Evaluating the proxy potential of land use and macroinvertebrates for water quality assessment in the Lower Mekong Basin

Abstract

Effective water quality monitoring in the Lower Mekong Basin (LMB) is challenged by the region’s vast spatial extent, limited station coverage, and the logistical complexity of chemical sampling. Yet, given the basin’s ecological significance and the millions of people who depend on its freshwater systems, scalable approaches to aquatic assessment are critically important. This study evaluates whether upstream land use patterns and downstream macroinvertebrate communities can reliably serve as proxy indicators for surface water quality in the LMB.

Using dry-season data from 34 monitoring stations spanning tributary and mainstem sites, the study integrated geospatial land cover metrics, physicochemical water quality variables, and benthic and littoral macroinvertebrate metrics to explore proxy relationships. Generalized Additive Models (GAMs), Redundancy Analysis (RDA), and variance partitioning were employed to assess predictive performance across parameters including total suspended solids, pH, dissolved oxygen, nutrient concentrations, and chemical oxygen demand.

Results indicate that both land use, particularly urban and agricultural cover, and macroinvertebrate metrics, such as ATSPT and richness, are independently and complementarily associated with water quality variation. Integrated models (with land use and macroinvertebrates as predictors) showed the strongest explanatory power, with proxy strength varying by water quality parameter. Temporal analysis revealed changing macroinvertebrate composition and increasing anthropogenic land use, underscoring the importance of multi-year monitoring.

By demonstrating the viability of spatial and biological proxies, this study supports a more scalable framework for freshwater assessment. It offers basin-scale insight into ecological condition and lays the groundwork for proxy-based monitoring strategies that are accessible, cost-effective, and applicable to other data-limited river systems.