Similarities in butterfly emergence dates among populations suggest local adaptation to climate

Phenology shifts are the most widely cited examples of the biological impact of climate change, yet there are few assessments of potential effects on the fitness of individual organisms or the persistence of populations. Despite extensive evidence of climate‐driven advances in phenological events over recent decades, comparable patterns across species' geographic ranges have seldom been described. Even fewer studies have quantified concurrent spatial gradients and temporal trends between phenology and climate. Here we analyse a large data set (~129 000 phenology measures) over 37 years across the UK to provide the first phylogenetic comparative analysis of the relative roles of plasticity and local adaptation in generating spatial and temporal patterns in butterfly mean flight dates. Although populations of all species exhibit a plastic response to temperature, with adult emergence dates earlier in warmer years by an average of 6.4 days per °C, among‐population differences are significantly lower on average, at 4.3 days per °C. Emergence dates of most species are more synchronised over their geographic range than is predicted by their relationship between mean flight date and temperature over time, suggesting local adaptation. Biological traits of species only weakly explained the variation in differences between space‐temperature and time‐temperature phenological responses, suggesting that multiple mechanisms may operate to maintain local adaptation. As niche models assume constant relationships between occurrence and environmental conditions across a species' entire range, an important implication of the temperature‐mediated local adaptation detected here is that populations of insects are much more sensitive to future climate changes than current projections suggest.     

Projecting marine species range shifts from only temperature can mask climate vulnerability

Climate change is causing range shifts in many marine species, with implications for biodiversity and fisheries. Previous research has mainly focused on how species' ranges will respond to changing ocean temperatures, without accounting for other environmental covariates that could affect future distribution patterns. Here, we integrate habitat suitability modeling approaches, a high‐resolution global climate model projection, and detailed fishery‐independent and ‐dependent faunal datasets from one of the most extensively monitored marine ecosystems—the U.S. Northeast Shelf. We project the responses of 125 species in this region to climate‐driven changes in multiple oceanographic factors (e.g., ocean temperature, salinity, sea surface height) and seabed characteristics (i.e., rugosity and depth). Comparing model outputs based on ocean temperature and seabed characteristics to those that also incorporated salinity and sea surface height (proxies for primary productivity and ocean circulation features), we explored how an emphasis on ocean temperature in projecting species' range shifts can impact assessments of species' climate vulnerability. We found that multifactor habitat suitability models performed better in explaining and predicting species historical distribution patterns than temperature‐based models. We also found that multifactor models provided more concerning assessments of species' future distribution patterns than temperature‐based models, projecting that species' ranges will largely shift northward and become more contracted and fragmented over time. Our results suggest that using ocean temperature as a primary determinant of range shifts can significantly alter projections, masking species' climate vulnerability, and potentially forestalling proactive management.     

Dynamic occupancy models for analyzing species' range dynamics across large geographic scales

Large‐scale biodiversity data are needed to predict species' responses to global change and to address basic questions in macroecology. While such data are increasingly becoming available, their analysis is challenging because of the typically large heterogeneity in spatial sampling intensity and the need to account for observation processes. Two further challenges are accounting for spatial effects that are not explained by covariates, and drawing inference on dynamics at these large spatial scales. We developed dynamic occupancy models to analyze large‐scale atlas data. In addition to occupancy, these models estimate local colonization and persistence probabilities. We accounted for spatial autocorrelation using conditional autoregressive models and autologistic models. We fitted the models to detection/nondetection data collected on a quarter‐degree grid across southern Africa during two atlas projects, using the hadeda ibis (Bostrychia hagedash) as an example. The model accurately reproduced the range expansion between the first (SABAP1: 1987–1992) and second (SABAP2: 2007–2012) Southern African Bird Atlas Project into the drier parts of interior South Africa. Grid cells occupied during SABAP1 generally remained occupied, but colonization of unoccupied grid cells was strongly dependent on the number of occupied grid cells in the neighborhood. The detection probability strongly varied across space due to variation in effort, observer identity, seasonality, and unexplained spatial effects. We present a flexible hierarchical approach for analyzing grid‐based atlas data using dynamical occupancy models. Our model is similar to a species' distribution model obtained using generalized additive models but has a number of advantages. Our model accounts for the heterogeneous sampling process, spatial correlation, and perhaps most importantly, allows us to examine dynamic aspects of species ranges.     

The influence of interspecific interactions on species range expansion rates

Ongoing and predicted global change makes understanding and predicting species' range shifts an urgent scientific priority. Here, we provide a synthetic perspective on the so far poorly understood effects of interspecific interactions on range expansion rates. We present theoretical foundations for how interspecific interactions may modulate range expansion rates, consider examples from empirical studies of biological invasions and natural range expansions as well as process‐based simulations, and discuss how interspecific interactions can be more broadly represented in process‐based, spatiotemporally explicit range forecasts. Theory tells us that interspecific interactions affect expansion rates via alteration of local population growth rates and spatial displacement rates, but also via effects on other demographic parameters. The best empirical evidence for interspecific effects on expansion rates comes from studies of biological invasions. Notably, invasion studies indicate that competitive dominance and release from specialized enemies can enhance expansion rates. Studies of natural range expansions especially point to the potential for competition from resident species to reduce expansion rates. Overall, it is clear that interspecific interactions may have important consequences for range dynamics, but also that their effects have received too little attention to robustly generalize on their importance. We then discuss how interspecific interactions effects can be more widely incorporated in dynamic modeling of range expansions. Importantly, models must describe spatiotemporal variation in both local population dynamics and dispersal. Finally, we derive the following guidelines for when it is particularly important to explicitly represent interspecific interactions in dynamic range expansion forecasts: if most interacting species show correlated spatial or temporal trends in their effects on the target species, if the number of interacting species is low, and if the abundance of one or more strongly interacting species is not closely linked to the abundance of the target species.     

Introducing BioSARN – an ecological niche model refinement tool

Environmental niche modeling outputs a biological species' potential distribution. Further work is needed to arrive at a species' realized distribution. The Biological Species Approximate Realized Niche (BioSARN) application provides the ecological modeler with a toolset to refine Environmental niche models (ENMs). These tools include soil and land class filtering, niche area quantification and novelties like enhanced temporal corridor definition, and output to a high spatial resolution land class model. BioSARN is exemplified with a study on Fraser fir, a tree species with strong land class and edaphic correlations. Soil and land class filtering caused the potential distribution area to decline 17%. Enhanced temporal corridor definition permitted distinction of current, continuing, and future niches, and thus niche change and movement. Tile quantification analysis provided further corroboration of these trends. BioSARN does not substitute other established ENM methods. Rather, it allows the experimenter to work with their preferred ENM, refining it using their knowledge and experience. Output from lower spatial resolution ENMs to a high spatial resolution land class model is a pseudo high‐resolution result. Still, it maybe the best that can be achieved until wide range high spatial resolution environmental data and accurate high precision species occurrence data become generally available.     

Spatial scale affects bioclimate model projections of climate change impacts on mountain plants

Plant species have responded to recent increases in global temperatures by shifting their geographical ranges poleward and to higher altitudes. Bioclimate models project future range contractions of montane species as suitable climate space shifts uphill. The species–climate relationships underlying such models are calibrated using data at either ‘macro’ scales (coarse resolution, e.g. 50 km × 50 km, and large spatial extent) or ‘local’ scales (fine resolution, e.g. 50 m × 50 m, and small spatial extent), but the two approaches have not been compared. This study projected macro (European) and local models for vascular plants at a mountain range in Scotland, UK, under low (+1.7 °C) and high (+3.3 °C) climate change scenarios for the 2080s. Depending on scenario, the local models projected that seven or eight out of 10 focal montane species would lose all suitable climate space at the site. However, the European models projected such a loss for only one species. The cause of this divergence was investigated by cross‐scale comparisons of estimated temperatures at montane species' warm range edges. The results indicate that European models overestimated species' thermal tolerances because the input coarse resolution climate data were biased against the cold, high‐altitude habitats of montane plants. Although tests at other mountain ranges are required, these results indicate that recent large‐scale modelling studies may have overestimated montane species' ability to cope with increasing temperatures, thereby underestimating the potential impacts of climate change. Furthermore, the results suggest that montane species persistence in microclimatic refugia might not be as widespread as previously speculated.     

Spatial processes and evolutionary models: a critical review

Evolution is a fundamentally population level process in which variation, drift and selection produce both temporal and spatial patterns of change. Statistical model fitting is now commonly used to estimate which kind of evolutionary process best explains patterns of change through time using models like Brownian motion, stabilizing selection (Ornstein–Uhlenbeck) and directional selection on traits measured from stratigraphic sequences or on phylogenetic trees. But these models assume that the traits possessed by a species are homogeneous. Spatial processes such as dispersal, gene flow and geographical range changes can produce patterns of trait evolution that do not fit the expectations of standard models, even when evolution at the local‐population level is governed by drift or a typical OU model of selection. The basic properties of population level processes (variation, drift, selection and population size) are reviewed and the relationship between their spatial and temporal dynamics is discussed. Typical evolutionary models used in palaeontology incorporate the temporal component of these dynamics, but not the spatial. Range expansions and contractions introduce rate variability into drift processes, range expansion under a drift model can drive directional change in trait evolution, and spatial selection gradients can create spatial variation in traits that can produce long‐term directional trends and punctuation events depending on the balance between selection strength, gene flow, extirpation probability and model of speciation. Using computational modelling that spatial processes can create evolutionary outcomes that depart from basic population‐level notions from these standard macroevolutionary models.     

Reconstructing the demise of Tethyan plants: climate‐driven range dynamics of Laurus since the Pliocene

Aim Climate changes are thought to be responsible for the retreat and eventual extinction of subtropical lauroid species that covered much of Europe and North Africa during the Palaeogene and early Neogene; little is known, however, of the spatial and temporal patterns of this demise. Herein we calibrate ecological niche models to assess the climatic requirements of Laurus L. (Lauraceae), an emblematic relic from the Tethyan subtropical flora, subsequently using these models to infer how the range dynamics of Laurus were affected by Plio‐Pleistocene climate changes. We also provide predictions of likely range changes resulting from future climatic scenarios. Location The Mediterranean Basin and Macaronesian islands (Canaries, Madeira, Azores). Methods We used a maximum‐entropy algorithm (Maxent) to model the relationship between climate and Laurus distribution over time. The models were fitted both to the present and to the middle Pliocene, based on fossil records. We employed climatic reconstructions for the mid‐Pliocene (3 Ma), the Last Glacial Maximum (21 ka) and a CO₂‐doubling future scenario to project putative species distribution in each period. We validated the model projections with Laurus fossil and present occurrences. Results Laurus preferentially occupied warm and moist areas with low seasonality, showing a marked stasis of its climatic niche. Models fitted to Pliocene conditions successfully predicted the current species distribution. Large suitable areas existed during the Pliocene, which were strongly reduced during the Pleistocene, but humid refugia within the Mediterranean Basin and Macaronesian islands enabled long‐term persistence. Future climate conditions are likely to re‐open areas suitable for colonization north of the current range. Main conclusions The climatic requirements of Laurus remained virtually unchanged over the last 3 Myr. This marked niche conservatism imposed largely deterministic range dynamics driven by climate conditions. This species's relatively high drought tolerance might account for the survival of Laurus in continental Europe throughout the Quaternary whilst other Lauraceae became extinct. Climatic scenarios for the end of this century would favour an expansion of the species's range towards northern latitudes, while severely limiting southern populations due to increased water stress.     

Spatial synchrony in stream fish populations: influence of species traits

Spatial synchrony in population dynamics has been identified in most taxonomic groups. Numerous studies have reported varying levels of spatial synchrony among closely‐related species, suggesting that species' characteristics may play a role in determining the level of synchrony. However, few studies have attempted to relate this synchrony to the ecological characteristics and/or life‐history traits of species. Yet, as to some extent the extinction risk may be related to synchrony patterns, identifying a link between species' characteristics and spatial synchrony is crucial, and would help us to define effective conservation planning. Here, we investigated whether species attributes and temperature synchrony (i.e. a proxy of the Moran effect) account for the differences in spatial population synchrony observed in 27 stream fish species in France. After measuring and testing the level of synchrony for each species, we performed a comparative analysis to detect the phylogenetic signal of these levels, and to construct various multi‐predictor models with species traits and temperature synchrony as covariates, while taking phylogenetic relatedness into account. We then performed model averaging on selected models to take model uncertainty into account in our parameter estimates. Fifteen of the 27 species displayed a significant level of synchrony. Synchrony was weak, but highly variable between species, and was not conserved across the phylogeny. We found that some species' characteristics significantly influenced synchrony levels. Indeed, the average model indicated that species associated with greater dispersal abilities, lower thermal tolerance, and opportunistic strategy displayed a higher degree of synchrony. These findings indicate that phylogeny and spatial temperature synchrony do not provide information pertinent for explaining the variations in species' synchrony levels, whereas the dispersal abilities, the life‐history strategies and the upper thermal tolerance limits of species do appear to be quite reliable predictors of synchrony levels.     

Predicting future climate at high spatial and temporal resolution

Most studies on the biological effects of future climatic changes rely on seasonally aggregated, coarse‐resolution data. Such data mask spatial and temporal variability in microclimate driven by terrain, wind and vegetation, and ultimately bear little resemblance to the conditions that organisms experience in the wild. Here, I present the methods for providing fine‐grained, hourly and daily estimates of current and future temperature and soil moisture over decadal timescales. Observed climate data and spatially coherent probabilistic projections of daily future weather were disaggregated to hourly and used to drive empirically calibrated physical models of thermal and hydrological microclimates. Mesoclimatic effects (cold‐air drainage, coastal exposure and elevation) were determined from the coarse‐resolution climate surfaces using thin‐plate spline models with coastal exposure and elevation as predictors. Differences between micro and mesoclimate temperatures were determined from terrain, vegetation and ground properties using energy balance equations. Soil moisture was computed in a thin upper layer and an underlying deeper layer, and the exchange of water between these layers was calculated using the van Genuchten equation. Code for processing the data and running the models is provided as a series of R packages. The methods were applied to the Lizard Peninsula, United Kingdom, to provide hourly estimates of temperature (100 m grid resolution over entire area, 1 m for a selected area) for the periods 1983–2017 and 2041–2049. Results indicated that there is a fine‐resolution variability in climatic changes, driven primarily by interactions between landscape features and decadal trends in weather conditions. High‐temporal resolution extremes in conditions under future climate change were predicted to be considerably less novel than the extremes estimated using seasonally aggregated variables. The study highlights the need to more accurately estimate the future climatic conditions experienced by organisms and equips biologists with the means to do so.     

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.     

The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling

Predicting which species will occur together in the future, and where, remains one of the greatest challenges in ecology, and requires a sound understanding of how the abiotic and biotic environments interact with dispersal processes and history across scales. Biotic interactions and their dynamics influence species' relationships to climate, and this also has important implications for predicting future distributions of species. It is already well accepted that biotic interactions shape species' spatial distributions at local spatial extents, but the role of these interactions beyond local extents (e.g. 10 km² to global extents) are usually dismissed as unimportant. In this review we consolidate evidence for how biotic interactions shape species distributions beyond local extents and review methods for integrating biotic interactions into species distribution modelling tools. Drawing upon evidence from contemporary and palaeoecological studies of individual species ranges, functional groups, and species richness patterns, we show that biotic interactions have clearly left their mark on species distributions and realised assemblages of species across all spatial extents. We demonstrate this with examples from within and across trophic groups. A range of species distribution modelling tools is available to quantify species environmental relationships and predict species occurrence, such as: (i) integrating pairwise dependencies, (ii) using integrative predictors, and (iii) hybridising species distribution models (SDMs) with dynamic models. These methods have typically only been applied to interacting pairs of species at a single time, require a priori ecological knowledge about which species interact, and due to data paucity must assume that biotic interactions are constant in space and time. To better inform the future development of these models across spatial scales, we call for accelerated collection of spatially and temporally explicit species data. Ideally, these data should be sampled to reflect variation in the underlying environment across large spatial extents, and at fine spatial resolution. Simplified ecosystems where there are relatively few interacting species and sometimes a wealth of existing ecosystem monitoring data (e.g. arctic, alpine or island habitats) offer settings where the development of modelling tools that account for biotic interactions may be less difficult than elsewhere.     

Mutualist‐mediated effects on species' range limits across large geographic scales

Understanding the processes determining species range limits is central to predicting species distributions under climate change. Projected future ranges are extrapolated from distribution models based on climate layers, and few models incorporate the effects of biotic interactions on species' distributions. Here, we show that a positive species interaction ameliorates abiotic stress, and has a profound effect on a species' range limits. Combining field surveys of 92 populations, 10 common garden experiments throughout the range, species distribution models and greenhouse experiments, we show that mutualistic fungal endophytes ameliorate drought stress and broaden the geographic range of their native grass host Bromus laevipes by thousands of square kilometres (~ 20% larger) into drier habitats. Range differentiation between fungal‐associated and fungal‐free grasses was comparable to species‐level range divergence of congeners, indicating large impacts on range limits. Positive biotic interactions may be underappreciated in determining species' ranges and species' responses to future climates across large geographic scales.     

Shifts in potential geographical distribution of Pterocarya stenoptera under climate change scenarios in China

Climate change poses a serious threat to biodiversity. Predicting the effects of climate change on the distribution of a species' habitat can help humans address the potential threats which may change the scope and distribution of species. Pterocarya stenoptera is a common fast‐growing tree species often used in the ecological restoration of riverbanks and alpine forests in central and eastern China. Until now, the characteristics of the distribution of this species' habitat are poorly known as are the environmental factors that influence its preferred habitat. In the present study, the Maximum Entropy Modeling (Maxent) algorithm and the Genetic Algorithm for Ruleset Production (GARP) were used to establish the models for the potential distribution of this species by selecting 236 sites with known occurrences and 14 environmental variables. The results indicate that both models have good predictive power. Minimum temperature of coldest month (Bio6), mean temperature of warmest quarter (Bio10), annual precipitation (Bio12), and precipitation of driest month (Bio14) were important environmental variables influencing the prediction of the Maxent model. According to the models, the temperate and subtropical regions of eastern China had high environmental suitability for this species, where the species had been recorded. Under each climate change scenario, climatic suitability of the existing range of this species increased, and its climatic niche expanded geographically to the north and higher elevation. GARP predicted a more conservative expansion. The projected spatial and temporal patterns of P. stenoptera can provide reference for the development of forest management and protection strategies.     

On using integral projection models to generate demographically driven predictions of species' distributions: development and validation using sparse data

Knowledge of species' geographic distributions is critical for understanding and forecasting population dynamics, responses to environmental change, biodiversity patterns, and conservation planning. While many suggestive correlative occurrence models have been used to these ends, progress lies in understanding the underlying population biology that generates patterns of range dynamics. Here, we show how to use a limited quantity of demographic data to produce demographic distribution models (DDMs) using integral projection models for size‐structured populations. By modeling survival, growth, and fecundity using regression, integral projection models can interpolate across missing size data and environmental conditions to compensate for limited data. To accommodate the uncertainty associated with limited data and model assumptions, we use Bayesian models to propagate uncertainty through all stages of model development to predictions. DDMs have a number of strengths: 1) DDMs allow a mechanistic understanding of spatial occurrence patterns; 2) DDMs can predict spatial and temporal variation in local population dynamics; 3) DDMs can facilitate extrapolation under altered environmental conditions because one can evaluate the consequences for individual vital rates. To illustrate these features, we construct DDMs for an overstory perennial shrub in the Proteaceae family in the Cape Floristic Region of South Africa. We find that the species' population growth rate is limited most strongly by adult survival throughout the range and by individual growth in higher rainfall regions. While the models predict higher population growth rates in the core of the range under projected climates for 2050, they also suggest that the species faces a threat along arid range margins from the interaction of more frequent fire and drying climate. The results (and uncertainties) are helpful for prioritizing additional sampling of particular demographic parameters along these gradients to iteratively refine projections. In the appendices, we provide fully functional R code to perform all analyses.     

Species dynamics alter community diversity–biomass stability relationships

The relationship between community diversity and biomass variability remains a crucial ecological topic, with positive, negative and neutral diversity–stability relationships reported from empirical studies. Theory highlights the relative importance of Species–Species or Species–Environment interactions in driving diversity–stability patterns. Much previous work is based on an assumption of identical (stable) species‐level dynamics. We studied ecosystem models incorporating stable, cyclic and more complex species‐level dynamics, with either linear or non‐linear density dependence, within a locally stable community framework. Species composition varies with increasing diversity, interacting with the correlation of species' environmental responses to drive either positive or negative diversity–stability patterns, which theory based on communities with only stable species‐level dynamics fails to predict. Including different dynamics points to new mechanisms that drive the full range of diversity–biomass stability relationships in empirical systems where a wider range of dynamical behaviours are important.     

Demographic consequences of climate change and land cover help explain a history of extirpations and range contraction in a declining snake species

Developing conservation strategies for threatened species increasingly requires understanding vulnerabilities to climate change, in terms of both demographic sensitivities to climatic and other environmental factors, and exposure to variability in those factors over time and space. We conducted a range‐wide, spatially explicit climate change vulnerability assessment for Eastern Massasauga (Sistrurus catenatus), a declining endemic species in a region showing strong environmental change. Using active season and winter adult survival estimates derived from 17 data sets throughout the species' range, we identified demographic sensitivities to winter drought, maximum precipitation during the summer, and the proportion of the surrounding landscape dominated by agricultural and urban land cover. Each of these factors was negatively associated with active season adult survival rates in binomial generalized linear models. We then used these relationships to back‐cast adult survival with dynamic climate variables from 1950 to 2008 using spatially explicit demographic models. Demographic models for 189 population locations predicted known extant and extirpated populations well (AUC = 0.75), and models based on climate and land cover variables were superior to models incorporating either of those effects independently. These results suggest that increasing frequencies and severities of extreme events, including drought and flooding, have been important drivers of the long‐term spatiotemporal variation in a demographic rate. We provide evidence that this variation reflects nonadaptive sensitivity to climatic stressors, which are contributing to long‐term demographic decline and range contraction for a species of high‐conservation concern. Range‐wide demographic modeling facilitated an understanding of spatial shifts in climatic suitability and exposure, allowing the identification of important climate refugia for a dispersal‐limited species. Climate change vulnerability assessment provides a framework for linking demographic and distributional dynamics to environmental change, and can thereby provide unique information for conservation planning and management.     

The range implications of lizard traits in changing environments

Aim Most predictions of species ranges are based on correlating current species localities to environmental conditions. These correlative models do not explicitly include a species' biology. In contrast, some mechanistic models link traits to energetics and population dynamics to predict species distributions. These models enable one to ask whether considering a species' biology is important for predicting its range. I implement mechanistic models to investigate how a species' morphology, physiology and life history influence its range. Location North America. Methods I compare the mechanistic model predictions with those of correlative models for eight species of North American lizards in both current environments and following a uniform 3 °C temperature warming. I then examine the implications of superimposing habitat and elevation requirements on constraints associated with environmental tolerances. Results In the mechanistic model, species with a narrower thermal range for activity are both predicted and observed to have more restricted distributions. Incorporating constraints on habitat and elevation further restricts species distributions beyond areas that are thermally suitable. While correlative models generally outperform mechanistic models at predicting current distributions, the performance of mechanistic models improves when incorporating additional factors. In response to a 3 °C temperature warming, the northward range shifts predicted by the mechanistic model vary between species according to trait differences and are of a greater extent than those predicted by correlative models. Main conclusions These findings highlight the importance of species traits for understanding the dynamics of species ranges in changing environments. The analysis demonstrates that mechanistic models may provide an important complement to correlative models for predicting range dynamics, which may underpredict climate‐induced range shifts.     

Illuminating geographical patterns in species' range shifts

Species' range shifts in response to ongoing climate change have been widely documented, but although complex spatial patterns in species' responses are expected to be common, comprehensive comparisons of species' ranges over time have undergone little investigation. Here, we outline a modeling framework based on historical and current species distribution records for disentangling different drivers (i.e. climatic vs. nonclimatic) and assessing distinct facets (i.e. colonization, extirpation, persistence, and lags) of species' range shifts. We used extensive monitoring data for stream fish assemblages throughout France to assess range shifts for 32 fish species between an initial period (1980–1992) and a contemporary one (2003–2009). Our results provide strong evidence that the responses of individual species varied considerably and exhibited complex mosaics of spatial rearrangements. By dissociating range shifts in climatically suitable and unsuitable habitats, we demonstrated that patterns in climate‐driven colonization and extirpation were less marked than those attributed to nonclimatic drivers, although this situation could rapidly shift in the near future. We also found evidence that range shifts could be related to some species' traits and that the traits involved varied depending on the facet of range shift considered. The persistence of populations in climatically unsuitable areas was greater for short‐lived species, whereas the extent of the lag behind climate change was greater for long‐lived, restricted‐range, and low‐elevation species. We further demonstrated that nonclimatic extirpations were primarily related to the size of the species' range, whereas climate‐driven extirpations were better explained by thermal tolerance. Thus, the proposed framework demonstrated its potential for markedly improving our understanding of the key processes involved in range shifting and also offers a template for informing management decisions. Conservation strategies would greatly benefit from identifying both the geographical patterns and the species' traits associated with complex modifications of species' distributions in response to global changes.     

Macro‐ and microclimatic interactions can drive variation in species' habitat associations

Many species are more restricted in their habitat associations at the leading edges of their range margins, but some species have broadened their habitat associations in these regions during recent climate change. We examine the effects of multiple, interacting climatic variables on spatial and temporal patterns of species' habitat associations, using the speckled wood butterfly, Pararge aegeria, in Britain, as our model taxon. Our analyses reveal that this species, traditionally regarded as a woodland‐dependent insect, is less restricted to woodland in regions with warmer winters and warmer and wetter summers. In addition, over the past 40 years of climate change, the species has become less restricted to woodland in locations where temperature and summer rainfall have increased most. We show that these patterns arise mechanistically because larval growth rates are slower in open (i.e. nonwoodland) habitats associated with colder microclimates in winter and greater host plant desiccation in summer. We conclude that macro‐ and microclimatic interactions drive variation in species' habitat associations, which for our study species resulted predominantly in a widening of habitat associations under climate change. However, species vary in their climatic and nonclimatic requirements, and so complex spatial and temporal patterns of changes in habitat associations are likely to be observed in future as the climate changes.