Conservation management and sustainable harvest quotas are sensitive to choice of climate modelling approach for two marine gastropods

Aim

To establish the robustness of two alternative methods for predicting the future ranges and abundances for two wild‐harvested abalone species (Haliotis rubra Donovan 1808 and H. laevigata Leach 1814): single atmosphere–ocean general circulation model (GCM) or ensemble‐averaged GCM forecasts.

Location

South Australia.

Methods

We assessed the ability of 20 GCMs to simulate observed seasonal sea surface temperature (SST) between 1980–1999, globally, and regionally for the Indian and Pacific Oceans south of the Equator. We used model rankings to characterize a set of representative climate futures, using three different‐sized GCM ensembles and two individual GCMs (the Parallel Climate Model and the Community Climate System Model, version 3.0). Ecological niche models were then coupled to physiological information to compare forecast changes in area of occupancy, population size and harvest area based on forecasts using the various GCM selection methods, as well as different greenhouse gas emission scenarios and climate sensitivities.

Results

We show that: (1) the skill with which climate models reproduce recent SST records varies considerably amongst GCMs, with multimodel ensemble averages showing closer agreement to observations than single models; (2) choice of GCM, and the decision on whether or not to use ensemble‐averaged climate forecasts, can strongly influence spatiotemporal predictions of range, abundance and fishing potential; and (3) comparable hindcasting skill does not necessarily guarantee that GCM forecasts and ecological and evolutionary responses to these forecast changes, will be similar amongst closely ranked models.

Conclusion

By averaging across an ensemble of seven highly ranked skilful GCMs, inherent uncertainties stemming from GCM differences are incorporated into forecasts of change in species range, abundance and sustainable fishing area. Our results highlight the need to make informed and explicit decisions on GCM choice, model sensitivity and emission scenarios when exploring conservation options for marine species and the sustainability of future harvests using ecological niche models.

     

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.     

Mopane (Colophospermum mopane): A potential winner under climate change in southern Africa

Distribution and abundance under climate change of particularly non-timber forest product tree species is vital since they sustain many livelihoods, especially in rural sub-Saharan Africa. The aim of the study was to determine the current and future natural range of mopane (Colophospermum mopane (J. Kirk ex Benth.) J. Léonard, Fabaceae), a dominant tree species in mopane woodlands of southern Africa. An ensemble model was built in ‘biomod2’ from eight algorithms and used to estimate the current and future distribution. Seven bioclimatic variables and 269 occurrence records were used to calibrate individual models that were later combined into an ensemble model. The ensemble model was projected to two time periods, 2041–2060 and 2081–2100, under two shared socio-economic pathways (SSPs), SSP2-4.5 and SSP5-8.5, and three general circulation models (GCMs). The ensemble model showed high performance (KAPPA = 0.770, ROC = 0.961, TSS = 0.792, ACCURACY = 0.900). A map of the current distribution shows occurrence predominantly in low-lying areas, including the Zambezi, Save and Limpopo valleys, Okavango and Cuvelai basins, and in southern and central Mozambique. Projection maps show expansion under all SSPs, GCMs and time periods. Averaged across GCMs in 2041–2060, the range expanded by 22.37% under SSP2-4.5, and by 19.94% under SSP5-8.5. In 2081–2100, the range expanded by 20.43% under SSP2-4.5, and by 27.62% under SSP5-8.5. Notably, the range expansion was highest under SSP5-8.5, an SSP that envisages unmitigated greenhouse gas release and the largest mean global temperature increase. It is highly likely that mopane is not directly threatened by climate change. Indirect climate change threats, however, remain uncertain.     

气候变化对物种分布影响模拟中的不确定性组分分割与制图——以油松为例

物种分布模型是预测评估气候变化对物种分布影响的主要工具。为了降低物种分布模型在预测过程中的不确定性,近期有学者提出了采用组合预测的新方法,即采用多套建模数据、模型技术,模型参数,以及环境情景数据对物种分布进行预测,构成物种分布预测集合。但是,组合预测中各组分对变异的贡献还知之甚少,因此有必要把变异组分来源进行分割,以更有效地利用组合预测方法来降低模型预测中的不确定性。以油松为例,采用8个生态位模型,9套模型训练数据,3个GCM模型和一个SRES(A2)排放情景,模型分析了油松当前(1961-1990年)和未来气候条件下3个时间段(2010-2039年,2040-2069年,2070-2099年)的潜在分布。共计得到当前分布预测数据72套,未来每个时间段分布数据216套。采用开发的ClimateChina软件进行当前和未来气候数据的降尺度处理。采用Kappa、真实技巧统计方法(TSS)和接收机工作特征曲线下的面积(AUC)对模型预测能力进行评估。结果表明,随机森林(RF)、广义线性模型(GLM),广义加法模型(GAM)、多元自适应样条函数(MARS)以及助推法(GBM)预测效果较好,几乎不受建模数据之间差异的影响。混合判别分析模型(MDA)对建模数据之间的差异非常敏感,甚至出现建模失败现象。采用三因素方差分析方法对组合预测中的不确定性来源进行变异分割,结果表明,模型之间的差异对模拟预测结果不确定性的贡献最大且所占比例极高,而建模数据之间的差异贡献最小,GCM贡献居中。研究将有助于加深对物种分布模拟预测中不确定性的认识。     

Uncertainties in the projection of species distributions related to general circulation models

Ecological Niche Models (ENMs) are increasingly used by ecologists to project species potential future distribution. However, the application of such models may be challenging, and some caveats have already been identified. While studies have generally shown that projections may be sensitive to the ENM applied or the emission scenario, to name just a few, the sensitivity of ENM‐based scenarios to General Circulation Models (GCMs) has been often underappreciated. Here, using a multi‐GCM and multi‐emission scenario approach, we evaluated the variability in projected distributions under future climate conditions. We modeled the ecological realized niche (sensu Hutchinson) and predicted the baseline distribution of species with contrasting spatial patterns and representative of two major functional groups of European trees: the dwarf birch and the sweet chestnut. Their future distributions were then projected onto future climatic conditions derived from seven GCMs and four emissions scenarios using the new Representative Concentration Pathways (RCPs) developed for the Intergovernmental Panel on Climate Change (IPCC) AR5 report. Uncertainties arising from GCMs and those resulting from emissions scenarios were quantified and compared. Our study reveals that scenarios of future species distribution exhibit broad differences, depending not only on emissions scenarios but also on GCMs. We found that the between‐GCM variability was greater than the between‐RCP variability for the next decades and both types of variability reached a similar level at the end of this century. Our result highlights that a combined multi‐GCM and multi‐RCP approach is needed to better consider potential trajectories and uncertainties in future species distributions. In all cases, between‐GCM variability increases with the level of warming, and if nothing is done to alleviate global warming, future species spatial distribution may become more and more difficult to anticipate. When future species spatial distributions are examined, we propose to use a large number of GCMs and RCPs to better anticipate potential trajectories and quantify uncertainties.     

Forecasting the future of biodiversity: a test of single‐ and multi‐species models for ants in North America

The geographic distributions of many taxonomic groups remain mostly unknown, hindering attempts to investigate the response of the majority of species on Earth to climate change using species distributions models (SDMs). Multi‐species models can incorporate data for rare or poorly‐sampled species, but their application to forecasting climate change impacts on biodiversity has been limited. Here we compare forecasts of changes in patterns of ant biodiversity in North America derived from ensembles of single‐species models to those from a multi‐species modeling approach, Generalized Dissimilarity Modeling (GDM). We found that both single‐ and multi‐species models forecasted large changes in ant community composition in relatively warm environments. GDM predicted higher turnover than SDMs and across a larger contiguous area, including the southern third of North America and notably Central America, where the proportion of ants with relatively small ranges is high and where data limitations are most likely to impede the application of SDMs. Differences between approaches were also influenced by assumptions regarding dispersal, with forecasts being more similar if no‐dispersal was assumed. When full‐dispersal was assumed, SDMs predicted higher turnover in southern Canada than did GDM. Taken together, our results suggest that 1) warm rather than cold regions potentially could experience the greatest changes in ant fauna under climate change and that 2) multi‐species models may represent an important complement to SDMs, particularly in analyses involving large numbers of rare or poorly‐sampled species. Comparisons of the ability of single‐ and multi‐species models to predict observed changes in community composition are needed in order to draw definitive conclusions regarding their application to investigating climate change impacts on biodiversity.     

Incorporating spatial autocorrelation into species distribution models alters forecasts of climate‐mediated range shifts

Species distribution models (SDMs) are widely used to forecast changes in the spatial distributions of species and communities in response to climate change. However, spatial autocorrelation (SA) is rarely accounted for in these models, despite its ubiquity in broad‐scale ecological data. While spatial autocorrelation in model residuals is known to result in biased parameter estimates and the inflation of type I errors, the influence of unmodeled SA on species' range forecasts is poorly understood. Here we quantify how accounting for SA in SDMs influences the magnitude of range shift forecasts produced by SDMs for multiple climate change scenarios. SDMs were fitted to simulated data with a known autocorrelation structure, and to field observations of three mangrove communities from northern Australia displaying strong spatial autocorrelation. Three modeling approaches were implemented: environment‐only models (most frequently applied in species' range forecasts), and two approaches that incorporate SA; autologistic models and residuals autocovariate (RAC) models. Differences in forecasts among modeling approaches and climate scenarios were quantified. While all model predictions at the current time closely matched that of the actual current distribution of the mangrove communities, under the climate change scenarios environment‐only models forecast substantially greater range shifts than models incorporating SA. Furthermore, the magnitude of these differences intensified with increasing increments of climate change across the scenarios. When models do not account for SA, forecasts of species' range shifts indicate more extreme impacts of climate change, compared to models that explicitly account for SA. Therefore, where biological or population processes induce substantial autocorrelation in the distribution of organisms, and this is not modeled, model predictions will be inaccurate. These results have global importance for conservation efforts as inaccurate forecasts lead to ineffective prioritization of conservation activities and potentially to avoidable species extinctions.     

Are fish outside their usual ranges early indicators of climate‐driven range shifts?

Shifts in species ranges are a global phenomenon, well known to occur in response to a changing climate. New species arriving in an area may become pest species, modify ecosystem structure, or represent challenges or opportunities for fisheries and recreation. Early detection of range shifts and prompt implementation of any appropriate management strategies is therefore crucial. This study investigates whether ‘first sightings’ of marine species outside their normal ranges could provide an early warning of impending climate‐driven range shifts. We examine the relationships between first sightings and marine regions defined by patterns of local climate velocities (calculated on a 50‐year timescale), while also considering the distribution of observational effort (i.e. number of sampling days recorded with biological observations in global databases). The marine trajectory regions include climate ‘source’ regions (areas lacking connections to warmer areas), ‘corridor’ regions (areas where moving isotherms converge), and ‘sink’ regions (areas where isotherms locally disappear). Additionally, we investigate the latitudinal band in which first sightings were recorded, and species’ thermal affiliations. We found that first sightings are more likely to occur in climate sink and ‘divergent’ regions (areas where many rapid and diverging climate trajectories pass through) indicating a role of temperature in driving changes in marine species distributions. The majority of our fish first sightings appear to be tropical and subtropical species moving towards high latitudes, as would be expected in climate warming. Our results indicate that first sightings are likely related to longer‐term climatic processes, and therefore have potential use to indicate likely climate‐driven range shifts. The development of an approach to detect impending range shifts at an early stage will allow resource managers and researchers to better manage opportunities resulting from range‐shifting species before they potentially colonize.     

Not‐so‐splendid isolation: modeling climate‐mediated range collapse of a montane mammal Ochotona princeps across numerous ecoregions

We modeled current and future distribution of suitable habitat for the talus‐obligate montane mammal Ochotona princeps (American pika) across the western USA under increases in temperature associated with contemporary climate change, to: a) compare forecasts using only climate variables vs using those plus habitat considerations; b) identify possible patterns of range collapse (center vs margins, and large‐ vs small‐sized patches); and c) compare conservation and management implications of changes at two taxonomic resolutions, and using binned‐ vs binary‐probability maps. We used MaxEnt to analyze relationships between occurrence records and climatic variables to develop a bioclimatic‐envelope model, which we refined by masking with a deductive appropriate‐habitat filter based on suitable land‐cover types. We used this final species‐distribution model to predict distribution of suitable habitat under range‐wide temperature increases from 1 to 7°C, in 1°C increments; we also compared these results to distribution under IPCC‐forecasted climates for 2050 and 2080. Though all currently recognized lineages and traditionally defined subspecies were predicted to lose increasing amounts of habitat as temperatures rose, the most‐dramatic range losses were predicted to occur among traditional subspecies. Nineteen of the 31 traditional US pika subspecies were predicted to lose > 98% of their suitable habitat under a 7?C increase in the mean temperature of the warmest quarter of the year, and lineages were predicted to lose 88 95% of suitable habitat. Under a 4?C increase, traditional subspecies averaged a predicted 73% (range = 44–99%) reduction. The appropriate‐habitat filter removed 40–6% of the predicted climatically suitable pixels, in a stepped and monotonically decreasing fashion as predicted temperatures rose. Predicted range collapse proceeded until only populations in island‐biogeographic ‘mainlands’ remained, which were not in the geographic range center. We used this model system to illustrate possible distributional shifts under stepped changes in biologically relevant aspects of climate, importance of land cover and taxonomic level in species‐distribution forecasts, and impact of using a single threshold vs multiple categories of persistence probability in predicted range maps; we encourage additional research to further investigate the generality of these patterns.     

Understanding global patterns in amphibian geographic range size: does Rapoport rule?

Aim Species geographic ranges are the ‘fundamental units’ of macroecology. Range size is a major correlate of extinction risk in many groups, and is also critical in studies of biotic responses to climate change. Despite this, there is a lack of studies exploring the role of environmental, historical and anthropogenic processes in determining large‐scale patterns in range size. We perform the first global analysis of putative drivers of range size variation in any group, choosing amphibians as our study taxon. Our aims are to disentangle the many hypothesized causes of range size variation and evaluate support for ‘Rapoport's rule’, the observation that range size correlates with latitude. Location Global. Methods We develop a global map of gridded median range size using the International Union for Conservation of Nature (IUCN) distribution maps. From this we perform spatial and non‐spatial regressions to explore relationships between range size and nine hypothesized variables in six biogeographic realms. We use information‐theoretic model selection to compare multiple competing variables, simultaneously evaluating the relative support for each one. Results Current climate – environmental water and energy, and temperature seasonality – is consistently highly ranked in spatial and non‐spatial analyses. Human impacts and other environmental measures (topographic and landscape complexity, effective area, climate extremes) show mixed support, and glacial history is consistently unimportant. Our findings add further evidence to the view that Rapoport's rule is a regional, not global, phenomenon. Main conclusions The primary importance of temperature seasonality may explain why Rapoport's rule is largely restricted to northern latitudes, as this is where seasonality is most pronounced. More generally, the dominance of contemporary climate in our analyses (even when accounting for space) has stark implications for the future status of amphibians. Changes in climate will almost certainly interact with the anthropogenic processes already threatening a third of amphibians globally, with the effects being most keenly felt by species with a restricted range.     

Equipped to cope with climate change: traits associated with range filling across European taxa

In order to understand the ecological effects of climate change it is essential to forecast suitable areas for species in the future. However, species’ ability to reach potentially suitable areas is also critical for species survival. These ‘range‐shift’ abilities can be studied using life‐history traits related to four range‐shift stages: emigration, movement, establishment, and proliferation. Here, we use the extent to which species’ ranges fill the climatically suitable area available (‘range filling’) as a proxy for the ability of European mammals and birds to shift their ranges under climate change. We detect which traits associate most closely with range filling. Drawing comparisons with a recent analysis for plants, we ask whether the latitudinal position of species’ ranges supports the assertion that post‐glacial range‐shift limitations cause disequilibrium between ranges and climate. We also disentangle which of the three taxonomic groups has greatest range filling. For mammals, generalists and early‐reproducing species have the greatest range filling. For birds, generalist species with high annual fecundity, which live longer than expected based on body size, have the greatest range filling. Although we consider traits related to the four range‐shift stages, only traits related to establishment and proliferation ability significantly influence range filling of mammals and birds. Species with the greatest range filling are those whose range centroid falls in the latitudinal centre of Europe, suggesting that post‐glacial range expansion is a leading cause of disequilibrium with climate, although other explanations are also possible. Range filling of plants is lower than that of mammals or birds, suggesting that plants are more range‐limited by non‐climatic factors. Therefore, plants might be face greater non‐climatic restraints on range shifts than mammals or birds.     

Effects of climate change on an emperor penguin population: analysis of coupled demographic and climate models

Sea ice conditions in the Antarctic affect the life cycle of the emperor penguin (Aptenodytes forsteri). We present a population projection for the emperor penguin population of Terre Adélie, Antarctica, by linking demographic models (stage‐structured, seasonal, nonlinear, two‐sex matrix population models) to sea ice forecasts from an ensemble of IPCC climate models. Based on maximum likelihood capture‐mark‐recapture analysis, we find that seasonal sea ice concentration anomalies (SIC_a) affect adult survival and breeding success. Demographic models show that both deterministic and stochastic population growth rates are maximized at intermediate values of annual SIC_a, because neither the complete absence of sea ice, nor heavy and persistent sea ice, would provide satisfactory conditions for the emperor penguin. We show that under some conditions the stochastic growth rate is positively affected by the variance in SIC_a. We identify an ensemble of five general circulation climate models whose output closely matches the historical record of sea ice concentration in Terre Adélie. The output of this ensemble is used to produce stochastic forecasts of SIC_a, which in turn drive the population model. Uncertainty is included by incorporating multiple climate models and by a parametric bootstrap procedure that includes parameter uncertainty due to both model selection and estimation error. The median of these simulations predicts a decline of the Terre Adélie emperor penguin population of 81% by the year 2100. We find a 43% chance of an even greater decline, of 90% or more. The uncertainty in population projections reflects large differences among climate models in their forecasts of future sea ice conditions. One such model predicts population increases over much of the century, but overall, the ensemble of models predicts that population declines are far more likely than population increases. We conclude that climate change is a significant risk for the emperor penguin. Our analytical approach, in which demographic models are linked to IPCC climate models, is powerful and generally applicable to other species and systems.     

Static species distribution models in dynamically changing systems: how good can predictions really be?

It is widely acknowledged that species respond to climate change by range shifts. Robust predictions of such changes in species’ distributions are pivotal for conservation planning and policy making, and are thus major challenges in ecological research. Statistical species distribution models (SDMs) have been widely applied in this context, though they remain subject to criticism as they implicitly assume equilibrium, and incorporate neither dispersal, demographic processes nor biotic interactions explicitly. In this study, the effects of transient dynamics and ecological properties and processes on the prediction accuracy of SDMs for climate change projections were tested. A spatially explicit multi‐species dynamic population model was built, incorporating species‐specific and interspecific ecological processes, environmental stochasticity and climate change. Species distributions were sampled in different scenarios, and SDMs were estimated by applying generalised linear models (GLMs) and boosted regression trees (BRTs). Resulting model performances were related to prevailing ecological processes and temporal dynamics. SDM performance varied for different range dynamics. Prediction accuracies decreased when abrupt range shifts occurred as species were outpaced by the rate of climate change, and increased again when a new equilibrium situation was realised. When ranges contracted, prediction accuracies increased as the absences were predicted well. Far‐dispersing species were faster in tracking climate change, and were predicted more accurately by SDMs than short‐dispersing species. BRTs mostly outperformed GLMs. The presence of a predator, and the inclusion of its incidence as an environmental predictor, made BRTs and GLMs perform similarly. Results are discussed in light of other studies dealing with effects of ecological traits and processes on SDM performance. Perspectives are given on further advancements of SDMs and for possible interfaces with more mechanistic approaches in order to improve predictions under environmental change.     

High-resolution bioclimatic dataset derived from future climate projections for plant species distribution modeling

Species distribution modeling is playing an increasingly prominent role in ecology and global change biology, owing to its potential to predict species range shifts, biodiversity losses, and biological invasion risks for future climates. Such models are now well-established as important tools for biological conservation. However, the lack of high-resolution data for future climate scenarios has seriously limited their application, particularly because of the scale gap between general circulation models (GCMs) and species distribution models (SDMs). A recently introduced change-factor downscaling technique provides a convenient way to build high-resolution datasets from GCM projections. Here, we present a high-resolution (10’ × 10’) global bioclimatic dataset (BioPlant) for plant species distribution. The 15 bioclimatic variables we select are considered those most eco-physiologically relevant. They can be easily calculated from climatic variables common to all GCM projections. In addition to the traditional classes of variables regarding temperature and precipitation, the BioPlant dataset emphasizes the interactions between temperature and precipitation, particularly within plant growing seasons. A preliminary visual analysis shows that variations among GCMs are more significant on a species range scale than on a global scale. Thus, the formerly advocated ensemble modeling method should be applied not only to different SDMs, but also to various GCMs. Statistic analysis suggests that divergent behavior among GCM variations for temperature class variables and classes of precipitation variables requires special attention. Our dataset may provide a common platform for ensemble modeling, and can serve as an example to develop higher-resolution regional datasets.     

Multi‐model comparison highlights consistency in predicted effect of warming on a semi‐arid shrub

A number of modeling approaches have been developed to predict the impacts of climate change on species distributions, performance, and abundance. The stronger the agreement from models that represent different processes and are based on distinct and independent sources of information, the greater the confidence we can have in their predictions. Evaluating the level of confidence is particularly important when predictions are used to guide conservation or restoration decisions. We used a multi‐model approach to predict climate change impacts on big sagebrush (Artemisia tridentata), the dominant plant species on roughly 43 million hectares in the western United States and a key resource for many endemic wildlife species. To evaluate the climate sensitivity of A. tridentata, we developed four predictive models, two based on empirically derived spatial and temporal relationships, and two that applied mechanistic approaches to simulate sagebrush recruitment and growth. This approach enabled us to produce an aggregate index of climate change vulnerability and uncertainty based on the level of agreement between models. Despite large differences in model structure, predictions of sagebrush response to climate change were largely consistent. Performance, as measured by change in cover, growth, or recruitment, was predicted to decrease at the warmest sites, but increase throughout the cooler portions of sagebrush's range. A sensitivity analysis indicated that sagebrush performance responds more strongly to changes in temperature than precipitation. Most of the uncertainty in model predictions reflected variation among the ecological models, raising questions about the reliability of forecasts based on a single modeling approach. Our results highlight the value of a multi‐model approach in forecasting climate change impacts and uncertainties and should help land managers to maximize the value of conservation investments.     

Novel spatial analysis methods reveal scale‐dependent spread and infer limiting factors of invasion by Sahara mustard

Multiple scale‐dependent ecological processes influence species distributions. Uncovering these drivers of dynamic range boundaries can provide fundamental ecological insights and vital knowledge for species management. We develop a transferable methodology that uses widely available data and tools to determine critical scales in range expansion and to infer dominating scale‐dependent forces that influence spread. We divide a focal geographic region into different sized square cells, representing different spatial scales. We then used herbarium records to determine the species' occupancy of cells at each spatial scale. We calculated the growth in cell occupancy across scales to infer the scale dependent expansion rate. This is the first time such a ‘box‐counting’ method is used to study range expansion. We coupled this multi‐scale analysis with species distribution models to determine the range and spatial scales where suitable climate allows the species to spread, and where other factors may be influencing the expansion. We demonstrate our methodology by assessing the spread of invasive Sahara mustard in North America. We detect critical scales where its spread is limited (100–500 km) or unconstrained (5–50 km) by climatic variables. Using climate‐based models to assess the similarity of climate envelopes in its native and invaded range, we find that the climate in the invaded range generally predicts the native distribution, suggesting that either there has been little local adaptation to climate occurring since introduction or the biological interaction experienced in the invaded range has not driven the species to occupy climatic conditions much different from its native range. Our novel method can be broadly utilized in other studies to generate critical insights into the scale dependency of different ecological drivers that influence the spread and distribution limits, as well as to help parameterizing predictions of future spread, and thus inform management decisions.     

To Be Or Not to Be? Variable selection can change the projected fate of a threatened species under future climate

Species distribution models (SDMs) are commonly used to project future changes in the geographic ranges of species, to estimate extinction rates and to plan biodiversity conservation. However, these models can produce a range of results depending on how they are parameterized, and over‐reliance on a single model may lead to overconfidence in maps of future distributions. The choice of predictor variable can have a greater influence on projected future habitat than the range of climate models used. We demonstrate this in the case of the Ptunarra Brown Butterfly, a species listed as vulnerable in Tasmania, Australia. We use the Maxent model to develop future projections for this species based on three variable sets; all 35 commonly used so‐called ‘bioclimatic’ variables, a subset of these based on expert knowledge, and a set of monthly climate variables relevant to the species’ primary activity period. We used a dynamically downscaled regional climate model based on three global climate models. Depending on the choice of variable set, the species is projected either to experience very little contraction of habitat or to come close to extinction by the end of the century due to lack of suitable climate. The different conclusions could have important consequences for conservation planning and management, including the perceived viability of habitat restoration. The output of SDMs should therefore be used to define the range of possible trajectories a species may be on, and ongoing monitoring used to inform management as changes occur.     

Integrating mechanistic and correlative niche models to unravel range‐limiting processes in a temperate amphibian

Insights into the causal mechanisms that limit species distributions are likely to improve our ability to anticipate species range shifts in response to climate change. For species with complex life histories, a mechanistic understanding of how climate affects different lifecycle stages may be crucial for making accurate forecasts. Here, we use mechanistic niche modeling (NicheMapR) to derive “proximate” (mechanistic) variables for tadpole, juvenile, and adult Rana temporaria. We modeled the hydroperiod, and maximum and minimum temperatures of shallow (30 cm) ponds, as well as activity windows for juveniles and adults. We then used those (“proximate”) variables in correlative ecological niche models (Maxent) to assess their role in limiting the species’ current distribution, and to investigate the potential effects of climate change on R. temporaria across Europe. We further compared the results with a model based on commonly used macroclimatic (“distal”) layers (i.e., bioclimatic layers from WorldClim). The maximum temperature of the warmest month (a macroclimatic variable) and maximum pond temperatures (a mechanistic variable) were the most important range‐limiting factors, and maximum temperature thresholds were consistent with the observed upper thermal limit of R. temporaria tadpoles. We found that range shift forecasts in central Europe are far more pessimistic when using distal macroclimatic variables, compared to projections based on proximate mechanistic variables. However, both approaches predicted extensive decreases in climatic suitability in southern Europe, which harbors a significant fraction of the species’ genetic diversity. We show how mechanistic modeling provides ways to depict gridded layers that directly reflect the microenvironments experienced by organisms at continental scales, and to reconstruct those predictors without extrapolation under novel future conditions. Furthermore, incorporating those predictors in correlative ecological niche models can help shed light on range‐limiting processes, and can have substantial impacts on predictions of climate‐induced range shifts.     

Phenology predicts the native and invasive range limits of common ragweed

Accurate models for species' distributions are needed to forecast the progress and impacts of alien invasive species and assess potential range‐shifting driven by global change. Although this has traditionally been achieved through data‐driven correlative modelling, robustly extrapolating these models into novel climatic conditions is challenging. Recently, a small number of process‐based or mechanistic distribution models have been developed to complement the correlative approaches. However, tests of these models are lacking, and there are very few process‐based models for invasive species. We develop a method for estimating the range of a globally invasive species, common ragweed (Ambrosia artemisiifolia L.), from a temperature‐ and photoperiod‐driven phenology model. The model predicts the region in which ragweed can reach reproductive maturity before frost kills the adult plants in autumn. This aligns well with the poleward and high‐elevation range limits in its native North America and in invaded Europe, clearly showing that phenological constraints determine the cold range margins of the species. Importantly, this is a ‘forward’ prediction made entirely independently of the distribution data. Therefore, it allows a confident and biologically informed forecasting of further invasion and range shifting driven by climate change. For ragweed, such forecasts are extremely important as the species is a serious crop weed and its airborne pollen is a major cause of allergy and asthma in humans. Our results show that phenology can be a key determinant of species' range margins, so integrating phenology into species distribution models offers great potential for the mechanistic modelling of range dynamics.     

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.