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.     

Incorporating intraspecific variation into species distribution models improves distribution predictions,but cannot predict species traits for a wide-spread plant species

The most common approach to predicting how species ranges and ecological functions will shift with climate change is to construct correlative species distribution models (SDMs). These models use a species’ climatic distribution to determine currently suitable areas for the species and project its potential distribution under future climate scenarios. A core, rarely tested, assumption of SDMs is that all populations will respond equivalently to climate. Few studies have examined this assumption, and those that have rarely dissect the reasons for intraspecific differences. Focusing on the arctic-alpine cushion plant Silene acaulis, we compared predictive accuracy from SDMs constructed using the species’ full global distribution with composite predictions from separate SDMs constructed using subpopulations defined either by genetic or habitat differences. This is one of the first studies to compare multiple ways of constructing intraspecific-level SDMs with a species-level SDM. We also examine the contested relationship between relative probability of occurrence and species performance or ecological function, testing if SDM output can predict individual performance (plant size) and biotic interactions (facilitation). We found that both genetic- and habitat-informed SDMs are considerably more accurate than a species-level SDM, and that the genetic model substantially differs from and outperforms the habitat model. While SDMs have been used to infer population performance and possibly even biotic interactions, in our system these relationships were extremely weak. Our results indicate that individual subpopulations may respond differently to climate, although we discuss and explore several alternative explanations for the superior performance of intraspecific-level SDMs. We emphasize the need to carefully examine how to best define intraspecific-level SDMs as well as how potential genetic, environmental, or sampling variation within species ranges can critically affect SDM predictions. We urge caution in inferring population performance or biotic interactions from SDM predictions, as these often-assumed relationships are not supported in our study.     

Host plant distributions and climate interact to affect the predicted geographic distribution of a Neotropical termite

Species distribution models (SDMs) have been widely used in the scientific literature. The majority of SDMs use climate data or other abiotic variables to forecast the potential distribution of a species in geographic space. Biotic interactions can affect the predicted spatial distribution of a species in many ways across multiple spatial scales, and incorporating these predictors in an SDM is a current topic in the scientific literature. Constrictotermes cyphergaster is a widely distributed termite in the Neotropics. This termite species nests in plants and more frequently nests in some arboreal species. Thus, this species is an excellent model to evaluate the influence of biotic interactions in SDMs. We evaluate the influences of climate and the geographic distribution of host plants on the potential distribution of C. cyphergaster. Three correlative models (MaxEnt) were built to predict the geographic distribution of the termite: (1) climate data, (2) biotic data (i.e., the geographic distribution of host plants), and (3) climate and biotic data. The models that were generated indicate that the potential geographic distribution of C. cyphergaster is concentrated in the Cerrado and Caatinga regions. In addition, path analysis and multiple regression revealed the importance of the direct effects of biological interactions in the geographic distribution of the termite, while climate affected the distribution of the termite mainly through indirect effects by influencing the geographic distributions of host plants. The current study endorses the importance of including biological interactions in SDMs. We recommend using biotic predictors in SDM studies of insect species, mainly because insects have important environmental services and biotic interaction data can improve the macroecological studies of this group.     

Predicting range shifts of Davidia involucrata Ball. under future climate change

Understanding and predicting how species will respond to climate change is crucial for biodiversity conservation. Here, we assessed future climate change impacts on the distribution of a rare and endangered plant species, Davidia involucrate in China, using the most recent global circulation models developed in the sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC6). We assessed the potential range shifts in this species by using an ensemble of species distribution models (SDMs). The ensemble SDMs exhibited high predictive ability and suggested that the temperature annual range, annual mean temperature, and precipitation of the driest month are the most influential predictors in shaping distribution patterns of this species. The projections of the ensemble SDMs also suggested that D. involucrate is very vulnerable to future climate change, with at least one‐third of its suitable range expected to be lost in all future climate change scenarios and will shift to the northward of high‐latitude regions. Similarly, at least one‐fifth of the overlap area of the current nature reserve networks and projected suitable habitat is also expected to be lost. These findings suggest that it is of great importance to ensure that adaptive conservation management strategies are in place to mitigate the impacts of climate change on D. involucrate.     

Biotic interactions influence the projected distribution of a specialist mammal under climate change

Aim

To measure the effects of including biotic interactions on climate‐based species distribution models (SDMs) used to predict distribution shifts under climate change. We evaluated the performance of distribution models for an endangered marsupial, the northern bettong (Bettongia tropica), comparing models that used only climate variables with models that also took into account biotic interactions.

Location

North‐east Queensland, Australia.

Methods

We developed separate climate‐based distribution models for the northern bettong, its two main resources and a competitor species. We then constructed models for the northern bettong by including climate suitability estimates for the resources and competitor as additional predictor variables to make climate + resource and climate + resource + competition models. We projected these models onto seven future climate scenarios and compared predictions of northern bettong distribution made by these differently structured models, using a ‘global’ metric, the I similarity statistic, to measure overlap in distribution and a ‘local’ metric to identify where predictions differed significantly.

Results

Inclusion of food resource biotic interactions improved model performance. Over moderate climate changes, up to 3.0 °C of warming, the climate‐only model for the northern bettong gave similar predictions of distribution to the more complex models including interactions, with differences only at the margins of predicted distributions. For climate changes beyond 3.0 °C, model predictions diverged significantly. The interactive model predicted less contraction of distribution than the simpler climate‐only model.

Main conclusions

Distribution models that account for interactions with other species, in particular direct resources, improve model predictions in the present‐day climate. For larger climate changes, shifts in distribution of interacting species cause predictions of interactive models to diverge from climate‐only models. Incorporating interactions with other species in SDMs may be needed for long‐term prediction of changes in distribution of species under climate change, particularly for specialized species strongly dependent on a small number of biotic interactions.     

Projecting climate change impacts on species distributions in megadiverse South African Cape and Southwest Australian Floristic Regions: Opportunities and challenges

Increasing evidence shows that anthropogenic climate change is affecting biodiversity. Reducing or stabilizing greenhouse gas emissions may slow global warming, but past emissions will continue to contribute to further unavoidable warming for more than a century. With obvious signs of difficulties in achieving effective mitigation worldwide in the short term at least, sound scientific predictions of future impacts on biodiversity will be required to guide conservation planning and adaptation. This is especially true in Mediterranean type ecosystems that are projected to be among the most significantly affected by anthropogenic climate change, and show the highest levels of confidence in rainfall projections. Multiple methods are available for projecting the consequences of climate change on the main unit of interest – the species – with each method having strengths and weaknesses. Species distribution models (SDMs) are increasingly applied for forecasting climate change impacts on species geographic ranges. Aggregation of models for different species allows inferences of impacts on biodiversity, though excluding the effects of species interactions. The modelling approach is based on several further assumptions and projections and should be treated cautiously. In the absence of comparable approaches that address large numbers of species, SDMs remain valuable in estimating the vulnerability of species. In this review we discuss the application of SDMs in predicting the impacts of climate change on biodiversity with special reference to the species‐rich South West Australian Floristic Region and South African Cape Floristic Region. We discuss the advantages and challenges in applying SDMs in biodiverse regions with high levels of endemicity, and how a similar biogeographical history in both regions may assist us in understanding their vulnerability to climate change. We suggest how the process of predicting the impacts of climate change on biodiversity with SDMs can be improved and emphasize the role of field monitoring and experiments in validating the predictions of SDMs.     

Combining physiological threshold knowledge to species distribution models is key to improving forecasts of the future niche for macroalgae

Species distribution models (SDM) are a useful tool for predicting species range shifts in response to global warming. However, they do not explore the mechanisms underlying biological processes, making it difficult to predict shifts outside the environmental gradient where the model was trained. In this study, we combine correlative SDMs and knowledge on physiological limits to provide more robust predictions. The thermal thresholds obtained in growth and survival experiments were used as proxies of the fundamental niches of two foundational marine macrophytes. The geographic projections of these species’ distributions obtained using these thresholds and existing SDMs were similar in areas where the species are either absent‐rare or frequent and where their potential and realized niches match, reaching consensus predictions. The cold‐temperate foundational seaweed Himanthalia elongata was predicted to become extinct at its southern limit in northern Spain in response to global warming, whereas the occupancy of southern‐lusitanic Bifurcaria bifurcata was expected to increase. Combined approaches such as this one may also highlight geographic areas where models disagree potentially due to biotic factors. Physiological thresholds alone tended to over‐predict species prevalence, as they cannot identify absences in climatic conditions within the species’ range of physiological tolerance or at the optima. Although SDMs tended to have higher sensitivity than threshold models, they may include regressions that do not reflect causal mechanisms, constraining their predictive power. We present a simple example of how combining correlative and mechanistic knowledge provides a rapid way to gain insight into a species’ niche resulting in consistent predictions and highlighting potential sources of uncertainty in forecasted responses to climate change.     

Climate change effects on behavioral and physiological ecology of predator–prey interactions: Implications for conservation biological control

Habitat management under the auspices of conservation biological control is a widely used approach to foster conditions that ensure a diversity of predator species can persist spatially and temporally within agricultural landscapes in order to control their prey (pest) species. However, an emerging new factor, global climate change, has the potential to disrupt existing conservation biological control programs. Climate change may alter abiotic conditions such as temperature, precipitation, humidity and wind that in turn could alter the life-cycle timing of predator and prey species and the behavioral nature and strength of their interactions. Anticipating how climate change will affect predator and prey communities represents an important research challenge. We present a conceptual framework—the habitat domain concept—that is useful for understanding contingencies in the nature of predator diversity effects on prey based on predator and prey spatial movement in their habitat. We illustrate how this framework can be used to forecast whether biological control by predators will become more effective or become disrupted due to changing climate. We discuss how changes in predator–prey interactions are contingent on the tolerances of predators and prey species to changing abiotic conditions as determined by the degree of local adaptation and phenotypic plasticity exhibited by species populations. We conclude by discussing research approaches that are needed to help adjust conservation biological control management to deal with a climate future.     

Arctic fox versus red fox in the warming Arctic: four decades of den surveys in north Yukon

During the last century, the red fox (Vulpes vulpes) has expanded its distribution into the Arctic, where it competes with the arctic fox (Vulpes lagopus), an ecologically similar tundra predator. The red fox expansion correlates with climate warming, and the ultimate determinant of the outcome of the competition between the two species is hypothesized to be climate. We conducted aerial and ground fox den surveys in the northern Yukon (Herschel Island and the coastal mainland) to investigate the relative abundance of red and arctic foxes over the last four decades. This region has undergone the most intense warming observed in North America, and we hypothesized that this climate change led to increasing dominance of red fox over arctic fox. Results of recent surveys fall within the range of previous ones, indicating little change in the relative abundance of the two species. North Yukon fox dens are mostly occupied by arctic fox, with active red fox dens occurring sympatrically. While vegetation changes have been reported, there is no indication that secondary productivity and food abundance for foxes have increased. Our study shows that in the western Arctic of North America, where climate warming was intense, the competitive balance between red and arctic foxes changed little in 40?years. Our results challenge the hypotheses linking climate to red fox expansion, and we discuss how climate warming’s negative effects on predators may be overriding positive effects of milder temperatures and longer growing seasons.     

Predicting changes in Fennoscandian vascular-plant species richness as a result of future climatic change

It is anticipated that future climatic warming following the currently enhanced greenhouse effect will change the distribution limits of many vascular plant species. Using annual accumulated respiration equivalents, calculated from January and July mean temperatures and total annual precipitation, simple presence–absence response surface plots are constructed for 1521 native vascular-plant species in 229 75×75-km grid squares within Fennoscandia. The contemporary occurrences in relation to present-day climate and to predicted changes in climate (and hence annual accumulated respiration equivalents) are used to predict possible future immigrations and extinctions within each grid square. The percentage of potential change in species richness for each grid square is estimated from these predictions. Results from this study suggest a mean increase in species richness per grid square of 26%. Increases in species richness are greatest in the southern parts of the alpine/boreal regions in Fennoscandia. There are ten species that potentially may become extinct in Fennoscandia as a result of predicted climatic warming. Possible conservation strategies to protect such endangered species are outlined.     

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.     

Predicting the distributions of predator (snow leopard) and prey (blue sheep) under climate change in the Himalaya

Future climate change is likely to affect distributions of species, disrupt biotic interactions, and cause spatial incongruity of predator–prey habitats. Understanding the impacts of future climate change on species distribution will help in the formulation of conservation policies to reduce the risks of future biodiversity losses. Using a species distribution modeling approach by MaxEnt, we modeled current and future distributions of snow leopard (Panthera uncia) and its common prey, blue sheep (Pseudois nayaur), and observed the changes in niche overlap in the Nepal Himalaya. Annual mean temperature is the major climatic factor responsible for the snow leopard and blue sheep distributions in the energy‐deficient environments of high altitudes. Currently, about 15.32% and 15.93% area of the Nepal Himalaya are suitable for snow leopard and blue sheep habitats, respectively. The bioclimatic models show that the current suitable habitats of both snow leopard and blue sheep will be reduced under future climate change. The predicted suitable habitat of the snow leopard is decreased when blue sheep habitats is incorporated in the model. Our climate‐only model shows that only 11.64% (17,190 km²) area of Nepal is suitable for the snow leopard under current climate and the suitable habitat reduces to 5,435 km² (reduced by 24.02%) after incorporating the predicted distribution of blue sheep. The predicted distribution of snow leopard reduces by 14.57% in 2030 and by 21.57% in 2050 when the predicted distribution of blue sheep is included as compared to 1.98% reduction in 2030 and 3.80% reduction in 2050 based on the climate‐only model. It is predicted that future climate may alter the predator–prey spatial interaction inducing a lower degree of overlap and a higher degree of mismatch between snow leopard and blue sheep niches. This suggests increased energetic costs of finding preferred prey for snow leopards – a species already facing energetic constraints due to the limited dietary resources in its alpine habitat. Our findings provide valuable information for extension of protected areas in future.     

The fluctuating world of a tundra predator guild: bottom‐up constraints overrule top‐down species interactions in winter

Global warming is predicted to change ecosystem functioning and structure in Arctic ecosystems by strengthening top‐down species interactions, i.e. predation pressure on small herbivores and interference between predators. Yet, previous research is biased towards the summer season. Due to greater abiotic constraints, Arctic ecosystem characteristics might be more pronounced in winter. Here we test the hypothesis that top‐down species interactions prevail over bottom‐up effects in Scandinavian mountain tundra (Northern Sweden) where effects of climate warming have been observed and top‐down interactions are expected to strengthen. But we test this ‘a priori’ hypothesis in winter and throughout the 3–4 yr rodent cycle, which imposes additional pulsed resource constraints. We used snowtracking data recorded in 12 winters (2004–2015) to analyse the spatial patterns of a tundra predator guild (arctic fox Vulpes lagopus, red fox Vulpes vulpes, wolverine Gulo gulo) and small prey (ptarmigan, Lagopus spp). The a priori top‐down hypothesis was then tested through structural equation modelling, for each phase of the rodent cycle. There was weak support for this hypothesis, with top‐down effects only discerned on arctic fox (weakly, by wolverine) and ptarmigan (by arctic fox) at intermediate and high rodent availability respectively. Overall, bottom‐up constraints appeared more influential on the winter community structure. Cold specialist predators (arctic fox and wolverine) showed variable landscape associations, while the boreal predator (red fox) appeared strongly dependent on productive habitats and ptarmigan abundance. Thus, we suggest that the unpredictability of food resources determines the winter ecology of the cold specialist predators, while the boreal predator relies on resource‐rich habitats. The constraints imposed by winters and temporary resource lows should therefore counteract productivity‐driven ecosystem change and have a stabilising effect on community structure. Hence, the interplay between summer and winter conditions should determine the rate of Arctic ecosystem change in the context of global warming.     

Prey switching as a means of enhancing persistence in predators at the trailing southern edge

Understanding the effects of climate change on species’ persistence is a major research interest; however, most studies have focused on responses at the northern or expanding range edge. There is a pressing need to explain how species can persist at their southern range when changing biotic interactions will influence species occurrence. For predators, variation in distribution of primary prey owing to climate change will lead to mismatched distribution and local extinction, unless their diet is altered to more extensively include alternate prey. We assessed whether addition of prey information in climate projections restricted projected habitat of a specialist predator, Canada lynx (Lynx canadensis), and if switching from their primary prey (snowshoe hare; Lepus americanus) to an alternate prey (red squirrel; Tamiasciurus hudsonicus) mitigates range restriction along the southern range edge. Our models projected distributions of each species to 2050 and 2080 to then refine predictions for southern lynx on the basis of varying combinations of prey availability. We found that models that incorporated information on prey substantially reduced the total predicted southern range of lynx in both 2050 and 2080. However, models that emphasized red squirrel as the primary species had 7–24% lower southern range loss than the corresponding snowshoe hare model. These results illustrate that (i) persistence at the southern range may require species to exploit higher portions of alternate food; (ii) selection may act on marginal populations to accommodate phenotypic changes that will allow increased use of alternate resources; and (iii) climate projections based solely on abiotic data can underestimate the severity of future range restriction. In the case of Canada lynx, our results indicate that the southern range likely will be characterized by locally varying levels of mismatch with prey such that the extent of range recession or local adaptation may appear as a geographical mosaic.     

Beyond species distribution modeling: A landscape genetics approach to investigating range shifts under future climate change

Understanding how biodiversity will respond to future climate change is a major conservation and societal challenge. Climate change is predicted to force many species to shift their ranges in pursuit of suitable conditions. This study aims to use landscape genetics, the study of the effects of environmental heterogeneity on the spatial distribution of genetic variation, as a predictive tool to assess how species will shift their ranges to track climatic changes and inform conservation measures that will facilitate movement. The approach is based on three steps: 1) using species distribution models (SDMs) to predict suitable ranges under future climate change, 2) using the landscape genetics framework to identify landscape variables that impede or facilitate movement, and 3) extrapolating the effect of landscape connectivity on range shifts in response to future climate change. I show how this approach can be implemented using the publicly available genetic dataset of the grey long-eared bat, Plecotus austriacus, in the Iberian Peninsula. Forest cover gradient was the main landscape variable affecting genetic connectivity between colonies. Forest availability is likely to limit future range shifts in response to climate change, primarily over the central plateau, but important range shift pathways have been identified along the eastern and western coasts. I provide outputs that can be directly used by conservation managers and review the viability of the approach. Using landscape genetics as a predictive tool in combination with SDMs enables the identification of potential pathways, whose loss can affect the ability of species to shift their range into future climatically suitable areas, and the appropriate conservation management measures to increase landscape connectivity and facilitate movement.     

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

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

Contrasting effects of biotic interactions on richness and distribution of vascular plants,bryophytes and lichens in an arctic–alpine landscape

Biotic interactions may strongly affect the distribution of individual species and the resulting patterns of species richness. However, the impacts can vary depending on the species or taxa examined, suggesting that the influences of interactions on species distributions and diversity are not always straightforward and can be taxon-contingent. The aim of this study was therefore to examine how the importance of biotic interactions varies within a community. We incorporated three biotic predictors (cover of the dominant vascular species) into two correlative species richness modelling frameworks to predict spatial variation in the number of vascular plants, bryophytes and lichens in arctic–alpine Fennoscandia, in N Europe. In addition, predictions based on single-species distribution models were used to determine the nature of the impact (negative vs. positive outcome) of the three dominant species on individual vascular plant, bryophyte and lichen species. Our results suggest that biotic variables can be as important as abiotic variables, but their relative contributions in explaining the richness of sub-dominant species vary among dominant species, species group and the modelling framework implemented. Similarly, the impacts of biotic interactions on individual species varied among the three species groups and dominant species, with the observed patterns partly reflecting species’ biogeographic range. Our study provides additional support for the importance of biotic interactions in modifying arctic–alpine biodiversity patterns and highlights that the impacts of interactions are not constant across taxa or biotic drivers. The influence of biotic interactions, including the taxon contingency and range-based impacts, should therefore be accounted for when developing biodiversity forecasts.     

Strength of asymmetric competition between predators in food webs ruled by fluctuating prey: the case of foxes in tundra

In food webs heavily influenced by multi‐annual population fluctuations of key herbivores, predator species may differ in their functional and numerical responses as well as their competitive ability. Focusing on red and arctic fox in tundra with cyclic populations of rodents as key prey, we develop a model to predict how population dynamics of a dominant and versatile predator (red fox) impacted long‐term growth rate of a subdominant and less versatile predator (arctic fox). We compare three realistic scenarios of red fox performance: (1) a numerical response scenario where red fox acted as a resident rodent specialist exhibiting population cycles lagging one year after the rodent cycle, (2) an aggregative response scenario where red fox shifted between tundra and a nearby ecosystem (i.e. boreal forest) so as to track rodent peaks in tundra without delay, and (3) a constant subsidy scenario in which the red fox population was stabilized at the same mean density as in the other two scenarios. For all three scenarios it is assumed that the arctic fox responded numerically as a rodent specialist and that the mechanisms of competition is of a interference type for space, in which the arctic fox is excluded from the most resource rich patches in tundra. Arctic fox is impacted most by the constant subsidy scenario and least by the numerical response scenario. The differential effects of the scenarios stemmed from cyclic phase‐dependent sensitivity to competition mediated by changes in temporal mean and variance of available prey to the subdominant predator. A general implication from our result is that external resource subsidies (prey or habitats), monopolized by the dominant competitor, can significantly reduce the likelihood for co‐existence within the predator guild. In terms of conservation of vulnerable arctic fox populations this means that the likelihood of extinction increases with increasing amount of subsidies (e.g. carcasses of large herbivores or marine resources) in tundra and nearby forest areas, since it will act to both increase and stabilize populations of red fox.     

Modelling the niche space of desert annuals needs to include positive interactions

The niche is a necessary consideration when estimating habitable area and geographic range of a species. Modellers often examine the fundamental niche and the environmental requirements for plant species, ignoring interactions among species. In deserts, positive plant interactions are important drivers of biodiversity and structure communities through many mechanistic pathways including modifying environmental conditions. Thus, we tested the hypothesis that desert shrubs increase the geographical extent of some annual species because, through modifying the microclimate, they match the niche requirements of beneficiary species. We used the database of the Global Biodiversity Information Facility to construct MaxEnt species distribution models (SDM) with and without reported benefactor species within the Mojave Desert in California. We chose 20 annual species to be modeled including 10 species that had been previously reported in the literature as being facilitated (beneficiary) and 10 that had no record of being facilitated (unreported). Beneficiary annuals co‐occurred significantly more with benefactor shrubs than the unreported annual species. The inclusion of shrubs into SDMs significantly improved model predictability and geographic range for all the beneficiary annual species, but not for the unreported annual species. Thus, positive interactions are species specific and it is possible to determine annual species dependency on benefactor shrubs at the regional scale. The co‐occurrence of benefactor shrubs and annual species can be used as a proxy for facilitation and recent developments in SDM techniques encourage the inclusion of biotic interactions. Species distribution models should include estimates of facilitation because biotic interactions determine the niche of species and can have implications with a changing climate.