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|Title:||Macroinvertebrates as indicators of ecological conditions in the rivers of KwaZulu-Natal, South Africa.||Authors:||O'Brien, Gordon Craig.
School of Biology and Environmental Sciences
|Keywords:||Macroinvertebrates.;Ecological traits.;Organic pollution.;Biomonitoring.||Issue Date:||2019||Publisher:||Elsevier||Abstract:||This study examined the effectiveness of macroinvertebrate community-based multimetrics to assess the ecological health of 38 rivers in KwaZulu-Natal (KZN) Province, South Africa. The study area comprised of headwater to lowland rivers determined by their hydro-morphology. Of the 40 tested metrics, only 11 core metrics were finally selected because of their ability to distinguish between reference and impaired sites, correlation strength with environmental variables and their reliability. Nine out of the selected metrics had strong correlations with environmental variables and these 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 chemical deterioration along longitudinal gradients of the rivers in KZN. We found that macroinvertebrate community metrics could detect nutrient pollution, organic pollution and physical habitat degradation in the rivers of KZN. We recommend more studies and validation of macroinvertebrate community-based metrics in the assessment of rivers in KZN, because they are relatively cheap and easy to use. The use of macroinvertebrate community metrics could be an effective alternative assessment method in the case of the lowland rivers where the lack of quality data often has negative impacts on the use of the biotic indices (South African Scoring System (SASS), Average Score Per Taxon (ASPT) and Macroinvertebrate Response Assessment Index (MIRAI)).||URI:||https://openscholar.ump.ac.za/handle/20.500.12714/108||DOI:||10.1016/j.ecolind.2019.105465|
|Appears in Collections:||Journal articles|
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checked on Jun 8, 2021
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