3.1.3 c). One way ANOVA demonstrated that there was i) no significant

58

difference between four sites (df = 3, F = 0.69, P = 0.56) (Table3.1.3 a), ii) a

significant effect of seasonal variations on species richness of rotifera (df = 3, F =

5.43, P = 0.003) (Table 3.1.3 b) and iii) a significant effect of monthly variations (df =

11, F = 4.25, P < 0.001) (Table 3.1.3 c) for species richness of rotifera.

Cladocera contributed 10 genera at WSS-1, 5 at WSS-2 & WSS-3 and 4 at WSS-4

(Table 3.1.3 a). Cladocera were 6 in winter, 5 in spring, 6 in summer and 5 in post-

monson (Table 3.1.3 b). Cladocera has maximum species richness (5 genera) in

September and minimum in April, May, June and July. One way ANOVA

demonstrated that there was i) a significant difference between four sites (df = 3, F =

2.90, P < 0.05) (Table 3.1.3 a), ii) non-significant effect of seasonal variations (df = 3,

F = 0.42, P = 0.73) (Table 3.1.3 b) and iii) a significant effect of monthly variations (df

= 11, F = 2.77, P = 0.01) (Table 3.1.3 c) for species richness of cladocera.

The total number of phytoplankton genera was 74 at WSS-1, 67 at WSS-2, 65 at WSS-

3 and 48 at WSS-4 (Table 3.1.3 a). Total genera of phytoplankton were 68 in winter

season, 48 in spring season, 56 in summer season and 48 in post-monsoon season

(Table 3.1.3 b).The total number of phytoplankton were maximum (42 genera)in

January and minimum (25 genera) in September (Table 3.1.3 c). One way ANOVA

demonstrated that there was i) a significant difference between four sites (df = 3, F =

4.25, P = 0.01) (Table 3.1.3 a), ii) a significant effect of seasonal variations (df = 3, F

= 3.95, P = 0.008) (Table 3.1.3 b) and iii) a significant effect of monthly variations (df

= 11, F = 3.01, P = 0.006) (Table 3.1.3 c) for species richness of total phytoplankton.

59

Total zooplankton genera were 34 at WSS-1, 23 at WSS-2, 27 at WSS-3 and 21 at

WSS-4 (Table 3.1.3 a). Total zooplankton genera were recorded 24 in winter, 21 in

spring, 23 in summer and 19 in post-monson (Table 3.1.3 b). The total number of

zooplankton genera were maximum (15 genera) in September and December and

minimum (12 genera) in January and May (Table 3.1.3. c). One way ANOVA

demonstrated that there was i) no significant difference between four sites (df = 3, F =

1.36, P = 0.26), (Table 3.1.3 a), ii) non-significant effect of seasonal variations (df = 3,

F = 1.70, P = 0.18) (Table 3.1.3 b) and iii) a significant effect of monthly variations (df

= 11, F = 5.03, P < 0.001) (Table 3.1.3 c) for species richness of total zooplankton.

The total number of planktonic genera was 108 at WSS-1, 90 at WSS-2, 92 at WSS-3

and 69 at WSS-4 (Table 3.1.3 a). The total plankton genera recorded were 92 in winter

season, 69 in spring season, 79 in summer season and 67 in post-monsoon (Table 3.1.3

b). The total number of plankton genera were maximum (54 genera) in January and

minimum (40 genera) in September (Table 3.1.3 c). One way ANOVA demonstrated

that there was i) a significant difference between four sites (df = 3, F = 6.60, P =

0.001) (Table 3.1.3 a), ii) non-significant effect of seasonal variations (df = 11, F =

2.53, P = 0.06) (Table 3.1.3 b) and iii) non-significant effect of monthly variations (df

= 11, F = 1.90, P = 0.75) (Table 3.1.3 c) for species richness of total plankton.

60

3.1.3.2 Relative abundance

The relative abundance of planktonic groups was calculated within each site (Table

3.1.5 a) and within all sites (Table 3.1.5 b); as well as within each seasons (Table 3.1.5

c) and within all seasons (Table 3.1.5 d).

The overall RA of phytoplankton was found to be 81.86% and of zooplankton was

18.14% at all sites of study area (Table 3.1.5 a, Figure 3.1.3.2a). Among

Phytoplankton groups, cyanophyta has relative abundance 26.82%, chlorophyta

28.80%, chrysophyta 3.34%, euglenophyta 4.59%, xanthophyta 0.87%, bacillariophyta

13.53%, and pyrrhophyta 3.91%. Among zooplankton groups, protozoa have relative

abundance 11.57%, rotifera 5.78% and cladocera 0.79%. The relative abundance of all

planktonic groups varied at sampling sites (Table 3.1.5 a, Table 3.1.5 b, Figure 3.1.3.2

a - 3.1.3.2 c) in seasons (Table 3.1.5 c, Table 3.1.5 d, Figure 3.1.3.2 d - 3.1.3.2 n) and

in months (Table 3.1.5 e, Figure 3.1.3.2 o - 3.1.3.2 z).

Among phytoplankton groups, cyanophyta has relative abundance of 24.23% at WSS-

1, 27.82% at WSS-2, 28.69% at WSS-3, 26.70% at WSS-4 (Table 3.1.5a). Cyanophyta

has relative abundance 8.75% in winter, 4.21% in spring, 8.73% in summer and 5.12%

in post monsoon (Table 3.1.5 d, Figure 3.1.3.2d). Cyanophyta has maximum relative

abundance in July (2.76%) and minimum in May (1.47%) (Table 3.1.5 e). One way

ANOVA demonstrated that there was i) a significant difference between four water

sampling sites (df = 3, F = 12.25, P < 0.001) (Table 3.1.4 a), ii) non-significant effect

of seasonal variations (df = 3, F = 0.84, P = 0.47) (Table 3.1.4 b) and iii) non-

significant effect of monthly variations (df = 11, F = 1.40, P = 0.21) (Table 3.1.4 c) for

number of organisms of cyanophyta.

61

The chlorophyta was 29.34% at WSS-1, 28.32% at WSS-2, 29.79% at WSS-3 and

27.25% at WSS-4 (Table 3.1.5 a). Chlorophyta has relative abundance 9.94% in

winter, 4.28% in spring, 10.34% in summer and 4.24% in post-monsoon (Table 3.1.5

d). Chlorophyta has maximum relative abundance in July (2.99%) and minimum in

March (1.79%) (Table 3.1.5 e). One way ANOVA demonstrated that there was i) a

significant difference between four sites (df = 3, F = 14.62, P < 0.001) (Table 3.1.4 a),

ii) no significant effect of seasonal variations (df = 3, F = 1.21, P = 0.31) (Table 3.1.4

b) and iii) no significant effect of monthly variations (df = 11, F = 1.00, P = 0.46)

(Table 3.1.4 c) for number of organisms ofchlorophyta.

The chrysophyta was 4.19% at WSS-1, 3.79% at WSS-2, 2.57% at WSS-3 and 2.29%

at WSS-4 (Table 3.1.5 a). Chrysophyta has relative abundance of 1.25% in winter,

0.68% in spring, 1.04% in summer and 0.37% in post monsoon (Table 3.1.5 d).

Chrysophyta has maximum relative abundance in February (0.48%) and minimum in

October (0.11%) (Table 3.1.5 e). One way ANOVA demonstrated that there was i) a

significant difference between four sites (df = 3, F = 9.98, P < 0.001) (Table 3.1.4 a),

ii) nosignificant effect of seasonal variations (df = 3, F = 1.56, P = 0.21) (Table 3.1.4

b) and iii) no significant effect of monthly variations (df = 11, F = 1.24, P = 0.29)

(Table 3.1.4c) for number of organisms of chrysophyta.

The euglenophyta was 5.28% at WSS-1, 4.20% at WSS-2, 4.32% at WSS-3 and 4.51%

at WSS-4 (Table 3.1.5 a). Euglenophyta has relative abundance of 1.73% in winter,

0.81% in spring, 1.34% in summer and 0.72% in post-monsoon (Table 3.1.5 d).

Euglenophyta has maximum relative abundance in March (0.50%) and minimum in

July (0.27%) (Table 3.1.5 e). One way ANOVA demonstrated that there was i) a

62

significant difference among four sites (df = 3, F = 5.26, P = 0.003) (Table 3.1.4 a), ii)

no significant effect of seasonal variations (df = 3, F = 0.85, P = 0.47) (Table 3.1.4 b)

and iii) no significant effect of monthly variations (df = 11, F = 1.14, P = 0.29) (Table

3.1.4c) for number of organisms of euglenophyta.

The xanthophyta was 1.38% at WSS-1, 0.71% at WSS-2, 0.57% at WSS-3 and 0.77%

at WSS-4 (Table 3.1.5 a). Xanthophyta has relative abundance in 0.37% winter, 0.14%

in spring, 0.26% in summer and 0.10% in post monsoon (Table 3.1.5 d.). Xanthophyta

has maximum relative abundance in November (0.13%) and minimum in February

(0.02%) (Table 3.1.5 e). One way ANOVA demonstrated that there was i) a significant

difference between four sites (df = 3, F = 4.12, P = 0.012) (Table 3.1.4 a), ii) no

significant effect of seasonal variations on (df = 3, F =0.61, P = 0.61) (Table 3.1.4 b)

and iii) no significant effect of monthly variations (df = 11, F = 1.45, P = 0.19) (Table

3.1.4 c) for number of organisms of xanthophyta.

The bacillariophyta was 12.49% at WSS-1, 13.73% at WSS-2, 13.16% at WSS-3 and

15.46% at WSS-4 (Table 3.1.5 a). Bacillariophyta has relative abundance7.78% in

winter, 2.00% in spring, 2.24% in summer and 1.49% in post monsoon (Table 3.1.5d).

Bacillariophyta has maximum relative abundance in January (2.87%) and minimum in

May (0.45%) (Table 3.1.5 e). One way ANOVA demonstrated that there was i) no

significant difference between four sites (df = 3, F = 1.66, P = 0.18) (Table 3.1.4 a), ii)

a significant effect of seasonal variations (df = 3, F = 14.02, P < 0.001) (Table 3.1.4 b)

and iii) a significant effect of monthly variations (df = 11, F = 10.23, P < 0.001) (Table

3.1.4 c) for number of organisms of bacillariophyta.

63

The pyrrhophyta was 5.36% at WSS-1, 3.83% at WSS-2, 3.04% at WSS-3 and 2.91%

at WSS-4 (Table 3.1.5 a). Pyrrhophyta has relative abundance of 1.32% in winter,

0.77% in spring, 0.92% in summer and 0.89% in post monsoon (Table 3.1.5 d).

Pyrrhophyta has maximum relative abundance in September (0.46%) and minimum in

July (0.17%) (Table 3.1.5 e). One way ANOVA demonstrated that there was i) a

significant difference between four sites (df = 3, F = 10.80, P < 0.001) (Table 3.1.4 a),

ii) no significant effect of seasonal variations (df = 3, F = 2.87, P = 0.47) (Table 3.1.4

b) and iii) no significant effect of monthly variations (df = 11, F = 0.82, P = 0.61)

(Table 3.1.4 c) for number of organisms of pyrrhophyta.

Among zooplankton groups, protozoa have relative abundance of 9.72% at WSS-1,

11.71% at WSS-2, 12.21% at WSS-3 and 13.45% at WSS-4 (Table 3.1.5a). Among

zooplankton group, protozoa have relative abundance in 4.10% winter, 1.67% in

spring, 3.87% in summer and 1.91% in post monsoon (Table 3.1.5 d). Among major

zooplankton group, protozoa has maximum relative abundance in July (1.17%) and

minimum in March 0.71% (Table 3.1.5 e). One way ANOVA demonstrated that there

was i) a significant difference between four sites (df = 3, F = 6.48, P = 0.003) (Table

3.1.4 a), ii) no significant effect of seasonal variations (df = 3, F = 1.09, P = 0.36)

(Table 3.1.4 b) and iii) no significant effect of monthly variations (df = 11, F = 1.24, P

= 0.29) (Table 3.1.4 c) for number of organisms of protozoa.

The rotifers were 7.21% at WSS-1, 5.07% at WSS-2, 4.89% at WSS-3 and 5.89% at

WSS-4 (Table 3.1.5 a, Figure 3.1.3.1 c). Rotifera have RA of 1.85% winter, 1.11% in

spring, 1.56% in summer and 1.24% in post monsoon (Table 3.1.5 d). Rotifera has

maximum relative abundance in March (0.65%) and minimum in July (0.27%) (Table

64

3.1.5 e). One way ANOVA demonstrated that there was i) a significant difference

between four sites (df = 3, F= 5.55, P = 0.003) (Table 3.1.4 a), ii) no significant effect

of seasonal variations (df = 3, F = 2.17, P = 0.10) (Table 3.1.4b) and iii) no significant

effect of monthly variations (df = 11, F = 1.28, P = 0.27) (Table 3.1.4 c) for number of

organisms of rotifera.

The cladocera were 0.80% at WSS-1, 0.82% at WSS-2, 0.76% at WSS-3 and 0.77% at

WSS-4 (Table 3.1.5 a). Cladocera have RA of 0.23% in winter, 0.19% in spring,

0.25% in summer and 0.17% in post monsoon (Table 3.1.5d). Cladocera has maximum

relative abundance in March (0.11%) and minimum in July (0.02%) (Table 3.1.5 e).

One way ANOVA demonstrated that there was i) no significant difference between

four sites (df = 3, F = 2.58, P = 0.065) (Table 3.1.4 a), ii) a significant effect of

seasonal variations (df = 3, F = 3.04, P = 0.038) (Table 3.1.4 b) and iii) no significant

effect of monthly variations (df = 11, F = 1.97, P = 0.062) (Table 3.1.4 c) for number

of organisms of cladocera.

The total phytoplanktons were 82.27% at WSS-1, 82.40% at WSS-2, 82.14% at WSS-

3 and 79.89% at WSS-4 (Table 3.1.5 a). The total phytoplankton showed relative

abundance of 31.14% in winter, 12.89% in spring, 24.80% in summer and 12.93% in

post monsoon (Table 3.1.4 d). Phytoplankton has maximum relative abundance in

January (8.30%) and minimum in May (5.02%) (Table 3.1.5 e). One way ANOVA

demonstrated that there was i) a significant difference between four sites (df = 3, F =

19.10, P < 0.001) (Table 3.1.4 a), ii) no significant effect of seasonal variations (df = 3,

F = 2.53, P = 0.06) (Table 3.1.4 b) and iii) no significant effect of monthly variations

65

(df = 11, F = 1.05, P = 0.42) (Table 3.1.4 c) for number of organisms of total

phytoplankton.

The total zooplanktons were 17.73% at WSS-1, 17.60% at WSS-2, 17.86% at WSS-3

and 20.11% at WSS-4 (Table 3.1.5 a). The zooplankton showed RA of 6.18% in

winter, 2.97% in spring, 5.68% in summer and 3.33% in post monsoon (Table 3.1.4 d).

Zooplankton has maximum relative abundance in September (1.72%) and minimum in

June (1.22%) (Table 3.1.3 e). One way ANOVA demonstrated that there was i) a

significant difference among four sites (df = 3, F = 9.95, P < 0.001) (Table 3.1.4 a), ii)

no significant effect of seasonal variations (df = 3, F = 0.99, P = 0.40) (Table 3.1.4 b)

and iii) no significant effect of monthly variations (df = 11, F = 0.53, P = 0.86) (Table

3.1.4 c) for number of organisms of total zooplankton.

One way ANOVA demonstrated that there was i) a significant difference among four

sites (df = 3, F = 21.96, P < 0.001) (Table 3.1.4 a), ii) no significant effect of seasonal

variations (df = 11, F = 2.15, P = 0.10), (Table 3.1.4 b) and iii) no significant effect of

monthly variations on number of organisms of total plankton (df = 11, F = 0.85, P =

0.42) (Table 3.1.4 c) for number of organisms of total plankton.

66

3.1.3.3 Diversity indices

Simpson diversity index

The overall value of Simpson diversity index was found to be 1.00 for all the plankton

genera at all study sites during study period. It was 0.67 for total phytoplankton and

0.03 for total zooplankton. The observed value for phytoplankton was 0.68 at WSS-1

and at WSS-2, 0.67 at WSS-3 and 0.64 at WSS-4. The observed value for total

zooplankton was 0.03 at WSS-1, WSS-2, WSS-3 and 0.04 at WSS-4. The value of

Simpson index for all plankton was 0.91 in winter, 0.88 in spring, 0.92 in summer and

0.87 in post-monsoon. The value of Simpson index for total phytoplankton was 0.70 in

winter, 0.66 in spring and summer, and 0.63 in post monsoon. The value of Simpson

index for total zooplankton was 0.03 in winter, spring and summer, and 0.04 in post-

monsoon (Table 3.1.6 a and Table 3.1.6 b).

Shannon - Weiner Diversity index

The value of Shannon-Weiner diversity index was found to be 0.16 for phytoplankton

genera and 0.31 for zooplankton genera at all study sites and all seasons during study

period. The overall value for total plankton was 2.38 at WSS-1, 2.31 at WSS-2, 2.26 at

WSS-3 and 2.34 at WSS-4 (Table 3.1.6 c). The observed value for phytoplankton was

1.46 at WSS-1, 1.40 at WSS-2, 1.35 at WSS-3 and 1.36 at WSS-4 (Table 3.1.6 c). The

observed value for zooplankton was 0.45 at WSS-1, 0.44 at WSS-2, WSS-3 and 0.47

at WSS-4 (Table 3.1.6 c). The value of Shannon-Weaner index for total plankton was

0.37 in winter, 0.29 in spring, 0.36 in summer and 0.30 in post monsoon (Table 3.1.6

d). The value of Shannon-Weiner index for total phytplankton was 0.15 in winter, 0.17

in spring, summer and 0.18 in post monsoon (Table 3.1.6 d). The value of Shannon -

67

Weiner index for total zooplankton was 0.30 in winter, 0.31 in spring, summer and

0.30 in post monsoon (Table 3.1.6 d).

Margalef Index (d)

The overall value of Margalef diversity index was found to be 12.13 for all the

plankton genera at all study sites. It was 8.61 for the phytoplankton and 4.37 for

zooplankton. The overall value for all plankton was 12.63 at WSS-1, 10.48 at WSS-2,

10.90 at WSS-3 and 8.53 at WSS-4. The observed value for total phytoplankton was

8.82 at WSS-1, 7.95 at WSS-2, 7.85 at WSS-3 and 6.05 at WSS-4. The observed value

for total zooplankton was 4.89 at WSS-1, 3.25 at WSS-2, 3.92 at WSS-3 and at 3.14

WSS-4 (Table 3.1.6 e). The value of margalef index for all plankton was 10.41 in

winter, 8.63 in spring, 9.14 in summer and 8.34 in post monsoon. The value of

margalef index for phytplankton was 7.83 in winter, 6.12 in spring, 6.60 in summer

and 6.12 in post monsoon. The value of margalef index for zooplankton was 3.31 in

winter, 3.22 in spring, 3.21 in summer and 2.85 in post monsoon (Table 3.1.6 f).

Evenness index (E)

The overall value of evenness index for phytoplankton was 0.04 and 0.09 for

zooplankton for all sites and seasons. The value of Evenness index for total plankton

was found to be 0.51 at WSS-1, WSS-2, 0.50 at WSS-3 and 0.55 at WSS-4. It was

0.34 for phytoplankton at WSS-1, 0.33 at WSS-2, 0.32 at WSS-3 and 0.35 at WSS-4.

It was 0.13 for zooplankton at WSS-1, 0.14 at WSS-2, 0.13 at WSS-3 and 0.15 at

WSS-4(Table 3.1.6 g). The value of evenness index for total plankton was 0.08 in

winter, 0.07 in spring, 0.08 in summer and 0.07 in post monsoon. The value of

evenness index for phytoplankton was 0.04 in winter, spring, summer and 0.05 in post-

68

monsoon. The value of evenness index for zooplankton was 0.09 in winter, 0.10 in

spring, summer and 0.11 in post monsoon (Table 3.1.6 h).

Sorenson Similarity index (S)

The value of Sorenson similarity index for all plankton genera among all sites was

found to be 0.33 while it was 0.36 for total phytoplankton and 0.24 for total

zooplankton. It was found maximum (0.87) between WSS-1 and WSS-2, and

minimum (0.65) between WSS-2 and WSS-3 for total plankton genera. It was found

maximum (0.90) between WSS-1 and WSS-2, and minimum (0.62) between WSS-2

and WSS-4 for total phytoplankton genera. It was found maximum (0.85) between

WSS-2 and WSS-3, and minimum (0.77) among WSS-2 and WSS-4 for zooplankton

genera. Among the major planktonic groups, cyanophyta, bacillariophyta and

pyrrhophyta have maximum similarity index value (0.42) between all sites. While

cladocera has minimum value (0.17) between all sites. Cyanophyta showed maximum

value (1) between WSS-1 and WSS-2, and xanthophyta showed maximum value (1)

between WSS-2 and WSS-3. The cladocera has minimum value (0.57) between WSS-

1 and WSS-4 (Table 3.1.6 i).

The similarity index value was maximum (0.84) between winter and post monsoon,

and minimum (0.70) between spring and post monsoon for total plankton genera. It

was maximum (0.85) between winter and post-monsoon, and minimum (0.68) between

spring and post monsoon for total phytoplankton genera. It was maximum (0.84)

between winter and post monsoon, and minimum (0.66) between summer and post-

monsoon for zooplankton genera. Several groups showed maximum value of Sorenson

similarity index (1) among different pairs of seasons. Cyanophyta showed maximum

69

value (1) between summer and post-monsoon, chrysophyta among winter and summer,

xanthophyta between winter and summer, bacillariophyta between spring and summer,

and pyrrhophyta between winter and spring and spring and summer. While

xanthophyta has minimum value (0.5) between winter and spring and cladocera (0.5)

between winter and summer (Table3.1.6 j).

3.1.3.4 Correlation Analysis

A number of strong and significant Pearson correlations were found among different

studied parameters at four study sites.

Correlations between Physico-chemical Parameters

Water temperature was positively correlated with pH at WSS-2 (r = 0.68, P< 0.014), at

WSS-3 (r = 0.86, P < 0.000); with TDS at WSS-4 (r = 0.61, P < 0.05); with CO 2 at

WSS-1 (r = 0.85, P < 0.001),WSS-2 (r = 0.87, P < 0.001), WSS-3 (r = 0.84, P < 0.001),

WSS-4 (r = 0.92, P < 0.001); with CO3-2 at WSS-4 (r = 0.61, P < 0.05); with HCO3

-1 at

WSS-4 (r = 0.57, P < 0.05); with TA at WSS-4 (r = 0.59, P < 0.05); and with SO4-2 at

WSS-4 (r = 0.54, P < 0.05). Water temperature was negatively correlated with DO at

WSS-1 (r = - 0.94, P < 0.001), WSS-2 (r = -0.91, P < 0.001), WSS-3 (r = -0.87, P <

0.001), WSS-4 (r = -0.89,P < 0.001).

Light penetration was positively correlated with TDS at WSS-2 (r = 0.73,P < 0.01),

WSS-3 (r = 0.82,P < 0.001); with DO at WSS-1 (r = 0.69, P < 0.05), at WSS-2 (r =

0.68, P < 0.05), at WSS-4 (r = 0.71, P < 0.01); with CO3-2 at WSS-3 (r = 0.78, P <

0.01); with HCO3-1 at WSS-2 (r = 0.59, P < 0.05), at WSS-3 (r = 0.82, P < 0.01); with

TA at WSS-2 (r = 0.64, P < 0.05), at WSS-3 (r = 0.82, P < 0.01); with TH at WSS-2 (r

= 0.70, P < 0.05), at WSS-3 (r = 0.74, P < 0.01); with Ca+2 at WSS-3 (r = 0.72, P

70

<0.01); with Mg+2 at WSS-3 (r = 0.82, P < 0.01); with SO4-2 at WSS-2 (r = 0.70, P <

0.05), at WSS-3 (r = 0.82, P < 0.01); with EC at WSS-2 (r = 0.72, P < 0.01), at WSS-3

(r = 0.62, P < 0.05); with SAR at WSS-3 (r = 0.88, P < 0.001). Light penetration was

negatively correlated with CO2 at WSS-2 (r = -0.64, P < 0.05) only.

TDS were positively correlated with CO3-2 at WSS-3 (r = 0.94, P < 0.001), at WSS-4 (r

= 0.96, P < 0.001); with HCO3-1 at WSS-1 (r = 0.97, P < 0.001),at WSS-2 (r = 0.59, P <

0.05), at WSS-3 (r = 0.94, P < 0.001),at WSS-4 (r = 0.92, P < 0.001); with TA at WSS-

1 (r = 0.95, P < 0.001),at WSS-2 (r = 0.96, P < 0.001), at WSS-3 (r = 0.95, P < 0.001),

at WSS-4 (r = 0.90, P < 0.001); with TH at WSS-1 (r = 0.92, P < 0.001), at WSS-2 (r =

0.91, P < 0.001), at WSS-3 (r = 0.92, P < 0.001),at WSS-4 (r = 0.94, P < 0.001); with

Na+1 at WSS-1 (r = 0.90, P < 0.001), at WSS-4 (r = 0.91, P < 0.001); with Ca +2 at

WSS-1 (r = 0.89, P < 0.001),at WSS-2 (r = 0.75, P < 0.01), at WSS-3 (r = 0.91, P <

0.001), at WSS-4 (r = 0.93, P < 0.001); with Mg+2 at WSS-1 (r = 0.82, P < 0.001),at

WSS-3 (r = 0.79, P < 0.01),at WSS-4 (r = 0.96, P < 0.001); with Cl -1 at WSS-3 (r =

0.87, P < 0.001), at WSS-4 (r = 0.95, P < 0.001); with SO4-2 at WSS-1 (r = 0.80, P <

0.01),at WSS-2 (r = 0.82, P < 0.001), at WSS-3 (r = 0.91, P < 0.001),at WSS-4 (r =

0.96, P < 0.001); with EC at WSS-1 (r = 0.99, P < 0.001),at WSS-2 (r = 0.99, P <

0.001), at WSS-3 (r = 0.77, P < 0.01), at WSS-4 (r = 0.99, P < 0.001); with SAR at

WSS-1 (r = 0.89, P < 0.001), at WSS-3 (r = 0.96, P < 0.001),at WSS-4 (r = 0.98, P <

0.001). TDS were negatively correlated with Cl-1 at WSS-1 (r = - 0.96, P < 0.001). The

pH waspositively correlated with CO2 at WSS-2 (r = 0.68, P < 0.05),at WSS-3 (r =

0.80, P < 0.01),at WSS-4 (r = 0.60, P < 0.05); with HCO3-1 at WSS-1 (r = 0.57, P <

0.05), at WSS-4 (r = 0.61, P < 0.05); with TA at WSS-4 (r = 0.90, P < 0.001).

71

The pH was negatively correlated with DOat WSS-4 (r = 0.61, P < 0.05). The DO

waspositively correlated with CO3-2 at WSS-3 (r = 0.64, P < 0.05); with Mg+2 at WSS-

3 (r = 0.57, P < 0.05); with EC atWSS-3 (r = 0.63, P < 0.05). The DO was negatively

correlated with CO2 at WSS-1 (r = -0.85, P < 0.001),WSS-2 (r = -0.97, P < 0.001), at

WSS-3 (r = - 0.58, P < 0.05), at WSS-4 (r = -0.71, P < 0.01).The CO2 was positively

correlated with CO3-2 at WSS-4 (r = 0.68, P < 0.05); with HCO3

-1 at WSS-4 (r = 0.73, P

< 0.01); with TA WSS-4 (r = 0.74, P < 0.01); with TH WSS-4 (r = 0.61, P < 0.05). The

CO3-2 were positively correlated with HCO3

-1 at WSS-3 (r = 0.86, P < 0.001), at WSS-4

(r = 0.94, P < 0.001); with TA at WSS-3 (r = 0.88, P < 0.001 ), at WSS-4 (r = 0.94, P <

0.001 ); with TH at WSS-3 (r = 0.88, P < 0.001), at WSS-4 (r = 0.94, P < 0.001); with

Ca+2 at WSS-3 (r = 0.86, P < 0.001),at WSS-4 (r = 0.91, P < 0.001); with Mg+2 at

WSS-3 (r = 0.81, P < 0.01 ), at WSS-4 (r = 0.98, P < 0.001); with Cl -1 at WSS-3 (r =

0.82, P < 0.01),at WSS-4 (r = 0.87, P < 0.001); with SO4 at WSS-3 (r = 0.82, P < 0.01),

at WSS-4 (r = 0.97, P < 0.001); with EC at WSS-3 (r = 0.88, P < 0.001), at WSS-4 (r =

0.96, P < 0.001); with SAR at WSS-3 (r = 0.92, P < 0.001), at WSS-4 (r = 0.94, P <

0.001). The HCO3-1 were positively correlated with TA at WSS-1 (r = 0.98, P <

0.001),WSS-2 (r = 0.71, P < 0.01), at WSS-3 (r = 0.99, P < 0.001), at WSS-4 (r = 0.99,

P < 0.001); with TH at WSS-1 (r = 0.91, P < 0.001),WSS-2 (r = 0.67, P < 0.05), at

WSS-3 (r = 0.82, P < 0.01), at WSS-4 (r = 0.94, P < 0.001); with Na+1 at WSS-1 (r =

0.89, P < 0.001), at WSS-4 (r = 0.78, P < 0.01); with Ca+2 at WSS-1 (r = 0.88, P <

0.001), WSS-2 (r = 0.87, P < 0.001), at WSS-3 (r = 0.80, P < 0.01), at WSS-4 (r =

0.93, P < 0.001); with Mg++ at WSS-1 (r = 0.79, P < 0.01), at WSS-3 (r = 0.73, P <

0.01), at WSS-4 (r = 0.98, P < 0.001); with Cl-1at WSS-1 (r = 0.94, P < 0.001),WSS-2

(r = 0.77, P < 0.01), at WSS-3 (r = 0.89, P < 0.001), at WSS-4 (r = 0.87, P < 0.001);

with SO4-2 at WSS-1 (r = 0.73, P < 0.01), WSS-2 (r = 0.83, P < 0.01), at WSS-3 (r =

72

0.81, P < 0.01), at WSS-4 (r = 0.97, P < 0.001); with EC at WSS-1 (r = 0.97, P <

0.001), WSS-2 (r = 0.73, P < 0.01), at WSS-3 (r = 0.69, P < 0.05), at WSS-4 (r = 0.96,

P < 0.001); with SAR at WSS-1 (r = 0.95, P < 0.001), at WSS-3 (r = 0.90, P < 0.001),

at WSS-4 (r = 0.94, P < 0.001).

TA was positively correlated with TH at WSS-1 (r = 0.90, P < 0.001),WSS-2 (r = 0.88,

P < 0.001), at WSS-3 (r = 0.84, P < 0.01), at WSS-4 (r = 0.93, P < 0.001); with Na+1 at

WSS-1 (r = 0.83, P < 0.01), WSS-4 (r = 0.75, P < 0.01); with Ca+2 at WSS-1 (r = 0.89,

P < 0.001), WSS-2 (r = 0.75, P < 0.01), at WSS-3 (r = 0.81, P < 0.01), at WSS-4 (r =

0.91, P < 0.001); with Mg+2 at WSS-1 (r = 0.73, P < 0.01), at WSS-3 (r = 0.74, P <

0.001), at WSS-4 (r = 0.90, P < 0.001); with Cl-1 at WSS-3 (r = 0.88, P < 0.001),at

WSS-4 (r = 0.89, P < 0.001); with SO4-2 at WSS-1 (r = 0.70, P < 0.05),WSS-2 (r =

0.74, P < 0.01), at WSS-3 (r = 0.81, P < 0.01), at WSS-4 (r = 0.95, P < 0.001); with EC

at WSS-1 (r = 0.95, P < 0.001),WSS-2 (r = 0.97, P < 0.001), at WSS-3 (r = 0.71, P <

0.01), at WSS-4 (r = 0.91, P < 0.001); with SAR at WSS-1 (r = 0.94, P < 0.001), at

WSS-3 (r = 0.91, P < 0.001), at WSS-4 (r = 0.97, P < 0.001). TA was negatively

correlated with Cl-1 at WSS-1 (r = - 0.92, P < 0.001).

TH was positively correlated with Na+1 at WSS-1 (r = 0.84, P < 0.001), at WSS-4 (r =

0.89, P < 0.001); with Ca+2 at WSS-1 (r = 0.95, P < 0.001),WSS-2 (r = 0.71, P <

0.001), at WSS-3 (r = 0.99, P < 0.001), at WSS-4 (r = 0.99, P < 0.001); with Mg+2 at

WSS-1 (r = 0.76, P < 0.01), at WSS-3 (r = 0.62, P < 0.05), at WSS-4 (r = 0.91, P <

0.001); with Cl-1 at WSS-1 (r = 0.92, P < 0.001), at WSS-3 (r = 0.72, P < 0.01), at

WSS-4 (r = 0.95, P < 0.001); with SO4-2 at WSS-1 (r = 0.74, P < 0.01),WSS-2 (r =

0.80, P < 0.01), at WSS-3 (r = 0.95, P < 0.001), at WSS-4 (r = 0.91, P < 0.001); with

73

EC at WSS-1 (r = 0.92, P < 0.001), WSS-2 (r = 0.92, P < 0.001), at WSS-3 (r = 0.69, P

< 0.05), at WSS-4 (r = 0.91, P < 0.001); with SAR at WSS-1 (r = 0.84, P < 0.01), at

WSS-3 (r = 0.87, P < 0.001 ),at WSS-4 (r = 0.97, P < 0.001).

The Na+1 was positively correlated with Ca+2 at WSS-4 (r = 0.89, P < 0.001 ); with

Mg+2 at WSS-1 (r = 0.79, P < 0.01), at WSS-3 (r = 0.86, P < 0.001); with Cl-1 at WSS-2

(r = 0.64, P < 0.001), at WSS-4 (r = 0.91, P < 0.001); with SO 4-2 at WSS-1 (r = 0.73, P

< 0.01), at WSS-4 (r = 0.82, P < 0.001); with EC at WSS-1 (r = 0.91, P < 0.001), at

WSS-4 (r = 0.91, P < 0.001); with SAR at WSS-1 (r = 0.82, P < 0.01),WSS-2 (r =

0.96, P < 0.001), at WSS-4 (r = 0.92, P < 0.001). The Na+1 was negatively correlated

with Mg+2 at WSS-2 (r = - 0.58, P < 0.05); with Cl-1 at WSS-1 (r = - 0.90, P < 0.001).

The Ca+2 was positively correlated with Mg+2 at WSS-1 (r = 0.63, P < 0.05 ),WSS-2 (r

= 0.57, P < 0.05), at WSS-3 (r = 0.59, P < 0.05), at WSS-4 (r = 0.89, P < 0.001); with

Cl-1 at WSS-2 (r = 0.67, P < 0.05), at WSS-3 (r = 0.69, P < 0.001), at WSS-4 (r = 0.95,

P < 0.001); with SO4-2 at WSS-1 (r = 0.70, P < 0.05), WSS-2 (r = 0.85, P < 0.001), at

WSS-3 (r = 0.96, P < 0.001), at WSS-4 (r = 0.89, P < 0.001); with EC at WSS-1 (r =

0.88, P < 0.001),WSS-2 (r = 0.73, P < 0.01), at WSS-3 (r = 0.68, P < 0.05), at WSS-4

(r = 0.95, P < 0.001); with SAR at WSS-1 (r = 0.85, P < 0.001), at WSS-3 (r = 0.85, P

< 0.001), at WSS-4 (r = 0.96, P < 0.001). The Ca+2 was negatively correlated with Cl-1

at WSS-1 (r = - 0.89, P < 0.001). The Mg+2 was positively correlated with Cl-1 at WSS-

2 (r = 0.77, P < 0.01), at WSS-3 (r = 0.82, P < 0.01),at WSS-4 (r = 0.87, P < 0.001);

with SO4-2 at WSS-1 (r = 0.73, P < 0.01),at WSS-2 (r = 0.74, P < 0.01), at WSS-3 (r =

0.65, P < 0.05), at WSS-4 (r = 0.97, P < 0.001); with EC at WSS-1 (r = 0.83, P < 0.01),

at WSS-3 (r = 0.77, P < 0.01), at WSS-4 (r = 0.96, P < 0.001).

74

Cl-1 were positively correlated with SO4-2 at WSS-3 (r = 0.71, P < 0.01), at WSS-4 (r =

0.88, P < 0.001); with EC at WSS-3 (r = 0.75, P < 0.01 ), at WSS-4 (r = 0.957, P <

0.001); with SAR at WSS-2 (r = 0.70, P = 0.01), at WSS-3 (r = 0.90, P < 0.001), at

WSS-4 (r = 0.97, P < 0.001). Cl-1 were negatively correlated with SO4-2 at WSS-1 (r = -

0.71, P < 0.01), at WSS-2 (r = - 0.61, P < 0.05); with EC at WSS-1 (r = - 0.96, P <

0.001), with SAR at WSS-1 (r = - 0.90, P < 0.001). The SO 4-2 were positively

correlated with EC at WSS-1 (r = 0.80, P < 0.01), at WSS-2 (r = 0.80, P < 0.01), at

WSS-3 (r = 0.61, P < 0.05), at WSS-4 (r = 0.95, P < 0.001); and with SAR at WSS-1 (r

= 0.68, P < 0.05), at WSS-3 (r = 0.89, P < 0.001), at WSS-4 (r = 0.95, P < 0.001). The

EC was positively correlated with SAR at WSS-1 (r = 0.90, P < 0.001), at WSS-3 (r =

0.79, P < 0.01), at WSS-4 (r = 0.98, P < 0.001).

Correlations of Physico-chemical Parameters with number of organisms

Water temperature had negative correlation with density of euglenophyta at WSS-4 (r

= - 0.79, P < 0.01); with bacillariophyta at WSS-1 (r = - 0.62, P < 0.05), at WSS-2 (r =

- 0.90, P < 0.001), WSS-3 (r = - 0.92, P < 0.001), WSS-4 (r = - 0.77, P < 0.01); and

with phytoplankton at WSS-3 (r = - 0.65, P < 0.05), at WSS-4 (r = - 0.64, P < 0.05).

Light penetration had positive correlation with density of chrysophyta at WSS-3 (r =

0.61, P < 0.05); with euglenophyta at WSS-4 (r = 0.61, P < 0.05);with pyrrhophyta at

WSS-4 (r = 0.77, P < 0.01). TDS had negative correlation with density ofchlorophyta

at WSS-1 (r = - 0.66, P < 0.05); with bacillariophyta at WSS-4 (r = - 0.61, P < 0.05),

with protozoa at WSS-4 (r = - 0.58, P < 0.05), with phytoplanktons at WSS-1 (r = -

0.67, P < 0.05).

75

The pH was negatively correlated with density of bacillariophyta at WSS-2 (r = - 0.61,

P < 0.05), at WSS-3 (r = - 0.68, P < 0.05); with zooplankton at WSS-1 (r = - 0.59, P <

0.05).DO had positive correlation with density ofeuglenophyta at WSS-4 (r = 0.73, P <

0.01); with pyrrhophyta at WSS-4 (r = 0.58, P < 0.05); with bacillariophyta at WSS-1

(r = 0.59, P < 0.05), at WSS-2 (r = 0.74, P < 0.01), at WSS-3 (r = 0.84, P < 0.001),

WSS-4 (r = 0.82, P < 0.01); with phytoplankton at WSS-3 (r = 0.60, P < 0.05),at WSS-

4 (r = 0.66, P < 0.05). The CO2 had negative correlation with euglenophyta atWSS-1 (r

= - 0.60, P < 0.05), at WSS-4 (r = - 0.79, P < 0.01), with bacillariophyta at WSS-1 (r =

- 0.61, P < 0.05), WSS-2 (r = - 0.66, P < 0.05), WSS-3 (r = - 0.69, P < 0.05). The CO 3-2

had positive correlation with rotifera at WSS-2 (r = 0.67, P < 0.01). The HCO 3-1 had

negative correlation with bacillariophyta at WSS-4 (r = - 0.59, P < 0.05); with protozoa

at WSS-4 (r = - 0.65, P < 0.05); with cladocera at WSS-1 (r = - 0.67, P < 0.05); with

zooplankton at WSS-4 (r = - 0.62, P < 0.05). The HCO3-1 had positive correlation with

bacillariophyta at WSS-1 (r = 0.67, P < 0.05).

The TA had negative correlation with chlorophyta at WSS-1 (r = - 0.63, P < 0.01);

with protozoa at WSS-4 (r = - 0.65, P < 0.01); with phytoplankton at WSS-1 (r = -

0.67, P < 0.05), WSS-2 (r = - 0.64, P < 0.05); and with zooplankton atWSS-4 (r = -

0.62, P < 0.05). The TH had negative correlation with cyanophyta at WSS-1 (r = -

0.64, P < 0.05); at WSS-2 (r = - 0.58, P < 0.05); witheuglenophyta at WSS-3 (r = -

0.60, P < 0.05); with protozoa at WSS-4 (r = - 0.60, P < 0.05); with phytoplankton at

WSS-1 (r = - 0.68, P < 0.05).

The Na+1 had negative correlation with cyanophyta at WSS-2 (r = - 0.59, P < 0.05).

The Ca+2 had negative correlation withcyanophyta at WSS-1 (r = - 0.66, P < 0.05);

76

chlorophytaat WSS-1 (r = - 0.58, P < 0.05); withprotozoa at WSS-4 (r = - 0.60, P <

0.05); with phytoplankton at WSS-1 (r = - 0.76, P < 0.01).The Mg+2 had negative

correlation with bacillariophyta at WSS-3 (r = - 0.58, P < 0.05). The Mg+2 had positive

correlation with xanthophyta at WSS-3 (r = 0.60, P < 0.05). The Cl -1 had negative

correlation with protozoaat WSS-4 (r = - 0.62, P < 0.05).

The Cl-1 had positive correlation with chlorophyta at WSS1 (r = 0.63, P < 0.05); with

phytoplankton at WSS-1 (r = 0.70, P < 0.05). The SO4-2 had negative correlation with

phytoplankton at WSS-1 (r = - 0.65, P < 0.05). The SO4-2 had positive correlation with

chrysophyta at WSS-1 (r = 0.58, P < 0.05). The EC had negative correlation with

chlorophyta at WSS-1 (r = - 0.66, P < 0.05); with bacillariophyta at WSS-4 (r = - 0.58,

P < 0.05); with protozoa at WSS-4 (r = - 0.59, P < 0.05); with phytoplankton at WSS-1

(r = - 0.68, P < 0.05). SAR had negative correlation with chlorophyta at WSS-1 (r = -

0.63, P < 0.05); with phytoplankton at WSS-1 (r = - 0.70, P < 0.05).

Correlations of Physico-chemical Parameters with Species richness

Water temperature had negative correlation with species richness of chrysophyta at

WSS-1 (r = - 0.59, P < 0.05); with bacillariophyta at WSS-3 (r = - 0.63, P < 0.05),

WSS-4 (r = - 0.83, P < 0.01); and with plankton at WSS-2 (r = - 0.59, P < 0.05), WSS-

3 (r = - 0.62, P < 0.05). Water temperature had positive correlation with species

richness euglenophyta at WSS-4 (r = 0.69, P < 0.05). Light penetration had negative

correlation with species richness of cyanophyta at WSS-1 (r = - 0.62, P < 0.05), WSS-2

(r = - 0.77, P < 0.05), WSS-3 (r = - 0.72, P < 0.05); with zooplankton at WSS-1 (r = -

0.65, P < 0.05). Light penetration had positive correlation with chlorophyta at WSS-1

(r = 0.61, P < 0.05); with bacillariophyta at WSS-2 (r = 0.63, P < 0.05), WSS-4 (r =

77

0.60, P < 0.05). TDS had negative correlation with species richness ofcyanophyta at

WSS-2 (r = - 0.73, P < 0.01); with planktons at WSS-1 (r = - 0.63, P < 0.05). TDS had

positive correlation with species richness ofchlorophyta at WSS-2 (r = 0.60, P < 0.05).

The pH was negatively correlated with species richness of bacillariophyta at WSS-3 (r

= - 0.62, P < 0.05); with chlorophyta at WSS-1 (r = - 0.78, P < 0.01); with pyrrhophyta

at WSS-2 (r = - 0.61, P < 0.05) and phytoplankton at WSS-4 (r = -0.61, P < 0.05. The

pH was positively correlated with species richness of euglenophyta at WSS-3 (r = 0.79,

P < 0.01).DO had negative correlation with species richness of euglenophyta at WSS-3

(r = - 0.63, P < 0.05). DO had positive correlation with species richness of

bacillariophyta at WSS-3 (r = 0.59, P < 0.05), WSS-4 (r = 0.84, P < 0.01); with

phytoplankton at WSS-2 (r = 0.61, P < 0.05). The CO2 had negative correlation with

euglenophyta at WSS- (r = - 0.69, P < 0.05), with bacillariophyta at WSS-3 (r = - 0.66,

P < 0.05), WSS-4 (r = - 0.76, P < 0.01) and with phytoplanktons at WSS-3 (r = - 0.63,

P < 0.05). CO3-2 had negative correlation with species richness of pyrrhophyta at WSS-

2 (r = - 0.61, P < 0.05), with cladocera at WSS-2 (r = - 0.62, P < 0.05) and positive

correlation with chlorophyta at WSS-3 (r = - 0.70, P < 0.05). The HCO3-1 had negative

correlation with cyanophyta at WSS-3 (r = - 0.58, P < 0.05); with cladocera at WSS-2

(r = - 0.67, P < 0.05); with zooplankton at WSS-2 (r = - 0.62, P < 0.05); with

phytoplankton at WSS-1 (r = - 0.64, P < 0.05).

The TA had negative correlation with cyanophyta at WSS-2 (r = - 0.64, P < 0.05); at

WSS-3 (r = - 0.58, P < 0.05); and with phytoplankton at WSS-1 (r = - 0.64, P < 0.05).

The TH had negative correlation with cyanophyta at WSS-2 (r = - 0.78, P < 0.01); at

WSS-3 (r = - 0.57, P < 0.05); with bacillariophyta at WSS-1 (r = - 0.67, P < 0.05); with

78

phytoplankton at WSS-1 (r = - 0.74, P < 0.05).The TH had positive correlation with

chlorophyta at WSS-3 (r = 0.61, P < 0.05).

The Na+1 had positive correlation with cyanophyta at WSS-4 (r = 0.59, P < 0.05). The

Ca+2 had negative correlation with chlorophytaat WSS-3 (r = - 0.62, P < 0.05);

withbacillariophyta at WSS-1 (r = - 0.63, P < 0.05); with phytoplankton at WSS-1 (r =

- 0.72, P < 0.01); and with zooplankton at WSS-2 (r = - 0.63, P < 0.05).

The Cl-1 had positive correlation with phytoplankton at WSS-1 (r = 0.57, P < 0.05);

with zooplankton at WSS-2 (r = 0.64, P < 0.05). The SO4-2 had negative correlation

with cyanophyta at WSS-3 (r = - 0.65, P < 0.05). The EC had negative correlation with

cyanophyta at WSS-2 (r = - 0.72, P < 0.01); with zooplankton at WSS-3 (r = - 0.63, P

< 0.05), and positive correlation with chlorophyta at WSS-2 (r = 0.60, P < 0.05); at

WSS-3 (r = 0.78, P < 0.01). SAR had negative correlation with cyanophyta at WSS-3

(r = - 0.57, P < 0.05); with phytoplankton at WSS-1 (r = - 0.62, P < 0.05); and positive

correlation with protozoa at WSS-2 (r = 0.66, P < 0.05).

79

3.2 Fish Biodiversity

3.2 a Species richness

In this study, twenty (20) species were recorded from ten sampling sites belonging to

different catchment areas (hill torrents) of Suleman Mountain Range, Dera Ghazi

Khan Region, Pakistan. These species belong to three orders (Cypriniformes,

Siluriformes and Synbranchiformes) and five families (Bagridae, Cobitidae,

Cyprinidae, Mastacembelidae and Siluridae). Of the sixteen genera, the two genera

Labeo and Barilius each with three species while rest of the genera each with single

species were recorded. Based on data, it was found that 16 species belong to family

Cyprinidae while of the remaining four species; each belongs to one of four families

(Table 3.2.1, Figure3.2.1 a). ANOVA demonstrated a significant difference (P < 0.05;

df = 4; F = 8.205) for occurrence of number of species between fish families in this

region. A Tucky post-hoc test revealed that Cyprinidae family was found to be

significantly different (P < 0.05) from other fish families (Figure 3.2.1 a).

Species richness was variable at sampling sites. Maximum species were found at FSS-

2 (8 species) followed by FSS-1, FSS-3 and FSS-8 (seven species each) and minimum

species were found on FSS-10 (2 species) (Table 3.2.1, Figure3.2.1 b).One way

ANOVA demonstrated that ten sites with respect to number of species were

significantly different (P < 0.05, df = 9, F = 33.55). A post-hoc Tukey test revealed

that FSS-1 (Vehowa) was significantly different (P < 0.001) from most of the studied

sites except FSS-2, FSS-3 and FSS-8 (P > 0.05). Statistical differences among other

sites for number of species, whether significant/non-significant are shown in (Table

3.2.2).

80

Barilius vagra was found to be the most frequent species recorded from eight sites

(FSS-1, FSS-2, FSS-3, FSS-4, FSS-5, FSS-6, FSS-8 and FSS-9). Among other

frequent species, Tor macrolepis recorded from five sites (FSS-1, FSS-2, FSS-6, FSS-

8 and FSS-9); Labeo diplostomus from five fish sampling sites (FSS-1, FSS-3, FSS-5,

FSS-8 and FSS-9), and Garra gotyla from five fish sampling sites (FSS-1, FSS-3,

FSS-4, FSS-5 and FSS-7) were other frequent species. Cyprinion watsoni was

recorded from four fish sampling sites (FSS-3, FSS-4, FSS-5 and FSS-10). Still other

species (Botia birdi, Barlius pakistanicus, Mastacembalus armatus and Securicula

gora) were the least frequent at two sites. The species such as Cirrhinus mrigala,

Labeo calbasu, Ompok pabda, Puntius sophore, Rita rita, Salmophasia punjabensis

and Schizothorax plagiostomus were only recorded from single site (Table 3.2.3,

Figure 3.2.2 c). The studied fish images are given as under:

81

Barilius modustus

Barilius pakistanicus

82

Barilius vagra

Botia birdi

Cirrhinus mrigala

Crossochelus diplocheilus

Cyprinion watsoni

83

Gara gotyla

Labeo calbasu

Labeo diplostomus

Labeo dyocheilus pakistanicus

Mastacembelus armatus

84

Ompok pabda

Puntius sophore

Rita rita

Salmophasia punjabensis

Salmostoma bacaila

85

Schizothorax plagiostomus

Securicula gora

Tor macrolepis

3.2 b Relative abundance (RA)

The recorded fishes in this study were categorized into highly abundant (RA > 10%),

moderately abundant (RA = 2-10%) and least abundanct (RA < 2%). Tor macrolepis

was the most abundant species (20.37%). Other abundant cyprinids species were

Labeo diplostomus (17.00%), Barilius vagra (11.48%) and Cyprinion watsoni

(10.62%) (Table 3.2.1). Abundance of other species of family Cyprinidae i.e. Barilius

modestus (4.17%), Garra gotyla (8.04%), Labeo dyocheilus pakistanicus (7.12%),

Schizothorax plagiostomus (7.55%) and Salmostoma bacaila (2.89%) was moderate.

Ompok pabda (2.46%) belonging to family Siluridae also showed moderate abundance

(RA 2-10%). Rest of the species of family Cyprinidae and other families were quite

rare (Figure 3.2.2 b, Figure 3.2.2 b). One way ANOVA revealed a significant

difference (P < 0.001; df = 19; F = 10.70) between number of individuals of species. A

Tukey post-hoc test revealed that Tor macrolepis was significantly different (P < 0.05)

from most of other fish species. However, no significant difference (P > 0.05) was

found with Labeo diplostomus, Labeo dyocheilus, Schizothorax plagiostomus,

Crossocheilus diplocheilus and Cyprinion watsoni (Table 3.2.1).

The family Cyprinidae showed highest relative abundance (96.40%) in the study.

Among other four families Siluridae contributed (2.45%) relative abundance while rest

of three families contributed 0.25% each (Figure 3.2.2 a). One way ANOVA

demonstrated a significant difference (P ≤ 0.001; df = 4; F = 8.77) for the number of

individuals between different fish families. The post-hoc Tucky test revealed a

significant difference for number of individuals of Cyprinidae from family Cobitidae

(P < 0.01), bagridae (P < 0.05) and Mastacembelidae (P < 0.05) (Figure 4.2.2 a).

86

However, no significant difference (P > 0.05) was observed between number of

individuals of family Cyprinidae and Siluridae.

The highest RA (21.11%) was found at FSS-3 (Hingloon) followed by FSS-1

(Vehowa) with RA (17.80%), FSS-2 (Barthi) with RA (13.50%) and FSS-8 (Murunj)

with RA (13.07%). FSS-4 (Zinda Peer) with RA (5.64%), FSS-5 (Jaj) with RA

(8.22%), FSS-6 (Harand) with RA (7.79%), FSS-7 (Siri) with RA (2.70%) and FSS-9

(Chitar Wata) with RA (8.34%) showed moderate abundance while FSS-10 (Rakhi

Gaj) with RA (1.78%) has lowest abundance (Table 3.2.2). One way ANOVA

demonstrated a significant difference (P < 0.001; df = 9; F = 25.85) for the abundance

of fish individuals between study sites. The post-hoc Tucky test revealed a significant

difference (P < 0.05) for number of fish individuals of FSS-1 from all of the studied

sites except FSS-2 and FSS-8. Similarly, when FSS-3 was compared, it also exhibited

significant difference (P < 0.001) from all sites except FSS-1. FSS-7 also showed

significant difference from all other fish sampling sites except FSS-6 and FSS-10.

FSS-9 showed non-significant difference (P > 0.05) from most of other site, except

FSS-1, FSS-3, FSS-7 and FSS-10 (Table 3.2.2). FSS-10 showed significant difference

with all other sites except FSS-7. All other sites showed variable pattern of

significane / non-significance levels with different sites.

The RA of fish species within each site are presented in (Table 3.2.3 a and Figure

3.2.2. b). At FSS-1, Labeo diplostmus showed maximum RA (29.65%) while

Securicula gora showed minimum RA (1.37%). At FSS-2, Tor macrolepis showed

maximum RA (36.93%) while two species i.e. Labeo calbasu, Mastacembalus

armatus both showed minimum RA (0.90%). At FSS-3, Schizothorax plagiostomus

87

exhibited maximum RA (35.75%) while Botia birdi minimum RA (0.29%). At FSS-4,

Cyprinion watsoni was recorded with maximum RA (40.21%) and Barilius vagra with

minimum RA (13.04%). At FSS-5, Labeo diplostomus was found to exhibit maximum

RA (37.81%) while Garra gotyla indicated minimum RA (15.12%). At FSS-6, Tor

macrolepis showed maximum RA (43.30%) while Mastacembelus armatus indicated

RA (1.60%). At FSS-7, Garra gotyla showed maximum RA (40.90%) while Barilius

pakistanicus indicated minimum RA (20.45%). At FSS-8, Labeo diplostomus showed

maximum RA (29.10%) while Botia birdi showed minimum RA (1.40%). At FSS-9,

Tor macrolepis with maximum RA (49.26%) while Salmophasia punjabensis with

minimum RA (2.94%) was recorded. At FSS-10, Cyprinion watsoni with maximum

RA (65.51%) and Barilius pakistanicus with minimum RA (34.48%) was recorded

(Table 3.2.3 Figure. 3.2.2 b).

It is important to state that RA of each species at different sites showed variations.

Among dominant species, maximum RA (5.03%) of Tor macrolepis was recorded at

FSS-2 and minimum RA (3.38%) at FSS-6. The RA of Labeo diplostomus was

maximum (5.28%) at FSS-1 and minimum (2.27%) at FSS-9. Barilius vagra showed

maximum RA (2.89%) at FSS-1 and minimum RA (0.74%) at FSS-4. The RA of

Cyprinion watsoni was maximum (5.10%) at FSS-3 and minimum (1.17%) at FSS-10.

Garra gotyla showed maximum RA (2.03%) at FSS-7 and minimum (1.10%) at FSS-5

(Figure 3.2.2 c).

3.2 c Diversity indices

The values of various diversity indices are presented in (Table 3.2.2). Simpson

diversity index (D) value was maximum at FSS-10 and minimum at FSS-1; Shannon-

88

Weaner index (H) was maximum at FSS-1 and minimum at FSS-10; Margalef index

(d) was maximum at FSS-2 and minimum at FSS-10. The evenness index (E) value

was maximum at FSS-2 and minimum at FSS-10 (Table 3.2.2). Calculations of the

Jaccards similarity index for comparison of species composition between different

sites showed highest value (0.6) between FSS-4 (Zinda Peer) and FSS-5 (Jaj). The

similarity index value was found to be zero between several pairs of data sets i.e. FSS-

6 (Harand) and FSS-7 (Siri), and FSS-8 (Murunj); and FSS-7 (Siri) and FSS-9 (Chitar

Wata). FSS-10 (Rakhi Gaj) demonstrated no similarity index with several other sites

i.e. FSS-1 (Vehowa), FSS-2 (Barthi), FSS-6 (Harand), FSS-8 (Murunj) and FSS-9

(Chitar wata) (Table 3.2.4). The dendrogram using Jaccards similarity index, based on

single linkage method demonstrated that FSS-4 (Zinda Peer) and FSS-5 (Jaj) showed

maximum similarity level, while FSS-1 and FSS-7 showed minimum similarity level

in species composition (Figure 3.2.3).

89

3. 3 Fish External Morphometry

3.3.1 Tor macrolepis

The central tendency values i.e. means ± standard deviation (SD) along with range of

measured morphometric data are given in table 3.3.1. Total length (TL) ranged from

7.8 – 31 cm with a mean value of 15.96 cm while wet body weight (W) ranged from

5.20 – 301.90 g with a mean value of 43.52 g.

Highly significant correlation values (P < 0.001) were observed for relationships of all

morphometric measurements with TL and W respectively. When least squared linear

regression correlation was calculated by keeping TL on X-axis and other

morphometric values on Y-axis, the maximum correlation (r = 0.984, P < 0.001) was

found for TL vs FL and minimum (r = 0.827 P <0.001) for TL vs PostOL (Table 3.3.2

a, Figure 3.3.1 a, Figure 3.3.1 b). The slope ‘b’ for TL vs W was found to be 2.637,

demonstrating negative allometric growth pattern. For other morphometric

measurements, the ‘b’ value ranged from 0.749 to 1.192. Most of the parameters

showed isometric growth pattern (b = 1), while TL vs ED and TL vs DFB showed

negative allometric growth (b < 1). TL vs PreOL, TL vs PostOL and TL vs CPL

showed positive allometric growth pattern (b > 1). Similarly, when least squared

regression correlation was calculated by keeping W on X-axis and other morphometric

measurements on Y-axis, maximum correlation (r = 0.953, P < 0.001) was found for

W vs BD and minimum (r = 0.863, P < 0.001) for W vs PelFB (Table 3.3.2 a, Figure

3.3.1 c, Figure 3.3.1 d). The slope ‘b’ value showed either isometric growth (b = 0.33)

or positive allometric growth pattern (b > 0.33) for most of the relationships. However,

W vs ED and W vs DFB showed negative allometric growth pattern. The condition

90

factor (K) was found to be 0.875 (Table 3.3.3). The correlation between K and W(r =

0.188); as well as between K and TL (r = 0.430) was significant (P < 0.05).

3.3.2 Schizothorax plagiostomus

The central tendency values i.e. means ± SD along with range of measured

morphometric data are given in Table 3.3.1 TL ranged from 8.08 – 17.20 cm with a

mean value of 14.13 cm while wet body weight (W) ranged from 7.05 – 67.08 g with

mean value of 32.00 g.

Highly significant relationships (P < 0.001) for all morphometric measurements with

both TL and W were observed. When least squared linear regression was calculated by

keeping TL on X-axis and other morphometric values on Y-axis, the maximum

correlation (r = 0.965, P < 0.001) was found for SL vs TL and minimum (r = 0.853, P

< 0.001) for GL vs TL (Table 3.3.2 b, Figure 3.3.2 a, Figure 3.3.2 b). The slope ‘b’ for

TL vs W was found to be 2.90, indicating optimal fish growth. The value of slope ‘b’

ranged from 0.420 – 1.220 for other morphometric measurements. For majority of

parameters, it reflected either isometric growth pattern (b = 1), or negative allometric

growth pattern (b < 1) for most of the parameters. However, AFL vs TL and AFB vs

TL showed positive allometric growth pattern (b > 1). Similarly, When least squared

linear regression was calculated by keeping W on X-axis and other morphometric

measurements on Y-axis, maximum correlation (r = 0.978, P < 0.001) was found for

SL vs W and minimum (r = 0.901, P < 0.001) for FL vs W (Table 3.3.2 b, Figure

3.3.2 c, Figure 3.3.2 d). The value of slope ‘b’ showed either isometric growth pattern

(b = 0.33) or negative allometric growth pattern (b < 0.33) for most of the

relationships. However, BD vs W, GL vs W, AFL vs W and AFB vs W showed

91

positive allometric growth pattern (b > 0.33). The value of K was found to be 1.08

(Table 3.3.3). The correlation between K and W(r = 0.182); as well as betweenK and

TL (r = 0.112) were non-significant (P > 0.05).

3.3.3 Labeo diplostomus

The central tendency values i.e. means ± SD along with ranges of measured

morphometric data are given in Table 3.3.1 TL values ranged from 9.00 – 20.50 cm

with a mean value of 12.72 cm while W ranged from 5.50 – 90.00 g with mean of

24.03 g.

Highly significant (P < 0.001) relationships for all morphometric measurements with

TL and W were recorded. When least squared linear regression correlation was

calculated by keeping TL on X-axis and other morphometric values on Y-axis, the

maximum correlation (r = 0.970, P < 0.001) was found for TL vs SL and minimum (r

= 0.558, P < 0.001) for TL vs GL (Table 3.3.2 c, Figure 3.3.3 c, Figure 3.3.3 d). The

slope ‘b’ for TL vs W found to be 2.601, which indicating optimal growth, while for

other morphometric measurements, it ranged from 0.472 to 1.393. Majority of the

parameters showed either isometric (b = 1) or negative (b < 1) allometric growth,

while TL vs SL and TL vs PecFB showed positive allometric growth (b > 1).

Similarly, when least squared linear regression correlation was calculated by keeping

W on X-axis and other morphometric measurements on Y-axis, maximum correlation

(r = 0.921, P < 0.001) was found between W and FL and W and PreOL and minimum

(r = 0.713, P < 0.001) between W and DFB (Table 4.3.2 c, Figure 4.3.3 c, Figure 4.3.3

d). The value of slope ‘b’ showed either isometric growth (b = 0.33) or negative

allometric growth (b < 0.33) for most of the relationships. However, W vs SL, W vs

92

PreOL, W vs PFB and W vs GL showed positive allometric growth (b > 0.33). The

value of K for Labeo diplostomus was 1.075 (Table 3.3.3). The correlation between K

and W (r = 0.136) was non-significant (P > 0.05) while correlation (0.305) between K

with TL was significant (P < 0.001).

3.3.4 Labeo dyocheilus pakistanicus

The central tendency values i.e. means ± SD along with range of measured

morphometric data are given in Table 3.3.1 TL ranged from 9.30 – 23.60 cm with a

mean value of 16.63 cm while W ranged from 10.20 – 125 g with mean value of

46.26g.

Highly significant relationships (P < 0.001) for all morphometric measurements with

both TL and W were recorded. When least squared regression correlation was

calculated by keeping TL on X-axis and other morphometric values on Y-axis, the

maximum correlation (r = 0.990, P < 0.001) was found for TL vs FL and minimum

(r = 0.648 P < 0.001) for TL vs PostOL (Table 3.3.2 d, Figure 3.3.4 a, Figure 3.3.4 b).

The value of slope ‘b’ for TL vs W was found to be 2.612, demonstrating optimum

growth. The value of slope ‘b’ for other morphometric measurements ranged from

0.727–1.387, reflecting isometric growth pattern (b = 1) for majority of the

parameters. However, for several parameters such as TL vs PostOL, TL vs PectFB, TL

vs AFL and TL vs CPW, the value of slope ‘b’ reflects negative (b < 1) allometric

growth pattern; while for TL vs SL, TL vs PectFL, TL vs PelFL and TL vs DFB, the

value of slope ‘b’ demonstated positive allometric growth pattern (b > 1). Similarly

when least squared linear regression correlation was calculated by keeping W on X-

axis and other morphometric measurements on Y-axis, maximum correlation (r =

93

0.975, P < 0.001) was found between W and FL and minimum (r = 0.640, P < 0.001)

between W and PostOL (Table 3.3.2 d, Figure 3.3.4 c, Figure 3.3.4 d). The value of

slope ‘b’ showed isometric growth pattern (b = 0.33) for majority of the relationships.

Few parameters i.e. W vs PostOL, W vs AFL demonstrated negative allometric growth

pattern (b < 0.33), while other parameters like, W vs SL, W vs PreOL, W vs PecFL, W

vs PelFL and W vs DFB demonstrated positive allometric growth pattern. The value of

K was found to be 0.933 (Table 3.3.3). The correlations between K and W (r = 0.262,

P < 0.05) and between K and TL (r = 0.503, P < 0.001) were significant.

3.3.5 Cyprinion watsoni

The central tendency values including means ±SD along with ranges of measured

morphometric data are given in Table 3.3.1 TL values ranged from 8.00 – 14.30 cm

with a mean value of 10.86 cm and W ranged from 7.90 – 30.70 g with a mean value

of 15.05 g.

Highly significant correlation values were found for relationships of all morphometric

measurements with TL and W. When least squared linear regression correlation was

calculated by keeping TL on X-axis and other morphometric values on Y-axis, the

maximum correlation (r = 0.957, P < 0.001) was found for TL vs FL and minimum (r

= 0.383 P = 0.001) for TL vs PecFL (Table 3.3.2 e, Figure 3.3.5 a, Figure 3.3.5 b). The

value of slope ʽbʼ for TL vs W was found to be 2.732, showing optimum growth,

while for other morphometric measurements, it ranged from 0.557 to 1.127. Most of

the parameters showed either isometric growth (b = 1) or negative allometric growth

(b < 1). Similarly, when least squared regression correlation was calculated by keeping

W on X-axis and other morphometric measurements on Y-axis, maximum correlation

94

(r = 0.929, P < 0.001) was found for W vs FL and W vs CPW and minimum (r =

0.600, P > 0.001) for W vs PecFL (Table 4.3.2 e, Figure 4.3.5 c, Figure 4.3.5 d). The

value of slope ʽbʼ showing either isometric growth (b = 0.33) or negative allometric

growth (b < 0.33) for most of the relationships. However, W vs PreOL and W vs CPL

showed positive allometric growth (b > 0.33). The value of K was found to be 1.136

(Table 3.3.3). The correlation between K and W(r = 0.168); as well as between K and

TL (r = 0.227) was non-significant (P > 0.05).

3.3.6 Ompok pabda

The central tendency values i.e. means ± SD along with ranges of measured

morphometric data are given in Table 3.3.1 TL values ranged from 10.30 – 18.90 cm

with a mean value of 15.06 cm while W ranged from 8.50 – 57.00 g with mean of

26.08 g.

Highly significant correlation values were observed for relationships of most of the

morphometric measurements with TL and W. When least squared regression

correlation was calculated by keeping TL on X-axis and other morphometric values on

Y-axis, the maximum correlation (r = 0.988, P < 0.001) was found for TL vs FL and

minimum (r = 0.194 P > 0.05) for TL vs CPL (Table 3.3.2 f, Figure 3.3.6 a, Figure

3.3.6 b). The value of slope ʽbʼ for TL vs W was found to be 3.048, showing isometric

growth, while for other morphometric measurements, it ranged from -0.864 to 1.630.

Most of the parameters showed isometric growth (b = 1), while TL vs DFB and TL vs

CPL showed negative allometric growth (b < 1). TL vs PostOL, TL vs BG and TL vs

CPW showed positive allometric growth (b > 1). Similarly, when least squared

regression correlation was calculated by keeping W on X-axis and other morphometric

95

measurements on Y-axis, maximum correlation (r = 0.950, P < 0.001) was found

between W vs TL and between W vs FL and minimum (r = 0.154, P > 0.05) between

W and CPL (Table 3.3.2 f, Figure 3.3.6 c, Figure 3.3.6 d). The value of slope ʽbʼ

showing either isometric growth (b = 0.33) or negative allometric growth (b < 0.33)

for most of the relationships. However, W vs Post OL and W vs PecFB and W vs

CPW showed positive allometric growth (b > 0.33). The value of condition factor K

was found to be 0.723 (Table 3.3.3). The correlation between K and W (r = 0.357, P <

0.05) was significant, however, between K and TL (r = 0.048) was non-significant (P

> 0.05).

3.3.7 Garra gotyla

The central tendency values including means ±SD along with ranges of measured

morphometric data are given in Table 3.3.1 TL values ranged from 9.00 – 14.10 cm

with a mean value of 11.06 cm and W ranged from 8.60 – 39.30 g with a mean value

of 17.66 g.

Highly significant correlation values were found for relationships of all morphometric

measurements TL and W. When least squared regression correlation was calculated by

keeping TL on X-axis and other morphometric values on Y-axis, the maximum

correlation (r = 0.991, P < 0.001) was found for TL vs FL and minimum (r = 0.761 P

= 0.001) for TL vs PostOL (Table 3.3.2 g, Figure 3.3.7 a, Figure 3.3.7 b). The value of

slope ʽbʼ for TL vs W was found to be 3.103, showing isometric growth, while for

other morphometric measurements, it ranged from 0.584 to 1.613. Most of the

parameters showed isometric growth (b = 1). Some showed either positive allometric

(b > 1) or negative allometric growth (b < 1). Similarly, when least squared linear

96

regression correlation was calculated by keeping W on X-axis and other morphometric

measurements on Y-axis, maximum correlation (r = 0.986, P < 0.001) was found for

W vs SL and minimum (r = 0.735, P > 0.001) for W vs PostOL (Table 3.3.2 g, Figure

3.3.7 c, Figure 3.3.7 b). The value of slope ʽbʼ showing either isometric growth (b =

0.33) or positive allometric growth (b < 0.33) for most of the relationships. However,

W vs PostOL and W vs ED showed negative allometric growth (b < 0.33). The value

of K was found to be 1.230 (Table 3.3.3). The correlation between K and W (r =

0.335) and between K with TL (r = 0.190) was non-significant (P > 0.05).

97

Table3.1.1 a: Mean values (±SD) of climatic factors at four water sampling sites and in four seasons (January 2012-December 2012) at Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.

Parameter WSS-1 WSS-2 WSS-3 WSS-4

Mean humidity* (%) 60.50±17.20a 62.04±17.06a 63.17±16.23a 60.71±15.42a

Photoperiod* (h) 11.99±1.35a 11.98±1.35a 11.98±1.35a 11.98±1.36a

Air temperature* (oC) 29.26±9.86a 30.72±8.46a 30.74±7.78a 33.31±6.14a

Winter Spring Summer Post-monsoon

Mean humidity** (%) 76.30±3.01a 50.44±14.34a 48.53±13.34b 69.44±3.34b

Photoperiod** (h) 10.51±0.38a 12.00±0.47b 13.54±0.35b 11.82±0.49c

Air temperature** (oC) 21.78±4.96a 30.66±3.44b 39.52±1.34b 32.90±2.01c

Mean values with different superscript letters are significantly different from one another. (LSD Posthoc analysis)*= non significant effect of site/season **= Significant effect of site/season

Table 3.1.1 b: Monthly variations in climatic factors (Mean values±SD) of four water sampling sites (January 2012-December 2012)at Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.

Months Mean humidity** (%) Photoperiod** (h) Air Temperature**(oC)

January 78.13±2.21a 10.28±0.02a 17.55±4.83a

February 73.63±2.87a 11.11±0.02a 22.73±5.11b

March 63.63±2.95ab 11.56±0.02a 27.85±2.02c

April 37.25±2.75bc 12.44±0.02a 33.48±1.58d

May 34.50±2.12c 13.38±0.01b 38.55±0.82e

June 37.50±2.92d 14.06±0.02b 39.90±0.90f

July 57.88±1.65d 13.51±0.02b 41.10±0.65g

August 64.25±1.85d 13.18±0.03c 38.28±0.10h

September 67.13±0.48e 12.27±0.03 c 34.40±1.51i

October 71.75±3.40f 11.36±0.03d 31.40±1.07j

November 76.00±2.80f 10.45±0.03de 26.80±1.20k

December 77.63±2.81f 10.18±0.03e 20.05±3.05l

TheMean values with different superscript letters are significantly different from one another (LSD post-hoc analysis).*= non significant effect of month**= Significant effect of month

98

Table. 3.1.2a: Mean values (±SD) of physico-chemical parameters at four water sampling sites of                          Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.

Parameter WSS-1 WSS-2 WSS-3 WSS-4

WT* (°C) 22.73±8.29a 24.10±8.02a 24.62±7.02a 27.82±5.81a

LP*(cm) 23.86±7.04a 23.32±6.42a 28.48±6.63a 26.50±4.88a

TDS**(mgL-1) 1182.50±198.50a 1174.17±179.97b 1209.33±123.11b 1636.58±126.09b

pH* 7.85±0.24a 7.91±0.31a 8.02±0.29a 7.85±0.27a

DO* (mgL-1) 7.30±1.16a 7.29±1.10a 7.03±0.91a 6.63±0.84a

CO2*(mgL-1) 7.84±1.14a 7.18±1.24a 7.35±1.19a 7.34±0.75a

CO3*(mgL-1) 12.08±3.10a 11.81±4.02a 16.06±2.72a 13.78±3.51a

HCO-3**(mgL-1) 623.11±160.61a 596.63±151.09a 600.39±41.69a 386.34±30.14b

TA** (mgL-1) 101.92±22.46a 107.75±25.26b 616.33±44.10c 401.00±35.37c

TH** (mgL-1) 264.17±25.75a 265.00±23.84a 201.58±32.36b 241.25±17.24c

Na** (mgL-1) 105.80±33.33a 125.35±55.46b 118.23±20.48b 200.85±14.73b

Ca** (mgL-1) 195.83±20.81ab 174.27±30.86b 183.50±28.05b 227.33±14.30b

Mg** (mgL-1) 56.50±12.33a 53.37±12.69ab 51.15±12.22b 63.40±8.68b

Cl** (mgL-1) 105.69±25.30a 160.87±99.74b 95.85±15.95c 382.34±37.26c

SO4** (mgL-1) 238.34±43.06a 203.67±79.10b 280.60±40.49bc 361.52±46.98c

EC** (dSm-1) 1.90±0.32a 1.91±0.28b 1.96±0.32b 2.57±0.20b

SAR** (mgL-1) 1.86±0.46a 2.32±1.02b 1.77±0.13bc 3.30±0.46c

TheMean values with different letters are significantly different from one another; (LSD post-hoc analysis)WT (water temperature), LP (light penetration), TDS (total dissolved solids), DO (dissolved oxygen), TA (total alkalinity), TH (total hardness), EC (electrical conductivity), SAR (sodium adsorption ratio).*= non significant effect of site**= Significant effect of site

Table 3.1.2 b: Mean values (±SD) of physico-chemical parameters in four seasons (January 2012- December 2012) of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.

Parameters Winter Spring Summer Post-monsoonWT**(°C) 16.27±5.07a 25.28±3.44b 32.01±1.55b 27.48±1.84c

LP** (cm) 28.21±3.80a 30.90±2.50a 22.48±7.27b 20.69±6.32b

TDS* (mgL-1) 1270.44±239.06a 1400.13±191.51a 1312.67±287.44a 1259.75±277.15a

pH** 7.73±0.18a 7.98±0.10ab 8.12±0.28bc 7.84±0.28c

DO** (mgL-1) 8.09±0.66a 7.43±0.35b 5.99±0.30c 6.68±0.72d

CO2**(mgL-1) 6.28±0.46a 7.66±0.52b 8.51±0.40b 7.16±0.73c

CO3*(mgL-1) 13.06±4.26a 14.20±2.55a 14.23±4.33a 12.11±1.88a

HCO-3* (mgL-1) 533.94±156.41a 588.57±141.47a 546.19±139.34a 553.42±172.28a

TA* (mgL-1) 303.00±233.10a 324.25±227.86a 326.00±233.98a 285.88±202.06a

TH* (mgL-1) 237.81±28.47a 256.63±39.04a 244.47±35.20a 233.63±48.67a

Na* (mgL-1) 128.05±47.11a 164.45±62.49a 136.28±50.83a 139.21±41.68a

Ca* (mgL-1) 194.35±23.91a 200.50±31.74a 198.16±36.64a 184.90±37.20a

Mg* (mgL-1) 55.91±11.88a 60.60±10.73a 56.45±13.24a 51.92±13.30a

Cl* (mgL-1) 170.84±114.77a 205.54±162.10a 198.34±133.18a 184.47±138.72a

SO4* (mgL-1) 271.50±57.09a 282.54±73.31a 280.56±100.97a 245.22±92.47a

EC* (dSm-1) 2.07±0.42a 2.23±0.30a 2.08±0.44a 2.01±0.41a

SAR** (mgL-1) 2.05±0.69a 2.80±1.29ab 2.38±0.81ab 2.29±0.67b

TheMean values with different letters are significantly different from one another; (LSD Post-hoc analysis)WT (water temperature), LP (light penetration), TDS (total dissolved solids), DO (dissolved oxygen), TA (total alkalinity), TH (total hardness), EC (electrical conductivity), SAR (sodium adsorption ratio)*= non significant effect of season**= Significant effect of season

99

Table 3.1.2 c: Monthly variations in physico-chemical parameters (Mean values±SD) at four water sampling sites(January 2012-December 2012) at Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.

Parameters WT** LP** TDS* pH** DO** CO2** CO3

-2** HCO3-1*

January 12.65±4.10a 31.75±0.88a 1287.75±158.45a 7.68±0.22a 8.65±0.42a 6.17±0.30a 13.58±4.69a 538.9±139.22a

February 18.00±4.85ab 29.68±1.96a 1346.75±114.67a 7.82±0.21a 7.92±0.25ab 6.80±0.24a 11.85±3.70ab 599.2±166.58a

March 22.55±2.23abc 31.48±2.63ab 1377.75±210.00a 7.97±0.10a 7.65±0.33bc 7.40±0.50ab 13.30±1.78ab 613.6±156.22a

April 28.00±1.70abc 30.33±2.61ab 1422.5±200.35a 7.97±0.13ab 7.20±0.22bcd 7.92±0.46ab 15.10±3.14ab 563.5±143.59a

May 30.65±1.48bcd 28.03±5.19ab 1415.0±263.53a 8.10±0.33abc 6.52±0.37cd 8.72±0.71bc 14.40±3.85ab 611.9±143.06a

June 32.10±1.82cd 25.30±6.91abc 1361.25±306.44a 8.10±0.40abc 5.97±0.15d 8.82±0.30c 17.90±5.07ab 578.6±105.69a

July 33.48±1.42d 21.68±6.40bcd 1268.0±331.18a 8.10±0.24 abc 5.77±0.09de 8.52±0.22cd 12.68±3.92ab 520.6±72.71a

August 30.98±0.75e 15.43±3.66bcd 1162.0±270.19a 8.05±0.34 abc 5.90±0.14ef 8.27±0.54de 11.33±1.16ab 488.5±205.88a

September 28.63±1.88ef 15.93±4.62cd 1186.5±334.65a 7.82±0.32 abc 6.30±0.37fg 7.55±0.70de 11.03±1.13ab 502.8±175.43a

October 26.33±0.91f 25.45±3.37d 1333.0±230.06a 7.85±0.28bc 7.05±0.83fg 6.77±0.59ef 13.20±1.94b 604.1±177.88a

November 21.35±1.88g 28.93±0.88e 1268.75±365.88a 7.70±0.21bc 7.52±0.67fg 6.25±0.40ef 12.85±4.96b 525.2±183.43a

December 13.08±3.92g 22.50±1.77e 1178.5±306.88a 7.71±0.10c 8.25±0.73g 5.90±0.42f 13.95±5.17b 472.5±175.18a

TA* TH** Na+1** Ca+2** Mg+2** Cl-1* SO4-2* EC* SAR**

January 301.25±262.13a 245.0±30.10a 122.5±44.40a 197.0±19.63a 62.4±6.72a 165.08±109.59a 278.15±38.43a 2.22±0.45a 2.01±0.42a

February 310.50±246.39a 242.2±39.81a 136.2±32.70ab 202.5±15.11ab 59.4±4.85ab 153.54±118.24a 292.55±36.42a 2.14±0.16a 2.15±0.40ab

March 316.25±236.60a 244.5±50.14ab 142.03±45.20ab 201.0±30.48b 64.2±1.20abc 167.65±160.41a 276.00±70.59a 2.18±0.32a 2.32±0.69 ab

April 332.25±254.93a 268.7±25.50ab 186.9±75.70ab 200.0±37.70ab 57±15.26 abc 243.44±178.17a 289.08±86.26a 2.28±0.30a 3.26±1.40 ab

May 342.50±267.38a 268.5±30.48ab 138.9±54.80ab 222.0±16.57ab 60.3±9.05 abc 194.79±147.58a 319.20±68.61a 2.25±0.40a 2.46±0.73 ab

June 326.50±280.98a 248.5±17.23ab 143.4±53.20ab 202.0±35.67ab 59.58±16.1abc 193.92±152.38a 284.62±117.21a 2.15±0.47a 2.49±0.91 ab

July 303.00±256.57a 246.0±25.11ab 107.5±72.47ab 200.0±29.66ab 58.5±13.30 abc 185.22±156.58a 295.40±93.05a 2.00±0.50a 2.26±0.89 ab

August 276.75±215.96a 221.0±50.98ab 141.4±28.20ab 170.6±45.44ab 46.2±11.36 abc 196.92±125.22a 211.66±111.91a 1.85±0.39a 2.18±0.34 ab

September 277.25±215.96a 213.7±55.64ab 138.7±39.50ab 169.8±45.27ab 44.28±13.49abc 206.34±148.06a 204.84±115.16a 1.89±0.49a 2.25±0.61 ab

October 294.50±220.06a 253.5±37.13ab 139.73±49.90ab 200.0±23.90ab 59.55±8.67 bc 162.59±147.30a 285.60±48.39a 2.13±0.34a 2.32±0.64 ab

November 306.75±265.65a 237.0±32.01ab 127.1±63.30ab 190.5±35.57b 56.47±12.94c 182.29±146.12a 275.52±82.74a 2.01±0.56a 2.11±0.92 ab

December 293.50±267.33a 227.0±16.87b 126.44±62.60b 187.4±28.23b 45.37±15.77c 182.47±133.10a 239.76±68.07a 1.87±0.49a 1.89±0.74b

TheMean values with different letters are significantly different from one another, (LSD post-hoc analysis)*= non significant effect of month**= Significant effect of month

100

Table 3.1.3 a: Species richness (as taxa number) of major planktonic groups at four water sampling sites (January 2012-December 2012)of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.

Divisions/Phyla WSS-1 WSS-2 WSS-3 WSS-4 All sitesCyanophyta* 11a 11a 12a 9a 12Chlorophyta** 31a 26a 24a 16b 33Chrysophyta* 7a 5a 4a 3a 7Euglenophyta* 5a 4a 4a 4a 6Xanthophyta* 4a 3a 3a 2a 4Bacillariophyta* 11a 12a 11a 9a 12Pyrrhophyta** 5a 6ab 7ab 5b 9Protozoa** 13a 10a 12ab 10b 15Rotifera* 11a 8a 10a 7a 11Cladocera** 10a 5ab 5ab 4b 10Phytoplankton** 74a 67a 65a 48b 83Zooplankton* 34a 23a 27a 21a 36All phyla** 108a 90a 92a 69b 119Thespecies richness values with different letters superscript are significantly different from one another, (LSD post-hoc analysis)*= non significant effect ofsite**= Significant effect of site .

Table 3.1.3 b: Species richness (as taxa number) of major planktonic groups in four seasons(January 2012-December 2012) of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.

Divisions/Phyla Winter Spring Summer Post Monsoon All seasons

Cyanophyta** 12a 8ab 10b 10c 12Chlorophyta* 28a 18a 21a 15a 33Chrysophyta** 5a 4ab 4ab 4b 7Euglenophyta* 5a 4a 3a 4a 6Xanthophyta** 3a 1b 3b 2b 4Bacillariophyta** 11a 9b 9b 9b 12Pyrrhophyta** 4a 4ab 6ab 4b 9Protozoa* 10a 8a 11a 7a 15Rotifera** 08a 8a 6a 7b 11Cladocera* 06a 5a 6a 5a 10Phytoplankton** 68a 48a 56a 48b 83Zooplankton** 24a 21a 23a 19b 36All phyla** 92a 69ab 79ab 67b 119Thespecies richness values with different letters superscript are significantly different from one another, (LSD post-hoc analysis).*= non significant effect of season **= Significant effect of season

101

Table 3.1.3 c: Monthly variation in species richness (as taxa number) of major planktonic groups(January 2012-December 2012)of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.

Divisions/Phyla Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecCyanophyta** 7a 8ab 8ab 7ab 7ab 7abc 7bcd 8bcd 7cde 9cde 7de 7e

Chlorophyta** 13a 11a 12a 13ab 13ab 9abc 13abc 10babc 9abc 12de 14de 13e

Chrysophyta** 4a 3ab 3ab 5ab 3ab 3abc 3abc 3abc 3bc 3bc 2bc 3c

Euglenophyta** 6a 4ab 5ab 3abc 3abc 3abc 2abc 3bc 3bc 4bc 3c 5c

Xanthophyta** 3a 1ab 1ab 1ab 1abc 1abc 1abc 2bc 0bc 2bc 1bc 2c

Bacillariophyta** 9a 8ab 8bc 6bcd 5cde 6def 6ef 4ef 6ef 7ef 6f 10f

Pyrrhophyta** 3a 2a 3a 4a 3ab 3ab 2ab 4ab 2ab 4ab 3ab 3b

Protozoa** 6a 7ab 6abc 8abc 6abc 5abc 8bc 5bc 5bcd 8cd 8cd 7d

Rotifera** 5a 6ab 7ab 5abc 4abcd 3abcd 3abcde 5bcdef 7cdef 5def 6ef 4f

Cladocera** 3a 3b 4b 2b 2b 2bc 2bc 3bc 5bc 4bc 4bc 4c

Phytoplankton** 42a 33ab 30abc 32bcd 32bcd 31bcde 30bcde 28cde 25de 32de 30ef 36f

Zooplankton* 12a 13a 14a 14a 12a 13a 14a 14a 15a 13a 14a 15a

All phyla** 54a 46ab 44ab 46abc 44abcd 44bcd 44bcd 42bcd 40bcd 45cd 44de 51e

Thespecies richness values with different letters are significantly different from one another, (LSD post-hoc analysis).*= non significant effect of month**= Significant effect of month

102

Table 3.1.4 a: Number of organisms of major planktonic groups at four sampling sites of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.

Divisions/Phyla WSS-1 WSS-2 WSS-3 WSS-4 All sitesCyanophyta** 1156a 1350a 1208a 770b 4484Chlorophyta** 1400a 1374a 1254a 786b 4814Chrysophyta** 200a 184a 108b 66b 558Euglenophyta** 252a 204ab 182bc 130c 768Xanthophyta** 66a 34b 24b 22b 146Bacillariophyta* 596a 666a 554a 446a 2262Pyrrhophyta** 256a 186b 128bc 84c 654Protozoa** 464a 568ab 514bc 388c 1934Rotifera** 344a 246b 206b 170b 966Cladocera** 38a 40a 32ab 22b 132Phytoplankton** 3926a 3998ab 3458b 2304c 13686Zooplankton** 846a 854a 752a 580b 3032All phyla** 4772a 4852a 4210b 2884c 16718The values with different letters superscript are significantly different from one another, (LSD post-hoc analysis).*= non significant effect of site**= Significant effect of site

Table 3.1.4 b: Number of organisms of major planktonic groups in four seasons,(January 2012-December 2012)of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.

Divisions/Phyla Winter Spring Summer Post Monsoon All seasonsCyanophyta* 1464a 704a 1460a 856a 4484Chlorophyta* 1662a 716a 1726a 710a 4814Chrysophyta* 210a 114a 172a 62a 558Euglenophyta* 286a 136a 224a 122a 768Xanthophyta* 62a 24a 42a 18a 146Bacillariophyta** 1300a 336b 376b 250b 2262Pyrrhophyta** 222a 130ab 152ab 150b 654Protozoa* 686a 280a 648a 320a 1934Rotifera** 310a 186ab 262ab 208b 966Cladocera** 36a 28ab 38bc 30c 132Phytoplankton** 5206a 2160ab 4152ab 2168b 13686Zooplankton* 1032a 494a 948a 558a 3032All phyla** 6238a 2654ab 5100ab 2726b 16718The values with different letters superscript are significantly different from one another, (LSD post-hoc analysis).*= non significant effect of season**= Significant effect of season

103

Table 3.1.4 c: Monthly variation (January 2012-December 2012) in number of organisms of major planktonic groups of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.

Divisions/Phyla Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec TotalCyanophyta** 350a 362ab 362ab 342ab 246ab 438abc 462abc 314abc 424abc 432abc 398bc 354c 4484Chlorophyta** 360a 454a 300ab 416ab 370ab 474ab 500ab 382ab 356ab 354ab 452ab 396b 4814Chrysophyta** 60a 80ab 58ab 56ab 46ab 28ab 56ab 42ab 44b 18b 34b 36b 558Euglenophyta* 72a 54a 84a 52a 48a 50a 46a 80a 72a 50a 82a 78a 768Xanthophyta** 18a 4ab 18ab 6ab 6ab 14abc 6abc 16abc 0abc 18abc 22bc 18c 146Bacillaphyta** 480a 290ab 224bc 112cd 76de 106de 96de 98e 98e 152e 146e 384e 2262Pyrrhophyta* 48a 60a 70a 60a 48a 44a 30a 30a 78a 72a 64a 50a 654Protozoa** 162a 188a 120ab 160ab 124ab 162ab 196ab 166ab 160ab 160ab 160b 176b 1934Rotifera** 60a 70ab 110ab 76ab 76abc 54abc 46abc 86abc 102abc 106abc 102bc 78c 966Cladocera** 8a 10ab 18abc 10abc 10bcd 4bcd 14bcd 10bcd 16bcd 14cd 10cd 8d 132Phytoplankton** 1388a 1304a 1116a 1044ab 840ab 1154ab 1196ab 962ab 1072ab 1096ab 1198ab 1316b 13686Zooplankton* 230a 268a 248a 246a 210a 220a 256a 262a 278a 280a 272a 262a 3032All phyla** 1618a 1572a 1364a 1290ab 1050ab 1374ab 1452ab 1224ab 1350ab 1376ab 1470ab 1578b 16718The values with differentsuperscriptletters are significantly different from one another, (LSD post-hoc).*= non significant effect of month**= Significant effect of month

104

Table 3.1.5 a: Relative abundance (%) of major planktonic groups within each site at four water                             sampling sites of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.

Divisions/Phyla WSS-1 WSS-2 WSS-3 WSS-4 All sitesCyanophyta 24.23 27.82 28.69 26.70 26.82Chlorophyta 29.34 28.32 29.79 27.25 28.80Chrysophyta 4.19 3.79 2.57 2.29 3.34Euglenophyta 5.28 4.20 4.32 4.51 4.59Xanthophyta 1.38 0.71 0.57 0.77 0.87Bacillariophyta 12.49 13.73 13.16 15.46 13.53Pyrrhophyta 5.36 3.83 3.04 2.91 3.91Protozoa 9.72 11.71 12.21 13.45 11.57Rotifera 7.21 5.07 4.89 5.89 5.78Cladocera 0.80 0.82 0.76 0.77 0.79Phytoplankton 82.27 82.40 82.14 79.89 81.86Zooplankton 17.73 17.60 17.86 20.11 18.14All phyla 100 100 100 100 100

Table 3.1.5 b: Relative abundance (%) of major planktonic groups within all sites of Suleman                            Mountain Range, Dera Ghazi Khan Region,Pakistan.

Divisions/Phyla WSS-1 WSS-2 WSS-3 WSS-4 All sitesCyanophyta 6.91 8.08 7.23 4.61 26.82Chlorophyta 8.37 8.22 7.50 4.70 28.80Chrysophyta 1.20 1.10 0.65 0.39 3.34Euglenophyta 1.51 1.22 1.09 0.78 4.59Xanthophyta 0.39 0.20 0.14 0.13 0.87Bacillariophyta 3.57 3.98 3.31 2.67 13.53Pyrrhophyta 1.53 1.11 0.77 0.50 3.91Protozoa 2.78 3.40 3.07 2.32 11.57Rotifera 2.06 1.47 1.23 1.02 5.78Cladocera 0.22 0.24 0.19 0.13 0.79Phytoplankton 23.48 23.91 20.69 13.78 81.86Zooplankton 5.06 5.11 4.49 3.47 18.14All phyla 28.54 29.02 25.18 17.25 100.00

105

Table 3.1.5 c: Relative abundance (%) of major planktonic groups within each season (January 2012-December 2012) of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.

Divisions/Phyla Winter Spring Summer Post Monsoon All seasonsCyanophyta 23.46 26.52 28.62 31.40 26.82Chlorophyta 26.65 26.97 33.84 26.04 28.80Chrysophyta 3.37 4.29 3.38 2.27 3.34Euglenophyta 4.59 5.12 4.39 4.47 4.59Xanthophyta 0.99 0.91 0.83 0.67 0.87Bacillariophyta 20.84 12.66 7.37 9.17 13.53Pyrrhophyta 3.55 4.89 2.98 5.50 3.91Protozoa 10.99 10.56 12.70 11.73 11.56Rotifera 4.98 7.00 5.14 7.64 5.78Cladocera 0.58 1.06 0.75 1.11 0.80Phytoplankton 83.45 81.38 81.39 79.52 81.86Zooplankton 16.55 18.62 18.59 20.48 18.14All phyla 100 100 100 100 100

Table 3.1.5 d: Relative abundance (%) of major planktonic groups in all seasons (January 2012 -December 2012)of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.

Divisions/Phyla Winter Spring Summer Post Monsoon All seasonsCyanophyta 8.75 4.21 8.73 5.12 26.82Chlorophyta 9.94 4.28 10.34 4.24 28.80Chrysophyta 1.25 0.68 1.04 0.37 3.34Euglenophyta 1.73 0.81 1.34 0.72 4.59Xanthophyta 0.37 0.14 0.26 0.10 0.87Bacillariophyta 7.78 2.00 2.24 1.49 13.53Pyrrhophyta 1.32 0.77 0.92 0.89 3.91Protozoa 4.10 1.67 3.87 1.91 11.56Rotifera 1.85 1.11 1.56 1.24 5.78Cladocera 0.23 0.19 0.25 0.17 0.80Phytoplankton 31.14 12.89 24.80 12.93 81.86Zooplankton 6.18 2.97 5.68 3.33 18.14All phyla 37.33 15.89 30.48 16.26 100

106

Table 3.1.5 e: Monthly variation (January 2012-December 2012) in relative abundance (%) of major planktonic groups of Suleman Mountain Range,  Dera Ghazi Khan Region, Pakistan.

Divisions/Phyla

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total

Cyanophyta 2.09 2.17 2.17 2.05 1.47 2.62 2.76 1.88 2.54 2.58 2.38 2.12 26.82Chlorophyta 2.15 2.72 1.79 2.49 2.21 2.84 2.99 2.28 2.13 2.12 2.70 2.37 28.80Chrysophyta 0.36 0.48 0.35 0.33 0.28 0.17 0.33 0.25 0.26 0.11 0.20 0.22 3.34Euglenophyta 0.43 0.32 0.50 0.31 0.29 0.30 0.28 0.48 0.43 0.30 0.49 0.47 4.59Xanthophyta 0.11 0.02 0.11 0.04 0.04 0.08 0.04 0.10 0.00 0.11 0.13 0.11 0.87Bacillaphyta 2.87 1.73 1.34 0.67 0.45 0.63 0.57 0.59 0.59 0.91 0.87 2.30 13.53Pyrrhophyta 0.29 0.36 0.42 0.36 0.29 0.26 0.18 0.18 0.47 0.43 0.38 0.30 3.91Protozoa 0.97 1.12 0.72 0.96 0.74 0.97 1.17 0.99 0.96 0.96 0.96 1.05 11.57Rotifera 0.36 0.42 0.66 0.45 0.45 0.32 0.28 0.51 0.61 0.63 0.61 0.47 5.78Cladocera 0.05 0.06 0.11 0.06 0.06 0.02 0.08 0.06 0.10 0.08 0.06 0.05 0.79Phytoplankton 8.30 7.80 6.68 6.24 5.02 6.90 7.15 5.75 6.41 6.56 7.17 7.87 81.86Zooplankton 1.38 1.60 1.48 1.47 1.26 1.32 1.53 1.57 1.66 1.67 1.63 1.57 18.14All phyla 9.68 9.40 8.16 7.72 6.28 8.22 8.69 7.32 8.08 8.23 8.79 9.44 100

107

Table 3.1.6 a: Simpson diversity index (D) values of major planktonic groups at four water sampling                          sites of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.

Divisions/Phyla WSS-1 WSS-2 WSS-3 WSS-4 All sitesCyanophyta 0.06 0.08 0.08 0.07 0.07Chlorophyta 0.09 0.08 0.09 0.07 0.08Chrysophyta 0.00 0.00 0.00 0.00 0.00Euglenophyta 0.00 0.00 0.00 0.00 0.00Xanthophyta 0.00 0.00 0.00 0.00 0.00Bacillariophyta 0.02 0.02 0.02 0.02 0.02Pyrrhophyta 0.00 0.00 0.00 0.00 0.00Protozoa 0.01 0.01 0.01 0.02 0.01Rotifera 0.01 0.00 0.00 0.00 0.00Cladocera 0.00 0.00 0.00 0.00 0.00Phytoplankton 0.68 0.68 0.67 0.64 0.67Zooplankton 0.03 0.03 0.03 0.04 0.03All phyla 1.00 1.00 1.00 1.00 1.00

Table 3.1.6 b: Simpson diversity index (D) values of major planktonic group in four seasons(January                        2012-December 2012)at Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.

Divisions/Phyla Winter Spring Summer Post-monsoon

All Seasons

Cyanophyta 0.06 0.07 0.08 0.10 0.07Chlorophyta 0.07 0.07 0.11 0.07 0.08Chrysophyta 0.00 0.00 0.00 0.00 0.00Euglenophyta 0.00 0.00 0.00 0.00 0.00Xanthophyta 0.00 0.00 0.00 0.00 0.00Bacillariophyta 0.04 0.02 0.01 0.01 0.02Pyrrhophyta 0.00 0.00 0.00 0.00 0.00Protozoa 0.01 0.01 0.02 0.01 0.01Rotifera 0.00 0.00 0.00 0.01 0.00Cladocera 0.00 0.00 0.00 0.00 0.00Phytoplankton 0.70 0.66 0.66 0.63 0.67Zooplankton 0.03 0.03 0.03 0.04 0.03All phyla 0.91 0.88 0.92 0.87 0.90

108

Table 3.1.6 c: Shannon-Weiner index (H) values of major planktonic group at four water sampling sites of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.

Divisions/Phyla WSS-1 WSS-2 WSS-3 WSS-4 All sitesCyanophyta 0.34 0.36 0.36 0.35 0.35Chlorophyta 0.36 0.36 0.36 0.35 0.36Chrysophyta 0.13 0.12 0.09 0.09 0.11Euglenophyta 0.16 0.13 0.14 0.14 0.14Xanthophyta 0.06 0.03 0.03 0.04 0.04Bacillariophyta 0.26 0.27 0.27 0.29 0.27Pyrrhophyta 0.16 0.13 0.11 0.10 0.13Protozoa 0.23 0.25 0.26 0.27 0.25Rotifera 0.19 0.15 0.15 0.17 0.16Cladocera 0.04 0.04 0.04 0.04 0.04Phytoplankton 1.46 1.40 1.35 1.36 0.16Zooplankton 0.45 0.44 0.44 0.47 0.31All phyla 2.38 2.31 2.26 2.34 ----

Table 3.1.6 d: Shannon-Weiner index (H) values of major planktonic group in four seasons (January                          2012-December 2012) at Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.

Divisions/Phyla Winter Spring Summer Post-monsoon All SeasonsCyanophyta 0.34 0.35 0.36 0.36 0.35Chlorophyta 0.35 0.35 0.37 0.35 0.36Chrysophyta 0.11 0.14 0.11 0.09 0.11Euglenophyta 0.14 0.15 0.14 0.14 0.14Xanthophyta 0.05 0.04 0.04 0.03 0.04Bacillariophyta 0.33 0.26 0.19 0.22 0.27Pyrrhophyta 0.12 0.15 0.10 0.16 0.13Protozoa 0.24 0.24 0.26 0.25 0.25Rotifera 0.15 0.19 0.15 0.20 0.16Cladocera 0.03 0.05 0.04 0.05 0.04Phytoplankton 0.15 0.17 0.17 0.18 0.16Zooplankton 0.30 0.31 0.31 0.33 0.31All phyla 0.37 0.29 0.36 0.30 ----

109

Table 3.1.6 e: Margalef index (d) values of major planktonic groups at four water sampling sites of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.

Divisions/Phyla WSS-1 WSS-2 WSS-3 WSS-4 All sitesCyanophyta 1.41 1.38 1.55 1.20 1.31Chlorophyta 4.14 3.45 3.22 2.24 3.77Chrysophyta 1.13 0.76 0.64 0.47 0.95Euglenophyta 0.72 0.56 0.76 0.61 0.75Xanthophyta 0.71 0.56 0.62 0.32 0.60Bacillariophyta 1.56 1.69 1.58 1.31 1.42Pyrrhophyta 0.72 0.95 1.23 1.12 1.23Protozoa 1.95 1.41 1.76 1.50 1.85Rotifera 1.71 1.27 1.68 1.16 1.45Cladocera 2.47 1.08 1.15 0.97 1.84Phytoplankton 8.82 7.95 7.85 6.07 8.61Zooplankton 4.89 3.25 3.92 3.14 4.37All phyla 12.6 10.48 10.90 8.53 12.13

Table 3.1.6 f: Margalef index (d) values of major planktonic group in four seasons (January 2012-December 2012) at Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.

Divisions/Phyla Winter Spring Summer Post-monsoon All Seasons

Cyanophyta 1.51 1.07 1.24 1.33 1.31Chlorophyta 3.64 2.59 2.68 2.13 3.77Chrysophyta 0.75 0.63 0.58 0.73 0.95Euglenophyta 0.71 0.61 0.37 0.62 0.75Xanthophyta 0.48 0.00 0.54 0.35 0.60Bacillariophyta 1.39 1.38 1.35 1.45 1.42Pyrrhophyta 0.56 0.62 1.00 0.60 1.23Protozoa 1.38 1.24 1.54 1.04 1.85Rotifera 1.22 1.34 0.90 1.12 1.45Cladocera 1.40 1.20 1.37 1.18 1.84Phytoplankton 7.83 6.12 6.60 6.12 8.61Zooplankton 3.31 3.22 3.21 2.85 4.37All phyla 10.41 8.63 9.14 8.34 12.13

110

Table 3.1.6 g: Evenness index (E) values of major planktonic group at four water sampling                          sites of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.

Divisions/Phyla WSS-1 WSS-2 WSS-3 WSS-4 All sitesCyanophyta 0.14 0.15 0.14 0.16 0.14Chlorophyta 0.10 0.11 0.11 0.13 0.10Chrysophyta 0.07 0.07 0.06 0.08 0.06Euglenophyta 0.10 0.09 0.09 0.10 0.08Xanthophyta 0.04 0.03 0.03 0.06 0.03Bacillariophyta 0.11 0.11 0.11 0.13 0.11Pyrrhophyta 0.10 0.07 0.06 0.06 0.06Protozoa 0.09 0.11 0.10 0.12 0.09Rotifera 0.08 0.07 0.07 0.09 0.07Cladocera 0.02 0.02 0.02 0.03 0.02Phytoplankton 0.34 0.33 0.32 0.35 0.04Zooplankton 0.13 0.14 0.13 0.15 0.09All phyla 0.51 0.51 0.50 0.55 -----

Table 3.1.6 h: Evenness index (E) values of major planktonic group in four seasons (from January 2012-December 2012) at Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.

Divisions/Phyla Winter Spring Summer Post-monsoon

All Seasons

Cyanophyta 0.14 0.17 0.16 0.16 0.14Chlorophyta 0.11 0.12 0.12 0.13 0.10Chrysophyta 0.07 0.10 0.08 0.06 0.06Euglenophyta 0.09 0.11 0.13 0.10 0.08Xanthophyta 0.05 0.00 0.04 0.04 0.03Bacillariophyta 0.14 0.12 0.09 0.10 0.11Pyrrhophyta 0.09 0.11 0.06 0.12 0.06Protozoa 0.10 0.12 0.11 0.13 0.09Rotifera 0.07 0.09 0.08 0.10 0.07Cladocera 0.02 0.03 0.02 0.03 0.02Phytoplankton 0.04 0.04 0.04 0.05 0.04Zooplankton 0.09 0.10 0.10 0.11 0.09All phyla 0.08 0.07 0.08 0.07 -----

111

Table 3.1.6 i: Sorenson index of similarity (SI) for major planktonic groups at four sites from Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.

Sites Cyanophyta Chlorophyta Chrysophyta Euglenophyta Xanthophyta Bacillariophyta PyrrhophytaWSS-1& WSS-2 1.00 0.894 0.833 0.888 0.857 0.916 0.909WSS-1& WSS-3 0.950 0.836 0.727 0.888 0.857 0.909 0.833WSS-1& WSS-4 0.900 0.680 s0.600 0.666 0.666 0.900 0.900WSS-2 & WSS-3 0.956 0.800 0.888 0.750 1.00 0.956 0.769WSS-2 & WSS-4 0.900 0.714 0.750 0.750 0.800 0.857 0.833WSS-3 & WSS-4 0.857 0.800 0.857 0.750 0.800 0.900 0.920WSS-1, WSS-2 & WSS-3 0.647 0.469 0.500 0.461 0.600 0.588 0.555WSS-1, WSS-2 & WSS-4 0.580 0.410 0.400 0.461 0.444 0.562 0.588WSS-1, WSS-3 & WSS-4 0.500 0.450 0.430 0.220 0.580 0.460 0.500WSS-2, WSS-3 & WSS-4 0.562 0.454 0.500 0.500 0.500 0.562 0.526WSS-1, WSS-2, WSS-3 & WSS-4 0.418 0.309 0.315 0.352 0.333 0.418 0.416

Protophyta Rotifera Cladocera Phytoplanktons Zooplanktons All PhylaWSS-1& WSS-2 0.860 0.842 0.666 0.901 0.807 0.870WSS-1& WSS-3 0.88 0.952 0.666 0.863 0.852 0.860WSS-1& WSS-4 0.782 0.777 0.571 0.764 0.727 0.752WSS-2 & WSS-3 0.819 0.888 0.800 0.863 0.840 0.857WSS-2 & WSS-4 0.800 0.666 0.666 0.620 0.727 0.650WSS-3 & WSS-4 0.909 0.705 0.666 0.789 0.791 0.790WSS-1, WSS-2 & WSS-3 0.514 0.551 0.400 0.533 0.500 0.524WSS-1, WSS-2 & WSS-4 0.484 0.384 0.315 0.484 0.410 0.582WSS-1, WSS-3 & WSS-4 0.460 0.430 0.320 0.470 0.410 0.450WSS-2, WSS-3 & WSS-4 0.50 0.480 0.285 0.500 0.450 0.492WSS-1, WSS-2, WSS-3 & WSS-4 0.311 0.222 0.166 0.362 0.240 0.328

112

Table 3.1.6 j: Sorenson index of similarity for major planktonic groups in different seasons (January 2012-December 2012) at Suleman Mountain Range,                        Dera Ghazi Khan, Region, Pakistan.

Phyla Winter & spring Winter & summer

Winter & post monsoon

Spring & summer Spring & post monsoon

Summer & post monsoon

Cyanophyta 0.80 0.90 0.90 0.73 0.77 1.00Chlorophyta 0.58 0.66 0.60 0.82 0.66 0.61Chrysophyta 0.88 1.00 0.66 0.75 0.75 0.75Euglenophyta 0.88 0.75 0.66 0.85 0.75 0.85Xanthophyta 0.50 1.00 0.80 0.00 0.00 0.80Bacillariophyta 0.85 0.85 0.76 1.00 0.66 0.66Pyrrhophyta 1.00 0.80 0.75 1.00 0.75 0.80Protozoa 0.77 0.76 0.82 0.73 0.80 0.77Rotifera 0.87 0.71 0.93 0.71 0.66 0.72Cladocera 0.54 0.50 0.72 0.54 0.80 0.54Phytoplankton 0.73 0.77 0.85 0.83 0.68 0.75Zooplankton 0.75 0.68 0.83 0.68 0.75 0.66

113

Table 3.2.1: Relative abundance (%) and status of each fish species from all sites of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.

Scientific name Common name n* RA (%)

Distributional status

IUCN statusⱡ

Commercial status#

Family CyprinidaeTor macrolepis Mahseer 340 20.87 Indigenous NE HighBarilius vagra Chalwa 187 11.48 Indigenous LC –Barilius modustus Chalwa 68 4.17 Indigenous NE –Labeo diplostomus Pahari rahu 277 17.00 Indigenous LC HighSalmostoma bacaila Choti Chal 47 2.89 Indigenous LC –Garra gotyla Pathar chat 131 8.04 Indigenous LC –Securicula gora Bari Chal 13 0.80 Indigenous LC –Labeo dyocheilusPakistan.icus Pakistani toraki 116 7.12 Indigenous LC High

Crossochelus diplocheilus DograPathar Chat 70 4.30 Indigenous NE –Schizothorax plagiostomus Sawati 123 7.55 Indigenous NE HighCyprinion watsoni Sabzak 173 10.62 Indigenous NE –Labeo calbasu Kalbans 2 0.12 Indigenous LC –Puntius sophore Sophore popara 3 0.18 Indigenous LC –Barilius Pakistan.icus Pakistani chalwa 19 1.17 Endemic NE –Salmophasia punjabensis Punjabi chal 4 0.25 Endemic NE –Cirrhinus mrigala Mori 4 0.25 Indigenous LC HighFamily CobitidaeBotia birdi Birdi loach 4 0.25 Indigenous NE –Family BagridaeRita rita Khaga 4 0.25 Indigenous LC Very HighFamily SiluridaeOmpok pabda Pahari pafta 40 2.46 Indigenous NT –Family MastacembelidaeMastacembalus armatus Bam 4 0.25 Indigenous LC High*n, number of organisms; ⱡ, IUCN status described is worldwide; RA, relative abundance; NE, not evaluated; LC, least concerned; NT, near threatened.ⱡ, # = Rafique and Khan (2012).

Table 3.2.2: Relative abundance (%) and diversity indices values of fish species from fish sampling sites of Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan

Site N SR RA (%) D H d EFSS-1 290a 7a 17.80 0.21 1.68 1.09 0.30FSS-2 220a 8ab 13.50 0.23 1.66 1.30 0.31FSS-3 344b 7ab 21.11 0.23 1.64 1.03 0.28FSS-4 92b 4bc 5.64 0.29 1.29 0.66 0.29FSS-5 134bc 4cd 8.22 0.26 1.36 0.61 0.28FSS-6 127bc 7d 7.79 0.31 1.37 1.24 0.28FSS-7 44bcd 3de 2.70 0.34 1.06 0.53 0.28FSS-8 213cde 7de 13.07 0.22 1.63 1.12 0.30FSS-9 136de 5ef 8.34 0.33 1.27 0.81 0.26FSS-10 29e 2f 1.78 0.53 0.64 0.30 0.19n, number of individuals; RA, relative abundance; D, Simpson diversity index; H, Shannon-Weiner index; d, Margalef index; E, evenness index; SR, Species richnessThe values with different superscript letters are significantly different from one another, (LSD post-hoc analysis).

114

Table 3.2.3: Relative abundance (%) of fish species within each fish sampling site from Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan

Species FSS-1 FSS-2 FSS-3 FSS-4 FSS-5 FSS-6 FSS-7 FSS-8 FSS-9 FSS-10Tor macrolepis 27.24 36.93 -- -- -- 44.00 -- 26.76 49.26 --Barilius vagra 16.20 10.81 7.26 13.04 18.48 15.20 -- 10.79 11.02 --Barilius modustus 12.75 -- -- -- -- -- -- 8.45 9.55 --Labeo diplostomus 29.65 -- 13.66 -- 37.81 -- -- 29.10 27.20 --Salmostoma bacaila 5.86 10.81 -- -- -- 4.80 -- -- -- --Garra gotyla 6.89 -- 9.30 30.43 15.12 -- 55.93 -- -- --Securicula gora 1.37 4.05 -- -- -- -- -- -- -- --Labeo dyocheilus Pakistanicus -- 24.77 -- 16.30 -- -- -- 21.59 -- --Crossochelus diplocheilus -- 9.00 9.59 -- -- -- 28.81 -- -- --Schizothorax plagiostomus -- -- 35.75 -- -- -- -- -- -- --Cyprinion watsoni -- -- 24.12 40.21 28.57 -- -- -- -- 65.51Labeo calbasu -- 0.90 -- -- -- -- -- -- -- --Puntius sophore -- -- -- -- -- 2.40 -- -- -- --Barilius Pakistan.icus -- -- -- -- -- -- 15.25 -- -- 34.48Salmophasia punjabensis -- -- -- -- -- -- -- -- 2.94 --Cirrhinus mrigala -- 1.80 -- -- -- -- -- -- -- --Botia birdi -- -- 0.29 -- -- -- -- 1.40 -- --Rita rita -- -- -- -- -- -- -- 1.87 -- --Ompok pabda -- -- -- -- -- 32.00 -- -- -- --Mastacembalus armatus -- 0.90 -- -- -- 1.60 -- -- -- --

115

Table 3.3.1: Central tendency values of various body measurements of different fish species from Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan

Body measurements Tor macrolepis Schizothorax plagiostomus Labeo diplostomus Labeo dyocheilus PakistanicusMean±SD Range Means±SD Range Means±SD Range Means±SD Range

Wet body weight (g) 43.52±44.05 5.20-301.90 32.00±12.04 7.05-67.08 24.03±15.64 5.50-90.00 46.26±23.44 10.20-125.00Total length (cm) 15.96±5.12 7.80-31.0 14.13±1.91 8.08-17.20 12.72±2.84 9.00-20.50 16.63±3.22 9.30-23.60Fork length (cm) 13.88±4.44 6.80-27.0 12.89±1.80 7.09-15.80 11.18±2.59 7.50-18.70 14.33±2.73 7.90-20.00Standard length (cm) 12.30±4.05 5.70-24.0 11.16±1.65 6.09-14.10 9.44±2.68 6.00-17.00 12.21±2.87 6.30-18.50Pre-orbital length (cm) 1.27±0.58 0.43-3.75 1.07±0.14 0.75-1.40 0.97±0.26 0.50-1.70 1.26±0.36 0.73-2.53Eye diameter (cm) 0.71±0.16 0.49-1.48 0.48±0.04 0.40-0.60 0.54±0.12 0.24-0.91 0.61±1.49 2.10-11.50Post-orbital length (cm) 1.63±0.81 0.5-5.0 1.16±0.15 0.80-1.60 1.05±0.24 0.60-1.95 1.39±0.40 0.72-2.56Head length (cm) 3.40±1.36 1.10-10.0 2.72±0.34 1.86-3.60 2.53±0.49 1.51-3.60 3.15±0.59 1.57-4.84Head depth (cm) 2.37±0.75 1.23-6.0 1.96±0.26 1.35-2.60 1.90±0.32 1.40-2.90 2.54±0.55 1.47-4.35Pectoral fin length (cm) 2.54±0.82 1.23-6.0 1.97±0.28 1.36-2.60 2.09±0.39 0.79-3.10 2.62±0.64 0.73-4.00Pectoral fin base (cm) 0.57±0.18 0.28-1.5 0.49±0.05 0.38-0.65 0.41±0.14 0.20-0.97 0.62±0.13 0.30-1.00Body depth (cm) 2.93±0.84 1.50-7.0 2.66±0.44 1.62-3.59 2.73±0.45 1.73-3.80 3.21±0.62 1.97-4.62Girth length (cm) 8.14±2.77 4.0-22.0 7.50±1.22 4.00-10.40 0.61±0,23 0.91-2.93 0.85±0.16 0.52-1.30Pelvic fin length (cm) 2.17±0.73 0.90-5.50 1.57±0.15 1.25-20.00 1.91±0.45 0.21-1.15 2.24±0.61 0.59-4.00Pelvic fin base (cm) 0.63±0.27 0.26-2.0 0.48±0.08 0.31-0.71 0.56±0.12 0.34-0.83 0.57±0.13 0.32-1.10Dorsal fin length (cm) 2.98±0.95 1.50-7.30 1.96±0.24 1.34-2.50 3.03±0.44 0.82-2.80 3.28±0.66 1.82-5.06Dorsal fin base (cm) 1.57±0.47 0.70-4.0 1.36±0.21 0.85-1.90 1.49±0.38 2.21-4.30 2.18±0.58 1.04-3.51Anal fin length (cm) 2.34±0.79 1.18-6.0 2.58±0.46 1.48-3.80 2.52±0.52 1.67-3.70 2.56±0.48 1.46-4.00Anal fin base (cm) 0.84±0.32 0.40-2.25 0.98±0.18 0.55-1.40 0.85±0.16 0.60-1.37 1.03±0.20 0.49-1.56Caudal peduncle width (cm) 1.26±0.40 0.70-3.35 1.02±0.14 0.66-1.35 1.16±0.23 0.80-1.93 1.39±0.26 0.82-2.00Caudal peduncle length(cm) 1.99±0.82 0.75-6.0 1.56±0.24 1.00-2.18 2.60±0.63 1.70-3.97 1.90±0.43 1.04-3.10Caudal fin length (cm) 3.80±1.20 1.92-9.50 2.90±0.35 2.00-3.7.00 2.53±0.63 1.70-5.30 4.22±0.91 1.18-6.19

Table 3.3.1: (continued) Central tendency values of various body measurements of different fish species from Suleman Mountain Range,

Dera Ghazi Khan Region, Pakistan.

116

Table 3.3.2 a: Relationship of total length (cm) and wet body weight (g) with different morphometric measurements of Tor macrolepis.

Equation Total length (cm) Equation Wet body weight (g)a b 95 % CL of a 95 % CL of b R a b 95 % CL of a 95 % CL of b r

117

Body measurements Cyprinion watsoni Ompok pabda Garra gotylaMean±S.D Range Means±SD Range Means±SD Range

Wet body weight (g) 15.05±4.51 7.90-30.70 26.08±11.08 8.50-57.00 17.66±8.42 8.60 – 39.30Total length (cm) 10.86±1.16 8.00-14.30 15.06±1.96 10.30-18.90 11.06±1.54 9.00 – 14.1Fork length (cm) 9.55±1.00 7.10-12.50 14.28±2.00 9.50-18.30 9.98±1.45 8.00 – 12.9Standard length (cm) 8.28±0.85 6.20-10.40 13.46±2.03 8.50-17.70 8.48±1.18 6.70 – 10.8Pre-orbital length (cm) 0.76±0.06 0.61-0.91 1.00±0.16 0.60-1.48 1.07±0.27 0.73 – 1.79Eye diameter (cm) 0.49±0.04 0.42-0.61 0.63±0.22 0.36-1.50 0.40±0.06 0.36 – 0.57Post-orbital length (cm) 0.89±0.11 0.69-1.20 1.39±0.34 0.67-2.00 0.70±0.07 0.53 – 0.80Head length (cm) 1.98±0.18 1.68-2.50 2.71±0.41 1.08-3.45 2.10±0.35 1.59 – 2.94Head depth (cm) 1.70±0.25 0.95-2.44 2.17±0.39 1.23-2.80 1.54±0.24 1.13 – 2.06Pectoral fin length (cm) 1.65±0.21 1.20-2.33 1.95±0.27 1.30-2.50 1.92±0.26 1.54 – 2.30Pectoral fin base (cm) 0.44±0.07 0.30-0.62 0.61±0.11 0.39-0.80 0.57±0.12 0.42 – 0.85Body depth (cm) 2.26±0.24 1.64-2.91 2.51±0.46 1.58-3.30 1.81±0.34 1.41 – 2.74Girth length (cm) 0.61±0.07 0.41-0.78 0.68±0.10 0.45-0.90 0.59±0.09 0.46 – 0.80Pelvic fin length (cm) 1.45±0.18 1.00-2.11 0.90±0.13 0.60-1.20 1.85±0.25 1.53 – 2.37Pelvic fin base (cm) 0.49±0.07 0.34-0.69 0.39±0.07 0.20-0.50 0.47±0.08 0.38 – 0.66Dorsal fin length (cm) 1.50±0.21 1.06-2.23 1.42±0.17 0.93-1.70 2.09±0.36 1.60 – 3.07Dorsal fin base (cm) 1.55±0.17 1.25-2.04 0.33±0.34 0.20-2.20 1.44±0.27 1.09 – 2.12Anal fin length (cm) 1.72±0.20 1.40-2.30 1.12±0.12 0.90-1.47 1.73±0.25 1.42 – 2.22Anal fin base (cm) 0.87±0.17 0.50-1.22 7.55±1.01 6.00-9.50 0.70±0.11 0.52 – 0.89Caudal peduncle width (cm) 0.76±0.07 0.65-1.00 0.84±0.19 0.40-1.27 1.01±0.21 0.80 – 1.59Caudal peduncle length (cm) 0.95±0.15 0.70-1.40 0.42±0.47 0.20-2.21 1.23±0.18 0.94 – 1.55Caudal fin length (cm) 2.52±0.35 1.70-3.45 1.67±0.27 1.05-2.27 2.50±0.36 2.08 – 3.32

W = a + b TL 0.022 2.637 0.016-0.032 2.510-2.765 0.952 TL = a + b W 4.753 0.344 4.487-5.035 0.327-0.360 0.952 FL = a + b TL 0.849 1.007 0.851-0.984 0.955-1.008 0.984 FL = a + b W 4.027 0.350 3.750-4.315 0.330-0.371 0.933 SL = a + b TL 0.789 0.990 0.789-0.879 0.951-1.030 0.967 SL = a + b W 3.451 0.363 3.184-3.750 0.340-0.386 0.920 PreOL = a + b TL 0.050 1.152 0.039-0.065 1.062-1.243 0.887 PreOL = a + b W 0.276 0.425 0.248-0.308 0.394-0.456 0.899 ED = a + b TL 0.153 0.557 0.133-0.177 0.504-0.610 0.847 ED = a + b W 0.338 0.214 0.320-0.358 0.198-0.230 0.896 PostOL = a + b TL 0.057 1.192 0.041-0.080 1.070-1.314 0.827 PostOL = a + b W 0.309 0.460 0.271-0.352 0.422-0.498 0.879 HL = a + b TL 0.202 1.012 0.158-0.258 0.922-1.102 0.862 HL = a + b W 0.851 0.389 0.776-0.933 0.362-0.415 0.911 HD = a + b TL 0.240 0.825 0.203-0.285 0.763-0.887 0.895 HD = a + b W 0.785 0.314 0.740-0.836 0.296-0.332 0.937 PecFL = a + b TL 0.203 0.911 0.176-0.236 0.858-0.964 0.932 PecFL = a + b W 0.782 0.335 0.735-0.830 0.318-0.353 0.944 PecFB = a + b TL 0.058 0.823 0.048-0.070 0.752-0.894 0.868 PecFB = a + b W 0.194 0.303 0.178-0.211 0.279-0.328 0.881 BD = a + b TL 0.319 0.801 0.275-0.370 0.748-0.855 0.914 BD = a + b W 1.012 0.304 1.040-1.064 0.289-0.318 0.953 GL = a + b TL 0.662 0.903 0.541-0.811 0.829-0.977 0.878 GL = a + b W 2.410 0.344 2.234-2.600 0.323-0.366 0.922 PelFL = a + b TL 0.153 0.955 0.129-0.181 0.894-1.016 0.921 PelFL = a + b W 0.628 0.350 0.585-0.676 0.330-0.371 0.930 PelFB = a + b TL 0.035 1.038 0.026-0.046 0.938-1.139 0.842 PelFB = a + b W 0.158 0.387 0.140-0.178 0.352-0.421 0.863 DFL = a + b TL 0.252 0.890 0.213-0.297 0.830-0.951 0.912 DFL = a + b W 0.929 0.331 0.867-0.993 0.311-0.350 0.932 DFB = a + b TL 0.197 0.749 0.165-0.236 0.684-0.815 0.866 DFB = a + b W 0.589 0.280 0.546-0.635 0.258-0.301 0.889 AFL = a + b TL 0.207 0.871 0.170-0.254 0.798-0.944 0.874 AFL = a + b W 0.716 0.335 0.667-0.736 0.314-0.355 0.924 AFB = a + b TL 0.057 0.961 0.045-0.074 0.868-1.053 0.843 AFB = a + b W 0.219 0.337 0.200-0.239 0.352-0.403 0.911 CPW = a + b TL 0.122 0.844 0.104-0.142 0.787-0.901 0.914 CPW = a + b W 0.413 0.318 0.390-0.437 0.302-0.334 0.947 CPL = a + b TL 0.089 1.113 0.071-0.111 1.032-1.194 0.901 CPL = a + b W 0.453 0.415 0.413-0.495 0.389-0.441 0.925CFL = a + b TL 0.325 0.887 0.280-0.378 0.832-1.023 0.942 CFL = a + b W 1.194 0.329 1.125-1.268 0.312-0.346 0.945 a = intercept, b = slope, r = correlation coefficient, CL = confidence limits, W = wet body weight, TL = total length, FL = fork length, SL = standard length, PreOL = pre- orbital length, ED = eye diameter, PostOL = post-orbital length, HL = head length, HD = head depth, PecFL = pectoral fin length, PecFB = pectoral fin base, BD = body depth, GL = girth length, PelFL = pelvic fin length, PelFB = pelvic fin base, DFL = dorsal fin length, DFB = dorsal fin base, AFL = anal fin length, AFB = anal fin base, CPW = caudal peduncle Width, CPL = caudal peduncle length, CFL = caudal fin length

118

Table 3.3.2 b: Relationship of total length (cm) and wet body weight (g) with different morphometric measurements of Shizothorax plagiostomus.

Equation Total length (cm) Equation Wet body weight (g)a b 95 % CL of a 95 % CL of b R a b 95 % CL of a 95 % CL of b r

W = a + b TL 0.014 2.900 0.008-0.023 2.707-3.093 0.957 TL = a + b W 4.819 0.315 4.487-5.176 0.294-0.336 0.957 FL = a + b TL 1.009 0.961 0.805-1.265 0.876-1.047 0.926 FL = a + b W 4.498 0.308 4.027-5.023 0.276-0.341 0.901 SL = a + b TL 0.690 1.050 0.586-0.813 0.988-1.112 0.965 SL = a + b W 3.373 0.351 3.184-3.565 0.334-0.367 0.978 PreOL = a + b TL 0.136 0.781 0.108-170 0.694-0.868 0.891 PreOL = a + b W 0.422 0.274 0.394-0.452 0.253-0.294 0.947 ED = a + b TL 0.160 0.420 0.137-0.185 0.363-0.477 0.849 ED = a + b W 0.291 0.150 0.277-0.305 0.136-0.164 0.921 PostOL = a + b TL 0.149 0.775 0.119-0.186 0.690-0.860 0.894 PostOL = a + b W 0.468 0.269 0.432-0.498 0.247-0.290 0.939 HL = a + b TL 0.314 0.814 0.264-0.374 0.749-0.880 0.938 HL = a + b W 1.054 0.278 1.000-1.109 0.263-0.293 0.970 HD = a + b TL 0.202 0.858 0.163-0.251 0.776-0.939 0.917 HD = a + b W 0.705 0.300 0.668-0.743 0.285-0.316 0.973 PecFL = a + b TL 0.187 0.888 0.144-0.242 0.790-0.986 0.893 PecFL = a + b W 0.684 0.310 0.631-0.741 0.286-0.333 0.943 PecFB = a + b TL 0.090 0.645 0.074-0.109 0.572-0.719 0.886 PecFB = a + b W 0.230 0.225 0.216-0.244 0.207-0.243 0.938 BD = a + b TL 0.169 1.038 0.125-0.229 0.925-1.152 0.894 BD = a + b W 0.750 0.370 0.697-0.807 0.349-0.392 0.966 GL = a + b TL 0.533 0.997 0.376-0.759 0.864-1.130 0.853 GL = a + b W 2.153 0.366 1.968-2.355 0.339-0.392 0.949 PelFL = a + b TL 0.349 0.567 0.288-0.423 0.495-0.639 0.864 PelFL = a + b W 0.794 0.199 0.746-0.847 0.181-0.218 0.920 PelFB = a + b TL 0.034 1.002 0.026-0.044 0.904-1.101 0.912 PelFB = a + b W 0.150 0.343 0.138-0.164 0.318-0.369 0.947 DFL = a + b TL 0.265 0.755 0.211-0.331 0.670-0.840 0.889 DFL = a + b W 0.794 0.264 0.741-0.853 0.244-0.285 0.943 DFB = a + b TL 0.102 0.976 0.077-0.135 0.870-1.082 0.896 DFB = a + b W 0.439 0.331 0.394-0.486 0.300-0.362 0.921 AFL = a + b TL 0.107 1.201 0.078-0.146 1.084-1.318 0.913 AFL = a + b W 0.637 0.410 0.571-0.708 0.378-0.441 0.944 AFB = a + b TL 0.039 1.220 0.028-0.054 1.094-1.345 0.905 AFB = a + b W 0.236 0.416 0.210-0.265 0.382-0.450 0.936 CPW = a + b TL 0.119 0.081 0.094-0.152 0.718-0.901 0.888 CPW = a + b W 0.394 0.278 0.362-0.430 0.253-0.303 0.924 CPL = a + b TL 0.118 0.973 0.088-0.158 0.862-1.084 0.887 CPL = a + b W 0.499 0.334 0.450-0.553 0.304-0.365 0.923 CFL = a + b TL 0.388 0.760 0.316-0.475 0.682-0.838 0.906 CFL = a + b W 1.186 0.263 1.112-1.265 0.244-0.281 0.950a = intercept, b = slope, r = correlation coefficient, CL = confidence limits, W = wet body weight, TL = total length, FL = fork length, SL = standard length, PreOL = pre-orbital length, ED = eye diameter, PostOL = post-orbital length, HL = head length, HD = head depth, PecFL = pectoral fin length, PecFB = pectoral fin base, BD = body depth, GL = girth length, PelFL = pelvic fin length, PelFB = pelvic fin base, DFL = dorsal fin length, DFB = dorsal fin base, AFL = anal fin length, AFB = anal fin base, CPW = caudal peduncle Width, CPL = caudal peduncle length, CFL = caudal fin length

Table 3.3.2 c: Relationship of total length (cm) and wet body weight (g) with different morphometric measurements of Labeo diplostomus.

119

Equation Total length (cm) Equation Wet body weight (g)a B 95 % CL of a 95 % CL of b R a b 95 % CL of a 95 % CL of b r

W = a + b TL 0.028 2.601 0.016-0.049 2.381-2.821 0.902 TL = a + b W 4.887 0.313 4.508-5.297 0.286-0.339 0.902 FL = a + b TL 0.838 1.018 0.745-0.942 0.972-1.064 0.969 FL = a + b W 3.999 0.336 3.698-4.315 0.311-0.361 0.921 SL = a + b TL 0.372 1.268 0.322-0.429 1.212-1.324 0.970 SL = a + b W 2.618 0.416 2.377-2.891 0.384-0.448 0.918 PreOL = a + b TL 0.068 1.038 0.046-0.100 0.887-1.189 0.773 PreOL = a + b W 0.258 0.429 0.234-0.285 0.397-0.461 0.921 ED = a + b TL 0.071 0.798 0.052-0.096 0.674-0.921 0.753 ED = a + b W 0.198 0.327 0.181-0.217 0.298-0.357 0.891 PostOL = a + b TL 0.116 0.864 11.246-0.152 0.757-0.970 0.821 PostOL = a + b W 0.384 0.330 0.352-0.417 0.303-0.358 0.905 HL = a + b TL 0.348 0.780 0.282-0.431 0.696-0.864 0.855 HL = a + b W 1.064 0.285 0.986-1.146 0.260-0.309 0.899 HD = a + b TL 0.352 0.664 0.300-0.414 0.600-0.727 0.879 HD = a + b W 0.984 0.216 0.910-1.067 0.189-0.242 0.824 PecFL = a + b TL 0.330 0.725 0.262-0.415 0.634-0.816 0.816 PecFL = a + b W 0.955 0.225 0.935-1.172 0.188-0.262 0.730 PecFB = a + b TL 0.011 1.393 0.008-0.016 1.260-1.526 0.880 PecFB = a + b W 0.117 0.397 0.095-0.143 0.330-0.456 0.724 BD = a + b TL 0.476 0.688 0.412-0.551 0.630-.745 0.905 BD = a + b W 1.352 0.231 1.262-1.449 0.208-0.253 0.875 GL = a + b TL 0.044 1.015 0.022-0.087 0.748-1.282 0.558 GL = a + b W 0.147 0.453 0.116-0.187 0.376-0.531 0.719 PelFL = a + b TL 0.209 0.866 0.149-0.292 0.733-0.999 0.755 PelFL = a + b W 0.738 0.308 0.644-1.183 0.263-0.353 0.775 PelFB = a + b TL 0.076 0.786 0.059-0.098 0.6865-0.887 0.811 PelFB = a + b W 0.252 0.262 0.225-0.283 0.224-0.299 0.778 DFL = a + b TL 0.916 0.472 0.791-0.944 0.414-1.530 0.821 DFL = a + b W 1.914 0.152 1.782-2.051 0.129-0.175 0.761 DFB = a + b TL 0.109 1.022 0.077-0.154 0.885-1.159 0.796 DFB = a + b W 0.555 0.317 0.469-0.656 0.262-0.372 0.713 AFL = a + b TL 0.265 0.885 0.225-0.312 0.821-.949 0.926 AFL = a + b W 1.019 0.296 0.940-1.102 0.270-0.323 0.895 AFB = a + b TL 0.149 0.684 0.120-0.185 0.597-0.770 0.814 AFB = a + b W 0.414 0.235 0.378-0.454 0.204-0.265 0.806 CPW = a + b TL 0.159 0.780 0.133-0.190 0.710-0.851 0.892 CPW = a + b W 0.522 0.260 0.480-0.568 0.233-0.288 0.858 CPL = a + b TL 0.184 1.040 0.151-0.224 0.961--1.118 0.920 CPL = a + b W 0.925 0.337 0.830-1.028 0.302--0.372 0.860 CFL = a + b TL 0.190 1.016 0.161-0.225 0.949-1.082 0.938 CFL = a + b W 0.925 0.328 0.838-1.019 0.295-0.360 0.873a = intercept, b = slope, r = correlation coefficient, CL = confidence limits, W = wet body weight, TL = total length, FL = fork length, SL = standard length, PreOL = pre-orbital length, ED = eye diameter, PostOL = post-orbital length, HL = head length, HD = head depth, PecFL = pectoral fin length, PecFB = pectoral fin base, BD = body depth, GL = girth length, PelFL = pelvic fin length, PelFB = pelvic fin base, DFL = dorsal fin length, DFB = dorsal fin base, AFL = anal fin length, AFB = anal fin base, CPW = caudal peduncle Width, CPL = caudal peduncle length, CFL = caudal fin length

Table 3.3.2 d: Relationship of total length (cm) and wet body weight (g) with different morphometric measurements of Labeo dyocheilus Pakistanicus.

EquationTotal length (cm)

EquationWet body weight (g)

a B 95 % CL of a 95 % CL of b R a b 95 % CL of a 95 % CL of b r

120

W = a + b TL 0.028 2.612 0.019-0.041 2.473-2.750 0.970 TL = a + b W 4.295 0.360 3.999-4.613 0.341-0.380 0.970 FL = a + b TL 0.925 0.975 0.853-0.998 0.946-1.004 0.990 FL = a + b W 3.758 0.357 3.524-4.009 0.339-0.374 0.975 SL = a + b TL 0.372 1.240 0.329-0.421 1.195-1.284 0.986 SL = a + b W 2.254 0.449 2.032-2.500 0.421-0.476 0.961 PreOL = a + b TL 0.044 1.188 0.031-0.062 1.065-1.312 0.899 PreOL = a + b W 0.242 0.436 0.202-0.290 0.388-0.484 0.887 ED = a + b TL 0.034 1.024 0.022-0.053 0.865-1.182 0.808 ED = a + b W 0.153 0.367 0.121-0.194 0.305-0.430 0.781 PostOL = a + b TL 0.146 0.796 0.083-0.255 0.597-0.996 0.648 PostOL = a + b W 0.457 0.292 0.345-0.604 0.217-0.267 0.640 HL = a + b TL 0.267 0.877 0.215-0.333 0.799-0.955 0.923 HL = a + b W 0.933 0.324 0.834-1.045 0.294-0.354 0.916 HD = a + b TL 0.161 0.981 0.119-0.217 0.874-1.088 0.890 HD = a + b W 0.698 0.343 0.585-0.836 0295-0.390 0.837 PecFL = a + b TL 0.068 1.296 0.044-0.104 1.142-1.451 0.873 PecFL = a + b W 0.476 0.450 0.370-0.615 0.382-0.518 0.816 PecFB = a + b TL 0.066 0.793 0.042-0.104 0.633-0.953 0.726 PecFB = a + b W 0.190 0.315 0.123-0.232 0.261-0.370 0.777 BD = a + b TL 0.235 0.930 0.193-0.285 0.860-0.999 0.943 BD = a + b W 0.877 0.346 0.796-0.964 0.320-0.371 0.944 GL = a + b TL 0.075 0.864 0.061-0.091 0.794-0.934 0.935 GL = a + b W 0.239 0.337 0.226-0.252 0.322-0.351 0.980 PelFL = a + b TL 0.045 1.387 0.027-0.074 1.208-1.567 0.855 PelFL = a + b W 0.342 0.496 0.261-0.450 0.423-0.569 0.823 PelFB = a + b TL 0.042 0.920 0.030-0.060 0.796-1.045 0.844 PelFB = a + b W 0.151 0.351 0.129-0.178 0.308-0.394 0.866 DFL = a + b TL 0.236 0.936 0.185-0.300 0.849-1.022 0.918 DFL = a + b W 0.891 0.346 0.789-1.009 0.313-0.313 0.914 DFB = a + b TL 0.069 1.223 0.042-0.112 1.048-1.398 0.831 DFB = a + b W 0.419 0.435 0.321-0.545 0.364-0.506 0.795 AFL = a + b TL 0.331 0.727 0.232-0.472 0.600-0.854 0.773 AFL = a + b W 0.863 0.289 0.738-1.007 0.248-0.331 0.829 AFB = a + b TL 0.084 0.890 0.062-0.114 0.779-0.100 0.864 AFB = a + b W 0.290 0.336 0.251-0.337 0.297-0.375 0.878 CPW = a + b TL 0.141 0.813 0.107-0.185 0.716-0.910 0.873 CPW = a + b W 0.429 0.313 0.381-0.482 0.281-0.344 0.904 CPL = a + b TL 0.097 1.058 0.076-0.123 0.971-1.145 0.934 CPL = a + b W 0.453 0.381 0.394-0.522 0.343-0.419 0.905 CFL = a + b TL 0.280 0.962 0.176-0.447 0.795-1.128 0.776 CFL = a + b W 1.146 0.345 0.899-1.462 0.280-0.410 0.748a = intercept, b = slope, r = correlation coefficient, CL = confidence limits, W = wet body weight, TL = total length, FL = fork length, SL = standard length, PreOL = pre-orbital length, ED = eye diameter, PostOL = post-orbital length, HL = head length, HD = head depth, PecFL = pectoral fin length, PecFB = pectoralfin base, BD = body depth, GL = girth length, PelFL = pelvic fin length, PelFB = pelvic fin base, DFL = dorsal fin length, DFB = dorsal fin base, AFL = anal fin length, AFB = anal fin base, CPW = caudal peduncle Width, CPL = caudal peduncle length, CFL = caudal fin length

121

Table 3.3.2 e: Relationship of total length (cm) and wet body weight (g) with different morphometric measurements of Cyprinion watsoni.

Equation Total length (cm) Equation Wet body weight (g)a B 95 % CL of a 95 % CL of b R a b 95 % CL of a 95 % CL of b r

W = a + b TL 0.021 2.732 0.011-0.041 2.461-3.002 0.922 TL = a + b W 4.732 0.311 4.365-5.140 0.280-0.342 0.922 FL = a + b TL 0.998 0.947 0.849-1.169 0.880-1.014 0.957 FL = a + b W 4.169 0.310 3.855-4.498 0.280-0.338 0.929 SL = a + b TL 0.962 0.903 0.787-1.172 0.820-0.986 0.931 SL = a + b W 0.268 0.298 0.291-0.246 0.267-0.330 0.912 PreOL = a + b TL 0.394 1.127 0.291-0.533 1.000-1.254 0.902 PreOL = a + b W 2.084 0.380 1.754-2.477 0.316-0.444 0.902 ED = a + b TL 0.107 0.637 0.090-0.127 0.566-0.709 0.903 ED = a + b W 0.274 0.217 0.248-0.301 0.181-0.252 0.903 PostOL = a + b TL 0.394 0.858 0.394-0.291 0.672-1.044 0.734 PostOL = a + b W 0.361 0.336 0.274-0.248 0.269-0.402 0.766 HL = a + b TL 0.457 0.721 0.072-0.344 0.617-0.825 0.852 HL = a + b W 0.918 0.288 0.843-1.000 0.255-0.319 0.904 HD = a + b TL 0.141 1.043 0.077-0.256 0.790-1.295 0.696 HD = a + b W 0.745 0.306 0.579-0.959 0.212-0.401 0.606 PecFL = a + b TL 0.188 0.909 0.121-0.294 0.722-1.096 0.752 PecFL = a + b W 0.773 0.283 0.643-0.927 0.215-0.352 0.695 PecFB = a + b TL 0.115 0.557 0.054-0.243 0.242-0.873 0.383 PecFB = a + b W 0.191 0.352 0.157-0.230 0.281-0.423 0.758 BD = a + b TL 0.303 0.841 0.218-0.421 0.703-0.979 0.820 BD = a + b W 1.045 0.288 0.925-1.178 0.243-0.333 0.832 GL = a + b TL 0.068 0.922 0.048-0.095 0.780-1.064 0.837 GL = a + b W 0.256 0.327 0.229-0.286 0.285-0.368 0.879 PelFL = a + b TL 0.258 0.722 0.157-0.422 0.515-0.930 0.633 PelFL = a + b W 0.778 0.231 0.643-0.944 0.159-0.303 0.600 PelFB = a + b TL 0.047 0.980 0.028-0.079 0.762-1.197 0.727 PelFB = a + b W 0.200 0.333 0.164-0.242 0.261-0.406 0.733 DFL = a + b TL 0.293 0.848 0.159-0.540 0.709-0.987 0.821 DFL = a + b W 0.703 0.285 0.590-0.839 0.219-0.351 0.712 DFB = a + b TL 0.166 0.925 0.096-0.286 0.696-1.155 0.688 DFB = a + b W 0.617 0.343 0.551-0.690 0.301-0.385 0.886 AFL = a + b TL 0.114 0.901 0.060-0.220 0.769-1.033 0.849 AFL = a + b W 6.577 0.358 5.888-7.345 0.317-0.399 0.899 AFB = a + b TL 0.070 1.047 0.028-0.175 0.665-1.429 0.541 AFB = a + b W 0.883 0.125 0.849-0.916 0.111-0.139 0.898 CPW = a + b TL 0.068 0.722 0.034-0.135 0.607-0.837 0.827 CPW = a + b W 0.337 0.305 0.313-0.362 0.277-0.333 0.932 CPL = a + b TL 0.116 0.954 0.056-0.239 0.650-1.258 0.593 CPL = a + b W 0.265 0.475 0.234-0.301 0.428-0.522 0.921 CFL = a + b TL 0.225 1.072 0.137-0.372 0.870-1.275 0.780 CFL = a + b W 1.186 0.280 0.575-1.486 0.196-0.364 0.616a = intercept, b = slope, r = correlation coefficient, CL = confidence limits, W = wet body weight, TL = total length, FL = fork length, SL = standard length, PreOL = pre-orbital length, ED = eye diameter, PostOL = post-orbital length, HL = head length, HD = head depth, PecFL = pectoral fin length, PecFB = pectoral fin base, BD = body depth, GL = girth length, PelFL = pelvic fin length, PelFB = pelvic fin base, DFL = dorsal fin length, DFB = dorsal fin base, AFL = anal fin length, AFB = anal fin base, CPW = caudal peduncle Width, CPL = caudal peduncle length, CFL = caudal fin length

Table 3.3.2 f: Relationship of total length (cm) and wet body weight (g) with different morphometric measurements of Ompok pabda.

Equation Total length (cm) Equation Wet body weight (g)

122

a B 95 % CL of a 95 % CL of b R a b 95 % CL of a 95 % CL of b r W = a + b TL 0.006 3.048 0.002-0.017 2.688-3.404 0.950 TL = a + b W 5.834 0.296 5.212-6.531 0.261-0.331 0.950 FL = a + b TL 0.805 1.060 0.684-1.054 1.000-1.120 0.988 FL = a + b W 5.164 0.318 4.571-5.821 0.280-0.355 0.950 SL = a + b TL 0.690 1.094 0.504-0.948 0.978-1.211 0.959 SL = a + b W 4.624 0.333 4.009-5.346 0.288-0.378 0.936 PreOL = a + b TL 0.108 0.816 0.046-0.255 0.500-1.132 0.681 PreOL = a + b W 0.505 0.211 0.354-0.719 0.100-0.322 0.564 ED = a + b TL 0.026 1.157 0.005-0.126 0.577-1.738 0.583 ED = a + b W 0.240 0.289 0.128-0.451 0.091-0.486 0.466 PostOL = a + b TL 0.020 1.559 0.005-0.060 1.152-1.966 0.810 PostOL = a + b W 0.253 0.526 0.182-0.352 0.423-0.630 0.877 HL = a + b TL 0.117 1.155 0.053-0.258 0.864-1.446 0.819 HL = a + b W 0.910 0.339 0.659-1.253 0.239-0.440 0.772 HD = a + b TL 0.097 1.145 0.041-0.225 0.833-1.457 0.797 HD = a + b W 0.692 0.355 0.506-0.948 0.257-0.453 0.793 PecFL = a + b TL 0.177 0.883 0.097-0.324 0.660-1.105 0.819 PecFL = a + b W 0.811 0.273 0.647-1.016 0.202-0.343 0.812 PecFB = a + b TL 0.024 1.186 0.012-0.051 0.911-1.461 0.841 PecFB = a + b W 0.177 0.385 0.139-0.226 0.309-0.462 0.876 BD = a + b TL 0.093 1.211 0.050-0.175 0.978-1.444 0.882 BD = a + b W 0.746 0.377 0.590-0.944 0.303-0.450 0.880 GL = a + b TL 0.044 1.014 0.026-0.072 0.828-1.200 0.891 GL = a + b W 0.252 0.311 0.207-0.308 0.249-0.372 0.875 PelFL = a + b TL 0.061 0.992 0.040-0.092 0.838-1.147 0.918 PelFL = a + b W 0.322 0.319 0.284-0.366 0.280-0.358 0.947 PelFB = a + b TL 0.035 0.889 0.011-0.112 0.456-1.322 0.594 PelFB = a + b W 0.136 0.327 0.093-0.200 0.208-0.447 0.702 DFL = a + b TL 0.234 0.665 0.124-0.444 0.428-0.901 0.712 DFL = a + b W 0.695 0.223 0.561-0.863 0.156-0.291 0.767 DFB = a + b TL 1.500 -0.615 0.086-26.303 -1.673-0.444 0.205 DFB = a + b W 0.482 -0.166 0.167-0.718 -0.498-0.165 0.178 AFL = a + b TL 0.229 0.588 0.148-0.354 0.426-0.749 0.795 AFL = a + b W 0.610 0.192 0.002-0.706 0.146-0.238 0.833 AFB = a + b TL 0.923 0.774 0.524-1.626 0.565-0.984 0.800 AFB = a + b W 3.281 0.260 2.748-3.908 0.205-0.315 0.862 CPW = a + b TL 0.010 1.630 0.005-0.020 1.380-1.880 0.920 CPW = a + b W 0.162 0.509 0.127-0.207 0.433-0.586 0.922 CPL = a + b TL 3.334 -0.864 0.047-2.370 -2.440-0.712 0.194 CPL = a + b W 0.637 0.214 0.131-3.105 -0.709-0.281 0.154 CFL = a + b TL 0.136 0.923 0.066-0.281 0.655-1.191 0.778 CFL = a + b W 0.701 0.269 0.524-0.940 0.178-0.361 0.729a = intercept, b = slope, r = correlation coefficient, CL = confidence limits, W = wet body weight, TL = total length, FL = fork length, SL = standard length, PreOL = pre-orbital length, ED = eye diameter, PostOL = post-orbital length, HL = head length, HD = head depth, PecFL = pectoral fin length, PecFB = pectoral fin base, BD = body depth, GL = girth length, PelFL = pelvic fin length, PelFB = pelvic fin base, DFL = dorsal fin length, DFB = dorsal fin base, AFL = anal fin length, AFB = anal fin base, CPW = caudal peduncle Width, CPL = caudal peduncle length, CFL = caudal fin length

Table 3.3.2 g: Relationship of total length (cm) and wet body weight (g) with different morphometric measurements of Garra gotyla.

Equation Total length (cm) Equation Wet body weight (g)a B 95 % CL of a 95 % CL of b R a b 95 % CL of a 95 % CL of b r

W = a + b TL 0.010 3.103 0.004-0.024 2.723-3.484 0.985 TL = a + b W 4.592 0.313 4.121-5.117 0.274-0.351 0.985 FL = a + b TL 0.818 1.041 0.646-1.035 0.943-1.139 0.991 FL = a + b W 3.954 0.330 3.581-4.365 0.294-0.365 0.977

123

SL = a + b TL 0.766 1.000 0.592-0.991 0.893-1.108 0.989 SL = a + b W 3.483 0.330 3.133-3.873 0.279-0.354 0.986 PreOL = a + b TL 0.022 1.613 0.009-0.051 1.258-1.968 0.954 PreOL = a + b W 0.242 0.524 0.168-0.304 0.443-0.605 0.977 ED = a + b TL 0.068 0.739 0.026-0.177 0.343-1.136 0.796 ED = a + b W 0.202 0.245 0.147-0.280 0.130-0.361 0.832 PostOL = a + b TL 0.172 0.584 0.074-0.399 0.234-0.935 0.761 PostOL = a + b W 0.424 0.179 0.305-0.587 0.063-0.296 0.735 HL = a + b TL 0.136 1.135 0.083-0.223 0.929-1.341 0.968 HL = a + b W 0.748 0.366 0.652-0.859 0.316-0.415 0.982 HD = a + b TL 0.106 1.111 0.055-0.206 0.835-1.387 0.943 HD = a + b W 0.553 0.363 0.466-0.959 0.302-0.425 0.972 PecFL = a + b TL 0.203 0.936 0.103-0.402 0.650-1.218 0.918 PecFL = a + b W 1.208 0.299 0.650-1.057 0.212-0.385 0.925 PecFB = a + b TL 0.029 1.237 0.009-0.090 0.760-1.713 0.877 PecFB = a + b W 0.181 0.404 0.124-0.264 0.269-0.539 0.903 BD = a + b TL 0.118 1.135 0.048-0.290 0.759-1.511 0.905 BD = a + b W 0.634 0.373 0.480-0.838 0.274-0.471 0.936 GL = a + b TL 0.043 1.090 0.027-0.067 0.903-1.278 0.972 GL = a + b W 0.219 0.353 0.198-0.241 0.318-0.388 0.990 PelFL = a + b TL 0.214 0.898 0.120-0.380 0.657-1.138 0.935 PelFL = a + b W 0.824 0.288 1.483 1.005 0.217-0.359 0.944 PelFB = a + b TL 0.036 1.065 0.020-0.066 0.815-1.316 0.949 PelFB = a + b W 0.182 0.338 0.145-0.228 0.257-0.418 0.947 DFL = a + b TL 0.160 1.068 0.070-0.363 0.726-1.409 0.910 DFL = a + b W 0.773 0.353 0.611-0.977 0.269-0.436 0.948 DFB = a + b TL 0.074 1.232 0.036-0.151 0.934-1.530 0.946 DFB = a + b W 0.467 0.399 0.377-0.578 0.323-0.475 0.965 AFL = a + b TL 0.161 0.986 0.090-0.289 0.743-1.229 0.944 AFL = a + b W 0.733 0.305 0.566-0.948 0.213-0.397 0.920 AFB = a + b TL 0.063 1.003 0.025-0.157 0.621-1.385 0.880 AFB = a + b W 0.284 0.321 0.204-0.395 0.203-0.439 0.887 CPW = a + b TL 0.049 1.258 0.019-0.123 0.872-1.645 0.917 CPW = a + b W 0.309 0.419 0.244-0.393 0.334-0.504 0.961 CPL = a + b TL 0.104 1.030 0.055-0.195 0.765-1.295 0.939 CPL = a + b W 2.000 0.320 0.381-0.656 0.223-0.416 0.919 CFL = a + b TL 0.243 0.970 0.381-0.656 0.742-1.197 0.949 CFL = a + b W 1.038 0.313 0.873-1.230 0.252-0.374 0.964a = intercept, b = slope, r = correlation coefficient, CL = confidence limits, W = wet body weight, TL = total length, FL = fork length, SL = standard length, PreOL = pre-orbital length, ED = eye diameter, PostOL = post-orbital length, HL = head length, HD = head depth, PecFL = pectoral fin length, PecFB = pectoral fin base, BD = body depth, GL = girth length, PelFL = pelvic fin length, PelFB = pelvic fin base, DFL = dorsal fin length, DFB = dorsal fin base, AFL = anal fin length, AFB = anal fin base, CPW = caudal peduncle Width, CPL = caudal peduncle length, CFL = caudal fin length

124

Table 3.3.3: Condition factor (K) of various fish species from Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.

125

Species Name Condition factor (K)

Tor macrolepis 0.875

Schizothorax plagiostomus 1.080

Labeo diplostomus 1.075

Labeo dyocheilus pakistanicus 0.933

Cyprinion watsoni 1.136

Ompok pabda 0.723

Garra gotyla 1.230

Jan Feb

Mar AprMay Jun Jul

AugSe

p OctNov

Dec0

10

20

30

40

50

60

70

80

90

Mea

n hu

mid

ity (%

)

Figure 3.1.1 a Monthly variation in mean humidity (%) at four sampling sites(January2012-December 2012).

Figure 3.1.1 b Monthly variation in air temperature (oC) at four sampling sites (January2012-December 2012).

Jan Feb

Mar AprMay Jun Jul

Aug Sep Oct

Nov Dec0

5

10

15

20

25

30

35

40

Wat

er te

mpe

ratu

re (°

C)

Figure 3.1.2 a Monthly variation in water temperature (oC) at four sampling sites(January2012-December 2012).

Jan Feb

Mar AprMay Jun Jul

AugSe

p OctNov Dec

0

5

10

15

20

25

30

35

40

Ligh

t pen

etra

tion

(cm

)

Figure 3.1.2 b Monthly variation in light penetration (cm) at four sampling sites (January2012-December 2012).

126

Jan Feb

Mar AprMay Jun Jul

Aug Sep Oct

Nov Dec0

5

10

15

20

25

30

35

40

45

Air

tem

pera

ture

(°C

)

Jan Feb

Mar AprMay Jun Jul Aug Se

p OctNov

Dec0

200

400

600

800

1000

1200

1400

1600

1800

2000

TDS

(mgL

-1)

Figure 3.1.2 c Monthly variation in TDS (mgL-1)at four sampling sites (January2012-December 2012).

Jan Feb

Mar AprMay Jun Jul

Aug Sep Oct

Nov Dec6.8

7

7.2

7.4

7.6

7.8

8

8.2

8.4

8.6

pH

Figure 3.1.2 d Monthly variation in pH at four sampling sites (January2012-December 2012).

Jan Feb Mar AprMay Jun Jul

AugSep Oct Nov

Dec0

1

2

3

4

5

6

7

8

9

10

Diss

olve

d ox

ygen

(mgL

-1)

Figure 3.1.2 e Monthly variation in dissolved oxygen (mgL-1)at four sampling sites (January2012-December 2012).

Jan Feb

Mar AprMay Jun Jul

Aug Sep Oct

Nov Dec0

2

4

6

8

10

12

Car

bon

diox

ide

(mgL

-1)

Figure 3.1.2 f Monthly variation in carbon dioxide (mgL-1)at four watersampling sites (January2012-December 2012).

127

Jan Feb

Mar AprMay Jun Jul Aug Se

p OctNov Dec

0

5

10

15

20

25

30

Car

bona

te io

ns (

mgL

-1)

Figure 3.1.2 g Monthly variation in carbonates (mgL-1)at four water sampling sites(January2012-December 2012).

Jan Feb

Mar AprMay Jun Jul

Aug Sep Oct

Nov Dec0

100

200

300

400

500

600

700

800

900

Bic

arbo

nate

ions

(m

gL-1

)

Figure 3.1.2 h Monthly variation in bicarbonates (mgL-1)at four water sampling sites (January2012-December 2012).

Jan Feb

Mar AprMay Jun Jul

Aug Sep Oct

Nov Dec0

100

200

300

400

500

600

700

800

Tota

l alk

alin

ity (

mgL

-1)

Figure 3.1.2 i Monthly variation in total alkalinity (mgL-1)at four water sampling sites (January2012-December 2012).

Jan Feb

Mar AprMay Jun Jul

Aug Sep Oct

Nov Dec0

50

100

150

200

250

300

350

Tota

l har

dnes

s (m

gL-1

)Figure 3.1.2 j Monthly variation in total hardness (mgL-1)at four water

sampling sites (January2012-December 2012).

128

Jan Feb

Mar AprMay Jun Jul

Aug Sep Oct

Nov Dec0

50

100

150

200

250

300

Sodi

um io

ns (

mgL

-1)

Figure 3.1.2 k Monthly variation in sodium ions (mgL-1)at four water sampling sites (January2012-December 2012).

Jan Feb Mar AprMay Jun Jul

AugSep Oct Nov

Dec0

50

100

150

200

250

300

Cal

cium

ions

(m

gL-1

)

Figure 3.1.2 l Monthly variation in calcium ions (mgL-1)at four water sampling sites(January2012-December 2012).

Jan Feb

Mar AprMay Jun Jul

Aug Sep Oct

Nov Dec0

10

20

30

40

50

60

70

80

Mag

nesi

um io

ns (

mgL

-1)

Figure 3.1.2 m Monthly variation in magnesium ions (mgL-1)at four water sampling sites (January2012-December 2012).

Jan Feb

Mar AprMay Jun Jul

Aug Sep Oct

Nov Dec0

50

100

150

200

250

300

350

400

450

Chl

orid

e io

ns (

mgL

-1)

Figure 3.1.2 n Monthly variation in chlorides ions (mgL-1)at four water sampling sites (January2012-December 2012).

129

Jan Feb

Mar AprMay Jun Jul

Aug Sep Oct

Nov Dec0

50

100

150

200

250

300

350

400

450

Sulp

hate

ions

(m

gL-1

)

Figure 3.1.2 o Monthly variation in sulphate ions (mgL-1) at four water sampling sites from (January2012-December 2012).

Jan Feb

Mar AprMay Jun Jul

Aug Sep Oct

NovDec

0

0.5

1

1.5

2

2.5

3

Elec

trica

l con

duct

ivity

(dSm

-1)

Figure 3.1.2 p Monthly variation in EC (mgL-1) at four water sampling sites (January2012-December 2012).

Jan Feb

Mar AprMay Jun Jul

Aug Sep Oct

Nov Dec0

1

2

3

4

5

6

SAR

Figure 3.1.2 q Monthly variation in SAR ions at four water sampling sites (January2012-December 2012).

130

WSS-1 WSS-2 WSS-3 WSS-4 All sites0

10

20

30

40

50

60

70

80

90

Spec

ies r

ichn

ess

Figure 3.1.3.1 a. Variations in species richness (as taxa number) of phytoplankton and zooplankton at four water sampling sites.

WSS-1 WSS-2 WSS-3 WSS-4 All sites0

5

10

15

20

25

30

35

Spec

ies r

ichn

ess

Figure 3.1.3.1 b. Variations in species richness (as taxa number) of major phytoplankton groups at four water sampling sites.

WSS-1 WSS-2 WSS-3 WSS-4 All sites0

2

4

6

8

10

12

14

16

Spec

ies r

ichn

ess

Figure 3.1.3.1 c. Variations in species richness (as taxa number) of zooplankton groups at four water sampling sites.

131

Cyanophyta Chlorophyta Bacillariophyta Protozoa Rotifera0

5

10

15

20

25

30

Spec

ies r

ichn

ess

Figure 3.1.3.1 d. Seasonal variation in species richness (as taxa number) ofmajorplanktonic groups atWSS-1.

Cyanophyta Chlorophyta Bacillariophyta Protozoa Rotifera0

5

10

15

20

25

Spec

ies r

ichn

ess

Figure 3.1.3.1 e. Seasonal variationin species richness (as taxa number) of major planktonic groupsatWSS-2.

Cyanophyta Chlorophyta Bacillariophyta Protozoa Rotifera0

5

10

15

20

25

Spec

ies r

ichn

ess

Figure 3.1.3.1 f. Seasonal variationin species richness (as taxa number) ofmajor planktonic groupsatWSS-3.

Cyanophyta Chlorophyta Bacillariophyta Protozoa Rotifera0

2

4

6

8

10

12

14

16

Spec

ies r

ichn

ess

Figure 3.1.3.1 g. Seasonal variation in species richness (as taxa number) of major planktonic groups at WSS-4.

132

WSS-1 WSS-2 WSS-3 WSS-40

10

20

30

40

50

60

Spec

ies r

ichne

s

Figure 3.1.3.1 h. Seasonal variationin species richness (as taxa number) ofphytoplanktons (January-December 2012) atfour water sampling sites.

WSS-1 WSS-2 WSS-3 WSS-40

5

10

15

20

25

Spec

ies r

ichne

ss

Figure 3.1.3.1 i. Seasonal variationin species richness (as taxa number) ofzooplanktons (January-December2012)

at four water sampling sites.

133

Winter Spring Summer Postmonsoon0

1

2

3

4

5

6

7

8

9

10

Spec

ies r

ichn

ess

Figure 3.1.3.1 j. Seasonal variation in species richness (as taxa number) of cyanophyta (Jan-Dec 2012) at

four water sampling sites.

Winter Spring Summer Postmonsoon0

5

10

15

20

25

30

Spec

ies r

ichn

ess

Figure 3.1.3.1 k. Seasonal variation in species richness (as taxa number) of chlorophyta (Jan-Dec 2012) at four water sampling sites.

Winter Spring Summer Postmonsoon0

2

4

6

8

10

12

Spec

ies r

ichn

ess

Figure 3.1.3.1 l. Seasonal variation in species richness (as taxa number) of bacillariophyta (Jan-Dec 2012) at four water sampling sites.

134

Winter Spring Summer Postmonsoon0

2

4

6

8

10

12

Spec

ies r

ichn

ess

Figure 3.1.3.1 m. Seasonal variation in species richness (as taxa number) of protozoa (January-December 2012)atfour water sampling sites.

Winter Spring Summer Postmonsoon0

1

2

3

4

5

6

7

8

Spec

ies r

ichn

ess

Figure 3.1.3.1 n. Seasonal variation in species richness (as taxa number) ofrotifers (January-December 2012) at four water sampling sites.

135

Jan Feb

Mar AprMay Jun Jul Aug Se

p OctNov Dec

0

1

2

3

4

5

6

7

8

9

10

Spec

ies r

ichn

ess

Figure 3.1.3.1 o. Monthly variation (January-December2012) in species richness (as taxa number) of phytoplankton and zooplankton at WSS-1.

Jan Feb Mar AprMay Jun Jul

AugSep Oct Nov

Dec0

1

2

3

4

5

6

7

8

9

10

Spec

ies r

ichn

ess

Figure 3.1.3.1 p. Monthly variation (January-December 2012) in species richness (as taxa number) of phytoplankton and zooplankton at WSS-2.

Jan Feb

Mar AprMay Jun Jul Aug Se

p OctNov Dec

0

2

4

6

8

10

12

Spec

ies r

ichn

ess

Figure 3.1.3.1 q. Monthly variation (Jan-Dec 2012) in species richness (as taxa number) of phytoplankton and zooplanktonatWSS-3.

Jan Feb

Mar AprMay Jun Jul Aug Se

p OctNov Dec

0

2

4

6

8

10

12

Spec

ies r

ichn

ess

Figure 3.1.3.1 r. Monthly variation (Jan-Dec2012) in species richness (as taxa number) of phytoplankton and zooplanktonatWSS-4.

136

Jan Feb

Mar Apr

May Ju

n Jul

Aug Sep

OctNov Dec

0

2

4

6

8

10

12

Spec

ies

rich

ness

Figure 3.1.3.1 s. Monthly variation (Jan-Dec 2012) in species richness (as taxa number) of major groups of phytoplankton at WSS-1.

Jan Feb

Mar Apr

May Ju

n Jul

Aug Sep

OctNov Dec

0

1

2

3

4

5

6

7

Spec

ies

rich

ness

Figure 3.1.3.1 t. Monthly variation (Jan-Dec2012) in species richness (as taxa number) of major groups of zooplankton at WSS-1.

Jan Feb

Mar AprM

ay Jun Jul

Aug Sep

OctNov Dec

0

2

4

6

8

10

12

Spec

ies r

ichn

ess

Figure 3.1.3.1 u. Monthly variation (Jan-Dec 2012) in species richness (as taxa number) of major groups of phytoplankton at WSS-2.

Jan Feb

Mar AprM

ay Jun Jul

Aug Sep

OctNov Dec

0

1

2

3

4

5

6

7

8

Spec

ies r

ichn

ess

Figure 3.1.3.1 v. Monthly variation (Jan-Dec 2012) in species richness (as taxa number) of major groups of zooplankton at WSS-2.

137

Jan Feb

Mar AprMay Jun Jul Aug Se

p OctNov Dec

0

2

4

6

8

10

12

Spec

ies r

ichn

ess

Figure 3.1.3.1 w. Monthly variation (Jan-Dec2012) in species richness (as taxa number) of major groups of phytoplankton at WSS-3.

Jan Feb

Mar AprMay Jun Jul

Aug Sep Oct

Nov Dec0

1

2

3

4

5

6

7

Spec

ies r

ichn

ess

Figure 3.1.3.1 x. Monthly variation (Jan-Dec 2012) in species richness (as taxa number) of major groups of zooplankton at WSS-3.

Jan Feb

Mar AprMay Jun Jul Aug Se

p OctNov Dec

0

1

2

3

4

5

6

7

8

9

10

Spec

ies r

ichn

ess

Figure 3.1.3.1 y. Monthly variation (Jan-Dec 2012) in species richness (as taxa number) of major groups of phytoplankton at WSS-4.

Jan Feb

Mar AprMay Jun Jul

Aug Sep Oct

Nov Dec0

1

2

3

4

5

6

Spec

ies r

ichn

ess

Figure 3.1.3.1 z. Monthly variation (Jan-Dec 2012) in species richness (as taxa number) of major groups of zooplankton at WSS-4.

138

WSS-1 WSS-2 WSS-3 WSS-4 All sites0

10

20

30

40

50

60

70

80

90

Rel

ativ

e ab

unda

nce

(%)

Figure 3.1.3.2 a. Variation in relative abundance (%) of phytoplankton and zooplankton at four water sampling sites.

WSS-1 WSS-2 WSS-3 WSS-4 All sites0

5

10

15

20

25

30

35

Rel

ativ

e ab

unda

nce

(%)

Figure 3.1.3.2 b. Variation in relative abundance (%) of major phytoplankton groups at four water sampling sites.

Site 1 Site 2 Site 3 Site 4 All sites0

2

4

6

8

10

12

14

16

Rel

ativ

e ab

unda

nce

(%)

Figure 3.1.3.2 c. Variation in relative abundance (%) of major zooplankton groupsat four water sampling sites.

139

Cyano

phyta

Chlorop

hyta

Bacilla

rioph

yta

Protoz

oa

Rotifer

a0

2

4

6

8

10

12

Rel

ativ

e ab

unda

nce

(%)

Figure 3.1.3.2 d. Seasonal variationin relative abundance (%) of major planktonic groups (Jan-Dec 2012) at WSS-1.

Cyanophyta Chlorophyta Bacillariophyta Protozoa Rotifera0

2

4

6

8

10

12

Rel

ativ

e ab

unda

nce

(%)

Figure 3.1.3.2 e. Seasonal variation in relative abundance (%) of major planktonic group (Jan-Dec 2012) atWSS-2.

Cyanophyta Chlorophyta Bacillariophyta Protozoa Rotifera0

2

4

6

8

10

12

Rel

ativ

e ab

unda

nce

(%)

Figure 3.1.3.2 f. Seasonal variationin relative abundance (%) of major planktonic groups (Jan-Dec 2012) atWSS-3.

Cyanophyta Chlorophyta Bacillariophyta Protozoa Rotifera0

2

4

6

8

10

12

Rel

ativ

e ab

unda

nce

(%)

Figure 3.1.3.2 g. Seasonal variation in relative abundance (%) of major planktonic groups (Jan-Dec 2012) at WSS-4.

140

WSS-1 WSS-2 WSS-3 WSS-40

5

10

15

20

25

30

35

Relati

ve ab

unda

nce (

%)

Figure 3.1.3.2 h. Seasonal variation in relative abundance (%) of phytoplankton (Jan-Dec 2012) at four water

sampling sites.

WSS-1 WSS-2 WSS-3 WSS-40

1

2

3

4

5

6

7

8

9

Relati

ve ab

unda

nce (

%)

Figure 3.1.3.2 i. Seasonal variation in relative abundance (%) of zooplankton (Jan-Dec 2012) at four water samping sites.

141

Winter Spring Summer Postmonsoon0

1

2

3

4

5

6

7

8

9

10

Rel

ativ

e ab

unda

nce

(%)

Figure 3.1.3.2 j. Seasonal variation in relative abundance (%) of cyanophyta at four water sampling sites.

Winter Spring Summer Postmonsoon0

5

10

15

20

25

30

Rel

ativ

e ab

unda

nce

(%)

Figure 3.1.3.2 k. Seasonal variationin relative abundance (%) of chlorophyta at four water sampling sites.

Winter Spring Summer Postmonsoon0

2

4

6

8

10

12

Rel

ativ

e ab

unda

nce

(%)

Figure 3.1.3.2 l Seasonal variationin relative abundance (%) of bacillariophyta at four water sampling sites.

142

Winter Spring Summer Postmonsoon0

2

4

6

8

10

12

Rel

ativ

e ab

unda

nce

(%)

Figure 3.1.3.2 m. Seasonal variation in relative abundance (%) of protozoa at four water sampling sites.

Winter Spring Summer Postmonsoon0

1

2

3

4

5

6

7

8

Rel

ativ

e ab

unda

nce

(%)

Figure 3.1.3.2 n. Seasonal variationin relative abundance (%) of rotifera at four water sampling sites.

143

Jan Feb

Mar AprMay Jun Jul Aug Se

p OctNov Dec

0

1

2

3

4

5

6

7

8

9

10

Rel

ativ

e ab

unda

nce

(%)

Figure 3.1.3.2 o. Monthly variation (Jan-Dec 2012) in relative abundance (%) of phytoplankton and zooplankton at WSS-1.

Jan Feb

Mar AprMay Jun Jul Aug Se

p OctNov Dec

0

1

2

3

4

5

6

7

8

9

10

Rel

ativ

e ab

unda

nce(

%)

Figure 3.1.3.2 p. Monthly variation (Jan-Dec 2012) in relative abundance (%) of phytoplankton and zooplankton at WSS-2.

Jan Feb

Mar AprMay Jun Jul Aug Se

p OctNov Dec

0

2

4

6

8

10

12

Rel

ativ

e ab

unda

nce

(%)

Figure 3.1.3.2 q. Monthly variation (Jan-Dec 2012) in relative abundance (%) of phytoplankton and zooplankton at WSS-3.

Jan Feb Mar AprMay Jun Jul

AugSep Oct Nov

Dec0

0.2

0.4

0.6

0.8

1

1.2

Rel

ativ

e ab

unda

nce

(%)

Figure 3.1.3.2 r. Monthly variation (Jan-Dec 2012) in relative abundance (%) of phytoplankton and zooplankton at WSS-4.

144

Jan Feb

Mar AprMay Jun Jul

Aug Sep Oct

Nov Dec0

0.5

1

1.5

2

2.5

3

3.5

Rel

ativ

e ab

unda

nce

(%)

Figure 3.1.3.2 s. Monthly variation (Jan-Dec 2012) in relative abundance (%) of major groups of phytoplankton at WSS-1.

Jan Feb Mar AprMay Jun Jul

AugSep Oct Nov

Dec0

0.2

0.4

0.6

0.8

1

1.2

Rel

ativ

e ab

unda

nce

(%)

Figure 3.1.3.2 t. Monthly variation (Jan-Dec 2012) in relative abundance (%) of major groups of zooplankton at WSS-1.

Jan Feb

Mar AprMay Jun Jul

Aug Sep Oct

Nov Dec0

0.5

1

1.5

2

2.5

3

3.5

Rel

ativ

e ab

unda

nce

(%)

Figure 3.1.3.2 u. Monthly variation (Jan-Dec 2012) in relative abundance (%) of major groups of phytoplankton at WSS-2.

Jan Feb Mar AprMay Jun Jul

Aug Sep Oct NovDec

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Rel

ativ

e ab

unda

nce

(%)

Figure 3.1.3.2 v. Monthly variation (Jan-Dec 2012) in relative abundance (%) of major groups of zooplanktonat WSS-2.

145

Jan Feb

Mar AprMay Jun Jul

Aug Sep Oct

Nov Dec0

0.5

1

1.5

2

2.5

3

3.5

4

Rel

ativ

e ab

unda

nce

(%)

Figure 3.1.3.2 w. Monthly variation (Jan-Dec 2012) in relative abundance (%) of major groupsphytoplankton at WSS-3.

Jan Feb

Mar AprMay Jun Jul

Aug Sep Oct

Nov Dec0

0.2

0.4

0.6

0.8

1

1.2

1.4

Rel

ativ

e ab

unda

nce

(%)

Figure 3.1.3.2 x. Monthly variation (Jan-Dec 2012) in relative abundance (%) of major groups of zooplankton at WSS-3.

Jan Feb

Mar AprMay Jun Jul

Aug Sep Oct

Nov Dec0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Rel

ativ

e ab

unda

nce

(%)

Figure 3.1.3.2 y. Monthly variation (Jan-Dec 2012) in relative abundance (%) of major groups of phytoplankton atWSS-4.

Jan Feb

Mar AprMay Jun Jul

Aug Sep Oct

Nov Dec0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Rel

ativ

e ab

unda

nce

(%)

Figure 3.1.3.2 z. Monthly variation (Jan-Dec 2012) in relative abundance (%) of major groups of zooplanktonaWSS-4.

146

Cyprinidae Cobitidae Bagridae Siluridae Mastacembelidae0

2

4

6

8

10

12

14

16

18

Rela

tive a

buda

nce (

%)

Figure 3.2.1 a. Species richness (as taxa number) of fish families at Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.

FSS-1 FSS-2 FSS-3 FSS-4 FSS-5 FSS-6 FSS-7 FSS-8 FSS-9 FSS-100

1

2

3

4

5

6

7

8

9

Spec

ies r

ichn

ess

Figure 3.2.1 b. Variation in species richness (as taxa number) at fish sampling sites of Suleman Mountain RangeRegion, Dera Ghazi Khan, Pakistan.

Cyprinidae

Cobitidae

Bagridae

Siluridae

Mastacembelidae

0 10 20 30 40 50 60 70 80 90 100

Species richness

Figure 3.2.2 a Relative abundance (%) of number of individuals of each fish family at Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.

147

FSS-1 FSS-2 FSS-3 FSS-4 FSS-5 FSS-6 FSS-7 FSS-8 FSS-9 FSS-100

10

20

30

40

50

60

70

80

90

100Mastacembalus arma-tusOmpok pabdaRita ritaBotia birdiCirrhnus mrigalaSalmophasia pun-jabensisBarilius pakistanicusPuntius sophoreLabeo calbasuCyprinion watsoniSchizothorax plagios-tomusCrossochelus diplocheilusLabeo dyocheilus pak-istanicusSecuricula goraGara gotylaSalmostoma bacaila

Rel

ativ

e ab

unda

nce

(%)

Figure 3.2.2 b Relative abundance (%) of each fish species within each site during study period at Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.

148

Tor m

acrolep

is

Bariliu

s vagr

a

Bariliu

s modustu

s

Labeo

diplostomus

Salmosto

ma baca

ila

Gara go

tyla

Securicu

la gora

Labeo

dyocheilu

s paki

stan...

Crossochelu

s diplocheilu

s

Schizo

thorax plag

iostomus

Cyprin

ion watsoni

Labeo

calbasu

Puntius sophore

Bariliu

s paki

stanicu

s

Salmophasi

a punjab

ensis

Cirrhnus m

rigala

Botia bird

i

Rita rit

a

Ompok pab

da

Mastace

mbalus a

rmatu

s

0

5

10

15

20

25FSS-10FSS-9FSS-8FSS-7FSS-6FSS-5FSS-4FSS-3FSS-2FSS-1

Relati

ve ab

unda

nce (

%)

Figure 3.2.2 c. Relative abundance (%) of twenty fish species at different sites during study period at Suleman Mountain Range, Dera Ghazi Khan Region, Pakistan.

149

Figure 3.2.3 Dendrogram showing similarity level (based on Jaccard similarity index) in species compositionacross ten fish sampling sites.

150

5 10 15 20 25 30 350

5

10

15

20

25

30

Total length (cm)

Leng

th o

f oth

er v

aria

bles

(cm

)

Figure 3.3.1a Relationship of total length (cm) with length of other variablesof Tor macrolepis.

5 10 15 20 25 30 350

1

2

3

4

5

6

7

8

9

10

Total length (cm)

Fin

leng

ths (

cm)

Figure 3.3.1b. Relationship of total length (cm) with fins length of Tor macrolepis.

0 50 100 150 200 250 300 3500

5

10

15

20

25

30

Wet body weight (g)

Leng

th o

f oth

er v

aria

bles

(cm

)

Figure 3.3.1c. Relationship of wet body weight (g) with length of other variables of Tor macrolepis.

0 50 100 150 200 250 300 3500

1

2

3

4

5

6

7

8

9

10

Wet body weight (g)

Fins

leng

th (c

m)

Figure 3.3.1d. Relationship of wet body weight (g) with fins length of Tor macrolepis.

151

6 8 10 12 14 16 180

2

4

6

8

10

12

14

16

18

Total length (cm)

Leng

th o

f oth

er v

aria

bles

(cm

)

Figure 3.3.2 a Relationship of total length (cm) with length of other variables of Schizothorax plagiostomus.

6 8 10 12 14 16 180

0.5

1

1.5

2

2.5

3

3.5

4

Total length (cm)

Fins

leng

th (c

m)

Figure 3.3.2 b Relationship of total length (cm) with fins lengthof Schizothorax plagiostomus.

0 10 20 30 40 50 60 70 800

2

4

6

8

10

12

14

16

18

Wet body weight (g)

Leng

th o

f oth

er v

aria

bles

(cm

)

Figure 3.3.2 c Relationship of wet body weight (g) with length of other variablesof Schizothorax plagiostomus

0 10 20 30 40 50 60 70 800

0.5

1

1.5

2

2.5

3

3.5

4

Wet body weight (g)

Fins

leng

th (c

m)

Figure 3.3.2 d Relationship of wet body weight (g) with fins length of Schizothorax plagiostomus.

152

8 10 12 14 16 18 20 220

5

10

15

20

25

30

35

40

Total length (cm)

Leng

th o

f oth

er v

aria

bles

(cm

)

Figure 3.3.3 a Relationship of total length (cm) with length of other variablesof Labeo diplostomus.

8 10 12 14 16 18 20 220

1

2

3

4

5

6

7

Total length (cm)

Fins

leng

th (c

m)

Figure 3.3.3 b Relationship of total length (cm) with fins length of Labeo diplostomus.

0 10 20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

Wet body weight (g)

Leng

th o

f oth

er v

aria

bles

(cm

)

Figure 3.3.3 c Relationship of wet body weight (g) with length of other variables of Labeo diplostomus.

0 10 20 30 40 50 60 70 80 90 1000

1

2

3

4

5

6

7

Wet body weight (g)

Fins

leng

th (c

m)

Figure 3.3.3 d Relationship of wet body weight (g) with fins length of Labeo diplostomus.

153

8 10 12 14 16 18 20 22 24 260

5

10

15

20

25

Total length (cm)

Leng

th o

f oth

er v

aria

bles

(cm

)

Figure 3.3.4 a Relationship of total length (cm) with length of other variables of Labeo dyocheilus pakistanicus.

8 10 12 14 16 18 20 22 24 260

1

2

3

4

5

6

7

Total length (cm)

Fins

leng

th (c

m)

Figure 3.3.4 b Relationship of total length (cm) with fins length of Labeo dyocheilus pakistanicus.

0 20 40 60 80 100 120 1400

5

10

15

20

25

Wet body weight (g)

Leng

th o

f oth

er v

aria

bles

(cm

)

Figure 3.3.4 c Relationship of wet body weight (g) with length of other variables of Labeo dyocheilus pakistanicus.

0 20 40 60 80 100 120 1400

1

2

3

4

5

6

7

Wet body weight (g)

Fins

leng

th (c

m)

Figure 3.3.4 d Relationship of wet body weight (g) with fins lengthof Labeo dyocheilus pakistanicus.

154

7 8 9 10 11 12 13 14 150

0.5

1

1.5

2

2.5

3

3.5

4

Total length (cm)

Fins

leng

th (c

m)

Figure 3.3.5 a Relationship of total length (cm) with length of other variables of Cyprinion watsoni.

7 8 9 10 11 12 13 14 150

2

4

6

8

10

12

14

Total length (cm)

Leng

th o

f oth

er v

aria

bles

(cm

)

Figure 3.3.5 b Relationship of total length (cm) with fins length of Cyprinion watsoni.

5 10 15 20 25 30 350

2

4

6

8

10

12

14

Wet body weight (g)

Leng

th o

f oth

er v

aria

bles

(cm

)

Figure 3.3.5 c Relationship of wet body weight (g) with length of other variables of Cyprinion watsoni.

5 10 15 20 25 30 350

0.5

1

1.5

2

2.5

3

3.5

4

Wet body weight (g)

Fins

leng

th (c

m)

Figure 3.3.5 d Relationship of wet body weight (g) with fins length of Cyprinion watsoni.

155

8 10 12 14 16 18 200

2

4

6

8

10

12

14

16

18

20

Total length (cm)

Leng

th o

f oth

er v

aria

bles

(cm

)

Figure 3.3.6 a Relationship of total length (cm) with length of other variables of Ompok pabda.

8 10 12 14 16 18 200

0.5

1

1.5

2

2.5

3

Total length (cm)

Fins

leng

th (c

m)

Figure 3.3.6 b Relationship of total length (cm) with fins length of Ompok pabda

0 10 20 30 40 50 600

2

4

6

8

10

12

14

16

18

20

Wet body weight (g)

Leng

th o

f oth

er v

aria

bles

(cm

)

Figure 3.3.6 c Relationship of wet body weight (g) with length of other variables of Ompok pabda.

0 10 20 30 40 50 600

0.5

1

1.5

2

2.5

3

Wet weight (g)

Fin

leng

th (c

m)

Figure 3.3.6 d Relationship of wet body weight (g) with fins length of Ompok pabda.

156

8 9 10 11 12 13 14 150

2

4

6

8

10

12

14

Total length (cm)

Leng

th o

f oth

er v

aria

bles

(cm

)

Figure 3.3.7 a Relationship of total length (cm) with length of other variables of Garra gotyla.

8 9 10 11 12 13 14 150

0.5

1

1.5

2

2.5

3

3.5

Total length (cm)

Fins

leng

th (c

m)

Figure 3.3.7 b Relationship of total length (cm) with fins length of Garra gotyla.

5 10 15 20 25 30 35 40 450

2

4

6

8

10

12

14

Wet body weight (g)

Leng

th o

f oth

er v

aria

bles

(cm

)

Figure 3.3.7 c Relationship of wet body weight (g) with length of other variables of Garra gotyla.

5 10 15 20 25 30 35 40 450

0.5

1

1.5

2

2.5

3

3.5

Wet body weight (g)

Fins

leng

th (c

m)

Figure 3.3.7 d Relationship of wet body weight (g) with fins length of Garra gotyla.

157

Discussion

4.1 Water Quality

4.1.1 Physico-Chemical Factors

Fresh water environments unlike the marine ones are subjected to variations in the

environmental factors such as temperature, DO, light penetration, turbidity, density,

etc. These factors are responsible for distribution of organisms in different fresh water

habitats according to their adaptations, which allow them to survive in that specific

habitat (Jaffries and Mills, 1990). Important climatic and phyico-chemical parameters

were estimated in present study. Several of the physico-chemical parameters showed

variations related either with site/season/months.

Variations in water temperature are usually governed by the climatic conditions.

Rainfall and solar radiations are the major climatic conditions that influence most of

the physico-chemical parameters of water bodies (Kadiri, 2000). Solar radiation is

dependent on the duration and intensity or iridescence received daily by the water

body. The intensity of solar radiations may be naturally modified by variations in

cloud cover, water flow, phytoplankton species composition and diversity, surface

area, depth, wind velocity, solid matter suspension, etc. All these factors influence

daily fluctuations in water temperature (Atoma, 2004). In present study, there were no

significant water temperature variations between sites, but water temperature varied

significantly in different months and seasons. The comparatively wide range of

maximum and minimum water temperature with 32.01±1.55 °C recorded in summer

and minimum 16.27±5.07 °C in winter clearly indicates pronounced seasonal

variations in surface water temperature of this region.

158

Light penetration values showed no significant difference between sites, however,

there was a significant difference between seasons and months. The lower limit of

light penetration is the limit of algal photosynthetic activity that has a major impact on

the primary productivity of the lake (Carpenter et al., 1998; Ask et al., 2009;

Vadeboncoeur et al., 2001). The light penetrationis affected by factors such as time of

the day, clarity of the sky at the time of measurement (cloudy or not) and suspended

solids in water including plankton (Smith et al., 1997; Julianet al., 2008). The mean

values of light penetration ranged from 23.32 cm (WSS-1) to 28.48 cm (WSS-3). It

also varied from 20.69 cm (post monsoon) to 30.90 cm (spring). Lower light

penetrationvaluesare recorded during rainy season when there is turbulence and high

turbidity (USEPA, 1992; APHA, 1992). Higher total solids, TDS and total suspended

solids, resulted in minimum transparency in monsoon season. This complies with the

reports on several water bodies especially in Indian climatic conditions (Zafar, 1964;

Singhai et al., 1990; Kaur et al., 1995; Ekhande, 2010) as well as other tropical

countries like Nigeria (Olele and Ekelemn, 2008). Khan and Chowdhury (1994)

reported that higher transparence occurred, during winter and summer due to absence

of rain, runoff and flood water as well as gradual settling of suspended particles.Light

penetration varying from 30 cm to above 60 cm was acknowledged to be favourable

for fish production (Boyd and Tucker, 1998; Ali et al., 2002). Our study results of

light penetration values also indicate suitable limits for aquatic life at four catchment

areas of Suleman Mountain Range.

TDS values were significantly different between four sampling sites. However, there

was no significant effect of seasonal variations on TDS, though it varied significantly

in months. TDS reduces solubility of gases (like oxygen), utility of water for drinking

159

purpose and also enhances eutrophication of the aquatic ecosystem (Mathur et al.,

2008). High concentration of TDS enriching the nutrient status of water body (Singh

and Mathur, 2005) beyond limit may enhance eutrophication (Mathur et al., 2008).

The TDS values ranged from 1174 to 1636 mgL-1 in present study. A maximum value

of 400 mgL-1 of total dissolved solids is permissible for diverse fish population (Boyd

and Tucker, 1998; Ali et al., 2003). Therefore, the TDS valuesin present study exceed

the recommended limit of suitable waters.

pH values demonstrated significant variations between sites as well as between

seasons. In a balanced ecosystem pH is maintained within the range of 5.5 to 8.5

(Chandrasekhar et al., 2003). Due to diurnal variations in the water temperature of a

system, pH of a water body is a diurnally variable property (Ojha and Mandloi, 2004).

Increased surface pH in water bodies is due to increased metabolic activities of

autotrophs, because in general they utilize the CO2 and liberate O2 thus reducing H+

ion concentration (Kaul and Handoo, 1980; Satpathy et al., 2007). In present study, the

pH values ranged between 7.85 to 8.02 for sites and 7.73 to 8.12 for seasons. Different

reports have given different pH ranges as ideal for supporting aquatic life. However,

all these ranges fall around same point between 6 to 8.5 (ICMR, 1975; Boyd and

Lichtkoppler, 1979; WHO, 1989). pH of lentic water bodies of Suleman Mountain

Range, Dera Ghazi Khan Region, Pakistan fall in ideal range.

DO is essential to all forms of aquatic life.Majority of chemical and biological

processes undergoing in the water body also depend on the presence of

oxygen.Oxygen is also known to affect the solubility and availability of many nutrients

and hence, it is one of the most significant parameter affecting the productivity of

160

aquatic systems (Wetzel, 1983). There was no significant difference between sites,

however, DO varied significantly with season and months. The DO ranged from 6.63

to 7.30 mgL-1 for sampling sites and 5.99 to 8.09 mgL-1 for seasons. The factors

affecting oxygen content in natural waters include input from atmosphere and

photosynthesis and output from respiration, decomposition and mineralization of

organic matter as well as losses to atmosphere (Wetzel, 1983; Ramacandra and

Solanki, 2007; Saravanakumar et al., 2008). The oxygen balance in water bodies

becomes poorer when the input and photosynthetic activity decrease and the metabolic

activities of heterotrophs enhance.The oxygen cycle in water involves a rapid decrease

during summer, a steady increase through autumn till maximum content is reached in

winter following the well-known theory of solubility of gases (Kaul and Handoo,

1980). Ideal DO for the fish, the final stage of productivity in the fresh water

ecosystem, is assumed to be between 6 and 7 mgL-1 (Edmondson, 1960). Low oxygen

concentration will also affect the types of fish and invertebrates that inhabit the area

(Gordon et al, 2004). The minimum limit of DO required for freshwaters as per ICMR

(1975) standards is 5 to 6 mgL-1. Therefore, DO of studied water bodies of Suleman

Mountain Range, Dera Ghazi Khan Region, Pakistan is within normal limits.

Free CO2 amount varied significantly between sites, with season and months. The

values ranged from 7.18 mgL-1 to 7.84 mgL-1for sites and 6.28 mgL-1 to 8.51 mgL-1for

seasons. The free CO2 limit values for drinking water have not been prescribed but the

permissible limit for fish culture is 6 mgL-1. The maximum free CO2 in summer may

be attributed to higher rate of decomposition of organic matter due to comparatively

higher temperature. During daytime due to photosynthesis water is generally CO2 free

(Sahu et al., 1995). Organic decomposition, respiration, photosynthesis, diffusion and

161

runoffs could also account for the variations seen in the CO2 levels (Renn, 1968;

APHA, 1995).

An inverse relationships exist between DO and CO2, pH, alkalinity and temperature

the parameter that are directly related to each other. The pH depends upon the free CO2

and bicarbonate-carbonate levels (Michael, 1984).

TA varied significantly between sites, and there was non significant effect of seasonal

variations. TA ranged from 101 mgL-1 to 401 mgL-1 for sites and 285 to 303 mgL-1 for

seasons. The main sources of natural alkalinity are rocks containing carbonate,

bicarbonate, and hydroxide compounds that are abundantly present. TA affects the

primary production and the other metabolic process of aquatic organisms (Singh and

Islam, 2000). It was reported earlier that during summer and winter season water was

alkaline, which is helpful for maximum population dynamics of planktons (Pundir and

Rana, 2002).

The values for TH, CO3-2 and HCO3

-1 were significantly different between sites;

however, there was non-significant effect of seasonal as well as monthly variations on

these parameters. The values of TH ranged from 201 mgL-1 to 265 mgL-1 for sites, CO3-

2 varied from 11.81 mgL-1to 13.78 mgL-1, and HCO3-1 ranged from 386 mgL-1 to 623

mgL-1 for sites. TH values were 146-257.3 mgL-1from River Indus at Beka Swabi

KPK, Pakistan Khan et al., 2014). Chughtai et al., (2013) described TH values 190-

320 mgL-1 at D. G. Khan Canal from Dera Ghazi Khan, Pakistan. These results

coincide with our results.

162

The hardness of water is not a pollution parameter but indicates water quality.Hardness

of water, mainly governed by the Ca+2 and to a lesser extent by Mg+2 contents with ions

such as Fe+2. Waters are often categorized according to degrees of hardness as follows:

0-75 mgL-1 = soft, 75-150 mgL-1 = moderately hard 150-300 mgL-1 = hard above 300

mgL-1 = very hard (WHO, 1996).Waters with more than 60 ppm which is equivalent to

60 mgL-1.CaCO3hardness are classified as ‘nutrient rich’ waters (Spence, 1964). The

desirable limit of hardness in drinking water according to ISI standards is below 300

mgL-1. While hardness levels above 500mgL-1 are generally considered to be

aesthetically unacceptable (WHO, 1996). The results of present study reflect hardness

value of water bodies are within acceptable range.

Calcium (Ca) is one of the most abundant substances of the natural waters. Being

present in higher quantities in rocks, it is leached from these to contaminate water. Ca

is an important element and associated with different cations like CO3-2, HCO3

-1 and

fluorides to exert hardness. As Ca+2 and Mg+2 bond with CO3-2 and HCO3

-1, alkalinity

and water hardness are closely interrelated and produce similar measured levels

(Matini et al., 2012).

Chlorides contents were significantly different between four sites.Higher concentration

of Cl-1 indicates higher degree of organic pollution (Munawar, 1974; Ramakrishna,

1990). Concentration of Cl-1 in sea water is around 20,000 mgL-1, in unpolluted rivers

between 2-10 mgL-1 and in rain water 2 mgL-1. When it is above 200 mgL-1, the water

is unsuitable for human consumption (Koshy and Nayar, 1999). Maximum permissible

limit with regard to Cl-1 content in natural freshwaters is 250 mgL-1(ICMR, 1975; ISI,

1991). In present study, three sites i.e. WSS-1, WSS-2, WSS-3 have lower Cl -1

163

contents than the permissible limit 250 mgL-1as per ISI. However, at WSS-4, higher

Cl-1 contents than permissible limits were recorded. In natural water excessive Cl -1 ions

are usually found associated with Na+1, K+1 and Ca+2 which produce salty taste when

concentration is 100mgL-1 (Kataria et al., 1996; Gowd et al., 1998).

EC values of four sites were significantly different. There was no significant effect of

seasons on EC. Several factors influence the conductivity including temperature, ionic

mobility and ionic valencies. In turn, conductivity provides a rapid mean of obtaining

knowledge of TDS concentration and salinity of water sample (Odum, 1971).

4.1.2 Phytoplankton

The freshwater habitat is a natural condition for the growth of aquatic flora and the

fluxing of the wastes by natural or anthropogenic activities cause disturbance in its

composition, this cause change in the optimum condition favourable for the growth of

the aquatic flora (Jaiswar and Mehta, 2014). Algal communities serve as a natural

oxygenator in any water system.Therefore its conservation is very important to

maintain the biotic community in the water. The present investigation on the

freshwater algal taxa of these unexplored sites of Suleman Mountain Range, Dera

Ghazi Khan Region, Pakistan would form valuable information which would be

further used for the environmental assessment and monitoring of water quality.

In our study, a considerable number of plankton genera i.e. 119 taxa including 83 of

phytoplankton and 36 of zooplankton were recorded from four sampling sites of the

area. A survey of literature has revealed slight or slightly larger variations in number

and types of planktonic genera from several water bodies located at different places in

Pakistan. A total number of 104 genera including 86 of phytoplankton and 18 of

164

zooplankton from Chenab River (Chughtai et al., 2011),a total number of 53 genera

including 39 of phytoplankton and 14 of zooplankton from D. G. Khan canal

(Chughtai et al., 2013), a total number of 103 genera including 32 of phytoplankton

and 71 of zooplankton from wetland complex Uchalli (Ali et al., 2007), a total number

of 179 genera including 142 of phytoplankton and 37 of zooplankton from Keenjhar

Lake (Korai et al., 2008), a total number of 169 genera including 125 of phytoplankton

and 44 of zooplankton from River Indus (Ali et al., 2003), a total number of 35

phytoplankton genera from ShaPur Dam (Janjua et al., 2009), a total number of 34

phytoplankton genera from KalaPani stream and adjoining area of stream area from

Mardan (Khan et al., 2011), a total number of 33 genera including 142 of

phytoplankton and 37 of zooplankton from Mangla Reservior (Rafique et al., 2002).

However, a greater variation was also noted when compared to planktonic biodiversity

analysis with other regions of the world. A total number of 54 phytoplankton genera

from Sapanca Lake, Turkey (Yilmaz and Aykulu, 2010), a total number of 39 genera

including 25 of phytoplankton and 14 of zooplankton from a tropical African

Reservior, Nigeria (Mustapha, 2010), a total number of 88 phytoplankton genera from

Lake Mogan, Turkey (Yerli et al., 2012), a total number of 53 species of

phytoplankton from three waterbodies of Satara district, India (Pawar and Sonavane,

2011), a total number of 53 genera of phytoplankton from Zayan Dh-Rood Dam Lake,

Iran (Shams et al., 2012).

In our study, phytoplanktons have been found to be abundant compared to

zooplankton. This is also reported by several other workers from various localities of

Pakistan (Ali et al., 2005; Chughtai et al., 2011; Janjua et al., 2009). The species

composition of phytoplankton was related with site, season and months, however,

165

there was no significant effect of site, season and month on abundance of total

phytoplankton.The composition and biomass of phytoplankton species in reservoirs

depends on a complex combination of factors, such as temperature, light, availability

of nutrients and zooplankton community (Reynolds, 2002). In subtropical regions, the

considerable variation in temperature and other environmental variables produces

predictable changes in the composition of phytoplankton in aquatic systems (Grover

and Chrzanowski, 2006).The composition of phytoplankton is very often an excellent

indication of the trophic state of a water body (Rosen, 1981; Reynolds, 1998). The

differences in number of taxa and number of individuals between sampling sites for

each class of phytoplankton may be due differences in environmental factors.

Chlorophyta demonstrated a significant difference of species richness and abundance

between four sites; however, there was no significant effect of season on species

composition and abundance. The optimal temperature reported for growth of green

algae ranged between 30-40°C (Palmer, 1984). High water temperature supported the

growth of chlorophyceae (Hegde and Sujata, 1997). During the present study

temperature was found to be the most important factor that influenced the other

physicochemical factors by forming positive/negative correlations with other

important environmental factors.

Chlorophyta was the most abundant group of phytoplankton and also with higher

species richness.This has been reported by several workers (Annalakshmi and Amsath,

2012; Jamal et al., 2014; Groga et al., 2014). Thus qualitatively chlorophyceae formed

the largest group and was followed by other groups. Quantitatively also chlorophyceae

dominanted over other groups and contributed as much as (48%) to the total

166

phytoplankton population (Rajagopal et al., 2010).Higher cholorophyceae are a large

and important group of freshwater algae. About 2650 species of chlorophyceae have

been described from the different parts of the world and 350 genera have so far been

authenticated (Ven DenHoeck et al., 1995).

In present study, chlorophyta demonstrated several significant correlations with

environmental variables. These include negative correlation of density of chlorophyta

with TDS at WS-1, with calcium at WSS-1, with EC at WSS-1, with SAR at WSS-1;

and a positive correlation of density of chlorophyta with Cl-1 at WSS-1. While species

composition of chlorophyta has negative correlation with pH at WSS-1, with Ca+2 at

WSS-3, and positive correlation with light penetration at WSS-1, with TDS at WSS-2,

with carbonates at WSS-1, with total hardness at WSS-3, with EC at WSS-2.Several

workers have reported positive correlation of chlorophyta abundance with DO (Kumar

and Oommen, 2009); chlorophyta with temperature (Hegde and Sujata, 1997); with

light penetration, DO and TA (Bhatt et al., 1999) and negative correlation with water

temperature, light penetration and dissolved oxygen (Cordero and Baldia, 2015);

chlorophyta with DO and CO2 (Hegde and Sujata, 1997) with pH (Bhatt et al., 1999).

Chlorophyta has been found to be dominant phyla in several studies. The

chlorophyceae dominance was followed by cyanophyceae (Janjua et al., 2009; Silva,

2005). The Oocystis was the most abundant genera of chlorophyta at WSS-2, WSS-3,

WSS-4 and second abundant genera at SWW-1. At WSS-1, Chlorella was the most

abundant genera. Oocystis was also the dominant genera in summer and post

monsoonseason at all sites, at WSS-2, WSS-3, WSS-4 in winter and at WSS-1 in

167

spring. Similarly, Chlorella was dominant in winter at WSS-1, and at WSS-2, WSS-3,

WSS-4 in spring.

Several genera of chlorophyta were recorded from all sites. These include

Ankistrodesmus, Carteria, Chlamydomonas, Chlorella, Cosmarium, Crucigenia,

Euastrum, Golenkinia, Oocytis, Spirogyra, Staurastrum, Tertaedon and Volvox. While

several species were recorded from single site only. These include Platymonas,

Plerotaenium and Spirotaenia from WSS-1, Treubaria from WSS-2, Heteromastrix

from WSS-3. Development of Oocystis and Sphaerocystis algae are typical of many

clear water bodies (Belkinova et al., 2014). Phacotus is an alga of stagnant inland

waters of various morphometric and ecological states from deep stratified oligotrophic

lakes to shallow polymictic hypertrophic waters (Schlegel et al., 1998).

Members of chlorophyta dominated in spring, summer and autumn. Chlamydomonas

globosa and Chlamydomonas pseudopertyii showed an increase in early spring and

composed of 82.3% of phytoplankton density. Chlamydomonas sp. and Carteria sp.

had been recorded as dominant in Lake Mogan during the spring (Obalı, 1984).

Chlorophyta members were usually found commonly and abundantly in

mesotrophicand eutrophic lakes (Trifonova, 1998). Some ofchlorococcales members

were found mostly in lakes which were heading towards fromoligotrophic period to

eutrophic period (Round, 1984). Scenedesmus, Oocystis and Pediastrum species were

found abundantly in eutrophic lakes and oligomesotrophic reservoirs in Turkey (Obali,

1984; Tas et al., 2002).

168

Cyanophyta demonstrated a non-significant difference of species richness between

four sites; however, there was a significant effect of seasonal variations on species

richness of cyanophyta. The cyanophyta demonstrated a significant difference of

abundance between four sites; however, there was no significant effect of season on

abundance of cyanophyta between sites.

In present study, Cyanophyta demonstrated significant correlations with

physicochemical factors. These include negative correlation of abundance of

cyanophyta with Na+1 at WSS-2, with Ca+2 at WSS-1. There are negative correlations

of species richness of cyanophyta with TDS at WSS-1, with HCO3-1 at WSS-3, with

TA at WSS-2 and WSS-3, with TH at WSS-2 and WSS-3, with SO4-2 at WSS-3, with

SAR at WSS-3, And positive correlation with Na+1 at WSS-4. Several studies showed

positive correlation of cyanophyta with DO (Kumar and Oommen, 2009); cyanophyta

with TH (Iqbal and Katariya, 1995); cyanophyta with CO2 (Johnston and Jacoby,

2003); TA with cyanophyta (Bhatt et al., 1999) and other showed negative correlations

of cyanophyta with temperature and pH (Bhatt et al., 1999). Microcystis was the most

abundant genera of cyanophyta at all sites and in all seasons. Several genera of

cyanophyta were recorded from all sites. These include Anabaena, Aphanothece,

Dactylococcopsis, Gloeocapsa, Microcystis, Ocillatoria, Phormidium and Snowella.

While Aphanizomenon was recorded from WSS-3 in winter season only.

Bacillariophyta demonstrated a non-significant difference of species richness and

abundance between four sites. However, there was a significant effect of seasonal

variations on species richness and abundance of bacillariophyta. In present study,

bacillariophyta was the third abundant group of phytoplankton. It has also been

169

reported to be the most abundant group in several studies (Yilmaz and Aykulu,

2010; Atici and Alas, 2012; Zakariya et al., 2013). However, results of several

studies from various localities of Pakistan show bacillariophyta to be second or third

abundant group (Ali et al., 2005; Korai et al., 2008; Janjua et al., 2009; Lashari et al.,

2014).

Bacillariophyta demonstrated several significant correlations with physico-chemical

factors. These include negative correlation of abundance of bacillariophyta with water

temperature at WSS-1 and WSS-2, with TDS at WSS-4, with pH at WSS-2, WSS-3,

with DO at all sites, with Mg atWSS-3, with EC at WSS-4; and positive correlation

with HCO3-1 at WSS-1. Similarly, there were negative correlations of species richness

of bacillariophyta with water temperature at all sites, with light penetration at WSS-2

and WSS-4, with pH at WSS-3, with DO at WSS-3, with TH at WSS-3, with Ca+2 at

WSS-1. Several studies showed positive correlation of bacillariphyta with temperature

and DO (Cordero and Baldia, 2015); with DO (Sarojini, 1996); with light penetration,

DO and pH (Bhatt et al., 1999); with EC and TDS (Singh et al., 2010) and other

showed negative correlations of bacillariphyta with temperature (Gupta and Pamposh,

2014); with light penetration (Cordero and Baldia, 2015).

Cyclotella was the most abundant genus at WSS-1, WSS-2, and the second abundant

genus at WSS-3 and WSS-4. While, Pinnularia was the most dominant genus at WSS-

3 and WSS-4 while second most abundant group at WSS-1 and WSS-2. Overall,

cyclotella was the most abundant bacillariophycean genus during study period.

Pinnularia was the dominant genus in summer and post-monsoon at all sites,

Cyclotella was the dominant genus at WSS-1, WSS-2, WSS-3 in winter and

170

Pinnularia at WSS-4 in winter. Cyclotella was dominant at WSS-1 in spring,

Stephanodiscus at WSS-2, Pinnularia at WSS-3 and WSS-4 in spring. Several species

of bacillariophyta were recorded from all sites. These include Cyclotella, Fragillaria,

Gomphonema, Melosira, Navicula, Pinnularia, Stephanodiscus, Synedra and

Tabellaria.

Cyclotella meneghinianahas been observed highly in the phytoplankton community

(Yerli et al., 2012).Cyclotella meneghiniana showed a regular distribution during the

study period and were recorded in high densities in summer months and early autumn

(Yerli et al., 2012). Cyclotella spp. are characteristic indicators of oligotrophy in lakes

and reservoirs (Trifonova, 1998), and they have been reported as dominant in several

oligotrophic and mesotrophic lakes and reservoirs of Turkey (Aykulu et al., 1983;

Altuner, 1984; Gonulol and Obali, 1998).

4.1.3 Zooplankton

The composition of total zooplankton did not vary significantly between sites,

however, zooplankton abundance showed significant variations between sites. The

composition of zooplankton was very similar at all sampling sites. Composition and

abundance of total zooplankton showed no significant effect of season. Several

workers have reported a positive correlation of temperature with zooplankton

population growth (Tripathi et al., 2006, Rainaet al., 2013). Temperature causes

zooplankton abundance particularly in shallow freshwaters (Ahangar et al., 2012).

However, in present study, we could not find any correlation of zooplankton

abundance with high temperature. The obvious reason could be the disturbance caused

by flowing water with high velocity, resulting in destabilization of planktonic

171

populations. It was observed that zooplankton diversity and abundance was influenced

by several physico-chemical parameters, showing a positive/negative correlation.

Zooplankton demonstrated severals significant correlations with physico-chemical

parameters. These include a negative correlation of zooplankton with pH and light

penetration at WSS-1; negative correlation of zooplankton with HCO3-1 and positive

correlation with Cl-1 at WSS-2; negative correlation of zooplankton with EC at WSS-3;

And negative correlation of zooplankton with HCO3-1 and TA at WSS-4.

Protozoa was found to be the most abundant group as well as with highest species

richness. Both species composition and abundance varied significantly between sites;

however, no significant seasonal effect was recorded. Several species were recorded

from one site only like Urotrichia and Stentor from WSS-1.Several species were

recorded from all sites like Acanthocystis, Actinophrys, Amoeba, Arcella, Difflugia,

Euglypha, Tintinnidium and Trinema. Centropyxis was found from three sites except

WSS-2. Some of the protozoan species of genus Arcella, Difflugia, Centropyxis and

Paramecium have been related with nutrients enrichment of water (Agarkar et al.,

1994, Wanganeo and Wanganeo, 2006, Kumar et al., 2010). The occurrence of thee

genera from water bodies of this locality suggest nutrient rich waters. Protozoan

demonstrated several significant correlations with physicochemical parameters. These

include negative correlation of protozoan abundance with HCO3- at WSS-4, with TA at

WSS-4, with Ca+2 at WSS-4, with Cl-1 at WSS-4, and with EC at WSS-4., whereas,

protozoan species richness has positive correlation with SAR at WSS-2.

Rotifera was the second abundant group as well as with species richness. Species

composition of rotifer varied significantly between sites; however, rotiferan abundance

172

did not varied significantly between sites. Species composition of rotifer did not varied

significantly between seaonss; however, rotiferan abundance varied significantly

between seasons. Several rotifera species were recorded from all sites. These include

Brachionus, Epiphanes, Keratella, Monotyla and Notholca. Brachionus and Keratella

are typical cosmopolitan genera (Mengestou et al., 1991). Brachionusis considered to

be abundant in eutrophic waters. Several species of Brachionus has high tolerance to

salinity (Mallin et al., 1995). Keratella is dominant rotifer of warm lakes (Fernando,

1980), and also occur with abundanceover whole range of temperatures (May, 1983).

The occurrence of MonostylaandEuchlanis sp. has been related with freshwaterbodies

of low trophic status (Baloch et al., 2004).Rotifera demonstrated a positive correlation

with CO3-2 at WSS-2.

Cladocera occurrence was inconsistent throughout study period at all sites and in all

seasons. Maximum species richness was observed at WSS-1 compared to other sites.

Alona sp. and Pleuroxus sp. was recorded from all sites.

4.1.4 Diversity indices

The major application of indices in plankton studies is their manipulation in pollution

assessment. The species diversity is a role of species richness and evenness in which

organisms are distributed in these species (Margalef, 1968). Present study indicated

diversity indices of phytoplankton and zooplankton are in accordance with the findings

of Ali et al. (2005) in which Margalef index ranged 5.98-8.15 and 1.11-3.85 for

phytoplankton and zooplankton, respectively.But our results of Margalef index differ

with the work of Chughtai et al. (2013) which ranged 2.53-2.99 and 1.08-1.68 for

phytoplankton and zooplankton, respectively. According to Mason (1998) diversity

index value greater than 3 reveals clean water, values from 1 to 3 indicated moderately

173

polluted water and having value less than 1 characterized heavily polluted water.

Therefore, our results showed that water conditions are appropriate for growth of

plankton in the region.

Shannon - Wiener Index was used to assess the diversity, this index can have a value

of 0 when there is single species and it is maximum when all species are equal to the

number of individuals (Ludwig and Reynolds, 1988). Shannon-Wiener Index values of

our results can be compared with findings of Onyema et al. (2014) which are ranged

0.73-1.13 and 0.24-0.93 for phytoplankton and zooplankton, respectively. Simpson

diversity index values found in our results for plankton are in agreement with the

results of Mishra et al. (2010) which ranged from 0.80-0.92.

174

4.2 Fish Biodiversity

The freshwater fish fauna of Pakistan is very rich and diverse. A number of recent and

comprehensive studies have described the freshwater fish fauna from natural

freshwater bodies at various localities of Pakistan (Mirza, 1994; Mirza, 2003; Rafique

and Qureshi, 1997; Rafique, 2000; Rafique, 2001; Ahmad and Mirza, 2002; Rafique et

al., 2003; Javed et al., 2005; Pervaiz, 2011; Khan et al., 2011). Most of these studies

focus on taxonomic identification thus provide information on fish species

composition and diversity of various water bodies. Mostly, these studies do not

consider other important aspects of fish fauna like population dynamics and

conservation status. Present study describes population dynamics, economic and

conservation status of fish fauna of hill torrents of Suleman Mountain Range, Dera

Ghazi Khan Region, Pakistan.

The freshwater fish fauna of Pakistan is represented by at least 193 fish species,

belonging to infraclass Teleostei of sub-class Actinopterygii, encompassing 3 cohorts,

6 superorders, 13 orders, 30 families and 86 genera (Rafique, 2007). In present study,

twenty fish species belonging to infraclass Teleostei of sub-class

Actinopterygiiencompassing 1 cohort (euteleostei), 2 superorders, 3 orders

(Cypriniformes, Siluriformes and Synbrancheiformes), 5 families (Cyprinidae,

Siluridae, Cobitidae, Bagridae and Mastacembalidae) and 16 genera were identified.

The twenty recorded fish species in present study have also been frequently reported

from other major mountainous, sub-mountainous and plain areas of Pakistan. The

number and type of fish species shared with several studies from other regions of

175

Pakistan is variable. Many of the fish species i.e. Bariliuspakistanicus, Barilius vagra,

Barilius modestus, Crossocheilus diplocheilus, Puntius sophore, Garra gotyla,

Schizothorax plagiostomus, Cirrhinus mrigala, Tor macrolepis, Salmophazia

punjabensis, Labeo diplostomus, and Mastacembelus armatus have been recorded

from River Swat at Charsada (Yousafzai et al., 2013). Same species except Barilius

modestus and Labeo diplostomus were reported from River Swat at Manglawar Valley

(Akhtar et al., 2014). Lesser number of fish species i.e. Barilius pakistanicus, Barilius

vagra, Barilius modestus, Crossocheilus diplocheilus, Mastacembelus armatus,

Salmophasia punjabensis and Schizothorax plagiostomus have been found to be

common with fish fauna from different mountainous streams of Bajour Agency (KPK)

(Hasan et al., 2014). Few fish species i.e. Labeo calbasu, Labeo dyocheilus

pakistanicus, Mastacembelus armatus, Cirrhinus mrigala and Puntius sophore were

found to be common with fish fauna from Baran Dam, District Bannu (KPK), major

part of which is included in Suleman Mountain Range (Ullah et al., 2014). Still five

fish species i.e. Cyprinion watsoni, Labeo dyocheilus pakistanicus, Cirrhinus mrigala,

Bariliuspakistanicusand Mastacembelus armatus has been found to be common with

one study from River Zhob, a mountainous region of Baloachistan, adjacent to

Suleman Mountain Range (Kakarabdullahzai and Kakarsulemankhel, 2004).

Four fish species i.e. Cirrhinus mrigala, Rita rita, Labeo calbasu, and Mastacembelus

armatus were found to be shared with Taunsa Barrage at River Indus (Khan et al.,

2012) near drainage place of Nallah Sanghar (Hill torrent of Suleman Mountain

Range, Dera Ghazi Khan Region). Maximum resemblance of fish fauna has been

found with that of River Indus at Attock region, a sub-mountainous area apparently

having no direct land connection, in present, with Suleman Mountain Range, Dera

176

Ghazi Khan Region. The common fish species at both studies are., Labeo calbasu,

Labeo diplostomus, Labeo dyocheilus pakistanicus, Cirrhinus mrigala, Tor

macrolepis, Crossocheilus diplocheilus, Salmophasia punjabensis, Securicula gora,

Barilius modestus, Barilius pakistanicus, Barilius vagra, Puntius sophore, Garra

gotyla, Schizothorax plagiostomus, Rita rita, Ompok pabda and Mastacembelus

armatus (Iqbal et al., 2013).

Majority of fish species described in present study are considered as common fish

fauna of the sub-continent region including India (Kumar et al., 2011; Sarkar et al.,

2008; Sarkar et al., 2012), Bangla Desh (Rahman et al., 2012; Galib et al., 2013) and

Nepal (Shrestha, 2001).

The most abundant fish species in our study was Tor macrolepis (RA = 20.87%) and

Labeo diplostomus (RA = 17.00%). Labeo calbasu (RA = 0.12%), Rita rita (RA =

0.25%) and Mastacembelus armatus (RA = 0.25%) were the least abundant species.

While Barilius vagra was the most frequent species recorded from eight fish sampling

sites. In several other studies, the abundance of various fish species is variable.

Crossocheilus diplocheilus (17.07%), Bariliuspakistanicus (16.53%) and Garra gotyla

(13.92%) have been described as the most abundant species while Barilius modestus

(2.98%) and Puntius sophore (2.43%) as least abundant at Rhound stream District

Lower Dir (Ullah et al., 2014). Crossocheilus diplocheilus (13.65%), Schistura

alipidota (10.97%) have been described as the most abundant species and Barilius

modestus (4.25%) as least abundant speceies, in 2010, at different streams of Bajour

Agency, KPK (Hasan et al., 2014)

177

The Cyprinidae has been observed the most abundant fish family from this region in

present study while other four fish families have low abundance. The several studies

have reported Cyprinids abundance from various bodies from different localities of

Pakistan. These include 77% Cyprinids from River Swat (Akhtar et al., 2014), 55.56%

Cyprinids from River Swat (Ishaq et al., 2014), 58.33% Cyprinids from River Indus at

Attock (Iqbal et al., 2013) and 85.91% Cypriniformes from Rhound stream Lower Dir

KPK (Ullah et al., 2014). Similarly, several studies from India also verify cyprinid

dominance like 88.67% from River Jammer (Vyas and Vishwakarma, 2013), 75%

from Barna stream network (Vishwakarma et al., 2014).

Two basic components i.e. richness (species number) and evenness (distribution of

relative abundance/biomass among species) dealt under the term “alpha diversity”are

taken into account to describe species diversity of a given area (Huston, 1994; Purvis

and Hector, 2000; Magurran, 2004). These aspects of species diversity are usually

measured by using several population indices as biodiversity indices. A biodiversity

index indicates presence of various taxa in a biological sample/community by a

number (Magurran, 1988). If all the species making up a community structure

contribute equal abundance then diversity is maximum. However, diversity also

depends on the varieties of habitats. Large number of species tends to be found in

more varied habitat compared to less variable habitat. And more species diversity is

found in older habitat compared to younger one. Among other factors resulting in

higher biodiversity are warmer temperatures, availability and stability of food. Still

latitudes and longitudes also affect biodiversity (Mulder et al., 2004; Varrin et al.,

2007; Wittebolle et al., 2009). To better describe the fish diversity including species

178

richness and evenness of the species distribution of this area, several biodiversity

indices were considered.

Several biodiversity indices like Simpson’s and Shannon-Weiner’s index reflect both

aspects of alpha diversity. Simpson Indexcalculatesabout the probability whether two

individual fish drawn from a large community will belong to the different species. The

value of this index ranges between 0 and 1, the greater the value, the greater the

sample diversity (Vyas and Vishwakarma, 2013). Simpson diversity index values

ranged from 0.96 to 1.00 at ten sampling sites with an overall value of 0.86 for whole

region, indicating greater diversity. Greater or lesser Simpson diversity index values

have been recorded in several studies from Pakistan like 0.37 at Chashma Reservoir

and 0.48 at Taunsa Reservoir (Khan et al, 2008), from Bangladesh like 0.93 to 0.95 at

upper Halda River (Alam et al., 2013), from India like 0.08 to 0.11 at Betwa River

(Vyas et al., 2012) and 0.72 to 0.98 at Aami River (Shukla and Singh, 2013).

The most commonly used index to compare diversity among various habitats

isShannon’s Index of diversity (Clarke and Warwick, 2001). It is a measure of

heterogeneity taking into account both the species number and their evenness

(Hollenbeck and Ripple, 2007). Increase in diversity of an area is directly proportional

to increase in both factors (Vyas and Vishwakarma, 2013). Shannon’s Index of

diversity is based on the mean value of uncertainty for predicting species richness for a

random sample of community.The uncertainty of species richness is directly

proportional to the number of species present more evenly in a community (Hasan et

al., 2014). Shannon’s Index of diversity is considered sensitive to the presence of rare

species in a sample compared to other indices like Simpson’s index (Krebs, 1989).The

179

values of Shannon-Weiner Diversity Index usually range between 1.5 and 3.5. The

higher values indicate greater diversity (Kent and Coker, 1992). Occassionally, the

values of Shannon-Weiner Diversity Index exceed 4.5. A value near 4.6 would

indicate that the numbers of individuals are evenly distributed between all the species.

In our study, Shannon-Weiner Diversity Index value ranged from 0.64-1.68 for ten

fish sampling sites with an overall value of 2.11 for the region. These values imply

lower diversity of the region. Relatively higher values have been reported for fish

biodiversity from other regions of Pakistan including value of 2.91 from River Jehlum,

Pakistan (Mirza et al., 2011), 2.49 from Rhound stream (Ullah et al., 2014) and 1.44 to

3.43 from River Ganga, India.

Another index which is used to measure species richness is the Margalef index which

is highly sensitive to sample size and also accounts for sampling effects (Magurran,

2004). The Margalef index values generally show deviation depending on the species

number when used to compare the sites (Vyas et al., 2012). In our study, the Margalef

index values ranged from 0.30 to 1.30 at ten fish sampling sites with an overall value

of 2.56. Relatively higher values have been found for fish biodiversity from various

regions of Pakistan including 2.19 for Rhound Stream (Ullah et al., 2014), 3.18 from

Chashma reservoir and 3.63 from Taunsa reservoir (Khan et al., 2008); while India

including 2.00-3.89 for Barna streams, Narmada Basin, Central India (Vishwakarma et

al., 2014) and 3.71-6.70 for Betwa River (Vyas et al., 2012).

Apart from above mentioned diversity indices which consider both number of species

and their abundance in a random sample of community, there are evenness indices that

standardize abundance (Smith and Wilson, 1996). Evenness indices range from near 0

180

to close to 1. If most of the individuals belong to a few species then values are near 0,

and when species are nearly equally abundantthen values are close to 1 (Smith and

Wilson, 1996). In our study, evenness index ranged from 0.19-0.31 for ten fish

sampling sites with overall value of 0.70. These imply that most of the individuals

belong to a few species at most sites. Variable values of evenness index has been

calculated for fish populations at various sites in Pakistan including 0.69 at Chashma

and 0.74 at Taunsa (Khan et al., 2008), 0.19 at River Jhelum (Mirza et al., 2011), 0.41

at Head Balloki and 0.90 at Head Trimmu (Khan et al., 2011) while India including

0.69-0.72 at Tunga river, India (Naik et al., 2013).

In addition to alpha diversity, beta diversity which is considered as variation in species

composition among different localities (Magurran, 2004) was also calculated. Several

methods are available for estimating beta diversity. There exists a wide variety of

methods for measuring beta diversity. The most widely used are similarity measures

which calculate beta diversity based on data for abundance or presence/absence

(Wolda, 1981; Koleff et al., 2003). One of the classical similarity index i.e. Jaccaard

index was used in this study. It identifies species composition of any of the two or

more sites and the shared species between then (Novotny and Weiblen, 2005).

The freshwater fish fauna of Pakistan is considered to have a highproportion of

endemism.Three fish species endemic to Pakistan i.e. Salmophasia punjabensis (Day,

1872), Barilius pakistamicus (Mirza and Sadiq, 1978) and Labeo dyocheilus

pakistanicus (Mirza and Awan, 1976) were also recorded from this region. While all

other species are considered indigenous species inhabiting the adjacent

countries/regions. Labeo diplostomus, Tor macrolepis, Barilius modestus and Botia

181

birdi are indigenous to India and Pakistan; Cirrhinus mrigala and Securicula gora are

indigenous to countries of sub-continent, Crossochilus diplocheilus, Barilius vagra,

Salmostoma bacaila and Cyprinion watsoni are indigenous to countries of south Asia

as well as adjacent countries of Iran and Afghanistan; Ompok pabda, Schizothorax

plagiostomus, Labeo calbasu, Rita rita, Mastacembelus armatus and Garra gotyla are

indigenous either South Asia and South East Asia and Puntius sophore is indigenous

to South Asia as well as Fareast Asia (Froese and Pauly, 2015).

The IUCN status of the two endemic species i.e. Salmophasia punjabensis and

Barilius pakistamicus has not been evaluated yet. Labeo dyocheilus pakistanicus has

also been described as least concern (Rafiq and Khan, 2012). The distributions of

endemic fish species is localised due to dispersal limitation and therefore restricted to

localized areas (Rosenfeld, 2002). The IUCN conservation status of most of the

endemic fish speciestends to be critically endangered or threatened with extinction due

to narrowrange of distribution, decline of population and fewer chances of

reproductive success.

Among indigenous species, IUCN status of Ompok pabda is near threatened; while for

Schizothorax plagiostomus, Tor macrolepis,Barilius modestus, Cyprinion watsoni and

Botia birdi, IUCN status has not been evaluated, and rest of the indigenous fish species

are least concerned (IUCN, 2014).

The commercialfisheries have a significant socio-economic contributionin the life of

peoples of Pakistan for a frequent part of country. Up till now, 31 fish species with

high commercial value has been recorded from fresh water bodies of Pakistan which is

182

fairly a high number. One of the commercially important fish species of the

mountainous /sub-mountainous region i.e. Tor macrolepishas been found to be the

most abundant and also the frequent species, being recorded from five sites of this

region. Previously, the population of Tor putitora,a closely related species was

considered to be declining (Rafiq and Khan, 2012). However, the population was

restored after several measures particularly farming. The frequent occurrence and

abundance of Tor macrolepis from various sites of this region indicate a considerable

conservation of population of this species. Tor macrolepis has been reported to be rare

from other regions (Iqbal et al., 2013; Ishaq et al., 2014). The abundance in wild of

this species from this region indicates the potential of this region to contribute in

sustaining the natural fish population of this species in Pakistan.

Schizothorax plagiostomus, essentially acold water fish of high altitudes i.e.

mountainous/sub-mountainous areas has been recorded with moderate abundance.This

species has high commercial importance and has also been described from Northern

hilly areas of Pakistan (Rafiq and Khan, 2012; Yousafzai et al., 2013). But from this

region it has been described for the first time in previous study (Ali et al., 2010) and

current study, only from a specific study site i.e. Hinglon kach.

Among other edible and commercially importantfish species Labeo diplostomus and

Labeo dyocheilus pakistanicus were recorded with moderate abundance in this area. It

may be assumed that the population of these species is conserved in this area and this

area has potential for cultivation of these species. Several other commercially

important fish species i.e.Cirrhinus mrigala, Labeo calbasu, Rita rita and

Mastacembelus armatus were found to be rare in this region.Some of thefish species

183

including Puntius sophore, Botia birdi, Barilus vagra, Barilius modestus, Barilius

pakistanicus and Securicula gora have the characteristics which impart them important

for ornamental purpose.

The fish faunal diversity of Pakistan is also attributable to the fact that Pakistan is

comprised of a transitional zone of three zoogeographic regions i.e. Oriental,

Palearctic and Ethiopian, which influence the composition of fish fauna (Mirza, 1994).

The present study area lies in the Oriental region. Ichthyogeograophically, it lies at the

borderline of Yaghistan (western) division and Mehran (eastern) division. Most of its

fish fauna is South Asianin origin (Puntius sophore, Tor macrolepis, Barilius vagra,

Barilius modestus, Cirrhinus mrigala, Crossocheilus diplochilus, Labeo calbasu,

Salmostoma bacaila, Securicula gora, Labeo diplostomus, Garra gotyla, Botia birdi,

Rita rita, Mastacembelus armatus, and Ompok pabda). Among others are West Asian

(Cyprinion watsoni), High Asian (Schizothorax plagiostomus), while Labeo dyocheilus

pakistanicus, Barilius pakistanicus and Salmophasia punjabensis are endemic to

Pakistan (Mirza, 2003; Ahmed and Niazi, 1998).

In present study, two more catchment areas i.e. Sori and Toe Sar were explored for

fish biodiversity compared to previous study (Ali et al., 2010). Alsofor previous

catchment areas, more number of water bodies was explored for fish diversity

compared to previous study (Ali et al., 2010). However, for one catchment area i.e.

Sanghar, new sites were explored for fish biodiversity instead of previous one. Several

species were recorded in present study in addition to previous study. These include

Salmophasia punjabensis, Securicula gora, Labeo dyoceilus pakistanicus, Labeo

calbau, Cirrhinus mrigala, Botia birdi, Rita rita and Mastacembelus armatus. Thus,

184

the total number of identified species reaches up to twenty three (23) at Suleman

Mountain Range, Dera Ghazi Khan Region, Pakistan. Three species namely Mystus

cavacius, Schistura sp and Glyptothorax cavia previously found (Ali et al., 2010) but

could not be recorded in present study from the same sites.

Barilius vagra and Labeo diplostomus were the species which were recorded from

maximum catchment areas being recorded from 8 sites and 7 sites respectively,

including both studies. Two species were identified to be recorded from same one

catchment area and site. These include Schizothorax plagiostomus (Hinglon) listed in

red data list as vulnerable, Ompok pabda (Harand) declared to be near threatened in

IUCN and Puntiussophore (Harand). As described above, Tor macrolepis,

Labeodiplostomus, Barilius vagra and Cyprinion watsoni have been among the

abundant species of present study. The previous study documented Cyprinion watsoni,

Garra gotyla, Crossocheilus diplocheilus and Labeo dero, as the abundant species. In

both studies, Hinglon is the most abundant site. Vehowa was the second abundant site

in present study while Harand was the second abundant site in previous site. Barthi

was the third abundant site in this study, while Zinda Peer was the third site in

previous study. Barthi was the site with highest species richness in present study and

Hinglon was the site with highest species richness in previous study.

The frequent presence of two very unique speciesi.e.Mahasheer (Tor macrolepis) and

Snow Carp (Shizothorax plagiostomus) is an indicator of the richness of the

biodiversity of this area.

185

4.3 Fish Morphometry

Generally, the fish growth do not obey the cube law as their shape changes with

growth, when the value of slope ‘b’ for length-weight relationship is either lower or

higher than 3, reflecting negative and positive allometric growth. The value of slope

‘b’ remains accurately 3, if fish grows isometrically. This applies to data of fish

growth from natural and commercial fisheries (Martin, 1949; Ricker, 1975).

According to Pauly and Gayanilo (1997) ‘b’ value may range from 2.5 to 3.5 for

length-weight relationship as has been previously described by Carlander (1969). The

variation in ‘b’ value of different species may be due to feeding, state of maturity, sex

and different population of species (Frost, 1945; LeCren, 1951; Jhingran, 1968). In the

present study, ‘b’ value for length-weight relationship of seven freshwater fish species

was between 2.5 to 3.5, the normal range as described by Carlander (1969). The

length-weight relationships were highly significant (P < 0.001) for all seven species

with ‘r’ values greater than 0.93 for most of the studied fish species.

Several workers have reported similar results for ‘b’ values for these species or related

species from different localities. These include, 3.02 for Catla catla (Zafar et al.,

2003), 3.27 for Labeo calbasu (Naeem et al., 2010), 2.52 for Tor puititora (Naeem et

al., 2011), 2.94 for Tor macrolepis from River Indus at Attok (Pervaiz et al., 2012),

2.92 for Labeo bata (Naeem et al., 2012), 3.46 for Labeo gonius (Dars et al., 2010),

2.87 for Ompok pabda(Banik et al., 2012), 3.24 for Garra orentalis (Zhang, 2005),

3.15 for Garra rufa (Hamidan and Britton; 2013), 2.95 for Schizothorax plagiostomus

(Bhat et al., 2010) and 2.86 for Schizothorax plagiostomus (Khan and Sabah, 2013).

186

Condition factor (K) reflects external measures of overall health. It decreases with

increase in length (Bakare, 1970; Fagade 1979). Condition factoris based on the

hypothesis that heavier fish of a given length are in better condition (Bagenal and

Tesch, 1978). Condition factor has been used as an index of growth and feeding

intensity (Fagade, 1979). It reflecs interactions between biotic and abiotic factors in

the physiological condition of the fishes. Environmental factors have been taken into

consideration in order to account for spatial and temporal differences in condition

factor of fish (Bagenal, 1978; Braga, 1986; Ekanem, 2004).Condition factor based on

the LWR is an indicator of the changes in food reserves and therefore an indicator of

the general fish condition (Offem et al., 2007).

The K values for seven fish species in this study were from 0.723 to 1.230. The K

valuesfor most of the studied fishes were ideal except Ompok pabda (0.723) and Tor

macrolepis (0.875). According to Rawal et al., (2013), K for Tor putitora and Labeo

dero was 0.922 and 1.183 respectively. Similarly K for Labeo cylindricus and

Oreochromis niloticus was 1.04 and 1.13 (Elijah et al., 2014). According to Naeem et

al., (2010) the condition factor of the hybrid (Catla catla ♂ × Labeo rohita ♀)

remained constant with increasing length or weight. In our study, most of the condition

factors are within the suitable recommended range for fresh water species. The value

of K in this study indicated good health of fish. This indicates that the environmental

conditions in suleman mountain range D. G. Khan region, Pakistan are favorable for

these fish species.

This study provides first records of length-length and length-weight relationships for

Labeo diplostomus, Labeo dyocheilus pakistanicus. The minimum (5.50 cm) and

187

maximum (90.00 cm) total length for Labeo diplostomus and minimum (14.30 cm) and

maximum (23.60 cm) total length for Labeo dyocheilus pakistanicus are the first

records (FishBase, 2015). Similarly, minimum (5.50 cm) and maximum (90.00 cm) TL

for Tor macrolepis and maximum (14.30 cm) TL for Cyprinion watsoni are also new

records (FishBase 2015).

The experiments explored the basic information on length-weight and length-length

relationships based on various body proportions as well as condition factor (K) for the

fish species of this mountainous area. This study also include information about two

important edible fishes i.e. Tor macrolepis and Schizothorax plagiostomus, the

population of which have now been reported to decline (Rafiq and Khan, 2012; Ishaq

et al., 2014) in Pakistan. According to the best of our knowledge this is the first

morphometric study conducted in this region.The morphometrics results indicate

optimal fish growth reflecting suitable and healthy environmental conditions for fish

culture. For several species including Garra gotyla, Labeo dyocheilus pakistanicus,

Labeo diplostomus, Schizothorax plagiostomus and Barilius pakistanicus, the lengths

and weight measurement are the first reports (FishBase, 2015). The current study will

provide a basic guideline to fishery biologist and conservationists for conducting

sustainable fishery management and conservation in Suleman Mountain Range, Dera

Ghazi Khan Region, Pakistan. However, further research work on morphometrics of

these studied species should be carried out considering different habitats, gender and

size groups.

188

4.4 Conclusions

1- Water quality of the area with physico-chemical parameters are within

tolerable limits.

2- The planktonic biodiversity shows variations.

3- Fish biodiversity is conserved very much in this area.The main factor in

this conservation is the behavior of Baloach Tribe. Baloach normally do

not like to consume aquatic life for example fish. Their internal

territorial behavior rarely allows other people to hunt the wildlife

specifically fish. Due to this reason very unique species such as

Mahasheer (Tor macrolepis) and Snow carp (Shizothorax

plagiostomus) are easily found in this area. Especially Shizothorax

plagiostomus is very good indicator of the richness of the biodiversity

of this area. This particular species normally associated with very cold

water and high altitude.

4- Fish morphometry indicates a good health of fishes of the region.

189

4.5 Future perspectives

The fisheries potential of the study area needs to be investigated in detail so that it can

be utilized by humans in future. The area receives lot of water in the form of irregular

seasonal rains which is mostly wasted as there is no management of its storage there is

need to adopt measures for the storage of water so that it can be wisely utilized for the

fish production and agriculture. Further, it is suggested that the nutritional status and

market value of the fishes of this area should be thoroughly assessed. Brsides this,

there is dire need for the analysis of water quality parameters of the studied area to

check the suitability of water for commercial fisheries in Suleman Mountain Range,

Drea Ghazi Khan, Region.

190

REFERENCES

ABBASI, S.A., 1998. Water quality sampling and analysis. 1st ed.Discovery Publishing

House, New Delhi.

AGARKER, M.S., GOSWAMI, H.K., KAUSHIK, S., MISHRA, S.M., BAJPAI, A.K.

ANDSHARMA, U.S., 1994. Biology, conservation and management of bhojwtland,

Upper lak ecosystem in Bhopal. Bionature, 14:250-273.

AHANGAR, I.A., SAKSENA, D.N. AND MIR, M.F., 2012. Seasonal Variation in

Zooplankton Community Structure of Anchar lake, Kashmir Uni. J. Environ. Res.

Technol., 2: 305-310.

AHMAD, Z. AND MIRZA, M.R., 2002. Fishes of River Ravi from Lahore at Head

Balloki.Biologia(Lahore),48:317-322.

AHMED, M.F. AND NIAZI, M.S., 1998. Important edible fishes of Pakistan. Zoological

Survey Department, Govt. of Pakistan. pp. 33-81.

AKHTAR, N., KHAN, S. AND SAEED,K., 2014. Exploring the Fish Fauna of River

Swat, Khyber Pakhtunkhwa, Pakistan, World J. Fish Mar. Sci.,6: 190-194.

ALABASTER, J.S., AND LLOYD, R., 1980. Water quality criteria for freshwater fish.

Butterworths. 297 p.

ALAM, M.S., HOSSAIN, M.S., MONWAR, M.M. AND HOQUE, M.E., 2013. Assessment

of fish distribution and biodiversity status in Upper Halda River, Chittagong,

Bangladesh.Int.J.Biodiv.Con.,5: 349-357.

ALI, M., HUSSAIN, S., MAHMOOD, J.A., IQBAL, R. AND FAROOQ, A., 2010. Fish

Diversity of Freshwater Bodies of Suleman Mountain Range, Dera Ghazi Khan

Region, Pakistan. PakistanJ. Zool.,42: 285-289.

191

ALI, M., SALAM, A., IRAM, S., BOKHARI, T.Z. AND QURESHI, K.A., 2005. Studies on

monthly variations in biological and physico-chemical parameters of brackishwater

fish pond, Muzaffargarh, PakistanJ. Res. Sci., 16: 27-38.

ALI, M., SALAM, A., JAMSHAID, S. AND ZAHRA, T., 2003. Studies on biodiversity in

relation to seasonal variations in water of river Indus at Ghazi Ghatt, Punjab, Pakistan.

Pak. J. Biol.Sci., 6: 1840-1844.

ALI, M.S., AZEEM, A., SHAFIQ, M. AND KHAN, B.A., 2000. Studies on the effect of

seasonal variation on physical and chemical charactristics of mixed water from rivers

Ravi and Chenab at union site in PakistanJ. Res.Sci., 11: 11-17.

ALI, S.S., 1999. Concepts of Ecology, Naseem Book Depot, Hyderabad, Karachi.

ALI, Z., AHMAD, S.S., AKHTAR, M., KHAN, M.A. AND KHAN, M.N., 2007. Ecology

and diversity of planktons in lakes of Uchalli wetlands complex, Pakistan J. Anim. Pl.

Sci., 17:1-2.

ALLAN, J.D. AND FLECKER,A.S., 1993. Biodiversity conservation in running waters.

Identifying themajor factors that threaten destruction of riverine species and

ecosystems. BioScience, 43: 32-43.

ALTUNER, Z., 1984. A qualitative and quantitative studies on the phytoplankton taken from

one station in Tortum Lake (in Turkish). DoğaBilim Dergisi,28: 162-182.

ANDERSON, R.O., AND NEUMANN, R.M., 1996. Length, weight, and associated structural

indices. Pages 447-482 in B.R. Murphy and D.W. Willis, editors. Fisheries

techniques, 2nd edition. American Fisheries Society, Bethesda, Maryland.

ANNALAKSHMI, G. AND AMSATH, A., 2012. Studies on the Hydrobiology of River

Cauvery and its tributaries Arasalar from Kumbakonam Region (Tamil Nadu, India)

with reference to Phytoplankton. Int. J. Plant, AnimalEnviron. Sci., pp. 37-46.

192

APHA (American Public Health Association), 1995. Standard methods for the examination of

water and waste water., 19th ed., American Public Health Association Inc., New

York, 1193 pp.

APHA, 1989. Standard Methods for the Examination of Water and Wastewater. 17th ed.,

American Public Health Association, Washington, D.C.

APHA, 1992.Standard Methods for the Examination of Water and Wastewater., 18th ed.,

American Public Health Association, Washington DC.

APHA, 1998. Standard methods for the examination of water and wastewater. 20thed.

American Public Health Association, 1015 Fifteenth Street, NW, Washington, DC. 3-

103.

ARIYADEJ, C, TANSAKUL, P AND TANSAKUL, R., 2008. Variation of phytoplankton

biomass as chlorophyll a in Banglang Reservior, Yala province, Songklanakarin J.

Sci.Technol.,30: 159-166.

ARORA, H.C., 1961. Rotifera as indicators of pollution, Bull. CPHERI Nagpur, 3/4: 24-26.

ASK, J., KARLSSON, J., PERSSON, L., ASK, P., BYSTROM, P. AND

JANSSON, M., 2009. Terrestrial organic matter and light penetration: Effects on

bacterial and primary production in lakes, Limnol. Oceanogr., 54: 2034-2040.

ATICI, T. AND ALAS, A., 2012. A Study on the Trophic Status and Phytoplanktonic

Algae of Mamasin Dam Lake (Aksaray-Turkey), Turk. J. Fish. Aquat. Sci., 12:

595-601.

ATOMA, O.C., 2004. Spatial and temporal characteristics of phytoplankton and nutrient

dynamics of the Imo River Estuary.South Eastern Nigeria.Ph.D Thesis.Department of

Botany, University of Benin, Benin City.

AYKULU, G., OBALI, O. AND GONULOL, A., 1983. The distribution of phytoplankton at

some lakes in the vicinity of Ankara (in Turkish). Doğa Bilim Dergisi., 7: 277-288.

193

BAGENAL, T., 1978. Methods for assessment of fish production in freshwaters, 3rd ed.

Edinburgh and Melbourne, Oxford, 365.

BAGENAL, T.B. AND TESCH, F.W., 1978. Age and growth. In: Bagenal T. (ed.), Fish

Production in Fresh Waters. I.B.P. Handbook No. 3. Blackwell Scientific Publications,

Oxford: 101-136.

BAGRA, K., KADU, K., SHARMA, K.N., LASKAR, B.A., SARKAR, U.K. AND DAS,

D.N., 2009. Ichthyological survey and review of the checklist of fish fauna of

Arunachal Pradesh, India.Check list, 5: 330-350.

BAKARE, O., 1970. Bottom Deposits as Food of Inland Fresh Water Fish. In: Kainji, A

Nigerian Manmade Lake S.A. Visser, (Ed.), Kanyi Lake Studies, Vol. 1. Ecology

Published for the Nigerian Institute.

BALOCH, W.A., JAFRI, S.I.H. AND SOOMRO, A.N., 2005. Spring zooplankton

composition of Rawal lake, Islamabad. Sindh Univ. Res. J.Sci. Ser., 37: 41-46.

BALOCH, W.A., SOOMRO, A.N. AND JAFRI, S.I.H., 2004. Zooplankton of a highly saline

water body, near Hyderabad, Sindh.Sindh Univ. Res. J. Sci. Ser., 36: 25- 28.

BANIK,S., GOSWAMI, P., ACHARJEE, T. AND MALLA, S., 2012. Ompok

pabda(Hamilton-Buchanan, 1822): An Endangered Catfish of Tripura, India:

Reproductive Physiology Related to Freshwater Lotic Environment., J. Environ., 2:

45-55.

BARNABE, G., 1990. “Aquaculture”, Vol.1, Ellis Horwood, New York.

BARNABE, G., 1994. “Aquaculture Biology and Ecology of Cultured Species”, Ellis

Horwood Ltd., New York.

BARON, J.S., POFF, N.L., ANGERMEIER, P.L., DAHM, C.N., GLEICK, P.H.,

HAIRSTON, N.G., JACKSON, R.B., JOHNSTON, C.A., RICHTER, B.D., AND

194

STEINMAN, A.D., 2002. Meeting ecological and societal needs for freshwater. Ecol.

Appl., 12: 1247-1260.

BASU, M., ROY, N. AND BARIK, A., 2010. Seasonal abundance of net zooplankton

correlated with physico-chemical parameters in a freshwater ecosystem. Int. J. Lakes

& Rivers, 3: 67-77.

BATTISH, S.K., 1992. Freshwater zooplankton of India. Oxford and IBH Publishing Co.

Ltd., New Delhi. pp. 233.

BELCHER, J.H. AND SWALE, E.M.F., 1979. English freshwater records of

Actinocyclusnormanii (Greg.) Hustedt (Bacillariophyceae). Br. Phycol. J., 14:225-229.

BELKINOVA, D., PADISÁK, J., GECHEVA, G. AND CHESHMEDJIEV, S., 2014.

Phytoplankton Based Assessment of Ecological status of Bulgarian Lakes and

Comparison of matrics within the water framework directive, Appl. Eco. Environ. Res.,

12: 83-103.

BERGSTAD, O.A., 1990. Distribution, population structure, growth and reproduction of the

roundnose grenadier Coryphaenoides rupestris (Pisces: Macrouridae) in the deep

waters of the Skagerrak. Mar. Biol., 107: 25-39.

BEYER, J.E., 1987. On length-weight Relationships. Part I: Computing the mean weight of

the Fish of a given length class. Fishbyte,5: 11-13.

BHAT, F.A., YOUSUF, A.R., BALKHI, M.H., MAHDI, M.D. AND SHAH, F.A., 2010.

Length-weight relationship and morphometric characteristics of Schizothorax spp. in

the River Lidder of Kashmir, Indian J. Fish., 57:73-76.

BHATT, L.R., LACOUL, P., LEKHAK, H.D. AND JHA, H.D., 1999. Physicochemical

characteristics and phytoplankton of Taudaha lake, Kathmandu. Poll. Res., 18: 353-

358.

195

BHUIYAN, A.S., ISLAM, M.T. AND SHARMEEN, R., 2008. Occurrence and abundance of

some copepods in a fish pond in Rajshahi, Bangladesh in relation to the physico-

chemical conditions. Journal of Bioscience, 16:115-119.

BIANCHI, F., ACRI, F., AUBRY, F.B., BERTON, A., BOLDRIN, A., CAMATTI, E.,

CASSIN, D. AND COMASCHI, A., 2003. Can plankton communities be considered

as bioindicators of water quality in the laggon of Venice? Mar. Pollut. Bull.,46:964-

971.

BINOHLAN, C. AND PAULY, D., 1998. The length-weight table, In: Froese R, Pauly D

(eds). FISHBASE 1998: concepts, design and data sources. ICLARM, Manila, pp.

121-123.

BOLGER, T. AND CONNOLLY, P.L., 1989. The selection of suitable indices for the

measurement and analysis of fish condition. J. Fish Biol.,34: 171-182.

BORGES, M.F., SANTOS, A.M.P., CRATO, N., MENDES, H. AND MOTA, B., 2003.

Sardine regime shifts off Portugal: a time series analysis of catches and wind

conditions. Sci. Mar. 67: 235–244.

BORSANI, O., VALPUESTA, V. AND BOTELLA, M.A., 2003. Developing salt tolerant

plants in a new century: A molecular biology approach. Plant Cell Tissue and Organ

Culture, 73: 101-115.

BOSTOCK, T., GREENHALGH, P. AND KLEIH, U., 2004. Policy research - implications of

liberalization of fish trade for developing countries. Synthesis report. Chatham, UK:

Natural Resources Institute, University of Greenwich, 68 p.

BOYD, C.E., 1979. Water quality in warm water fish ponds. Craftmaster, Printers Inc.

Auburn, Alabama, USA, 353 pp.

BOYD, C.E. AND LICHTKOLPLER, R., 1979. Water quality management in pond fish

culture, Res. Dev. Ser. No. 22. Auburn University, Aubum, Alabama.

196

BOYD, C.E. AND TUCKER, C.S., 1998. Pond aquaculture water quality management.

Kluwer Academic Publishers, London.

BOYD, C.E., 1981. Water quality in warm water fish ponds.Craftmaster Printers, Inc.

Opelika, Alabama.

BOYD, C.E., 1998. Water Quality for Pond Aquaculture. Research and development

Series No.43. ICAAE. Auburn University, USA.

BOYLE, M. AND SENIOR, K., 2002. Collins advanced science: Biology. 2nd ed.London:

Harper Collins.

BRAGA, F.M.S., 1986. Estudo entre o fator de condição e relação peso/comprimento para

alguns peixesmarinhos. Rev. Brasil. Biol., 46: 339-346.

BRAZNER, J.C., DANZ, N.P., NIEMI, G.J., REGAL, R.R., TREBITZ, A.S., HOWE, R.W.,

HANOWSKI, J.M., JOHNSON, L.B., CIBOROWSKI, J.J.H., JOHNSTON, C.A.,

REAVIE, E.D., BRADY, V.J. AND SGRO, G.V., 2007. Evaluation of geographic,

geomorphic and human influences on Great Lakes wetland indicators: a multi-

assemblage approach. Ecolog. Indicat., 7: 610-635.

BRIONES, V., TE´LLEZ, S., GOYACHE, J., BALLESTEROS, C., LANZAROT, M.P.,

DOM´INGUEZ, L. AND FERNA´NDEZ-GARAYZA´BAL, J., 2004. Salmonella

diversity associated with wild reptiles and amphibians in Spain. Environ. Microbiol.,

6: 868-871.

BURTON, P.J., BALISKY, A.E., COWARD, L.P., CUMMING, S.G. AND KNESHWAW,

D.D., 1992. The value of managing biodiversity. The Forestry Chronicle., 68: 225-

237.

BUZZI, F., 1999. Phytoplankton assemblages in two sub-basins of Lake Como, J. Limonol.,

61: 117-128.

197

CARLANDER, K.D., 1969. Handbook of freshwater fishery biology. The lowa State

University Press, Ames, IA, 1:752.

CARPENTER, S.R., COLE, J.J., KITCHELL,J.F. AND PACE, M.L., 1998. Impact of

dissolved organic carbon, phosphorus, and grazing on phytoplankton biomass and

production in experimental lakes, Limnol. Oceanogr., 43: 73-80.

CHANDRASEKHAR, J.S., LENIN BABU, K. AND SOMASEKHAR, R.K., 2003. Impact of

urbanization on Bellandurlake, Bangalore - A case study. J.Environ.Biol., 24: 223-227.

CHANG, J.S., YU, K.C., LIN, C.Z., SUN, J.F., HO, S.T. AND LAI, S.D., 1995. A study of

the eutrophic status on the Tseng-Wen reservoir and the Wu-Sun-Tu reservoir. J.

Environ. Engin., 5: 95-105.

CHAPMAN, V.J. AND CHAPMAN, D.J., 1981. The algae. 18. Freshwater ecology.

Macmillan. Hong Kong. 381-404.

CHATTA, A.M. AND AYUB, M., 2010.Growth performance of hatchery reared

golden mahseer (Tor macrolepis) at Sialkot, Pakistan. Biologia, 56: 1-8.

CHAUDHRY, Q.Z. AND RASUL, G., 2004.Climatic classification of Pakistan. Science

Vision, 9: 59-66

CHELLAPPA, N.T., BORBA, J.M. AND ROCHA, O., 2008. Phytoplankton community and

physical-chemical characteristics of water in the public reservoir of Cruzeta, RN,

Brazil. Braz. J. Biol., 68: 477-494.

CHUGHTAI, M.I., KAUSAR, T., MAHMOOD, K., NAEEM, M. AND AWAN, A.R., 2013.

Studies onLimnologicalCharacteristics and Planktonic Diversity in D.G. Khan Canal

Water at D.G. Khan (Pakistan). Pak. J. Bot., 45: 599-604.

CHUGHTAI, M.I., MAHMOOD, K. AND AWAN, A.R., 2011. Assessment of planktonic

diversity in river Chenab as affected by sewage of Multan city. Pak. J. Bot., 43: 2551-

2555.

198

CLARKE, K.R. AND WARWICK, R.M., 2001. Changes in marine communities: an approach

to statistical analysis and interpretation, 2nd edition, PRIMERE: Plymouth. 172pp.

COGGAN, R.A., GORDON, J.D.M. AND MERRETT, N.R., 1999. Aspects of the biology of

Nezumia aequalis from the continental slope west of the British Isles. J. Fish Biol., 54:

152-170.

COLLARES-PEREIRA, M.J. AND COWX, I.G., 2004. The role of catchment scale

environmental management in freshwater fish conservation. Fisheries Manag. Ecol.,

11: 303-312.

COLLARES-PEREIRA, M.J., COELHO, M.M. AND COWX, I.G., 2002. Conservation of

Freshwater Fishes: Options for the Future. Fishing News Books Ltd., Oxford, UK,

462pp.

CORDERO, C.S. AND BALDIA, S.F., 2015. Temporal variation of phytoplankton

community structure in Lake Mohicap, San Pablo City, Laguna, Philippines, Int. J.

Pure App. Biosci., 3: 377-385.

COSTANZA, R., D’ARGE, R., DE-GROOT, R., FARBER, S., GRASSO, M., HANNON, B.,

LIMBURG, K., NAEEM, S., O’NEIL, R., PARUELO, J., RASKIN, R., SUTTON, P.

AND VANDEN BELT, J., 1997. The value of the world’s ecosystem services and

natural capital. Ecol. Econom., 25: 3-15.

COWX I.G. AND COLLARES-PEREIRA, M.J., 2000. Conservation of endangered fish

species in the face of water resource devel- opment schemes in the Guadiana River,

Portugal: Harmony of the incompatible. In: Management and Ecology of River Fish-

eries (Cowx I.G., ed.), pp. 428-438. Fishing News Books. Oxford: Blackwell Science.

199

DANZ, N.P., NIEMI, G.J., REGAL, R.R., HOLLENHORST, T.P., JOHNSON, L.B.,

HANOWSKI, J.M., AXLER, R.P., CIBOROWSKI, J.J.H., HRABIK, T., BRADY,

V.J., KELLY, J.R., MORRICE, J.A., BRAZNER, J.C., HOWE, R.W., JOHNSTON,

C.A. AND HOST, G.E., 2007. Integrated gradients of anthropogenic stress in the U.S.

Great Lakes basin. Environ. Manage., 39: 631-647.

DARS, B.A., NAREJO, N.T. AND DAYO, A., 2010. Relative condition factor and length-

weight relationship of a carp, Labeogonius(Hamilton) from Keenjharlake, district

Thatta, Sindh, Pakistan. SindhUniv. Res. J. Sci. Ser., 42:67-70.

DARWALL, W.R.T. AND VIE, J.C., 2005. Identifying important sites for conservation of

freshwater biodiversity: extending the species based approach.Fisheries Manage.

Ecol., 12: 287-293.

DASTI, A.A., SAIMA, S., MAHMOOD, Z., ATHAR, M. AND GOHAR, S., 2010.

Vegetation zonation along the geological and geomorphological gradient at Eastern

slope of Sulaiman range, Pakistan. African J. Biotechnol., 9: 6105-6115.

DAWSON, T.P., BERRY, P.M. AND KAMPA, E., 2003. Climate change impacts on

freshwater wetland habitat. J. Nature Conser., 11: 25-30.

DECLINCE, G., 1992. The ecology of the fish pond ecosystem. Kluwer Academic Publishers.

DIAZ, L.S., ROA, A., GARCIA, C.B., ACERO, A. AND NAVAS, G., 2000. Length-weight

relationships of demersal fishes from the upper continental slope off Colombia. The

ICLARM Quarterly, 23: 23-25.

DIAZ, S., FARGIONE, J., STUART CHAPIN F III AND TILMAN, D., 2006. Biodiversity

loss threatens human well-being. PLoS Biol.4: e277.

DOBSON, M. AND FRID, C., 1998. Ecology of aquatic systems. Longman, Harlow.

DOLAN, J.R., 2005. “An introduction to the biogeography of aquatic microbes.” Aquat.

Microbial Ecol., 41:39-48.

200

DUDGEON, D., ARTHINGTON, A.H. AND GESSNER, M.O., 2006. Freshwater

biodiversity: importance, threats, status and conservation challenges. Biological

Rev.Cambridge Philosophical Soci., 81: 163-182.

DUNCAN, J.R. AND LOCKWOOD, J.L., 2001. Extinction in a field of bullets: a search for

the causes in the decline of the world’s freshwater fishes. Biol. Conser., 102:97-105.

DZIOCK, F., HENLE, K.,FOECKLER, F., FOLLNER K. AND SCHOLZ,M., 2006.

Biological indicator systems in floodplains, a review, Internat. Rev. Hydrobiol., 91:

271-291.

ECOUTIN, J.M., ALBARET, J.J. AND TRAPE, S., 2005. Length-weight relationships for

fish populations of a relatively undistributed tropical estuary: the Gambia. Fish. Res.,

72: 347-351.

EDMONDSON, W.T., 1960. Reproductive rates of rotifers in natural populations.

Memoriedell’IstitutoItaliano di Idrobiologia Dott. Marco de Marchi., 12:21-77.

EGGS, J.K. AND AKSNES, D.L., 1992. Silicate as regulating nutrient in phytoplankton

competition. Mar. Ecol. Proc. Ser., 83:281-289.

EHRLICH, P.R. AND WILSON, E.O., 1991. Biodiversity studies science and policy. Sci.,

253: 758-762.

EKANEM, S.B., 2004. The biology and culture of the silver catfish (Chrysichthysnigro

digitatus). J. Sustainable Tropical Agri. Res., 10: 1-7.

EKHANDE, A.P., 2010. Hydrobiological studies of Yashwant Lake with special reference to

selected biodiversity. Ph. D. Thesis submitted to the Maharaja Sayajirao University

of Baroda, Vadodara, India.

ELIJAH, M.K., ERICK, O.O., CECILIA, M.G., CALLEN, N.A., REUBEN, O. AND

JONATHAN, M.M., 2014. Seasonal Changes of Length -Weight Relationship and

201

Condition Factor of Five Fish Species in Lake Baringo, Kenya. Inter. J. Sci. Bas. Appl.

Res., 14: 130-140.

EPA (Environmental Protection Agency), 1976.Quality criteria for water by United

States.E.P.A office of water planning and standards, Washington, 20460 pp.

EPA (Environmental Protection Agency), 1980. Ambient water quality criteria for zinc,

publication-440/8-80-79 EPA.

EPA (Environmental Protection Agency), 1998. Final Guidline for Implimentation of

Drinking Water State Resolving Fund Programme, Federal Register.

FAFIOYE, O.O. AND OLUAJO,O.A., 2005. length-weight relationships of five fish species

in Epe Lagoon, Nigeria. African J. Biotechnol., 4:749-751.

FAGADE, S.O., 1979. Observation of the biology of two species of Tilapia from the Lagos

lagoon Nigeria. Bull. Inst. Fond Afr. Nore Ser. A., 41: 627-658.

FAO, 2007. The state of World Fisheries and Aquacutlure 2006. Food and Agriculture

Organization of the United Nations. Fisheries and Aquaculture Department, Rome

Italy.

FAO, 2008. (Food and Agriculture Organization). State of food insecurity in the world. Rome.

FAO, 2010. Report of the Inception Workshop of the FAO Extrabudgetary Programme on

Fisheries and Aquaculture for Poverty Alleviation and Food Security. Rome, 27-30

October 2009. FAO Fisheries and Aquaculture Report No. 930. Rome. 68 pp.

FAO, 2012. The state of world fisheries and aquaculture 2012. Food and Agriculture

Organization of the United Nations, Rome.

FERNANDO, C.H., 1980. The freshwater zooplankton of Sri Lanka with a discussion of

tropical freshwater zooplankton composition. Int. Rev. ges. Hydrobiol., 65: 85-125.

FIGUEREDO, C.C. AND GIANI, A., 2001. Seasonal variation in the diversity and species

richness of phytoplankton in a tropical eutrophic reservoir. Hydrobiol., 445: 165-174.

202

FISHBASE, 2012. http://www.fishbase.org.

FRANK, E.H., GRODZINSKY, A.J., PHILLIPS, S.L. AND GRIMSHAW, P.E.,

1990. Physiochemical and bioelectrical determinants of cartilage material properties.

In: Biomechanics of diarthrodial joints. Vol 1 Mow VC, Ratcliffe A, Woo SL, editors.

New York: Springer-Verlag, pp. 261-282

FRITSCH, F.E., 1979. “The Structure and Reproduction of Algae”, Vol.2, Vikas Publ. House

Pvt. Ltd., New Delhi.

FROESE, R. AND PAULY, D., 1998. Fishbase 1998, concepts, design and data sources.

ICLARM, Manila.

FROESE, R. AND PAULY, D., (eds.) 2010. FishBase.World Wide Web electronic

publication. www.fishbase.org, version (07/2006. Cited 18 April).

FROESE, R. AND PAULY, D., (eds.) 2015. FishBase. World Wide Web electronic

publication. www.fishbase.org, version ( Cited 18 April, 2015).

FROESE, R., 1998. Length-weight relationships for 18 less-studied fish species. J. Appl.

Ichthyol., 14: 117-118.

FROESE, R., 2006. Cube law, condition factor and weight-length relationships: history, meta-

analysis and recommendations. J. Applied Ichthyol., 22: 241-253.

FROESE, R., TSIKLIRAS, A.C. AND STERGIOU, K.I., 2011. Editorial note on weight–

length relations of fishes. Acta Ichthyologica et Piscatoria, 41: 261-263.

FROST, W.E., 1945. The age and growth of eels, (Anguilla anguilla) from the windermere

catchment area. Part II. J. Anim. Ecol., 4:106-124.

GALIB, S.M., ABU NASER, S.M., MOHSIN, A.B.M., CHAKI, N. AND FAHAD, F.H.,

2013. Fish diversity of the River ChotoJamuna, Bangladesh: Present status and

conservation needs. Int. J. Biodiver. Conserv., 5: 389-395.

203

GANAI, A.H. AND PARVEEN, S., 2014. Effect of physico-chemical conditions on the

structure and composition of the phytoplankton community in Wular Lake at

Lankrishipora, Kashmir. Int. J. Biodivers. Conserv., 6: 71-84.

GANY, H., 2006. An outlook of the recent development of irrigation and propensity for

transformation into an efficient engine of growth. International seminar on the

occasion of 70th anniversary of the RCWR, Bandung, Indonesia.

GELDIAY, R. AND BALIK, S., 1998. Turkish Freshwater Fishes. Ege University Press,

Izmir, Turkey.

GERTEN, D., HOFF, H., ROCKSTROM, J., JAGERMEYR, J., KUMMU, M. AND

PASTOR, A.V., 2013. Towards a revised planetary boundary for consumptive

freshwater use: role of 30 environmental flow requirements, Current Opinion in

Environmental Sustainability, 5: 551-558.

GHAZALA, B. AND ARIFA, H., 2011. Distribution of family Fragilaraceae

(Bacillarophycota) in the region of Multan, Pakistan.Pak. J. Bot., 43: 15-27.

GIBBS. J.P., 2000. Wetland loss and Biodiversity conservation, Conservation Biology, 14:

314-317.

GLEICK, P.H., ALLEN, L., COHEN, M.J., COOLEY, H., CHRISTIAN-SMITH, J.,

HEBERGER, M., MORRISON, J., PALANIAPPAN, M. AND SCHULTE, P., 2011.

The World’s water, vol 7, The biennial report on freshwater resources. Island Press,

Washington.

GLEICK, P.H., ED., 1993. Water in Crisis: A Guide to the World's Freshwater Resources.

Oxford University Press. p. 13, Table 2.1 "Water reserves on the earth".

GOLDMAN, C.K. AND HORNE, A.J., 1983. Limnology, McGraw Hill, Tokyo. Lewis WM.

2000. Basis for the protection and Management of tropical lakes. Lakes Reserv. Res.

Manage., 5: 35-48.

204

GONCALVES, J.M.S., BENTES, L., LINO, P.G., RIBEIRO, J., CANARIO, A.V.M. AND

ERZINI, K., 1997. Weight–length relationships for selected fish species of the small-

scale demersal fisheries of the south and south-west coast of Portugal. Fish. Res., 30:

253-256.

GONULOL, A. AND OBALI, O., 1998. A study on the phytoplankton of HasanUğurlu Dam

Lake (Samsun-Turkey). Turkish J. Biol.,22: 447-461.

GORDON, J.D.M., 1978. Some notes on the biology of the roundnose grenadier

Coryphaenoides rupestris to the West of Scotland. ICES C.M. Doc. No. G: 40.

GORDON, N.D., MCMAHON, T.A., FINLAYSON, B.L., GIPPEL, C.J. AND NATHAN,

R.J., 2004. Stream hydrology; an introduction for ecologists.

GOWD, T.N., SRIRAMA, R.S.V. AND CHARY, K.B., 1998. Stress field and seismicity in

the Indian shield: Effects of the collision between Indian and Eurasia.Curr. Sci., 74:

75-80.

GRIZZLE, J.M., 1981, Effects of hypolimnetic discharge on fish health below a reservoir:

Transactions American Fisheries Society, v. 110, p. 29-43.

GROGA, N., QUATTARA, A., KOULIBALY, A., DAUTA, A., AMBARD, C.,

LAFFAILLE, P.AND GOURENE, G., 2014. Dynamic and Structure of

phytoplankton community and environment in the lake Taabo (Cote d,lvoire). Int.

Res. J. Public Environ. Health, 1: 70-86.

GROVER, J.P. AND CHRZANOWSKI, T.H., 2006. Seasonal dynamics of phytoplankton in

two warm temperate reservoirs: association of taxonomic composition with

temperature., J. Plankton Res., 28: 1-17.

GUPTA, K. AND PAMPOSH., 2014. Algal Flora of some selected water bodies of Delhi,

Biol. Form., 6: 181-188.

205

GUY, D., 1992. The ecology of the fish pond ecosystem with special reference to Africa, 220-

230. Pergamon Press.

HAMIDAN, N., AND BRITTON, J.R., 2013.Length-weight relationships for three fish

species (Capoeta damascina, Garra rufa, andNemacheilus insignis) native to the

Mujib Basin, Jordan, J. Appl. Ich., 29:480-481. 

HANSON, G., HASHMI, M. A., VIAK, V.B.B., Sweeden, 1995. Conservation and

development of land and water resources of upper Kaha hill torrent catchment, Report

No. NES/4804.

HARRIS, G.P. AND BAXTER, G., 1996. Inter annual variability in phytoplankton biomass

and species composition in a subtropical reservoir. Freshwater Biol., B: 545-560.

HARRISON, T.D., 2001. Length-weight relationships of fishes from South African estuaries.

J. Applied Ichthyol., 17:46-48.

HASAN, Z., AHMAD, I., YOUSAF, M., REHMAN, L. AND KHAN, J., 2013. Fish

biodiversity of River Swat.Pakistan J. Zool., 45: 283-289.

HASAN, Z., KHAN, W., KHAN, M.A., REHMAN, L.U., KHAN, J. AND SANAULLAH,

2014. Comparative Abundance of Fish Fauna of Different Streams of Bajaur Agency,

Khyber Pakhtunkhwa, Pakistan. Biologia, 60: 159-163.

HASSAN, M., BHATTI, M.A., BHUTTA, A.M. AND ABBASS, S.Q., 2001. Geology and

mineral resources of D.G. Khan and Rajan Pur areas, eastern Sulaiman Range. 747:

107.

HEGDE, G.R. AND SUJATA, T., 1997. Distribution of planktonic algae in three freshwater

lentic habitats of Dharwad.Phykos., 36: 49-53.

HOCH, M.P., DILLON, K.S., COFFIN, R.B. AND CIFUENTES, L.A., 2008. Sensitivity of

bacterioplankton nitrogen metabolism to eutrophication in sub-tropical coastal water of

Key West. Florida. Mar. Pollut. Bull., 56: 913-926.

206

HOLLENBECK, J.P. AND RIPPLE, W.J., 2007. Aspen and Conifer Heterogeneity Effects

on Bird Diversity in the Northern Yellowstone Ecosystem. Western

NorthAmericanNaturalist, 67: 92-101.

HOLMBORN, T., 2009. Zooplankton growth and trophic linkages. Ph.D. thesis Stockholm

University.

HOLMLUND, C.M. AND HAMMER, M., 1999. Ecosystem services generated by fish

populations.Ecol. Econ., 29: 253-268.

HOOPER, D.U., CHAPIN, F.S. III., EWEL, J.J., HECTOR, A., INCHAUSTI, P.,

LAVOREL, S., LAWTON, J.H., LODGE, D.M., LOREAU, M., NAEEM, S.,

SCHMID, B., SETA, L.H., SYMSTAD, A.J., VANDERMEER, J. AND WARDLE,

D.A., 2005. Effects of biodiversity on ecosystem functioning: a consensus of current

knowledge. Ecol.Monographs, 75: 3-35.

HUSSAIN, A., SULEHRIA, A.Q.K., EJAZ, M. AND MAQBOOL, A., 2014. Assessment

ofwater quality of a flood plain reservoir for the development of aquaculture in

Pakistan, BIOLOGIA (PAKISTAN)., 60: 1-9.

HUSTON, M.A., 1994. Biological Diversity: The Coexistence of Species on Changing

Landscapes. Cambridge University Press, New York.

HUSZAR, V.L.D. AND CARACO, N.F., 1998. The relationship between phytoplankton

composition and physical-chemical variables: a comparision of taxanomic and

morphological- functional descriptors in six temperate lakes. Freshwater Biol., 40:

679-696.

ICMR, 1975. Manual of Standards of Quality of Drinking Water Supplies, Indian Council of

Medical Research, New Delhi.

IQBAL AND KATARIYA, H.C., 1995. Physico-chemical analysis and water quality

assessment of upper lake of Bhopal. Int. J. Environ. Pollu., 15: 504-509.

207

IQBAL, F., Ali, M., SALAM, A., KHAN,B.A., AHMAD,S., QAMAR, M. AND KASHIF,

U., 2004. Seasonal Variations of Physico- Chemical Characteristics of River Soan

Water at Dhoak Pathan Bridge (Chakwal), Pakistan. Int. J. Agri. Biol., 6: 89-92.

IQBAL, Z., PERVAIZ, K. AND JAVED, M. N., 2013. Population Dynamics of Tor

Macrolepis (Teleostei: Cyprinidae) and Other Fishes Of Attock Region,

Pakistan.Canadian J. Pure Applied Sci., 1: 2195-2201.

ISHAQ, M., KHAN,S., KHAN,J., AKHTAR, N. AND SAEED,K., 2014. Study on

Ichthyofaunal Biodiversity of River Swat. World J. Fish Marine Sci., 6: 313-318.

ISI, 1991. Indian Standard Specification for Drinking water 1510500 ISI, New Delhi.

IUCN, 2001. Report on the State of Conservation of Natural and Mixed Sites Inscribed on the

World Heritage List and the List of World Heritage in Danger. Gland, Switzerland.

IUCN, 2014. The IUCN red list of threatened species. Version, 2014.

http://www.iucnredlist.org.

JACCARD, P., 1912. The distribution of flora in alpine zone. New Phytol., 11: 37-50.

JAISWAR, S. AND MEHTA, S., 2014. Occurrence of freshwater algae and water qualityin

Bhavnagar, Gujarat, India. J. Biol. Earth Sci., 4: 6-20.

JAMAL, A., YUSOFF, F.M., BANERJEE, S AND SHARIFF, M., 2014. Littoral and Littoral

Phytoplankton Distribution and Biodiversity in a Tropical Man-Made Lake, Malaysia.

Adv. St. Biol., 6: 149-168.

JANJUA, M.Y., AHMAD, T. AND AKHTAR, N., 2009. Limnology and trophic status of

shahpur dam reservoir, Pakistan. J. Animal Plant Sci., 19: 224-273.

JAVED, M.N., PERVAIZ, K., MIRZA, M.R. AND BHATTI, M.N., 2005. Fishes of River

Indus from Ghazi to Garyala, Pakistan. Biologia, 51:1-13.

JEFFRIES, M. AND MILLS, D., 1990. Freshwater Ecology: Principles and Applications John

Wiley and Sons, Chichester.

208

JHINGRAN, V.G., 1968. Synopsis on biological data on Catlacatla (Ham., 1822) FAO Fish

synops. (32):1.

JIVENDRA,1995. Water pollution management, A.P.H. publishing corporation. India.

JOHNSTON, R.B. AND JACOBY, M.J., 2003, Cyanobacterial toxicity and migration in a

mesotrophic lake in western Washington, USA. Hydrobiol., 495: 79-91.

JORGENSEN, O.A., 1996. Distribution and biology of grenadiers (Macrouridae) in West

Greenland waters. Journal of Northwest Atlantic Fishery Science, 18: 7-29.

JULIAN, J.P., DOYLE, M.W., POWERS, S.M., STANLEY, E.H. AND RIGGSBEE, J.A.,

2008. Optical water quality in rivers, Water Resources Research, 44: 1-19.

KADIRI, M.O., 2000. Limnological studies of two contrasting but closely linked springs in

Nigeria, West Africa. Plant Biosyst., 134 :123- 131.

KAKARABDULLAHZAI, A.S. AND KAKARSULEMANKHEL, J.K., 2004. Additions to

the Fish Fauna of River Zhob, Balochistan, Pakistan. J. Biol. Sci., 4: 293-297.

KANG, B., HE, D., PERRETT, L., WANG, H., HU, W., DENG, W. AND WU, Y., 2009.

Fish and fisheries in the Upper Mekong: current assessment of the fish community,

threats and conservation, Reviews in Fish Biology and Fisheries, 19: 465-480.

KARACHLE, P.K. AND STERGIOU, K.I., 2008. Length-length and length-weigh

relationships of several fish species from the North Aegean Sea (Greece). J.Biological

Res., 10: 149-157.

KARATAS, M., 2005. Research Techniques in Fish Biology. Nobel Press, Ankara, Turkey.

KATARIA, H.C., QUERESHI, H.A., IQBAL, S.A. AND SHANDILYA, A.K., 1996.

Assessment ofwater quality of Kolar Reservoir in Bhopal (MP). Pollution Res., 15:

191-193.

209

KAUL, V. AND HANDOO, J.K., 1980. Physicochemical characteristics of Nilang-a high

altitude forest Lake of Kashmir and its comparison with valley lakes. Proc Indian

National Sci. Acad. B., 46: 528-541.

KAUR, S., DHILLON, S.S., KAUR, N. AND BATH, K.S., 1995. Physico-chemical

charaterstics of freshwater ecosystem prevailing in and around Patiala (Punjab). J.

Environ. Ecol., 13: 523-528.

KELLY, C.J., CONNOLLY, P.L. AND BRACKEN, J.J., 1997. Age estimation, growth,

maturity and distribution of the roundnose grenadier from the Rockall Trough. J. Fish

Biol., 50: 1-17.

KENT, M., AND COKER, P., 1992.Vegetation description and analysis a practical

approach. John Wiley and Sons, New York.

KHAN, A.M., ALI, Z., SHELLY, S.Y. AND MIRZA, M.R., 2011. Aliens; a catastrophe for

native freshwater fish diversityin Pakistan. J. Anim. Plants Sci., 21: 435-440.

KHAN, A.M., SHAKIR, H.A., KHAN, M.N., ABID, M., MIRZA, M.R., 2008. Ichthyofaunal

survey of some freshwater reservoirs in Punjab. J. Anim. Plant Sci., 18: 151-154.

KHAN, H.Z., KHAN, W., REHMAN, M.A., KHAN, L.J. AND SANAULLAH., 2014,

Comparative Abundance of fish fauna of different stream of Bajaur, Agency Khyber

PakhtonkhwaPakistan. Biologia (Pakistan), 60: 159-163.

KHAN, M.A.G. AND CHOUDHARY, S.H., 1994. Physical and chemical limnology of lake

Kaptai, Bangladesh. Trop. Ecol., 35: 35-51.

KHAN, M., HUSSAIN, F. AND MUSHARAF, S., 2011, A fraction of fresh water algae of

Kalpani stream and adjoining area of district Mardan, Pakistan. Inter. J. Biosci., 1: 45-

50.

KHAN, M.A. AND HASAN, Z., 2011. A Preliminary survey of fish fauna of Changhoz Dam,

Karak, K.P.K. Pakistan, World J. Fish Marine Sci., 3: 376-378.

210

KHAN, M.A. AND SABAH., 2013. Length–weight and length-length relationships for five

fish species from Kashmir Valley. J Appl. Ichthyol., 29: 283-284.

KHAN, M.A.G. AND CHOUDHARY, S.H., 1994, Physical and chemical limnology of lake

Kaptai: Bangladesh.Trop. Ecol., 35: 35-51.

KING, R.P., 1996. Length-weight relationship of Nigerian Coastal water fishes. Fishbyte, 19:

53-58.

KOLEFF, P., GASTON, K.J. AND LENNON, J.J. 2003. Measuring beta diversity for

presence-absence data. J. Anim. Ecol., 72: 367-382.

KOLTUNIEWICZ, A. AND DRIOLI, E., 2008. Membranes in Clean Technologies. Wiley-

Verlag GmbH & Co. KGaA, Weinheim, Germany.

KORAI, A.L., SAHATO, G.A., LASHARI, K.H., AND ARBANI, S.N., 2008. Biodiversity in

relation to physicochemical properties of Keenjhar Lake, Thatta district, Sindh,

Pakistan.Turk. J. Fish. Aquatic Sci., 8: 259-268.

KOSHY, M. AND NAYAR, T.V., 1999. Water quality aspects of river Pamba.Pollut. Res., 8:

501-510.

KOUTRAKIS, E.T. AND TSIKLIRAS, A.C., 2003. Length-weight relationships of fishes

from three northern Aegean estuarine systems (Greece). J. Appl. Ichthyol.,19: 258-260.

KOVAC, V., COPP, G.H. AND FRANICIS, M.P., 1999. Morphometry of the stone loach

Barbatula barbatula (L.): do mensural characters reflect the species’ life history

thresholds? Environ. Biol. Fish., 56: 105-115.

KREBS, C.J., 1989. Ecological Methodology. Harper- Collins Publishers, New York. 654 pp.

KUMAR, C.R.R., HARIKRISHNAN, M. AND KURUP, B.M., 2011. Exploited fisheries

resources of the Pampa River, Kerala, India. Ind. J. Fish, 58: 13-22.

KUMAR, N.J.I. AND OOMMEN, C., 2009. Influence of limiting factors on phytoplankton

and coliform population in an inunolated, isolated wetland, J. Wet. Eco., 3: 43-55.

211

KUMAR, P., SONAULLAH, F. AND WANGANEO, A., 2010. A preliminary limnological

study on Shershah Suri Pond, Sasaram, Bihar. Asian J. Exp. Sci., 24: 219- 226.

KUMAR, U. AND KAKRANI, B., 2000. Water environment and pollution. Agrobios, India.

KUMAR, V.N.S. AND HOSHMANI, S.P., 2006. Algal biodiversity in fresh

waters and related physico-chemical factors. Nat. Environ. And Poll.

Tec., 5: 37-40.

LAFFAILLE, P., ACOU, A., GUILLOUET, J. AND LEGULT, A., 2005. Temporal change in

European eel, Anguilla anguilla, stock in a small catchment after installation of fish

passes.FishManag. Ecol., 12: 123-129.

LAGHARI, K.Q., LASHARI, B.K. AND MEMON, H.M., 2008. Perceptive research on

wheat evapotranspiration in Pakistan. Irrigationand Drainage, 57: 571-584.

LAGHARI, M. Y., NAREJO, N.T., MAHESAR,H., LASHARI, P.K. AND ABID, M., 2009,

Length-weight relationship and condition of indigenous catfish, Ritarita (Hamilton)

from cemented ponds of University ofSindh, Jamshoro. Sindh. Univ. Res. J., 41: 47-

52.

LAGHARI, M.Y., DARS, B.A. AND NAREJO, N.T., 2011. Length-weight relationship of

Tilapia niloticus in concrete pond of Habib ADM, Hub, Balochistan. Sindh Univ. Res.

J., 43:29-32.

LASHARI, K.H., NAQVI, S.H., PALH, Z.A., LAGHARI, MASTOI,Z.A., SAHATO, G.A.

AND MASTOI,G.M., 2014. The effects of physiochemical parameters on planktonic

species population of Keenjhar lake, district Thatta, Sindh, Pakistan.American J.

BioSci., 2: 38-44.

LECREN, E.D., 1951. The length-weight relationship and seasonal cycle in gonad weight and

condition in the perch, Perca fluviatilis. J. Anim. Ecol. 20: 201-219.

212

LEUNDA, P.M., OSCOZ, J., MIRANDA, R. AND 2006. Length-weight relationshipsof

fishes from tributaries of the Ebro River, Spain. J. Appl.Ichthyol., 22: 299-300.

LEVEQUE, C., BALIAN, E.V. AND MARTENS, K., 2005. An assessment of animal species

diversity in continental waters. Hydrobiol., 542:32-67.

LIOUSIA, V., BATZIAKAS, S., PANAGIOTOU, N., DAOUTI, I., KOUTRAKIS, E. AND

LEONARDOS, I.D., 2012. Length-weight relations of 22 fishspecies from the littoral

zone of the eastern Ionian Sea, Greece. Acta Ichthyologica et Piscatoria, 42: 69-72.

LUDWIG, J.A. AND REYNOLDS, J.F., 1988. Statistical ecology, A Wiley-Interscience

Publication (John Wiley & Sons), New York.

MACE, G.M., MASUNDIRE, H. AND BAILLIE, J., 2005. Biodiversity. In: Scholes R and

Hassan R (eds.) Ecosystems and Human Well-Being: Current State and Trends, pp.

77-122. Washington, DC: Island Press.

MAGURRAN, A.E., 1988. Ecological diversity and its measurement, Princeton University

press, Princeton.

MAGURRAN, A.E., 2004, Measuring Biological Diversity. Blackwell, Malden.

MALKANI, M.S., 2010. Stratigraphy and Mineral potential of Sulaiman (Middle Indus)

basin, Pakistan.Sindh Univ. Res. J., 42: 39-66.

MALKANI, M.S., 2011. Stratigraphy, Mineral Potential, Geological History and

Paleobiogeography of Balochistan Province, Pakistan, Sindh Univ. Res. J., 43: 269-

290.

MALLIN, M.A., BURKHOLDER, G.M., LARSEN, L.M. AND GLASGOW, H.B. JR., 1995.

Response of two plankton grazers to an ichthyotoxic estuarine dinoflagillate.J. Plank.

Res., 17: 351-363.

MALLIN, M.A., STONE, K.L. AND PAMPERL, M.A., 1994. Phytoplankton community

assessments of seven southeast U.S. cooling reservoirs, Water Res., 28: 665-673.

213

MALMQVIST, B. AND RUNDLE, S., 2002. Threats to running water ecosystems of the

world. Environ. Conserv., 29: 134-153.

MARGALEF, R., 1968. Perspective in Ecological Theory, Uni. of Chicago Press. 112.

MARINHO, M.M. AND HUSZAR, V.L.M., 2002. Nutrient availability and physical

conditions as controlling factors of phytoplankton composition and biomass in a

tropical reservoir (Southeastern Brazil). Arch. Hydrobiol. 153: 443-468.

MARTIN, S.E., IRIGOIEN, X., HARRIS, R.P., LOPEZ-URRUTIA, A., ZUBKOV, M.V.

AND HEYWOOD, J.L., 2006. Variation in the transfer of energy in marine plankton

along a productivity gradient in the Atlantic Ocean. Limnol. Ocean., 51: 2084-2091.

MARTIN, W.R., 1949. The mechanic of environmental control of body form in fishes,

Univ.Toronto . Stud. Biol., 58: 1- 91.

MAS-MARTI, E., GARCIA-BERTHOU, E., SABATER, S., TOMANOVA, S. AND

MONOZ, I., 2010. Comparing fish assemblages and trophic ecology of permanent and

intermittent researches in a Mediterranean stream, Hydrobiol., 657: 167-180.

MASON, C.F., 1998. Biology of freshwater pollution. Longman scientific and technical, New

York, USA. 351pp.

MATHUR, P., AGARWAL, S. AND NAG, M., 2008. Proce.Taal 2007: The 12th World

Lake Conference: 1518-1529.

MATINI, L., TATHY, C. AND MOUTOU, J.M., 2012. Seasonal groundwater quality

variation in Brazzaville, Congo. Res. J. Chem. Sci., 2: 7-14.

MAY, L., 1983. Rotifer occurrence in relation to water temperature in Loch Leven,

Scotland.Hydrobiol., 104: 311-315.

MENDES, B., FONSECA, P. AND CAMPOS, A., 2004. Weight–length relationships for 46

fish species of the Portuguese west coast. J. Appl. Ichthyol., 20: 355-361.

214

MENGESTOU, S., GREEN, J. AND FERNANDO, C.H., 1991. Species composition,

Distribution and seasonal dynamics of rotifera in Rift Valley Lake in Ethiopia. Hydrobiol.,

209: 203-214.

MICHAEL, A.M., PAERL, H.W., 1994. Planktonic trophic transfer in an estuary: Seasonal,

diel, and community structure effects. Ecology, 75: 2168-2184.

MICHAEL, P., 1984. Ecological Methods for Field and Laboratory Investigations, 

Tata McGraw-Hill Publishing Co. Ltd., New Delhi.

MILLER, R.R., WILLIAMS, J.D. AND WILLIAMS, J.E., 1989. Extinction of North

American fishes during the past century. Fisheries, 14: 22-38.

MIRANDA, R., OSCOZ, J., LEUNDA, P.M. AND ESCALA, M.C., 2006. Weight length of

cyprinid fishes of the Iberian Peninsula. J. Appl. Ichthyol., 22:297-298.

MIRZA, M.R. AND BHATTI, M.N., 1999. Biodiversity of the freshwater fishes of Pakistan

and Azad Kashmir. In: Proc. Sem. Aquatic Biodiversity of Pakistan (eds. Q.B. Kazmi

and M.A. Kazmi). pp. 136-144.

MIRZA, M.R., 1990. Freshwater fishes of Pakistan. Published by Urdu Science Board

Lahore. Pp. 125.

MIRZA, M.R., 1994. Geographical distribution of freshwater fishes in Pakistan: a review.

Punjab Univ. J. Zool., 9: 93-108.

MIRZA, M.R., 2003. Checklist of freshwater fishes of Pakistan.Pakistan J. Zool., suppl. 3: 1-

30.

MIRZA, Z.S., MIRZA, M.R., MIRZA, M.A. AND SULEHRIA, A.Q.K., 2011. Icthyofaunal

diversity of the River Jhelum, Pakistan. Biologia, 57: 23-32.

MIRZA, M.R., SHARIF, H.M., 1996.A Key to the Fishes of Punjab, 1st. Ed., Ilmi Kitab

Khana, Lahore, Pakistan.

215

MISHRA, A., CHAKRABORTY, S.K., JAISWAR, A.K., SHARMA, A.P., GEETANJALI,

D. AND MADAN, M., 2010. Plankton diversity in Dhaura and Baigul reservoirs

of Uttarakhand, Indian. J. Fish., 57: 19-27.

MOMMSEN, T.P., 1998. Growth and metabolism. In: Evans, D.H. (Ed.), The Physiology of

Fishes. CRC Press, Boca Raton, pp. 65-97.

MOUTOPOULOS, D.K. AND STERGIOU, K.I., 2002. Length–weight and length–length

relationships of fish species from the Aegean Sea (Greece). J. Appl. Ichthyol., 18: 200-

203.

MOYLE, P. B. AND CECH, J.J., 1996. Fishes: An introduction to ichthyology. New Jersey.

MOYLE, P.B. AND LEIDY, R.A., 1992. Loss of biodiversity in aquatic ecosystems; evidence

from fishfaunas. In: Fielder P.L. and Jain S.K. (eds), Conservation Biology: The

Theory and Practice of Nature Conservation. Chapman and Hall, UK 129-161.

MULDER, C.P.H., BAZELEY-WHITE, E., DIMITRAKOPOULOS, P.G., HECTOR, A.,

SCHERER-LORENZEN, M. AND SCHMID, B., 2004. Species evenness and

productivity in experimental plant communities. Oikos., 107: 50-63.

MUNAWAR, M., 1974. Limnological studies on freshwater ponds of Hyderabad, India

IV. The biocenose. Periodicity and species composition of unicellular and colonial

phytoplankton in polluted and unpolluted environments, Hydrobiol., 45: 1-32.

MUSTAPHA, M.K., 2010. Seasonal Influence of Limnological Variables on Plankton

Dynamics of a Small, Shallow, Tropical African Reservoir. Asian J. Exp. Biol. Sci., 1:

60-79.

NAEEM, M., SALAM, A., AND ISHTIAQ, A., 2011. Length-weight relationships of wild

and farmed Tor putitorafrom Pakistan. J. Appl. Ichthyol., 27: 1133-1134.

216

NAEEM, M., SALAM, A., ASHRAF, M., KHALID, M. AND ISHTIAQ, A., 2011. External

morphometric study of hatchery reared mahseer (Tor putitora) in relation to body size

and condition factor. Afr. J. Biotechnol., 10: 7071-7077.

NAEEM, M., SALAM, A., GILLANI, Q. AND ISHTIAQ, A., 2010. Length-weight

relationships of Notopterusnotopterusand introduced Oreochromisniloticusfrom the

Indus River, southern Punjab, Pakistan. J. Appl. Ichthyol., 26: 620.

NAEEM, M., SALAM, A., ISHTIAQ, A. AND SHAFIQUE, S., 2010. Length-weight and

condition factor relationship of farmed hybrid (Catla catla X Labeo rohita) from

Multan, Pakistan. Shindh Uni. Res. J. Sci. Ser., 42: 35-38.

NAEEM, M., ZUBERI, A., HASAN,Z., SALAM, A., KHALID, M., KHAN, M.J., AYAZ,

M.M., ASHRAF, M., NASIR, M.F.,RASOOL, S.A., AZIZ, M. AND ISHTIAQ, A.,

2012. Length-weight and length-length relationships of freshwater wild catfish Mystus

bleekeri from Nala Daik, Sialkot, Pakistan. Afr. J.Biot., 11: 11168-11172.

NAIK, A.S.K., KUMAR, J., MAHESH, V. AND BENAKAPPA, S., 2013. Assessment of

Fish diversity of Tunga River, Karnataka, India, Nature and Science, 11: 82-87.

NAREJO, N.T., 2006. Length-weight relationship and relative condition of a carp, Cirrhinus

reba (Hamilton) from Manchar Lake district Dadu, Sindh. Pakistan J. Zool., 38: 11-

14.

NAREJO, N.T., LASHARI, P.K. AND JAFRI, S.I.H., 2008.Morphometric and meristic

differences between two types of palla, Tenualosa ilisha (Hamilton) from River

Indus Pakistan.Pakistan J. Zool., 40: 31-35.

NELSON, J.S., 1994. Fishes of the World, 3rd ed. John Wiley & Sons, New York, USA.

NELSON, J.S., 2006. Fishes of the world, 4th ed., John Wiley and Sons, Inc., New York, 601.

NIELSEN, C., 1995. Animal Evolution: Interrelationships of the Living Phyla; Oxford

University Press. Oxford.

217

NOGUEIRA, M.G., 2000. Phytoplankton composition, dominance and abundance as

indicators of environmental compartmentalization in Jurumirim Reservoir

(Paranapanema River), São Paulo, Brazil. Hydrobiol., 431: 115-128.

NOVOTNY, V. AND WEIBLEN, G.D., 2005.  From communities to continents: beta

diversity of herbivorous insects.Annales Zoologici Fennici, 42: 463-475.

OBALI, O., 1984. MoganGölüfitoplanktonununmevsimseldeğişimi(in Turkish). Doğa Bilim

Dergisi, A2, 8: 9-104.

OBALI, O., GONULOL, A. AND DERE, S., 1989. Algal flora in the littoral zone of Lake

Mogan.OndokuzMayıs University.J. Sci., 1: 33-53.

ODUM, E.P., 1971. Fundamentals of ecology, 3rd Eds. Toppan Company, Ltd., Japan.

OFFEM, B.O., AKEGBEJO-SAMSONS, Y. AND OMONIYI, I.T., 2007. Biological

assessment of Oreochromis niloticus (Pisces, Cichlidae, Linne, 1958) in a tropical

floodplain river. Afr. J. Biotechnol., 6: 1966-1971.

OHTA, T., 2003. On the molecular kinetics of acoustic cavitation and nuclear emission. In.

J. Hydrogen Energy, 28: p. 437.

OJHA, P. AND MANDLOI. A.K., 2004. Diurnal variation of pH in freshwater fish culture

pond, Ecol., Env.and Cons., 10: 85-86.

 OLELE, N.F. AND EKELEMU, J.K., 2008. Physicochemical and Periphyton/Phytoplankton

Study of Onah Lake, Asaba, Nigeria,African J. Genl. Agri., 4 : 183-193.

OMUDU, E.A. AND ODEH, P., 2006. A survey of zooplankton and macroinvertebrates of

Agi Stream in Ojo Benue State, and their implications for transmission of endemic

diseases, Biol. Environ.Sci. J. Trop., 3: 10-17.

ONYEMA, C., NWABUZOR, E.J. AND IGWE, C.O., 2014. The Water Quality

Phytoplankton and Zooplankton of the Lower Ogun River Lagos, Intern. J. Life Sci.,

3: 16-25.

218

ORMEROD, S.J., 2003. Current issues with fish and fisheries: editor’s overview and

introduction. J. Appl. Ecol., 40: 204-213.

OSCOZ, J., CAMPOS, F. AND ESCALA, M.C., 2005. Weight-length relationships of some

fish species of the Iberian Peninsula. J Appl. Ichthyol., 21: 73-74.

PALMER, C.M., 1984. Algae and water pollution. Castle house publication Ltd, California.

PAULY, D. AND GAYANILO, JR. F.C., 1997. A Bee: An alternative approach to estimating

the parameters of a length-weight relationship from lengthfrequency samples and their

bulk weights. NAGA ICLARM, Manila, Philippines.

PAULY, D., 1983. Some simple methods for the assessment of tropical fish stocks. FAO.

Fisheries Techn. Pap. (234) FAO, Rome.

PAULY, D., 1993. Editorial . Fish byte section. Naga, The ICLARM Q, 16: 26.

PAULY, D., CHRISTENSEN, V. AND WALTERS, C., 2000. Ecopath, Ecosim, and

Ecospace as tools for evaluating ecosystem impact of fisheries. ICES J. Mar. Sci., 57:

697-706.

PAWAR, S.M. AND SONAWANE, S.R., 2011. Diversity of phytoplankton from three water

bodies of Satara district (M.S.) India, Int. J. Biosci., 1: 81-87.

PECKHAM, S.D., CHIPMAN, J.W., LILLESAND, T.M. AND DODSON, S.I., 2006.

Alternate stable states and the shape of the lake trophic distribution. Hydrobiol., 57:

401-407.

PEERAPORNPISAL, Y., CHAIUBOL, C., PEKKO, J., KRAIBUT, H., CHORUM, M.,

WANNATHONG, P., NGEARNPAT, N., JUSAKUL, K., THAMMATHIWAT, A.,

CHUANUNTA, J.AND INTHASOTTI, T., 2004. Monitoring of Water Quality in Ang

Kaew Reservoir of Chiang Mai University Using Phytoplankton as Bioindicator from

1995-2002., Chiang Mai. J. Sci., 31: 85-94.

219

PERVAIZ, K., 2011. Aspects of Biology of Mahseer fish species from Attack Region,

Pakistan. Ph.D. Thesis, Department of Zoology, University of the Punjab, Lahore,

Pakistan.Pp195.

PERVAIZ, K., IQBAL,Z., MIRZA, M.R., JAVED, M.N., NAEEM, M. AND ISHTIAQ, A.,

2012. Length-weight, length-length relationships and feeding habits of wild Indus

Mahseer, Tor macrolepis, from Attock, Pakistan. J. Appl. Ichthyol., 1-4.

PERVIN, M.R. AND MORTUZA, M.G., 2008. Notes on length-weight relationship and

condition factor of fresh water fish, Labeo boga (Hamilton) (Cypriniformes:

Cyprinidae). Univ. J. Rajshahi Univ., 27: 97-98.

PETRAKIS, G. AND STERGIOU, K.I., 1995. Weight–length relationships for 33 fish species

in Greek waters. Fish Res., 21: 465-469.

PIELOU, E.C., 1966. The measurement of diversity in different types of biological

collections. J. Theor. Biol., 13: 131-144.

POSTEL, S.L., DAILY, G.C. AND EHRLICH, P.R., 1996. Human appropriation of

renewable fresh water. Science, 271:785-788.

PRABHAHAR, C., SALESHRANI, K. AND THARMARAJ, K., 2011. Hydrobiological

investigations on the planktonic diversity of Vellar River, Vellar Estuary and

Portonovo coastal waters, South East Coast of India. Int. J. Pharm. Biol. Arch., 2:

1699-1704.

PRENDA, J., CLAVERO, M., BLANCO-GARRIDO, F., MENOR, A. AND HERMOSO, V.,

2006. Threats to the conservation to of biotic integrity in Iberian fluvial ecosystems.

Linmnetica, 25: 377-388.

PRESCOTT, G. W., 1978. How to Know Freshwater Algae.Pictured Key Nature Series, third

edn. W. M. C, Brown Company Publishers, Dubugue, lowa.

220

PUNDIR, P. AND RANA, K.S., 2002. population dynamics of phytoplanktons in the wetland

area of Keolado National Park, Bharathpur (Rajasthan). Eco. Enviro. Cons., 8: 253-

255.

PURVIS, A. AND HECTOR, A., 2000. Getting the measure of biodiversity.Nature, 405: 212-

219.

QUYANG, Y., NKEDI-KIZZA, P., WU, Q.T., SHINDE, D. AND HUANG, C.H., 2006.

Assessment of seasonal variations in surface water quality. Water Res., 40: 3800-3810.

RAFIQUE, M. AND KHAN, N.U.H., 2012. Distribution and status of significant freshwater

fishes of Pakistan. Zool. Surv. Pak., 21: 90-95.

RAFIQUE, M. AND QURESHI, M.Y., 1997. A contribution to the fish and fisheries of Azad

Kashmir. In: S.A. Mufti, C.A. Woods and S.A. Hasan, (eds.), Biodiversity of Pakistan.

Pak. Mus. Nat. Hist. Islamabad and Fl. Mus. Nat. Hist. USA. p 335-343.

RAFIQUE, M., 2000. Fish diversity and distribution in Indus River and its drainage system.

Pak. J. Zool., 32: 321-332.

RAFIQUE, M., 2001. Fish fauna of Himalayas in Pakistan with comments on the origin and

dispersal of its high Asianelements. Pak. J. Zool., 33: 279-288.

RAFIQUE, M., 2007. Biosystematics and distribution of the freshwater fishes of Pakistan

with special references to the subfamilies Noemacheilinae and Schizothoracinae.Ph.D.

dissertation, UAAR.Pp 220.

RAFIQUE, M., AKHTAR, S. AND NIAZI, H.K., 2003. Fish fauna of Jinnah Barrage and

adjoining areas. Pak. J. Zool., 35: 95-98.

RAFIQUE, R.M., MAHBOOB, S., AHMAD, M. AND SALEEM, S., 2002. Seasonal

limnological variations in Mangla Reservoir at Sukhian, Mirpur (Azad Kashmir) Int. J.

Agri. Biol.,4: 223-226.

221

RAHMAN, M.M., HOSSAIN, M.Y., AHAMED, F., FATEMATUZZHURASUBBA, B.R.,

ABDALLAH, E.M. AND OHTOMI, J., 2012. Biodiversity in the Padma Distributary

of the Ganges River, Northwestern Bangladesh: Recommendations for Conservation.

World J. Zool., 7: 328-337.

RAINA, R., KUMAR, P., SONAULLAH, F. AND WANGANEO, A., 2013. Limnological

study on a Samrat Ashok Sagar with special reference to zooplankton population

International J. Biodiversity Conserv., 5: 317-332.

RAJAGOPAL, T., THANGAMANI, A. AND ARCHUNAN, G., 2010. Comparison of

physico-chemical parameters and phytoplankton speciesdiversity of two perennial

ponds in Sattur area, Tamil Nadu. J. Env. Biol., 31:787-794.

RAMACHANDRA, T.V. AND SOLANKI, M., 2007. Ecological Assessment of Lentic Water

Bodies of Bangalore. ENVIS Technical Report 25, Bangalore: Centre for Ecological

Sciences, Indian Institute of Science.

RAMAKRISHNA, R.S., 1990.Bioenergetics and methodlogy of energy flow on important

wetlands and lake systems of Eastern Ghats. Environ. Research 1: 42. Pub. by

Ministry of Environ, and Forest, New Delhi, India.

RATH, R.K., 1993. Freshwater aquaculture, Scientific Publisher 5 A, New Pali Road,

Jodhpur, India.

RAWAL, Y.K., KAUR, A. AND KAUR, A., 2013. Analysis of Length-Weight

Relationship and Condition Factor of Tor Putitora (Hamilton) and Labeo Dero

(Hamilton) From Nangal Wetland, Punjab, India. Inter. J. Sci. Res., 2: 268-291.

REID, G.M., CONTRERAS, M.T. AND CSATADI, K., 2013. Global challenges in

freshwater fish conservation related to public aquariums and the aquarium

industry. Int. Zoo Yearbook, 47: 6-45.

222

RENN, C.E., 1968. A study of water quality: Lamotte chemical products company,

Chestertown, Maryland, 46 pp.

REVENGA, C. AND MOCK, G., 2000. Pilot analysis of global ecosystems: Freshwater

systems. World Resources Institute, Washington, DC.

REYNOLDS, C.S.,1984. The ecology of freshwater phytoplankton. Cambridge Univ. Press,

Cambridge and New York. 384 p.

REYNOLDS, C.S., 1990. Temporal scales of variability in pelagic environments and the

response of phytoplankton. Freshwater Biol., 23: 25-53.

REYNOLDS, C.S., 1996. Plant life of the pelagic. Verh. int. Ver. theor. angew. Limnol., 26:

97-113.

REYNOLDS, C.S., 1998. What factors influence the species composition of phytoplankton in

lakes of different trophic status? Hydrobiol., 369: 11-26.

REYNOLDS, C.S., 2002. Resilience in aquatic ecosystems-hysteresis, homeostasis and

health. Aquat. Ecosyst. Health Management. 5: 3-17.

RICHMOND, A., 2004. Handbook of microalgal culture: biotechnology and applied

phycology. Blackwell Science Ltd.

RICKER, W.E., 1975. Computation and interpretation of biological statistics of fish

populastions. Bull. FishRes. Board Can., 191: 203-233.

RICKLEFS, R. AND MILLER, G., 2000.Ecology, Fourth Edition. W.H. Freeman & Co.

ROSEN, G., 1981. Phytoplankton indicators and their relations to certain chemical and

physical factors. Limnologica, 13: 263-290.

ROSENFELD, J.A., 2002. Patterns and process in the geographical ranges of fishes. Global

Eco. Biogeography, 11: 323-332.

ROUND, F.E., 1984. The Ecology of Algae. Cambridge University Press, Cambridge, 664.

223

SAHU, B.K., RAO, R.J. AND BEHERA, S.K., 1995. Studies of some physico-chemical

characteristics of Ganga river water (Rishikesh-Kanpur) within 24 hours during winter

1994. Ecol. Env. Cons., 1: 35-38.

SANTOS, M.N., GASPAR, M.B., VASCONCELOS, P. AND MONTEIRO, C.C., 2002.

Weigth-length relationships for 50 fish species of the algarve coast (southern

Portugal). Fish Res., 59: 289-295.

SARAVANAKUMAR, A., RAJKUMAR, M., SEREBIAH, J.S. AND THIVAKARAN, G.A.,

2008. Seasonal variations in physico-chemical characteristics of water, sediment and

soil texture in arid zone mangroves of Kachchh-Gujarat. J. Environ. Biol., 29: 725-

732.

SARKAR, U.K., PATHAK, A.K. AND LAKRA, W.S., 2008. Conservation of freshwater fish

resources of India: New approaches, assessment and challenges. Biodivers.Conserv.,

17: 2495-2511.

SARKAR, U.K., PATHAK, A.K., SINHA, R.K., SIVAKUMAR, K., PANDIAN, A.K.,

PANDEY, A., DUBEY, V.K. AND LAKRA, W.S., 2012. Freshwater fish biodiversity

in the River Ganga (India): changing pattern, threats and conservation perspectives.

Rev. Fish Biol. Fisheries, 22: 251-272

SAROJINI, Y., 1996. Physico-chemical characteristics and phytoplankton assemblages of

sewage entering harbour water at Visakapatanam, East Coast of India. Indian J.

Environ. Prot., 16: 645-650.

SATPATHY, K.K., PRASAD, M.V.R., SHEELASHANTA, A., BHASKAR, S.,

JEBAKUMAR, K.E., RAJAN, M. AND ARULWAMY, K.S., 2007. A comparison of

water quality of two fresh water sources at Kalpakkam. Proceedings of NSL-2007,

140-144.

224

SCHEFFER, M., 2004.Ecology of Shallow Lakes. Kluwer Academic Publishers, Dordrecht,

Netherlands.

SCHIPPERS, A., JOZSA, P.G.AND SAND, W., 1996. Sulfur chemistry in bacterial leaching

of pyrite. Appl. Environ. Microbiol. 62: 3424-3431.

SCHLEGEL, I., KOSCHEL, RAND KRIENITZ, L., 1998. On the occurrence of Phacotus

lenticularis (Chlorophyta) in lakes of different trophic state. Hydrobiol., 370: 353-361.

SCHWOERBEL, J., 1987. Handbook of limnology. Ellis Horwood Limited Publishers,

Chichester.

SHAMS, M., AFSHARZADEH, S. AND ATICI, T., 2012. Seasonal variations in

phytoplankton communities in Zayandeh-Rood Dam Lake (Isfahan, Iran). Turk. J.

Bot., 36: 715-726.

SHANNON, C.E. AND WIENER, W. 1963. The mathematical theory of communication.

University Illinois Press, Urbana, 360 ppg.

SHEARER, K.D., 1994. Factors affecting the proximate composition of cultured fishes with

emphasis on salmonids. Aquaculture, 119: 63-88.

SHRESTHA, J., 2001. Taxonomic Revision of Fishes of Nepal. Environment and

Agriculture. In: Biodiversity, Agriculture and Pollution in South Asia (P.K. Jha et. al.,

eds): 171-180. ECOS, Kathmandu.

SHUKLA, P. AND SINGH, A., 2013. Distribution and diversity of freshwater fishes in Aami

River, Gorakhpur, India.Advances in Biological Research, 7: 26-31.

SILVA, E.I.L., 2005. Ecology of phytoplankton in tropical waters: Introduction to the topic

and ecosystem changes from Sri Lanka. Asian J. Water Environ. Pollut., 4:25-35.

SIMPSON, E.H., 1949. Measurment of diversity. Nature, 163: 688.

225

SINGH, B. AND ISLAM, M.R., 2000. Seasonal variation in zooplankton population of two

lentic bodies and Assam state Zoo cum botanical garden, Guwahati, Assam, Eco.

Environ. Cons., 8: 273-278.

SINGH, M., LODHA, P. AND SINGH, G.P., 2010. Seasonal Diatom variations with

reference to physico-chemical properties of waterof Mansagar Lake, Jaipur, Rajistan.

Res. J. Agri. Sci., 1: 451-457.

SINGH, R.P. AND MATHUR, P., 2005. Invstigation in physico-chemical characteristics of a

freshwater reservoir of Ajmer city, Rajistan. Indian J. Environ. Sci., 9: 57-61.

SINGHAI, S., RAMANI, G.M.A. AND GUPTA, U.S., 1990. Seasonal variations and

relationship of different physic-chemical characteristics in the newly made

Tawa.Limnologica (Berlin), 21: 293-301.

SINHA, V.R.P. AND SRIVASTAVA, H.C., 1991. “Aquaculture Productivity”, Oxford and

IHB Publishing Co. Pvt. Ltd. New Delhi.

SINOVCIC, G., FRANICEVIC, M., ZORICA, B. AND CILES-KEC, V., 2004. Length-

weight and length-length relationships for 10 pelagic fish speciesfrom the Adriatic Sea

(Croatia). J. Appl. Ichthyol., 20: 156-158.

SKIPTON, S.O. AND DVORAK, B.I., 2009.Drinking water hard water (calcium and

magnesium). Division of institute of agriculture and nature resource. University of

Nebraska-Lincoln.

SMITH, B. AND WILSON, J.B., 1996. A consumer's guide to evenness indices. Oikos, 76:

70-82.

SMITH, R.A., SCHWARZ, G.E., ALEXANDER, R.B., 1997. Regional interpretation of

water-quality monitoring data. Water Resources Research, 33: 2781-2798.

SMITH, V.H., 2003. Eutrophication of freshwater and coastal marineecosystems. A global

problem. Environ. Sci. Pollut. Res., 10: 126-139.

226

SORENSEN, T., 1948. “A method of establishing groups of equal amplitude in plant society

based similarity of species content,” K. Danske.Videns.Selsk., 5: 1-34.

SPARRE, P. AND VENEMA, S.C., 1998. Introduction to tropical fish stock assessment. Part

1. Manual. FAO Fisheries TechnicalPaper No. 306.1, Rev. 2. FAO, Rome.

SPENCE, D.H.N., 1964. The Macrophytic Vegetation of Freshwater Loch, In the Vegetation

of Scotland. Ed. A.H. Burnell, Oliver and Boyd, Edinburg and London: 306-425.

STEFFEN, W., RICHARDSON, K., ROCKSTROM, J., CORNELL, S., FETZER, I.,

BENNETT, E., BIGGS, R., CARPENTER, S. R., DE WIT, C.A., FOLKE, C., MACE,

G., PERSSON, L.M., VEERABHADRAN, R., REYERS, B. AND SORLIN, S., 2015.

Planetary boundaries: guiding human development on a changing planet.Science, 347:

1-10.

STEPHEN, M.G. AND GANAPATHY, M., 2011. Cell Counting with the Sedgewick-

Rafter Chamber and Whipple Micrometer Disc.

STRAYER, D.L. AND DUDGEON, D., 2010. Freshwater biodiversity conservation, recent

progress and future challenges. Journal of the North American Benthological Society,

29:344-358.

SWAN, S.C., GORDON, J.D.M., MORALES-NIN, B., SHIMMIELD, T., SAWYER, T.

AND GEFFEN, A.J., 2003. Otolith microchemistryof Nezumia aequalis (Pisces:

Macrouridae) from widely differenthabitats in the Atlantic and Mediterranean.

J.Marine Biological Association UK, 83: 883-886.

TALLING, J.F., 1987. The phytoplankton of Lake Victoria (East Africa). Arch. Hydrobiol.,

25: 229-256.

TALWAR, P.K. AND JHINGRAN, A.G., 1991. Inland fishes of India and adjacent

countries.Vol.I and II.Oxford andIBH publishing Co. Pvt. Ltd. New Delhi, Bombay.

227

TAS, B., GONULOL, A. AND TAS, E., 2002. A study on the seasonal variation of the

phytoplankton ofLake Cenek (Samsun-Turkey).Turkish J. Fisheries Aquat. Sci., 2:

121-128.

TIWARI, A. AND CHAUHAN, S.V.S., 2006. Seasonal phytoplanktonic diversity of Kitham

lake.Agra. J. Environ. Biol., 27:35-38.

TONAPI, G.T., 1980. Freshwater animals of India. Oxford and IBHPublishing Co. Ltd., New

Delhi.

TREER, T., PIRIA, M. AND SPREM, N., 2009. The relationship between condition

and form factors of freshwater fishes of Croatia. J. Appl. Ichthyol., 25: 608-

610.

TREVEDY, S.R. AND RAJ, S.S., 1992. River pollution in India. Ashish publishing House,

New Delhi, India.

TRIFONOVA, I.S., 1998. Phytoplankton composition and biomass structure in relation to

trophic gradient in some temperate and subarctic lakes of north-western Russia and the

Prebaltic. Hydrobiol., 370: 99-108.

TRIPATHI. R.B., SINGH, I. AND TIWARI, D.D., 2006. Qualitative and Quantitative study

of zooplankton in Seetawar Lake of Shravasti, U.P, India. Flora and Fauna, 12: 37-40.

TURAN, C., 2004. Stock identification of Mediterranean horse mackerel

(Trachurusmediterraneus) using morphometric and meristic characters. J. Mar. Sci.,

61: 774-781.

TURAN, C., ERGUDEN, D., GURLEK, M., BAŞUSTA, N. AND TURAN, F., 2004.

Morphometric Structuring of the Anchovy (Engraulis encrasicolus L.) in the

Black, Aegean and Northeastern Mediterranean Seas. Turk. J. Vet. Anim. Sci.,

28: 865-871.

228

ULLAH, A., ULLAH, H., REHMAN, A., MASOOD, Z., REHMAN, F.U., REHMAN, H.U.,

HASSAN, Z., AYAZ, S. AND ULLAH. A., 2014. The diversity of fish faunain

Baran Dam of district Bannu, (KPK) Pakistan. Int. J. Adva. Res., 2: 136-145.

ULLAH, S., HASAN, Z., AHMAD, S., RAUF, M. AND KHAN, B., 2014. Ichthyofaunal

diversity of Rhound stream at district Lower Dir, KPK, Pakistan. Int. J. Biosci. 4: 241-

247.

USEPA., 1992. Handbook of RCRA Ground-Water Monitoring Constituents: Chemical

and Physical Properties, 40 CFR Part 264, Appendix IX, EPA/530/R-92/022.

VADEBONCOEUR, Y., LODGE,D.M. ANDCARPENTER, S.R., 2001. Whole-lake

fertilization effects on distribution of primary production between benthic and

pelagichabitats,Ecology, 82: 1065-1077.

VALDIMARSSON, G. AND JAMES, D., 2001. World Fisheries - utilisation of catches.

Ocean and Coastal Management, 44: 619-633.

VAN DE MEENE, S.J., BROWN, R.R. AND FARRELLY, R.R., 2001. Towards

understanding governance for sustainable urban water management. Global

Environmental Change, 21: 1117-1127.

VAREETHIAH, K. AND HANIFFA, M.A., 1998. Phytoplankton pollution indicators of coir

retting. J. Environ. Pollut., 3:117-122.

VARRIN, R., BOWMAN, J. AND GRAY, P.A., 2007. The known and potential impacts of

climate change on biodiversity in Ontario’s terrestrial ecosystems: case studies and

recommendations for adaptation; Climate Change Research Report CCRR-09, Applied

Research and Development Branch, Ontario Ministry of Natural Resources, Sault Ste.

Marie, Ontario, 48 p.

VEERESHAKUMAR, N.S. AND HOSMANI S.P., 2006. Algal biodiversity in frashwaters

and related physiochemical factors. NatureEnv. Polln. Technol., 5: 37-40.

229

VEN DEN HOECK, C., MANN, D.G. AND JAHNS, H.M., 1995. Alage: An introduction to

phycology, Cambridge University Press, Cambridge.

VIDYA, P.J., KUMAR, S.P., GAUNS, M., VERENKAR, A., UNGER, D. AND

RAMASWAMY, V.,2013.Influence of physical and biological processes on the

seasonal cycle of biogenic flux in the equatorial Indian Ocean. Biogeosciences, 10:

7493-7507.

VISHWAKARMA, K.S., MIR, A.A., BHAWSAR, A. AND VYAS, V., 2014.

Assessment of Fish assemblage and distribution in Barna Stream Network in

Narmada basin (Central India). Int. J. Adv. Res.,2: 888-897.

VYAS, V. AND VISHWAKARMA, K.S., 2013. Species diversity and assemblage of fish

fauna of Sip River: A tributary of Narmada River. J. Res. Biol., 3: 1003-1008.

VYAS, V., DAMDE, D. AND PARASHAR, V., 2012. Fish Biodiversity of Betwa River in

Madhya Pradesh, India with Special reference to Sacred Ghat. Int. J. Biodiv. Con., 4:

71-77.

WANGANEO, A. AND WANGANEO, R., 2006. Variation in Zooplankton population in two

morphologically dissimilar rural lakes in Kashmir Himalaya. Nat. Acad. Sci., 76: 222-

239.

WARD, H.B. AND WHIPPLE, G.C., 1959. Fresh Water Biology 2nd Edition, John Willey

London.

WATSON, S. B., MCCAULEY, E. AND DOWNING, J.A., 1997. Patterns in Phytoplankton

Taxonomic Composition Across Temperate Lakes of Differing Nutrient Status.

Limnol. Oceanog., 42: 487-495.

WEBSTER, M., 2006. Introduction to Geometric Morphometrics. Department of the

Geophysical Sciences, Univ. Chicago.

230

WETZEL, R.G., 1983.Limnology, second edition. Saunders College Publishing, Philadelphia,

858 pp.

WHITTON, B.A. AND PATTS, M., 2000. The Ecology of Cyanobacteria. Kluwer Academic

Publishers, Netherlands.

WHO, 1989. Health Guidelines for the Use of Wastewater in Agriculture and Aquaculture.

Report of a Scientific Group Meeting. Technical Report Series, No. 778, World Health

Organization, Geneva.

WHO, 1996. Water Quality Monitoring: A Practical Guide to the Design and Implementation

of Freshwater Quality Studies and Monitoring Programmes. E and FN Spon, London,

UK.

WILCOX, B.A., 1984. In situ conservation of genetic resources: determinants of minimum

area requirements. In: MCNEELY, J.A., MILLER, K.R., (Eds.) National Parks,

Conservation and development, Smithsonian Inxtitution Press, Washington. D.C.

WILLIAMS, J.E., 2000. The coefficient of condition of fish. Chapter 13. In: Manual of

Fisheries Survey Methods II: with periodic updates. Schneider, J.C. (ed.). Michigan

Dep. of Natural Resources, Fisheries Special Report 25, Ann Arbor.

WILSON, E. M., 1990. Engineering Hydrol., Macmillan, London, 149-152.

WILSON,E.O., 1988. Biodiversity; Haward Univ. National Academy of

Sciences/Smithsonian Institution.

WITTEBOLLE, L., MARZORATI, M., CLEMENT, L., BALLOI, A. AND DAFFONCHIO,

D., 2009. Initial community evenness favours functionality under selective

stress. Nature, 458: 623-626. 

WOLDA, H., 1981. Similarity indices, sample size and diversity. Oecologia, 50: 296-302.

www.fishbase.org.

www.hopkin.dartmooth.edu .

231

www.iucnredlist.org.

www.latlong.net/c/?lat.

YAKUBU, A.F., SIKOKI, F.D., ABOWEI, J.F.N. AND HART, S.A., 2000. A comparative

study of phytoplankton communities of some rivers creeks and borrow pits in the

Niger Delta Area. J. Applied Sci. Env.Manag., 4: 41-46.

YALCIN, H. AND GURU, M., 2002. Corrosion of Metals, Palme Publication-Ankara,

Turkey, 173-183, 225-228.

YERLI, S.V., KIVRAK, E., GURBUZ, H., MANAV, E., MANGIT, F. AND TURKECAN,

O., 2102. Phytoplankton Community, Nutrients and Chlorophyll a in Lake Mogan

(Turkey); with Comparison Between Current and Old Data. Turkish J. Fisheries

Aquat. Sci., 12: 95-104

YILMAZ, N. AND AYKULU, G., 2010. An investigation on the seasonal variation of the

phytoplankton density on the surface water of Sapanca Lake, Turkey. Pak J. Bot., 42:

1213-1224.

YOUSAF, M., SALAM, A. ANDNAEEM, M., 2011. Body composition of freshwater

Wallago attu in relation to body size, condition factor and sex from southern Punjab,

Pakistan. Afr. J. Biotech.,10: 4265-4268.

YOUSAFZAI, A.M., KHAN, W.AND HASAN, Z., 2013. Fresh Records on Water Quality

and Ichthyodiversity of River Swat at Charsadda, Khyber Pakhtunkhwa.Pakistan J.

Zool., 45: 1727-1734.

YUNFANG, H.M.S., 1995. Atlas of Fresh-Water Biota in China.Yauton University, Fishery

College, China Ocean Press, Beijing. 375.

ZAFAR, A.R., 1964. On the ecology of Algae in Certain fish ponds of Hydrabad, India. I.

Physico-chemical complexes. Hydrobiol., 23: 179-195.

232

ZAFAR, M., MUSSADDEQ, Y., AKHTER, S. AND SULTAN, A., 2003. Weight- length and

condition factor relationship of Thaila, Catlacatlafrom Rawal Dam Islamabad,

Pakistan. Pak.J. of Biol. Sci., 6: 1532- 1534.

ZAKARIYA, A.M., ABIODUNADELANWA, M. AND TANIMU, Y., 2013. Physico-

Chemical Characteristics and Phytoplankton Abundance of the Lower Niger River,

Kogi State, Nigeria.Journal of Environmental Science, Toxicology and Food

Technology, 4: 31-37.

ZHANG, E., 2005. Garra bispinosa, a new species of cyprinid fish (Teleostei: Cypriniformes)

from Yunnan, South China. Raffles Bulletin of Zoology, Supplement, 13: 9-15.

233