Abteilung Aquatische Ökologie

In times of rapid environmental changes, baseline biodiversity data are crucial for management. In freshwaters, fish inventories are commonly based on the capture and morphological identification of specimens. The sampling of environmental DNA (eDNA) provides an alternative to assess diversity across large catchments. Here, we used extensive historic data of fish communities collected across 89 river sites in all major catchments of Switzerland and compared their diversity and community composition to a single campaign of eDNA and electrofishing, respectively. Locally, we found that eDNA provided diversity estimates similar to the integrated historic richness, while the electrofishing campaign captured a significantly lower local richness. Fish species locally recorded by electrofishing were nested (Jaccard's dissimilarity index) within the respective eDNA community for most sites. Finally, eDNA sequence reads positively correlated with the overall electrofishing biomass. Despite the congruences, the eDNA data did not correlate well with the electrofishing water quality index. Overall, eDNA was more accurately assessing overall diversity than a simultaneous electrofishing campaign, but yet cannot be directly used to calculate fish-based water quality indices.

Ecological and ecosystem monitoring is rapidly shifting towards using environmental DNA (eDNA) data, particularly in aquatic systems. This approach enables a combined coverage of biodiversity across all major organismal groups and the assessment of ecological indices. Yet, most current approaches are not exploiting the full potential of eDNA data, largely interpreting results in a localized perspective. In riverine networks, by explicitly modelling hydrological transport and associated DNA decay, hydrology-based models enable upscaling eDNA-based diversity information, providing spatially integrated inference. To capitalize on these unprecedented biodiversity data and translate it into space-filling biodiversity projections, a streamlined implementation is needed. Here, we introduce the eDITH R-package, implementing the eDITH model to project biodiversity across riverine networks with minimal prior information. eDITH couples a species distribution model relating a local taxon's eDNA shedding rate in streamwater to environmental covariates, a mass balance expressing the eDNA concentration at a river's cross-section as a weighted sum of upstream contributions, and an observational model accounting for uncertainties in eDNA measurements. By leveraging on spatially replicated eDNA measurements and minimal hydro-morphological data, eDITH enables disentangling the various upstream eDNA sources, and produces space-filling maps of a taxon's spatial distribution at any chosen resolution. eDITH is applicable to both eDNA concentration and metabarcoding data, and to any taxon whose DNA can be retrieved in streamwater. The eDITH package provides user-friendly functions for single-run execution and fitting of eDITH to eDNA data with both Bayesian methods (via the BayesianTools package) and non-linear optimization. An interface to the DHARMa package allows model validation via posterior predictive checks. Necessary preliminary steps such as watershed delineation and hydrological characterization are implemented via the rivnet package. We illustrate eDITH's workflow and functionalities with two case studies from published fish eDNA data. The eDITH package provides a user-friendly implementation of eDITH, specifically intended for ecologists and conservation biologists. It can be used without previous modelling knowledge but also allows customization for experienced users. Ultimately, eDITH allows upscaling eDNA biodiversity data for any river globally, transforming how state and change in biodiversity in riverine systems can be tracked at high resolution in a highly versatile manner.

Catchment urbanisation results in urban streams being exposed to a multitude of stressors. Notably, stressors originating from diffuse sources have received less attention than stressors originating from point sources. Here, advances related to diffuse urban stressors and their consequences for stream benthic communities are summarised by reviewing 92 articles. Based on the search criteria, the number of articles dealing with diffuse urban stressors in streams has been increasing, and most of them focused on North America, Europe, and China. Land use was the most common measure used to characterize diffuse stressor sources in urban streams (70.7 % of the articles characterised land use), and chemical stressors (inorganic nutrients, xenobiotics, metals, and water properties, including pH and conductivity) were more frequently reported than physical or biological stressors. A total of 53.3 % of the articles addressed the impact of urban stressors on macroinvertebrates, while 35.9 % focused on bacteria, 9.8 % on fungi, and 8.7 % on algae. Regarding ecosystem functions, almost half of the articles (43.5 %) addressed changes in community dynamics, 40.3 % addressed organic matter decomposition, and 33.9 % addressed nutrient cycling. When comparing urban and non-urban streams, the reviewed studies suggest that urbanisation negatively impacts the diversity of benthic organisms, leading to shifts in community composition. These changes imply functional degradation of streams. The results of the present review summarise the knowledge gained to date and identify its main gaps to help improve our understanding of urban streams.

La urbanización de las cuencas expone a los arroyos urbanos a multitud de factores de estrés. Destacan aquellos que tienen su origen en fuentes difusas, los cuales han recibido menos atención que aquellos estresores procedentes de fuentes puntuales. Este estudio resume los avances relacionados con los estresores urbanos difusos y sus consecuencias para las comunidades bentónicas fluviales, a partir de la revisión de 92 artículos. En base a los criterios de búsqueda, el número de artículos que tratan sobre estresores urbanos difusos en arroyos ha ido en aumento, la mayoría de ellos centrados en América del Norte, Europa y China. Los usos del suelo fueron la variable más utilizada para caracterizar las fuentes difusas de estrés (el 70.7 % de los artículos caracterizó los usos del suelo), y los factores de estrés químico (nutrientes inorgánicos o propiedades del agua, como pH o conductividad) se mencionaron con más frecuencia que los factores de estrés físico o biológico. El 53.3 % de los trabajos abordaron el impacto de los estresores urbanos difusos sobre los macroinvertebrados, mientras que el 35.9 % se centraron en bacterias, el 9.8 % en hongos y el 8.7 % en algas. En cuanto al funcionamiento de los ecosistemas, prácticamente la mitad de los trabajos (43.5 %) analizó cambios en la dinámica de las comunidades, el 40.3 % en la descomposición de materia orgánica y el 33.9 % en los ciclos de los nutrientes. Al comparar los arroyos urbanos y no urbanos, los trabajos revisados sugieren que la urbanización afecta negativamente a la diversidad de organismos bentónicos, provocando cambios en la composición de la comunidad. Estos cambios implican la degradación funcional de los arroyos. Los resultados de la presente revisión resumen los conocimientos adquiridos hasta hoy e identifican sus principales carencias a fin de ayudar a mejorar nuestra comprensión de los arroyos urbanos.

Facing climate change and biodiversity loss, it is critical that ecology advances so that processes, such as species interactions and dynamics, can be correctly estimated and skillfully forecasted. As different processes occur on different time scales, the sampling frequency used to record them should intuitively match these scales. Yet, the effect of data sampling frequency on ecological forecasting accuracy is understudied. Using a simple simulated dataset as a baseline and a more complex high-frequency plankton dataset, we tested how different sampling frequencies impacted abundance forecasts of different plankton classes and the estimation of their interactions. We then investigated whether plankton growth rates and body sizes could be used to select the most appropriate sampling frequency. The simple simulated dataset showed that the optimal sampling frequency scaled positively with growth rate. This finding was not repeated in the analyses of the plankton time series, however. There, we found that a reduction in sampling frequency worsened forecasts and led us to both over- and underestimate plankton interactions. This suggests that forecasting can be used to determine the ideal sampling frequency in scientific and monitoring programs. A better study design will improve theoretical understanding of ecology and advance policy measures dealing with current global challenges.