Species–area relationships (SAR) are fundamental in the understanding of biodiversity patterns and of critical importance for predicting species extinction risk worldwide. Despite the enormous attention given to SAR in the form of many individual analyses, little attempt has been made to synthesize these studies. We conducted a quantitative meta‐analysis of 794 SAR, comprising a wide span of organisms, habitats and locations. We identified factors reflecting both pattern‐based and dynamic approaches to SAR and tested whether these factors leave significant imprints on the slope and strength of SAR. Our analysis revealed that SAR are significantly affected by variables characterizing the sampling scheme, the spatial scale, and the types of organisms or habitats involved. We found that steeper SAR are generated at lower latitudes and by larger organisms. SAR varied significantly between nested and independent sampling schemes and between major ecosystem types, but not generally between the terrestrial and the aquatic realm. Both the fit and the slope of the SAR were scale‐dependent. We conclude that factors dynamically regulating species richness at different spatial scales strongly affect the shape of SAR. We highlight important consequences of this systematic variation in SAR for ecological theory, conservation management and extinction risk predictions.

The protection, preservation and restoration of aquatic ecosystems and their functions are of global importance. For European states it became legally binding mainly through the EU-Water Framework Directive (WFD). In order to assess the ecological status of a given water body, aquatic biodiversity data are obtained and compared to a reference water body. The quantified mismatch obtained determines the extent of potential management actions. The current approach to biodiversity assessment is based on morpho-taxonomy. This approach has many drawbacks such as being time consuming, limited in temporal and spatial resolution, and error-prone due to the varying individual taxonomic expertise of the analysts. Novel genomic tools can overcome many of the aforementioned problems and could complement or even replace traditional bioassessment. Yet, a plethora of approaches are independently developed in different institutions, thereby hampering any concerted routine application. The goal of this Action is to nucleate a group of researchers across disciplines with the task to identify gold-standard genomic tools and novel eco-genomic indices for routine application in biodiversity assessments of European fresh- and marine water bodies. Furthermore, DNAqua-Net will provide a platform for training of the next generation of European researchers preparing them for the new technologies. Jointly with water managers, politicians, and other stakeholders, the group will develop a conceptual framework for the standard application of eco-genomic tools as part of legally binding assessments.

The recognition and discrimination of phytoplankton species is one of the foundations of freshwater biodiversity research and environmental monitoring. This step is frequently a bottleneck in the analytical chain from sampling to data analysis and subsequent environmental status evaluation. Here we present phytoplankton diversity data from 49 lakes including three seasonal surveys assessed by next generation sequencing (NGS) of 16S ribosomal RNA chloroplast and cyanobacterial gene amplicons and also compare part of these datasets with identification based on morphology. Direct comparison of NGS to microscopic data from three time-series showed that NGS was able to capture the seasonality in phytoplankton succession as observed by microscopy. Still, the PCR-based approach was only semi-quantitative, and detailed NGS and microscopy taxa lists had only low taxonomic correspondence. This is probably due to, both, methodological constraints and current discrepancies in taxonomic frameworks. Discrepancies included Euglenophyta and Heterokonta that were scarce in the NGS but frequently detected by microscopy and Cyanobacteria that were in general more abundant and classified with high resolution by NGS. A deep-branching taxonomically unclassified cluster was frequently detected by NGS but could not be linked to any group identified by microscopy. NGS derived phytoplankton composition differed significantly among lakes with different trophic status, showing that our approach can resolve phytoplankton communities at a level relevant for ecosystem management. The high reproducibility and potential for standardization and parallelization makes our NGS approach an excellent candidate for simultaneous monitoring of prokaryotic and eukaryotic phytoplankton in inland waters.