Performance tradeoffs in target‐group bias correction for species distribution models

Species distribution models (SDMs) are often calibrated using presence‐only datasets plagued with environmental sampling bias, which leads to a decrease of model accuracy. In order to compensate for this bias, it has been suggested that background data (or pseudoabsences) should represent the area that has been sampled. However, spatially‐explicit knowledge of sampling effort is rarely available. In multi‐species studies, sampling effort has been inferred following the target‐group (TG) approach, where aggregated occurrence of TG species informs the selection of background data. However, little is known about the species‐ specific response to this type of bias correction. The present study aims at evaluating the impacts of sampling bias and bias correction on SDM performance. To this end, we designed a realistic system of sampling bias and virtual species based on 92 terrestrial mammal species occurring in the Mediterranean basin. We manipulated presence and background data selection to calibrate four SDM types. Unbiased (unbiased presence data) and biased (biased presence data) SDMs were calibrated using randomly distributed background data. We used real and TG‐estimated sampling efforts in background selection to correct for sampling bias in presence data. Overall, environmental sampling bias had a deleterious effect on SDM performance. In addition, bias correction improved model accuracy, and especially when based on spatially‐explicit knowledge of sampling effort. However, our results highlight important species‐specific variations in susceptibility to sampling bias, which were largely explained by range size: widely‐distributed species were most vulnerable to sampling bias and bias correction was even detrimental for narrow‐ranging species. Furthermore, spatial discrepancies in SDM predictions suggest that bias correction effectively replaces an underestimation bias with an overestimation bias, particularly in areas of low sampling intensity. Thus, our results call for a better estimation of sampling effort in multispecies system, and cautions the uninformed and automatic application of TG bias correction.     

Evaluating model transferability for a threatened species to adjacent areas: Implications for rock‐wallaby conservation

When modelling the distribution of a species, it is often not possible to comprehensively sample the whole distribution of the species and managers may have habitat models based on data from one area that they want to apply in other areas. Hence, an important question is: how accurate are models of the distributions of species when applied beyond the areas where they were developed? A first step in measuring model transferability could be testing models in adjacent areas. We predicted the habitat associations of the brush‐tailed rock‐wallaby (Petrogale penicillata) across two spatial scales in two neighbouring study areas in eastern Australia, south‐east Queensland and north‐east New South Wales. We used classification trees for exploratory data analysis of habitat relationships and then applied logistic regression models to predict species occurrence. We assessed the within‐area discriminative ability of the habitat models using cross‐validation and threshold plots, and tested the predictive ability of the models for adjacent areas using the receiver operating characteristic statistic to determine the area under the curve. We found that models performed well within an area and extrapolating them to adjacent areas resulted in good predictive performance at the site scale but substantially poorer predictive performance at the landscape scale. We conclude that distribution models for wildlife species should only be extrapolated to neighbouring areas with caution when using landscape‐scale environmental variables. Alternatively, only key habitat associations predicted by the models at this scale should be transferred across adjacent areas once verified against local knowledge of the ecology of the study species.     

A dynamic multi‐scale occupancy model to estimate temporal dynamics and hierarchical habitat use for nomadic species

Distribution models are increasingly being used to understand how landscape and climatic changes are affecting the processes driving spatial and temporal distributions of plants and animals. However, many modeling efforts ignore the dynamic processes that drive distributional patterns at different scales, which may result in misleading inference about the factors influencing species distributions. Current occupancy models allow estimation of occupancy at different scales and, separately, estimation of immigration and emigration. However, joint estimation of local extinction, colonization, and occupancy within a multi‐scale model is currently unpublished. We extended multi‐scale models to account for the dynamic processes governing species distributions, while concurrently modeling local‐scale availability. We fit the model to data for lark buntings and chestnut‐collared longspurs in the Great Plains, USA, collected under the Integrated Monitoring in Bird Conservation Regions program. We investigate how the amount of grassland and shrubland and annual vegetation conditions affect bird occupancy dynamics and local vegetation structure affects fine‐scale occupancy. Buntings were prevalent and longspurs rare in our study area, but both species were locally prevalent when present. Buntings colonized sites with preferred habitat configurations, longspurs colonized a wider range of landscape conditions, and site persistence of both was higher at sites with greener vegetation. Turnover rates were high for both species, quantifying the nomadic behavior of the species. Our model allows researchers to jointly investigate temporal dynamics of species distributions and hierarchical habitat use. Our results indicate that grassland birds respond to different covariates at landscape and local scales suggesting different conservation goals at each scale. High turnover rates of these species highlight the need to account for the dynamics of nomadic species, and our model can help inform how to coordinate management efforts to provide appropriate habitat configurations at the landscape scale and provide habitat targets for local managers.     

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.     

Relationships between survival and habitat suitability of semi‐aquatic mammals

Spatial distribution and habitat selection are integral to the study of animal ecology. Habitat selection may optimize the fitness of individuals. Hutchinsonian niche theory posits the fundamental niche of species would support the persistence or growth of populations. Although niche‐based species distribution models (SDMs) and habitat suitability models (HSMs) such as maximum entropy (Maxent) have demonstrated fair to excellent predictive power, few studies have linked the prediction of HSMs to demographic rates. We aimed to test the prediction of Hutchinsonian niche theory that habitat suitability (i.e., likelihood of occurrence) would be positively related to survival of American beaver (Castor canadensis), a North American semi‐aquatic, herbivorous, habitat generalist. We also tested the prediction of ideal free distribution that animal fitness, or its surrogate, is independent of habitat suitability at the equilibrium. We estimated beaver monthly survival probability using the Barker model and radio telemetry data collected in northern Alabama, United States from January 2011 to April 2012. A habitat suitability map was generated with Maxent for the entire study site using landscape variables derived from the 2011 National Land Cover Database (30‐m resolution). We found an inverse relationship between habitat suitability index and beaver survival, contradicting the predictions of niche theory and ideal free distribution. Furthermore, four landscape variables selected by American beaver did not predict survival. The beaver population on our study site has been established for 20 or more years and, subsequently, may be approaching or have reached the carrying capacity. Maxent‐predicted increases in habitat use and subsequent intraspecific competition may have reduced beaver survival. Habitat suitability‐fitness relationships may be complex and, in part, contingent upon local animal abundance. Future studies of mechanistic SDMs incorporating local abundance and demographic rates are needed.     

Using historical woodland creation to construct a long‐term,large‐scale natural experiment: the WrEN project

Natural experiments have been proposed as a way of complementing manipulative experiments to improve ecological understanding and guide management. There is a pressing need for evidence from such studies to inform a shift to landscape‐scale conservation, including the design of ecological networks. Although this shift has been widely embraced by conservation communities worldwide, the empirical evidence is limited and equivocal, and may be limiting effective conservation. We present principles for well‐designed natural experiments to inform landscape‐scale conservation and outline how they are being applied in the WrEN project, which is studying the effects of 160 years of woodland creation on biodiversity in UK landscapes. We describe the study areas and outline the systematic process used to select suitable historical woodland creation sites based on key site‐ and landscape‐scale variables – including size, age, and proximity to other woodland. We present the results of an analysis to explore variation in these variables across sites to test their suitability as a basis for a natural experiment. Our results confirm that this landscape satisfies the principles we have identified and provides an ideal study system for a long‐term, large‐scale natural experiment to explore how woodland biodiversity is affected by different site and landscape attributes. The WrEN sites are now being surveyed for a wide selection of species that are likely to respond differently to site‐ and landscape‐scale attributes and at different spatial and temporal scales. The results from WrEN will help develop detailed recommendations to guide landscape‐scale conservation, including the design of ecological networks. We also believe that the approach presented demonstrates the wider utility of well‐designed natural experiments to improve our understanding of ecological systems and inform policy and practice.     

Scale‐dependent occupancy patterns in reptiles across topographically different landscapes

Understanding what factors influence species occupancy in human‐modified landscapes is a central theme in ecology. We examined scale‐dependent habitat relationships and site occupancy in reptiles across three topographically different study areas in south‐eastern Australia. We collected presence–absence data on reptiles from 443 sites associated with three long‐term biodiversity monitoring programs, on four to seven occasions, between 2001 and 2013. We characterised sites by the following four variable domains: 1) field design, 2) topography, 3) local‐scale vegetation attributes and 4) landscape‐scale vegetation cover. We constructed occupancy models for 14 species and used an information‐theoretic approach to compare multiple alternative hypotheses to explain occupancy within and between study areas. We modelled detection probability and used the model with the lowest AIC in subsequent analyses. We then modelled occupancy probability against all subsets of the variable groups (field design, topography, local‐ and landscape‐scale vegetation), as well as a model that held occupancy constant (null model). We found that local‐scale vegetation attributes were important for explaining site occupancy in 12/19 possible models, although, in several cases model fit was improved by the addition of topographic variables or native vegetation cover in the surrounding landscape. Occupancy models for widespread species were broadly congruent across study areas. We demonstrate that topographic variables are important for explaining reptile occupancy in hilly landscapes, and local‐ and landscape‐scale variables are important for explaining reptile occupancy in flat or gently undulating landscapes. Management actions that improve habitat complexity at a site‐level, and encompass entire topographic gradients, will have greater benefit to woodland reptiles than simply increasing vegetation cover in the surrounding landscape.     

Predicting global population connectivity and targeting conservation action for snow leopard across its range

Movements of individuals within and among populations help to maintain genetic variability and population viability. Therefore, understanding landscape connectivity is vital for effective species conservation. The snow leopard is endemic to mountainous areas of central Asia and occurs within 12 countries. We assess potential connectivity across the species’ range to highlight corridors for dispersal and genetic flow between populations, prioritizing research and conservation action for this wide‐ranging, endangered top‐predator. We used resistant kernel modeling to assess snow leopard population connectivity across its global range. We developed an expert‐based resistance surface that predicted cost of movement as functions of topographical complexity and land cover. The distribution of individuals was simulated as a uniform density of points throughout the currently accepted global range. We modeled population connectivity from these source points across the resistance surface using three different dispersal scenarios that likely bracket the lifetime movements of individual snow leopard: 100 km, 500 km and 1000 km. The resistant kernel models produced predictive surfaces of dispersal frequency across the snow leopard range for each distance scenario. We evaluated the pattern of connectivity in each of these scenarios and identified potentially important movement corridors and areas where connectivity might be impeded. The models predicted two regional populations, in the north and south of the species range respectively, and revealed a number of potentially important connecting areas. Discrepancies between model outputs and observations highlight unsurveyed areas of connected habitat that urgently require surveying to improve understanding of the global distribution and ecology of snow leopard, and target land management actions to prevent population isolation. The connectivity maps provide a strong basis for directed research and conservation action, and usefully direct the attention of policy makers.     

Sex‐dependent habitat selection in a high‐density Little Bustard Tetrax tetrax population in southern France,and the implications for conservation

Conservation measures often rely on habitat management, so knowledge about a species’ habitat use is a prerequisite for effective conservation planning. The Little Bustard Tetrax tetrax, a medium‐sized bird native to the Palaearctic steppes and today found in extensively farmed habitats, is a threatened species. Its population experienced a 94% decline in farmland habitats in France between 1982 and 1996, and populations all over Europe have suffered equally sharp declines. Due to this steep negative trend, this species has been the subject of a number of habitat selection studies in order to develop relevant conservation measures based on its habitat requirements. In this study, we investigated the habitat selection of a range of habitat types by both sexes and at two nested spatial scales: plot scale and landscape scale. In addition, we analysed intra‐specific social interactions by incorporating conspecific density in the statistical models of habitat use. The study was conducted on a very high‐density population, perhaps the highest ever recorded for this species at around 50 Bustards per 100 ha of suitable habitat. Our methodology combined two field approaches (point counts and quadrat counts). The findings showed rather limited sexual dimorphism in terms of habitat selection at a local scale, with only vegetation height differing between sexes at a micro‐habitat scale, no selection at landscape scale, and a prevailing role of social factors at both scales. The implications for future conservation strategies in relation to population density and landscape composition are discussed.     

In the absence of a “landscape of fear”: How lions,hyenas, and cheetahs coexist

Aggression by top predators can create a “landscape of fear” in which subordinate predators restrict their activity to low‐risk areas or times of day. At large spatial or temporal scales, this can result in the costly loss of access to resources. However, fine‐scale reactive avoidance may minimize the risk of aggressive encounters for subordinate predators while maintaining access to resources, thereby providing a mechanism for coexistence. We investigated fine‐scale spatiotemporal avoidance in a guild of African predators characterized by intense interference competition. Vulnerable to food stealing and direct killing, cheetahs are expected to avoid both larger predators; hyenas are expected to avoid lions. We deployed a grid of 225 camera traps across 1,125 km² in Serengeti National Park, Tanzania, to evaluate concurrent patterns of habitat use by lions, hyenas, cheetahs, and their primary prey. We used hurdle models to evaluate whether smaller species avoided areas preferred by larger species, and we used time‐to‐event models to evaluate fine‐scale temporal avoidance in the hours immediately surrounding top predator activity. We found no evidence of long‐term displacement of subordinate species, even at fine spatial scales. Instead, hyenas and cheetahs were positively associated with lions except in areas with exceptionally high lion use. Hyenas and lions appeared to actively track each, while cheetahs appear to maintain long‐term access to sites with high lion use by actively avoiding those areas just in the hours immediately following lion activity. Our results suggest that cheetahs are able to use patches of preferred habitat by avoiding lions on a moment‐to‐moment basis. Such fine‐scale temporal avoidance is likely to be less costly than long‐term avoidance of preferred areas: This may help explain why cheetahs are able to coexist with lions despite high rates of lion‐inflicted mortality, and highlights reactive avoidance as a general mechanism for predator coexistence.     

A species‐centered approach for uncovering generalities in organism responses to habitat loss and fragmentation

Theoretical models predict strong influences of habitat loss and fragmentation on species distributions and demography, but empirical studies have shown relatively inconsistent support across species and systems. We argue that species’ responses to landscape‐scale habitat loss and fragmentation are likely to appear less idiosyncratic if it is recognized that species perceive the same landscapes in different ways. We present a new quantitative approach that uses species distribution models (SDMs) to measure landscapes (e.g. patch size, isolation, matrix amount) from the perspective of individual species. First, we briefly summarize the few efforts to date demonstrating that once differences in habitat distributions are controlled, consistencies in species’ responses to landscape structure emerge. Second, we present a detailed example providing step‐by‐step methods for application of a species‐centered approach using freely available land‐cover data and recent statistical modeling approaches. Third, we discuss pitfalls in current applications of the approach and recommend avenues for future developments. We conclude that the species‐centered approach offers considerable promise as a means to test whether sensitivity to habitat loss and fragmentation is mediated by phylogenetic, ecological, and life‐history traits. Cross‐species generalities in responses to habitat loss and fragmentation will be challenging to uncover unless landscape mosaics are defined using models that reflect differing species‐specific distributions, functional connectivity, and domains of scale. The emergence of such generalities would not only enhance scientific understanding of biotic processes driving fragmentation effects, but would allow managers to estimate species sensitivities in new regions.     

Integrating anthropogenic factors into regional‐scale species distribution models—A novel application in the imperiled sagebrush biome

Species distribution models (SDMs) that rely on regional‐scale environmental variables will play a key role in forecasting species occurrence in the face of climate change. However, in the Anthropocene, a number of local‐scale anthropogenic variables, including wildfire history, land‐use change, invasive species, and ecological restoration practices can override regional‐scale variables to drive patterns of species distribution. Incorporating these human‐induced factors into SDMs remains a major research challenge, in part because spatial variability in these factors occurs at fine scales, rendering prediction over regional extents problematic. Here, we used big sagebrush (Artemisia tridentata Nutt.) as a model species to explore whether including human‐induced factors improves the fit of the SDM. We applied a Bayesian hurdle spatial approach using 21,753 data points of field‐sampled vegetation obtained from the LANDFIRE program to model sagebrush occurrence and cover by incorporating fire history metrics and restoration treatments from 1980 to 2015 throughout the Great Basin of North America. Models including fire attributes and restoration treatments performed better than those including only climate and topographic variables. Number of fires and fire occurrence had the strongest relative effects on big sagebrush occurrence and cover, respectively. The models predicted that the probability of big sagebrush occurrence decreases by 1.2% (95% CI: ?6.9%, 0.6%) when one fire occurs and cover decreases by 44.7% (95% CI: ?47.9%, ?41.3%) if at least one fire occurred over the 36 year period of record. Restoration practices increased the probability of big sagebrush occurrence but had minimal effect on cover. Our results demonstrate the potential value of including disturbance and land management along with climate in models to predict species distributions. As an increasing number of datasets representing land‐use history become available, we anticipate that our modeling framework will have broad relevance across a range of biomes and species.     

Forest cover and level of protection influence the island-wide distribution of an apex carnivore and umbrella species,the Sri Lankan leopard (<Emphasis Type="Italic">Panthera pardus kotiya</Emphasis>)

Apex predators fulfil potentially vital ecological roles. Typically wide-ranging and charismatic, they can also be useful surrogates for biodiversity preservation, making their targeted conservation imperative. The Sri Lankan leopard (Panthera pardus kotiya), an endangered, endemic sub-species, is the island’s apex predator. Of potential keystone importance, this carnivore also fulfills “umbrella” and “flagship” criterion and is of high ecological and existence value. Apex predator conservation requires identifying factors underlying distribution, so we used multi-scale maximum entropy modelling with sampling bias correction to investigate a broad suite of relevant ecological, climatic and anthropogenic factors in order to identify potentially suitable leopard habitat. Presence locations were determined from 15 years of surveys, observations and verified reports. The best bias correction procedure and scale were uncertain, so we employed a novel method of using information from all models across analyses to determine top models and identify influential variables. Leopard presence was most strongly linked to the landscape proportion encompassed by Protected Areas strictly limiting human presence, with more porous Protected Areas less influential. All three forest composition and configuration metrics investigated (area weighted mean patch size, patch density and forest connectivity) were influential, with increased patch size and higher connectivity predicting better habitat suitability for leopards. Habitat suitability was also better where cropland extent and urban patch size were small. In summary, ground-level protection and natural forest extent and connectivity are of profound importance to Sri Lankan leopard distribution and are key factors in ensuring the ecological integrity of the island’s faunal assemblages.     

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.     

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.     

Multi‐scale effects of habitat structure and landscape context on a vertebrate with limited dispersal ability (the brown‐throated sloth,Bradypus variegatus)

As human population, food consumption, and demand for forest products continue to rise over the next century, the pressures of land‐use change on biodiversity are projected to intensify. In tropical regions, countryside habitats that retain abundant tree cover and structurally complex canopies may complement protected areas by providing suitable habitats and landscape connectivity for a significant portion of the native biota. Species with low dispersal capabilities are among the most at risk of extinction as a consequence of land‐use change. We assessed how the spatial distribution of the brown‐throated sloth (Bradypus variegatus), a model species for a vertebrate with limited dispersal ability, is shaped by differences in habitat structure and landscape patterns of countryside habitats in north‐central Costa Rica using a multi‐scale framework. We quantified the influence of local habitat characteristics and landscape context on sloth occurrence using mixed‐effects logistic regression models. We recorded 27 sloths within countryside habitats and found that both local and landscape factors significantly influenced their spatial distribution. Locally, sloths favored structurally complex habitats, with greater canopy cover and variation in tree height and basal area. At the landscape scale, sloths demonstrated a preference for habitats with high proportions of forest and nearby large tracts of forest. Although mixed‐use areas and tree plantations are not substitutes for protected forests, our results suggest they provide important supplemental habitats for sloths. To promote the conservation and long‐term viability of sloth populations in the tropical countryside, we recommend that land managers retain structurally complex vegetation and large patches of native habitat.     

Influence of fine‐scale habitat requirements and riparian degradation on the distribution of the purple‐crowned fairy‐wren (Malurus coronatus coronatus) in northern Australia

Species distributions are influenced by variation in environmental conditions across many scales. Knowledge of fine‐scale habitat requirements is important for predicting species occurrence and identifying suitable habitat for target species. Here we investigate the perplexing distribution of a riparian habitat specialist, the western subspecies of the purple‐crowned fairy‐wren (Malurus coronatus coronatus), in relation to fine‐scale habitat associations and patterns of riparian degradation. Surveys of vegetation attributes, river structure and disturbance indicators that are likely to be causal determinants of the species occurrence were undertaken at 635 sites across 14 catchments. Generalized Linear Mixed Modelling demonstrated that the probability of purple‐crowned fairy‐wren occurrence increased with Pandanus aquaticus crown cover, shrub density and height of emergent trees, while riparian structure and signs of cattle were indirect predictors of occurrence. As our study area predominantly contained Pandanus type habitat, we failed to identify river grass as an important component of habitat. Predictions from a cross‐validated model of purple‐crowned fairy‐wren occurrence suggested distribution is constrained by three factors: (i) low quality of local habitat within catchments where the species occurs; (ii) broad‐scale reduction in habitat quality that has resulted in extinction of the species from parts of its range; and (iii) unmeasured variables that limit the exploitation of suitable habitat. The reliance of the species on dense shrubby understorey suggests conservation efforts should aim to maintain the complexity of understorey structure by managing fire and grazing intensity. Efforts to halt the continuing decline of riparian condition and maintain connectivity between areas of quality habitat will help to ensure persistence of riparian habitat specialists in northern Australia.     

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.     

Diurnal space use and nocturnal roost‐site selection by male Cerulean Warblers during the breeding season

Detailed information about space use during the breeding season is limited for most Nearctic‐Neotropical migratory species of songbirds because of their small size and often cryptic behaviors. We monitored male Cerulean Warblers (Setophaga cerulea), a species of conservation concern, using radio‐telemetry during the 2006–2008 breeding seasons in northern Alabama to better understand their space use and habitat selection. We estimated diurnal home range and core areas using information theoretic criteria, located nocturnal roost sites, and related day and evening locations to surrounding landscape habitat, including features representative of canopy disturbances. Mean home range size was 6.7 ha (N = 10), and home ranges included an average of at least 2 core areas encompassing 0.7 ha. We located 53 nocturnal roost sites that were an average 159.0 m from the center of the nearest core area. More than one‐third (36.6%) of roost sites were located outside the diurnal home ranges of male Cerulean Warblers; only 13.6% were located in core areas. Males in our study moved much farther than reported in previous studies, with some singing in areas > 300 m from previously used song perches, a behavior suggesting pursuit of extra‐pair copulations. Cerulean Warblers in our study preferentially selected a heavily forested landscape composed of mesic, floodplain bottomlands with little man‐made disturbance. Within their home ranges, diurnal locations of males in core areas were located significantly closer to a creek than locations outside of core areas. Our results suggest that male Cerulean Warblers require much larger areas than previously reported and underscore the importance of a predominately forested landscape in their habitat selection process. Although edge habitats appeared to influence space use by male Cerulean Warblers in our study, the extent to which this is an essential requirement is unclear. Our results and those of previous studies suggest that specific habitat requirements of this species can vary at the local scale throughout its breeding range.     

Wrong,but useful: regional species distribution models may not be improved by range‐wide data under biased sampling

Species distribution modeling (SDM) is an essential method in ecology and conservation. SDMs are often calibrated within one country's borders, typically along a limited environmental gradient with biased and incomplete data, making the quality of these models questionable. In this study, we evaluated how adequate are national presence‐only data for calibrating regional SDMs. We trained SDMs for Egyptian bat species at two different scales: only within Egypt and at a species‐specific global extent. We used two modeling algorithms: Maxent and elastic net, both under the point‐process modeling framework. For each modeling algorithm, we measured the congruence of the predictions of global and regional models for Egypt, assuming that the lower the congruence, the lower the appropriateness of the Egyptian dataset to describe the species' niche. We inspected the effect of incorporating predictions from global models as additional predictor (“prior”) to regional models, and quantified the improvement in terms of AUC and the congruence between regional models run with and without priors. Moreover, we analyzed predictive performance improvements after correction for sampling bias at both scales. On average, predictions from global and regional models in Egypt only weakly concur. Collectively, the use of priors did not lead to much improvement: similar AUC and high congruence between regional models calibrated with and without priors. Correction for sampling bias led to higher model performance, whatever prior used, making the use of priors less pronounced. Under biased and incomplete sampling, the use of global bats data did not improve regional model performance. Without enough bias‐free regional data, we cannot objectively identify the actual improvement of regional models after incorporating information from the global niche. However, we still believe in great potential for global model predictions to guide future surveys and improve regional sampling in data‐poor regions.