Feld, C. K. | bioRxiv Science

bioRxiv2024

Global-scale quantification of responses to anthropogenic stressors in six riverine organism groups

Rivers globally are impacted by numerous anthropogenic stressors, including water pollution, habitat degradation, and climate change, which collectively stress biodiversity and ecosystem functioning. This study systematically reviews and analyses published and unpublished data to understand how five aquatic organism groups (bacteria, algae, macrophytes, invertebrates, fish) respond to seven common stressors (salinization, oxygen depletion, fine sediment enrichment, temperature increase, flow modifications and nitrogen or phosphorus enrichment). Using an analytical framework that includes Generalized Linear Models (GLMs) and Robust Bayesian Meta Analysis (RoBMA), we extracted data from 143 relevant datasets out of 29,749 screened articles. Our results reveal a negative relationship between invertebrates and salinity, fine sediment enrichment, and temperature increase, while fish respond positively to increased oxygen levels and temperature. Bacteria and algae show variable responses, with algae positively associated with nitrogen. The findings highlight strong variability in stressor-response relations across organism groups and stressor types, and emphasize the need for more targeted studies on underrepresented groups like macrophytes and microorganisms. This analysis enhances the predictive understanding of stressor impacts on riverine biodiversity, informing future river ecosystem management and restoration efforts.

ecology↗

bioRxiv2024

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

Freshwater ecosystems increasingly face pressures from climate change induced extreme events, like droughts, posing significant threats to biodiversity. While Species Distribution Models (SDMs) serve as vital tools for predicting species responses to environmental shifts, their transferability to novel environmental conditions, especially during and after drought remains poorly understood. In this study, we delve into the transferability of SDMs for freshwater macroinvertebrates from drought-free to drought-influenced conditions. We examine how sensitive the transferability is to traits such as tolerance scores according to their distribution along longitudinal gradients, as well as the used modelling method. We constructed and validated SDMs for freshwater macroinvertebrates in a central German catchment under drought-free conditions using four different algorithms (Generalised linear models; GLM, Spatial Stream Networks; SSN, Random Forests; RF, and Maximum Entropy; MaxEnt). We then projected these models to environmental conditions influenced by drought, and obtained their transferability by computing the difference in accuracy when predicting under drought-free and drought-influenced conditions (AUCgap). Our findings reveal a marked reduction in SDM accuracy under drought conditions, illustrating the challenges of accurately predicting species distributions into novel environmental conditions. Our results show a slightly better transferability when using SSN and RF. In addition, we observed that SDM transferability can be influenced by species tolerance, with sensitive and tolerant species presenting higher AUCgap (i.e. lower transferability). Furthermore, we found that when sensitive species were modelled using SSNs, the AUCgap was reduced. Our study underscores the limitations of SDMs in capturing species responses to drought and advocates for integrating ecologically relevant predictors and modelling methods that account for the stream connectivity to allow for robust predictions. These considerations could enhance the ability of SDMs to effectively estimate the impacts of extreme events on freshwater biodiversity.

ecology↗