3.2. Biomonitoring

Biomonitoring integrates multiple effects of stressors including chemical (e.g. salinisation), physical (e.g. sedimentation) and biological (e.g. parasitism) to evaluate aquatic ecosystem health [15]. It relies on the sound ecological understanding that in-stream biota, for example, plants, algae, animals and microorganisms integrate the conditions of their environment and are therefore able to provide an indication of the health of the ecosystem in which they live [16]. Biomonitoring can be applied at multiple biological organisations including suborganismal (e.g. gene mutation and cell alteration), individual species composition, population, community and ecosystem levels. In South Africa, for example, the science of biomonitoring is well developed compared to the rest of sub-Saharan African countries. The design of the National Aquatic Ecosystem Health Monitoring Programme (NAEHMP) is met to generate information needed regarding the ecological conditions of aquatic ecosystems in South Africa [17]. The NAEHMP utilises the responses of in-stream biota and system drivers to characterise the impacts of disturbances in aquatic ecosystems and to determine present ecological states of the systems. The NAEHMP uses fish, macroinvertebrate and riparian vegetation as its primary biological indicators, while abiotic indicators such as habitat, geomorphology, hydrology and water chemistry form the framework for the interpretation of the biotic results. In terms of the NAEHMP, assessment models such as the fish response assessment index (FRAI), vegetation response assessment index (VEGRAI) and macroinvertebrate response assessment index (MIRAI) have been developed for assessing the ecological states of riverine ecosystems [18–20].

At the core of biomonitoring is the search for and identification of suitable biological indicators (i.e. bioindicators), whose presence or absence, abundance and diversity, and behaviour reflect environmental conditions. Over the years, many studies have used bioindicators such as fish, diatoms, algae and macroinvertebrates to assess ecological water quality [21]. However, among the bioindicators, macroinvertebrates are arguably the most widely used groups [22]. Their wide application in biomonitoring can be attributed to their ubiquitous occurrence, abundance and diversity in the aquatic ecosystems. In addition, they can be easily collected and identified to the family level, though species-level identification requires more time and for some taxa may not be possible especially in the Afrotropical region. They have a huge species richness that offers a wide spectrum of environmental responses and they are relatively sedentary, representing local conditions. They provide an indication of environmental conditions over varying times and are differentially sensitive to a variety of pollutants and, consequently, capable of a graded response to stress. They also serve as a critical pathway for transporting and utilising energy and matter in the aquatic ecosystem.

Freshwater macroinvertebrates spend at least part of their lifecycles in the aquatic environment and are large enough to be seen unaided [23]. Depending on the goal of the biomonitoring, they can be monitored for changes in population, community, growth rate and cohorts. They can also be monitored for bioaccumulation of pollutants, as well as for morphological and biochemical changes in cells, tissues, organs and systems. Macroinvertebrates-based biomonitoring approaches include single biotic indices such as the Biological Monitoring Working Party (BMWP) and the South African Scoring System version 5 (SASS5) [24, 25]; multimetric indices, for example, the Index of Biotic Integrity 12 (IBI 12) and the Serra dos Órgãos Multimetric Index (SOMI) [26]; multivariate predictive techniques, for example, the Australian River Assessment System (AUSRIVAS) and the United Kingdom's River Invertebrate Prediction and Classification System (RIVPACS, UK) [27] and finally the traits-based techniques.

A multivariate predictive technique evaluates aquatic ecosystem condition by comparing biota at a site to those expected to occur in the absence of human disturbances [16]. A predictive model is constructed using reference sites' biotic communities and correlating the community to natural environmental variables using multivariate statistics to predict expected communities at the impacted sites. A multimetric approach on the other hand combines metrics representing several aspects of macroinvertebrate attributes (e.g. structure, function and processes) to indicate river health. Bonada et al. [16] assessed the utilities, strengths and weaknesses of both approaches using a set of 12 criteria in 3 categories: rationale, implementation and performance. Out of the 12 criteria evaluated, the multivariate approach satisfies 9, while the multimetric fulfils 10.
