Habitat microclimates drive fine‐scale variation in extreme temperatures

Most multicellular terrestrial organisms experience climate at scales of millimetres to metres, yet most species‐climate associations are analysed at resolutions of kilometres or more. Because individuals experience heterogeneous microclimates in the landscape, species sometimes survive where the average background climate appears unsuitable, and equally may be eliminated from sites within apparently suitable grid cells where microclimatic extremes are intolerable. Local vegetation structure and topography can be important determinants of fine‐resolution microclimate, but a literature search revealed that the vast majority of bioclimate studies do not include fine‐scale habitat information, let alone a representation of how habitat affects microclimate. In this paper, we show that habitat type (grassland, heathland, deciduous woodland) is a major modifier of the temperature extremes experienced by organisms. We recorded differences among these habitats of more than 5°C in monthly temperature maxima and minima, and of 10°C in thermal range, on a par with the level of warming expected for extreme future climate change scenarios. Comparable differences were found in relation to variation in local topography (slope and aspect). Hence, we argue that the microclimatic effects of habitat and topography must be included in studies if we are to obtain sufficiently detailed projections of the ecological impacts of climate change to develop detailed adaptation strategies for the conservation of biodiversity.     

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.     

From topography to hydrology—The modifiable area unit problem impacts freshwater species distribution models

Species distribution models (SDMs) are statistical tools to identify potentially suitable habitats for species. For SDMs in river ecosystems, species occurrences and predictor data are often aggregated across subcatchments that serve as modeling units. The level of aggregation (i.e., model resolution) influences the statistical relationships between species occurrences and environmental predictors—a phenomenon known as the modifiable area unit problem (MAUP), making model outputs directly contingent on the model resolution. Here, we test how model performance, predictor importance, and the spatial congruence of species predictions depend on the model resolution (i.e., average subcatchment size) of SDMs. We modeled the potential habitat suitability of 50 native fish species in the upper Danube catchment at 10 different model resolutions. Model resolutions were derived using a 90‐m digital‐elevation model by using the GRASS‐GIS module r.watershed. Here, we decreased the average subcatchment size gradually from 632 to 2 km². We then ran ensemble SDMs based on five algorithms using topographical, climatic, hydrological, and land‐use predictors for each species and resolution. Model evaluation scores were consistently high, as sensitivity and True Skill Statistic values ranged from 86.1–93.2 and 0.61–0.73, respectively. The most contributing predictor changed from topography at coarse, to hydrology at fine resolutions. Climate predictors played an intermediate role for all resolutions, while land use was of little importance. Regarding the predicted habitat suitability, we identified a spatial filtering from coarse to intermediate resolutions. The predicted habitat suitability within a coarse resolution was not ported to all smaller, nested subcatchments, but only to a fraction that held the suitable environmental conditions. Across finer resolutions, the mapped predictions were spatially congruent without such filter effect. We show that freshwater SDM predictions can have consistently high evaluation scores while mapped predictions differ significantly and are highly contingent on the underlying subcatchment size. We encourage building freshwater SDMs across multiple catchment sizes, to assess model variability and uncertainties in model outcomes emerging from the MAUP.     

Improving species distribution models for climate change studies: variable selection and scale

Statistical species distribution models (SDMs) are widely used to predict the potential changes in species distributions under climate change scenarios. We suggest that we need to revisit the conceptual framework and ecological assumptions on which the relationship between species distributions and environment is based. We present a simple conceptual framework to examine the selection of environmental predictors and data resolution scales. These vary widely in recent papers, with light inconsistently included in the models. Focusing on light as a necessary component of plant SDMs, we briefly review its dependence on aspect and slope and existing knowledge of its influence on plant distribution. Differences in light regimes between north‐ and south‐facing aspects in temperate latitudes can produce differences in temperature equivalent to moves 200 km polewards. Local topography may create refugia that are not recognized in many climate change SDMs using coarse‐scale data. We argue that current assumptions about the selection of predictors and data resolution need further testing. Application of these ideas can clarify many issues of scale, extent and choice of predictors, and potentially improve the use of SDMs for climate change modelling of biodiversity.     

Microclimate species distribution models estimate lower levels of climate-related habitat loss for salamanders

Species distribution models (SDMs) largely rely on free-air temperatures at coarse spatial resolutions to predict habitat suitability, potentially overlooking important microhabitat. Integrating microclimate data into SDMs may improve predictions of organismal responses to climate change and support targeting of conservation assets at biologically relevant scales, especially for small, dispersal-limited species vulnerable to climate-change-induced range loss. We integrated microclimate data that account for the buffering effects of forest vegetation into SDMs at a very high spatial resolution (3 m²) for three plethodontid salamander species in Great Smoky Mountains National Park (North Carolina and Tennessee). Microclimate SDMs were used to characterize potential changes to future plethodontid habitat, including habitat suitability and habitat spatial patterns. Additionally, we evaluated spatial discrepancies between predictions of habitat suitability developed with microclimate and coarse-resolution, free-air climate data. Microclimate SDMs indicated substantial losses to plethodontid ranges and highly suitable habitat by mid-century, but at much more conservative levels than coarse-resolution models. Coarse-resolution SDMs generally estimated higher mid-century losses to plethodontid habitat compared to microclimate models and consistently undervalued areas containing highly suitable microhabitat. Furthermore, microclimate SDMs revealed potential areas of future gain in highly suitable habitat within current species’ ranges, which may serve as climatic microrefugia. Taken together, this study highlights the need to develop microclimate SDMs that account for vegetation and its biophysical effects on near-surface temperatures. As microclimate datasets become increasingly available across the world, their integration into correlative and mechanistic SDMs will be imperative for accurately estimating organismal responses to climate change and helping environmental managers tasked with spatially prioritizing conservation assets.     

Fine‐grain,large‐domain climate models based on climate station and comprehensive topographic information improve microrefugia detection

Large‐domain species distribution models (SDMs) fail to identify microrefugia, as they are based on climate estimates that are either too coarse or that ignore relevant topographic climate‐forcing factors. Climate station data are considered inadequate to produce such estimates, a viewpoint we challenge here. Using climate stations and topographic data, we developed three sets of large‐domain (450 000 km²), fine‐grain (50 m) temperature grids accounting for different levels of topographic complexity. Using these fine‐grain grids and the Worldclim data, we fitted SDMs for 78 alpine species over Sweden, and assessed over‐ versus underestimations of local extinction and area of microrefugia by comparing modelled distributions at species' rear edges. Accounting for well‐known topographic climate‐forcing factors improved our ability to model fine‐scale climate, despite using only climate station data. This approach captured the effect of cool air pooling, distance to sea, and relative humidity on local‐scale temperature, but the effect of solar radiation could not be accurately accounted for. Predicted extinction rate decreased with increasing spatial resolution of the climate models and with increasing number of topographic climate‐forcing factors accounted for. About half of the microrefugia detected in the most topographically complete models were not detected in the coarser SDMs and in the models calibrated from climate variables extracted from elevation only. Although major limitations remain, climate station data can potentially be used to produce fine‐grain topoclimate grids, opening up the opportunity to model local‐scale ecological processes over large domains. Accounting for the topographic complexity encountered within landscapes permits the detection of microrefugia that would otherwise remain undetected. Topographic heterogeneity is likely to have a massive impact on species persistence, and should be included in studies on the effects of climate change.     

Ecological principles of species distribution models: the habitat matching rule

Most species distribution models (SDMs) assume that habitats are closed, stable and without competition. In that environmental context, it is ecologically correct to assume that members of a species will be distributed in direct relation to the suitability of the habitat, that is, according to the so‐called habitat matching rule. This paper examines whether it is possible to maintain the assumption of the habitat matching rule in the following circumstances: (1) when habitats are connected and organisms can move between them, (2) when there are disturbances and seasonal cycles that generate instability, and (3) when there is inter‐specific and intra‐specific competition. Here I argue that it is possible as long as the following aspects are taken into account. In open habitats at equilibrium, in which habitat selection and competition operate, the habitat matching rule can be applied in some conditions, while competition tends to homogenize the species distribution in other environmental contexts. In the latter case, two methods can be used to incorporate these effects into SDMs: new parameters can be incorporated into the response functions, or the occurrence of proportions of categories of individuals (adult/young, male/female, or dominant/subordinate species in guilds) can be used instead of the occurrence of organisms. The habitat matching rule is not fulfilled in non‐equilibrium environments. The solution to this problem lies in the design of SDMs with two strategies that depend on scale. Locally, the disequilibrium can be encapsulated using average environmental conditions, with sufficiently large cells (in the case of metapopulations) and/or long enough sampling periods (in the case of seasonal cycles). At coarse scales, the use of presence‐only models can in some cases avoid the destabilizing effect of catastrophic historical processes. The matching law is a strong assumption of SDMs because it is based on population ecology theory and the principle of evolution by natural selection.     

Climate,competition and connectivity affect future migration and ranges of European trees

Aim Species ranges have adapted during the Holocene to altering climate conditions, but it remains unclear if species will be able to keep pace with recent and future climate change. The goal of our study is to assess the influence of changing macroclimate, competition and habitat connectivity on the migration rates of 14 tree species. We also compare the projections of range shifts from species distribution models (SDMs) that incorporate realistic migration rates with classical models that assume no or unlimited migration. Location Europe. Methods We calibrated SDMs with species abundance data from 5768 forest plots from ICP Forest Level 1 in relation to climate, topography, soil and land‐use data to predict current and future tree distributions. To predict future species ranges from these models, we applied three migration scenarios: no migration, unlimited migration and realistic migration. The migration rates for the SDMs incorporating realistic migration were estimated according to macroclimate, inter‐specific competition and habitat connectivity from simulation experiments with a spatially explicit process model (TreeMig). From these relationships, we then developed a migration cost surface to constrain the predicted distributions of the SDMs. Results The distributions of early‐successional species during the 21st century predicted by SDMs that incorporate realistic migration matched quite well with the unlimited migration assumption (mean migration rate over Europe for A1fi/GRAS climate and land‐use change scenario 156.7 ± 79.1 m year^?1 and for B1/SEDG 164.3 ± 84.2 m year^?1). The predicted distributions of mid‐ to late‐successional species matched better with the no migration assumption (A1fi/GRAS, 15.2 ± 24.5 m year^?1 and B1/SEDG, 16.0 ± 25.6 m year^?1). Inter‐specific competition, which is higher under favourable growing conditions, reduced range shift velocity more than did adverse macroclimatic conditions (i.e. very cold or dry climate). Habitat fragmentation also led to considerable time lags in range shifts. Main conclusions Migration rates depend on species traits, competition, spatial habitat configuration and climatic conditions. As a result, re‐adjustments of species ranges to climate and land‐use change are complex and very individualistic, yet still quite predictable. Early‐successional species track climate change almost instantaneously while mid‐ to late‐ successional species were predicted to migrate very slowly.     

21st century climate change threatens mountain flora unequally across Europe

Continental‐scale assessments of 21st century global impacts of climate change on biodiversity have forecasted range contractions for many species. These coarse resolution studies are, however, of limited relevance for projecting risks to biodiversity in mountain systems, where pronounced microclimatic variation could allow species to persist locally, and are ill‐suited for assessment of species‐specific threat in particular regions. Here, we assess the impacts of climate change on 2632 plant species across all major European mountain ranges, using high‐resolution (ca. 100 m) species samples and data expressing four future climate scenarios. Projected habitat loss is greater for species distributed at higher elevations; depending on the climate scenario, we find 36–55% of alpine species, 31–51% of subalpine species and 19–46% of montane species lose more than 80% of their suitable habitat by 2070–2100. While our high‐resolution analyses consistently indicate marked levels of threat to cold‐adapted mountain florae across Europe, they also reveal unequal distribution of this threat across the various mountain ranges. Impacts on florae from regions projected to undergo increased warming accompanied by decreased precipitation, such as the Pyrenees and the Eastern Austrian Alps, will likely be greater than on florae in regions where the increase in temperature is less pronounced and rainfall increases concomitantly, such as in the Norwegian Scandes and the Scottish Highlands. This suggests that change in precipitation, not only warming, plays an important role in determining the potential impacts of climate change on vegetation.     

Effects of ecohydrological variables on current and future ranges,local suitability patterns,and model accuracy in big sagebrush

Forecasting of species and ecosystem responses to novel conditions, including climate change, is one of the major challenges facing ecologists at the start of the 21st century. Climate change studies based on species distribution models (SDMs) have been criticized because they extend correlational relationships beyond the observed data. Here, we compared conventional climate‐based SDMs against ecohydrological SDMs that include information from process‐based simulations of water balance. We examined the current and future distribution of Artemisia tridentata (big sagebrush) representing sagebrush ecosystems, which are widespread in semiarid western North America. For each approach, we calculated ensemble models from nine SDM methods and tested accuracy of each SDM with a null distribution. Climatic conditions included current conditions for 1970–1999 and two IPCC projections B1 and A2 for 2070–2099. Ecohydrological conditions were assessed by simulating soil water balance with SOILWAT, a daily time‐step, multiple layer, mechanistic, soil water model. Under current conditions, both climatic and ecohydrological SDM approaches produced comparable sagebrush distributions. Overall, sagebrush distribution is forecasted to decrease, with larger decreases under the A2 than under the B1 scenario and strong decreases in the southern part of the range. Increases were forecasted in the northern parts and at higher elevations. Both SDM approaches produced accurate predictions. However, the ecohydrological SDM approach was slightly less accurate than climatic SDMs (?1% in AUC, ?4% in Kappa and TSS) and predicted a higher number of habitat patches than observed in the input data. Future predictions of ecohydrological SDMs included an increased number of habitat patches whereas climatic SDMs predicted a decrease. This difference is important for understanding landscape‐scale patterns of sagebrush ecosystems and management of sagebrush obligate species for future conditions. Several mechanisms can explain the diverging forecasts; however, we need better insights into the consequences of different datasets for SDMs and how these affect our understanding of future trajectories.     

On the use of climate covariates in aquatic species distribution models: are we at risk of throwing out the baby with the bath water?

Species distribution models (SDMs) in river ecosystems can incorporate climate information by using air temperature and precipitation as surrogate measures of instream conditions or by using independent models of water temperature and hydrology to link climate to instream habitat. The latter approach is preferable but constrained by the logistical burden of developing water temperature and hydrology models. We therefore assessed whether regional scale, freshwater SDM predictions are fundamentally different when climate data versus instream temperature and hydrology are used as covariates. Maximum entropy (MaxEnt) SDMs were built for 15 freshwater fishes using one of two covariate sets: 1) air temperature and precipitation (climate variables) in combination with physical habitat variables; or 2) water temperature, hydrology (instream variables) and physical habitat. Three procedures were then used to compare results from climate vs instream models. First, equivalence tests assessed average pairwise differences (site‐specific comparisons throughout each species’ range) among climate and instream models. Second, ‘congruence’ tests determined how often the same stream segments were assigned high habitat suitability by climate and instream models. Third, Schoener's D and Warren's I niche overlap statistics quantified range‐wide similarity in predicted habitat suitability from climate vs instream models. Equivalence tests revealed small, pairwise differences in habitat suitability between climate and instream models (mean pairwise differences in MaxEnt raw scores for all species < 3 × 10^–4). Congruence tests showed a strong tendency for climate and instream models to predict high habitat suitability at the same stream segments (median congruence = 68%). D and I statistics reflected a high margin of overlap among climate and instream models (median D = 0.78, median I = 0.96). Overall, we found little support for the hypothesis that SDM predictions are fundamentally different when climate versus instream covariates are used to model fish species’ distributions at the scale of the Columbia Basin.     

Back to the future: using historical climate variation to project near‐term shifts in habitat suitable for coast redwood

Studies that model the effect of climate change on terrestrial ecosystems often use climate projections from downscaled global climate models (GCMs). These simulations are generally too coarse to capture patterns of fine‐scale climate variation, such as the sharp coastal energy and moisture gradients associated with wind‐driven upwelling of cold water. Coastal upwelling may limit future increases in coastal temperatures, compromising GCMs’ ability to provide realistic scenarios of future climate in these coastal ecosystems. Taking advantage of naturally occurring variability in the high‐resolution historic climatic record, we developed multiple fine‐scale scenarios of California climate that maintain coherent relationships between regional climate and coastal upwelling. We compared these scenarios against coarse resolution GCM projections at a regional scale to evaluate their temporal equivalency. We used these historically based scenarios to estimate potential suitable habitat for coast redwood (Sequoia sempervirens D. Don) under ‘normal’ combinations of temperature and precipitation, and under anomalous combinations representative of potential future climates. We found that a scenario of warmer temperature with historically normal precipitation is equivalent to climate projected by GCMs for California by 2020–2030 and that under these conditions, climatically suitable habitat for coast redwood significantly contracts at the southern end of its current range. Our results suggest that historical climate data provide a high‐resolution alternative to downscaled GCM outputs for near‐term ecological forecasts. This method may be particularly useful in other regions where local climate is strongly influenced by ocean–atmosphere dynamics that are not represented by coarse‐scale GCMs.     

Climate and land use changes will degrade the configuration of the landscape for titi monkeys in eastern Brazil

Land use changes have profound effects on populations of Neotropical primates, and ongoing climate change is expected to aggravate this scenario. The titi monkeys from eastern Brazil (Callicebus personatus group) have been particularly affected by this process, with four of the five species now allocated to threatened conservation status categories. Here, we estimate the changes in the distribution of these titi monkeys caused by changes in both climate and land use. We also use demographic‐based, functional landscape metrics to assess the magnitude of the change in landscape conditions for the distribution predicted for each species. We built species distribution models (SDMs) based on maximum entropy for current and future conditions (2070), allowing for different global circulation models and contrasting scenarios of glasshouse gas concentrations. We refined the SDMs using a high‐resolution map of habitat remnants. We then calculated habitat availability and connectivity based on home‐range size and the dispersal limitations of the individual, in the context of a predicted loss of 10% of forest cover in the future. The landscape configuration is predicted to be degraded for all species, regardless of the climatic settings. This include reductions in the total cover of forest remnants, patch size and functional connectivity. As the landscape configuration should deteriorate severely in the future for all species, the prevention of further loss of populations will only be achieved through habitat restoration and reconnection to counteract the negative effects for these and several other co‐occurring species.     

Butterfly Response to Microclimatic Conditions Following Ponderosa Pine Restoration

Efforts to restore ponderosa pine ecosystems to open, park‐like conditions that predominated prior to European‐American settlement result in altered stand structure and increased landscape heterogeneity, potentially altering habitat suitability for invertebrates and other forest organisms. We examined the responses of two butterfly species, Colias eurytheme and Neophasia menapia, to microclimatic changes at structural edges created by experimental restoration treatments in northern Arizona. We monitored microclimate, including air temperature, light intensity, and vapor pressure deficit (VPD), on several mornings during butterfly releases. We placed adult butterflies at east‐ and west‐facing edges approximately one half‐hour before dawn to determine their behavioral response to microclimatic differences between east‐ and west‐facing edges. After sunrise, all three microclimatic variables were higher at east‐facing edges, and the difference in microclimate between the two edge orientations increased through early morning. For both species, butterflies placed at east‐facing edges flew earlier than butterflies at west‐facing edges. Colias eurytheme, an open‐habitat species, tended to move toward the treated forest during initial flight, while movements of Neophasia menapia, a forest‐dwelling species, did not differ from random flight. Our results indicate that butterflies respond to microclimatic factors associated with restoration treatments, while responses to structural changes in habitat vary among species, based on habitat and food plant preferences. These changes in forest structure and microclimate may affect the distribution of many mobile invertebrates in forested landscapes undergoing restoration treatments.     

Habitat availability and gene flow influence diverging local population trajectories under scenarios of climate change: a place‐based approach

Ecological niche theory holds that species distributions are shaped by a large and complex suite of interacting factors. Species distribution models (SDMs) are increasingly used to describe species’ niches and predict the effects of future environmental change, including climate change. Currently, SDMs often fail to capture the complexity of species’ niches, resulting in predictions that are generally limited to climate‐occupancy interactions. Here, we explore the potential impact of climate change on the American pika using a replicated place‐based approach that incorporates climate, gene flow, habitat configuration, and microhabitat complexity into SDMs. Using contemporary presence–absence data from occupancy surveys, genetic data to infer connectivity between habitat patches, and 21 environmental niche variables, we built separate SDMs for pika populations inhabiting eight US National Park Service units representing the habitat and climatic breadth of the species across the western United States. We then predicted occurrence probability under current (1981–2010) and three future time periods (out to 2100). Occurrence probabilities and the relative importance of predictor variables varied widely among study areas, revealing important local‐scale differences in the realized niche of the American pika. This variation resulted in diverse and – in some cases – highly divergent future potential occupancy patterns for pikas, ranging from complete extirpation in some study areas to stable occupancy patterns in others. Habitat composition and connectivity, which are rarely incorporated in SDM projections, were influential in predicting pika occupancy in all study areas and frequently outranked climate variables. Our findings illustrate the importance of a place‐based approach to species distribution modeling that includes fine‐scale factors when assessing current and future climate impacts on species’ distributions, especially when predictions are intended to manage and conserve species of concern within individual protected areas.     

Testing projected wild bee distributions in agricultural habitats: predictive power depends on species traits and habitat type

Species distribution models (SDM) are increasingly used to understand the factors that regulate variation in biodiversity patterns and to help plan conservation strategies. However, these models are rarely validated with independently collected data and it is unclear whether SDM performance is maintained across distinct habitats and for species with different functional traits. Highly mobile species, such as bees, can be particularly challenging to model. Here, we use independent sets of occurrence data collected systematically in several agricultural habitats to test how the predictive performance of SDMs for wild bee species depends on species traits, habitat type, and sampling technique. We used a species distribution modeling approach parametrized for the Netherlands, with presence records from 1990 to 2010 for 193 Dutch wild bees. For each species, we built a Maxent model based on 13 climate and landscape variables. We tested the predictive performance of the SDMs with independent datasets collected from orchards and arable fields across the Netherlands from 2010 to 2013, using transect surveys or pan traps. Model predictive performance depended on species traits and habitat type. Occurrence of bee species specialized in habitat and diet was better predicted than generalist bees. Predictions of habitat suitability were also more precise for habitats that are temporally more stable (orchards) than for habitats that suffer regular alterations (arable), particularly for small, solitary bees. As a conservation tool, SDMs are best suited to modeling rarer, specialist species than more generalist and will work best in long‐term stable habitats. The variability of complex, short‐term habitats is difficult to capture in such models and historical land use generally has low thematic resolution. To improve SDMs’ usefulness, models require explanatory variables and collection data that include detailed landscape characteristics, for example, variability of crops and flower availability. Additionally, testing SDMs with field surveys should involve multiple collection techniques.     

Uncertainty in predictions of range dynamics: black grouse climbing the Swiss Alps

Empirical species distribution models (SDMs) constitute often the tool of choice for the assessment of rapid climate change effects on species’ vulnerability. Conclusions regarding extinction risks might be misleading, however, because SDMs do not explicitly incorporate dispersal or other demographic processes. Here, we supplement SDMs with a dynamic population model 1) to predict climate‐induced range dynamics for black grouse in Switzerland, 2) to compare direct and indirect measures of extinction risks, and 3) to quantify uncertainty in predictions as well as the sources of that uncertainty. To this end, we linked models of habitat suitability to a spatially explicit, individual‐based model. In an extensive sensitivity analysis, we quantified uncertainty in various model outputs introduced by different SDM algorithms, by different climate scenarios and by demographic model parameters. Potentially suitable habitats were predicted to shift uphill and eastwards. By the end of the 21st century, abrupt habitat losses were predicted in the western Prealps for some climate scenarios. In contrast, population size and occupied area were primarily controlled by currently negative population growth and gradually declined from the beginning of the century across all climate scenarios and SDM algorithms. However, predictions of population dynamic features were highly variable across simulations. Results indicate that inferring extinction probabilities simply from the quantity of suitable habitat may underestimate extinction risks because this may ignore important interactions between life history traits and available habitat. Also, in dynamic range predictions uncertainty in SDM algorithms and climate scenarios can become secondary to uncertainty in dynamic model components. Our study emphasises the need for principal evaluation tools like sensitivity analysis in order to assess uncertainty and robustness in dynamic range predictions. A more direct benefit of such robustness analysis is an improved mechanistic understanding of dynamic species’ responses to climate change.     

Unpacking the mechanisms captured by a correlative species distribution model to improve predictions of climate refugia

Climate refugia are regions that animals can retreat to, persist in and potentially then expand from under changing environmental conditions. Most forecasts of climate change refugia for species are based on correlative species distribution models (SDMs) using long‐term climate averages, projected to future climate scenarios. Limitations of such methods include the need to extrapolate into novel environments and uncertainty regarding the extent to which proximate variables included in the model capture processes driving distribution limits (and thus can be assumed to provide reliable predictions under new conditions). These limitations are well documented; however, their impact on the quality of climate refugia predictions is difficult to quantify. Here, we develop a detailed bioenergetics model for the koala. It indicates that range limits are driven by heat‐induced water stress, with the timing of rainfall and heat waves limiting the koala in the warmer parts of its range. We compare refugia predictions from the bioenergetics model with predictions from a suite of competing correlative SDMs under a range of future climate scenarios. SDMs were fitted using combinations of long‐term climate and weather extremes variables, to test how well each set of predictions captures the knowledge embedded in the bioenergetics model. Correlative models produced broadly similar predictions to the bioenergetics model across much of the species' current range – with SDMs that included weather extremes showing highest congruence. However, predictions in some regions diverged significantly when projecting to future climates due to the breakdown in correlation between climate variables. We provide unique insight into the mechanisms driving koala distribution and illustrate the importance of subtle relationships between the timing of weather events, particularly rain relative to hot‐spells, in driving species–climate relationships and distributions. By unpacking the mechanisms captured by correlative SDMs, we can increase our certainty in forecasts of climate change impacts on species.     

The fate of European breeding birds under climate,land‐use and dispersal scenarios

Many species have already shifted their distributions in response to recent climate change. Here, we aimed at predicting the future breeding distributions of European birds under climate, land‐use, and dispersal scenarios. We predicted current and future distributions of 409 species within an ensemble forecast framework using seven species distribution models (SDMs), five climate scenarios and three emission and land‐use scenarios. We then compared results from SDMs using climate‐only variables, habitat‐only variables or both climate and habitat variables. In order to account for a species’ dispersal abilities, we used natal dispersal estimates and developed a probabilistic method that produced a dispersal scenario intermediate between the null and full dispersal scenarios generally considered in such studies. We then compared results from all scenarios in terms of future predicted range changes, range shifts, and variations in species richness. Modeling accuracy was better with climate‐only variables than with habitat‐only variables, and better with both climate and habitat variables. Habitat models predicted smaller range shifts and smaller variations in range size and species richness than climate models. Using both climate and habitat variables, it was predicted that the range of 71% of the species would decrease by 2050, with a 335 km median shift. Predicted variations in species richness showed large decreases in the southern regions of Europe, as well as increases, mainly in Scandinavia and northern Russia. The partial dispersal scenario was significantly different from the full dispersal scenario for 25% of the species, resulting in the local reduction of the future predicted species richness of up to 10%. We concluded that the breeding range of most European birds will decrease in spite of dispersal abilities close to a full dispersal hypothesis, and that given the contrasted predictions obtained when modeling climate change only and land‐use change only, both scenarios must be taken into consideration.     

Dispersal limitation and spatial scale affect model based projections of Pinus uncinata response to climate change in the Pyrenees

Species Distribution Models (SDMs) were employed to assess the potential impact of climate change on the distribution of Pinus uncinata in the Pyrenees, where it is the dominant tree species in subalpine forest and alpine tree lines. Predicting forest response to climate change is a challenging task in mountain regions but also a conservation priority. We examined the potential impact of spatial scale on SDM projections by conducting all analyses at four spatial resolutions. We further examined the potential effect of dispersal constraints by applying a threshold distance of maximal advancement derived from a spatially explicit, individual‐based simulation model of tree line dynamics. Under current conditions, SDMs including climatic factors related to stress or growth limitation performed best. These models were then employed to project P. uncinata distribution under two emission scenarios, using data generated from several regional climate models. At the end of this century, P. uncinata is expected to migrate northward and upward, occupying habitat currently inhabited by alpine plant species. However, consideration of dispersal limitation and/or changing the spatial resolution of the analysis modified the assessment of climate change impact on mountain ecosystems, especially in the case of estimates of colonization and extinction at the regional scale. Our study highlights the need to improve the characterization of biological processes within SDMs, as well as to consider simultaneously different scales when assessing potential habitat loss under future climate conditions.