Abteilung Aquatische Ökologie

1. Species distribution models (SDMs) relate species observations to mapped environmental variables to estimate the realized niche of species and predict their distribution. SDMs are key tools for projecting the impact of climate change on species and have been used in many biodiversity assessments. However, when fitted within spatial extents that do not encompass the whole species range (i.e. subrange), the estimated realized environmental niche can be truncated, which can lead to wrong or inaccurate predictions.
2. A simple solution to this niche truncation consists in fitting SDMs at a spatial extent that encompasses the whole species range, but this often implies using a spatial resolution too coarse for local conservation assessments. To keep a fine resolution, a solution is to fit spatially nested SDMs (N-SDMs), where a whole range, coarse-grain SDM is combined with a subrange, fine-grain SDM. N-SDMs have demonstrated superior performance to subrange (truncated) SDMs in projecting species distributions under climate change and have accordingly regained considerable interest.
3. Here, we review developments, applications and effectiveness of N-SDMs. We present and discuss existing methods and tools to fit N-SDMs, and assess when N-SDMs are not needed. We highlight strengths and weaknesses of N-SDMs, underline their importance in reducing niche truncation, and identify remaining challenges and future perspectives. Our review highlights that subrange SDMs most often lead to niche truncation and thus to incorrect spatial projections, a problem that can be overcome by using N-SDMs. We show that the various N-SDM methods come with their strengths and weaknesses and should be selected depending on the intended goal of the study.
4. Synthesis. N-SDMs are key tools to develop untruncated regional climate change forecasts of species distributions at fine resolution over restricted extent. While several N-SDM approaches were proposed, there is currently no universal solution suggesting that further developments and testing are crucial if we are to derive robust future projections of species distributions, at least until SDMs can be applied for most species at high resolution over large geographic extents.

Rising temperatures and an increasing frequency of extreme events, such as heatwaves pose major threats to ecosystems, impacting ectothermic organisms including fish. Elevated water temperatures, coupled with reduced oxygen levels, intensify stress and mortality among fish fauna, necessitating urgent action to address the impacts of heatwaves on biodiversity. Here, we propose to combine sub-seasonal to seasonal (S2S) forecasting methodologies with ecological modeling integrated with physiological parameters. Specifically, we developed a sub-seasonal forecast model designed to assess fish stress during heatwaves in Swiss rivers. We first compiled from the literature physiological parameters related to fish thermal stress to construct a thermal stress index. The developed model integrates deep-learning forecasts of water temperature with fish physiological data and species distribution modelling to offer a process-driven prediction of heat-stress responses. To validate its efficacy, we retrospectively applied the model to the 2018 heatwave across 20 measurement stations on lowland Swiss rivers, comparing its results with assessments from experts and practitioners. We found that the model successfully forecast stress peaks 2–3 weeks in advance at two validated key sites, where high mortality was reported by practitioners. By classifying fish species based on their thermal sensitivity, we identified a high vulnerability of salmonids. Applications of our model intend to alleviate climate change impacts, resulting in targeted actions to reduce local fish mortality.

La biodiversité macroscopique (visible à l'oeil nu) du Léman, le plus grand lac d'Europe occidentale, est particulièrement riche. Le déclin irréversible des anodontes vient toutefois nous alerter sur la fragilité de la faune et de la flore de ce magnifique écosystème.