final report of kscste (back-to-lab) project

ii

Final Report of KSCSTE (Back-to-lab) Project

Functional Diversity of Biyyam Backwaters: Relating

the Biodiversity to the Varying Environmental and

Climatic Changes.

Project Reference No. 04-30/WSD-BLS/2016/CSTE

Submitted by

Dr. SINI ANOOP

Post-doctoral Fellow

Dr. Razia Beevi M Scientist Mentor

Postgraduate Department and Research Center in Aquaculture

and Fishery Microbiology

MES Ponnani College

Ponnani, 679586, Malappuram (Dist.), Kerala

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CONTENTS

Sl.

No

Chapter Page No.

1 Authorization 1

2 Acknowledgement 2

3 Abstract 3

4 Introduction and review of literature 4-7

5 Objectives of the study 8

6 Materials and Methods 9-15

7 Results and Discussion 16-78

8 Summary 79

9 Outcomes of the Project (Brief summary) 80-82

i. Salient findings (in bullet points) including technical details and

innovations

ii. Publications i. Journals (a. International, b. National), ii. Papers

presented in Conferences iii. Other publications

10 Scope of future work 83

11 Bibliography 84-89

1

1. Authorization

The work entitled ― Functional Diversity of Biyyam backwaters: relating the

biodiversity to the varying environmental and climatic changes, by, Dr. Sini Anoop was

carried out under the Kerala State Council for Science Technology and Environment,

Women Scientist Division, Back to lab programme for Women Scientists, Govt. of Kerala at

MES Ponnani College, Ponnani. The project work was carried out under the mentorship of

Dr. Razia Beevi, Associate Professor in Postgraduate Department and Research Center in

Aquaculture and Fishery Microbiology MES Ponnani College Ponnani, 679586. The project

was initiated wide sanction No: 879/2016/KSCSTE dated 21/12/2016, with commencement

date as 08/03/2017 and completion date as 07/03 /2020. The project was completed with

financial expenditure of Rs. 21,53,983/-.

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2. Acknowledgement

I take this as a great opportunity to express my gratitude to all those who helped me to

complete this work. The financial support received from KSCSTE was key to complete the

project in time.

I wish to express my sincere gratitude to my guide Dr. M. Razia Beevi, Associate

Professor and Head of the Department of Aquaculture and Fishery microbiology, MES

Ponnani College, for her support, and encouragement throughout my project.

I am very much grateful to Dr. Lekha, Head, Women Scientist Division, KSCSTE,

Thiruvananthapuram for all her care and support.

My sincere thanks to Dr. A. Biju Kumar, University of Kerala, for his timely

suggestions, mentoring and reviews which helped me immensely with my work. I would also

like to thank all the panel members (progress review committee members) for their guidance

throughout.

My thanks to Dr. T.P. Abbas (Former Principal, MES Ponnani College) and Capt.

M.N. Mohamed Koya, Principal, MES Ponnani College for their help and support. I would

also like to thank all the faculty members, non-teaching staff of Dept. of Aquaculture and

Fishery microbiology, MES Ponnani College for their help and guidance. My thanks to my

friends, office staff and other faculty members of MES Ponnani college for the help rendered

by them.

My gratefulness to my family for the help and support I received throughout my

work.

There are no words to thank God almighty for his countless blessings in my life.

3. Abstract

Biyyam backwater is a developing eco-tourism hub and subjected to increased pollution due

to urbanization and agricultural waste disposal. In view of its ecological and economic

importance, the present study investigated the spatiotemporal patterns in the environmental

variables, heavy metal (Mn, Co, Zn, Ni, Cr, Pb, Sc, Cd and, As) and biological component of

the area. To the best of our knowledge, this is the first detailed study that has used an

integrated approach that considers the various component of the ecosystem (environment,

microbes to fish). Moreover, only a few studies have attempted to assess the relation between

the functional diversity and environmental conditions in coastal system from India.

Sampling was conducted during June 2017-Dec 2018 for the biological components

and June 2017-May 2018 for environmental variables. Spatiotemporal variability observed in

the environmental variables was influenced by runoff during the monsoon and the natural

estuarine gradient. Heavy metals distribution showed enrichment and accumulation in the

upstream sediments. The relatively high Igeo factor for all metals indicates that agricultural

and urban waste disposal were the major sources of contamination in the area.

3

High coliform load and total heterotrophic bacteria (THB) observed during monsoons

was due to the land runoff during this period. Phytoplankton community was represented by

67 taxa. Phytoplankton community pattern was influenced by various environmental

variables. Zooplankton community were represented by 14 groups and dominated by the

calonoid copepods. Macrofauna was represented by 16 taxa dominated by Polychaeta. In

general, the functional diversity indices values were low and showed spatiotemporal

variability.

The finfish diversity was represented by 48 species belonging to 14 orders, 31

families and 41 genera. Functional diversity of finfish indicates higher diversity in the

upstream locations.

High bacterial load and heavy metal contamination in this ecologically and

economically important system indicates that the region is affected by multiple

anthropogenic activities. The modeling studies confirmed that the Biyyam backwater system

was affected by anthropogenic disturbances. The results and the inferences provided as part

of this study can serve as a model for monitoring and assessment of other tropical estuaries

which exhibit similar features all along the Kerala coast.

4. Introduction and review of literature

A unique ecosystem of the state of Kerala is the large number of perennial or temporary

estuaries popularly known as “backwaters”. The term backwater is of local origin and refers

to a system of shallow, brackish water lagoons and swamps. Thirty backwaters occur in

Kerala covering an area 2, 42, 00 ha. (Grace, 2014). Out of the 30 backwaters in the Kerala

coast, seven are characteristically river mouth estuaries (Bijoy Nandan, 2004). Backwaters

are well known for their high productivity, high carrying capacity supporting the diverse and

abundant fish and invertebrate species (Grace, 2014; Vimal Raj et al. 2014; Ansar et al.

2017; Kiranya et al. 2018). Apart from the resident species, a variety of migratory fishes and

birds reside in this ecosystem sustaining the economy of local population (Grace, 2014).

Microbes such as Bacteria, Archaea, fungi, viruses and protists dominate the living

biomass on Earth. The metabolic processes of microbes is essential for transformation of

elements, degradation of organic matter, and recycling of nutrients hence, they play a central

role in innumerable activities that is crucial to the functioning of the biosphere. Thus,

understandings of the microbial community are critical for our understanding of any system,

for its sustainable use and predict the changes in the ecosystem due to natural variation and

anthropogenic disturbances.

Like any other aquatic system, phytoplankton forms the base of the food chain in the

backwater system and also plays an important role in the biogeochemistry. This energy is

transferred to higher organisms through food chain. Apart from the primary production, they

play an important role in the climate change by regulating the atmospheric level of CO2. The

species composition, abundance and diversity are regulated by environmental factors such as

physicochemical properties of water, meteorological characteristics of the region and

morphometric and hydrographic features of the water body (Dahl and Wilson 2000). Since

phytoplankton is extremely sensitive to the environmental changes they can be used as a

reliable tool to assess the health status of aquatic bodies (Mathivanan et al., 2008).

Zooplanktons are the primary consumers or secondary producers hence, the vital link

in the trophic tier of aquatic environment. In addition, the abundance and availability of

plankton has direct influence on the fish population of any aquatic system. Therefore study

of zooplankton community reveals not only the productivity of a system but also the

ecological status.

The organisms that inhabit the sediments are known as “benthos” and constitute the

largest faunal assemblages on Earth. Benthos play a significant role in key ecosystem

processes such as food for higher organism including demersal fish, nutrient recycling, and

https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/biogeochemistry

4

sediment oxygenation (Snelgrove 1999; Meysman et al. 2006). Because of the longer life

cycle and relatively low mobility they are considered as efficient bioindicator. Hence, in the

recent years benthic invertebrates are used in environmental monitoring and assessment of

aquatic systems (Sivadas et al. 2016).

Fishes constitute a key component of biodiversity in the backwaters since they have a

large range of morphologies, life-history traits, behaviors, and diets. Therefore, the fish

communities are central in controlling fluxes of matter and energy within aquatic systems.

Since fisheries are an essential source of protein for billions of people, contaminated aquatic

system can affect the fisheries quality and quantity.

Like most aquatic systems, the backwater systems are overexploited for the abundant

resources and severely impacted by various human activities. Moreover, biodiversity loss

due to habitat degradation and overharvesting will be further exacerbated by human induced

climate change that will have social and economic consequences. Therefore, the gradual

deterioration of the backwater system has demanded for a comprehensive and

comprehensible ecological assessment from societal, economic and political heads. If we do

not manage the anthropogenic activities from a holistic environmental point of view, we

stand to lose out in the future.

Most biodiversity programme use a single ecosystem component approach to assess

the ecological status of a system, for example, the use of species abundance and

composition. However, these biological variables are unable to reflect the interconnections

between the various ecosystem components and its role in the ecosystem process. The

functional approach in biodiversity study can provide information about species functional

role in the ecosystem processes and its vulnerability to human disturbance and/or climatic

change. Although the functional diversity concept has been widely used in freshwater and

terrestrial ecosystems, it has not been explored effectively in the marine system (Bremner

2008). Therefore, the use of functional traits may provide additional information on the

response of the community to environmental gradient and also facilitate better comparison

with other geographical regions (Bremner 2008).

Literature Review

In contrast to the well-studied coastal system, fundamental questions regarding the

distribution and diversity patterns of species from the backwaters are largely unanswered.

Number of studies has reported the deteriorating water quality of the various aquatic system

of Kerala. For e.g. the Cochin backwaters system is one of the largest tropical estuaries of

India and is facing massive pollution due to the release of untreated effluents from the

industries and domestic sectors (Martin et al., 2008; Madhu et al. 2010). Menon et al. (2000)

documented the presence of more than 240 industrial units operating in Eloor, Kalamasery

industrial belt with an average of 2.6 million liters of untreated effluents released into the

adjoining backwaters per day. Similarly, the anthropogenic disturbances in the largest lake of

Kerala, the Vembanad Kayal, have led to the decline of flora and fauna (Sashikumar and

Jayarajan, 2007; Duncan, 2009).

Biodiversity of the Kerala backwaters were studied by various authors (Grace 2014;

Vimal Raj et al. 2014; Ansar et al. 2017; Kiranya et al. 2018). Ali Akshad et al. (2019)

assessed the abundance and diversity of phytoplankton of Kadalundi estuary, while Santhosh

et al., (2009) studied the impact of sea sand filling on the phytoplankton community of

Paravur-Kappil backwaters. Walmiki et al. (2016) studied the plankton and benthic diversity

of Vembanad Lake. However, compared to the other backwater and estuarine system of

Kerala, there are very few biological studies from the Biyyam backwaters (Razia Beevi et.al,

2009).

5

Most of these studies in the estuarine and backwater system are restricted to species

composition and diversity, limiting the ability to understand the role of the species in the

ecosystem functioning. In fact, there are scant studies using the functional approach from the

Indian aquatic system (Vijapure et al. 2019; Sivadas et al. 2020) despite its applicability in

understanding the ecosystem functioning

In view of the importance of the Biyyam backwaters for tourism enhancement

(mainly water sport activities) and the non-availability of detailed ecological studies, the

present study was conducted. In addition, the kayal is under threat from the effluents flowing

from the adjacent agriculture fields. To our knowledge, this is the first attempt to study the

plankton, benthic, fish and microbial community and the environmental variables structuring

the biodiversity pattern of the Biyyam Kayal backwaters. In the last few decades it is stressed

for an integrated approach to managing the world‟s resources that consider the entire

ecosystem, from microbes to vertebrates. Also for the first time the functional approach is

used to understand the biodiversity-environmental relationship in a backwater system using

macrobenthos and Ichthyofauna.

5. Objectives of the study

The main objective in this project was to study the complete ecological status of Biyyam

backwater relating to its biodiversity and come up with inferences and recommendations

which will help the monitoring and assessment of similar backwaters in Kerala coast.

The proposed study will help in fulfilling some of the identified gaps of the biodiversity-

ecosystem functioning of a tropical estuarine environment. With these goals in mind, the

objectives of the present study are:

1. To develop a benchmark data on biodiversity of the lake, with special reference to

plankton, benthos and nekton

2. To analyse the water quality of the lake water downstream and upstream areas of the

barrage with respect to space and time

3. To correlate the relation between biodiversity and water quality using appropriate

statistical software

4. Develop an ecological model for trophic interactions

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6. Materials and Methods

Study Area

The Malappuram district of Kerala have large number of backwaters and among them

Biyyam Kayal is the biggest with an area of 15 sq.km. Biyyam backwater popularly known

as Biyyam Kayal (1045‟ - 10

48‟ N and 75

56‟ - 75

58‟ E), is situated proximal to Ponnani

township and the total basin area includes parts of Malappuram, Palakkad and Thrissur

districts (Fig. 1). This water body lies almost parallel to the Arabian Sea and makes the

southern boundary of Ponnani municipality. Kanjiramukkupuzha originates from this kayal

and joins the Arabian Sea at Puduponnani through the Velayangod Azhi. At the northern side

lies Bharathapuzha, the second largest river of Kerala and it gets connected to the backwater

during monsoon season. The backwater is also connected with Canoli-Canal, the artificial

waterway. The whole backwater area is interspersed with abundant kole lands (paddy fields)

where, Puncha (seasonal cultivation of paddy) cultivation is done regularly during summer

(Razia Beevi et.al, 2009).

Fig.1: Map of the study area (a) sampling location for biological and environmental

variables; (b) stations for heavy metals study

Field Sampling and Laboratory Analysis

Sampling was carried out seven stations (Fig.1a) for physicochemical parameters, heavy

metals and biological components.

Physicochemical Parameters

Sampling for the physicochemical parameters was conducted from June 2017 to May 2018 at

all the seven stations. Temperature and pH were recorded in situ. Water samples for the

analysis of salinity and nutrients were collected in pre-cleaned polythene bottles. Salinity

was measured using the Salinometer. Nutrients (nitrite, nitrate, phosphate, ammonia and

silicate) were analysed following standard procedure (Grasshoff, 1983). Water samples for

dissolved oxygen (DO) were collected in 250 ml stoppered glass bottles without trapping air

bubbles and fixed immediately with manganous chloride (Winkler A) followed by alkaline

potassium iodide (Winkler B) solution. In the laboratory, DO was analysed following

standard procedure (Grasshoff, 1983). For estimation of chlorophyll a (chl-a) 1 liter water

samples were filtered through Whatman GF/F filter papers (0.7 µm pores size) and chl-a

was extracted using 90% acetone and measured spectrophotometrically (Strickland and

Parsons 1972).

7

Sediment texture was analysed using the formulae of Folk and Ward (1957) and Chatterjee

et al., (2007). Sediment organic matter (OM) and inorganic matter (IO) was estimated by

the loss-on-ignition method (Wang et al. 2011).

Heavy Metals

Sediment samples were collected using Van Veen grab (250 cm2) from four stations during

three seasons - monsoon (December 2017), post-monsoon (March 2018) and pre-monsoon

(July 2018). Stns. 1 and 2 are situated in the upstream, while Stns. 3 and 4 towards the

seaward side (Fig. 1b). Geochemical analyses of the sediments were carried out on 12

sediment samples. A known weight of the powdered sediments were transferred into Teflon

beakers and 10 ml of the acid mixture (7: 3: 1 of HF, HNO3 and HClO4, respectively) was

added to each beaker, kept overnight and then dried on a hot plate at 180 to 190°C. The

procedure was repeated until the samples in the beakers were completely digested. The final

digested sample was made up to 50 ml volume with 2 % HNO3 and elemental concentrations

on Biyyam backwater sediment samples were measured using an Inductively Coupled

Plasma-Mass Spectrometry (ICP-MS, Agilent 7700 series) at Birbal Sahni Institute of

Palaeosciences (BSIP), Lucknow. A similar digestion procedure was followed for reference

standards (IAEA (SL1 and SL3) and blank (without sediment). Reference standards analyzed

along with the samples, and the precision of the analyses was better than 5%.

Geoaccumulation index (Igeo)

To estimate the enrichment of metal concentrations, Igeo was calculated as proposed by

Müller (1969). The method assesses the degree of metal pollution in terms of different

enrichment classes based on the increasing numerical values of the index. The following

equation defines the value of Igeo:

Igeo = log2(Cn/1.5Bn)

where Cn is the measured concentration of the metal in the samples, and the Bn is the

geochemical background concentration of Upper Continental Crust (Taylor and McLennan,

1995; McLennan 2001). Factor 1.5 is introduced to minimize the effect of lithologic

variations in the sediments. Different descriptive categories ranging from uncontaminated to

extremely contaminated based on the Igeo values are:- Igeo ≤ 0 is uncontaminated, 0<Igeo<1 is

uncontaminated to moderately contaminated, 1<Igeo>2 is moderately contaminated, 2<Igeo>3

is moderately to strongly contaminated, 3<Igeo>4 is strongly contaminated, 4<Igeo>5 is

strongly to extremely contaminated, and 5 < Igeo is extremely contaminated (Müller 1969).

Modified degree of contamination (mCd)

The mCd is a measure of the degree of overall contamination of surface layers in a sampled

site. Abrahim (2005) presented a modified and generalized form of the Hakanson (1980)

equation for the calculation of the overall degree of contamination at a given sampling site

given below:

∑

Where N is the number of metal analyzed, i is the metal (or pollutant) and CF is the

contamination factor.

The CF was calculated using the equation below:

CF = CmSample/CmBackground

where CmSample and CmBackground are the concentration of the measured metal and reference

values, respectively. The estuarine sediments are classified in quality as: mCd < 1.5 (Nil to

very low degree of contamination), 1.5≤ mCd < 2 (Low degree of contamination), 2 ≤ mCd

< 4 (Moderate degree of contamination), 4 ≤ mCd < 8 (High degree of contamination), 8 ≤

mCd < 16 (Very high degree of contamination), 16 ≤ mCd < 32 (Extremely high degree of

contamination), mCd ≥ 32 (Ultra high degree of contamination).

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Microbiological Parameters

Water and sediment samples were collected in sterile vials for bacteriological analysis.

Water and sediment samples were analyzed for coliform load and were enumerated by three

tube Most Probable Number (MPN) technique using MacConkey broth (Speck, 1976).

Samples were analyzed for heterotrophic bacterial load by pour plate method using Nutrient

agar (APHA, 1998). The colonies were isolated and identified up to genus level by following

the standard scheme of Buchanan and Gibbons (1974).

Antibiotic sensitivity studies

Antibiotic resistance of bacteria was determined for sediment samples (post-monsoon)

following the disc diffusion method (Bauer et.al., 1966). The isolates were tested with 10

different antibiotics each for gram negative and gram positive bacteria. The isolates were

scored as resistant, intermediate and sensitive according to the inhibition zone around the

disc.

Multiple antibiotic resistance index

The MAR index was calculated as the ratio of the number of antibiotics to which the isolate

displayed resistance to the number of antibiotics to which the isolate had been evaluated for

susceptibility (Krumperman 1983).

Phytoplankton

Phytoplankton samples were collected at seven stations from June 2017 to December 2018.

One liter water sample collected from the surface and was fixed with 4% formalin-Lugol‟s

iodine solution. The samples were allowed to settle for 48 hour and later back filtered with a

10µ mesh and was made up to 50 ml. 1ml of this sample was drawn and placed into the

Sedgewick rafter counting chamber (Welch, 1952). The phytoplankton samples were

identified upto the lowest possible taxa using standard literatures. (Desikachary 1987; Anand

1998).

Zooplankton

Zooplankton samples were collected using zooplankton net from seven stations during three

seasons - monsoon (January 2017), post-monsoon (February 2018) and pre-monsoon

(September 2018). The zooplankton net was towed horizontally and samples collected were

preserved in 5% formalin solution. The initial and final flow meter reading was noted.

Zooplankton was counted and identified to group level under microscope. Biomass was

estimated using the volume displacement method.

Macrobenthos

Macrofauna was collected using Van Veen grab sampler (250 cm2). The sediment samples

were sieved using 0.5 mm sieve and material retained on the sieve was preserved in 5%

formalin Rose-Bengal solution. In the laboratory, the organisms were sieved again and

sorted. Macrofauna were counted and identified upto lowest possible taxa using standard

identification keys (Fauvel 1953 and other published works). We included four functional

traits with 17 modalities in the analysis: bioturbation, mobility, size and feeding type for the

functional diversity indices.

Ichthyofauna

The ichthyofauna data of Biyyam Kayal was collected using different methods. Fish was

sampled during several ecological and artisanal fisheries studies. In addition, surveys were

carried out in collaboration with local fishermen. Fish samples were also collected from

traditional fish aggregating devices (FAD) like kuruthi and thuruth. Kuruthi and thuruth are

local FAD (Plate 1), which are used mainly during the post-monsoon and pre-monsoon

seasons when the water depth decreases and fishing nets cannot be operated. Species were

scientifically classified following Nelson (2006).

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Genera and species were arranged alphabetically; scientific names and authorities

were confirmed following Eschmeyer et al. (2016). Vernacular names of the fish (Biju

Kumar and Raghavan, 2015; Razia Beevi et.al. 2009) and IUCN status have also been

provided. Each species was photographed and deposited in the Aquatic Biodiversity

Museum, PG & Research Department of Aquaculture & Fishery Microbiology, MES

Ponnani College. Feeding type and size were the functional traits used for the functional

diversity analyses.

Plate 1: Fish aggregating devices Kuruthi and Thuruth

Data Processing and Statistical Analysis

The data in the present study for phytoplankton and macrofauna are divided based on rainfall

data into onset of monsoon (OM- June), Peak Monsoon (PM-July, August), Post-monsoon

(September and October), Dry season (DS-November to April), Pre-monsoon (May). The DS

for 2017-2018 consist of months from November 2017 to April 2018, whereas DS for 2018

consist of November and December 2018.

Principal Components Analysis (PCA) is a procedure that allows exploring the data

structure, the relationship between objects, the relationship between objects and variables

and overall correlation of the variables. In general, PCA reduces the number of variables

(environmental variables) in the dataset through a projection of objects onto a smaller

number of new orthogonal variables, so-called principal components (PCs) / Factors (Word

et al. 1987). In the present study, the PCA used to designate the main factors (environmental

variables) that contributed to the variability in the study area. The Kaiser‟s criterion (Kaiser

1960) was used to determine the total number of axis to be retained. Only eigenvalues > 1 is

considered. Pearson‟s correlation was carried to find the relationship between water and

environmental variables.

To test for significant differences in the community patterns, we used the distance-

based permutation multivariate analysis of variance (PERMANOVA) (Anderson 2001;

McArdle and Anderson 2001). The design included stations and seasons as factors and, the

analysis were based on Bray-Curtis similarity of log transformed data, using 9999

permutations. The tests were carried out using the permutation of residuals under a reduced

model.

To investigate the effect of environmental variables on the phytoplankton

community, the DISTLM (Distance based Linear Model) forward routine was used

(McArdle and Anderson, 2001). The analysis was based on the Bray-Curtis similarity for

abundance and species diversity indices. Pearson‟s correlation was carried to find the

relationship of macrofaunal and environmental variables. The insufficient data point due to

absence of macrofauna in many of the upstream locations during most of the year did not

allow the DISTLM analyses to be used for the benthic data.

The classical univariate measures such as species number (S), Margalef‟s species

richness (d), Pielou's species evenness (J), Shannon species diversity (H′ log2) and

Simpson's Evenness Index (1-λ′) were calculated. The spatial pattern of phytoplankton and

macrofaunal community was analysed using the cluster and non-metric multidimensional

scaling (nMDS) ordination on the log (x+1) transformed abundance data and the Bray–Curtis

10

similarity measure. The Similarity of Percentage (SIMPER) analysis was used to determine

the taxa that structured the grouping and contributed to similarity.

The multivariate functional indices such as Rao‟s Entropy (Rao Q), Functional

Richness (FRic), Functional Evenness (FEve) and Functional Dispersal (FD) were also

calculated. CWM and functional diversity indices were calculated using the R-based

software package FDiversity (Casanoves et al. 2011). To characterize trait distributions in

fish communities Community-Weighted Mean (CWM) was calculated after fuzzy coding of

the traits (Chevnet et al. 1994). The statistical package, PRIMER 6, STATISTICA 10 and

FDiversity (Casanoves et al. 2011) was used for the analysis.

7. Results and Discussion

Spatiotemporal variability of water parameters Depth

The water depth in the study area ranged between 4.5 m to 0.5 m in the freshwater sector

(stn. 1- 4) and 4.5 to 3.5 m in downstream locations (Fig. 2). PERMANOVA detected

significant seasonal variation in the depth (Table 1).

Fig. 2: Depth

variation in the (a)

up stream and (b)

downstream,

Biyyam Kayal

Table 1: PERMANOVA results for water parameters

Source Df SS MS Pseudo-F P(perm)

Water Parameters

Depth Season (S) 4 15.74 3.94 7.23 0.001

Station (St) 6 5.28 0.88 1.62 0.16

S x St 23 5.16 0.22 0.41 0.97

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Temperature Season 4 61.95 15.49 12.26 0.001

Station 6 3.84 0.64 0.51 0.74

S x St 23 7.54 0.33 0.26 0.99

Salinity Season 4 2227.8 556.96 18.53 0.001

Station 6 2564 427.33 14.22 0.001

S x St 23 2903.5 126.24 4.19 0.001

pH Season 4 1.89 0.47 2.38 0.05

Station 6 1.51 0.25 1.26 0.29

S x St 23 3.59 0.16 0.78 0.75

DO Season 4 8.33 2.08 1.43 0.23

Station 6 2.93 0.49 0.33 0.91

S x St 23 19.39 0.84 0.57 0.90

NO3 Season 4 12.73 3.18 3.18 0.019

Station 6 1.82 0.30 0.30 0.92

S x St 23 11.44 0.49 0.49 0.93

NO2 Season 4 12.73 3.18 3.18 0.019

Station 6 1.82 0.30 0.30 0.91

S x St 23 11.44 0.49 0.49 0.93

SiO2 Season 4 1247 311.74 1.10 0.36

Station 6 6245.7 1040.9 3.68 0.002

S x St 23 34795 1512.8 5.35 0.001

PO4 Season 4 0.99 0.25 5.74 0.002

Station 6 3.41 0.57 13.19 0.001

S x St 23 7.92 0.34 7.99 0.002

Chl-a Season 4 1171.9 292.98 15.12 0.001

Station 6 49.63 8.27 0.43 0.79

S x St 23 286.14 12.44 0.64 0.77

Temperature

The variation in temperature in the study area was typical of tropical estuaries with highest

values recorded during the pre-monsoon period (29- 30C; Fig. 3). Low values were

recorded during monsoon (26- 29C) and during Dec-Jan (26 -28.8C). PERMANOVA

showed a significant variation in temperature among seasons (p<0.05, Table 1). High solar

radiation during dry period, low rainfall and relatively stagnant condition during these

months accounted for the high temperature (Meera and Nandan 2010).

Salinity

The upstream stations (Stns. 1-4) showed freshwater conditions throughout the year (salinity

0). The downstream stations, Stns. 5 to 7 had low to zero salinity during monsoon (June to

August) and during rest of the seasons the salinity is high (20-35; Fig. 4). The seasonal

variation in the downstream stations is mainly due to the opening of the regulator of the

Biyyam bridge to avoid flooding of kole farming area which lies in the proximity of

upstream station (Stns.1 to 4). The closure of the regulator after monsoon, the salinity

gradually increased reaching a value of 35 from January onwards. PERMANOVA confirmed

that the salinity in the study area varied among the stations and seasons (Table 1).

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Fig. 3: Spatiotemporal variation in water temperature (a) up stream and (b)

downstream, Biyyam Kayal

Fig. 4: Spatiotemporal variation of water salinity in the downstream stations, Biyyam

Kayal

pH

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pH in the present study ranged between 6.2 to 8.15, with highest value in January at stn. 7

and lowest value in November recorded at stn. 3 (Fig.5). The pH is a significant index in

water quality assessment and indicates acidic and basic nature of the aquatic system. The pH

showed a significant

seasonal variation (Table

1). In general, the pH

showed decrease from

marine to freshwater zone,

a trend that was also

observed in the Ashtamudi

estuary (Nair et. al.,

1983).

Fig. 5: Spatiotemporal

variation in water pH (a)

up stream and (b)

downstream, Biyyam

Kayal

Dissolved Oxygen (DO)

The DO in the study area

was generally low

throughout the study

period (Fig. 6). The values

ranged from 1 to 5 mg/L,

except for the highest

value recorded at Stn. 6

during March 2018 (7.5

mg/L). The DO values did not show significant variation amongst the stations and season

(Table 1). The temperature of the water, the partial pressure of the gas in the atmosphere in

contact with water, oxygen demand of the system, hydrodynamic features, salinity and

biological activities are the important factors affecting the concentration of influences the

DO in aquatic system (Lunardini and Cola 2000). High temperature, reduced freshwater

input, stagnation, changes in the phytoplankton community and nutrients in the water could

be some of the reasons for the comparatively lowers values (< 4 mg/L) at most stations

observed during non-monsoon period (Mandal et al. 2012).

Nutrients

The NO2 concentration in the upstream ranged from 0.004–0.24 mol L-1

whereas the

values in the downstream ranged from 0.015–0.25 mol L-1

(Fig. 7). The concentration of

NO3 in the surface water of Biyyam Kayal ranged from 0.06–4.68 mol L-1

0.01– 2.97

mol L-1

in the upstream and downstream stations, respectively (Fig. 7). PERMANOVA

showed a significant variation between seasons for NO3 and NO2 with the highest values

recorded from December to February (Table 1).

14

Fig. 6: Spatiotemporal variations in water DO (a) up stream and (b) downstream,

Biyyam Kayal

Fig. 7: Spatiotemporal variation in water nitrite (NO2) and nitrate (NO3), Biyyam

Kayal

15

Phosphate concentration was higher in the downstream (0.024–1.16 mol L-1

) compared to

the upstream (0.01-0.38 mol L-1

), except in June 2017 when high values were recorded

(2.99 mol L-1

; Fig. 8). The PERMANOVA detected significant variation between station,

season and their interactive effect (Table 1). Phosphate showed high concentration during

monsoon and again increased from November to April. Silicate showed a reverse trend to

that of phosphate with higher values in upstream (0.64–103.74 mol L-1

; Fig. 8) and in the

downstream values ranged from 1.77– 94.68 mol L-1

. Silicate showed significant spatial

variation and season x station effect (PERMANOVA; Table 1). Further, in the upstream

SiO2 showed an increase trend from June 2017 to May 2018 (Fig. 8). On the hand highest

values of SiO2 were observed during the monsoon period (June to September).

Fig. 8: Spatiotemporal variation in water phosphate and silicate, Biyyam Kayal

Chlorophyll -a

Chlorophyll-a (Chl-a) in the upstream ranged from 1.42–26.57 mg/m3 while in the

downstream the values were 1.27–28.30 mg/m3. High chlorophyll values were observed

during April-May in (Fig. 9). PERMANOVA showed significant variation in chl-a between

seasons (Table 1). The high Chl-a value in the study areas during this period could be due to

the high phytoplankton productivity, as reported by Redekar and Wagh (2000). The

reduction in Chl-a during the onset of monsoon (June), peak-monsoon (July-August) and

post-monsoon (Sep-Oct) season may be due to the dilution of sea water with freshwater

discharged from the rivers causing turbidity and lower light availability.

16

Fig. 9: Spatiotemporal variation in water Chl-a, Biyyam Kayal

Clustering and nMDS based on water parameters

The nMDS plot (Fig. 10a) showed a clear spatiotemporal variability in the study area based

on the environmental parameters. The station grouping shows that the upstream stations

(Stns. 1-4) separated from the downstream stations (Stns. 5-7). Further, within each group,

the locations showed a temporal variability. The Euclidean based cluster analysis (Fig 10 b)

indicates that the rainfall associated changes and the natural estuarine gradient were the main

factors influencing the environmental condition in the study area. The single cluster with a

distance of 6, comprised of nutrients (except silicate), pH, DO, Chl-a, depth and rainfall.

Silicate remained an outlier. Pearsons‟s correlation confirmed the result of cluster analysis

(Table 2). All the environmental parameters showed significant correlation among

themselves (p<0.0005), except silicate.

17

Fig. 10: (a) nMDS grouping of the stations and (b) clustering of parameters based on

Euclidean distance of normalized data.

18

Table 2: Pearson’s Correlation between water parameters. Values in bold are significant (p<0.0005)

Rainfall salinity Temp pH DO Depth NO3 NO2 PO4 SiO2 Chl-a

Rainfall 1

p= ---

Salinity 0.84 1

p=0.00 p= ---

Temp 0.84 0.66 1

p=0.00 p=.000 p= ---

pH 0.98 0.91 0.81 1

p=0.00 p=0.00 p=0.00 p= ---

DO 0.97 0.92 0.74 0.99 1

p=0.00 p=0.00 p=.000 p=0.00 p= ---

Depth 0.97 0.92 0.75 1.00 1.00 1

p=0.00 p=0.00 p=.000 p=0.00 p=0.00 p= ---

NO3 0.95 0.93 0.71 0.99 1.00 1.00 1

p=0.00 p=0.00 p=.000 p=0.00 p=0.00 p=0.00 p= ---

NO2 0.95 0.92 0.70 0.99 1.00 1.00 1.00 1

p=0.00 p=0.00 p=.000 p=0.00 p=0.00 p=0.00 p=0.00 p= ---

PO4 0.95 0.92 0.71 0.99 1.00 1.00 1.00 1 1

p=0.00 p=0.00 p=.000 p=0.00 p=0.00 p=0.00 p=0.00 p=0.00 p= ---

SiO2 0.41 0.08 0.77 0.33 0.24 0.25 0.20 0.19 0.19 1

p=0.000 p=0.458 p=.000 p=.002 p=.027 p=.023 p=.069 p=.082 p=.082 p= ---

Chl-a 0.96 0.94 0.74 0.99 1.00 1.00 1.00 0.99 1.00 0.22 1

p=0.00 p=0.00 p=.000 p=0.00 p=0.00 p=0.00 p=0.00 p=0.00 p=0.00 p=.043 p= ---

19

The water salinity was strongly influenced by seasonality and the natural estuarine gradient.

During the dry season decreasing freshwater entering the backwaters significantly increased

the salinity of the stations in the downstream, while the upstream showed freshwater

condition through the year. Dissolved oxygen was also influenced as the rainfall events

renews and possibly diluting the effluents and thus, increasing the oxygen in the water

(Delpla and Rodriguez, 2016). The levels of dissolved oxygen recommended for aquatic

conservation are > 4–5 mgL−1 (e.g. Osode and Okoh, 2009) however; in the study most of

the DO values were > 4 mgL−1 (Fig. 6) which may have resulted from anthropic interference.

Although pH did not show a significant variation low values (6- 6.8) were recorded at the

upstream station during the dry season. This is typical of aquatic system that transports large

quantities of organic material (humic material) resulting in acidic nature (Reid 1961).

Rainfall was also a significant factor in the high concentration of all the nutrients

studied, except silicate. The nutrients values were higher in the upstream during the monsoon

period indicating the influence of runoff during the monsoon period. The primary production,

depth, hydrodynamic of the system, tidal flux, vertical mixing, decomposition of the aquatic

vegetation and, runoff (land, agriculture) are some of the factors influencing the nutrient

variability (Jayachandran et al. 2012).

Spatiotemporal

variability of

sediment parameters Sediment texture

The sediment texture in the

upstream stations (Stns. 1 -

4) was dominated by finer

fraction, while the

downstream locations

(Stns. 5-7) had higher sand

content (Fig. 11).

PERMANOVA analysis

showed that sand and silt

and clay content showed

significant variation among

the stations (Table 3).

Fig. 11: Spatiotemporal

variation in sand and

mud (silt & clay)

composition, Biyyam

Kayal

20

Organic and inorganic matter

In the present study, high organic matter was obtained in upstream station (5.30–31.62 %)

compared to the downstream stations (1.35–12.31 %; Fig. 12). The upstream stations harbor

high densities of macrophytes which is the main reason for the increased organic matter

content in that area. PERMANOVA showed a significant variation between stations for

organic matter (Table 3). The inorganic matter was also higher in the upstream stations with

values ranging from 0.74–5.71 % compared to 0.35–3.51 % observed in the downstream

(Fig. 12). PERMANOVA detected significant spatial variation in only sediment organic

matter (Table 3).

Fig. 12: Spatiotemporal variation in sediment organic matter and inorganic matter,

Biyyam Kayal

Table 3: PERMANOVA results for sediment parameters

Source Df SS MS Pseudo-F P(perm)

Sand Season 4 197.76 49.44 0.42 0.71

Station 6 1.08E+05 18062 153.66 0.001

S x St 23 930.27 40.447 0.34 0.84

Silt-Clay Season 4 197.76 49.44 0.42 0.73

Station 6 1.08E+05 18062 153.66 0.001

S x St 23 930.27 40.45 0.34 0.85

Organic Matter Season 4 159.66 39.91 2.75 0.055

Station 6 3944.3 657.38 45.33 0.001

21

S x St 23 271.26 11.79 0.81 0.65

Inorganic matter Season 4 8238 2059.5 1.48 0.29

Station 6 9517.3 1586.2 1.141 0.34

S x St 23 50617 2200.8 1.58 0.27

As seen from Table 4, the organic matter and inorganic matter was strongly correlated

with rainfall, nutrients, Chl-a and fine mud. A high production of both submerged and

emergent macrophytes is characteristic for most littorals, and in addition to phytoplankton,

other algal sources like epiphytes and benthic algae are present. Also, terrestrial material can

be transported to the littoral by run-off or winds (Wetzel 2004). Information on the relative

importance of these carbon sources is important to our understanding of the structure and

functioning of wetlands and of littoral regions in particular.

Table 4: Pearson’s correlation between sediment and water parameter. Bold

values significant at p<0.0005.

Sand Silt+Clay Organic Matter Inorganic matter

Rainfall -0.09 0.67 0.95 0.92

Salinity -0.23 0.56 0.88 0.87

Temp 0.32 0.71 0.77 0.69

pH -0.19 0.70 0.98 0.95

DO -0.27 0.67 0.97 0.95

Depth -0.26 0.68 0.97 0.95

NO3 -0.31 0.66 0.97 0.95

NO2 -0.31 0.65 0.97 0.95

PO4 -0.31 0.65 0.97 0.95

SiO2 0.63 0.43 0.29 0.21

Chl-a -0.26 0.66 0.97 0.95

Sand 1.00 -0.31 -0.31 -0.30

Silt+Clay -0.31 1.00 0.80 0.66

Org Matt -0.31 0.80 1.00 0.93

Inorg matt -0.30 0.66 0.93 1.00

Spatiotemporal variation in Heavy metals All the nine heavy metals (Mn, Co, Zn, Ni, Cr, Pb, Sc, Cd, As) had highest

concentrations at the Stns. 1 and 2 which was also dominate by fine sediment particles (Fig.

13). The PCA analysis was performed on the normalized heavy metal data to evaluate the

variability in the sediment and its interrelationship with sediment parameters (sediment

texture, organic and inorganic content (Fig.14 a). Based on the Kaiser criterion, the first two

PCA explained 88% of the variability of the heavy metals. PC1 with 62% of the variability

showed strong positive (>0.7) loadings for Cr, Mn and Zn whereas Sc, Co and Cd had the

highest negative loading on this axis. Inorganic matter and organic matter showed a strong

correlation with Cd, Co, Sc, As, while Pb showed with silt and clay. The first PC showed a

clear spatiotemporal trend and separated the upstream stations, Stn. 2 during all three seasons

22

and Stn1 during monsoon from the downstream Stns. 3 and 4 (Post and Pre-monsoon; Fig. 14

b).

The PC2 with 27% of total variance with concentration of Ni and Pb were the major

contributor to this component. The PC2 was responsible for separating Stns. 1 and 2 of pre-

monsoon and Stn. 1 of post-monsoon. PERMANOVA detected significant station differences

for Ni, Zn, Cr, (p<0.05) (Table 5), however, no significant differences between seasons were

observed for any of the metals.

Fig 13: Spatiotemporal variation in heavy metals in Biyyam backwater

Table 5: Results of PERMANOVA (only significantly high values

are given)

Parameter Df SS MS Pseudo-F P (perm)

Ni 3 9.8598 3.2866 18.247 0.004

Zn 3 7.387 2.4623 5.629 0.049

Cr 3 9.9127 3.3042 23.711 0.013

The Euclidean based cluster analysis (Fig 15 a and b) confirmed the results of PCA.

Two groups were formed for the heavy metal and stations which correspond completely to

the two PC identified in the PCA. The first cluster with < 2 distance comprised of Cd, Co,

Sc, As and sediment variables such as silt, clay, organic matter and inorganic fraction. The

second cluster composed for Ni and Zn (< 2 distance) (Fig. 15a). The station clustering

shows that the upstream stations (1 and 2) separated from the downstream stations, Stns. 3

and 4 (Fig. 15 b). Further, within each cluster, the locations showed a temporal variability.

23

Fig. 14: Principal Component

Analysis of heavy metals,

organic matter, inorganic

matter and sediment texture for

the study area. (a) loading of

sediment parameters and (b)

sites and season.

The correlation between heavy metal and sediment variables is represented in Table

6. A strong positive correlation between metals indicates that they may have common

sources of origin with mutual dependence and similar behavior during transport (Suresh et

al., 2011). Salinity and inorganic matter showed a strong correlation with most metals except

Pb and As. Similarly, a strong negative correlation was observed for sand with Ni and Pb,

while clay showed a positive correlation with As and Pb. Similarly, As, Cd, Co, Sc showed a

positive correlation with organic matter. The presence of heavy metals in the stations

dominated by clay-silt particles is due to the increase in specific surface properties of fine

fraction (Okeweye et al., 2016). Sand and silt fractions are composed mainly of the primary

mineral quartz (e.g., SiO2), which is a very weak adsorbent for heavy metals (Yao et al.,

2015). Also, the input of waste from agriculture from the kole farming site could also

contribute to the high heavy metals in the water near the kole farming site (Stns. 1 and 2).

Since, these paddy fields dominate a major section of backwater where seasonal paddy

cultivation is carried out mainly during the summer season (Razia Beevi et al., 2009). It is

also observed that Mn was high during monsoons even at Stns. 3 and 4 (Fig. 13), this could

24

be due to their transport

due to heavy water

flow, since different

hydrological regimes

(i.e., dry, rain, flooding,

inundation) can

influence metal

transport (Conrad et al.,

2020). There are several

finding which reported

groundwater discharge

following floodwater

subsidence can

constitute a

considerable portion of

metal loading to

downstream waterways

(Berka et al., 2001;

Santos et al., 2011).

Studies in coastal

catchments have shown

export of dissolved

heavy metal inputs to

the coastal ocean

(Duarte et al., 2017;

González-Ortegón et

al., 2019).

Fig. 15: Euclidean distance analysis of heavy metals, organic matter, inorganic matter

and sediment texture for the study area A) between sediment parameters B) between

sites and seasons

25

Table 6: Pearson’s correlation between sediment parameters. Bold values are significant at p <0 .05.

Sc Cr Mn Co Ni Zn As Cd Pb Salinity OM IO Sand Silt

Cla

y

Sc 1

Cr -0.63 1

Mn -0.71 0.66 1

Co 0.85 -0.6 -0.43 1

Ni -0.29 0.89 0.55 -0.23 1

Zn -0.64 0.95 0.61 -0.56 0.91 1

As 0.78 -0.36 -0.46 0.68 -0.06 -0.36 1

Cd 0.88 -0.91 -0.77 0.79 -0.69 -0.89 0.64 1

Pb 0.42 0.24 0.12 0.46 0.57 0.28 0.57 0.07 1

Salinity 0.68 -0.92 -0.8 0.6 -0.77 -0.83 0.38 0.89 -0.18 1

OM 0.66 -0.41 -0.47 0.67 -0.26 -0.53 0.7 0.62 0.13 0.41 1

IO 0.9 -0.89 -0.79 0.79 -0.66 -0.86 0.66 0.99 0.10 0.88 0.63 1

Sand -0.32 -0.39 -0.12 -0.42 -0.69 -0.37 -0.40 0.07 0.58 0.28 -0.40 0.04 1

Silt 0.13 0.13 0.006 0.35 0.18 0.00 0.14 0.01 0.11 -0.11 0.66 0.02 -0.53 1

Clay 0.46 0.13 -0.08 0.39 0.48 0.25 0.62 0.16 0.72 0.02 0.09 0.19 -0.62 -0.23 1

26

Ecological risk assessment parameters

Geoaccumulation index was assessed using the seven classes for increasing Igeo values proposed

by Müller (1969). The present study reveals uncontaminated to moderate contamination of

sediments by Mn, and moderate to strongly contamination by Zn and Co with Stn. 1 being

strongly to extremely contaminated (Table 7). Sediments at Stns. 3 and 4 were moderately to

strongly contaminated by Pb and Ni, however Stns. 1 and 2 were strongly to extremely

contaminate by Ni during all the 3 seasons. Stn. 3 was moderately contaminated by Sc during

pre-monsoon and post-monsoon, while the other stations showed moderate to strong

contamination. All the stations were strongly to extremely contaminated by Cd in all the three

seasons with stations 1 showing extremely contaminated (Igeo 7) during pre-monsoon. An

extreme contamination is observed by As in all the stations in all three seasons. The mCd values

lies between 2 – 3.8 suggesting moderate degree of contamination, however stations 1 had mCd

values of 6.8 (pre-monsoon) and 4.5 (monsoon) indicating high degree of contamination. This is

mainly due to the fertilizers and pesticides used for kole farming in this area and urban waste.

These indices suggest the present study area is exposed to high levels of contamination by these

heavy metals.

Accumulation of heavy metal in sediments poses an environmental threat, since these

toxic elements are taken up by benthic organisms which are further taken up by the fishes which

eventually lead to bioaccumulation. Several studies have shown that heavy metals in sediments

could significantly impact the health of the marine ecosystem (Marchand et al., 2006; Zhang et

al., 2012; Rahman and Ishiga, 2012). Results of the present study indicate that the Biyyam

backwater is heavily contaminated mainly due to the kole farming which may pose a major

health risk to the ecosystem and human health as fishery is an important food source for the local

population. Heavy metal enrichment of the downstream stations indicates that the Biyyam Kayal

backwater is an essential pathway for the transport of heavy metals to the adjacent coastal

system. As the present study provides evidence that the Biyyam backwater is moderately to

extremely polluted, continuous development without proper management will further increase the

metals concentration. Therefore, the authorities and stakeholder needs to take urgent preventive

measures to preserve, conserve and protect the ecologically sensitive and economically important

Biyyam backwater system.

Table 7: Geoaccumulation index and modified degree of contamination for

Biyyam backwater. PostM- postmonsoon; PreM-premonsoon; Mon-monsoon.

Igeo

Stations Mn I Co Zn Ni Cr Pb Sc Cd As mCd

Stn 1_PostM 2.04 4.24 2.93 4.77 4.92 3.49 3.3 4.07 6.52 3.69

Stn 2_PostM 1.29 3.22 2.64 4.1 4.85 3.01 2.92 3.69 6.73 3.49

Stn 3_PostM 1.06 2.06 1.6 2.37 3.32 2.43 1.63 2.44 6.13 2.04

Stn 4_PostM 1.11 2.57 2.39 3.6 4.24 2.77 2.3 3.74 6.1 2.46

Stn 1_PreM 2.09 3.76 2.79 4.59 4.67 3.29 3.07 7.23 6.88 6.78

Stn 2_PreM 0.79 3.23 3.05 4.77 5.04 3.33 3.37 4.27 6.71 3.85

Stn 3_PreM 0.94 2.5 2.46 2.62 3.7 2.39 1.74 4.48 5.98 2.35

Stn 4_PreM 1.38 2.91 2.4 3.03 3.87 2.77 2.04 4.95 6.22 2.88

Stn 1_Mon 1.7 3.89 3.1 4.88 4.99 3.44 3.34 4.91 6.87 4.47

27

Stn 2_Mon 1.93 3.11 2.76 4.28 4.94 3.22 3.31 4.45 6.62 3.67

Stn 3_Mon 1.64 2.98 2.6 3.31 4.19 2.72 2.33 5.29 6.39 3.33

Stn 4_Mon 1.7 2.64 2.21 3.18 4.01 3.03 2.12 4.41 6.35 2.86

Microbial Community Coliform and total heterotrophic bacteria

In the present study, highest coliform load and total heterotrophic bacteria (THB) was observed

during the monsoon season in most of the stations followed by post-monsoon and pre-monsoon

both in the water and sediments (Table 8 and 9). Higher counts during monsoon months may be

due to low salinity, less exposure to light and temperature since these organisms cannot tolerate

high salinity (Nallathambi et. al., 2002). Also, a heavy rain in the monsoon is known to increase

the coliform load in both the water and the sediment (Goyal et.al 1977). Salinity showed a

seasonal variation during the present study (Fig. 4). This is mainly due to the presence of the

regulator with 24 shutters (between station 4 and 5) to avoid the saline water intrusion into the

kole lands (proximity of station 1 to 4). These shutters are opened during the monsoons to avoid

flooding of kole lands. Hence, stations 5 to 7 are observed to be freshwater during monsoon

season, which is otherwise saline (once the regulator is closed after monsoons).

Table 8: Seasonal variation of MPN index and THB of water

Post-monsoon Pre-monsoon Monsoon

Stations MPN/100ml THB

Cfu/ml

MPN/100ml THB

Cfu/ml

MPN/100ml THB

Cfu/ml

1 20 0.1×106 150 0.4×10

6 1400+ 2×10

6

2 150 0.4×106 20 0.1×10

6 1400+ 1.6×10

6

3 1100 1.1×106 75 0.2×10

6 450 0.6×10

6

4 1100 1×106 1100 0.9×10

6 250 0.4×10

6

5 40 0.2×106 11 0.1×10

6 1100 0.7×10

6

6 450 0.6×106 9 0.1×10

6 1400+ 1.3×10

6

7 75 0.3×106 1400+ 1.2×10

6 95 0.2×10

6

Table 9: Seasonal variation of MPN index and THB of sediment

Post monsoon Pre monsoon Monsoon

Stations MPN/ 100g THB

Cfu/g

MPN/100g THB

Cfu/g

MP/100g THB

Cfu/g

1 15 0.1×106 150 0.2×10

6 20 0.1×10

6

2 30 0.3×106 20 0.1×10

6 115 0.3×10

6

3 1100 2.1×106 95 0.2×10

6 40 0.2×10

6

4 1100 1×106 20 0.2×10

6 200 0.4×10

6

5 45 0.4×106 450 0.2×10

6 1100 0.9×10

6

6 20 0.2×106 450 0.2×10

6 1400+ 1.3×10

6

7 150 0.6×106 160 0.2×10

6 1400+ 1.5×10

6

28

Large variation in coliform load and THB was observed between stations, especially

during the pre-monsoon and post-monsoon (Table 8 and 9). It is not uncommon to find difference

of 2-5 orders of magnitude between maximum and minimum concentrations observed at the same

site or in the same watershed (Pachepsky and Shelton, 2011). In a laboratory study higher

variability between replicate sediment samples were observed compared to the water samples

(Anderson et.al 2005).

Though higher nutrients were detected during pre-monsoon and post-monsoon compared

to monsoon season (Fig. 7 and 8), the bacterial count in the present study did not show any

significant correlation with the physicochemical parameters. Hence, the variation between

stations in the present study could be due to other contributing factors, the patchy distribution of

organisms in sediments and difficulty in dissociating bacteria from sediment particles (Pachepsky

and Shelton, 2011) could also be a key factor.

Water and sediment samples from Biyyam backwaters showed the presence of different

genus of bacteria. The isolated bacteria from the present study were further characterized and

identified upto genus level (Table 10). A total of 224 isolates from water and sediment samples

were obtained in the present study, from which 84 isolates were recorded during post monsoon,

43 in pre-mosoon and 97 in monsoon. Out of the 84 isolates (sediment and water) in post-

monsoon, 62 (74%) were Enterobacteriacae and 7 (8%) were Bacillus. The remaining isolates of

12 (14%) Micrococcus sp. and 3 (4%) Staphylococcus sp. were present only in the sediment. In

the pre-monsoon, Bacillus sp. were dominant with 24 (56%) isolates followed by

Enterobacteriacae 6 (14%) isolates, Micrococcus sp. 4 (9%) isolates and Aeromonas sp. 3 (7%)

isolates in the water and sediment. However, 1 (2%) isolate of Vibrio sp. and 5 (12%) isolates of

Staphylococcus sp. were present only in sediment and water respectively. Highest number of

isolates 97 (43%) were observed in monsoon season. Of the 97 isolates 47 (48%) isolates were

Enterobacteriacae followed by Bacillus sp .25 (26%), Vibrio sp. 5 (5%), Listeria sp. 11 (11%),

and Aeromonas sp.9 (9%).

Table 10: Seasonal variation of number of isolates, Biyyam backwaters

Post monsoon Pre monsoon Monsoon

Isolates Water Sediment Water Sediment Water Sediment

Enterobacteriacae 32 30 5 1 22 25

Bacillus sp. 5 2 15 9 15 10

Micrococcus sp. - 12 3 1 - -

Aeromonas sp. - - 2 1 4 5

Vibrio sp. - - - 1 3 2

Listeria sp. - - - - 6 5

Staphylococcus sp. - 3 5 - - -

Antibiotic resistance of selected isolated strains:

The antibiotic sensitivity for both gram negative and gram-positive isolates revealed antibiotic

resistant bacteria in the backwater of Biyyam. Among the 10 antibiotics applied, 5 of them viz.,

cefuroxime, cefoxitin, nitrofurantoin, nalidixic acid and tetracycline were resistant to gram

negative Enterobacteriacae. It was also found to be a multidrug resistant. The findings of

29

antibiotic resistance in Enterobacteriacae are extremely important as they taper towards the

prevalence of antibiotic resistant microorganisms in drinking water sources. It has lately been

discovered that such multiple drug resistant bacteria „superbugs‟ are the suit of the worry nation‟s

worldwide (Walsh et al., 2011).

The antibiotic sensitivity study for gram positive isolates are given in the table 11. Gram

positive isolates (Staphylococcus sp., Bacillus sp., and Micrococcus sp.) showed 100% resistance

to ampicillin and penicillin and also were resistant to gentamicin (33.33%), erythromycin

(66.66%), norfloxacin (66.66%), linezolid (66.66%), ciprofloxacin (33.33%), vancomycin

(66.66%), and tigecycline (33.33%). Most of the bacteria isolated were resistant to commonly

used antibiotics, ampicillin (100%), penicillin (100%), cotrimoxazole (57.1%), cefuroxime

(92.9%), erythromycin (92.9%) and therefore represent a public health concern (Khan and Malik,

2001). Koesak et al., (2012) have also previously detected bacterial resistance against ampicillin,

gentamicin, erythromycin, tetracycline and, ciprofloxacin at different times.

Table 11: Antibiotic sensitivity study of selected gram positive isolates

(Bacillus sp., Staphylococcus sp., Micrococcus sp.)

Antibiotic Sensitive Strains

(%)

Moderately

sensitive strains (%)

Resistant

strains (%)

Gentamicin 66.66 Nil 33.33

Erythromycin Nil 33.33 66.66

Levofloxacin 100 Nil Nil

Norfloxacin Nil 33.33 66.66

Ampicillin Nil Nil 100

Penicillin Nil Nil 100

Linezolid 33.33 Nil 66.66

Ciprofloxacin 33.33 33.33 33.33

Vancomycin 33.33 Nil 66.66

Tigecycline Nil 66.66 33.33

The Multiple Antibiotic Resistance (MAR) index is a good tool for health risk assessment

which identifies if isolates are from a region of high or low antibiotic use. A MAR index > 0.2

indicates a „high-risk‟ source of contamination (Davis and Brown 2016). In the present study, for

all the isolates the MAR index ranged from 0.2 to 0.8 (Table 12). This may be due to their long-

term exposure to the pollutants in the backwater. The high incidence of multiple antibiotic

resistances has been reported in the aquatic environment. (Hatha et al., 2005).

Table 12: MAR index of selected isolates Gentamicin (GEN), Erythromycin (E), Levofloxacin (LE), Norfloxacin (NX), Ampicillin (AMP), Penicillin

(P), Linezolid (LZ), Ciprofloxacin (CIP),Vancomycin (V), Tigecycline (TGC).

Isolates Multiple antibiotic resistance MAR index No. of isolates

Enterobacteriacae CXM,CX,NIT,NX.TE 0.5 30

Bacillus sp. E,NX,AMP,P,LZ,CIP,VA 0.7 2

Micrococcus sp. GEN,E,NX,AMP,P,LZ,TGC,VA 0.8 12

Staphylococcus sp. AMP,P 0.2 3

Resistance to multiple antibiotics can lead to occurrence of newly emerging resistant

bacteria which may be transferred to consumers causing infections that are hard to handle. The

incidence of antibiotic resistance in this study corresponds with the findings of Rakic-Martinez et

30

al., 2011, who reported the prevalence of MAR bacteria in waste water. According to Harakeh et

al., (2006), the emergence of antimicrobial bacteria increases in environments where

antimicrobials are indiscriminately used by the public. Antibiotic resistant organisms are tough to

be treated with, so proper monitoring should be carried out for better health of the backwater and

as per report of Das et al., (2013) there is a need of effluent/run off treatment to avoid spread of

antibiotic resistant bacteria in the aquatic environment.

Phytoplankton Community Structure Phytoplankton species composition

The phytoplankton community of Biyyam Kayal was represented by 67 taxa (Table 13). The

density ranged between 1400– 75, 1600 cells/l with maximum density observed at Stn. 1 (pre-

monsoon; Table 14 and Fig. 16). In general, high phytoplankton density at most stations (Stns. 1-

2 and 4-5) were observed during the pre-monsoon (Fig. 16). Stns. 3 and 7 showed highest

densities during post-monsoon (2017) while Stn. 6 recorded highest values during pre-monsoon

2018.

Table 13: Occurrence of phytoplankton taxa in the study area

Taxa Stn. 1 Stn. 2 Stn. 3 Stn. 4 Stn. 5 Stn. 6 Stn. 7

Amphipora sp - + + + - + +

Ankistrodemus falcatus + + + + + + +

Ankistrodemus sp 2 + + + + - - -

Aulacoseira granulata + + + + + + -

Arthrodesmus sp + + + + + - -

Asterionella fermosa - - - - + + +

Asterionellopsis glacialis - - - - - + +

Ceratium furca - - - - + + +

Chaeatoceros curvisetus - - - - + + +

Chaetoceros lorenzianus - - - - - + +

Chaeatoceros spp. - - - - + + +

Chrococcus sp. + + + + + + -

Coelastrum sp. + + + + + + -

Comastella sp. + - - - - - -

Closterium sp. + + + + + + -

Cosmarium sp. + + + + + + +

Craticula sp. - - - - + - -

Crucigenia fenestrata + + + + + - -

Crucigenia quadrata + + + + + + -

Crucigenia sp 3 + + + + - + -

Cyclotella spp. - + - + + + +

Cylindrotheca clostrium - - - - + + +

Cymbella sp. - - + + + - -

Diplonis sp. - - - - - - -

Dinobryon sertularia + + + + + - -

31

Ditylium brightwellii - - - - - + +

Elakotothrix sp. + - - + - - -

Euastrum spp. + - + + - - -

Euglena sp + + + + + + +

Gleocapsa sp. - - + + - - -

Golenkinia sp. + + + + + + -

Guinardia sp. - - - + - - -

Gymnodinium spp. + + + + + + +

Gyrodinium sp. + + - + - + +

Table 13: contd..


Haematococcus sp. + + + + + + +

Hemialus sp - - - + - + +

Kirchneriella contorta + + + - - + +

Kirchneriella spp. + + + + + + +

Micrasterias pinnatifida + - - - - - -

Micrasterias sp. 2 - + - + - - -

Microsystis spp. + + + + + + -

Monorhaphidium sp + + + + - + +

Navicula spp. + + + + + + +

Nephrocyium sp + + + + + + -

Nitzchia spp. + + + + + + +

Nostoc sp. + + + + + + -

Onychonema sp. + + - - + - -

Oscillatoria sp. + + + + + + +

Pediastrum sp. + + + + + + +

Peridinium sp. - + - - - + +

Phacus sp. + + + + + + +

Pleurosigma spp. - - - - - + +

Proceratium sp. + - - + - - -

Prorocentrum sp + - + - + + -

Psudonizchia sp. - - - - + + +

Pyrocystis sp. - - - + + + -

Raphidiopsis sp. + - - - + - -

Rhadomonas sp + + + + + + -

Rhizosolenia spp. + + + - + + +

Scenedesmus quadricauda + + + + + + +

Scenedesmus ecornis + + + + + + +

Scenedesmus arcuatus + + + + - + -

Scenedesmus bernadri + + + + - + -

Scenedesmus dimorphus - + - - - - -

Scenedesmus accuminatus + + - - - - -

Scenedesmus perforatus + + + + - - -

32

Scenedesmus spp. + + + + + + -

Screppsiella hangoei - - - + - - +

Selenastrum sp. + - - + - + -

Skeletonema sp. - - - - + + +

Snowella sp. + + - - - - +

Sphaerozoma sp. + + + + + + +

Spirulina sp. + + - - - + +

Stuarastrum spp. + + + + + + +

Staurodesmus spp. + + + + + + +

Table 13: contd..


Synedra spp. + + - + + + +

Tabellria sp. - - - + - - -

Tetrastrum sp. + - - - - - -

Tetraedron sp. + + + + - - -

Trachelomonas sp. + + + + + + -

Treubaria sp. + + - - - - -

Xanthidium spp. + + + + + - +

Table 14: Phytoplankton species diversity. S- no of species; N-Density; d-Margalef

species richness; J’- Pielou’s eveness; H’ –Shannon Diversity; 1-Lambda- Simpson

Dominance.

S N (cells L-1

) D J' H'(loge) 1-Lambda'

Stn 1 1-54 2000-7,51600 0-4.64 0-0.97 0-3.1 0-0.92

Stn 2 5-45 6400-1,49200 0.45-3.9 0.44-0.92 1.02-3.07 0.50-0.92

Stn 3 4-38 2000-43,097 0.38-3.56 0.53-1 1.19-2.83 0.48-0.92

Stn 4 4-28 3000-51,200 0.37-2.86 0.61-0.93 0.85-2.97 0.43-0.93

Stn 5 2-21 3200-2,24800 0.40-2.05 0.14-0.97 0.27-2.65 0.10-0.90

Stn 6 4-34 1901-2,06984 0.38-2.69 0.33-0.97 1.18-2.51 0.43-0.89

Stn 7 2-29 1400-1,50075 0.12-2.47 0.46-0.97 0.32-2.19 0.18-0.83

33

Fig. 16: Spatiotemporal variation in the phytoplankton species abundance and diversity indices.

Phytoplankton Species Composition

The Bray–Curtis cluster analysis performed on the phytoplankton abundance data grouped the

sampling months in seven groups (Fig. 17). The SIMPER analysis showed that Group 1 with

average similarity of 47.06% was dominated by Nitzchia spp. (50 %), Asterionella fermosa (25

%) and Stuarastrum sp. (25%; Table 15). In group 2, Skeletonema sp., Cylindrotheca clostrium,

Cyclotella sp 1 and, Gyrodinium sp together contributed to 77% of the similarity (Table 15).

Synedra sp 2 and Microsystis sp contributed to 47 % of the similarity in Group 3. Genera such as

Chrococcus sp., Closterium sp., Cosmarium sp., Gymnodinium sp 1, Navicula sp 1, Nitzchia sp 1

and, Pyrocystis sp. contributed to 6.67 %, each to group 3. Group 4 was dominated by Synedra

sp. 2, Nitzchia sp. 1, Navicula sp.1 and, Cosmarium sp and together contributed to 93% of the

similarity (Table 15). The dominance of Kirchneriella sp., Haematococcus sp., Nitzchia sp. 1,

Stuarastrum sp. 1 and, Crucigenia quadrata which contributed to 42% of the similarity grouped

34

Group 5. In Group 6, Euglena sp., Aulacoseira granulate, Microsystis sp., Scenedesmus

quadricauda, Rhadomonas sp., Haematococcus sp., and, Nitzchia sp 1 dominated and together

contributed to 81 % of the similarity in this group. Grp 7 showed an average similarity of 19 %.

Haemetococcus sp. and Navicula spp. were the only species present in this group. In Plate 2 and

3 some of the phytoplankton species recorded during the present study are presented.

Fig. 17: Bray-Curtis cluster analysis of phytoplankton species abundance

Table 15: SIMPER result for phytoplankton taxa based on the clustering

(Fig. 17).

Species Av.Abund Av.Sim Contrib% Cum.%

Group 1 (Average similarity: 47.06)

Nitzchia sp 1 800 23.53 50 50

Asterionella fermosa 400 11.76 25 75

Stuarastrum sp 1 600 11.76 25 100


Skeletonema sp 85907.25 12.57 39.66 39.66

Cylindrotheca clostrium 22181.83 7.03 22.17 61.83

35

Cyclotella sp 1 5096.96 2.6 8.2 70.04

Gyrodinium sp 2806.96 1.78 5.62 75.66

Psudonizchia sp 2451.21 1.39 4.4 80.05

Nitzchia sp 1 2601.08 1.28 4.05 84.1

Navicula sp 1 3517.21 1.04 3.3 87.4

Peridinium sp 1472.88 1 3.15 90.55


Synedra sp 2 2334.5 11.49 33.33 33.33

Microsystis sp 1334 4.6 13.33 46.67

Chrococcus sp 667 2.3 6.67 53.33

Closterium sp 333.5 2.3 6.67 60

Cosmarium sp 500.25 2.3 6.67 66.67

Gymnodinium sp 1 1000.5 2.3 6.67 73.33

Navicula sp 1 500.25 2.3 6.67 80

Nitzchia sp 1 500.25 2.3 6.67 86.67

Pyrocystis sp 333.5 2.3 6.67 93.33


Synedra sp 2 2267.13 15.21 36.89 36.89

Nitzchia sp 1 1233.5 10.72 26.01 62.9

Navicula sp 1 941.94 8.26 20.03 82.92

Cosmarium sp 425.06 4.03 9.76 92.69


Kirchneriella sp. 2546.3 3.07 15.46 15.46

Haematococcus sp 3466.73 1.62 8.17 23.63

Nitzchia sp 1 1289.52 1.51 7.58 31.21

Stuarastrum sp 1 1741.68 1.08 5.41 36.62

Crucigenia quadrata 1673.59 1.04 5.22 41.84

Gymnodinium sp 2 708.34 0.97 4.87 46.71

Euglena sp 706.06 0.9 4.51 51.22

Scenedesmus ecornis 881.39 0.81 4.08 55.3

Microsystis sp 2406.08 0.63 3.16 58.46

Rhadomonas sp 1741.67 0.62 3.14 61.6

Gymnodinium sp 1 405.31 0.6 3 64.59

Table 15: contd…


Species Av.Abund Av.Sim Contrib% Cum.%

Euglena sp 1000 3.71 16.9 16.9

Aulacoseira granulata 1000 3.04 13.84 30.74

Microsystis sp 533.33 2.24 10.22 40.96

Scenedesmus quadricauda 1600 2.24 10.22 51.18

Rhadomonas sp 1466.67 2.2 10.02 61.2

Haematococcus sp 1666.67 2.2 10.01 71.21

Nitzchia sp 1 600 2.2 10.01 81.22

36

Coelastrum sp 333.33 1.48 6.74 87.97

Pediastrum sp 333.33 1.48 6.74 94.71


Haematococcus sp 2400 9.52 50 50

Navicula sp 1 400 9.52 50 100

Plate 2: Phytoplankton species observed during the present study

37

Plate 3: Phytoplankton species observed during the present study

Phytoplankton species diversity

Number of phytoplankton taxa recorded ranged from 1 to 54 and highest value was

recorded at Stn. 1 (Fig. 16). In fact, highest number of taxa at all the stations was recorded during

the dry season of 2017-18. Margalef‟s species richness ranged from 0.12 to 4.64 recorded at Stns.

7 (Premonsoon 2018) and 1 (dry season 2017-18), respectively (Table 14 and Fig. 16). The

highest values at all stations were recorded during dry season (2017-18), except in Stn. 5 which

recorded the high values in post-monsoon 2017. The Pielou‟ evenness ranged from 0.14 (Stn. 5

pre-monsoon 2018) to 1 (Stn. 3 peak monsoon 2018). In general the evenness values at most

stations were > 0.5 during most of the study period (Fig. 16). Shannon Diversity values ranged

from 0.27 (Stn. 5 pre-monsoon 2018) to 3 recorded at Stns. 1 and 2 during dry season 2017-18.

Simpson‟s Dominance followed a similar pattern as that of Shannon Diversity with highest

values (0.92) at Stn. 1 and 2 during dry season 2017-18 and lowest values of 0.10 was recorded at

Stn 5 (5 pre-monsoon 2018).

Relation between phytoplankton community and environmental variables

To determine the environmental features that influence the phytoplankton community, the

DISTLM analysis was carried out. The phytoplankton community showed a significant relation

with rainfall, temperature, depth, surface salinity, pH and, silicate (Table 16). Total density of

phytoplankton showed significant relation with salinity, temperature and SiO2 (Table 17). All the

38

species diversity indices showed significant relation with salinity and silicate, except for

evenness (Table 17). In addition to salinity and silicate, Shannon diversity also showed strong

relation with NO3.

Table 16: Result of DISTLM forward analysis. Values in bold

are significant.

Variable SS (trace) Pseudo-F P

Rainfall 11366 3.1774 0.001

Temp 6151.7 1.6881 0.033

Salinity 29615 8.858 0.001

Depth 7582.3 2.0912 0.006

DO 4256.1 1.1602 0.289

pH 13012 3.659 0.001

NO3 4791.4 1.3086 0.161

NO2 4523.3 1.2342 0.201

SiO2 18610 5.341 0.001

PO4 5195.6 1.421 0.089

Table 17: Result of DISTLM forward analysis. Only significant data presented

Variable SS(trace) Pseudo-F P

Total Density (N) Salinity 740.49 9.1489 0.006

Temp 411 4.8261 0.019

SiO2 1122.3 14.758 0.001

Species number (S) Salinity 2273.6 11.36 0.002

SiO2 3032.3 15.92 0.001

Margalef species richness (d) Salinity 3421.5 7.52E+00 0.003

SiO2 4944.4 11.36 0.001

Shannon Diversity (H‟) Salinity 1336 6.00 0.003

NO3 749.02 3.26 0.049

SiO2 1447.7 6.55 0.001

Simpson‟s Dominance (1-) Salinity 645.41 4.44 0.003

Among different environmental parameters, rainfall, salinity temperature, depth, pH and,

silicate played an important role in the phytoplankton species composition variation. While the

phytoplankton diversity indices were mainly influenced by salinity, silicate and NO3. Salinity

plays a key role for the phytoplankton density and species succession of phytoplankton. The

present study reveals that the phytoplankton in the backwaters can broadly be divided into (1)

flora which is well adapted to the fluctuating salinity and (2) those which are not adapted or a

little adapted. While the former comprises of typical estuarine forms which may be permanent

residents, the latter represent either fresh water or marine forms migrated to the marine waters

and seen only for short periods. Salinity is one of the important factors which control the species

composition. Salinity and temperature were the main factor controlling their abundance. Silicates

39

are often considered important for the diatoms (Wu and Chou 2003) while pH affect the species

composition (Berge et al. 2010).

Zooplankton community of Biyyam Kayal

Zooplankton sampling was carried during three seasons (pre-monsoon, monsoon and post-

monsoon). The density ranged from 21 to 3287 ind. m-3

recorded at Stn. 6 (monsoon) and Stn. 1

(post monsoon), respectively (Fig. 18 a). In general, the upstream stations (Stns. 1-4) showed

comparatively higher density than the downstream stations (Stns. 5-7). Further, zooplankton

density was lowest during the monsoon period.

Zooplankton biomass ranged from 0.01 to 19.8 ml m-3

(Fig. 18 b). The highest values

were recorded at Stn. 1 in post-monsoon while low value was observed in Stn. 7 (pre monsoon).

During the post-monsoon, the biomass was higher in the upstream locations, while downstream

stations showed higher value during pre-monsoon and monsoon period.

Fig. 18: Spatiotemporal variation in zooplankton (a) abundance and (b) biomass

During the post-monsoon period Daphnia spp. with 54% of the total abundance was the

most dominant taxa. The highest abundance (2821 ind. m-3

) was recorded at Stn.1 (Fig. 19).

Cladocerans (23%) and Calanoid copepod (10%) were the other dominant groups. In pre-

40

monsoon Calanoid copepod (64%) dominated the community with abundance ranging from 35-

372 ind. m-3

recorded at Stn. 7 and 4, respectively (Fig. 19). Rotifer (0-147 ind. m-3

; 16%),

cladocerans (0-50 ind. m-3

7%) and Nauplii (0-34 ind. m-3

; 5%) were the other dominant groups

during pre-monsoon (Fig. 19). During monsoon zooplankton were recorded at only three stations

(Fig. 19). Calanoid copepod (0-62 ind. m-3

; 28%) and Rotifers 0-59; 25%) were the dominant

groups (Fig. 19).

The zooplankton community was represented by 14 groups with highest number of

groups recorded in pre-monsoon (11 group) followed by post-monsoon (12 group) and monsoon

had only 6 group (Fig. 19). Stn. 5 (post-monsoon) and Stn. 6 (pre-monsoon) had the highest

group of 9, while lowest groups were recorded at Stns. 6 (post-monsoon) and Stn. 1 (monsoon).

Fig. 19: Spatiotemporal variation in zooplankton group

41

The seasonal changes in the zooplankton composition could be due to the fresh water

inflow due to the monsoons and change in salinity (in downstream stations). Fresh water inflow

events can rapidly alter the composition of planktonic communities (Hoover et al. 2006). Direct

changes occur through physical forcing (Kobayashi et al. 1998) and changing salinity (Primo et

al. 2009). Indirectly, freshwater inflow may change resource availability. This may occur through

promoting or disrupting primary production (Saeck et al. 2013) and by the delivery of

allochthonous organic matter, driving microbial food-web production (Findlay et al. 1992).

The Pearson‟s correlation between zooplankton and environmental variables (abundance,

biomass and, number of taxa) did not show any significant relation.

Spatiotemporal variation in Macrofaunal community

A total of 16 macrofaunal taxa were obtained in the present study (Table 18). Polychaeta were

the dominant group in terms of diversity with 14 taxa, followed by crustacean with five taxa and

oligochaetes (2 taxa). Molluscs were represented by Bivalvia (2 families) and Gastropoda.

Nemotoda, Nemertea and, Phoronoid were the other macrofaunal groups recorded in the study

area.

Table 18: Macrofaunal taxa observed in the study area

Stn 1 Stn 2 Stn 3 Stn 4 Stn 5 Stn 6 Stn 7

Polychaeta

Mediomastus sp. + + + + + + +

Prionospio sp. - + + + + + +

Pallasia sp. - - - - - - +

Sigambra sp. - - - + - + +

Nereidae - - - - - + -

Nepthys sp. - - - + + + +

Paraprinospio - - - - - + +

Eunicidae - - - - - - +

Dendroneries sp. - - - - + + +

Polydora sp. - - - - - + +

Glycera sp. - - - - + - -

Lycastis sp. - - - - + - -

Pisionidens sp. - - - - - - -

Pseudopolydora sp. - - - - - - +

Oligochaeta - - - - - - -

Naididae sp.1 - - - + + + +

Naididae sp.2 + + + + + + +

Nematoda - - - - - - +

Nemertea - - - - - + -

Phoronida - - - - + + +

Crustacean

Table 18: contd..

42

Stn 1 Stn 2 Stn 3 Stn 4 Stn 5 Stn 6 Stn 7

Amphipoda - - + - + + +

Isopoda - - - - - + +

Tanaidacea - - - - + + +

Stomatopoda - - - - - - +

Prawn - - - - - - +

Insecta

Chironomidae Larvae + + + + + + +

Bivalvia

Myidae - - - - + - -

Corbuculidae - - - - - - +

Bivalvia (unidentiifed) - - - - + + -

Gastropoda - + - - - - +

Total macrofaunal abundance ranged from 0- 21,180 ind. m-2

(Table 19 and Fig. 20). The

highest abundance was recorded at Stn. 7 during post-monsoon 2018. In general, macrofaunal

abundance was lowest or completely absent in the upstream stations (Stn. 1 to 4) and high values

were reported from the downstream locations (Stn. 5 to 7; Fig. 20). Polychaetes also dominated

in terms of abundance with values ranging from 40-18,400 ind. m-2

.

Table 19: Macrofaunal species diversity indices

S N D J' H'(log2) 1-Lambda'

Stn. 1 0-2 0-40 0-0.23 0-1 0-1 0-0.51

Stn. 2 0-4 0-100 0-0.57 0-0.96 0-1.92 0-0.72

Stn. 3 0-3 0-540 0-0.29 0.29-0.97 0-0.97 0-0.48

Stn. 4 0-4 0-920 0-0.57 0.50-1 0-1.92 0-0.72

Stn. 5 0.6 80-14,240 0-0.63 0.03-0.85 0-1.98 0-0.68

Stn. 6 1-8 80-9,380 0-1 0.34-1 0-2.69 0-0.83

Stn. 7 0-11 0-21,180 0-1.12 0.42-0.96 0-2.26 0-0.73

Bray–Curtis cluster analysis based on the macrofaunal abundance data grouped the

samples into 10 groups (Fig. 21). The species that contributed to the clustering were analysed

using SIMPER (Table 20). Group 1 formed the major cluster with 11 stations and 38% average

similarity. Chironomidae larvae (52 %) and the oligocheata Naididae sp.2 (47%) completely

dominated this group. Group 2 was dominated by Chironomidae larvae (97 %), while Group 3

showed dominance of Nepthys sp., (40%), Naididae sp.2 (47%) and, Sigambra sp. (20 %). Group

4 and 5 were dominated by Naididae sp.2 (99 %) and Mediomastus sp. (100%), respectively.

Group 6 with average similarity of 21% was dominated by two polychaete species Prionospio

sp., and Mediomastus sp. and together contributed to 98% of the similarity. In Group 7,

Prionospio sp., Naididae sp.1, Amphipoda and, Mediomastus sp. contributed to 96% of the

43

similarity. Group 8 (Average similarity: 64.71) was dominated by Dendroneries sp. and

Mediomastus sp. In Group 9, three taxa (Naididae sp.1, Amphipoda and, Dendroneries sp.) and

two taxa (Amphipoda and Prionospio sp.) in Group 10 contributed to 100% of the similarity.

Fig 20:

Spatiotemporal variability in the macrofaunal abundance and diversity indices.

The high tolerance to organic enrichment and reduced oxygen level allow oligochaetes

(Naididae spp.) to increase under unfavourable conditions (Caspers 1971). On the other hand,

species belonging to polychaete family Spionidae (Prionospio sp.,) and Capitellidae

(Mediomastus sp.) are adapted to areas with high organic matter and unstable habitat (Sivadas et

al. 2016; 2020).

44

Fig 21: Bray -Curtis cluster analysis of macrofaunal abundance

Table 20: SIMPER result for macrofauna taxa based on the clustering in

Fig. 21.

Taxa Av.Abund Av.Sim Contrib% Cum.%


Chironomidae larvae 71.52 19.72 52.24 52.24

Naididae sp.2 133.33 17.61 46.65 98.9


Chironomidae larvae 114.29 39.43 97.07 97.07


Nepthys sp. 26.67 14.81 40 40

Naididae sp.2 16.67 14.81 40 80

Sigambra sp. 13.33 7.41 20 100


Naididae sp.2 270 32.54 98.51 98.51


Mediomastus sp. 30 22.22 100 100


Prionospio sp 2068.33 17.55 81.95 81.95

Mediomastus sp. 658.33 3.46 16.15 98.1


Prionospio sp 4317.78 14.07 50.2 50.2

45

Naididae sp.1 801.11 4.92 17.56 67.76

Amphipoda 2754.44 4.31 15.39 83.14

Mediomastus sp. 930 3.63 12.96 96.1


Dendroneries sp. 460 52.94 81.82 81.82

Mediomastus sp. 60 5.88 9.09 90.91


Naididae sp.1 220 8.89 40 40

Amphipoda 640 8.89 40 80

Dendroneries sp. 130 4.44 20 100


Amphipoda 530 8.33 75 75

Prionospio sp 30 2.78 25 100

Macrofaunal Species Diversity

The number of taxa in the region ranged from 1 to 14. Lower values were mostly recorded in the upstream

locations (Table 19 and Fig. 20) while high values were observed in the Stns. 6-7 during DS 2017-18.

Margalef‟s species richness ranged from 0.14 to 1.83 (Stn. 7 DS 2017-18). Peilou‟s species evenness

ranged from 0 to 1 with high values recorded in some of the upstream and downstream stations. Shannon

Diversity ranged from 0-2.5 and highest values were recorded at Stn. 4 (Dry season 2017-18). Simpson‟s

Dominance ranged from 0- 0.83 with lower values observed mostly in the upstream locations. Highest

Simpson‟s Dominance was recorded at Stn. 4 (Dry season 2017-18). Plate 4 represents some of the

macrofaunal taxa observed in the Biyyam Kayal during the study period

Plate 4: Macrobenthos from Biyyam Kayal

46

Macrofaunal functional diversity

In Table 21, the summary of the functional diversity indices is presented. In general the Rao‟s Q

was low and ranged from 0 to 16.03. The highest value was recorded at Stn. 6 (DS 2017-18).

Functional richness (FRic) ranged from 0 to 31.82 with highest values observed at Stn. 6 during

the dry seasons. Functional Evennes (FEve) ranged from 0 to 0.95. FEve values were highest tn

Stn 5 (0.94) and Stn. 6 (0.96) which was reported in the dry season 2017-18. Functional dispersal

(FDis) values ranged from 0 to 3.96 (Stn6 DS 2017-18). In general the functional diversity

indices were higher in the downstream locations and during the dry season. Two-way ANOVA

showed a spatial significant variation for only for FRic (F=3.55; p=0.0055).

Table 21: Functional diversity indices of macrofaunal community

Stations Rao FRic FEve FDis

Stn. 1 0-11.75 0-6.86 0 0-3.43

Stn. 2 0-12.94 0-17.68 0-0.77 0-3.52

Stn. 3 0-10.44 0-17.68 0-0.14 0-3.05

Stn. 4 0-11.75 0-11.91 0-0.39 0-3.43

Stn. 5 0-9.53 0-22.95 0-0.95 0-2.75

Stn. 6 0-16.03 4.8-31.82 0.3-0.96 1.29-3.96

Stn. 7 0-13.68 0-37.56 0-0.79 0-3.6

Macrofaunal Environmental Relationship

Macrofaunal total abundance showed significant relationship with only temperature (Table 22).

Species number (S) was negatively correlated with organic matter and fine sediment and

positively related to sand. Species richness (d) showed significant negative relation with organic

matter (Table 22). Pielou‟s evenness (J) was positively related to temperature while Simpson‟s

dominance showed negative relation with organic matter (Table 22).

Table 22: Pearson’s correlation between macrofaunal parameters. Values in

bold significant (p<0.05)

Abundance S d J H' 1-Lambda'

Organic Matter -0.35 -0.51 -0.52 0.34 -0.38 -0.47

Inorganic matter -0.38 -0.25 -0.20 0.11 -0.15 0.00

Salinity 0.08 0.38 0.36 -0.17 0.22 0.33

Sand 0.35 0.45 0.42 -0.41 0.24 0.39

Silt+Clay -0.35 -0.45 -0.42 0.41 -0.24 -0.39

DO 0.30 0.38 0.33 -0.23 0.07 -0.05

pH -0.03 0.34 0.33 -0.31 0.14 0.39

Temp 0.47 0.18 0.08 -0.45 -0.19 -0.30

47

Depth 0.17 0.27 0.33 0.08 0.39 0.41

The lack of clear pattern in the some of the macrofaunal variables (species and functional

diversity indices) and the measured environmental variables may be related to the fact that

functional traits used in the present study might not encompass the array of functions performed

by the macrofaunal community. Relations between macrofaunal community and environmental

condition are complex, often difficult to interpret and environmental parameters other than those

measured during the present study, such as biotic interaction (e.g prey-predator, competition)

may also influence the observed pattern.

Finfish diversity of Biyyam Kayal The finfish diversity of Biyyam Kayal was represented by 48 species belonging to14 orders, 31

families and 41 genera and (Table. 23). Previous study from Biyyam kayal reported a total of 36

finfishes (Razia Beevi et.al 2009). Of the 31 families obtained in the present study, Cyprinidae

family dominated with 6 species (12.5%), followed by Carangidae and Bagridae with 4 species,

each (8.3%) each (Fig. 22 and Table 23). As per the IUCN status 35 species are of least concern

and 4 species each had data deficient, not evaluated and not threatened (Table 23).

Fig. 22: Species number in finfish families, Biyyam Kayal

Table 23: Finfish diversity of Biyyam Kayal. DD - Data deficient; LC - least concern; NE - Not

evaluated; VU – Vulnerable; NT - Not threatened; F- Freshwater; M – Marine; B - Brackish

Scientific Name English name Vernacular Name IUCN Habitat

Order Anguilliformes

Family Anguillidae

Anguilla bicolor, McClelland, 1844 Indonesian Shortfin Eel Kaṟutta Maliññīl NT F

48

(Shortfin Eel)

Order Beloniformes

Family Belonidae

Xenentodon cancila (Hamilton, 1822) Needlefish Kēālān LC F

Order Cichliformes

Family Cichlidae

Etroplus suratensis (Bloch, 1790) Pearl Spot Karimīn LC B

Etroplus maculatus (Bloch, 1795) Orange chromide Paḷḷatti LC B

Oreochromis mossambicus (Peters, 1852) Mozambique tilapia

Meāsāmbikk

Tilāppiya NT F

Order Clupeiformes

Family Clupeidae

Nematalosa nasus (Bloch, 1795)

Bloch's Gizzard Shad

(Hairback) Nūlciṟakan Nūna LC B

Tenualosa ilisha (Hamilton, 1822) Hilsa (Hilsa Shad) Hilsa LC B

Dayella malabarica (Day, 1873) Day's Round Herring Ḍēyuṭe Uruḷan

Nettēāli LC B

Family Engraulidae

Stolephorus indicus (van Hasselt, 1823) Indian Anchovy Intyan Nettēāli LC B

Order Cypriniformes

Family Cyprinidae

Systomus sarana (Hamilton, 1822) Olive barb Kuruva / Mundathi LC F

Dawkinsia filamentosa (Valenciennes,

1844) Filament barb Pūvāli Paral LC F

Amblypharyngodon microlepis (Bleeker,

1853) Indian carplet Peruvayamp LC F

Puntius vittatus, Day, 1865 Green Stripe Barb Kaypa Paral LC F

Puntius amphibius (Valenciennes, 1842) Scarlet-banded barb Paral DD F

Rasbora daniconius (Hamilton, 1822) Slender barb Thuppalkudiyan LC F

Order Cyprinodontiformes

Family Aplocheilidae

Aplocheilus lineatus (Valenciennes, 1846) Striped Panchax Mānattukaṇṇi LC F

Order Elopiformes

Family Megalopidae

Megalops cyprinoides (Broussonet, 1782) Indo-Pacific Tarpon (Oxeye

Tarpon) Pālānkaṇṇi DD B

Order Gonorhynchiformes

Family Chanidae

Chanos chanos (Forsskål, 1775) Milkfish Pūmīn LC B

Order Mugiliformes

Family Mugilidae

Mugil cephalus, Linnaeus, 1758 Flathead Mullet

Chappattalayan

Kaṇamp LC B

Table 23: contd…


Order Perciformes

Family Ambassidae

Parambassis thomassi (Day 1870) Western Ghats Glassy Perchlet Āṟṟunandan LC B

Parambassis dayi (Bleeker, 1874) Day's Glassy Perchlet Ḍē Glāsṁ LC F and B

49

Ambassis gymnocephalus (Lacepède, 1802)

Bald Glassy (Naked- Head

Glass Perchelet) Kaṣaṇṭi Glāsṁ LC B and M

Family Anabantidae

Anabas testudineus (Bloch 1792) Climbing Perch Karippiṭi DD F

Family Carangidae

Alepes djedaba (Forsskål, 1775) Shrimp scad Chem'mīn Pāra LC B

Alepes kleinii (Bloch, 1793) Razorbelly Scad Kattivayaṟan Pāra LC B and M

Caranx heberi (Bennett, 1830) Blacktip trevally Aṟṟakkaṟu Ppan Pāra LC M

Caranx ignobilis (Forsskål, 1775)

Giant Trevally (Yellowfin

Jack) Bhīman Pāra LC M

Family Channidae

Channa striata (Bloch, 1793) Striped Snakehead Varāl LC F

Family Gerreidae

Gerres filamentosus, Cuvier, 1829 Whipfin mojarra Keāṭiyan Prāññil LC B

Family Gobiidae

Glossogobius giuris (Hamilton, 1822) Tank Goby¹ Ṭāṅk Pūḻān LC B

Family Latidae

Lates calcarifer (Bloch 1970) Barramundi (Giant Seaperch) Kāḷāñci NE B

Family Leiognathidae

Leiognathus brevirostris (Valenciennes,

1835) Short nose ponyfish

Ceṟu Mūkkan

Muḷḷankāra NE M

Family Lutjanidae

Lutjanus argentimaculatus (Forsskål, 1775)

Mangrove Red Snapper (River

Snapper

Kaṇṭal Chempalli

LC M

Family Nandidae

Nandus nandus (Hamilton, 1822) Gengetic leaf fish Kariyila Mīn LC B

Family Osphronemidae

Pseudosphromenus cupanus (Cuvier, 1831) Spike Tailed Paradise Fish Kariṅkaṇṇi LC F

Family Scatophagidae

Scatophagus argus (Linnaeus, 1766)

Spotted Scat (Spotted

Butterfish) Puḷḷi Nacchār LC B and M

Family Sillaginidae

Sillago sihama (Forsskål, 1775) Silver Sillago Veḷḷi Pūḻān LC B and M

Family Sphyraenidae

Sphyraena jello, Cuvier, 1829 Pickhandle Barracuda (Banded

Barracuda) Varayan Śīlāv NE B and M

Family Terapontidae

Terapon jarbua (Forsskal, 1775) Crescent Perch (Jarbua

Terapon, Squeaking Perch)

Chandrakkala

Kīrimīn LC B

Order Pleuronectiformes

Family Cynoglossidae

Cynoglossus lida (Bleeker, 1851) Shoulderspot tonguesole

Tēāḷeppuḷḷi

Nākkmāntaḷ NE M

Table 23: contd…


Order Siluriformes

Family Bagridae

Horabagrus brachysoma (Günther, 1864)

Yellow Catfish (Gunther's

Catfish) Maññakkūri VU F

50

Mystus malabaricus (Jerdon, 1849) Malabar mystus Malabār Kūri NT B

Mystus oculatus (Valenciennes, 1840) Spotted Mystus Cuṭṭikkūri LC B

Mystus gulio (Hamilton, 1822) Long whiskers catfish Chiggu LC B

Family Heteropneustidae

Heteropneustes fossilis (Bloch, 1794) Stinging catfish Kāri LC F

Family Siluridae

Ompok bimaculatus (Bloch, 1794) Butter Catfish Teāṇṇivāḷa NT F

Order Synbranchiformes Family Mastacembelidae

Mastacembelus armatus (Lacepède, 1800) Zig-zag Eel (Tyre-track Eel) Malayārakan LC F

Order Scorpaeniformes Family Platycephalidae

Platycephalus indicus (Linnaeus, 1758) Bartail Flathead Varavālan

Cappattalayan DD B and M

Among the 48 species only yellow catfish, Horabagrus brachysoma (Plate. 5) belongs to

the vulnerable category. Horabagrus is one of the three endemic genera of catfish that occur in

peninsular India and is an endangered and threatened species (Raghavan et.al 2016).

Padmakumar et al. (2010) has cited a drastic decrease in the population of this species in the

Vembanad Lake. H. brachysoma has not been reported from Biyyam Backwaters and may have

migrated during the devastating floods that occurred in Kerala in August 2018.

Plate 5: Fishes from Biyyam kayal

Despite the extensive sampling, no specimens of the two earlier reported species, Clarius

dussumieri and Devario malabaricus from Biyyam Kayal (Razia Beevi et.al 2009) could be

observed during the present study. The local fishermen confirmed that these species have not

been reported from Biyyam Kayal for many years. Relative abundance of D. malabaricus in the

Biyyam Kayal was reported to be rare in the study (Razia Beevi et.al 2009) however IUNC status

is reported to be of Least Concern. Currently C. dussumieri is classified as Nearly Threatened in

the IUCN red list (Shaji and Biju 2016) and is considered as an endangered species (Binoy,

2010). Overexploitation and reduction in the habitat area due to the reclamation of wetlands,

extensive use of pesticides, weedicides and fertilizers in the agriculture fields may be responsible

51

for the decline in population of C. dussumieri (Padmakumar et. al. 2010). Extensive survey

conducted by Salin (2013) reports the vulnerability of C. dussumieri to extinction. Owing to the

vulnerability and absence of these species, and continues dwindling density of the finfishes (as

per the data collected by the local fisherman) for the past several years, it is suggested that a

proper management plan needs to be implemented, which otherwise may eventually lead to the

collapse of the fishery. Knowledge of the local biodiversity, of which checklists are a first step, is

essential for effective management plans.

The commonly occurring fishery resources in the Biyyam kayal are given in fig.23. In the

upstream stations Rasbora daniconius were dominant during post-monsoon comprising of 37%

of the total composition while Etroplus maculatus (27.5%) dominated during premonsoon

season. In the downstream stations Macrobrachium equidens and Macrobrachium rosenbergii

dominanted during premonsoon and post-monsoon, respectively. Due to the variation in salinity

the species composition is seen to vary between seasons in the downstream and upstream stations

(Fig. 23).

Fig. 23: Species composition (%) of commonly occurring fishery (a) Upstream and (b)

downstream, Biyyam Kayal

52

Functional diversity of fish

The functional diversity indices analysis was carried out for the most commonly occurring fish

species from the study area. The Rao‟s Q values ranged from 4.81 (May) to 9.15 (August) in the

upstream, while in the downstream values ranged from 1.51 to 6.62 recorded during August and

June, respectively (Fig. 24). Functional richness (FRic) ranged from 0.72 (March) to 11.91

(December) and 4.5 (August) to 10.55 (May) in the upstream and downstream, respectively.

Functional Evenness (FEve) in the upstream was highest during March (0.88) and lowest values

were recorded during February (0.42). In the downstream, FEve ranged from 0.18 (January) to

0.7 (August). Functional Dispersal (FDis) values ranged from 2.13(May) to 2.95(August) in the

upstream and from 0.71(August) to 2.37 (October) in the downstream. ANOVA analysis detected

significant spatial variation for Rao‟s Q, FEve and, FDis (Table 24).

Fig. 24: Functional diversity indices for commonly occurring fish species.

Table 24: ANOVA result for fish functional diversity. Only

significant data presented

Sum Sq Mean Sq F value P

Rao's Q 45.47 45.47 19.2 0.0003

FEve 0.22 0.22 7.17 0.0145

FDis 4.24 4.24 19.26 0.0003

Community Weighted Mean (CWM)

The community weighted mean (CWM) indicates that the herbivores fishes were higher in the

upstream locations throughout the year. On the other hand the carnivore‟s species were higher in

53

the downstream, except during March to May (Fig. 25). In general, the upstream showed the

dominance of most size classes during most of the year, except for size class 10-30 cm and 50-

100 cm which were higher the downstream locations (Fig. 26). All measures of diversity

examined here suggest that the up steam areas of the Biyyam kayal may be more diverse than

assemblages in the downstream regions.

Fig. 25. Community weighted mean (CWM) for fish feeding type

54

Fig. 26. Community weighted mean (CWM) for fish size classes

Statistical Analysis and Modeling The data was subjected to statistical analyses to understand the co-relation and significant

differences between different environmental parameters. Factor analysis was conducted for all

the parameters to understand the trophic interaction, to enable to capture important pattern in the

dataset and understand how variables are interrelated with each other.

Pre-monsoon

In PCA, component 1 and 2 explained 72.5% of cumulative variance (Table 25). Nitrate, Nitrite

and chlorophyll values had high contribution towards component 2 (Fig. 27). During pre-

55

monsoon, chlorophyll values were negatively correlated with nitrate whereas positively

correlated with nitrite (Table 26). This may be due to the high abundance of Cyanophyceae,

especially Scenedesmus sp. that dominated in the study area. Silicate concentration failed to show

any relationship with phytoplankton density which was logical as phytoplankton was dominated

by non-diatomaceous sp.

In factor analysis, all the parameters except chlorophyll, phytoplankton, temperature and

inorganic matter showed high values for communalities (Table 27) which explains the

dependency of system on benthic parameters (organic matter, sediment structure, macrofauna

distribution etc.) rather than phytoplankton. Lack of relationship between phytoplankton

density/chlorophyll and zooplankton confirms the same.

Table 25: Total variance explained for different seasons

Initial Eigenvalues

Extraction Sums of

Squared Loadings

Seasons Components Total

%

Variance

Cumulative

%

Tota

l

% of

Variance

Cumulative

%

Pre-monsoon 1 7.8 46.1 46.1 7.8 46.1 46.1

2 4.5 26.4 72.5 4.5 26.4 72.5

Dry Season 1 6.3 37.1 37.1 6.3 37.1 37.1

2 2.9 17.0 54.0 2.9 17.0 54.0

Peak monsoon 1 7.8 46.1 46.1 7.8 46.1 46.1

2 4.5 26.4 72.5 4.5 26.4 72.5

Post-monsoon 1 6.302 37.073 37.073

6.30

2 37.073 37.073

2 2.883 16.959 54.032 2.88

3 16.959 54.032

56

Table 26: Correlation of variables during pre-monsoon. OM-organic matter, IM-Inorganic matter, MF-Macrofauna, S-Salinity, SND-Sand, SLC-

Silt+Clay, DO-Dissolved oxygen, T-Temperature, D-Depth, Phyto – Phytoplankton density, zooplankton – zooplankton biomass.

OM IM MF S SND SLC DO PH Temp. Depth Chl-a NO3 NO2 SIO2 PO4 PHYTO ZOO

OM 1

IM 0.56 1

MF -0.52 -0.46 1 .

S . . . 1 . . . . . . . . . . . . .

SND -0.88 -0.78 0.59 . 1

SLC 0.88 0.78 -0.59 . -1.00 1

DO 0.24 -0.24 -0.17 . -0.12 0.12 1

pH 0.20 0.57 0.09 . -0.24 0.24 -0.49 1

Temp. -0.43 0.11 0.22 . 0.37 -0.37 -0.64 0.53 1

Depth 0.09 0.32 -0.51 . -0.19 0.19 0.26 -0.08 -0.24 1

Chl-a 0.46 0.14 0.20 . -0.44 0.44 0.06 0.22 -0.03 -0.59 1

NO3 0.40 0.22 -0.44 . -0.37 0.37 0.28 -0.17 -0.42 0.28 0.03 1

NO2 -0.44 -0.64 0.30 . 0.57 -0.57 0.23 -0.29 0.26 -0.43 -0.05 -0.38 1

SiO2 -0.39 -0.25 0.48 . 0.38 -0.38 -0.31 0.25 0.58 -0.44 0.28 -0.05 0.43 1

PO4 -0.35 -0.33 0.20 . 0.22 -0.22 -0.27 -0.07 0.19 -0.32 0.23 -0.61 0.20 0.04 1

Phyto 0.10 0.73 -0.31 . -0.38 0.38 -0.53 0.50 0.39 0.18 -0.05 -0.24 -0.38 -0.25 0.19 1

Zoo -0.36 0.18 -0.02 . 0.25 -0.25 -0.76 0.35 0.59 0.10 -0.35 0.17 -0.24 0.42 -0.12 0.33 1

57

Table 27: Communalities for all the seasons

Premonsoon Dry Season Peakmonsoon Postmonsoon

Initial Extraction Initial Extraction Initial Extraction Initial Extraction

OM 1 0.88 1 0.93 1 0.88 1 0.93

IM 1 0.49 1 0.06 1 0.49 1 0.06

MF 1 0.74 1 0.73 1 0.74 1 0.73

S 1 0.98 1 0.94 1 0.98 1 0.94

SND 1 0.99 1 0.97 1 0.99 1 0.97

SLC 1 0.99 1 0.97 1 0.99 1 0.97

DO 1 0.88 1 0.31 1 0.88 1 0.31

pH 1 0.71 1 0.82 1 0.71 1 0.82

Temp. 1 0.41 1 0.18 1 0.41 1 0.18

Depth 1 0.69 1 0.24 1 0.69 1 0.24

CHL-a 1 0.48 1 0.61 1 0.48 1 0.61

NO3 1 0.58 1 0.26 1 0.58 1 0.26

NO2 1 0.70 1 0.58 1 0.70 1 0.58

SiO2 1 0.95 1 0.08 1 0.95 1 0.08

PO4 1 0.61 1 0.60 1 0.61 1 0.60

Phyto 1 0.90 1 0.27 1 0.90 1 0.27

Zoo 1 0.36 1 0.63 1 0.36 1 0.63

58

Fig. 27. PCA plot for different season. OM-organic matter, IM-Inorganic matter, MF-

Macrofauna, S-Salinity, SND-Sand, SLC- Silt+Clay, DO-Dissolved oxygen, T-

Temperature, D-Depth, Phyto – Phytoplankton density, zooplankton – zooplankton

biomass.

Dry season

In PCA component 2 explained 54% of total variance (Table 25). Phytoplankton density,

chlorophyll and zooplankton biomass contributed significantly towards component 2 (Fig.

27). In dry season, phytoplankton density/chl-a was not correlated with any of the nutrient

parameters (Table 28), which could be due to the dominance of cyanophytes which have

accessory pigments that overpower chlorophyll. But phytoplankton density was significantly

correlated with zooplankton biomass which indicated a disparity between benthic and pelagic

flux. Values for communalities are given in table 27.

59

Table 28: Correlation of variables during dry season. OM-organic matter, IM-Inorganic matter, MF-Macrofauna, S-Salinity, SND-Sand,

SLC- Silt+Clay, DO-Dissolved oxygen, T-Temperature, D-Depth, Phyto – Phytoplankton density, zooplankton – zooplankton biomass.

OM IM MF S SND SLC DO pH Temp. Depth

CHL-

a NO3 NO2 SiO2 PO4 Phyto Zoo

OM 1

IM 0.39 1.00

MF -0.82 -0.18 1.00

S -0.96 -0.24 0.90 1.00

SND -0.98 -0.24 0.88 0.98 1.00 -1.00

SLC 0.98 0.24 -0.88 -0.98 -1.00 1.00

DO 0.08 0.04 -0.25 -0.11 -0.07 0.07 1.00 0.45

pH -0.58 -0.16 0.44 0.53 0.61 -0.61 0.45 1.00

Temp. 0.22 -0.37 -0.15 -0.18 -0.28 0.28 0.24 -0.37 1.00

Depth 0.27 -0.10 -0.03 -0.19 -0.31 0.31 -0.05 -0.46 0.70 1.00

CHL-a -0.58 -0.17 0.29 0.57 0.51 -0.51 -0.21 0.04 -0.13 -0.03 1.00

NO3 0.24 -0.20 -0.06 -0.22 -0.24 0.24 -0.09 0.11 0.13 0.22 -0.37 1.00

NO2 -0.05 -0.09 0.14 0.09 0.11 -0.11 0.26 0.51 -0.29 -0.37 -0.16 0.41 1.00

SiO2 0.08 -0.13 -0.05 -0.12 -0.11 0.11 -0.01 0.13 -0.13 0.25 0.22 0.26 0.41 1.00

PO4 0.01 0.05 -0.18 0.03 -0.07 0.07 -0.29 -0.36 0.12 0.14 0.68 -0.34 -0.39 0.01 1.00

Phyto 0.30 0.06 -0.30 -0.34 -0.32 0.32 0.35 -0.04 0.27 0.43 -0.16 0.12 0.09 0.59 -0.25 1.00

Zoo -0.59 -0.07 0.57 0.65 0.61 -0.61 -0.47 -0.05 -0.22 -0.21 0.66 -0.21 0.02 0.03 0.23 -0.34 1.00

60

Onset of monsoon

Phytoplankton density showed significant negative correlation with nitrate and nitrate. Chl-a

did not show any correlation with nutrient parameters (Table 29). There was a high

abundance of cyanophyceae and other non-diatomaceous species in the area. Further analysis

was impossible as data were missing in some stations.

Table 29: Correlation of variables during onset monsoon. OM-organic matter, IM-Inorganic matter, MF-Macrofauna, S-Salinity, SND-

Sand, SLC- Silt+Clay, DO-Dissolved oxygen, T-Temperature, D-Depth, Phyto – Phytoplankton density, zooplankton – zooplankton

biomass.

OM IM MF S SND SLC DO pH

Tem

p. Depth

Chl-

a NO3 NO2 SiO2 PO4

Ph

yto

Zo

o

OM 1

.

IM 0.09

.

MF -

0.64

-

0.10 1 .

S . . . 1 .

SND -

0.86

-

0.36 0.83 . 1

SLC 0.86 0.36 -

0.83 .

-1.00

1

DO 0.66 0.30 -

0.77 .

-

0.93 0.93 1

pH -

0.03

-

0.30

-

0.68 .

-

0.26 0.26 0.43 1

Temp. -

0.96

-

0.25 0.47 . 0.84 -0.84 -0.65 0.24 1

Depth -

0.69 0.21

-0.09

. 0.28 -0.28 -0.07 0.57 0.74 1

Chl-a -

0.17 0.51 0.09 .

-

0.21 0.21 0.48 0.01 -0.02 0.29 1

NO3 -

0.72

-

0.12 0.16 . 0.64 -0.64 -0.63 0.30 0.85 0.73 -0.38 1

NO2 -

0.77 -

0.65 0.47 . 0.87 -0.87 -0.76 0.21 0.88 0.41 -0.40 0.76 1

SiO2 -

0.64

-

0.56 0.06 . 0.44 -0.44 -0.14 0.68 0.74 0.63 0.12 0.49 0.72 1

PO4 0.13 0.77 -

0.49 .

-

0.39 0.39 0.26 0.15 -0.10 0.44 0.04 0.29

-

0.40 -0.36 1

Phyto 0.18 0.71 -

0.13 .

-0.51

0.51 0.66 -0.09 -0.37 0.09 0.92 -0.57 -

0.72 -0.23 0.26 1

Zoo 0.48 -

0.25

-

0.42 .

-

0.59 0.59 0.77 0.39 -0.48 -0.27 0.44 -0.72

-

0.40 0.20

-

0.41

0.4

4 1

Peak monsoon

In PCA, component 2 explained 72.5% of cumulative variance (Table 25). Fig. 20 shows the

PCA plot. In peak monsoon there was a highly significant positive correlation between

phytoplankton density, nitrate and phosphate values (Table 30). At the same time

phytoplankton density showed a significant negative correlation with nitrite. Yet,

zooplankton biomass did not show any relationship with phytoplankton density and biomass.

Communality values showed that both pelagic and benthic factors drive the system during

peak monsoon (Table 27).

61

Table 30: Correlation of variables during peak monsoon. OM-organic matter, IM-Inorganic matter, MF-Macrofauna, S-Salinity, SND-Sand,

SLC- Silt+Clay, DO-Dissolved oxygen, T-Temperature, D-Depth, Phyto – Phytoplankton density, zooplankton – zooplankton biomass.

OM IM MF S SND SLC DO pH Temp. Depth Chl-a NO3 NO2 SiO2 PO4 Phyto Zoo

OM 1.00

IM 0.22 1.00

MF 0.76 -0.09 1.00

S -0.80 -0.51 -0.38 1.00

SND -0.83 -0.53 -0.37 0.99 1.00 -1.00

SLC 0.83 0.53 0.37 -0.99 -1.00 1.00

DO 0.82 0.14 0.43 -0.89 -0.89 0.89 1.00

pH -0.80 0.42 -0.73 0.45 0.46 -0.46 -0.71 1.00

Temp. 0.11 0.31 -0.19 -0.64 -0.58 0.58 0.52 0.07 1.00

Depth -0.80 0.33 -0.87 0.31 0.34 -0.34 -0.52 0.93 0.34 1.00

Chl-a 0.28 -0.07 0.74 -0.13 -0.06 0.06 0.06 -0.25 0.08 -0.37 1.00

NO3 -0.39 -0.08 -0.57 0.30 0.25 -0.25 -0.08 0.26 -0.14 0.33 -0.73 1.00

NO2 0.18 -0.23 0.49 -0.01 0.05 -0.05 -0.12 -0.25 -0.03 -0.32 0.69 -0.94 1.00

SiO2 0.78 0.56 0.34 -0.99 -0.99 0.99 0.84 -0.39 0.64 -0.27 0.11 -0.35 0.05 1.00

PO4 -0.47 0.09 -0.38 0.58 0.51 -0.51 -0.50 0.47 -0.58 0.31 -0.46 0.77 -0.71 -0.60 1.00

Phyto -0.07 0.62 -0.43 -0.25 -0.29 0.29 0.19 0.39 0.25 0.44 -0.53 0.71 -0.90 0.23 0.56 1.00

Zoo 0.63 -0.47 0.38 -0.37 -0.40 0.40 0.70 -0.90 -0.05 -0.75 -0.18 0.07 -0.03 0.31 -0.32 -0.20 1.00

Post monsoon

In PCA, component 2 explained 54% of cumulative variance (Table 25). Fig. 27 shows the

PCA plot. During post monsoon zooplankton density was positively correlated with Chl-a

values. Chl-a showed positive correlation with phosphate but not with any other nutrient

parameters (Table 31).Communality values indicate that system is dependent both, pelagic

and benthic flux during post monsoon (Table 27).

The analyses show that the ecosystem is not a healthy one. The reasons are: 1) dominance of

cyanophytes which indicates pollution of the system, 2) non-significant relation between

silicate and phytoplankton even in monsoon season, 3) poor representation of diatoms

62

Table 31: Correlation of variables during post-monsoon. OM-organic matter, IM-Inorganic matter, MF-Macrofauna, S-Salinity, SND-Sand, SLC- Silt+Clay, DO-

Dissolved oxygen, T-Temperature, D-Depth, Phyto – Phytoplankton density, zooplankton – zooplankton biomass.

OM IM MF S SND SLC DO pH Temp. Depth CHL-a NO3 NO2 SiO2 PO4 Phyto Zoo

OM 1

IM 0.39 1

MF -0.82 -0.18 1

S -0.96 -0.24 0.90 1

SND -0.98 -0.24 0.88 0.98 1

SLC 0.98 0.24 -0.88 -0.98 -1.00 1

DO 0.08 0.04 -0.25 -0.11 -0.07 0.07 1

pH -0.58 -0.16 0.44 0.53 0.61 -0.61 0.45 1

Temp. 0.22 -0.37 -0.15 -0.18 -0.28 0.28 0.24 -0.37 1

Depth 0.27 -0.10 -0.03 -0.19 -0.31 0.31 -0.05 -0.46 0.70 1

CHL-a -0.58 -0.17 0.29 0.57 0.51 -0.51 -0.21 0.04 -0.13 -0.03 1

NO3 0.24 -0.20 -0.06 -0.22 -0.24 0.24 -0.09 0.11 0.13 0.22 -0.37 1

NO2 -0.05 -0.09 0.14 0.09 0.11 -0.11 0.26 0.51 -0.29 -0.37 -0.16 0.41 1

SiO2 0.08 -0.13 -0.05 -0.12 -0.11 0.11 -0.01 0.13 -0.13 0.25 0.22 0.26 0.41 1

PO4 0.01 0.05 -0.18 0.03 -0.07 0.07 -0.29 -0.36 0.12 0.14 0.68 -0.34 -0.39 0.01 1

Phyto 0.30 0.06 -0.30 -0.34 -0.32 0.32 0.35 -0.04 0.27 0.43 -0.16 0.12 0.09 0.59 -0.25 1

Zoo -0.59 -0.07 0.57 0.65 0.61 -0.61 -0.47 -0.05 -0.22 -0.21 0.66 -0.21 0.02 0.03 0.23 -0.34 1

63

8. Summary This is the first study that investigated (1) the spatiotemporal patterns of various biological

components and the factors governing this pattern and (2) compared the relationship between

environmental variables and species and functional diversity (macrofauna and fish) from

backwater system of India.

The phytoplankton community was represented by 67 taxa, zooplankton (14 groups),

macrofauna (16 taxa) and finfish by 48 species. The results of the study indicate that different

environmental factors may be important for different aspects of taxonomic and functional

diversity pattern. Studies using the species and functional diversity will provide a better

understanding of the role of biodiversity in the ecosystem functioning. Much to be gained

from consideration of multiple component of an ecosystem particularly the backwater system

where there the environmental heterogeneity is high, and hence, allowing the testing of

different environment factors in governing the biodiversity pattern. Although the present

study provides important information on the factors governing the biodiversity pattern, more

detailed studies considering the lacunae such as species level identification and species

specific trait information are required in order to test the observed patterns in the ecologically

and economically important backwater system of the country. The results of our study

contribute to the growing knowledge on the diversity patterns for backwater diversity, which

may be critical for understanding ecological patterns and its responses to both habitat and

climatic change. A clear understanding of the various aspect of the ecosystem is fundamental

to evaluate the potential ecological risk and, develop management strategies.

9. Outcomes of the Project (Brief Summary)

Salient findings including technical details and innovations

The present study is the first detailed report that investigated the spatiotemporal

variability of various biological (phytoplankton, macrobenthos, fishery resources and

zooplankton) and environmental components from the Biyyam Kayal.

The study is also the first that used the functional approach to assess macrofaunal and

finfish diversity

The project was able to produce inventories and understanding of the spatiotemporal

variability in the abundance, biomass and diversity of plankton, macrobenthos and

fish of Biyyam Kayal.

Phytoplankton diversity reported in the Biyyam Kayal (67 taxa) is higher than those

reported in Vembanad Lake (40 taxa), Ashtamudi (3 taxa), Kadalundi (24 taxa) and,

Kerala Backwaters (9 taxa).

A total of 48 species of finfish were reported in Biyyam Kayal and is comparable to

larger aquatic system like the Vembanad Lake (52 species) and Ashtamudi (68

species).

An important finding was that all metals studied were above the acceptable limits.

Further, low macrofaunal community (16 taxa) may indicate the negative impact of

pollution, which needs to be further investigated.

64

Since backwater system supports the livelihood of the local fisherman, the high metal

content is of great concern.

Metal water concentration and bioaccumulation of metals in fish and benthic

organism need to be considered in future studies to understand the potential health

impact.

As the study area is a Ramsar site and of socio-economic importance to the locals, the

present study forms an important baseline data for future monitoring studies.

Technical Details

Biological (microbes, phytoplankton, zooplankton, macrobenthos and fish) and

environmental variables (water- chlorophyll a, temperature, pH, salinity, nutrients

and dissolved oxygen and sediment texture, organic and inorganic matter) were

collected from seven stations (June 2017 to Dec 2018)

Standard protocols were followed during collection and processing of the samples

Plankton and benthos were analysed for density, biomass and diversity.

Functional diversity was calculated for macrobenthic and fish community

Fish samples were collected during ecological and artisanal fisheries and from local

using traditionally fish aggregating devices (FAD).

Seasonally analyses of nine heavy metals from sediment

Statistical analyses were carried out using the softwares PRIMER-6, STATISTICA 8

and FDiversity.

Innovations

Tropical estuaries lack baseline data for assessing their ecological health status

and this project is a model which can be utilized for the same.

Statistical modelling on ecological variables can provide cues for the monitoring

and assessment of estuarine Lakes.

Educational Institutions adjacent to Lakes can be empowered using academic

grants for such Lake monitoring and assessments

ii. Publications

i. Journals papers

(a) International: Nil

(b) National:

Sini Anoop, Risvana Ummer, Rasiya Beevi M (2019). “Seasonal

variation in coliform and total heterotrophic bacterial load and

Antibiotic resistance of selected bacteria from Biyyam Kayal”, Journal

of Emerging Technologies and Innovative Research, 6(2), 605-613.

ii. Conference/Workshops etc

(a) International:

Sini Anoop, Ansha M.A., Razia Beevi M (2019) Seasonal composition

of phytoplankton in the Biyyam kayal, Kerala. Proceedings -

International conference on advances in Biosciences, 27-28 Aug 2019,

EMEA College of arts and Science, Kondotty, Kerala.

65

(b) National: Nil

iii. Papers Communicated :

(a) International:

Sini Anoop, Sanitha K. Sivadas, M.C. Manoj, M. Raziya Beevi ,

Spatial and seasonal characteristics of the heavy metals in the surface

sediments of Biyyam kayal, Kerala, Southwest India. (Under review:

Arabian journal of Geosciences)

(b) National:

Sini Anoop, Rajool Shaniz, Rasiya Beevi M. “Checklist of

commercially important finfish and shellfish from Biyyam Kayal,

North Kerala” (Under review: Journal of marine biological

association India).

10. Scope of future work

In view of the current disturbances in the region (domestic, agriculture and rapid

urbanization) and the plans to develop the region for eco-tourism, there was a need to

understand the existing environmental condition of the region. High quality baseline data has

been linked to the success of any developmental project as it forms the basis of the

environmental impact assessment (EIA) process from which predictions and decisions can

made on the impact of the developmental activities on the ecosystem.

The present project investigated the spatiotemporal variation in the sediments heavy

metals, sediment and environmental variables (Nutrients, DO, pH, Salinity, Temperature,

Chl-a,) and biological parameters (Phytoplankton, Zooplankton, Macrofauna, Fin-Fish

resources, E-coli). The data were collected for 18 months to understand, first, the current

environmental conditions of the study area and secondly, this data can also be used to assess

and predict the possible environmental changes that could occur, due to the developmental

activities in the region. Also, based on study it was concluded that the Biyyam Kayal

backwater is threatened by metal contamination therefore, this information can be beneficial

to the local environmental managers to develop strategies to manage the sources of pollution

in the area and also appropriated remediation of this ecologically and economically important

backwater system. We suggest that present integrated approach need to be applied to monitor

the other backwater and coasts system for better understanding of the ecosystem processes.

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final report of kscste (back-to-lab) project

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