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Species distribution models are widely used to identify potential and high‐quality habitat of endangered species to inform conservation decisions. However, their usefulness is constrained by the amount and quality of biodiversity data and the approaches for dealing with data deficiencies. Presence‐only data, used in presence/background modelling methods, are widely available but are often affected by sampling bias. Presence/absence modelling methods are less affected by biases, but data are less common. We modelled the distribution of a widely distributed, endangered species from Australia – the greater glider – and tested how predictions were influenced by data treatment and modelling framework. We collated available species data and fitted generalized linear models and boosted regression trees using presence/absence data, as well as using an augmented dataset that included additional presences alongside absences inferred from survey data. We also fitted presence/background models, adopting three common strategies for bias correction. We compared model performance quantitatively through evaluation metrics calculated internally and on held out data, and qualitatively by identifying areas of agreement of spatial predictions. We found that presence/background models with bias correction performed better than not corrected, though evaluation metrics did not favour a single strategy. Presence/absence models outperformed presence/background models in comparable metrics and delivered different spatial predictions. Importantly, differences in spatial predictions between models had the potential to substantially alter decisions about where to protect high‐quality habitat. The approach to inferring absences proved useful, as models fitted with these outperformed all other models. Dealing with sampling bias requires additional time and data management strategies, but we found that the time invested allowed improvement of models and more reliable predictions. Our resu

A core challenge in global change biology is to predict how species will respond to future environmental change and to manage these responses. To make such predictions and management actions robust to novel futures, we need to accurately characterize how organisms experience their environments and the biological mechanisms by which they respond. All organisms are thermodynamically connected to their environments through the exchange of heat and water at fine spatial and temporal scales and this exchange can be captured with biophysical models. Although mechanistic models based on biophysical ecology have a long history of development and application, their use in global change biology remains limited despite their enormous promise and increasingly accessible software. We contend that greater understanding and training in the theory and methods of biophysical ecology is vital to expand their application. Our review shows how biophysical models can be implemented to understand and predict climate change impacts on species' behavior, phenology, survival, distribution, and abundance. It also illustrates the types of outputs that can be generated, and the data inputs required for different implementations. Examples range from simple calculations of body temperature at a particular site and time, to more complex analyses of species' distribution limits based on projected energy and water balances, accounting for behavior and phenology. We outline challenges that currently limit the widespread application of biophysical models relating to data availability, training, and the lack of common software ecosystems. We also discuss progress and future developments that could allow these models to be applied to many species across large spatial extents and timeframes. Finally, we highlight how biophysical models are uniquely suited to solve global change biology problems that involve predicting and interpreting responses to environmental variability and extremes, multiple or shif

Aim Occurrence records for cryptic species are typically limited or highly uncertain, leaving their distributions poorly resolved and hampering conservation. This can apply to well‐studied species, and increased survey effort and/or novel methods are required to improve distribution data. Here, we paired species distribution modelling (SDM) with decision tools to direct surveys for the critically endangered Leadbeater's possum (Gymnobelideus leadbeateri) outside its current restricted range. We also assessed survey areas for their suitability to host translocations. Location Victoria, Australia. Method We used both recent and historic records (now out of range and spatially uncertain) of Leadbeater's possum to build SDMs using MaxEnt. The SDMs informed an initial multi‐criteria decision analysis (MCDA) that enabled prioritization of 80 survey sites across seven forest patches (13–145 km outside the known range), which we surveyed using camera traps. Site and vegetation data were used in a post‐survey MCDA to rank their potential translocation suitability. Results The SDM predictions were consistent with the species’ ecology, identifying cold areas with high rainfall that had not recently burnt as suitable. The spatial uncertainty of records did not exert a strong influence on either model predictions or the ranking of patches for surveys. Camera trap surveys yielded records of 19 native species, with Leadbeater's possum detected in only one survey patch, 13 km outside of its previously known range. The post‐survey MCDA identified three forest patches as potentially suitable for conservation translocations, and these priorities were not sensitive to the decision criteria used. Main conclusions The approach outlined here prioritized survey effort over a large area, resulting in detection of Leadbeater's possum in one new patch. The potential translocation sites identified could present an important risk‐spreading measure for the species given the threat posed

Artificial hollows are commonly used to mitigate the scarcity of den and nest sites that threatens fauna species globally, but there is a risk that these do not provide suitable microclimates for the populations they are intended to support. We monitored temperature, humidity and occupancy in artificial dens (nest boxes and chainsaw‐carved cavities) provided for the critically endangered Leadbeater’s possum (Gymnobelideus leadbeateri) in south‐eastern Australian forests. Monitoring occurred over both winter and summer in five habitat types spanning a broad elevational gradient (110–1580 m asl). We then used a biophysical model to explore the physiological consequences of the den temperatures recorded, and the potential effectiveness of behavioural strategies in reducing thermoregulatory costs. Monitoring revealed that nest box temperatures tracked ambient over both seasons, ranging from −5.5°C to 48.5°C, while chainsaw hollows provided more stable internal conditions. However, we found that inferences regarding the thermal suitability of artificial dens were site‐specific, and dependent upon habitat type, season and assumptions around behavioural strategies. During winter, behaviours such as the construction of large, insulative nests and huddling are likely critical in overcoming high energy costs associated with poorly insulated artificial dens. In contrast, during summer high thermoregulatory costs were predicted for the warmest (lowland) site and coincided with low nest box occupancy. Our study reinforces the importance of factoring in local climates, and behavioural and physiological strategies employed by target taxa, when implementing artificial den programs to support fauna populations.

Many threatened species depend on climatic microrefugia, but places with harsh climates for predators may also play a refugial role. Feral cats threaten many native species in arid Australia. Although cats can persist in regions with no free water, their abundance should depend on the availability of microclimates that protect them from harsh environmental conditions. We developed a biophysical model of feral cat heat stress and used it to explore how behavior and microhabitat features influence water requirements and activity. Tests of model predictions against fine‐scale GPS and microclimate data highlight the importance of refuges, particularly rabbit burrows. Continent‐wide simulations show large but temporally varying areas of the arid zone that would be lethal for cats without access to deep or shaded burrows. Our approach can identify locations that may act as natural refuges for native prey, and where habitat management strategies may be effective in controlling cat abundance.

Accurately predicting species ranges is a primary goal of ecology. Demographic distribution models (DDMs), which correlate underlying vital rates (e.g. survival and reproduction) with environmental conditions, can potentially predict species ranges through time and space. However, tests of DDM accuracy across wide ranges of species' life histories are surprisingly lacking. Using simulations of 1.5 million hypothetical species' range dynamics, we evaluated when DDMs accurately predicted future ranges, to provide clear guidelines for the use of this emerging approach. We limited our study to deterministic demographic models ignoring density dependence, since these models are the most commonly used in the literature. We found that density‐independent DDMs overpredicted extinction if populations were near carrying capacity in the locations where demographic data were available. However, DDMs accurately predicted species ranges if demographic data were limited to sites with mean initial abundance less than one half of carrying capacity. Additionally, the DDMs required demographic data from at least 25 sites, over a short time‐interval (< 10 time‐steps), as populations initially below carrying capacity can saturate in long‐term studies. For species with demographic data from many low density sites, DDMs predicted occurrence more accurately than correlative species distribution models (SDMs) in locations where the species eventually persisted, but not where the species went extinct. These results were insensitive to differences in simulated dispersal, levels of environmental stochasticity, the effects of the environmental variables and the functional forms of density dependence. Our findings suggest that deterministic, density‐independent DDMs are appropriate for applications where locating all possible sites the species might occur in is prioritized over reducing false presence predictions in absent sites. This makes DDMs a promising tool for mapping invasion risk. Howeve

Endothermic organisms typically maintain high and relatively constant body temperatures in the face of environmental variation by regulating their metabolic and water loss rates in conjunction with behavioural and postural adjustments. A mechanistic understanding of these processes provides a perspective of the fundamental niches of endotherms by quantifying their energy and water requirements, restrictions on habitat use and survival limits. Here we introduce and document the endotherm model of NicheMapR, an R package that includes a suite of programs for the mechanistic modelling of energy and mass exchange between organisms and their environments. The NicheMapR endotherm model is based on a Fortran program that simultaneously solves for the metabolic rate, water loss rate, fur/feather temperature and skin temperature required to balance the heat budget in a given microclimate. These calculations are a function of traits relating to insulation, shape, physiological and behavioural responses. The program contains algorithms for computing heat transfer through a porous insulative layer (fur/feathers), including solar and infrared radiation fluxes as well as convective and conductive exchange, and can handle differing dorsal and ventral traits and environments. Here we configure the program to be called from R as part of the NicheMapR package and describe the model in detail. We break the program into a set of modules that can be run in isolation, or jointly, and combined in different ways to capture species‐specific thermoregulatory responses. We explain the theory and underlying subroutines, and the default thermoregulatory settings. We discuss how the model can be adapted to incorporate species‐specific behaviours and extended to incorporate multiple body parts. We also compare the endotherm modelling setup with the ectotherm model of the NicheMapR package and discuss how the different modelling strategies they require reflect fundamental biological issues.

Predictions of species' current and future ranges are needed to effectively manage species under environmental change. Species ranges are typically estimated using correlative species distribution models (SDMs), which have been criticized for their static nature. In contrast, dynamic occupancy models (DOMs) explicitily describe temporal changes in species' occupancy via colonization and local extinction probabilities, estimated from time series of occurrence data. Yet, tests of whether these models improve predictive accuracy under current or future conditions are rare. Using a long-term data set on 69 Swiss birds, we tested whether DOMs improve the predictions of distribution changes over time compared to SDMs. We evaluated the accuracy of spatial predictions and their ability to detect population trends. We also explored how predictions differed when we accounted for imperfect detection and parameterized models using calibration data sets of different time series lengths. All model types had high spatial predictive performance when assessed across all sites (mean AUC > 0.8), with flexible machine learning SDM algorithms outperforming parametric static and DOMs. However, none of the models performed well at identifying sites where range changes are likely to occur. In terms of estimating population trends, DOMs performed best, particularly for species with strong population changes and when fit with sufficient data, while static SDMs performed very poorly. Overall, our study highlights the importance of considering what aspects of performance matter most when selecting a modelling method for a particular application and the need for further research to improve model utility. While DOMs show promise for capturing range dynamics and inferring population trends when fitted with sufficient data, computational constraints on variable selection and model fitting can lead to reduced spatial accuracy of predictions, an area warranting more attention.

Species population dynamics are driven by spatial and temporal changes in the environment, anthropogenic activities and conservation management actions. Understanding how populations will change in response to these drivers is fundamental to a wide range of ecological applications, but there are few open‐source software options accessible to researchers and managers that allow them to predict these changes in a flexible and transparent way. We introduce an open‐source, multi‐platformrpackage,steps, that models spatial changes in species populations as a function of drivers of distribution and abundance, such as climate, disturbance, landscape dynamics and species ecological and physiological requirements. To illustrate the functionality ofsteps, we model the population dynamics of the greater gliderPetauroides volans, an arboreal Australian mammal. We demonstrate howstepscan be used to simulate population responses of the glider to forest dynamics and management with the types of data commonly used in ecological analyses. stepsexpands on the features found in existing software packages, can easily incorporate a range of spatial layers (e.g. habitat suitability, vegetation dynamics and disturbances), facilitates integrated and transparent analyses within a single platform and produces interpretable outputs of changes in species' populations through space and time. Further,stepsoffers both ready‐to‐use, built‐in functionality, as well as the ability for advanced users to define their own modules for custom analyses. Thus, we anticipate thatstepswill be of significant value to environment and wildlife managers and researchers from a broad range of disciplines.

Knowing where species occur is fundamental to many ecological and environmental applications. Species distribution models (SDMs) are typically based on correlations between species occurrence data and environmental predictors, with ecological processes captured only implicitly. However, there is a growing interest in approaches that explicitly model processes such as physiology, dispersal, demography and biotic interactions. These models are believed to offer more robust predictions, particularly when extrapolating to novel conditions. Many process-explicit approaches are now available, but it is not clear how we can best draw on this expanded modelling toolbox to address ecological problems and inform management decisions. Here, we review a range of process-explicit models to determine their strengths and limitations, as well as their current use. Focusing on four common applications of SDMs - regulatory planning, extinction risk, climate refugia and invasive species - we then explore which models best meet management needs. We identify barriers to more widespread and effective use of process-explicit models and outline how these might be overcome. As well as technical and data challenges, there is a pressing need for more thorough evaluation of model predictions to guide investment in method development and ensure the promise of these new approaches is fully realised.

Conservation of species under climate change relies on accurate predictions of species ranges under current and future climate conditions. To date, modelling studies have focused primarily on how changes in long‐term averaged climate conditions are likely to influence species distributions with much less attention paid to the potential effect of extreme events such as droughts and heatwaves which are expected to increase in frequency over coming decades. In this study we explore the benefits of tailoring predictor variables to the specific physiological constraints of species, or groups of species. We show how utilizing spatial predictors of extreme temperature and water availability (heat‐waves and droughts), derived from high‐temporal resolution, long‐term weather records, provides categorically different predictions about the future (2070) distribution of suitable environments for 188 mammal species across different biomes (from arid zones to tropical environments) covering the whole of continental Australia. Models based on long‐term averages‐only and extreme conditions‐only showed similarly high predictive performance tested by hold‐out cross‐validation on current data, and yet some predicted dramatically different future geographic ranges for the same species under 2070 climate scenarios. Our results highlight the importance of accounting for extreme conditions/events by identifying areas in the landscape where species may cope with average conditions, but cannot persist under extreme conditions known or predicted to occur there. Our approach provides an important step toward identifying the location of climate change refuges and danger zones that goes beyond the current standard of extrapolating long‐term climate averages.

How climate constrains species' distributions through time and space is an important question in the context of conservation planning for climate change. Despite increasing awareness of the need to incorporate mechanism into species distribution models (SDMs), mechanistic modeling of endotherm distributions remains limited in this literature. Using the American pika (Ochotona princeps) as an example, we present a framework whereby mechanism can be incorporated into endotherm SDMs. Pika distribution has repeatedly been found to be constrained by warm temperatures, so we used Niche Mapper, a mechanistic heat-balance model, to convert macroclimate data to pika-specific surface activity time in summer across the western United States. We then explored the difference between using a macroclimate predictor (summer temperature) and using a mechanistic predictor (predicted surface activity time) in SDMs. Both approaches accurately predicted pika presences in current and past climate regimes. However, the activity models predicted 8-19% less habitat loss in response to annual temperature increases of ~3-5 °C predicted in the region by 2070, suggesting that pikas may be able to buffer some climate change effects through behavioral thermoregulation that can be captured by mechanistic modeling. Incorporating mechanism added value to the modeling by providing increased confidence in areas where different modeling approaches agreed and providing a range of outcomes in areas of disagreement. It also provided a more proximate variable relating animal distribution to climate, allowing investigations into how unique habitat characteristics and intraspecific phenotypic variation may allow pikas to exist in areas outside those predicted by generic SDMs. Only a small number of easily obtainable data are required to parameterize this mechanistic model for any endotherm, and its use can improve SDM predictions by explicitly modeling a widely applicable direct physiological effect: climate

Thermal properties of tree hollows play a major role in survival and reproduction of hollow-dependent fauna. Artificial hollows (nest boxes) are increasingly being used to supplement the loss of natural hollows; however, the factors that drive nest box thermal profiles have received surprisingly little attention. We investigated how differences in surface reflectance influenced temperature profiles of nest boxes painted three different colors (dark-green, light-green, and white: total solar reflectance 5.9%, 64.4%, and 90.3% respectively) using boxes designed for three groups of mammals: insectivorous bats, marsupial gliders and brushtail possums. Across the three different box designs, dark-green (low reflectance) boxes experienced the highest average and maximum daytime temperatures, had the greatest magnitude of variation in daytime temperatures within the box, and were consistently substantially warmer than light-green boxes (medium reflectance), white boxes (high reflectance), and ambient air temperatures. Results from biophysical model simulations demonstrated that variation in diurnal temperature profiles generated by painting boxes either high or low reflectance colors could have significant ecophysiological consequences for animals occupying boxes, with animals in dark-green boxes at high risk of acute heat-stress and dehydration during extreme heat events. Conversely in cold weather, our modelling indicated that there are higher cumulative energy costs for mammals, particularly smaller animals, occupying light-green boxes. Given their widespread use as a conservation tool, we suggest that before boxes are installed, consideration should be given to the effect of color on nest box temperature profiles, and the resultant thermal suitability of boxes for wildlife, particularly during extremes in weather. Managers of nest box programs should consider using several different colors and installing boxes across a range of both orientations and shade profiles (i.e

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

Animals can exploit spatial and temporal variation in microclimates to avoid stressful conditions, behavior that is likely to become increasingly important in a warming world. Recent research shows that during hot weather cool tree trunk surfaces can provide an important heat-loss avenue for arboreal mammals and other tree-dwelling animals.