Open Access

The conservation of freshwater ecosystems has lagged behind that of marine and terrestrial ecosystems and often requires the integration of large-scale approaches and transboundary considerations. This study aims to set the foundations of a spatial conservation strategy by identifying the most important catchments for the conservation of freshwater biodiversity in Europe. Using data on 1296 species of fish, mollusc, odonate and aquatic plant, and the key biodiversity area criteria (species Red List status, range restriction and uniqueness of species assemblages), we identified a network of Critical Catchments for the conservation of freshwater biodiversity. Applying spatial prioritisation, we show how the prioritised network differs from the ideal case of protecting all Critical Catchments and how it changes when protected areas are included, and we also identify gaps between the prioritised network and existing protected areas. Critical Catchments (n = 8423) covered 45% of the area of Europe, with 766 qualifying (‘trigger’) species located primarily in southern Europe. The prioritised network, limited to 17% of the area of Europe, comprised 3492 catchments mostly in southern and eastern Europe and species targets were met for at least 96% of the trigger species. We found the majority of Critical Catchments to be inadequately covered by protected areas. However, our prioritised network presents a possible solution to augment protected areas to meet policy targets while also achieving good species coverage. Policy implications. While Critical Catchments cover almost half of Europe, priority catchments are mostly in southern and eastern Europe where the current level of protection is not sufficient. This study presents a foundation for a Europe-wide systematic conservation plan to ensure the persistence of freshwater biodiversity. Our study provides a powerful new tool for optimising investment on the conservation of freshwater biodiversity and for meeting targets set forth in international biodiversity policies, conventions and strategies.

Model-derived relationships between chlorophyll a (Chl-a) and nutrients and temperature have fundamental implications for understanding complex interactions among water quality measures used for lake classification, yet accuracy comparisons of different approaches are scarce. Here, we (1) compared Chl-a model performances across linear and nonlinear statistical approaches; (2) evaluated single and combined effects of nutrients, depth, and temperature as lake surface water temperature (LSWT) or altitude on Chl-a; and (3) investigated the reliability of the best water quality model across 13 lakes from perialpine and central Balkan mountain regions. Chl-a was modelled using in situ water quality data from 157 European lakes; elevation data and LSWT in situ data were complemented by remote sensing measurements. Nonlinear approaches performed better, implying complex relationships between Chl-a and the explanatory variables. Boosted regression trees, as the best performing approach, accommodated interactions among predictor variables. Chl-a–nutrient relationships were characterized by sigmoidal curves, with total phosphorus having the largest explanatory power for our study region. In comparison with LSWT, utilization of altitude, the often-used temperature surrogate, led to different influence directions but similar predictive performances. These results support utilizing altitude in models for Chl-a predictions. Compared to Chl-a observations, Chl-a predictions of the best performing approach for mountain lakes (oligotrophic–eutrophic) led to minor differences in trophic state categorizations. Our findings suggest that both models with LSWT and altitude are appropriate for water quality predictions of lakes in mountain regions and emphasize the importance of incorporating interactions among variables when facing lake management challenges.