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Browsing School of Life Sciences by Author "Agboola, Olalekan A."

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    Monitoring and assessment of macroinvertebrate communities in support of river health management in KwaZulu-Natal, South Africa.
    (2017) Agboola, Olalekan A.; O’Brien, Gordon.; Downs, Colleen Thelma.
    Conservation of freshwater systems is globally weak and generally declining, with rivers and wetlands being the most threatened ecosystems by anthropogenic impacts. Though they are highly import, freshwater ecosystems remain poorly understood and insufficient data often limit conservation efforts on many freshwater ecosystems. KwaZulu-Natal (KZN) Province is an important high water yield area of South Africa, but the sustainability of the rivers is being threatened. Macroinvertebrates are good indicators of water quality and ecosystem degradation, but their biodiversity and ecosystem conservation depend largely on the quality of the available data and the efficiency of the methods used in the data analysis. Each aspect of the research provides results that can be used in current and future conservation planning for rivers and aquatic macroinvertebrates. The reference condition approach is an effective bioassessment technique closely related to the biological/ecological integrity concept, which is based on the evaluation of the deviation of the ecological quality of a test site’s biological community from that of a near-pristine “reference” condition having very similar characteristics. Although the term reference condition, is used to describe near-natural or pristine condition, several practitioners believe that only a few pristine ecosystems still exist in the world. Hence, the reference condition (RC) defines the representative of a group of undisturbed or minimally disturbed sites by anthropogenic activities, while biological reference condition is the description of the biological elements that exist under no or very minor anthropogenic activities. This study applied the multivariate method of selecting and validating reference conditions, using ecoregions, river types and seasonal changes as grouping criteria for the reference sites. The ecoregions and river types were more adequate than the seasonal variations in the selection of reference conditions. Although there is currently no consensus about the most appropriate and informative index, biodiversity indices are essential for environmental monitoring and conservation management decisions. This study compared a series of macroinvertebrate data from the rivers of KZN according to nine diversity indices (total number of species/taxa, total number of individuals, Margalef’s, Pielou’s, Brillouin’s, Hill’s, Simpson’s, Fisher’s and Shannon’s indices), one similarity index (similarity percentage – SIMPER) and three biotic indices (SASS5, ASPT, and MIRAI). There were clear connections between water quality, and abundance of macroinvertebrates with the decrease in the diversity values of macroinvertebrates along pollution gradients. Fisher’s index, similarity percentage, SASS5, ASPT and MIRAI were suitable indices for comparing degraded and least degreaded sites in this study. However, small changes in community compositions were better revealed by the Fisher’s diversity index, similarity percentage and SASS5. The MIRAI was better than SASS5 as an ecological tool for the rivers of KZN, but it can further be improved by incorporating measures of diversity and taxa richness into the model. Also, this study examined the effectiveness of macroinvertebrate taxa composition metrics to assess the ecological health of the rivers in KZN. Nine taxa metrics were able to distinguish between reference and impaired sites, through correlation strength with environmental variables and their reliability. The nine metrics were total number of taxa, total number of Diptera taxa, total number of Plecoptera individuals, percentage of Ephemeroptera, Plecoptera and Trichoptera taxa, percentage of Odonata taxa, total number of Trichoptera individuals, total number of Gastropoda individuals, total number of Oligochaeta individuals and total number of Coleoptera individuals. This study showed increasing water quality deterioration along the longitudinal gradients of the rivers in KwaZulu-Natal, from the upper reaches towards the lower reaches of the rivers. We found that macroinvertebrate community composition metrics could detect nutrient pollution, organic pollution and physical habitat degradation in KZN rivers. Thus it is recommended that more studies and validation of macroinvertebrate community-based metrics in the assessment of rivers in KZN are conducted. Furthermore, they are relatively cheap and easy to use. Macroinvertebrate community-based indices could be an effective alternative assessment method in the case of the lowland rivers where the lack of quality data often have negative impacts on the use of the biotic indices (SASS5, ASPT and MIRAI). In addition, this study demonstrated how Bayesian networks can be used to conduct an environmental risk assessment of macroinvertebrate biodiversity and their associated river ecosystem to assess the overall effects of multiple anthropogenic stressors in rivers of KZN. Cause-effect exposure pathways were established between the sources of stressors, habitats and endpoints (macroinvertebrate biodiversity and river ecosystem wellbeing) using using a conceptual model. The resulting conceptual model was then used to construct the Bayesian network models for each study site (risk regions) to estimate the overall risk from water quality, flow and habitat stressors. The model outputs and sensitivity analysis showed ecosystem threat and river health (represented by MIRAI) as the top factors posing the highest risks to macroinvertebrate biodiversity and the river ecosystem wellbeing respectively. The Bayesian network model was used to estimate the risk across the sites in the current scenario and three other scenarios that could occur if there were inadequate management practices. The current scenario was developed from field data collected during this study, while the other three scenarios were simulated to predict potential risk to the selected endpoints. We further simulated the low and high risks to the endpoints in order to demonstrate that the Bayesian network can be an effective adaptive management tool for decision making. The results of this study demonstrated that Bayesian networks can be used to calculate risk for multiple stressors, and that they are a powerful tool for informing future management strategies for achieving best management practices and policy making in the rivers of KZN.