Department Aquatic Ecology

Accurate characterisation of ecological communities with respect to their biodiversity and food-web structure is essential for conservation. However, combined empirical study of biodiversity and multi-trophic food webs at a large spatial and temporal resolution has been prohibited by the lack of appropriate access to such data from natural systems. Here, we assessed biodiversity and food-web characteristics across a 700 km2 riverine network over seasons using environmental DNA. We found contrasting biodiversity patterns between major taxonomic groups. Local richness showed statistically significant, season-dependent increases and decreases towards downstream location within the catchment for fish and bacteria, respectively. Meanwhile, invertebrate richness remained spatially unchanged but varied across seasons. The structure of local food webs, such as link density and nestedness, also varied across space and time. However, these patterns did not necessarily mirror those observed for biodiversity and functional feeding characteristics. Our results suggest that biodiversity patterns and food-web dynamics are not directly scalable to each other even at the same spatial and temporal scales. In order to conserve species diversity as well as the functional trophic integrity of communities, patterns of biodiversity and food-web characteristics must thus be jointly studied.

The alarming declines of freshwater biodiversity call for efficient biomonitoring at fine spatiotemporal scales, such that conservation measures be grounded upon accurate biodiversity data. Here, we show that combining environmental DNA (eDNA) extracted from stream water samples with models based on hydrological first principles allows upscaling biodiversity estimates for aquatic insects at very high spatial resolution. Our model decouples the diverse upstream contributions to the eDNA data, enabling the reconstruction of taxa distribution patterns. Across a 740-km² basin, we obtain a space-filling biodiversity prediction at a grain size resolution of 1-km long stream sections. The model’s accuracy in matching direct observations of aquatic insects' local occurrence ranges between 57-100%. Our results demonstrate how eDNA can be used for high-resolution biodiversity assessments in rivers with minimal prior knowledge of the system. Our approach allows identification of biodiversity hotspots that could be otherwise overlooked, enabling implementation of focused conservation strategies.

DNA metabarcoding is increasingly used for the assessment of aquatic communities, and numerous studies have investigated the consistency of this technique with traditional morpho-taxonomic approaches. These individual studies have used DNA metabarcoding to assess diversity and community structure of aquatic organisms both in marine and freshwater systems globally over the last decade. However, a systematic analysis of the comparability and effectiveness of DNA-based community assessment across all of these studies has hitherto been lacking. Here, we performed the first meta-analysis of available studies comparing traditional methods and DNA metabarcoding to measure and assess biological diversity of key aquatic groups, including plankton, microphytobentos, macroinvertebrates, and fish. Across 215 data sets, we found that DNA metabarcoding provides richness estimates that are globally consistent to those obtained using traditional methods, both at local and regional scale. DNA metabarcoding also generates species inventories that are highly congruent with traditional methods for fish. Contrastingly, species inventories of plankton, microphytobenthos and macroinvertebrates obtained by DNA metabarcoding showed pronounced differences to traditional methods, missing some taxa but at the same time detecting otherwise overseen diversity. The method is generally sufficiently advanced to study the composition of fish communities and replace more invasive traditional methods. For smaller organisms, like macroinvertebrates, plankton and microphytobenthos, DNA metabarcoding may continue to give complementary rather than identical estimates compared to traditional approaches. Systematic and comparable data collection will increase the understanding of different aspects of this complementarity, and increase the effectiveness of the method and adequate interpretation of the results.