Understanding the intricate dynamics of biodiversity within and across riverine ecosystems, influenced by geological history and environmental factors, is crucial for effective conservation and management strategies. Italy, particularly the Ligurian region, harbors diverse freshwater fish communities and populations shaped by unique geological and hydrological conditions. Here, we investigated the suitability of eDNA-metabarcoding to identify inter- and intraspecific diversity patterns of riverine fish populations along the main drainage divide (MDD) between the Adriatic and Ligurian basins in Northern Italy. We collected 96 aquatic eDNA samples across 48 riverine sites, amplified them using a cytb primer pair and denoised the sequences to retrieve amplicon sequence variants (ASV). We calculated communities’ phylogenetic distance with betaMPD based on genetic distances derived from the ASVs, combined them with conductance-based landscape metrics and applied generalized dissimilarity models (GDM) to assess spatial genetic structure. Our results reveal genetic differentiation among populations of several fish species, with some displaying clustering patterns across the drainage divide and isolation by distance patterns. Overall, taxon richness was significantly higher in the Ligurian sites (25) than in the Adriatic side of the MDD (22), as was ASV richness (205 vs. 196). Our findings highlight the effectiveness of eDNA-metabarcoding in uncovering various facets of diversity, shedding light on hidden genetic diversity within ASVs, and revealing significant spatial genetic structuring in freshwater fish populations across multiple species.

The integration of ecosystem processes over large spatial extents is critical to predicting whether and how global changes may impact biodiversity and ecosystem functions. Yet, there remains an important gap in meta-ecosystem models to predict multiple functions (e.g., carbon sequestration, elemental cycling, trophic efficiency) across ecosystem types (e.g., terrestrial-aquatic, benthic-pelagic). We derive a flexible meta-ecosystem model to predict ecosystem functions at landscape extents by integrating the spatial dimension of natural systems as spatial networks of different habitat types connected by cross-ecosystem flows of materials and organisms. We partition the physical connectedness of ecosystems from the spatial flow rates of materials and organisms, allowing the representation of all types of connectivity across ecosystem boundaries as well as the interaction(s) between them. Through simulating a forest-lake-stream meta-ecosystem, our model illustrated that even if spatial flows induced significant local losses of nutrients, differences in local ecosystem efficiencies could lead to increased secondary production at regional scale. This emergent result, which we dub the ‘cross-ecosystem efficiency hypothesis’, emphasizes the importance of integrating ecosystem diversity and complementarity in meta-ecosystem models to generate empirically testable hypotheses for ecosystem functions.

A document by Jan Pawlowski. Click on the document to view its contents.