Spatially Explicit Flow-Ecology Modeling to Inform Biomonitoring in Data-Scarce Afromontane Ecosystems

The global imperative to address the biodiversity crisis requires transparent and trustworthy spatial data products to measure ecological state, assess trends, and inform policy. A critical data gap persists in sensitive Afromontane ecosystems of the Global South, such as the Ruo River in Malawi, a key biodiversity area facing intensifying pressure from irrigation and hydropower. An urgent priority is establishing a scientifically rigorous baseline by quantitatively linking hydrological variability (river flow) to biological response. This study contributes directly to the session’s objectives by employing a quantitative longitudinal design and time-tested statistical models to generate meaningful biodiversity indicators. The approach integrated newly generated in situ biodiversity data aquatic macroinvertebrates, classified using SASS version 5 with measured river flow rates and physicochemical parameters across a natural spatial gradient(upper stream, middle stream, and lower stream).. Crucially, the sampling design accounted for microhabitat diversity by sampling three diverse biotopes (gravel, sand, and mud; stone; and vegetation). Analysis relied on ANOVA to assess spatial and biotope heterogeneity and Generalized Linear Models (GLMs), specifically Poisson Regression, to explicitly model the non-linear relationship between flow parameters and key macroinvertebrate diversity indicators.

The results yield crucial, ready-to-use metrics for decision-making. The statistical models established a significant spatial gradient and confirmed that flow rate and biotope-specific conditions are strong predictors of community structure.  A clear community turnover was observed, with sensitive families (e.g., EPT groups) dominating less-disturbed reaches, and tolerant taxa increasing in areas impacted by reduced flow. By focusing on transparent GLMs, the study facilitated clear uncertainty quantification and enhanced model transparency for the derived indicators (richness, EPT scores). Furthermore, the project produced a new macroinvertebrate database for the Ruo River, advancing regional data management and benchmarking.

In conclusion, this research provides a rigorous example of generating high-fidelity spatial indicators from fundamental data. The established flow-ecology relationships form a vital biodiversity baseline and directly inform the urgent need for a locally calibrated biomonitoring index, supporting national decision-making and prioritizing the crucial role of transparent, validated models in global conservation strategy and development.