Abteilung Aquatische Ökologie

Genetic diversity is a fundamental aspect of biodiversity, yet it is rarely assessed and monitored in conservation practice. Unionid freshwater mussels exemplify the dramatic loss of biodiversity in freshwater ecosystems, yet genomic data for these ecologically important species remain scarce. Here, we conducted a high-resolution population genomics study of all Anodonta species in Switzerland, with a focus on two species with contrasting reproductive strategies. After generating draft genomes of the hermaphroditic Anodonta cygnea and the gonochoric Anodonta anatina, we performed whole-genome resequencing of 421 individuals collected in 31 localities. While A. anatina populations followed a metapopulation structure shaped by catchment areas, genetic diversity correlated positively with waterbody size, suggesting greater vulnerability in small ponds compared with large lakes. Inbreeding levels were low; however, effective population sizes were consistently below 100, indicating serious extinction risks. Strong divergence between A. anatina populations north and south of the Alps suggests a putative undescribed Anodonta species in the Ticino area. Furthermore, we detected hybridisation between A. cygnea and A. exulcerata, indicating genomic permeability between these species. In addition, genomic data suggested facultative selfing in A. cygnea, leading to a marked reduction in genetic diversity, increased population structure and inbreeding and a decline in effective population size compared to the outcrossing A. anatina. Our study underscores that reproductive strategy fundamentally shapes genetic indicators of biodiversity and influences extinction risk; conservation targets should therefore be adapted to the biology of the species of interest. To conclude, we advocate for integrating reproductive mode and genomic data into conservation planning to more accurately assess vulnerability and guide effective action.

Dreissena rostriformis bugensis and Dreissena polymorpha, collectively known as dreissenid mussels, were first documented in Lake Erie nearly 40 years ago and continue to strongly impact ecosystem structure and function. Recently, dreissenids have been recognized as the primary regulator of phosphorus dynamics in the Great Lakes, potentially reducing external phosphorus load management strategies. To evaluate the extent that dreissenids may modulate phosphorus dynamics and assess the effectiveness of external load management strategies, ecosystem models need to adequately represent spatial patterns of dreissenid biomass densities. Assessment of dreissenid populations in the Great Lakes are complicated by their vast size, as well as the heterogeneity and stochastic nature of critical physical variables that shape habitat suitability (e.g., depth, hypoxia, substrate type, and benthic shear stress). We used observations from a 2019 benthic survey and 10-fold cross validation to assess spatial prediction methods to create a spatially explicit data layer representing dreissenid biomass in Lake Erie, including inverse distance weighting, nearest neighbour, depth binned average, random forest, generalized linear, and general additive models. A random forest spatial prediction approach produced the best predictive surface for dreissenid biomass in Lake Erie. The estimated spatial pattern of dreissenid biomass may be used in nutrient modeling exercises across Lake Erie to improve estimates of dreissenid impacts on biogeochemistry and lower food web productivity. This work also provides insight for potential sampling design improvements for benthic surveys in future years that could help refine the predictive capacity of the developed spatial prediction framework.