Transferability of stream benthic macroinvertebrate distribution models to drought-related conditions

Freshwater ecosystems, which include rivers and streams, are increasingly threatened by climate change-induced extreme events, such as droughts, which disrupt hydrological processes and biodiversity. Species distribution models (SDMs) are essential for predicting species responses to environmental change. However, the transferability of SDMs beyond the conditions under which they were trained, such as from drought-free to drought-influenced scenarios, remains limited. These drought-influenced conditions represent novel environmental conditions for the models, posing challenges for accurate predictions. This study investigated the transferability of SDMs for freshwater macroinvertebrates from drought-free to drought-influenced conditions in a central German catchment via four modeling techniques (generalized linear models (GLMs); spatial stream networks (SSNs); random forests (RF) and maximum entropy (MaxEnt)) and species tolerance scores to assess how these factors independently and jointly affect model transferability. The transferability is evaluated on the basis of the accuracy gap (AUC gap/TSS gap), which quantifies performance differences between drought-free and drought conditions. Our findings reveal a marked decline in model performance under drought-influenced conditions, highlighting the challenges of predicting species distributions in novel environments. SDM transferability varied by species tolerance, with tolerant species exhibiting lower transferability. Additionally, SSN and RF models demonstrated slightly greater transferability for specific tolerances, suggesting their potential for modeling species responses under hydrological stress. Our study underscores the limitations of conventional SDMs in capturing species responses to extreme hydrological events, such as droughts, and advocates for integrating ecologically relevant predictors (such as stream connectivity) and accounting for species traits in SDMs to increase predictive accuracy in novel environmental scenarios.