Correlative species distribution models are frequently used to predict species’ range shifts under climate change. However, climate variables often show high collinearity and most statistical approaches require the selection of one among strongly correlated variables. When causal relationships between species presence and climate parameters are unknown, variable selection is often arbitrary, or based on predictive performance under current conditions. While this should only marginally affect current range predictions, future distributions may vary considerably when climate parameters do not change in concert. We investigated this source of uncertainty using four highly correlated climate variables together with a constant set of landscape variables in order to predict current (2010) and future (2050) distributions of four mountain bird species in central Europe. Simulating different parameterization decisions, we generated a) four models including each of the climate variables singly, b) a model taking advantage of all variables simultaneously and c) an un‐weighted average of the predictions of a). We compared model accuracy under current conditions, predicted distributions under four scenarios of climate change, and – for one species – evaluated back‐projections using historical occurrence data. Although current and future variable‐correlations remained constant, and the models’ accuracy under contemporary conditions did not differ, future range predictions varied considerably in all climate change scenarios. Averaged models and models containing all climate variables simultaneously produced intermediate predictions; the latter, however, performed best in back‐projections. This pattern, consistent across different modelling methods, indicates a benefit from including multiple climate predictors in ambiguous situations. Variable selection proved to be an important source of uncertainty for future range predictions, difficult to control using contemporary information. Small, but diverging changes of climate variables, masked by constant overall correlation patterns, can cause substantial differences between future range predictions which need to be accounted for, particularly when outcomes are intended for conservation decisions.

The rapid spread and diversification of outdoor recreation can impact on wildlife in various ways, often leading to the avoidance of disturbed habitats. To mitigate human-wildlife conflicts, spatial zonation schemes can be implemented to separate human activities from key wildlife habitats, e.g., by designating undisturbed wildlife refuges or areas with some level of restriction to human recreation and land use. However, mitigation practice rarely considers temporal differences in human-wildlife interactions. We used GPS telemetry data from 15 red deer to study the seasonal (winter vs. summer) and diurnal (day vs. night) variation in recreation effects on habitat use in a study region in south-western Germany where a spatial zonation scheme has been established. Our study aimed to determine if recreation infrastructure and spatial zonation affected red deer habitat use and whether these effects varied daily or seasonally. Recreation infrastructure did not affect home range selection in the study area, but strongly determined habitat use within the home range. The spatial zonation scheme was reflected in both of these two levels of habitat selection, with refuges and core areas being more frequently used than the border zones. Habitat use differed significantly between day and night in both seasons. Both summer and winter recreation trails, and nearby foraging habitats, were avoided during day, whereas a positive association was found during night. We conclude that human recreation has an effect on red deer habitat use, and when designing mitigation measures daily and seasonal variation in human-wildlife interactions should be taken into account. We advocate using spatial zonation in conjunction with temporal restrictions (i.e., banning nocturnal recreation activities) and the creation of suitable foraging habitats away from recreation trails.

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Species adapted to cold-climatic mountain environments are expected to face a high risk of range contractions, if not local extinctions under climate change. Yet, the populations of many endothermic species may not be primarily affected by physiological constraints, but indirectly by climate-induced changes of habitat characteristics. In mountain forests, where vertebrate species largely depend on vegetation composition and structure, deteriorating habitat suitability may thus be mitigated or even compensated by habitat management aiming at compositional and structural enhancement. We tested this possibility using four cold-adapted bird species with complementary habitat requirements as model organisms. Based on species data and environmental information collected in 300 1-km2 grid cells distributed across four mountain ranges in central Europe, we investigated (1) how species’ occurrence is explained by climate, landscape, and vegetation, (2) to what extent climate change and climate-induced vegetation changes will affect habitat suitability, and (3) whether these changes could be compensated by adaptive habitat management. Species presence was modelled as a function of climate, landscape and vegetation variables under current climate; moreover, vegetation-climate relationships were assessed. The models were extrapolated to the climatic conditions of 2050, assuming the moderate IPCC-scenario A1B, and changes in species’ occurrence probability were quantified. Finally, we assessed the maximum increase in occurrence probability that could be achieved by modifying one or multiple vegetation variables under altered climate conditions. Climate variables contributed significantly to explaining species occurrence, and expected climatic changes, as well as climate-induced vegetation trends, decreased the occurrence probability of all four species, particularly at the low-altitudinal margins of their distribution. These effects could be partly compensated by modifying single vegetation factors, but full compensation would only be achieved if several factors were changed in concert. The results illustrate the possibilities and limitations of adaptive species conservation management under climate change.

Systematic species surveys over large areas are mostly not affordable, constraining conservation planners to make best use of incomplete data. Spatially explicit species distribution models (SDM) may be useful to detect and compensate for incomplete information. SDMs can either be based on standardized, systematic sampling in a restricted subarea, or – as a cost‐effective alternative – on data haphazardly collated by “volunteer‐based monitoring schemes” (VMS), area‐wide but inherently biased and of heterogeneous spatial precision. Using data on capercaillie Tetrao urogallus, we evaluated the capacity of SDMs generated from incomplete survey data to localise unknown areas inhabited by the species and to predict relative local observation density. Addressing the trade‐off between data precision, sample size and spatial extent of the sampling area, we compared three different sampling strategies: VMS‐data collected throughout the whole study area (7000 km2) using either 1) exact locations or 2) locations aggregated to grid cells of the size of an average individual home range, and 3) systematic transect counts conducted within a small subarea (23.8 km2). For each strategy, we compared two sample sizes and two modelling methods (ENFA and Maxent), which were evaluated using cross‐validation and independent data. Models based on VMS‐data (strategies 1 and 2) performed equally well in predicting relative observation density and in localizing “unknown” occurrences. They always outperformed strategy 3‐models, irrespective of sample size and modelling method, partly because the VMS‐data provided the more comprehensive clues for setting the discrimination‐threshold for predicting presence or absence. Accounting for potential errors due to extrapolation (e.g. projections outside the environmental domain or potentially biasing variables) reduced, but did not fully compensate for the observed discrepancies. As they cover a broader range of species‐habitat relations, the area‐wide data achieved a better model quality with less a‐priori knowledge. Furthermore, in a highly mobile species like capercaillie a sampling resolution corresponding to an individuals' home range can lead to equally good predictions as the use of exact locations. Consequently, when a trade‐off between the sampling effort and the spatial extent of the sampling area is necessary, less precise data unsystematically collected over a large representative region are preferable to systematically sampled data from a restricted region.