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Aim: Streamflow and water temperature are primary variables influencing the distribution of freshwater taxa. Climate‐induced changes in these variables are already causing shifts in species distributions, with continued changes projected in the coming decades. The Mobile River Basin (MRB), located in the southeastern United States, contains some of the highest levels of temperate freshwater biodiversity in North America. We integrated species distribution data with contemporary and future streamflow and water temperature data as well as other physical habitat data to characterize occurrence probabilities of fish species in the MRB with the goal of identifying current and future areas of high conservation value. Location: Mobile River Basin, southeastern United States. Methods: We used a maximum entropy approach to estimate baseline and future occurrence probability distributions for 88 fish species in the MRB based on model‐generated streamflow and water temperature as well as geologic, topographic and land cover data. Areas of conservation prioritization were identified based on regions that contain suitable habitat for high levels of biodiversity according to baseline and future conditions while accounting for uncertainty associated with multiple future climate projections. Results: On average, flow (28%), water temperature (28%) and geology (30%) contribute evenly to determining suitable habitat for fish species in the MRB. Based on baseline and future species distribution model estimates, high priority streams (best 10%) are largely concentrated in the eastern portion of the MRB, with a majority (51%) located within the Coosa and Tallapoosa River systems. Main conclusion: We provide a framework that uses relevant hydrologic and environmental data in the context of future climatic uncertainty to estimate areas of freshwater conservation opportunity in the coming decades. While streamflow and water temperature represent important habitat for freshwater fishes in the MRB, distributions are also constrained by other aspects of the physical environment. [ABSTRACT FROM AUTHOR]

Aim: Species distribution models are an important conservation tool; however, performance can vary with factors including data inputs and modelling method. Model outputs are often under‐evaluated for explanatory and predictive capacity. Our aim was to evaluate the capacity of existing data for seven small mammal species to provide useful inferences for management planning. Location: Bathurst and Melville (collectively the Tiwi) Islands, Northern Territory, Australia. Methods: We developed species distribution models (SDMs) with generalized linear models (GLMs) and boosted regression trees (BRTs) using survey data (351 sites) of small mammals, with two sets of environmental predictors: (a) field‐study measurements and (b) available remotely sensed rasters. Predictive capacity of models was evaluated using percentage of deviance explained (%DE) and area under the receiver operating characteristic curve (AUC). We used Marxan to evaluate the influence of different model and data types as input for identifying spatial priorities. Results: Field‐informed SDMs performed well across both modelling methods, with relatively high test AUC values (mean = 0.82, range = 0.64–0.97) and test %DE (mean = 22.5%, range = 3.5%–65.8%). Remotely sensed models performed relatively poorly, with lower test AUC values (mean = 0.7, range = 0.56–0.86) and lower test %DE (mean = 8.9%, range = 0.03%–24.9%). A notable exception was remotely sensed models for Melomys burtoni (AUC = 0.85 & 0.86, %DE = 23.3% & 24.9%, Bathurst and Melville respectively). Marxan site irreplaceability rankings demonstrated low to marginal agreement using field‐informed and remotely sensed inputs (Pearson correlation coefficient = 0.3), and similarly, using GLM and BRT model inputs (0.29). Main conclusions: The occurrence of small mammals on the Tiwi Islands can be reasonably explained with field‐informed variables, but not with remotely sensed alternatives. Different models lead to different conservation priorities. Our work emphasizes the importance of thoroughly testing SDMs prior to decision‐making. [ABSTRACT FROM AUTHOR]

Aim: Contemporary climate change is predicted to impact all levels of biodiversity, including intraspecific genetic diversity, the evolutionary basis for future adaptation. While numerous studies use species distribution models (SDMs) to predict species' future distributions, relatively few investigate potential climatic impacts on the spatial structure of genetic diversity, and how it varies across species ranges. We revisited phylogeographic data for three New Zealand forest beetles to predict the effects of climate change on the geographic distributions, genetic diversity and phylogeographic structure for each species. Location: New Zealand Methods: We used ensemble SDMs to predict potential distributions for Agyrtodes labralis (Leiodidae), Brachynopus scutellaris (Staphylinidae) and Epistranus lawsoni (Zopheridae) in 2035, 2065 and 2100. To assess the impact of predicted range loss on genetic diversity and phylogeographic structure, we estimated haplotype and nucleotide diversity, ΦST, Average Taxonomic Distinctness (AvTax), Phylogenetic Diversity (PD) and Net Relatedness Index (NRI) under current and future climatic scenarios, excluding sequences from localities predicted to become unsuitable. We tested whether predicted population loss was spatially clustered and how losses were distributed across the phylogenies of each species. Results: Agyrtodes labralis is predicted to lose parts of its current distribution by 2100, with the loss of 50% of unique haplotypes and a significant reduction in PD, while Brachynopus scutellaris and Epistranus lawsoni will likely experience an expansion in climatically suitable area and little change in genetic diversity. Brachynopus scutellaris populations are predicted to be more phylogenetically clustered than expected by 2100, but changes in AvTax were negligible for all species. Main conclusions: We demonstrate that the loss of genetic diversity under climate change is significant; however, intraspecific lineages with deep genetic divergences are widely distributed, buffering against greater change in phylogeographic structure. For species with strong geographic clustering of genetic diversity, climate change impacts may be quite different. [ABSTRACT FROM AUTHOR]

Aim: Large carnivore populations in Europe are expanding into new areas. This generates opportunities to improve their conservation status, but also creates a need to address new conflicts with humans. Species management units are constrained by administrative boundaries, but effective conservation and conflict management require a continental‐scale perspective on the opportunities and limitations for expanding populations. We assessed the conservation applicability and the uncertainties of transferring and up‐scaling local habitat suitability models from multiple populations in support of large‐scale, transboundary species conservation. Location: Europe. Methods: We evaluated the accuracy of local population models to predict European brown bear (Ursus arctos) distribution patterns in other populations and at the continental scale. We also assessed the benefits of combining predictions from multiple local population models, and we evaluated the limitations of transferring models among populations and environmental settings. Last, we estimated the availability of unoccupied suitable habitats in Europe for colonization by expanding populations. Results: We found that integrating habitat predictions from multiple populations outperformed predictions from most individual populations. Results showed that about 37% of potentially suitable brown bear habitat in Europe remains unoccupied. As a synthesis of our results, we provide a set of predictive maps for the expansion of brown bears at the continental scale, including predictions from individual habitat models and a multimodel predictive map. Main conclusions: We show that integrating habitat models from multiple populations provides richer and more reliable information on the distribution of suitable habitats in data deficient areas. This integration yields more reliable predictions compared to those based on individual populations and has important implications to manage species expansions and the associated conflicts. We also identified major limitations in transferring predictions among habitats; therefore, it is critical to enhance the reproducibility of habitat suitability models to apply local‐scale habitat studies to broader‐scale conservation practice. [ABSTRACT FROM AUTHOR]

Aim: Modelling the response of β‐diversity (i.e., the turnover in species composition among sites) to environmental variation has wide‐ranging applications, including informing conservation planning, understanding community assembly and forecasting the impacts of climate change. However, modelling β‐diversity is challenging, especially for multiple diversity facets (i.e., taxonomic, functional and phylogenetic diversity), and current methods have important limitations. Here, we present a new approach for predicting the response of multifaceted β‐diversity to the environment, called Multifaceted Biodiversity Modelling (MBM). We illustrate the approach using both a plant diversity dataset from the French Alps and a set of simulated data. We also provide an implementation via an R package. Location: French Alps. Methods: For both the French Alps and the simulated communities, we compute β‐diversity indices (e.g., Sørensen dissimilarity, mean functional/phylogenetic pairwise distance) among site pairs. We then apply Gaussian process regression, a flexible nonlinear modelling technique, to predict β‐diversity in response to environmental distance among site pairs. For comparison, we also perform similar analyses using Generalized Dissimilarity Modelling (GDM), a well‐established method for modelling β‐diversity in response to environmental distance. Results: In the Alps, we observed a general increase in taxonomic (TD) and functional (FD) β‐diversity (i.e., site pairs were more different from each other) as the climatic distance between site pairs increased. GDM performed better for TD and FD when fitting to calibration data, whereas MBM performed better for both when predicting to a validation dataset. For phylogenetic β‐diversity, MBM outperformed GDM in predicting the observed decrease in phylogenetic β‐diversity with increasing climatic distance. Main conclusions: Multifaceted Biodiversity Modelling provides a flexible new approach that expands our capacity to model multiple facets of β‐diversity. Advantages of MBM over existing methods include simpler assumptions, more flexible modelling, potential to consider multiple facets of diversity across a range of diversity indices, and robust uncertainty estimation. [ABSTRACT FROM AUTHOR]

Agricultural intensification has caused drastic declines in the area and species richness of semi-natural grasslands across Europe. Novel habitats, such as power line clearings, provide alternative habitats and niches for grassland species, and might therefore mitigate these declines. However, it is not fully understood which environmental factors determine the occurrence of grassland species in the clearings. Identifying the most important drivers for grassland species occurrence would help understand the value of the clearings for grassland conservation and target enhanced management into clearings with most potential as grassland habitat. We studied the effects of local environmental conditions, and past and present connectivity to semi-natural grasslands, on the species richness of grassland plants and butterflies in 43 power line clearings in Finland. The results of generalized linear models and hierarchical partitioning showed that increasing time since clear-cut and amount of clearing residue decreased the species richness of both species groups, while the cover of mesic habitats increased it. However, the two species groups showed also divergent responses. Present-day local environmental conditions appeared to be the sole driver of grassland butterfly species richness, whereas the richness of grassland plants was related both to current conditions and historical connectivity to grasslands in 1870-1880s. This suggests the presence of an extinction debt in the studied grassland plant communities, emphasizing the need for enhanced management to increase suitable grassland habitat in the clearings. This would diminish the potential future losses of grassland plant species in the clearings and create valuable habitat for grassland butterflies as well. [ABSTRACT FROM AUTHOR]