Testing the response of macroinvertebrate communities and biomonitoring indices under multiple stressors in a lowland regulated river

Abstract

River systems and their communities are exposed to diverse and multiple threats. Understanding how these threats affect the behaviour of biomonitoring indices is essential in order to provide reliable tools for the management and conservation of watercourses. To this purpose we tested the relationship of the STAR_ICMi index and its metrics, LIFE index and macroinvertebrate community with hydrology, water chemistry and land use in 8 sites located along the Oglio River course (Northern Italy), a watercourse originating from a large and deep lake. Macroinvertebrates and water samples were collected seasonally from summer 2013 to summer 2015 and daily discharge data were used to calculate several indices of hydrological alteration. A subset of these variables was selected by principal component analysis for using in the data analysis. The influence of hydrology, water chemistry and land use on macroinvertebrate community structure was explored with the variance partitioning method, while their influence on biomonitoring indices was analysed in a linear mixed effect model framework. Temporal and spatial constraints were explicitly considered in both analyses. Macroinvertebrate community structure was mainly related to these last two factors and to their joint effects with water chemistry and hydrology. STAR_ICMi, its metrics (with the exception of the Shannon index) and LIFE were related to the distance from the lake outlet, a proxy of mean annual discharge, groundwater input and artificial land use. STAR_ICMi, ASPT and EPT richness were also inversely related to the flow variability in the 3 months preceding sample collection. Surprisingly LIFE index was not related to any of the hydrological variables. The results of this study highlight weakness in the current biomonitoring tools and support the need for further investigations on macroinvertebrate interrelations with environmental drivers and their spatial and temporal structure. This is essential to overcome the limitations that may affect the reliability of macroinvertebrate-based indices in aquatic biomonitoring.