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The changes in the publishing landscape are inevitable and the transition is bound to create challenges and occasional dysfunctions. Ecography is a journal of the Nordic Society Oikos and, as such, is committed to explore solutions that will enable adherence to open science policies while minimising the adverse effects of inequality that can emerge from them.

Identifying the key factors driving invasion processes is crucial for designing and implementing appropriate management strategies. In fact, the importance of (model-based) prevention and early detection was highlighted in the recent European Union regulation on Invasive Alien Species. Models based on abundance estimates for different age/size classes would represent a significant improvement relative to the more usual models based only on species’ occurrence data. Here, we evaluate the relative contribution of different environmental drivers to the spatial patterns of abundance of several height classes (or life-stages) of invasive tree populations at the regional scale, using a data-driven hierarchical modelling approach. A framework for modelling life-stages to obtain spatial projections of their potential occurrence or abundance has not been formalized before. We used Acacia dealbata (Silver-wattle) as a test species in northwest of Portugal, a heavily invaded region, and applied a multimodel inference to test the importance of various environmental drivers in explaining the abundance patterns of five plant height classes in local landscape mosaics. The ensemble of height classes is considered here as a proxy for population dynamics, life-stages and age of adult trees. In this test with A. dealbata, we used detailed field data on population height structure and calibrated an independent model for each height class. We found evidence to support our hypothesis that the distribution of height classes is mostly influenced by distinct factors operating at different scales. The spatial projections which resulted from several height class models provide an overview of population structure and invasion dynamics considering various life-stages, that is widely used in biodiversity and invasion research. The approach proposed here provides a framework to guide forest management to deal more effectively with plant invasions. It allows to test the effects of key invasion

How climate affects species distributions is a longstanding question receiving renewed interest owing to the need to predict the impacts of global warming on biodiversity. Is climate change forcing species to live near their critical thermal limits? Are these limits likely to change through natural selection? These and other important questions can be addressed with models relating geographical distributions of species with climate data, but inferences made with these models are highly contingent on non-climatic factors such as biotic interactions. Improved understanding of climate change effects on species will require extensive analysis of thermal physiological traits, but such data are scarce and scattered. To overcome current limitations, we created the GlobTherm database. The database contains experimentally derived species’ thermal tolerance data currently comprising over 2,000 species of terrestrial, freshwater, intertidal and marine multicellular algae, pl ants, fungi, and animals. The GlobTherm database will be maintained and curated by iDiv with the aim of expanding it, and enable further investigations on the effects of climate on the distribution of life on Earth.

Demand for models in biodiversity assessments is rising, but which models are adequate for the task? We propose a set of best-practice standards and detailed guidelines enabling scoring of studies based on species distribution models for use in biodiversity assessments. We reviewed and scored 400 modeling studies over the past 20 years using the proposed standards and guidelines. We detected low model adequacy overall, but with a marked tendency of improvement over time in model building and, to a lesser degree, in biological data and model evaluation. We argue that implementation of agreed-upon standards for models in biodiversity assessments would promote transparency and repeatability, eventually leading to higher quality of the models and the inferences used in assessments. We encourage broad community participation toward the expansion and ongoing development of the proposed standards and guidelines.

Ecological Niche Models (ENMs) have different performances in predicting potential geographic distributions. Here we meta-analyzed the likely effects of climate change on the potential geographic distribution of 1,205 bird species from the Neotropical region, modeled using eight ENMs and three Atmosphere-Ocean General Circulation Models (AOGCM). We considered the variability in ENMs performance to estimate a weighted mean difference between potential geographic distributions for baseline and future climates. On average, potential future ranges were projected to be from 25.7% to 44.5% smaller than current potential ranges across species. However, we found that 0.2% to 18.3% of the total variance in range shifts occurred “within species” (i.e., owing to the use of different modeling techniques and climate models) and 81.7% to 99.8% remained between species (i.e., it could be explained by ecological correlates). Using meta-analytical techniques akin to regression, we also showed that potential range shifts are barely predicted by bird biological traits. We demonstrated that one can combine and reduce species-specific effects with high uncertainty in ENMs and also explore potential causes of climate change effect on species using meta-analytical tools. We also highlight that the search for powerful correlates of climate change-induced range shifts can be a promising line of investigation.

Research addressing the effects of global warming on the distribution and persistence of species generally assumes that population variation in thermal tolerance is spatially constant or overridden by interspecific variation. Typically, this rationale is implicit in sourcing one critical thermal maximum (CTmax) population estimate per species to model spatiotemporal cross‐taxa variation in heat tolerance. Theory suggests that such an approach could result in biased or imprecise estimates and forecasts of impact from climate warming, but limited empirical evidence in support of those expectations exists. We experimentally quantify the magnitude of intraspecific variation in CTmax among lizard populations, and the extent to which incorporating such variability can alter estimates of climate impact through a biophysical model. To do so, we measured CTmax from 59 populations of 15 Iberian lizard species (304 individuals). The overall median CTmax across all individuals from all species was 42.8°C and ranged from 40.5 to 48.3°C, with species medians decreasing through xeric, climate‐generalist and mesic taxa. We found strong statistical support for intraspecific differentiation in CTmax by up to a median of 3°C among populations. We show that annual restricted activity (operative temperature > CTmax) over the Iberian distribution of our study species differs by a median of >80 hr per 25‐km2 grid cell based on different population‐level CTmax estimates. This discrepancy leads to predictions of spatial variation in annual restricted activity to change by more than 20 days for six of the study species. Considering that during restriction periods, reptiles should be unable to feed and reproduce, current projections of climate‐change impacts on the fitness of ectotherm fauna could be under‐ or over‐estimated depending on which population is chosen to represent the physiological spectra of the species in question. Mapping heat tolerance over the full geographical ranges of sin

Identifying the key factors driving invasion processes is crucial for designing and implementing appropriate management strategies. In fact, the importance of (model-based) prevention and early detection was highlighted in the recent European Union regulation on Invasive Alien Species. Models based on abundance estimates for different age/size classes would represent a significant improvement relative to the more usual models based only on species’ occurrence data. Here, we evaluate the relative contribution of different environmental drivers to the spatial patterns of abundance of several height classes (or life-stages) of invasive tree populations at the regional scale, using a data-driven hierarchical modelling approach. A framework for modelling life-stages to obtain spatial projections of their potential occurrence or abundance has not been formalized before. We used Acacia dealbata (Silver-wattle) as a test species in northwest of Portugal, a heavily invaded region, and applied a multimodel inference to test the importance of various environmental drivers in explaining the abundance patterns of five plant height classes in local landscape mosaics. The ensemble of height classes is considered here as a proxy for population dynamics, life-stages and age of adult trees. In this test with A. dealbata, we used detailed field data on population height structure and calibrated an independent model for each height class. We found evidence to support our hypothesis that the distribution of height classes is mostly influenced by distinct factors operating at different scales. The spatial projections which resulted from several height class models provide an overview of population structure and invasion dynamics considering various life-stages, that is widely used in biodiversity and invasion research. The approach proposed here provides a framework to guide forest management to deal more effectively with plant invasions. It allows to test the effects of key invasion fac

A large array of species distribution model (SDM) approaches has been developed for explaining and predicting the occurrences of individual species or species assemblages. Given the wealth of existing models, it is unclear which models perform best for interpolation or extrapolation of existing data sets, particularly when one is concerned with species assemblages. We compared the predictive performance of 33 variants of 15 widely applied and recently emerged SDMs in the context of multispecies data, including both joint SDMs that model multiple species together, and stacked SDMs that model each species individually combining the predictions afterward. We offer a comprehensive evaluation of these SDM approaches by examining their performance in predicting withheld empirical validation data of different sizes representing five different taxonomic groups, and for prediction tasks related to both interpolation and extrapolation. We measure predictive performance by 12 measures of accuracy, discrimination power, calibration, and precision of predictions, for the biological levels of species occurrence, species richness, and community composition. Our results show large variation among the models in their predictive performance, especially for communities comprising many species that are rare. The results do not reveal any major trade-offs among measures of model performance; the same models performed generally well in terms of accuracy, discrimination, and calibration, and for the biological levels of individual species, species richness, and community composition. In contrast, the models that gave the most precise predictions were not well calibrated, suggesting that poorly performing models can make overconfident predictions. However, none of the models performed well for all prediction tasks. As a general strategy, we therefore propose that researchers fit a small set of models showing complementary performance, and then apply a cross-validation procedure involving s

Wildlife trade is a profitable economic activity. Birds are among the most heavily traded animals worldwide, with numerous species threatened by pet trade. Information on both legal and illegal aspects of trade and consumer demand is difficult to obtain across different countries, particularly given substantial socio-economic and cultural variation. Focusing on consumer demand in each country, we conducted a global survey among 105 international experts on bird conservation to identify expected trends, drivers and market characteristics of legal and illegal wild-caught pet bird trade. Our results suggest that future trends in legal bird trade will be mostly driven by socio-cultural motivations and intentional demand for wild-caught, rather than captive-bred birds. Bird popularity and rarity are the main factors expected to influence the choice of which bird species will be the most traded legally. Percentage of rural population was the main national-level socio-economic predictor for legal bird trade in the future. Demand for future illegal trade is expected to be driven by bird popularity and particular species identity. Experts consider illegal trade to be sustained mainly by consumers from higher socio-economic and educational backgrounds. Human population growth rate was the main national-level socio-economic predictor of illegal trade expected for the future. Legislation enforcement remains a critical issue in wildlife trade. Expanding trade networks and socio-economic changes continue to incorporate new regions into the wildlife trade. Investigating the multidimensional and synergistic determinants of wildlife trade will thus help address potential detrimental impacts bird trade might cause on biodiversity.

[Aim]: Climate change alters the water cycle, potentially affecting the distribution of species. Using an ensemble of species distribution models (SDMs), we predicted changes in distribution of the Asian elephant in South Asia due to increasing climatic variability under warming climate and human pressures., [Location]: India and Nepal., [Methods]: We compiled a comprehensive geodatabase of 115 predictor variables, which included climatic, topographic, human pressures and land use, at a resolution of 1 km2, and an extensive database on current distribution of elephants. For variable selection, we first developed 14 candidate models based on different hypotheses on elephant habitat selection. For each candidate model, a series of 240 individual models were evaluated using several metrics. Using three climatic and one land use change datasets for two greenhouse gas scenarios, ensemble SDMs were used to predict future projections., [Results]: Nine predictor variables were selected for ensemble SDMs. Elephant distribution is driven predominantly by changes in climatic water balance (>60%), followed by changes in temperature and human‐induced disturbance. The results suggest that around 41.8% of the 256,518 km2 of habitat available at present will be lost by the end of this century due to combined effects of climate change and human pressure. Projected habitat loss will be higher in human‐dominated sites at lower elevations due to intensifying droughts, leading elephants to seek refuge at higher elevations along valleys with greater water availability in the Himalayan Mountains., [Main conclusions]: Changes in climatic water balance could play a crucial role in driving species distributions in regions with monsoonal climates. In response, species would shift their range upwards along gradients of water availability and seasonal droughts. Conservation and management of elephant populations under global change should include design of movement corridors to enable dispersal of the elephant and other associated species to more conducive environments.

The productivity of marine ecosystems and the services they provide to humans are largely dependent on complex interactions between prey and predators. These are embedded in a diverse network of trophic interactions, resulting in a cascade of events following perturbations such as species extinction. The sheer scale of oceans, however, precludes the characterization of marine feeding networks through de novo sampling. This effort ought instead to rely on a combination of extensive data and inference. Here we investigate how the distribution of trophic interactions at the global scale shapes the marine fish food web structure. We hypothesize that the heterogeneous distribution of species ranges in biogeographic regions should concentrate interactions in the warmest areas and within species groups. We find that the inferred global metaweb of marine fish—that is, all possible potential feeding links between co-occurring species—is highly connected geographically with a low degree of spatial modularity. Metrics of network structure correlate with sea surface temperature and tend to peak towards the tropics. In contrast to open-water communities, coastal food webs have greater interaction redundancy, which may confer robustness to species extinction. Our results suggest that marine ecosystems are connected yet display some resistance to perturbations because of high robustness at most locations.Using a global interaction dataset, the authors quantify the distribution of trophic interactions among marine fish, finding a high degree of geographic connectivity but low spatial modularity.

How climate affects species distributions is a longstanding question receiving renewed interest owing to the need to predict the impacts of global warming on biodiversity. Is climate change forcing species to live near their critical thermal limits? Are these limits likely to change through natural selection? These and other important questions can be addressed with models relating geographical distributions of species with climate data, but inferences made with these models are highly contingent on non-climatic factors such as biotic interactions. Improved understanding of climate change effects on species will require extensive analysis of thermal physiological traits, but such data are both scarce and scattered. To overcome current limitations, we created the GlobTherm database. The database contains experimentally derived species’ thermal tolerance data currently comprising over 2,000 species of terrestrial, freshwater, intertidal and marine multicellular algae, plants, fungi, and animals. The GlobTherm database will be maintained and curated by iDiv with the aim to keep expanding it, and enable further investigations on the effects of climate on the distribution of life on Earth.

No species can persist in isolation from other species, but how biotic interactions affect species persistence is still a matter of inquiry. Is persistence more likely in communities with higher proportion of competing species, or in communities with more positive interactions? How do different components of community structure mediate this relationship? We address these questions using a novel simulation framework that generates realistic communities with varying numbers of species and different proportions of biotic interaction types within and across trophic levels. We show that when communities have fewer species, persistence is more likely if positive interactions—such as mutualism and commensalism—are prevalent. In species‐rich communities, the disproportionate effect of positive interactions on persistence is diluted and different combinations of biotic interaction types can coexist without affecting persistence significantly. We present the first theoretical examination of how multiple‐interaction networks with varying architectures relate to local species persistence, and provide insight about the underlying causes of stability in communities.

The relative importance of deterministic and neutral processes in shaping assembly of communities remains controversial, partly due to inconsistencies between theoretical, empirical, and experimental studies. We investigate the interplay between local (productivity) and regional (size of species pool) assembly mechanisms in communities of phytoplankton and zooplankton in 72 experimental microcosms. Local environmental conditions were manipulated by varying the level of nutrients in the water (ambient, low, high). The size of regional species pool colonizing each microcosm was manipulated by mixing phytoplankton and zooplankton species from different numbers of source ponds (n = 2, 4, 8 and 16). Our results show that local communities assembled differently depending on the numbers of sources available for colonization. Microcosms with larger species pools supported greater numbers of species. In contrast, the effects of productivity led to different results across trophic groups. Phytoplankton communities were, on average, more diverse on more productive treatments, while zooplankton communities were more diverse under less productive treatments. Phytoplankton and zooplankton communities responded to both sources of variation, although the size of species pool was a better predictor of communities’ composition than the local effects of productivity. These results reinforce the view that community assembly is influenced by the interplay of both local and regional drivers but that the relative importance of these factors varies with trophic groups.

Several studies estimating the effects of global environmental change on biodiversity are focused on climate change. Yet, non-climatic factors such as changes in land cover can also be of paramount importance. This may be particularly important for habitat specialists associated with human-dominated landscapes, where land cover and climate changes may be largely decoupled. Here, we tested this idea by modelling the influence of climate, landscape composition and pattern, on the predicted future (2021–2050) distributions of 21 farmland bird species in the Iberian Peninsula, using boosted regression trees and 10-km resolution presence/absence data. We also evaluated whether habitat specialist species were more affected by landscape factors than generalist species. Overall, this study showed that the contribution of current landscape composition and pattern to the performance of species distribution models (SDMs) was relatively low. However, SDMs built using either climate or climate plus landscape variables yielded very different predictions of future species range shifts and, hence, of the geographical patterns of change in species richness. Our results indicate that open habitat specialist species tend to expand their range, whereas habitat generalist species tend to retract under climate change scenarios. The effect of incorporating landscape factors were particularly marked on open habitat specialists of conservation concern, for which the expected expansion under climate change seems to be severely constrained by land cover change. Overall, results suggest that particular attention should be given to landscape change in addition to climate when modelling the impacts of environmental changes for both farmland specialist and generalist bird distributions.

Forecasts of species range shifts under climate change most often rely on ecological niche models, in which characterizations of climate suitability are highly contingent on the species range data used. If ranges are far from equilibrium under current environmental conditions, for instance owing to local extinctions in otherwise suitable areas, modelled environmental suitability can be truncated, leading to biased estimates of the effects of climate change. Here we examine the impact of such biases on estimated risks from climate change by comparing models of the distribution of North American mammals based on current ranges with ranges accounting for historical information on species ranges. We find that estimated future diversity, almost everywhere, except in coastal Alaska, is drastically underestimated unless the full historical distribution of the species is included in the models. Consequently forecasts of climate change impacts on biodiversity for many clades are unlikely to be reliable without acknowledging anthropogenic influences on contemporary ranges.

How climate affects species distributions is a longstanding question receiving renewed interest owing to the need to predict the impacts of global warming on biodiversity. Is climate change forcing species to live near their critical thermal limits? Are these limits likely to change through natural selection? These and other important questions can be addressed with models relating geographical distributions of species with climate data, but inferences made with these models are highly contingent on non-climatic factors such as biotic interactions. Improved understanding of climate change effects on species will require extensive analysis of thermal physiological traits, but such data are both scarce and scattered. To overcome current limitations, we created the GlobTherm database. The database contains experimentally derived species’ thermal tolerance data currently comprising over 2,000 species of terrestrial, freshwater, intertidal and marine multicellular algae, plants, fungi, and animals. The GlobTherm database will be maintained and curated by iDiv with the aim to keep expanding it, and enable further investigations on the effects of climate on the distribution of life on Earth.

Coastal lobsters support important fsheries all over the world, but there is evidence that climate-induced changes may jeopardize some stocks. Here we present the frst global forecasts of changes in coastal lobster species distribution under climate change, using an ensemble of ecological niche models (ENMs). Global changes in richness were projected for 125 coastal lobster species for the end of the century, using a stabilization scenario (4.5 RCP). We compared projected changes in diversity with lobster fsheries data and found that losses in suitable habitat for coastal lobster species were mainly projected in areas with high commercial fshing interest, with species projected to contract their climatic envelope between 40 and 100%. Higher losses of spiny lobsters are projected in the coasts of wider Caribbean/Brazil, eastern Africa and Indo-Pacifc region, areas with several directed fsheries and aquacultures, while clawed lobsters are projected to shifts their envelope to northern latitudes likely afecting the North European, North American and Canadian fsheries. Fisheries represent an important resource for local and global economies and understanding how they might be afected by climate change scenarios is paramount when developing specifc or regional management strategies.

[Context]: Species are expected to shift their distributions in response to global environmental changes and additional protected areas are needed to encompass the corresponding changes in the distributions of their habitats. Conservation policies are likely to become obsolete unless they integrate the potential impacts of climate and land-use change on biodiversity., [Objectives]: We identify conservation priority areas for current and future projected distributions of Iberian bird species. We then investigate the extent to which global change informed priority areas are: (i) covered by existing protected area networks (national protected areas and Natura 2000); (ii) threatened by agricultural or urban land-use changes., [Methods]: We use outputs of species distributions models fitted with climatic data as inputs in spatial prioritization tools to identify conservation priority areas for 168 bird species. We use projections of land-use change to then discriminate between threatened and non-threatened priority areas., [Results]: 19% of the priority areas for birds are covered by national protected areas and 23% are covered by Natura 2000 sites. The spatial mismatch between protected area networks and priority areas for birds is projected to increase with climate change. But there are opportunities to improve the protection of birds under climate change, as half of the priority areas are currently neither protected nor in conflict with urban or agricultural land-uses., [Conclusions]: We identify critical areas for bird conservation both under current and climate change conditions, and propose that they could guide the establishment of new conservation areas across the Iberian Peninsula complementing existing protected areas.

Ecological communities are defined by species interacting dynamically in a given location at a given time, and can be conveniently represented as networks of interactions. Pairwise interactions can be ascribed to one of five main types, depending on their outcome for the species involved: amensalism, antagonism (including predation, parasitism and disease), commensalism, competition or mutualism. While most studies have dealt so far with networks involving one single type of interaction at a time, often focusing on a specific clade and/or guild, recent studies are being developed that consider networks with more than one interaction type and across several levels of biological organisation. We review these developments and suggest that three main frameworks are in use to investigate the properties of multiple interactions networks: ‘expanded food‐webs’, ‘multilayer networks’ and ‘equal footing networks’. They differ on how interactions are classified and implemented in mathematical models, and on whether the effect of different interaction types is expressed in the same units. We analyse the mathematical and ecological assumptions of these three approaches, and identify some of the questions that can be addressed with each one of them. Since the overwhelming majority of studies on multiple interactions are theoretical and use artificially generated data, we also provide recommendations for the incorporation of field data in such studies.

Understanding the mechanisms driving geographic range sizes of species is a central issue in ecology, but remarkably few rules link physiology with the distributions of species. Maximal metabolic rate (MMR) during exercise is an important measure of physiological performance. It sets an upper limit to sustained activity and locomotor capacity, so MMR may influence ability to migrate, disperse and maintain population connectivity. Using both conventional ordinary least squares (OLS) analyses and phylogenetically generalized least squares (PGLS), we tested whether MMR helps explain geographic range size in 51 species of placental mammals. Log body mass alone (OLS r =.074, p =.053; PGLS r =.016, p =.373) and log MMR alone (OLS r =.140, p =.007; PGLS r =.061, p =.081) were weak predictors of log range size. However, multiple regression of log body mass and log MMR accounted for over half of the variation in log range size (OLS R =.527, p <.001). The relationship was also strong after correcting for the phylogenetic non-independence (PGLS R =.417, p <.001). In analyses restricted to rodents (34 species), neither log body mass alone (OLS r =.004, p =.720; PGLS r =.003, p =.77) nor log MMR alone was useful in predicting log geographic range size (OLS r =.008, p =.626; PGLS r =.046, p =.225), but multiple regressions of log body mass and log MMR accounted for roughly a third to a half of the variation in log range size (OLS R =.443, p <.001, PGLS R =.381, p <.001). Mass-independent MMR is a strong predictor of mass-independent geographic range size in placental mammals. The ability of body mass and MMR to explain nearly 50% of the variation in the geographic ranges of mammals is surprising and powerful, particularly when neither variable alone is strongly predictive. A better understanding of MMR during exercise may be important to understanding the limits of geographic ranges of mammals, and perhaps other animal groups.

Intraspecific genetic variability is critical for species adaptation and evolution and yet it is generally overlooked in projections of the biological consequences of climate change. We ask whether ongoing climate changes can cause the loss of important gene pools from North Atlantic relict kelp forests that persisted over glacial–interglacial cycles. We use ecological niche modelling to predict genetic diversity hotspots for eight species of large brown algae with different thermal tolerances (Arctic to warm temperate), estimated as regions of persistence throughout the Last Glacial Maximum (20,000 YBP), the warmer Mid‐Holocene (6,000 YBP), and the present. Changes in the genetic diversity within ancient refugia were projected for the future (year 2100) under two contrasting climate change scenarios (RCP2.6 and RCP8.5). Models predicted distributions that matched empirical distributions in cross‐validation, and identified distinct refugia at the low latitude ranges, which largely coincide among species with similar ecological niches. Transferred models into the future projected polewards expansions and substantial range losses in lower latitudes, where richer gene pools are expected (in Nova Scotia and Iberia for cold affinity species and Gibraltar, Alboran, and Morocco for warm‐temperate species). These effects were projected for both scenarios but were intensified under the extreme RCP8.5 scenario, with the complete borealization (circum‐Arctic colonization) of kelp forests, the redistribution of the biogeographical transitional zones of the North Atlantic, and the erosion of global gene pools across all species. As the geographic distribution of genetic variability is unknown for most marine species, our results represent a baseline for identification of locations potentially rich in unique phylogeographic lineages that are also climatic relics in threat of disappearing.

Criticism has been levelled at climate-change-induced forecasts of species range shifts that do not account explicitly for complex population dynamics. The relative importance of such dynamics under climate change is, however, undetermined because direct tests comparing the performance of demographic models vs. simpler ecological niche models are still lacking owing to difficulties in evaluating forecasts using real-world data. We provide the first comparison of the skill of coupled ecological-niche-population models and ecological niche models in predicting documented shifts in the ranges of 20 British breeding bird species across a 40-year period. Forecasts from models calibrated with data centred on 1970 were evaluated using data centred on 2010. We found that more complex coupled ecological-niche-population models (that account for dispersal and metapopulation dynamics) tend to have higher predictive accuracy in forecasting species range shifts than structurally simpler models that only account for variation in climate. However, these better forecasts are achieved only if ecological responses to climate change are simulated without static snapshots of historic land use, taken at a single point in time. In contrast, including both static land use and dynamic climate variables in simpler ecological niche models improve forecasts of observed range shifts. Despite being less skilful at predicting range changes at the grid-cell level, ecological niche models do as well, or better, than more complex models at predicting the magnitude of relative change in range size. Therefore, ecological niche models can provide a reasonable first approximation of the magnitude of species' potential range shifts, especially when more detailed data are lacking on dispersal dynamics, demographic processes underpinning population performance, and change in land cover.