Disentangling the effect of climatic and hydrological predictor variables on benthic macroinvertebrate distributions from predictive models

Abstract

AbstractLotic freshwater macroinvertebrate species distribution models (SDMs) have been shown to improve when hydrological variables are included. However, most studies to date only include data describing climate or stream flow-related surrogates. We assessed the relative influence of climatic and hydrological predictor variables on the modelled distribution of macroinvertebrates, expecting model performance to improve when hydrological variables are included. We calibrated five SDMs using combinations of bioclimatic (bC), hydrological (H) and hydroclimatic (hC) predictor datasets and compared model performance as well as variance partition of all combinations. We investigated the difference in trait composition of communities that responded better to either bC or H configurations. The dataset bC had the most influence in terms of proportional variance, however model performance was increased with the addition of hC or H. Trait composition demonstrated distinct patterns between associated model configurations, where species that prefer intermediate to slow-flowing current conditions in regions further downstream performed better with bC–H. Including hydrological variables in SDMs contributes to improved performance, it is however, species-specific and future studies would benefit from hydrology-related variables to link environmental conditions and diverse communities. Consequently, SDMs that include climatic and hydrological variables could more accurately guide sustainable river ecosystem management.