ASSESSING THE ECOLOGICAL INTEGRITY OF FRESHWATER

ECOSYSTEMS IN SOUTH AFRICA

By

Mao Angua Amis

MSc. Conservation Biology Programme,

Percy FitzPatrick Institute of African Ornithology,

University of Cape Town

and

GISI Conservation Planning Unit

Kirstenbosch Research Centre

South African National Biodiversity Institute

Report submitted to the Council for Scientific and Industrial Research (CSIR)

February 2005

The copyright of this thesis rests with the University of Cape Town. No

quotation from it or information derived from it is to be published

without full acknowledgement of the source. The thesis is to be used

for private study or non-commercial research purposes only.

Univers

ity of

Cap

e Tow

n

ABSTRACT

Freshwater ecosystems are highly imperiled the world over, yet conservation planning is

mainly focused on terrestrial and marine ecosystems. One of the reasons is that, few

criteria exist to assess the ecological integrity of rivers for conservation planning.

Another cause for the disparity between terrestrial and freshwater conservation planning

is that, data for assessing ecological integrity of rivers generally have limited

geographical coverage. Here, I use a fine-scale dataset from South Africa to explore

how well measures of ecological integrity can be predicted from readily available

remotely sensed data. I built a spatial statistical model that uses broad land use

variables for predicting the ecological integrity of rivers (subdivided into riparian and

instream integrity). I also tested the importance of the spatial scale of land use variables

in predicting ecological integrity in order to identify what scale such predictive models

should be built. Results showed that riparian and instream integrity of river systems

could be reasonably predicted, although riparian integrity was more accurately predicted

than instream integrity. The total area under natural cover is the most significant

variable for assessing riparian integrity. Riparian integrity is best predicted by land use

activities at the catchments level rather than more locally. This GIS-based model

provides a fine-filter approach to supplement landscape-level conservation plans of river

systems. The model represents a significant contribution towards the monitoring

component of the River Health Program (RHP), which reports on the state of rivers in

South Africa.

KEYWORDS

Freshwater ecosystems, ecological integrity, conservation planning, predictive modeling

INTRODUCTION

The conservation status of freshwater ecosystems worldwide is poor and declining fast,

with rivers and wetlands among the most threatened ecosystems (Vitousek et al. 1997,

Revenga et al. 2000). This trend is attributable to aquatic systems having being

severely altered by human activity (Moyle & Williams 1990, Jensen et al. 1993).

Although a comprehensive global assessment of the status of freshwater

biodiversity is difficult to carryout (Revenga et al. 2000), the few estimates that exist are

shocking. About 30% of freshwater vertebrate species in the world have become

extinct, threatened or endangered (World Conservation Union 2000). The situation is

even more serious for other freshwater faunal groups (Abell 2002). The challenge to

conservation biologists is to acknowledge the freshwater biodiversity crisis, and to

reflect it through focused research on topical issues in freshwater. Current research

does not seem to however, reflect the concern for this crisis (Abell 2002).

Despite their obvious importance and the seriousness of the threats that they

face, freshwater ecosystems remain poorly understood and inadequately represented in

biodiversity assessments (Higgins 2003). One reason for the disparity between

freshwater conservation and terrestrial conservation is the lack rapid assessment

techniques of ecological integrity of freshwater ecosystems. Assessment of river

ecological integrity is resource intensive, and so there is a dearth of information on

many freshwater ecosystems. Those data that are available for conservation planning

are often too coarse for fine-scale planning.

In South Africa, the status of freshwater ecosystems matches trends worldwide,

with recent assessments showing that 44% of South Africa's mainstem rivers are

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critically endangered (Nel et al. 2004). Sixty Three percent of freshwater fish in South

Africa are extinct, threatened or endangered (Moyle & Leidy 1992). These statistics

reflect the dire need to incorporate freshwater ecosystems into the mainstream

conservation planning process. However, there are very limited ecological data on

South Africa's rivers, with the assessment of freshwater ecosystems having focused to

date on mainstem rivers e.g. the National Spatial Biodiversity Assessment (Nel et al.

2004). Yet most of the mainstems are highly transformed by human impacts. With many

of these already seriously damaged, conservation attention could usefully focus on

tributaries, though data on these is extremely limited.

Fortunately for South Africa, there are relatively good Geographical Information

Systems (GIS) data on terrestrial biodiversity and land cover. Recent terrestrial

conservation plans developed in South Africa, which use these data for systematic

planning, are probably the most detailed and explicit for any part of the developing world

(Balmford 2003). The availability of this information presents an opportunity for

freshwater systems too: if available fine-scale information on the ecological integrity of

rivers can be predicted with reasonable confidence using remotely-sensed data, then in

principle GIS data could be used to estimate freshwater integrity more widely, and

hence accelerate freshwater conservation planning.

The overall aim of this study is therefore to use South African data to test the

feasibility of GIS-based assessment of the ecological integrity of rivers. Specifically my

objectives are to: -

1. Review the different criteria for assessing river health and ecological integrity in

South Africa and the suitability of these criteria for conservation planning;

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2. Develop a GIS-based model for predicting river ecological integrity;

3. Determine the spatial scale at which GIS data best predict ecological integrity;

4. Test whether the relationship between land use and ecological integrity holds at

a finer scale than the entire catchments so as to predict the integrity of

tributaries.

RIVER HEALTH AND ECOLOGICAL INTEGRITY

Definitions of river health and ecological integrity and methods for their assessment are

still a subject of considerable debate (Wicklum & Davies 1995, Norris & Norris 1995,

Norris &Thoms 1999, Quigley et al. 2001). Ecological integrity may be defined as" the

ability to support and maintain a balanced, integrated, adaptive community of organisms

having a species composition, diversity, and functional organization comparable to that

of natural habitat of the region" (Angermeier & Karr 1994). Unlike ecological integrity

that is based purely on ecological criteria, river health refers to the ecological condition

of a river and its ability to provide ecosystem service (Boulton 1999, RHP 2003). River

health is thus the incorporation of ecological values of a river system and the human

values associated with such a river system (Boulton 1999). Here, my primary concern is

to assess the ecological status of riverine ecosystems, regardless of their potential to

provide ecosystem services, and so in the context of this paper I use both terms

interchangeably.

In order to design a conservation plan for freshwater ecosystems and to

determine the impact of anthropogenic activities, it is important that river integrity is

mapped across the planning domain (Nel et al. 2004). In South Africa, various indices

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have been developed for assessing ecological integrity and water quality e.g. the South

African Scoring System (SASS) (Chutter 1998), the index of habitat integrity (Kleynhans

1996), the riparian vegetation index, (Kemper 1998), and the fish assemblage index

(Kleynhans 1999). In the following section, I review these indices, their usefulness for

freshwater conservation planning, and how they have been applied in the River Health

Program.

Methods of Assessing River Ecological Integrity

Several methods and indices have been developed in South Africa for assessing

ecological integrity of riverine ecosystems (Table 1). Most of the criteria are data

intensive, and most of them do not have a wide geographic coverage. Together with the

lack of manpower, it has made their use unsuitable for landscape scale conservation

planning. Most of the indices are biotic indices that use the presence of organisms as

indicators of river integrity. The use of broad surrogates may offer a more robust and

less data intensive means of assessing the ecological integrity of freshwater

ecosystems in cases where data from the field is not readily available. Thus the choice

of suitable surrogates that represent both the abiotic and biotic characteristics of aquatic

ecosystems is critical.

Fish Assemblage Integrity Index (FAil)

This index measures the biotic integrity of river systems based on attributes of native

fish (with the presence of alien fish reducing the FAil score). The index is based on the

relative intolerance of fish occurring in a given river segment (Kleynhans 1999). Four

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components are taken into consideration in estimating intolerance - habitat preferences

and specialisation, food preferences and specialisation, requirements for flowing water

during different life stages, and association with habitats with unmodified water quality

(Kleynhans 1999). The shortfall of this index is that "experimental information on the

tolerance of various South African fish species is lacking" (Kleynhans 1999). As a result

the assessment of fish intolerance is based entirely on field observation. Since the

representativeness of FAil is dependent on sampling intensity, its wide application for

freshwater conservation planning is again limited.

South African Scoring System (SASS)

SASS is an index of water quality based on the presence of families of aquatic macro­

invertebrates (Chutter 1998). Aquatic macro-invertebrates form one of the most

commonly assessed components of the freshwater biota in South Africa (Dallas & Day

2004). Each macro-invertebrate taxon is allocated a sensitivity or tolerance score,

according to the water quality conditions it is known to tolerate (Dallas 1997). SASS was

developed in response to a need by resource managers for a rapid and cost-effective

means of assessing water quality (Dallas 2000). The major shortfall in the application of

SASS is that it is resource intensive.

Water Quality Index (Diatoms)

This index uses diatoms as an indicator of water quality (Bate et al. 2004). Diatoms are

widely used throughout the world as bioindicators of water quality (Bate et al. 2004).

The shortage of expertise in the identification has made the use of this index unsuitable

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for use in South Africa (Uys 1996). However, it is in the process of being developed for

estuaries and wetlands (Day, J. personal comm.)

PESC (present ecological status)

The PESC index was derived in a scoping exercise during the planning phase of the

river health program (Kleynhans, C.J. personal comm.). It was used to give a very

rough idea of the river systems of South Africa, and was mainly based on expert

opinion. PESC is still in use in some instances for aquatic reserve determination. There

is a national coverage of PESC, but the data is too coarse (only available for mainstem

rivers of quaternary catchments), and it was only based on expert knowledge.

Riparian Vegetation Index (RVI)

The Riparian Vegetation Index was developed by Kemper (1998). The index uses

physical and biological attributes of the riparian zone to assign an ecological integrity

class for river segments. The attributes assessed for the determination of the riparian

index are: - vegetation decrease, alien vegetation encroachment, bank erosion, channel

modification, water abstraction, inundation, flow modification, and water quality. Each

attribute is assigned a weight, and a multicriteria decision analysis (MCDA) is used to

arrive at a final integrity score. Deriving this index requires a thorough knowledge of all

the river systems under review. It is thus impractical for developing a conservation plan

for large areas or in cases where there is lack of expert knowledge. The riparian

vegetation index is used to determine riparian integrity while compiling the index of

habitat integrity discussed below.

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Index of Habitat Integrity (/HI)

The IHI index was developed by Kleynhans (1996). It assesses the impact of

disturbance both in the riparian zone and in the instream habitat (RHP 2003). The index

of habitat integrity (IHI) is the overall index used most frequently for determining river

integrity in South Africa. It assesses the impact of disturbance both in the riparian zone

and in the instream habitat (RHP 2003).

Instream habitat integrity

In the determination of instream integrity, both measures of physical and biotic

characteristics are used to estimate overall instream integrity (Table 2). Fish

assemblages and macro-invertebrates are used to estimate the biotic integrity. Other

biotic attributes include, the presence of exotic macrophytes and fauna (Kleynhans

1996). For measures of physical characteristics the following attributes are measured: ­

water abstraction, flow modification, bed modification, channel modification, water

quality, inundation, and solid waste disposal.

Riparian habitat integrity

Riparian habitat integrity is determined using the RVI, where vegetation removal,

cultivation, flow modification, channel modification, inundation, erosion, alien vegetation,

and water quality are assessed (Kleynhans

1996). Most of the measures of riparian integrity are physical rather than biotic (Table

2).

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The River Health Program (RHP) of South Africa

The RHP is a national biomonitoring program, whose mandate is to assess and monitor

the ecological integrity of riverine ecosystems in South Africa (Roux et al. 1999). It was

established in 1997, and incorporates several components of the biota (Dallas 2000).

The RHP uses instream and riparian integrity monitoring to characterize the response of

aquatic environments to multiple stressors (Roux et al. 1999). The indices used in the

RHP represent the most widely available indices for assessing ecological integrity in

South Africa (Fig. 1).

In the RHP, an overall integrity score of the river system was calculated based on

riparian and instream integrity. However, this was done only for the purposes of

reporting on the state of rivers (SOR), and there was no scientific basis for this

derivation (Kleynhans, C.J personal comm.). Scores of river integrity (instream and

riparian) were categorized in the following manner, (90-100)= A; (80-90)= B; (60-80)= C;

(40-60)= D; (20-40)= E; (0-20)= F. A category of (A) represents a natural unmodified

river system, while a category of (F) represents a very highly modified system with

almost a complete loss of natural habitat.

Land use as a Measure of River ecological Integrity

Ideal indicators of river health should have features that are easy to quantify and are of

direct relevance to river health (Boulton 1999). In the absence of suitable indicators, a

method needs to be developed for rapid assessment of the ecological integrity of river

systems.

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Anthropogenic activities impose a huge stress on river systems (Allan & Flecker

1993, Dynesius & Nilsson 1994). And therefore land use could be a potential predictor

of integrity. By modeling potential predictors of ecological integrity on a GIS platform, it

may be possible to develop criteria for assessing the ecological integrity of river

systems. In this study, I explore the suitability of variables associated with land use, and

others like population density as proxy for assessing river ecological integrity for the

purposes of conservation planning.

METHODS

Study Area

The Crocodile (West) and Marico Water Management Area (WMA) (Fig. 2 & 3), is one

of 19 WMAs in South Africa. It covers an area of 4.7 million hectares, and forms part of

the Limpopo River basin, which spans four countries (Botswana, Zimbabwe, South

Africa and Mozambique). The main rivers in the water WMA are the Crocodile and

Marico rivers and their tributaries. Other important rivers include the Elands, Pienaars

and the Bierspruit (DWAF 2002a).

The location of the Crocodile and Marico, covering South Africa's economic

centre, has bearing on both water requirements and the ecological integrity of its rivers.

The WMA has a semi-arid climate, and water requirements are very considerable.

Irrigation accounts for 37% of water requirements, the rest being attributable to urban,

industrial and mining needs (DWAF 2004). Agriculture is the most predominant land

use, accounting for 53% of area; other major economic activities include mining and

light industries (Fig. 3) ( DWAF 2002b). The WMA supports a human population of 4.9

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million (1995 census), of which about 70% live in urban areas with the remainder living

in rural areas (DWAF 2004). I chose this study area for my research because it has

excellent data on river integrity, and because of the diverse land use in the area.

Delineation of the study area

The study area was divided into 25 regions (hereafter referred to as 'catchments'),

based on Level 2 ecoregions (Roux 1999) and the location of monitoring sites.

Ecoregions denote areas with similarity in ecosystems and environmental resources.

The classification of ecoregions is based on a hierarchical procedure involving the

delineation of spatial units, with a progressive increase in detail at each higher level of

hierarchy (Roux et a/1999). In South Africa, level 1 and level 2 ecoregion typology have

been done for the entire country. The level 1 ecoregion was based on physiography,

climate, geology, soils and potential natural vegetation (Roux et a/2002, DWAF 1999).

While the level 2 ecoregion was a refinement of the level 1, and was based on

variations in geology, natural vegetation, altitude, as well as expert knowledge of the

rivers that flow through the planning domain (Low & Rebelo 1996, Roux et a/1999).

The 25 catchments were subdivided into smaller units based on tributaries

(hereafter referred to as 'sub-catchments') giving rise to 47 sub-catchments. Sub­

catchments were created to isolate tributaries independent of the mainstem rivers.

Present Ecological Integrity of the River System

Data on the ecological integrity of the river systems in the study area were obtained at

RHP monitoring sites and expert knowledge (RHP in press). The overall scores of river

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ecological integrity were based at the catchments level (RHP in press). Scores

comprised two measures, riparian zone habitat integrity, and instream habitat integrity.

Riparian and instream habitat integrity were based on proxy variables to define habitat

integrity (Table 2). I chose to build the model based on the RHP assessments, because

they have the most fine scale data on river ecological integrity available for any WMA in

South Africa.

In order to explore potential predictors of ecological integrity as a whole, I next

identified a suite of potential predictors (Table 3), which were available in GIS format,

and which I considered might be linked to these individual components of river integrity

(whilst bearing in mind that some components may not have suitable remotely-sensed

surrogates) .

Quantification of potential predictors of ecological integrity

I chose a series of land use variables that might be suitable surrogates for assessing

ecological integrity (Fig, 3) from the land cover/land use map of 2000 (CSIR in press)

and incorporated separate GIS surfaces of population and number of mines. Together

with the total area of catchments, 14 variables were used to predict ecological integrity

(Table 3); waterbodies were excluded from the analysis. I derived the land use variables

by reclassifying the initial 49 land use types from the land cover map. For instance, I

lumped all naturally occurring vegetation types together to form a single land use type.

This reclassification was necessary so as to avoid too many independent variables for

subsequent modeling.

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Because river integrity may be influenced not just by local conditions but also by

more distant patterns of landuse (Allan et al. 1997), I attempted to predict river integrity

scores using land use data at a range of spatial scales. Riparian strips were created on

both sides of the river to capture land use patterns up to 100m, 500m, 1000m, 2000m,

and 3000m from the river of interest, using Arcview 3.2a and Arclnfo (ESRI, Redlands,

CA). The choice of the size of riparian strip was arbitrary, and the riparian strips covered

the entire length of the river in each of the 22 catchments. I also compiled catchments­

wide summaries of land use, so that together with the riparian strips, analyses were

carried out at six spatial scales.

The Model

The model predicting river ecological integrity was derived based on 22 of the

catchments because three did not have sufficient data. The model was then applied to

predict the integrity of the 47 sub-catchments. I chose a Generalized Linear Modeling

(GLM) approach (Chambers & Hastie 1992) to predict integrity scores because I was

looking for simple relationships and also due to my small sample size of catchments

(22), I wanted to avoid over-fitting the model, which might tailor the current data too

much to the detriment of future predictions (Vanderpoorten et al. 2005). With more data,

a generalized additive model (GAM) could have been more suitable. I assessed

the potential predictors of riparian and instream integrity independently of each other,

using the overall riparian and the instream integrity scores as dependent variables in a

series of GLMs. I predicted the components of riparian integrity using the best model

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out of the six models that were built. I did not attempt to predict the components of

instream integrity.

Fitting the Model

I built six models for each of the different spatial scales of analyses in S-Plus software

(Mathsoft 1999) using GLM with Poisson error distribution (link

function =log (mu), variance =mu), because non-negative variables are best modeled

using a Poisson-error distribution (Crawley 1993). Due to limited sample size I

constrained the models in various ways. No attempt was made to mix the spatial scales

at which predictor variables were measured within a given model (i.e. the same model

did not include land use variables derived from 100m and 500m riparian strips). I also

did not consider any possible interactions between the various predictor variables, even

though it is possible that the magnitude of the effect of one variable on river integrity

may depend on the effect of other variables (Chambers & Hastie 1999). Interactions

were not considered because I had a small sample size (22), with too many variables

already. Thus interactions could result in over fitting the model, yet they are difficult to

interpret.

In all instances where the model output contained several predictors, I limited the

number to the best four explanatory variables, based on Akaike Information Criterion

(AIC) scores (Akaike 1974). The maximum number of parameters used in the model

was limited to four due to the small sample size, and to enable comparison of

competing explanatory models compiled at differing spatial scales.

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Testing the accuracy of the models

To test for the strength of each model, r2 values were calculated. I also measured how

well the integrity categories (A to F) assigned to catchment in the RHP compared to the

category (A to F) predicted by a model.

Application of the Model at a finer Scale (sub-catchments)

I used the best model among the six developed to predict riparian and instream integrity

of the sub-catchments. The choice of this model was based on its ability to predict

integrity accurately according to the categories assigned in RHP and whether there is a

logical explanation of the potential predictors used in the model. An expert who is well

versed with the study area evaluated the results of the predictions by giving his opinion

on what he thinks is the actual ecological integrity of the sub-catchment.

RESULTS

Overall predictive power of the models

Modeling riparian and instream integrity in the 22 catchments of the study area revealed

that riparian integrity (Fig. 4) could be predicted more accurately (Table 5 &6) than

instream integrity (Fig. Sa), and that land use at the catchments scale is a better

predictor (~ = 0.79, P < 0.001) than at the smaller scales of riparian strips (r2 = 0.5, P <

0.001) (Table 5). Instream integrity is also somewhat better predicted at the catchments

scale than at smaller riparian strips (Table 6). When the model for instream integrity

was run for headwaters only; it yielded a better fit (~ =0.97, p< 0.001) (Fig. 5b) using

four land use variables only (natural vegetation, population density, area of mines, and

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density of dams), than the fit for models in all the catchments (Fig. 5a), where upstream

effect was not taken into consideration.

Most important predictor variables of river integrity

The relative extent of cover by natural vegetation was the single most powerful land use

variable for predicting riparian integrity scores, with a positive correlation in all the

spatial scales of analyses. Regardless of the spatial scale of analyses, natural

vegetation cover emerged as the key determinant of riparian integrity (Table 5). Another

important and independent predictor was the presence of a mine in the vicinity, which

appeared to be more important than the relative area occupied by mines. With the

exception of the scenarios with the riparian strip nearest the river (100 meters), all other

spatial scales featured the number of mines as a significant predictor of riparian

integrity. Other land use variables important for predicting riparian integrity, but

restricted to specific spatial scales include the relative extent of land under degradation,

the relative area occupied by mines, the relative area of rural dwellings and eroded

areas (Table 5). The rather surprising result was that the density of roads, the

proportion of cultivated area, plantations, and the relative extent of dams, industry,

eroded areas, and built-up residential or commercial areas were not important

predictors of riparian integrity (Table 5).

The most important predictors of instream integrity are not very different from

those of riparian integrity. Natural vegetation cover was again the key determinant of

instream integrity regardless of the spatial scale of analyses (Table 6). The relative

extents of plantations, of rural dwellings, and of industrial developments were also found

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to be important predictors of instream integrity at most scales, while relative area of

mines, population density and the density of dams are important predictors at the

catchments scale only (Table 6). The relative extents of cultivated lands, road density,

and the total area of the planning unit do not feature as significant predictors of instream

integrity in most spatial scales (Table 6).

Predicting the components of riparian integrity

Attempts to predict the components of riparian integrity (i.e. vegetation decrease, bank

erosion, channel and flow modification, water abstraction, inundation and water quality)

yielded poor results because fitting the model involved too many predictors, making it

difficult to explain their effect. However, when the model was restricted to the best four

variables (based on AIC scores) in predicting each of the eight components of riparian

integrity there was an improvement, although it was still difficult to explain the effect of

the potential predictors on the components (Table 7). The most important variables in

the model output were: - natural vegetation cover, the relative area of mines, eroded

areas and population. Channel modification yielded the best prediction (r2 = 0.89, p<

0.001). The most important variable for determining channel modification was the total

area of mines. Other important variables for determining channel modification were

population, natural vegetation cover, and eroded areas.

Model application for State of the Rivers (SOR) reporting

Most of the rivers were predicted in the appropriate category of ecological integrity

according to the River Health Program categorization mentioned earlier. Riparian

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integrity was estimated with an accuracy of up to 77% (Table 5). Incorrect Predictions

were either overestimated or underestimated by only one class (Fig. 6). For instream

integrity, 41% of predictions were in the correct category of classification according to

the river health program (Table 6). Some predictions were overestimated or

underestimated by more than one category (Fig. 7).

Model application for predicting ecological integrity at a finer scale (sub­

catchments)

The results of the predictions of the sub-catchments based on the best model were

mapped (fig. 8).

Discussion

Prediction of riparian and instream integrity

My analyses suggests that both riparian and instream integrity can be predicted with

reasonable accuracy using a series of variables covering land use, settlement, and

infrastructure. Riparian integrity was predicted more accurately than instream integrity

however, because riparian integrity is primarily a reflection of physical characteristics

such as bank condition and riparian vegetation cover. Adjacent land cover has a direct

impact on these characteristics. In contrast, land-use data are relatively poor predictors

of instream integrity, which in large part is linked to the structure of the biotic community

of the river, itself only indirectly impacted by adjacent land use. It appears that activities

upstream of the catchments maybe better predictors of instream integrity than is

adjacent land use. This finding is consistent with Roth et al (1996) and Allan et al.

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(1997) who found that habitat condition and index of biotic integrity correlated strongly

with regional land use upstream of a site.

In the derivation of riparian and instream integrity, numerous characteristics were

used (Table 2) and thus, the potential of the model to estimate the magnitudes of the

components of integrity was determined by the choice of predictors. Some components

e.g. channel modification (Table 7) was better predicted because it was directly related

to potential predictors such as dams and mines. In general, the application of the model

to predict components of integrity was unsuitable. This was the only case throughout my

analyses where the model was over-fitted, with more than eight potential predictors

used in the initial attempt to predict the components. I had to limit the number of

predictors to four and run the model again.

The most important predictor variables of river ecological integrity

Natural vegetation cover was the most important predictor of both riparian and instream

integrity. This was not surprising because, natural vegetation may influence stream

processes like regulation of sediment transport (Osborne & Kovacic 1993),

development of channel morphology (Naiman et al. 1993), and regulation of erosion

(Berkman & Rabeni 1987). In the absence of other data on the state of a river,

assessing the natural vegetation cover alone can provide a fairly accurate prediction of

integrity. Such rapid assessments are very useful for purposes of prioritising river

systems for conservation interventions.

The surprising result from the prediction of ecological integrity is that potential

predictors like road density, relative coverage of dams, industries, eroded areas and

relative area covered by cultivated lands were not important in predicting river integrity.

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This may imply that even if these are major factors in the alteration of ecological

integrity of river systems (Roth et al. 1996, Quigley et al. 2001, Higgins 2003), they are

not necessarily the best broad surrogates for predicting river integrity.

The application of land cover as a potential surrogate for developing biodiversity

plans have been widely used by the Nature Conservancy of the US and World Wildlife

Fund (WWF). The surrogates they use remain largely untested (Abell et al. 2002). For

example, they place a lot of emphasis on the importance of road density as a potential

predictor of river integrity, but this study has shown that road density is not an important

predictor of integrity, instead natural vegetation cover, and population were more

important in predicting river integrity.

Spatial scale of analysing potential predictors of river integrity

The influence of land use on river integrity is scale dependent (Allan et al. 1997,

Sponseller et al. 2001), and I found that land use at the catchments level provided the

best scale for predicting overall river integrity. In a similar study, Morelyand Karr (2002)

found that the "effectiveness of localized patches of riparian corridor in maintaining

biological integrity varies as a function of basin-wide urbanization". Instream processes

and patterns such as organic matter input may depend on local vegetation cover, while

nutrient supply, channel characteristics and hydrology are influenced by activities at the

catchment scale. The fact that some potential surrogates were only useful in predicting

integrity at specific spatial scales implies that, the scale of analyses also affects

individual predictors. However, this needs to be investigated further. GIS is very

appropriate for modeling such varying scenarios, because it enables assessments to be

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carried over a large geographic area and different spatial scales, although there is

always a tradeoff between the geographic extent and the resolution of analyses.

Limitations of the model

The model needs validation on an independent dataset preferably in an entirely

different region to ascertain if the findings in this study hold. A similar approach, with

reasonable sample of field-based data, which span a broad range of ecological and land

use conditions (e.g. all those in a country), which can then be divided into reasonably

sized calibration and validation datasets could be useful. Also more experts need to

evaluate the result of the predictions in cases where there is no independent dataset

available, as was the case in this study.

The challenge for such predictive modelling is to use potential predictors that

closely mimic both, the abiotic and biotic characteristics of aquatic ecosystems,

because the explanatory power of the model is limited by the choice of predictors.

Another important determinant of the robustness of a predictive model is the sample

size. In this study, the sample size of 22 catchments was quite small. A small sample

size affects the ability of the model to explain the relationship between potential

predictors and the dependent variables, and also makes validation of the model difficult

(Stockwell & Peterson 2002).

Implications for conservation planning

Tributaries contribute significantly to the overall conservation status of a river, because

in many cases mainstem rivers are highly disturbed. The potential of this model to

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predict the integrity of the sub-catchments is thus a significant contribution to freshwater

conservation efforts. The resource intensive nature of freshwater assessments means

that very often data is unavailable for certain river systems or some areas are

inaccessible. In such a situation, predictive modelling offers an opportunity for

assessing the ecological integrity of the river systems.

The dependency of river integrity on catchment-wide activities has considerable

implications for conservation planning. In the past there has been strong focus on

protecting riparian corridors (Gregory et 81. 1991), as key to conserving freshwater

ecosystems. But it appears that catchment-wide activities are vital in assessing river

integrity as well. In South Africa, buffer strips of 500m on both sides of the river have

been widely used by freshwater conservation planners in assessing ecological integrity

(Roux. D. personal comm.). My findings show that this is perhaps not an appropriate

scale, a wider buffer zone of at least 1000m maybe more useful.

It should be emphasised that predictive modelling cannot substitute field-based

surveys, because field assessments still offer the best means of assessing ecological

integrity (Roth et 81. 1996). But with the advances in GIS technology and increasing

focus on landscape scale conservation planning, predictive modelling may offer the only

solution in bridging the gap between freshwater conservation planning and terrestrial

conservation planning.

ACKNOWLEDGEMENT

I am grateful to my supervisors Dr. Mathieu Rouget, Dr. Andrew Balmford, and Prof.

Jenny Day for their close guidance and excellent advice throughout the course of this

23

research project. I'm thankful to Jeanne Nel for conceiving this project idea and making

sure it goes all the way. This project was shaped in many ways through extensive

discussions and logistical support from C.J. Kleynhans, Dirk Roux, Gillian Maree, and

Juanita Moolman. Wilfried Thuiller provided statistical and modelling expertise, for that

I'm grateful. I acknowledge my colleagues at the GIS/conservation planning unit

(SANBI) for assistance with GIS analyses. Funding and logistical support came from the

Tropical Biology Asscociation (TBA), South African National Biodiversity Institute

(SANBI), and the Council for Scientific and Industrial Research (CSIR). Finally I would

like to thank Prof. Morne du Plessis and all the staff at the FitzPatrick Institute for

making sure nothing went wrong during the course of my studies.

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30

LIST OF TABLES AND FIGURES

Table 1. Indices presently used for assessing river ecological integrity in South Africa;

parameters measured, indicator, type of index, potential surrogates for the indices and the

possible GIS measures! measures of estimating the potential surrogates

Index Measure Indicator Type PotentialSurrogatesfor theindices

Possible GIS ReferencesAnalyses/measures

Bate (2004)

Chutter(1998)

Kleynhans(1996)

Kemper(1998)

Unpublished

Level of Kleynhanstransformation in (1999)riparian zone

None

None

None

Land use

None

Biotic

Biotic

Biotic

Abiotic Land use, Transformation,/biotic erosion, dam coverage,

impoundmentdigital elevations, models, mining,

agriculture,urbanization,alien vegetation

Habitatsuitability &water quality

WaterqualityHabitatsuitability

Assemblage of Wateraquatic qualityinvertebratesTrophic levelpreference,requirements forflowing water,habitat integrity,health of fish,Assemblage ofdiatomsVegetationdecrease, exoticvegetation,channelmodification, waterabstraction,inundation, flowmodification, waterquality,

PESC (present Bed modification, Habitat Abiotic/ Land use, Transformation,ecological flow modification, suitability & biotic impoundmentdam coverage,status inundation, water water quality s digital elevationcondition) quality, introduced models, mining,

alien biota, agriculture,riparian vegetation urbanization,conditions alien vegetation

Index of HabitatWater abstraction, Habitat Abiotic/ Land use, Transformation,Integrity (IHI) flow modification, sultabillty biotic impoundmentdam coverage,

Channel s digital elevationmodification, solid models, mining,waste, exotic agriculture,organisms, urbanization, andinundation, water alien vegetationquality, alienvegetation

Water qualityindexRiparianvegetationindex (RVI)

SASS (SouthAfrica Scoringsystems)FAil (fishassemblageintegrity index)

31

Table 2. Parameters used as indicator of riparian and instream Integrity, and the

weights of their relative contribution to the overall river integrity, as used in the RHP

assessments (RHP in press)

Riparian habitat integrity ParameterVegetation decrease

Exotic vegetation

Bank erosion

Channel modification

Water abstraction

Inundation

Flow modification

Water quality

Instream habitat integrity ParametersWater abstraction

Flow modification

Bed modification

Channel Modification

Water quality

Inundation

Exotic macrophytes

Exotic fauna

Solid waste

Weights13

12

14

12

131112

13

Weights14

131313

14

10

9

8

6

32

Table 3. Variables used as potential surrogates for predicting integrity, and a description

of the measures used in their quantification

Predictors Description

Natural areas Area under natural vegetation cover (%)

Plantations Area under forest & grassland plantations (%)

Degraded Areas Area under man-induced low vegetation cover (%)

Cultivated areas Area under all forms of agriculture (%)

Rural clusters Area under clustered rural dwellings (%)

Residential/commercial areas Area under residential or commercial usage (%)

Industrial Area built up of industries (%)

Mines Area used for mining (%)

Eroded areas Area under bare rock/soil (gully or sheet erosion) (%)

Roads Length of roads per Km2 (%)

Dams Area under dams (%)

Population* Population density (KM2)

Number of mines Total number of active mines

Area Total area of catchments/planning unit (m2)

* Assessed only at the scale of regions

33

Table 4. Correlation matrix between potential predictors summarized at the catchments

scale

rJJ"0 3 c ......

C "0 rJJ .9 0.9 Q) ..... Q)

Q) ..... ~

"@ ..... "0 ce C .;:: ...... "0..... Q)ce

~ ce ce .::: Q) ..... 0 rJJ Q) rJJ rJJ "3 ..0 rJJ..... "@ "0 rJJ;:l C

~ ..... ;:l ce Q) "0 "0 6 6 Q)..... eo "3 ~ 'Vi Q) c 0 ce 0.. Cce ce Q) ;:l Q) "0 ~ ·s 0 ce 0 ;:l ·SZ is:: 0 c <t:~

0 Zu ~ ~ - ~ ~ P-.

Natural

Plantations

Degraded

Cultivated

Rural

Residential

Industries

Area of mines

Eroded

Roads

Dams

PopulationNumber of mines

1

-0.66 1

-0.23 -0.39 1

-0.30 -0.11 0.11 1

-0.20 -0.18 0.31 0.29 1

-0.86 0.84 -0.10 -0.12 -0.08 1

-0.78 0.86 -0.20 -0.19 -0.11 0.97 1

-0.38 0.16 0.10 0.16 0.69 0.22 0.14 1

0.09 -0.09 -0.01 -0.30 0.00 0.03 0.03 -0.14 1

-0.67 0.81 -0.29 0.08 0.10 0.73 0.73 0.33 -0.12 1

-0.03 0.22 -0.22 -0.13 0.39 0.06 0.15 0.51 -0.08 0.46 1

-0.17 0.41 -0.21 -0.20 -0.18 0.30 0.42 -0.02 -0.17 0.45 0.61 1-0.11 0.17 0.25 -0.18 -0.05 0.08 0.07 0.22 -0.04 0.06 0.20 0.29 1

34

Table 5. Output of the six different models showing the effect of potential predictors on

riparian integrity, the strength of prediction (r2) , and the % accuracy of the predictions

according to the State of the River (SOR) categorization. The (+) and (-) signs signify

the effect of the parameter on integrity according to the model (e.g. increase in natural

cover (+) results in higher integrity). Blank columns show that the variable did not

appear in the model output as a predictor of integrity. All the predictions in the different

spatial scales (models) were significant at p < 0.001

I/)(1)

I/)c

RIPARIAN (1) 'E cI/) ro c .... 0C "0 "0 I/) 'E c

0 1:50 (1) (1) +:i (1) 0..... C +:i .... :0ro +:i "0 ro (1) 'C .... "0 ro (1).... ro ro > "0..... 0 (1) I/)

I/) "5 .0 (1)..... ro I/) "0::J C....

+:i .iii ::J ro "0 E E ro ......... ro 0>::J

.... (1) 0 ro 0. (1) 0-ro (1) ::J (1) "0 0 ro 0 ::J0- c .... .... .... <'L :::RZ 0 U 0:::: 0:::: « w 0:::: 0 0- Z « 0

1OOm riparian strip + + + + 0.5 45.5

500m riparian strip + + 0.76 63.6

1000m riparian strip + + 0.68 77.3

2000m riparian strip + + 0.71 72.7

3000m riparian strip + + + 0.78 59.1

Catchments + + + 0.79 68.2

35

Table 6. Output of the six different models showing the effect of potential predictors on

instream integrity, the strength of prediction (r2) , and the % accuracy of the predictions

according to the SOR categorization. The (+) and (-) signs signify the effect of the

parameter on integrity according to the model (e.g. increase in natural cover (+) results

in higher integrity). Blank columns show that the variable did not appear in the model

output as a predictor of integrity. All the predictions in the different spatial scales

(models) were significant at p < 0.001

l/)Q)

l/)c

INSTREAMQ) 'E c

l/) CllC

C ..... 0C "0 "0 :p l/) 'E 0 0 ~0 Q) Q) c Q) :p ....:p "0 ..... 'C ..... "0 Q) "0Cll Cll Q) 0 l/) CllCll

~..... Q) l/) .c Q).... ..... > Cll

"0 l/) "0 :::::l ....:::::l C C) :p .iii :::::l Cll "0 Cll E c.. E Cll o,..... Cll :::::l....

~ 0 ~Cll Q) :::::l Q) "0 .... 0 Cll 0 :::::l "LZ a.. 0 0 0::: 0::: c <! LU 0::: 0 o, z <! :::R0

100m riparian strip 0.5 50

500m riparian strip + 0.47 45

1000m riparian strip + 0.52 41

2000m riparian strip + + 0.64 68

3000m riparian strip + + 0.64 64

Catchments

Headwaters

+

+ +

+ 0.68 55

0.97

36

Table 7. Model of catchment showing the effect of potential predictors at the catchments

scale on the components of riparian integrity, and the strength of prediction (r2) . The (+)

and (-) signs signify the effect of the parameter on the component of riparian integrity

according to the model (e.g. increase in natural cover (+) results in higher integrity).

Blank columns show that the variable did not appear in the model output as a predictor

of integrity. All the predictions in the different spatial scales were significant at p < 0.001

Components(/)

of riparianQ)

(/)c:

Q) 'E(/) m c: c: -integrity c: "0 "0 :;::::; (/) 'E 0 00 Q) Q) c: Q) :;::::; -ן

m :;::::; "0 ..... 'C - "0 Q)ro ro ro Q) ..... 0 Q) rn ro

.0I- ..... > m "0 (/) 0(/) "5:::::l I- ro "0 E E roc: 0> :;::::; '00 :::::l ttl 0-.... ro "5

I- Q) 0 Q)ro Q) :::::l Q) "0 l- I- 0 ro 0 :::::l I-

Z a.. 0 0 0::: 0::: c .g:: W l::: 0 o, z <! "L

Vegetation

Decrease + + 0.65

Exotic

Vegetation + + + 0.62

Bank erosion + + 0.82

Inundation + + 0.86

Channel

Modification + + 0.89

Water

Abstraction + + 0.8

Flow

Modification 0.5

Water Quality + + 0.8

37

INSTREAM HABITATINTEGRIT

INDEX OF HABITATINTEGRITY llHI\

RIPARIAN ZONEHABITAT

FISH ASSEMBLAGElFAII\

DIATOMINDEX

MACRO·INVERTEBRATERESPONSE lSASS)

INTEGRATEDINSTREAM BIOTIC

RIPARIAN VEGETATIONINDEX lRVI\

INTEGRATED RIVERBIOLOGICAL

Figure 1. A schematic representation of the different biotic indices developed in South

Africa, and their application in the determination of river health

38

• Monitoring sitesRivers

. I Catchments

Figure. 2. Monitoring sites and major river systems in the Crocodile (West) and Marico

WMA.

39

N

D Cidc hllleutsL.11H11IS e types

C10 11 " .lIod0 0 09,.111..1o Eroded tl lellS

. lndlO slIyMines_(llI,lI liesPl oll1 t.l 1l0ll S

• Resl dollHolOlOll O' cialD RIlI.,1Chiste..o W.11 0,h odles• Roads. 0~II1 S

o 50 100

Figure. 3. Major land use types in the Crocodile (West) Marico water management area

40

a0:>

0

aco

0

UJ @

0:: 00 ao I"-(J)

0 0

~ 0

0:: a 0 0o <00

UJ 0I-z0 a 0L()UJ 0 00::~

~ a 0"'":2 0

aC')

0

0

30 40 50 60 70 80

PREDICTED INTEGRITY SCORE

Figure 4. Accuracy of predicted riparian integrity based on the model at the scale of

catchments (r2 =0.68)

41

a)

a0 00 000

aw a a~

0UC/) 0 a~

coa

0::(') aw.... a~ a0w 0 a a a~ ... a::::JC/) a "i'1i:;; 00

1') a

a

b)

20 30 40 50 60

PREDICTED INTEGRITY SCORE

70

a0 a a a00

aw a a~

0UC/) 0

~co

~(')w....;:::0w 0 a~ ...::::JC/)

i'1i:;; a a

0

'"a

20 40 60 80

PREDICTED INTEGRITYSCORE

Figure 5. Prediction of instream integrity for; a) all the catchments (r2= 0.68), b) headwater

catchments (r2= 0.97) only. The Predictions were based on the model forentire catchment

(Table 6).

42

A 8 c

Inlegrity CID~U~

ABCoEF

• OVER ESTIMATED

• UI JDERESTIMATED

D E F

Figure 6. Crocodile (West) and Marico WMA showing predictions of riparian Integrity

using different spatial scales (Table 5), (A=100m, 8= 500m, C= 1000m, D= 2000m, E=

3000m , F= entire catchment). The map in the center depicts measured integrity. The

dots show whether the model has over estimated riparian integrity or underestimated

based on the measured integrity.

43

A

D

B

E

c

, -fIJI ly 1.1~~~ .. \

AelCo[

J

• OVER ESTMATED

• I ~lrH< F TI Tf r ,

F

Figure 7. Crocodile (West) and Marico WMA showing predictions of instream Integrity

using different spatial scales (Table 6), (A=100m, B= SOOm, C= 1000m, D= 2000m, E=

3000m, F= entire catchment). The map in the center depicts measured integrity. The

dots show whether the model has over estimated instream integrity or underestimated

based on the measured integrity.

44

N"-et'IIlrEGAll ' CLo' 5E5

~

CoE•Sow ( I::h rnrt ,

/'N

Figure 8. Predicted riparian integrity of sub-catchments and river systems in the

Crocodile (West) and Marico. The predictions were based on the best model

(Catchments model) . ( integrity classes:- A= intact, unmodified habitat; F= highly

modified habitat due to anthropogenic activities).

45