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

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

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

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

物种分布模型在海洋潜在生境预测的应用研究进展

海洋生物的栖息分布与环境要素的关联性一直是海洋生态学研究的热点之一.近年来,物种分布模型被广泛应用于预测海洋物种分布、潜在适宜性生境评价等研究,为保护海洋生物多样性、防治外来物种入侵及制定渔业管理措施等提供了一条有效途径.物种分布模型主要包括生境适宜性指数模型、机理模型和统计模型.本文对物种分布模型的理论基础进行了归纳和总结,回顾了物种分布模型在预测海洋物种潜在地理分布研究中的开发与应用,重点介绍了不同类型统计模型在海洋物种潜在分布预测中的研究实例.比较各种选取变量和模型验证方法,认为赤池信息准则对于选取模型变量具有优势,Kappa系数和受试者操作特征曲线下面积在验证模型精度中应用最广泛.阐述了物种分布模型存在的问题及未来发展趋势,随着海洋生物生理机制研究的进一步深入,机理模型将是今后物种分布模型发展的重点.     

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

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

Responses of coral reef fishes to past climate changes are related to life‐history traits

Coral reefs and their associated fauna are largely impacted by ongoing climate change. Unravelling species responses to past climatic variations might provide clues on the consequence of ongoing changes. Here, we tested the relationship between changes in sea surface temperature and sea levels during the Quaternary and present‐day distributions of coral reef fish species. We investigated whether species‐specific responses are associated with life‐history traits. We collected a database of coral reef fish distribution together with life‐history traits for the Indo‐Pacific Ocean. We ran species distribution models (SDMs) on 3,725 tropical reef fish species using contemporary environmental factors together with a variable describing isolation from stable coral reef areas during the Quaternary. We quantified the variance explained independently by isolation from stable areas in the SDMs and related it to a set of species traits including body size and mobility. The variance purely explained by isolation from stable coral reef areas on the distribution of extant coral reef fish species largely varied across species. We observed a triangular relationship between the contribution of isolation from stable areas in the SDMs and body size. Species, whose distribution is more associated with historical changes, occurred predominantly in the Indo‐Australian archipelago, where the mean size of fish assemblages is the lowest. Our results suggest that the legacy of habitat changes of the Quaternary is still detectable in the extant distribution of many fish species, especially those with small body size and the most sedentary. Because they were the least able to colonize distant habitats in the past, fish species with smaller body size might have the most pronounced lags in tracking ongoing climate change.     

Niche variability and its consequences for species distribution modeling

When species distribution models (SDMs) are used to predict how a species will respond to environmental change, an important assumption is that the environmental niche of the species is conserved over evolutionary time-scales. Empirical studies conducted at ecological time-scales, however, demonstrate that the niche of some species can vary in response to environmental change. We use habitat and locality data of five species of stream fishes collected across seasons to examine the effects of niche variability on the accuracy of projections from Maxent, a popular SDM. We then compare these predictions to those from an alternate method of creating SDM projections in which a transformation of the environmental data to similar scales is applied. The niche of each species varied to some degree in response to seasonal variation in environmental variables, with most species shifting habitat use in response to changes in canopy cover or flow rate. SDMs constructed from the original environmental data accurately predicted the occurrences of one species across all seasons and a subset of seasons for two other species. A similar result was found for SDMs constructed from the transformed environmental data. However, the transformed SDMs produced better models in ten of the 14 total SDMs, as judged by ratios of mean probability values at known presences to mean probability values at all other locations. Niche variability should be an important consideration when using SDMs to predict future distributions of species because of its prevalence among natural populations. The framework we present here may potentially improve these predictions by accounting for such variability.     

Nice weather for bettongs: using weather events,not climate means,in species distribution models

Current applications of species distribution models (SDM) are typically static, in that they are based on correlations between where a species has been observed (ignoring the date of the observation) and environmental features, such as long‐term climate means, that are assumed to be constant for each site. Because of this SDMs do not account for temporal variation in the distribution of suitable habitat across the range of a species. Here, we demonstrate the temporal variability in the potential geographic distributions of an endangered marsupial, the northern bettong Bettongia tropica as a case study. Models of the species distribution using temporally matched observations of the species with weather data (including extreme weather events) at the time of species observations, were better able to define habitat suitability, identify range edges and uncover competitive interactions than models based on static long‐term climate means. Droughts and variable temperature are implicated in low densities and local extinctions of northern bettong populations close to range edges. Further, we show how variable weather can influence the results of competition with the common rufous bettong Aepyprymnus rufescens. Because traditional SDMs do not account for temporal variability of suitable habitat, static SDMs may underestimate the impacts of climate change particularly as the incidence of extreme weather events is likely to rise.     

Incorporating microclimate into species distribution models

Species distribution models (SDMs) have rapidly evolved into one of the most widely used tools to answer a broad range of ecological questions, from the effects of climate change to challenges for species management. Current SDMs and their predictions under anthropogenic climate change are, however, often based on free‐air or synoptic temperature conditions with a coarse resolution, and thus fail to capture apparent temperature (cf. microclimate) experienced by living organisms within their habitats. Yet microclimate operates as soon as a habitat can be characterized by a vertical component (e.g. forests, mountains, or cities) or by horizontal variation in surface cover. The mismatch between how we usually express climate (cf. coarse‐grained free‐air conditions) and the apparent microclimatic conditions that living organisms experience has only recently been acknowledged in SDMs, yet several studies have already made considerable progress in tackling this problem from different angles. In this review, we summarize the currently available methods to obtain meaningful microclimatic data for use in distribution modelling. We discuss the issue of extent and resolution, and propose an integrated framework using a selection of appropriately‐placed sensors in combination with both the detailed measurements of the habitat 3D structure, for example derived from digital elevation models or airborne laser scanning, and the long‐term records of free‐air conditions from weather stations. As such, we can obtain microclimatic data with a relevant spatiotemporal resolution and extent to dynamically model current and future species distributions.     

Effects of biotic interactions on modeled species' distribution can be masked by environmental gradients

A fundamental goal of ecology is to understand the determinants of species' distributions (i.e., the set of locations where a species is present). Competition among species (i.e., interactions among species that harms each of the species involved) is common in nature and it would be tremendously useful to quantify its effects on species' distributions. An approach to studying the large‐scale effects of competition or other biotic interactions is to fit species' distributions models (SDMs) and assess the effect of competitors on the distribution and abundance of the species of interest. It is often difficult to validate the accuracy of this approach with available data. Here, we simulate virtual species that experience competition. In these simulated datasets, we can unambiguously identify the effects that competition has on a species' distribution. We then fit SDMs to the simulated datasets and test whether we can use the outputs of the SDMs to infer the true effect of competition in each simulated dataset. In our simulations, the abiotic environment influenced the effects of competition. Thus, our SDMs often inferred that the abiotic environment was a strong predictor of species abundance, even when the species' distribution was strongly affected by competition. The severity of this problem depended on whether the competitor excluded the focal species from highly suitable sites or marginally suitable sites. Our results highlight how correlations between biotic interactions and the abiotic environment make it difficult to infer the effects of competition using SDMs.     

种间作用对物种分布模拟的影响及建模方法

物种分布模型(SDMs)通过量化物种分布和环境变量之间的关系,并将其外推到未知的景观单元,模拟、预测地理空间中生物的潜在分布,是生态学、生物地理学、保护生物学等研究领域的重要工具.然而,目前物种分布模型主要采用非生物因素作为预测变量,由于数据量化和建模表达困难,生物因素特别是种间作用在物种分布模型中常被忽略,将种间作用...     

Topographically controlled thermal‐habitat differentiation buffers alpine plant diversity against climate warming

Aim We aim to: (1) explore thermal habitat preferences in alpine plant species across mosaics of topographically controlled micro‐habitats; (2) test the predictive value of so‐called ‘indicator values’; and (3) quantify the shift in micro‐habitat conditions under the influence of climate warming. Location Alpine vegetation 2200–2800 m a.s.l., Swiss central Alps. Methods High‐resolution infra‐red thermometry and large numbers of small data loggers were used to assess the spatial and temporal variation of plant‐surface and ground temperatures as well as snow‐melt patterns for 889 plots distributed across three alpine slopes of contrasting exposure. These environmental data were then correlated with Landolt indicator values for temperature preferences of different plant species and vegetation units. By simulating a uniform 2 K warming we estimated the changes in abundance of micro‐habitat temperatures within the study area. Results Within the study area we observed a substantial variation between micro‐habitats in seasonal mean soil temperature (ΔT = 7.2 K), surface temperature (ΔT = 10.5 K) and season length (>32 days). Plant species with low indicator values for temperature (plants commonly found in cool habitats) grew in significantly colder micro‐habitats than plants with higher indicator values found on the same slope. A 2 K warming will lead to the loss of the coldest habitats (3% of current area), 75% of the current thermal micro‐habitats will be reduced in abundance (crowding effect) and 22% will become more abundant. Main conclusions Our results demonstrate that the topographically induced mosaics of micro‐climatic conditions in an alpine landscape are associated with local plant species distribution. Semi‐quantitative plant species indicator values based on expert knowledge and aggregated to community means match measured thermal habitat conditions. Metre‐scale thermal contrasts significantly exceed IPCC warming projections for the next 100 years. The data presented here thus indicate a great risk of overestimating alpine habitat losses in isotherm‐based model scenarios. While all but the species depending on the very coldest micro‐habitats will find thermally suitable ‘escape’ habitats within short distances, there will be enhanced competition for those cooler places on a given slope in an alpine climate that is 2 K warmer. Yet, due to their topographic variability, alpine landscapes are likely to be safer places for most species than lowland terrain in a warming world.     

Predicting distributions,habitat preferences and associated conservation implications for a genus of rare fishes,seahorses (Hippocampus spp.)

Aim

To identify useful sources of species data and appropriate habitat variables for species distribution modelling on rare species, with seahorses as an example, deriving ecological knowledge and spatially explicit maps to advance global seahorse conservation.

Location

The shallow seas.

Methods

We applied a typical species distribution model (SDM), maximum entropy, to examine the utility of (1) two versions of habitat variables (habitat occurrences vs. proximity to habitats) and (2) three sources of species data: quality research‐grade (RG) data, quality‐unknown citizen science (CS) and museum‐collection (MC) data. We used the best combinations of species data and habitat variables to predict distributions and estimate species–habitat relations and threatened status for seahorse species.

Results

We demonstrated that using “proximity to habitats” and integrating all species datasets (RG, CS and MC) derived models with the highest accuracies among all dataset variations. Based on this finding, we derived reliable models for 33 species. Our models suggested that only 0.4% of potential seahorse range was suitable to more than three species together; seahorse biogeographic epicentres were mainly in the Philippines; and proximity to sponges was an important habitat variable. We found that 12 “Data Deficient” species might be threatened based on our predictions according to IUCN criteria.

Main conclusions

We highlight that using proper habitat variables (e.g., proximity to habitats) is critical to determine distributions and key habitats for low‐mobility animals; collating and integrating quality‐unknown occurrences (e.g., CS and MC) with quality research data are meaningful for building SDMs for rare species. We encourage the application of SDMs to estimate area of occupancy for rare organisms to facilitate their conservation status assessment.

     

Used‐habitat calibration plots: a new procedure for validating species distribution,resource selection,and step‐selection models

‘Species distribution modeling’ was recently ranked as one of the top five ‘research fronts’ in ecology and the environmental sciences by ISI's Essential Science Indicators, reflecting the importance of predicting how species distributions will respond to anthropogenic change. Unfortunately, species distribution models (SDMs) often perform poorly when applied to novel environments. Compounding on this problem is the shortage of methods for evaluating SDMs (hence, we may be getting our predictions wrong and not even know it). Traditional methods for validating SDMs quantify a model's ability to classify locations as used or unused. Instead, we propose to focus on how well SDMs can predict the characteristics of used locations. This subtle shift in viewpoint leads to a more natural and informative evaluation and validation of models across the entire spectrum of SDMs. Through a series of examples, we show how simple graphical methods can help with three fundamental challenges of habitat modeling: identifying missing covariates, non‐linearity, and multicollinearity. Identifying habitat characteristics that are not well‐predicted by the model can provide insights into variables affecting the distribution of species, suggest appropriate model modifications, and ultimately improve the reliability and generality of conservation and management recommendations.     

Quantifying the degree of bias from using county‐scale data in species distribution modeling: Can increasing sample size or using county‐averaged environmental data reduce distributional overprediction?

Citizen‐science databases have been used to develop species distribution models (SDMs), although many taxa may be only georeferenced to county. It is tacitly assumed that SDMs built from county‐scale data should be less precise than those built with more accurate localities, but the extent of the bias is currently unknown. Our aims in this study were to illustrate the effects of using county‐scale data on the spatial extent and accuracy of SDMs relative to true locality data and to compare potential compensatory methods (including increased sample size and using overall county environmental averages rather than point locality environmental data). To do so, we developed SDMs in maxent with PRISM‐derived BIOCLIM parameters for 283 and 230 species of odonates (dragonflies and damselflies) and butterflies, respectively, for five subsets from the OdonataCentral and Butterflies and Moths of North America citizen‐science databases: (1) a true locality dataset, (2) a corresponding sister dataset of county‐centroid coordinates, (3) a dataset where the average environmental conditions within each county were assigned to each record, (4) a 50/50% mix of true localities and county‐centroid coordinates, and (5) a 50/50% mix of true localities and records assigned the average environmental conditions within each county. These mixtures allowed us to quantify the degree of bias from county‐scale data. Models developed with county centroids overpredicted the extent of suitable habitat by 15% on average compared to true locality models, although larger sample sizes (>100 locality records) reduced this disparity. Assigning county‐averaged environmental conditions did not offer consistent improvement, however. Because county‐level data are of limited value for developing SDMs except for species that are widespread and well collected or that inhabit regions where small, climatically uniform counties predominate, three means of encouraging more accurate georeferencing in citizen‐science databases are provided.     

Genetic structure and life history are key factors in species distribution models of spiny lobsters

AimWe incorporated genetic structure and life history phase in species distribution models (SDMs) constructed for a widespread spiny lobster, to reveal local adaptations specific to individual subspecies and predict future range shifts under the RCP 8.5 climate change scenario.LocationIndo‐West Pacific.MethodsMaxEnt was used to construct present‐day SDMs for the spiny lobster Panulirus homarus and individually for the three genetically distinct subspecies of which it comprises. SDMs incorporated both sea surface and benthic (seafloor) climate layers to recreate discrete influences of these habitats during the drifting larval and benthic juvenile and adult life history phases. Principle component analysis (PCA) was used to infer environmental variables to which individual subspecies were adapted. SDM projections of present‐day habitat suitability were compared with predictions for the year 2,100, under the RCP 8.5 climate change scenario.ResultsIn the PCA, salinity best explained P. h. megasculptus habitat suitability, compared with current velocity in P. h. rubellus and sea surface temperature in P. h. homarus. Drifting and benthic life history phases were adapted to different combinations of sea surface and benthic environmental variables considered. Highly suitable habitats for benthic phases were spatially enveloped within more extensive sea surface habitats suitable for drifting larvae. SDMs predicted that present‐day highly suitable habitats for P. homarus will decrease by the year 2,100.Main conclusionsIncorporating genetic structure in SDMs showed that individual spiny lobster subspecies had unique adaptations, which could not be resolved in species‐level models. The use of sea surface and benthic climate layers revealed the relative importance of environmental variables during drifting and benthic life history phases. SDMs that included genetic structure and life history were more informative in predictive models of climate change effects.     

Equilibrium or not? Modelling potential distribution of invasive species in different stages of invasion

Aim The assumption of equilibrium between organisms and their environment is a standard working postulate in species distribution models (SDMs). However, this assumption is typically violated in models of biological invasions where range expansions are highly constrained by dispersal and colonization processes. Here, we examined how stage of invasion affects the extent to which occurrence data represent the ecological niche of organisms and, in turn, influences spatial prediction of species’ potential distributions. Location Six ecoregions in western Oregon, USA. Methods We compiled occurrence data from 697 field plots collected over a 9‐year period (2001–09) of monitoring the spread of invasive forest pathogen Phytophthora ramorum. Using these data, we applied ecological‐niche factor analysis to calibrate models of potential distribution across different years of colonization. We accounted for natural variation and uncertainties in model evaluation by further investigating three hypothetical scenarios of varying equilibrium in a simulated virtual species, for which the ‘true’ potential distribution was known. Results We confirm our hypothesis that SDMs calibrated in early stages of invasion are less accurate than models calibrated under scenarios closer to equilibrium. SDMs that are developed in early stages of invasion tend to underpredict the potential range compared to models that are built in later stages of invasion. Main conclusions A full environmental niche of invasive species cannot be effectively captured with data from a realized distribution that is restricted by processes preventing full occupancy of suitable habitats. If SDMs are to be used effectively in conservation and management, stage of invasion needs to be considered to avoid underestimation of habitats at risk of invasion.     

No species is an island: testing the effects of biotic interactions on models of avian niche occupation

Traditionally, the niche of a species is described as a hypothetical 3D space, constituted by well‐known biotic interactions (e.g. predation, competition, trophic relationships, resource–consumer interactions, etc.) and various abiotic environmental factors. Species distribution models (SDMs), also called “niche models” and often used to predict wildlife distribution at landscape scale, are typically constructed using abiotic factors with biotic interactions generally been ignored. Here, we compared the goodness of fit of SDMs for red‐backed shrike Lanius collurio in farmlands of Western Poland, using both the classical approach (modeled only on environmental variables) and the approach which included also other potentially associated bird species. The potential associations among species were derived from the relevant ecological literature and by a correlation matrix of occurrences. Our findings highlight the importance of including heterospecific interactions in improving our understanding of niche occupation for bird species. We suggest that suite of measures currently used to quantify realized species niches could be improved by also considering the occurrence of certain associated species. Then, an hypothetical “species 1” can use the occurrence of a successfully established individual of “species 2” as indicator or “trace” of the location of available suitable habitat to breed. We hypothesize this kind of biotic interaction as the “heterospecific trace effect” (HTE): an interaction based on the availability and use of “public information” provided by individuals from different species. Finally, we discuss about the incomes of biotic interactions for enhancing the predictive capacities on species distribution models.     

Transferable species distribution modelling: Comparative performance of Generalised Functional Response models

Predictive species distribution models (SDMs) are becoming increasingly important in ecology, in the light of rapid environmental change. However, the predictions of most current SDMs are specific to the habitat composition of the environments in which they were fitted. This may limit SDM predictive power because species may respond differently to a given habitat depending on the availability of all habitats in their environment, a phenomenon known as a functional response in resource selection. The Generalised Functional Response (GFR) framework captures this dependence by formulating the SDM coefficients as functions of habitat availability. The original GFR implementation used global polynomial functions of habitat availability to describe the functional responses. In this study, we develop several refinements of this approach and compare their predictive performance using two simulated and two real datasets. We first use local radial basis functions (RBF), a more flexible approach than global polynomials, to represent the habitat selection coefficients, and balance bias with precision via regularization to prevent overfitting. Second, we use the RBF-GFR and GFR models in combination with the classification and regression tree CART, which has more flexibility and better predictive powers for non-linear modelling. As further extensions, we use random forests (RFs) and extreme gradient boosting (XGBoost), ensemble approaches that consistently lead to variance reduction in generalization error. We find that the different methods are ranked consistently across the datasets for out-of-data prediction. The traditional stationary approach to SDMs and the GFR model consistently perform at the bottom of the ranking (simple SDMs underfit, and polynomial GFRs overfit the data). The best methods in our list provide non-negligible improvements in predictive performance, in some cases taking the out-of-sample R2 from 0.3 up to 0.7 across datasets. At times of rapid environmental change and spatial non-stationarity ignoring the effects of functional responses on SDMs, results in two different types of prediction bias (under-prediction or mis-positioning of distribution hotspots). However, not all functional response models perform equally well. The more volatile polynomial GFR models can generate biases through over-prediction. Our results indicate that there are consistently robust GFR approaches that achieve impressive gains in transferability across very different datasets.     

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

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

Habitat matching: Alternatives and implications to populations and communities

Summary I evaluate habitat matching rules based on ideal distribution models of density-dependent habitat use. Recent approaches and the ideal free continuous input matching rule on which they depend, are restricted to only those habitats that are jointly occupied across the full range of population sizes. These assumptions may often be inappropriate to field applications of habitat matching. I develop alternatives that can be applied to a wide array of ideal forms of habitat selection, including the ideal free, continuous input example. Input matching can be distinguished from assumptions of consumer-resource models and preemptive habitat use by regressions of density between paired habitats (isodars). Isodars for continuous input models should be linear on a logarithmic scale, while those for consumer-resource models should be linear on an arithmetic scale. Pre-emptive isodars can be distinguished from the others by dramatic non-linearities at both low and high densities. Field data on white-footed mice support the consumer-resource theory. Implications of the rules for population regulation and community organization are highlighted by new models that specify how the fitness of pre-emptive habitat selectors should decline with increasing density. Strong non-linearities produced by comparisons between variable and homogeneous habitats produce reversing source-sink population regulation and a new form of cyclical community dynamics. Variable habitats act as a source of emigrants at low density and a sink for immigrants at high density. Subordinate species may occupy only the variable habitat at both low and high density.