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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.
Climate change is expected to exacerbate the current threats to freshwater ecosystems, yet multifaceted studies on the
potential impacts of climate change on freshwater biodiversity at scales that inform management planning are lacking. The aim of this study was to fill this void through the development of a novel framework for assessing climate
change vulnerability tailored to freshwater ecosystems. The three dimensions of climate change vulnerability are as
follows: (i) exposure to climate change, (ii) sensitivity to altered environmental conditions and (iii) resilience potential.
Our vulnerability framework includes 1685 freshwater species of plants, fishes, molluscs, odonates, amphibians, crayfish and turtles alongside key features within and between catchments, such as topography and connectivity. Several
methodologies were used to combine these dimensions across a variety of future climate change models and scenarios. The resulting indices were overlaid to assess the vulnerability of European freshwater ecosystems at the catchment scale (18 783 catchments). The Balkan Lakes Ohrid and Prespa and Mediterranean islands emerge as most
vulnerable to climate change. For the 2030s, we showed a consensus among the applied methods whereby up to 573
lake and river catchments are highly vulnerable to climate change. The anthropogenic disruption of hydrological
habitat connectivity by dams is the major factor reducing climate change resilience. A gap analysis demonstrated that
the current European protected area network covers <25% of the most vulnerable catchments. Practical steps need to
be taken to ensure the persistence of freshwater biodiversity under climate change. Priority should be placed on
enhancing stakeholder cooperation at the major basin scale towards preventing further degradation of freshwater
ecosystems and maintaining connectivity among catchments. The catchments identified as most vulnerable to climate
change provide preliminary targets for development of climate change conservation management and mitigation
strategies.