Biotic interactions improve prediction of boreal bird distributions at macro-scales

Aim The role of biotic interactions in influencing species distributions at macro‐scales 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. Results At 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.     

Do we need land‐cover data to model species distributions in Europe?

Aim To assess the influence of land cover and climate on species distributions across Europe. To quantify the importance of land cover to describe and predict species distributions after using climate as the main driver. Location The study area is Europe. Methods (1) A multivariate analysis was applied to describe land‐cover distribution across Europe and assess if the land cover is determined by climate at large spatial scales. (2) To evaluate the importance of land cover to predict species distributions, we implemented a spatially explicit iterative procedure to predict species distributions of plants (2603 species), mammals (186 species), breeding birds (440 species), amphibian and reptiles (143 species). First, we ran bioclimatic models using stepwise generalized additive models using bioclimatic variables. Secondly, we carried out a regression of land cover (LC) variables against residuals from the bioclimatic models to select the most relevant LC variables. Finally, we produced mixed models including climatic variables and those LC variables selected as decreasing the residual of bioclimatic models. Then we compared the explanatory and predictive power of the pure bioclimatic against the mixed model. Results (1) At the European coarse resolution, land cover is mainly driven by climate. Two bioclimatic axes representing a gradient of temperature and a gradient of precipitation explained most variation of land‐cover distribution. (2) The inclusion of land cover improved significantly the explanatory power of bioclimatic models and the most relevant variables across groups were those not explained or poorly explained by climate. However, the predictive power of bioclimatic model was not improved by the inclusion of LC variables in the iterative model selection process. Main conclusion Climate is the major driver of both species and land‐cover distributions over Europe. Yet, LC variables that are not explained or weakly associated with climate (inland water, sea or arable land) are interesting to describe particular mammal, bird and tree distributions. However, the addition of LC variables to pure bioclimatic models does not improve their predictive accuracy.     

Does seasonal fine‐tuning of climatic variables improve the performance of bioclimatic envelope models for migratory birds?

We examined the influence of 'seasonal fine-tuning' of climatic variables on the performance of bioclimatic envelope models of migrating birds. Using climate data and national bird atlas data from a 10 × 10 km uniform grid system in Finland, we tested whether the replacement of one 'baseline' set of variables including summer (June–August) temperature and precipitation variables with climate variables tailored ('fine-tuned') for each species individually improved the bird-climate models. The fine-tuning was conducted on the basis of time of arrival and early breeding of the species. Two generalized additive models (GAMs) were constructed for each of the 63 bird species studied, employing (1) the baseline climate variables and (2) the fine-tuned climate variables. Model performance was measured as explanatory power (deviance change) and predictive power (area under the curve; AUC) statistics derived from cross-validation. Fine-tuned climate variables provided, in many cases, statistically significantly improved model performance compared to using the same baseline set of variables for all the species. Model improvements mainly concerned bird species arriving and starting their breeding in May–June. We conclude that the use of the fine-tuned climate variables tailored for each species individually on the basis of their arrival and critical breeding periods can provide important benefits for bioclimatic modelling.     

Land-cover data improve bioclimatic models for anurans and turtles at a regional scale

Aim We investigated whether accounting for land cover could improve bioclimatic models for eight species of anurans and three species of turtles at a regional scale. We then tested whether accounting for spatial autocorrelation could significantly improve bioclimatic models after statistically controlling for the effects of land cover. Location Nova Scotia, eastern Canada. Methods Species distribution data were taken from a recent (1999–2003) herpetofaunal atlas. Generalized linear models were used to relate the presence or absence of each species to climate and land‐cover variables at a 10‐km resolution. We then accounted for spatial autocorrelation using an autocovariate or third‐order trend surface of the geographical coordinates of each grid square. Finally, variance partitioning was used to explore the independent and joint contributions of climate, land cover and spatial autocorrelation. Results The inclusion of land cover significantly increased the explanatory power of bioclimatic models for 10 of the 11 species. Furthermore, including land cover significantly increased predictive performance for eight of the 11 species. Accounting for spatial autocorrelation improved model fit for rare species but generally did not improve prediction success. Variance partitioning demonstrated that this lack of improvement was a result of the high correlation between climate and trend‐surface variables. Main conclusions The results of this study suggest that accounting for the effects of land cover can significantly improve the explanatory and predictive power of bioclimatic models for anurans and turtles at a regional scale. We argue that the integration of climate and land‐cover data is likely to produce more accurate spatial predictions of contemporary herpetofaunal diversity. However, the use of land‐cover simulations in climate‐induced range‐shift projections introduces additional uncertainty into the predictions of bioclimatic models. Further research is therefore needed to determine whether accounting for the effects of land cover in range‐shift projections is merited.     

Climate and satellite-derived land cover for predicting breeding bird distribution in the Great Lakes Basin

Aim We examined relationships between breeding bird distribution of 10 forest songbirds in the Great Lakes Basin, large‐scale climate and the distribution of land cover types as estimated by advanced very high resolution radiometer (AVHRR) and multi‐spectral scanner (MSS) land cover classifications. Our objective was to examine the ability of regional climate, AVHRR (1 km resolution) land cover and MSS (200 m resolution) land cover to predict the distribution of breeding forest birds at the scale of the Great Lakes Basin and at the resolution of Breeding Bird Atlas data (5–10 km²). Specifically we addressed the following questions. (1) How well do AVHRR or MSS classifications capture the variation in distribution of bird species? (2) Is one land cover classification more useful than the other for predicting distribution? (3) How do models based on climate compare with models based on land cover? (4) Can the combination of both climate and land cover improve the predictive ability of these models. Location Modelling was conducted over the area of the Great Lakes Basin including parts of Ontario, Canada and parts of Illinois, Indiana, Michigan, New York, Ohio, Pennsylvania Wisconsin, and Minnesota, USA. Methods We conducted single variable logistic regression with the forest classes of AVHRR and MSS land cover using evidence of breeding as the response variable. We conducted multiple logistic regression with stepwise selection to select models from five sets of explanatory variables (AVHRR, MSS, climate, AVHRR + climate, MSS + climate). Results Generally, species were related to both AVHRR and MSS land cover types in the direction expected based on the known local habitat use of the species. Neither land cover classification appeared to produce consistently more intuitive results. Good models were generated using each of the explanatory data sets examined here. And at least one but usually all five variable sets produced acceptable or excellent models for each species. Main conclusions Both climate and large scale land cover were effective predictors of the distribution of the 10 forest bird species examined here. Models generated from these data had good classification accuracy of independent validation data. Good models were produced from all explanatory data sets or combinations suggesting that the distribution of climate, AVHRR land cover, and MSS land cover all captured similar variance in the distribution of the birds. It is difficult to separate the effects of climate and vegetation on the species’ distributions at this scale.     

Inclusion of soil data improves the performance of bioclimatic envelope models for insect species distributions in temperate Europe

Aim To analyse the effect of the inclusion of soil and land‐cover data on the performance of bioclimatic envelope models for the regional‐scale prediction of butterfly (Rhopalocera) and grasshopper (Orthoptera) distributions. Location Temperate Europe (Belgium). Methods Distributional data were extracted from butterfly and grasshopper atlases at a resolution of 5 km for the period 1991–2006 in Belgium. For each group separately, the well‐surveyed squares (n = 366 for butterflies and n = 322 for grasshoppers) were identified using an environmental stratification design and were randomly divided into calibration (70%) and evaluation (30%) datasets. Generalized additive models were applied to the calibration dataset to estimate occurrence probabilities for 63 butterfly and 33 grasshopper species, as a function of: (1) climate, (2) climate and land‐cover, (3) climate and soil, and (4) climate, land‐cover and soil variables. Models were evaluated as: (1) the amount of explained deviance in the calibration dataset, (2) Akaike’s information criterion, and (3) the number of omission and commission errors in the evaluation dataset. Results Information on broad land‐cover classes or predominant soil types led to similar improvements in the performance relative to the climate‐only models for both taxonomic groups. In addition, the joint inclusion of land‐cover and soil variables in the models provided predictions that fitted more closely to the species distributions than the predictions obtained from bioclimatic models incorporating only land‐cover or only soil variables. The combined models exhibited higher discrimination ability between the presence and absence of species in the evaluation dataset. Main conclusions These results draw attention to the importance of soil data for species distribution models at regional scales of analysis. The combined inclusion of land‐cover and soil data in the models makes it possible to identify areas with suitable climatic conditions but unsuitable combinations of vegetation and soil types. While contingent on the species, the results indicate the need to consider soil information in regional‐scale species–climate impact models, particularly when predicting future range shifts of species under climate change.     

Modeling plant species distributions under future climates: how fine scale do climate projections need to be?

Recent studies suggest that species distribution models (SDMs) based on fine‐scale climate data may provide markedly different estimates of climate‐change impacts than coarse‐scale models. However, these studies disagree in their conclusions of how scale influences projected species distributions. In rugged terrain, coarse‐scale climate grids may not capture topographically controlled climate variation at the scale that constitutes microhabitat or refugia for some species. Although finer scale data are therefore considered to better reflect climatic conditions experienced by species, there have been few formal analyses of how modeled distributions differ with scale. We modeled distributions for 52 plant species endemic to the California Floristic Province of different life forms and range sizes under recent and future climate across a 2000‐fold range of spatial scales (0.008–16 km²). We produced unique current and future climate datasets by separately downscaling 4 km climate models to three finer resolutions based on 800, 270, and 90 m digital elevation models and deriving bioclimatic predictors from them. As climate‐data resolution became coarser, SDMs predicted larger habitat area with diminishing spatial congruence between fine‐ and coarse‐scale predictions. These trends were most pronounced at the coarsest resolutions and depended on climate scenario and species' range size. On average, SDMs projected onto 4 km climate data predicted 42% more stable habitat (the amount of spatial overlap between predicted current and future climatically suitable habitat) compared with 800 m data. We found only modest agreement between areas predicted to be stable by 90 m models generalized to 4 km grids compared with areas classified as stable based on 4 km models, suggesting that some climate refugia captured at finer scales may be missed using coarser scale data. These differences in projected locations of habitat change may have more serious implications than net habitat area when predictive maps form the basis of conservation decision making.     

Threat spots and environmental determinants of red-listed plant,butterfly and bird species in boreal agricultural environments

The aims of this study were (1) to examine the geographic distribution of red-listed species of agricultural environments and identify their national threat spots (areas with high diversity of threatened species) in Finland and (2) to determine the main environmental variables related to the richness and occurrence patterns of red-listed species. Atlas data of 21 plant, 17 butterfly and 11 bird species recorded using 10 km grid squares were employed in the study. Generalized additive models (GAMs) were constructed separately for species richness and occurrence of individual species of the three species groups using climate and land cover predictor variables. The predictive accuracy of models, as measured using correlation between the observed and predicted values and AUC statistics, was generally good. Temperature-related variables were the most important determinants of species richness and occurrence of all three taxa. In addition, land cover variables had a strong effect on the distribution of species. Plants and butterflies were positively related to the cover of grasslands and birds to small-scale agricultural mosaic as well as to arable land. Spatial coincidence of threat spots of plants, butterflies and birds was limited, which emphasizes the importance of considering the potentially contrasting environmental requirements of different taxa in conservation planning. Further, it is obvious that the maintenance of various non-crop habitats and heterogeneous agricultural landscapes has an essential role in the preservation of red-listed species of boreal rural environments.     

Species traits are associated with the quality of bioclimatic models

Aim To analyse the effects of nine species trait variables on the accuracy of bioclimatic envelope models built for 98 butterfly species. Location Finland, northern Europe. Methods Data from a national butterfly atlas monitoring scheme (NAFI) collected from 1991–2003 with a resolution of 10 × 10 km were used in the analyses. Generalized additive models (GAMs) were constructed for 98 butterfly species to predict their occurrence as a function of climatic variables. Modelling accuracy was measured as the cross‐validation area under the curve (AUC) of the receiver–operating characteristic plot. Observed variation in modelling accuracy was related to species traits using multiple GAMs. The effects of phylogenetic relatedness among butterflies were accounted for by using generalized estimation equations. Results The values of the cross‐validation AUC for the 98 species varied between 0.56 and 1.00 with a mean of 0.79. Five species trait variables were included in the GAM that explained 71.4% of the observed variation in modelling accuracy. Four variables remained significant after accounting for phylogenetic relatedness. Species with high mobility and a long flight period were modelled less accurately than species with low mobility and a short flight period. Large species (>50 mm in wing span) were modelled more accurately than small ones. Species inhabiting mires had especially poor models, whereas the models for species inhabiting rocky outcrops, field verges and open fells were more accurate compared with other habitats. Main conclusions These results draw attention to the importance of species traits variables for species–climate impact models. Most importantly, species traits may have a strong impact on the performance of bioclimatic envelope models, and certain trait groups can be inherently difficult to model reliably. These uncertainties should be taken into account by downweighting or excluding species with such traits in studies applying bioclimatic modelling and making assessments of the impacts of climate change.     

Species richness, area and climate correlates

Aim Species richness–area theory predicts that more species should be found if one samples a larger area. To avoid biases from comparing species richness in areas of very different sizes, area is often controlled by counting the numbers of co‐occupying species in near‐equal area grid cells. The assumption is that variation in grid cell size accrued from working in a three‐dimensional world is negligible. Here we provide a first test of this idea. We measure the surface area of c. 50 × 50 km and c. 220 × 220 km grid cells across western Europe. We then ask how variation in the area of grid cells affects: (1) the selection of climate variables entering a species richness model; and (2) the accuracy of models in predicting species richness in unsampled grid cells. Location Western Europe. Methods Models are developed for European plant, breeding bird, mammal and herptile species richness using seven climate variables. Generalized additive models are used to relate species richness, climate and area. Results We found that variation in the grid cell area was large (50 × 50 km: 8–3311 km²; 220 × 220: 193–55,100 km²), but this did not affect the selection of variables in the models. Similarly, the predictive accuracy was affected only marginally by exclusion of area within models developed at the c. 50 × 50 km grid cells, although predictive accuracy suffered greater reductions when area was not included as a covariate in models developed for c. 220 × 220 km grid cells. Main conclusions Our results support the assumption that variation in near‐equal area cells may be of second‐order importance for models explaining or predicting species richness in relation to climate, although there is a possibility that drops in accuracy might increase with grid cell size. The results are, however, contingent on this particular data set, grain and extent of the analyses, and more empirical work is required.     

Patterns of beta diversity in Europe: the role of climate,land cover and distance across scales

Aim We test the prediction that beta diversity (species turnover) and the decay of community similarity with distance depend on spatial resolution (grain). We also study whether patterns of beta diversity are related to variability in climate, land cover or geographic distance and how the independent effects of these variables depend on the spatial grain of the data. Location Europe, Great Britain, Finland and Catalonia. Methods We used data on European birds, plants, butterflies, amphibians and reptiles, and data on British plants, Catalonian birds and Finnish butterflies. We fitted two or three nested grids of varying resolutions to each of these datasets. For each grid we calculated differences in climate, differences in land‐cover composition (CORINE) and beta diversity (β_sim, β_Jaccard) between all pairs of grid cells. In a separate analysis we looked specifically at pairs of adjacent grid cells (the first distance class). We then used variation partitioning to identify the magnitude of independent statistical associations (i.e. independent effects in the statistical sense) of climate, land cover and geographic distance with spatial patterns of beta diversity. Results Beta diversity between grid cells at any given distance decreased with increasing grain. Geographic distance was always the most important predictor of beta diversity for all pairwise comparisons at the extent of Europe. Climate and land cover had weaker but distinct and grain‐dependent effects. Climate was more important at relatively coarse grains, whereas land‐cover effects were stronger at finer grains. In the country‐wide analyses, climate and land cover were more important than geographic distance. Climatic and land‐cover models performed poorly and showed no systematic grain dependence for beta diversity between adjacent grid cells. Main conclusions We found that relationships between geographic distance and beta diversity, as well as the environmental correlates of beta diversity, are systematically grain dependent. The strong independent effect of distance indicates that, contrary to the current belief, a substantial fraction of species are missing from areas with a suitable environment. Moreover, the effects of geographic distance (at continental extents) and land cover (at fine grains) indicate that any species distribution modelling should take both environment and dispersal limitation into account.     

Distribution patterns of boreal marshland birds: modelling the relationships to land cover and climate

Aim The aims of this work were (1) to study how well land‐cover and climatic data are capable of explaining distribution patterns of ten bird species breeding and/or feeding primarily on marshes and other wetlands and (2) to compare the differences between red‐listed and common marshland species in explanatory variables, and to study the predictability of their distribution patterns. Location Finland, northern Europe. Methods The data of the bird atlas survey carried out in 1986–89 using a 10 × 10 km uniform grid system in Finland were used in the analyses. Land‐cover data based on CORINE (Coordination of Information on the Environment) classification and climatic variables were compiled using the same 10 × 10 km grid. Generalized additive models (GAM) with a stepwise selection procedure were used to select relevant explanatory variables and to examine the complexity of the response shapes of the different species to each variable. The original data set was randomly divided into model training (70%) and model evaluation (30%) sets. The final models of common and red‐listed bird species richness were validated by fitting them to the model evaluation set, and the correlation between observed and predicted species richness was calculated. We assessed the discrimination ability of the binary models (single species) with the area under the curve (AUC) of a receiver operating characteristic (ROC) plot and the Kappa coefficient. Results Cover of marshland, shoreline length and mean temperature in April–June were significantly (P < 0.01) related to the common marshland species richness. Cover and clumping of marshland and mean temperature and precipitation in April–June were selected in the model of red‐listed marshland species richness. The level of discrimination in our single species models varied in ROC from fair to excellent (AUC values 0.70–0.95). Cover of marshland was included in all GAM models built for the target species, but clumping of marshland, shoreline length and cover of mires also appeared as important predictors in single species models. Seven species had statistically significant relationships with climatic variables in the multivariate GAMs. Cover of marshland was highest in squares in which the red‐listed bittern Botaurus stellaris, marsh harrier Circus aeruginosus and great reed warbler Acrocephalus arundinaceus and the water rail Rallus aquaticus were observed. Main conclusions Cover of marshland was the only variable which was included in all the models, reinforcing the close connection between the studied species and marshlands. Broad‐scale clumping of marshlands was important for the red‐listed species, probably due to the much lower population sizes of red‐listed species than those of common species. Land‐cover data produced in CORINE seems to be well suited for modelling the distribution patterns of marshland birds. Although climatic variables also strongly affect the studied marshland birds, habitat availability plays a crucial role in their occurrence. The distribution patterns of marshland birds at the scale of 10 × 10 km reflect the interplay between habitat availability and direct climatic variables.     

An empirical test of the relative and combined effects of land‐cover and climate change on local colonization and extinction

Land‐cover and climate change are two main drivers of changes in species ranges. Yet, the majority of studies investigating the impacts of global change on biodiversity focus on one global change driver and usually use simulations to project biodiversity responses to future conditions. We conduct an empirical test of the relative and combined effects of land‐cover and climate change on species occurrence changes. Specifically, we examine whether observed local colonization and extinctions of North American birds between 1981–1985 and 2001–2005 are correlated with land‐cover and climate change and whether bird life history and ecological traits explain interspecific variation in observed occurrence changes. We fit logistic regression models to test the impact of physical land‐cover change, changes in net primary productivity, winter precipitation, mean summer temperature, and mean winter temperature on the probability of Ontario breeding bird local colonization and extinction. Models with climate change, land‐cover change, and the combination of these two drivers were the top ranked models of local colonization for 30%, 27%, and 29% of species, respectively. Conversely, models with climate change, land‐cover change, and the combination of these two drivers were the top ranked models of local extinction for 61%, 7%, and 9% of species, respectively. The quantitative impacts of land‐cover and climate change variables also vary among bird species. We then fit linear regression models to test whether the variation in regional colonization and extinction rate could be explained by mean body mass, migratory strategy, and habitat preference of birds. Overall, species traits were weakly correlated with heterogeneity in species occurrence changes. We provide empirical evidence showing that land‐cover change, climate change, and the combination of multiple global change drivers can differentially explain observed species local colonization and extinction.     

Downscaling European species atlas distributions to a finer resolution: implications for conservation planning

Aim One of the limitations to using species’ distribution atlases in conservation planning is their coarse resolution relative to the needs of local planners. In this study, a simple approach to downscale original species atlas distributions to a finer resolution is outlined. If such a procedure yielded accurate downscaled predictions, then it could be an aid to using available distribution atlases in real‐world local conservation decisions. Location Europe. Methods An iterative procedure based on generalized additive modelling is used to downscale original European 50 × 50 km distributions of 2189 plant and terrestrial vertebrate species to c. 10 × 10 km grid resolution. Models are trained on 70% of the original data and evaluated on the remaining 30%, using the receiver operating characteristic (ROC) procedure. Fitted models are then interpolated to a finer resolution. A British dataset comprising distributions of 81 passerine‐bird species in a 10 × 10 km grid is used as a test bed to assess the accuracy of the downscaled predictions. European‐wide, downscaled predictions are further evaluated in terms of their ability to reproduce: (1) spatial patterns of coincidence in species richness scores among different groups; and (2) spatial patterns of coincidence in richness, rarity and complementarity hotspots. Results There was a generally good agreement between downscaled and observed fine‐resolution distributions for passerine species in Britain (median Jaccard similarity = 70%; lower quartile = 36%; upper quartile = 88%). In contrast, the correlation between downscaled and observed passerine species richness was relatively low (rho = 0.31) indicating a pattern of error propagation through the process of overlaying downscaled distributions for many species. It was also found that measures of model accuracy in fitting original data (ROC) were a poor predictor of models’ ability to interpolate distributions at fine resolutions (rho = ?0.10). Although European hotspots were not fully coincident between observed and modelled coarse‐resolution data, or between modelled coarse resolution and modelled downscaled data, there was evidence that downscaled distributions were able to maintain original cross‐taxon coincidence of species‐richness scores, at least for terrestrial vertebrate groups. Downscaled distributions were also able to uncover important environmental gradients otherwise blurred by coarse‐resolution data. Main conclusions Despite uncertainties, downscaling procedures may prove useful to identify reserves that are more meaningfully related to local patterns of environmental variation. Potential errors arising from the presence of false positives may be reduced if downscaled‐distribution records projected to occur outside the range of original coarse‐resolution data are excluded. However, the usefulness of this procedure may be limited to data‐rich regions. If downscaling procedures are applied to data‐poor regions, then there is a need to undertake further research to understand the structure of error in models. In particular, it would be important to investigate which species are poorly modelled, where and why. Without such an assessment it is difficult to support unsupervised use of downscaled data in most real‐world situations.     

Climate change and the future distributions of aquatic macrophytes across boreal catchments

Aim Aquatic–terrestrial ecotones are vulnerable to climate change, and degradation of the emergent aquatic macrophyte zone would have severe ecological consequences for freshwater, wetland and terrestrial ecosystems. Our aim was to uncover future changes in boreal emergent aquatic macrophyte zones by modelling the occurrence and percentage cover of emergent aquatic vegetation under different climate scenarios in Finland by the 2050s. Location Finland, northern Europe. Methods Data derived from different GIS sources were used to estimate future emergent aquatic macrophyte distributions in all catchments in Finland (848 in total). We used generalized additive models (GAM) with a full stepwise selection algorithm and Akaike information criterion to explore the main environmental determinates (climate and geomorphology) of emergent aquatic macrophyte distributions, which were derived from the national subclass of CORINE land‐cover classification. The accuracy of the distribution models (GAMs) was cross‐validated, using percentage of explained deviance and the area under the curve derived from the receiver‐operating characteristic plots. Results Our results indicated that emergent aquatic macrophytes will expand their distributions northwards from the current catchments and percentage cover will increase in all of the catchments in all climate scenarios. Growing degree‐days was the primary determinant affecting distributions of emergent aquatic macrophytes. Inclusion of geomorphological variables clearly improved model performance in both model exercises compared with pure climate variables. Main conclusions Emergent aquatic macrophyte distributions will expand due to climate change. Many emergent aquatic plant species have already expanded their distributions during the past decades, and this process will continue in the years 2051–80. Emergent aquatic macrophytes pose an increasing overgrowth risk for sensitive macrophyte species in boreal freshwater ecosystems, which should be acknowledged in management and conservation actions. We conclude that predictions based on GIS data can provide useful ‘first‐filter’ estimates of changes in aquatic–terrestrial ecotones.     

Birds on the move in the face of climate change: High species turnover in northern Europe

Species richness is predicted to increase in the northern latitudes in the warming climate due to ranges of many southern species expanding northwards. We studied changes in the composition of the whole avifauna and in bird species richness in a period of already warming climate in Finland (in northern Europe) covering 1,100 km in south–north gradient across the boreal zone (over 300,000 km²). We compared bird species richness and species‐specific changes (for all 235 bird species that occur in Finland) in range size (number of squares occupied) and range shifts (measured as median of area of occupancy) based on bird atlas studies between 1974–1989 and 2006–2010. In addition, we tested how the habitat preference and migration strategy of species explain species‐specific variation in the change of the range size. The study was carried out in 10 km squares with similar research intensity in both time periods. The species richness did not change significantly between the two time periods. The composition of the bird fauna, however, changed considerably with 37.0% of species showing an increase and 34.9% a decrease in the numbers of occupied squares, that is, about equal number of species gained and lost their range. Altogether 95.7% of all species (225/235) showed changes either in the numbers of occupied squares or they experienced a range shift (or both). The range size of archipelago birds increased and long‐distance migrants declined significantly. Range loss observed in long‐distance migrants is in line with the observed population declines of long‐distance migrants in the whole Europe. The results show that there is an ongoing considerable species turnover due to climate change and due to land use and other direct human influence. High bird species turnover observed in northern Europe may also affect the functional diversity of species communities.     

Fine‐scale determinants of butterfly species richness and composition in a mountain region

Aim Global patterns of species richness are often considered to depend primarily on climate. We aimed to determine how topography and land cover affect species richness and composition at finer scales. Location Sierra de Guadarrama (central Iberian Peninsula). Methods We sampled the butterfly fauna of 180 locations (89 in 2004, 91 in 2005) at 600–2300 m elevation in a region of 10800 km². We recorded environmental variables at 100‐m resolution using GIS, and derived generalized linear models for species density (number of species per unit area) and expected richness (number of species standardized to number of individuals) based on variables of topoclimate (elevation and insolation) or land cover (vegetation type, geology and hydrology), or both (combined). We evaluated the models against independent data from the alternative study year. We also tested for differences in species composition among sites and years using constrained ordination (canonical correspondence analysis), and used variation partitioning analyses to quantify the independent and combined roles of topoclimate and land cover. Results Topoclimatic, land cover and combined models were significantly related to observed species density and expected richness. Topoclimatic and combined models outperformed models based on land cover variables, showing a humped elevational diversity gradient. Both topoclimate and land cover made significant contributions to models of species composition. Main conclusions Topoclimatic factors may dominate species richness patterns in regions with pronounced elevational gradients, as long as large areas of natural habitat remain. In contrast, both topoclimate and land cover may have important effects on species composition. Biodiversity conservation in mountainous regions therefore requires protection and management of natural habitats over a wide range of topoclimatic conditions, which may assist in facilitating range shifts and alleviating declines in species richness related to climate change.     

Uncertainty of bioclimate envelope models based on the geographical distribution of species

Aim We explored the effects of prevalence, latitudinal range and spatial autocorrelation of species distribution patterns on the accuracy of bioclimate envelope models of butterflies. Location Finland, northern Europe. Methods The data of a national butterfly atlas survey (NAFI) carried out in 1991–2003 with a resolution of 10 × 10 km were used in the analyses. Generalized additive models (GAM) were constructed, for each of 98 species, to estimate the probability of occurrence as a function of climate variables. Model performance was measured using the area under the curve (AUC) of a receiver operating characteristic (ROC) plot. Observed differences in modelling accuracy among species were related to the species’ geographical attributes using multivariate GAM. Results Accuracies of the climate–butterfly models varied from low to very high (AUC values 0.59–0.99), with a mean of 0.79. The modelling performance was related negatively to the latitudinal range and prevalence, and positively to the spatial autocorrelation of the species distribution. These three factors accounted for 75.2% of the variation in the modelling accuracy. Species at the margin of their range or with low prevalence were better predicted than widespread species, and species with clumped distributions better than scattered dispersed species. Main conclusions The results from this study indicate that species’ geographical attributes highly influence the behaviour and uncertainty of species–climate models, which should be taken into account in biogeographical modelling studies and assessments of climate change impacts.     

Does bird community structure vary with landscape patchiness? A Chihuahuan Desert perspective

During the springs of 1995–1997, we studied birds and landscapes at 70 sites in the Chihuahuan Desert to assess relations between bird community structure and landscape patchiness. Within each of two spatial extents (1‐km and 2‐km‐radius areas centered on each site), we measured the number of patches of individual land‐cover types and the total number of patches of all land‐cover types. Mean bird richness, and the mean abundance and probability of occurrence of most bird species were significantly correlated with one or more of these variables. Contrary to evidence from other systems, positive association with landscape patchiness did not increase with the degree to which species were habitat generalists, was not negatively related to body size, and did not differ between neotropical migrants and nonmigrants. For the communities’ primary constituent species as a group, the strength of positive and negative associations with patchiness did not differ between landscape extents. Within the 1‐km but not the 2‐km extent, habitat specialists were more positively and negatively associated with patchiness than were habitat generalists. In general, however, neither habitat breadth, body size, nor migratory status seemed to be responsible for associations with landscape patchiness. Mean richness, and the mean abundance and probability of occurrence of most species were significantly correlated with patchiness within one or both extents, and patchiness of all of the most extensive land‐cover types was influential. The simplest explanation for most of the bird‐patchiness relations we found is that the associations reflected species‐specific habitat needs. Through effects on avian richness, abundance, and occurrence, landscape patchiness affected bird community structure. A more complete understanding of the effects of landscape patchiness on bird community structure is likely to emerge when ecologists study the patchiness of major land‐cover types at various spatial extents.     

Climate and topography drives macroscale biodiversity through land‐use change in a human‐dominated world

Drivers of biodiversity at macroscales have long been of interest in ecology, and climate and topography are now considered to be major drivers. Because humans have transformed most of the Earth's land surface, land use may play a significant role as a driver of biodiversity at a macroscale. Here we disentangle the relationships among climate, topography, land use, available energy (measured by the normalized difference vegetation index [NDVI]), and species richness of Japanese forest birds. Species richness was better explained at 40‐ and 80‐km resolutions than at 5‐, 10‐ and 20‐km resolutions; it was explained by climate, topography, and land use, and the effects of land use were fully incorporated into those of climate and topography. As temperature increased and elevation decreased, natural forest area decreased, and this decrease intensified in warm lowland areas. With the loss of natural forest, species richness decreased below a certain threshold. As temperature increased and elevation decreased, species richness and NDVI increased slightly or were unchanged in cool highland areas and decreased in warm lowland areas. Species richness increased linearly with the increase in NDVI. Most effects of climate/topography on species richness in warm lowland areas were shared by those of land use, suggesting that the decrease in species richness in warm lowland areas has been caused by loss of natural forest. Therefore, it is suggested that climate and topography determined land use intensity, which in turn, drove species richness through the depletion of available energy. Increasing temperature and decreasing elevation leads to both benefits (increase in potential available energy) and costs (depletion of energy by human land‐use change) for forest birds. These costs seem to override benefits in warm lowland areas.