Rwenzori Score (RS): A Benthic Macroinvertebrate Index for Biomonitoring Rivers and Streams in the Rwenzori Region, Uganda

A Bayesian Belief Network learning tool integrates multi-scale effects of riparian buffers on stream invertebrates

Riparian forest buffers have multiple benefits for biodiversity and ecosystem services in both freshwater and terrestrial habitats but are rarely implemented in water ecosystem management, partly reflecting the lack of information on the effectiveness of this measure. In this context, social learning is valuable to inform stakeholders of the efficacy of riparian vegetation in mitigating stream degradation. We aim to develop a Bayesian belief network (BBN) model for application as a learning tool to simulate and assess the reach- and segment-scale effects of riparian vegetation properties and land use on instream invertebrates. We surveyed reach-scale riparian conditions, extracted segment-scale riparian and subcatchment land use information from geographic information system data, and collected macroinvertebrate samples from four catchments in Europe (Belgium, Norway, Romania, and Sweden). We modelled the ecological condition based on the Average Score Per Taxon (ASPT) index, a macroinvertebrate-based index widely used in European bioassessment, as a function of different riparian variables using the BBN modelling approach. The results of the model simulations provided insights into the usefulness of riparian vegetation attributes in enhancing the ecological condition, with reach-scale riparian vegetation quality associated with the strongest improvements in ecological status. Specifically, reach-scale buffer vegetation of score 3 (i.e. moderate quality) generally results in the highest probability of a good ASPT score (99-100%). In contrast, a site with a narrow width of riparian trees and a small area of trees with reach-scale buffer vegetation of score 1 (i.e. low quality) predicts a high probability of a bad ASPT score (74%). The strengths of the BBN model are the ease of interpretation, fast simulation, ability to explicitly indicate uncertainty in model outcomes, and interactivity. These merits point to the potential use of the BBN model in workshop activities to stimulate key learning processes that help inform the management of riparian zones.

Chemical pollution as a driver of biodiversity loss and potential deterioration of ecosystem services in Eastern Africa: A critical review

Chemical pollution, i.e. the release of anthropogenic chemical substances into the environment, is a driver of biodiversity loss. Although this issue has been widely investigated in high-income countries of temperate regions, there is a lack of data for tropical areas of middle- or low-income countries, such as those in Eastern Africa. Some of the world’s richest biomes that are affected by multiple pressures, including chemical pollution, are hosted in this macro-region. However, few studies have addressed the impact of the release of anthropogenic chemical pollutants on the biodiversity, and the related potential implications for the deterioration of ecosystem goods and services in this area. A contribution in systemising the scientific literature related to this topic is, therefore, urgently needed. We reviewed studies published from 2001 to 2021, focusing on the chemical pollution impact on Eastern African wildlife. Despite an extensive literature search, we found only 43 papers according to our survey methods. We focused on wildlife inhabiting terrestrial ecosystems and inland waters. According to our search, Kenya and Uganda are the most represented countries accounting for about half of the total number of reviewed articles. Moreover, 67.4% of the studies focus on inland waters. The spread of anthropogenic chemicals into tropical areas, e.g. Eastern Africa, and their effects on living organisms deserve greater attention in research and politics. We report a weak increasing trend in publishing studies addressing this topic that might bode well. The combined effort of science and governments is crucial in improving the management of chemical pollutants in the environment for achieving the goals of biodiversity conservation.

Identification of Potential Surface Water Resources for Inland Aquaculture from Sentinel-2 Images of the Rwenzori Region of Uganda

Aquaculture has the potential to sustainably meet the growing demand for animal protein. The availability of water is essential for aquaculture development, but there is no knowledge about the potential inland water resources of the Rwenzori region of Uganda. Though remote sensing is popularly utilized during studies involving various aspects of surface water, it has never been employed in mapping inland water bodies of Uganda. In this study, we assessed the efficiency of seven remote-sensing derived water index methods to map the available surface water resources in the Rwenzori region using moderate resolution Sentinel 2A/B imagery. From the four targeted sites, the Automated Water Extraction Index for urban areas (AWEInsh) and shadow removal (AWEIsh) were the best at identifying inland water bodies in the region. Both AWEIsh and AWEInsh consistently had the highest overall accuracy (OA) and kappa (OA > 90%, kappa > 0.8 in sites 1 and 2; OA > 84.9%, kappa > 0.61 in sites 3 and 4), as well as the lowest omission errors in all sites. AWEI was able to suppress classification noise from shadows and other non-water dark surfaces. However, none of the seven water indices used during this study was able to efficiently extract narrow water bodies such as streams. This was due to a combination of factors like the presence of terrain shadows, a dense vegetation cover, and the image resolution. Nonetheless, AWEI can efficiently identify other surface water resources such as crater lakes and rivers/streams that are potentially suitable for aquaculture from moderate resolution Sentinel 2A/B imagery.