International audiencePotential impacts of projected climate change on biodiversity are often assessed using single-species bioclimatic 'envelope' models. Such models are a special case of species distribution models in which the current geographical distribution of species is related to climatic variables so to enable projections of distributions under future climate change scenarios. This work reviews a number of critical methodological issues that may lead to uncertainty in predictions from bioclimatic modelling. Particular attention is paid to recent developments of bioclimatic modelling that address some of these issues as well as to the topics where more progress needs to be made. Developing and applying bioclimatic models in a informative way requires good understanding of a wide range of methodologies, including the choice of modelling technique, model validation, collinearity, autocorrelation, biased sampling of explanatory variables, scaling and impacts of non-climatic factors. A key challenge for future research is integrating factors such as land cover, direct CO2 effects, biotic interactions and dispersal mechanisms into species-climate models. We conclude that, although bioclimatic envelope models have a number of important advantages, they need to be applied only when users of models have a thorough understanding of their limitations and uncertainties

Current rates of climate change are unprecedented, and biological responses to these changes have also been rapid at the levels of ecosystems, communities, and species. Most research on climate change effects on biodiversity has concentrated on the terrestrial realm, and considerable changes in terrestrial biodiversity and species' distributions have already been detected in response to climate change. The studies that have considered organisms in the freshwater realm have also shown that freshwater biodiversity is highly vulnerable to climate change, with extinction rates and extirpations of freshwater species matching or exceeding those suggested for better-known terrestrial taxa. There is some evidence that freshwater species have exhibited range shifts in response to climate change in the last millennia, centuries, and decades. However, the effects are typically species-specific, with cold-water organisms being generally negatively affected and warm-water organisms positively affected. However, detected range shifts are based on findings from a relatively low number of taxonomic groups, samples from few freshwater ecosystems, and few regions. The lack of a wider knowledge hinders predictions of the responses of much of freshwater biodiversity to climate change and other major anthropogenic stressors. Due to the lack of detailed distributional information for most freshwater taxonomic groups and the absence of distribution-climate models, future studies should aim at furthering our knowledge about these aspects of the ecology of freshwater organisms. Such information is not only important with regard to the basic ecological issue of predicting the responses of freshwater species to climate variables, but also when assessing the applied issue of the capacity of protected areas to accommodate future changes in the distributions of freshwater species. This is a huge challenge, because most current protected areas have not been delineated based on the requirements of freshwater organisms. Thus, the requirements of freshwater organisms should be taken into account in the future delineation of protected areas and in the estimation of the degree to which protected areas accommodate freshwater biodiversity in the changing climate and associated environmental changes.

AimThe role of biotic interactions in influencing species distributions at macroscales remains poorly understood. Here we test whether predictions of distributions for four boreal owl species at two macro-scales (10 × 10 km and 40 × 40 km grid resolutions) are improved by incorporating interactions with woodpeckers into climate envelope models.Location Finland, northern Europe.Methods Distribution data for four owl and six woodpecker species, along with data for six land cover and three climatic variables, were collated from 2861 10 × 10 km grid cells. Generalized additive models were calibrated using a 50% random sample of the species data from western Finland, and by repeating this procedure 20 times for each of the four owl species. Models were fitted using three sets of explanatory variables: (1) climate only; (2) climate and land cover; and (3) climate, land cover and two woodpecker interaction variables. Models were evaluated using three approaches: (1) examination of explained deviance; (2) four-fold cross-validation using the model calibration data; and (3) comparison of predicted and observed values for independent grid cells in eastern Finland. The model accuracy for approaches (2) and (3) was measured using the area under the curve of a receiver operating characteristic plot. ResultsAt 10-km resolution, inclusion of the distribution of woodpeckers as a predictor variable significantly improved the explanatory power, cross-validation statistics and the predictive accuracy of the models. Inclusion of land cover led to similar improvements at 10-km resolution, although these improvements were less apparent at 40-km resolution for both land cover and biotic interactions.Main conclusions Predictions of species distributions at macro-scales may be significantly improved by incorporating biotic interactions and land cover variables into models. Our results are important for models used to predict the impacts of climate change, and emphasize the need for comprehensive evaluation of the reliability of species-climate impact models.

Summary1. Numerical studies of the relationship between birds and their habitat are important because they provide understanding of the impacts of natural and human factors on avian diversity. However, collinearity between explanatory variables and spatial autocorrelation can hamper the detection of key environmental factors underlying birdenvironment relationships identified by traditional regression approaches. This study utilized two alternative statistical methods to address these difficulties in biodiversity modelling. 2. We examined bird abundance patterns, spatial structure and relationship to environmental factors in an agricultural-forest mosaic landscape in Finland. We used data from 105 grid squares each 25 ha in size. Using variation partitioning and hierarchical partitioning methods, we determined the independent and joint effects of habitat cover, landscape structure and spatial variables on the total number of bird pairs and that of agricultural bird pairs. 3. The explanatory variables highlighted as important predictors of bird patterns by the two methods generally coincided well. The total number of bird pairs was negatively related to agricultural land, and positively to cover of forests and landscape heterogeneity. However, a clear majority of the explained variation in bird patterns was related to the joint effect of predictors, and the independent contributions of predictors were small. The univariate importance of landscape heterogeneity decreased greatly if the habitat cover variables were considered simultaneously. 4. Most of the explained variation in the number of agricultural bird pairs was related to the joint effects of the explanatory variables. In addition, the independent effect of habitat cover variables was considerable and agricultural birds showed a positive relationship with semi-natural grasslands. 5. Synthesis and applications . Variation partitioning and hierarchical partitioning approaches provide deeper insights into bird-environment relationships than traditional regression methods. This is particularly so when they are employed in a complementary manner. Our results indicate that a major part of the spatial structure in bird patterns in agricultural-forest mosaics can be caused by the clumping of habitats either preferred or avoided by birds. Moreover, at a scale of 25 ha, the abundance of bird pairs is not necessarily related to landscape heterogeneity as such, but depends more on the distribution of the most important habitats for birds.

Aim We explored the importance of climate and land cover in bird species distribution models on multiple spatial scales. In particular, we tested whether the integration of land cover data improves the performance of pure bioclimatic models.Location Finland, northern Europe. MethodsThe data of the bird atlas survey carried out in 1986 -89 using a 10 × 10 km uniform grid system in Finland were employed in the analyses. Land cover and climatic variables were compiled using the same grid system. The dependent and explanatory variables were resampled to 20-km, 40-km and 80-km resolutions. Generalized additive models (GAM) were constructed for each of the 88 land bird species studied in order to estimate the probability of occurrence as a function of (1) climate and (2) climate and land cover variables. Model accuracy was measured by a cross-validation approach using the area under the curve (AUC) of a receiver operating characteristic (ROC) plot. ResultsIn general, the accuracies of the 88 bird-climate models were good at all studied resolutions. However, the inclusion of land cover increased the performance of 79 and 78 of the 88 bioclimatic models at 10-km and 20-km resolutions, respectively. There was no significant improvement at the 40-km resolution. In contrast to the finer resolutions, the inclusion of land cover variables decreased the modelling accuracy at 80km resolution. Main conclusionsOur results suggest that the determinants of bird species distributions are hierarchically structured: climatic variables are large-scale determinants, followed by land cover at finer resolutions. The majority of the land bird species in Finland are rather clearly correlated with climate, and bioclimate envelope models can provide useful tools for identifying the relationships between these species and the environment at resolutions ranging from 10 km to 80 km. However, the notable contribution of land cover to the accuracy of bioclimatic models at 10-20-km resolutions indicates that the integration of climate and land cover information can improve our understanding and model predictions of biogeographical patterns under global change.