A machine‐learning approach for extending classical wildlife resource selection analyses

Resource selection functions (RSFs) are tremendously valuable for ecologists and resource managers because they quantify spatial patterns in resource utilization by wildlife, thereby facilitating identification of critical habitat areas and characterizing specific habitat features that are selected or avoided. RSFs discriminate between known‐use resource units (e.g., telemetry locations) and available (or randomly selected) resource units based on an array of environmental features, and in their standard form are performed using logistic regression. As generalized linear models, standard RSFs have some notable limitations, such as difficulties in accommodating nonlinear (e.g., humped or threshold) relationships and complex interactions. Increasingly, ecologists are using flexible machine‐learning methods (e.g., random forests, neural networks) to overcome these limitations. Herein, we investigate the seasonal resource selection patterns of mule deer (Odocoileus hemionus) by comparing a logistic regression framework with random forest (RF), a popular machine‐learning algorithm. Random forest (RF) models detected nonlinear relationships (e.g., optimal ranges for slope and elevation) and complex interactions which would have been very challenging to discover and characterize using standard model‐based approaches. Compared with standard RSF models, RF models exhibited improved predictive skill, provided novel insights about resource selection patterns of mule deer, and, when projected across a relevant geographic space, manifested notable differences in predicted habitat suitability. We recommend that wildlife researchers harness the strengths of machine‐learning tools like RF in addition to “classical” tools (e.g., mixed‐effects logistic regression) for evaluating resource selection, especially in cases where extensive telemetry data sets are available.     

Modelling the distribution of badgers Meles meles: comparing predictions from field-based and remotely derived habitat data

• 1 Environmental heterogeneity is important in determining the distribution and abundance of organisms at various spatial scales. The ability to understand and predict distribution patterns is important for solving many management problems in conservation biology and wildlife epidemiology.
• 2 The badger Meles meles is a highly adaptable, medium‐sized carnivore, distributed throughout temperate Eurasia, which shows a wide diversity of social and spatial organization. Within Britain, badgers are not only legally protected, but they also serve as a wildlife host for bovine tuberculosis Mycobacterium bovis. An evaluation of the role of badgers in the dynamics of this infection depends on understanding the responses of badgers to the environment at different spatial scales.
• 3 The use of digital data to provide information on habitats for distribution models is becoming common. Digital data are increasingly accessible and are generally cheaper than field surveys. There has been little research, however, to compare the accuracy of models based on field‐derived and remotely derived data.
• 4 In this paper, we make quantified comparisons between large‐scale presence/absence models for badgers in Britain, based on field‐surveyed habitat data and remotely derived digital data, comprising elevation, geology and soil.
• 5 We developed four models: 1980s badger survey data using field‐based and digital data, and 1990s badger survey data using field‐based and digital data. We divided each of the four datasets into two subsets and used one subset for training (developing) the model and the other for testing it.
• 6 All four training models had classification accuracies in excess of 69%. The models generated from digital data were slightly more accurate than those generated from field‐derived habitat data.
• 7 The high classificatory ability of the digital‐based models suggests that the use of digital data may overcome many of the problems associated with field data in wildlife‐habitat modelling, such as cost and restricted geographical coverage, without any significant impact on model performance for some species. The more widespread use of digital data in wildlife‐habitat models should enhance their accuracy, repeatability and applicability and make them better‐suited as tools to aid policy‐ and decision‐making processes.

     

Can We Accurately Characterize Wildlife Resource Use When Telemetry Data Are Imprecise?

ABSTRACT Telemetry data have been widely used to quantify wildlife habitat relationships despite the fact that these data are inherently imprecise. All telemetry data have positional error, and failure to account for that error can lead to incorrect predictions of wildlife resource use. Several techniques have been used to account for positional error in wildlife studies. These techniques have been described in the literature, but their ability to accurately characterize wildlife resource use has never been tested. We evaluated the performance of techniques commonly used for incorporating telemetry error into studies of wildlife resource use. Our evaluation was based on imprecise telemetry data (mean telemetry error = 174 m, SD = 130 m) typical of field-based studies. We tested 5 techniques in 10 virtual environments and in one real-world environment for categorical (i.e., habitat types) and continuous (i.e., distances or elevations) rasters. Technique accuracy varied by patch size for the categorical rasters, with higher accuracy as patch size increased. At the smallest patch size (1 ha), the technique that ignores error performed best on categorical data (0.31 and 0.30 accuracy for virtual and real data, respectively); however, as patch size increased the bivariate-weighted technique performed better (0.56 accuracy at patch sizes >31 ha) and achieved complete accuracy (i.e., 1.00 accuracy) at smaller patch sizes (472 ha and 1,522 ha for virtual and real data, respectively) than any other technique. We quantified the accuracy of the continuous covariates using the mean absolute difference (MAD) in covariate value between true and estimated locations. We found that average MAD varied between 104 m (ignore telemetry error) and 140 m (rescale the covariate data) for our continuous covariate surfaces across virtual and real data sets. Techniques that rescale continuous covariate data or use a zonal mean on values within a telemetry error polygon were significantly less accurate than other techniques. Although the technique that ignored telemetry error performed best on categorical rasters with smaller average patch sizes (i.e., ≤31 ha) and on continuous rasters in our study, accuracy was so low that the utility of using point-based approaches for quantifying resource use is questionable when telemetry data are imprecise, particularly for small-patch habitat relationships.     

A temporally explicit species distribution model for a long distance avian migrant,the common cuckoo

Modelling the distribution of migratory species has rarely been extended beyond breeding and wintering ranges despite many species showing much more complex movement patterns with multiple stopovers. We aimed to create a temporally explicit species distribution model describing the full annual distribution cycle, and use it to model the complex seasonal shifts in distribution of the common cuckoo Cuculus canorus, a declining long‐distance migrant. To do this we used full‐year satellite telemetry occurrence data, with their associated temporal information, to inform a temporally explicit species distribution model using MaxEnt. The resulting full‐year distribution model was highly predictive (AUC = 0.894) and appeared to have generality at the species‐level despite being informed by data from a single breeding population. Comparison of our methodology with seasonal distribution models describing the breeding, winter and migration ranges separately showed that our full‐year method provided more general and extensive predictions and performed better when tested with an independent dataset. When species distribution models based on a single season exclude environmental conditions experienced by birds in other parts of the annual cycle they risk underestimating niche breadth and neglecting the importance of stopover habitat. Conversely, models which simply average conditions across a season may miss the significance of finer scale within‐season movements and overestimate niche breadth. In contrast, our framework for a full‐year migrant distribution model successfully captures the finer‐scale changes expected in seasonal environments and can be used to inform conservation management at every stage of migration. The full‐year model framework appears to produce temporal distribution models generalised to the species‐level from occurrence data limited to few individuals of a single population and may have particular utility when aiming to describe the distribution of species with complex migration patterns from telemetry data.     

Postfledging survival of eastern bluebirds in an urbanized landscape

Golf courses ostensibly offer green space in urbanized areas, but it is unclear how suitable these human-modified habitats are for wildlife populations. Golf courses are home to a variety of wildlife, but in particular they have been the focus of research on avian responses to urbanization. Although numerous reproductive and diversity studies have been conducted on birds of golf courses, no research exists on postfledging survival in this created landscape. In 2008 and 2009, we estimated survival of eastern bluebird (Sialia sialis) fledglings using radio telemetry on golf course and other developed sites in Williamsburg, Virginia. We used nest survival models in Program MARK set in an information theoretic framework to assess whether the golf course habitat predicted mortality along with other previously studied variables, such as fledgling age, year, site, body condition, fledging date, and transmitter weight. We found no evidence that inhabiting a golf course increased mortality during the fledgling period, but we did find support for both fledgling age and fledging date as predictors of survival. Mortality decreased for older fledglings and those that fledged later in the season. Cause-specific postfledging survival rates did not differ among sites. Fledgling bluebirds did, however, move into habitat that was significantly more forested and less grassy than their natal habitat. For managers of wildlife on golf courses and other urbanized sites, our study is the first to show that placing nest boxes in manicured habitat may attract birds to areas without suitable habitat for fledglings. © 2011 The Wildlife Society.     

Sea ice resource selection models for polar bears in the Barents Sea subpopulation

The extent, thickness and age of Arctic sea ice has dramatically declined since the late 1990s, and these trends are predicted to continue. Exploring the habitat use of sea‐ice‐dependent species can help us understand which resources they use and how their distribution responds to a changing environment. The goal of this study was to develop predictive models of the habitat use of an Arctic apex predator. Polar bear Ursus maritimus habitat use in the Barents Sea subpopulation was modelled with seasonal resource selection functions (RSFs) using satellite‐linked telemetry data from 294 collars deployed on female polar bears between 1991 and 2015. Polar bears selected habitat in the Marginal Ice Zone, with a preference for intermediate sea ice concentrations (40–80%). They spent most time in areas with relatively short travel distances to 15 or 75% ice concentration, and during spring and autumn they exhibited a preference for sea ice areas over the continental shelf or over shallower bathymetry). Predictions of the distribution of polar bears in the Barents Sea area can be made for specific sea ice scenarios using these models. Two such predictive distribution maps based on the autumn seasonal model were made and validated against two independent polar bear survey datasets collected in August 2004 and August 2015. The distribution of optimal polar bear habitat has shifted strongly northwards in all seasons of the year during the 25 yr study period.     

Living on the edge: Multiscale habitat selection by cheetahs in a human‐wildlife landscape

Animals select habitats that will ultimately optimize their fitness through access to favorable resources, such as food, mates, and breeding sites. However, access to these resources may be limited by bottom‐up effects, such as availability, and top‐down effects, such as risk avoidance and competition, including that with humans. Competition between wildlife and people over resources, specifically over space, has played a significant role in the worldwide decrease in large carnivores. The goal of this study was to determine the habitat selection of cheetahs (Acinonyx jubatus) in a human‐wildlife landscape at multiple spatial scales. Cheetahs are a wide‐ranging, large carnivore, whose significant decline is largely attributed to habitat loss and fragmentation. It is believed that 77% of the global cheetah population ranges outside protected areas, yet little is known about cheetahs’ resource use in areas where they co‐occur with people. The selection, or avoidance, of three anthropogenic variables (human footprint density, distance to main roads and wildlife areas) and five environmental variables (open habitat, semiclosed habitat, edge density, patch density and slope), at multiple spatial scales, was determined by analyzing collar data from six cheetahs. Cheetahs selected variables at different scales; anthropogenic variables were selected at broader scales (720–1440 m) than environmental variables (90–180 m), suggesting that anthropogenic pressures affect habitat selection at a home‐range level, whilst environmental variables influence site‐level habitat selection. Cheetah presence was best explained by human presence, wildlife areas, semiclosed habitat, edge density and slope. Cheetahs showed avoidance for humans and steep slopes and selected for wildlife areas and areas with high proportions of semiclosed habitat and edge density. Understanding a species’ resource requirements, and how these might be affected by humans, is crucial for conservation. Using a multiscale approach, we provide new insights into the habitat selection of a large carnivore living in a human‐wildlife landscape.     

Implications of ignoring telemetry error on inference in wildlife resource use models

Global Positioning System (GPS) and very high frequency (VHF) telemetry data redefined the examination of wildlife resource use. Researchers collar animals, relocate those animals over time, and utilize the estimated locations to infer resource use and build predictive models. Precision of these estimated wildlife locations, however, influences the reliability of point-based models with accuracy depending on the interaction between mean telemetry error and how habitat characteristics are mapped (categorical raster resolution and patch size). Telemetry data often foster the assumption that locational error can be ignored without biasing study results. We evaluated the effects of mean telemetry error and categorical raster resolution on the correct characterization of patch use when locational error is ignored. We found that our ability to accurately attribute patch type to an estimated telemetry location improved nonlinearly as patch size increased and mean telemetry error decreased. Furthermore, the exact shape of these relationships was directly influenced by categorical raster resolution. Accuracy ranged from 100% (200-ha patch size, 1- to 5-m telemetry error) to 46% (0.5-ha patch size, 56- to 60-m telemetry error) for 10 m resolution rasters. Accuracy ranged from 99% (200-ha patch size, 1- to 5-m telemetry error) to 57% (0.5-ha patch size, 56- to 60-m telemetry error) for 30-m resolution rasters. When covariate rasters were less resolute (30 m vs. 10 m) estimates for the ignore technique were more accurate at smaller patch sizes. Hence, both fine resolution (10 m) covariate rasters and small patch sizes increased probability of patch misidentification. Our results help frame the scope of ecological inference made from point-based wildlife resource use models. For instance, to make ecological inferences with 90% accuracy at small patch sizes (≤5 ha) mean telemetry error ≤5 m is required for 10-m resolution categorical rasters. To achieve the same inference on 30-m resolution categorical rasters, mean telemetry error ≤10 m is required. We encourage wildlife professionals creating point-based models to assess whether reasonable estimates of resource use can be expected given their telemetry error, covariate raster resolution, and range of patch sizes. © 2011 The Wildlife Society.     

Functional responses in the habitat selection of a generalist mega‐herbivore,the African savannah elephant

Resource selection function (RSF) models are commonly used to quantify species/habitat associations and predict species occurrence on the landscape. However, these models are sensitive to changes in resource availability and can result in a functional response to resource abundance, where preferences change as a function of availability. For generalist species, which utilize a wide range of habitats and resources, quantifying habitat selection is particularly challenging. Spatial and temporal changes in resource abundance can result in changes in selection preference affecting the robustness of habitat selection models. We examined selection preference across a wide range of ecological conditions for a generalist mega‐herbivore, the African savanna elephant Loxodonta africana, to quantify general patterns in selection and to illustrate the importance of functional responses in elephant habitat selection. We found a functional response in habitat selection across both space and time for tree cover, with tree cover being unimportant to habitat selection in the mesic, eastern populations during the wet season. A temporal functional response for water was also evident, with greater variability in selection during the wet season. Selection for low slopes, high tree cover, and far distance from people was consistent across populations; however, variability in selection coefficients changed as a function of the abundance of a given resource within the home range. This variability of selection coefficients could be used to improve confidence estimations for inferences drawn from habitat selection models. Quantifying functional responses in habitat selection is one way to better predict how wildlife will respond to an ever‐changing environment, and they provide promising insights into the habitat selection of generalist species.     

Improving population estimates by quantifying diving and surfacing patterns: A dugong example

Diving animals are available for detection from above the water when environmental conditions are favorable and the animals are near the surface. The number of animals that are unavailable for detection needs to be estimated to obtain unbiased population estimates. The current availability correction factors used in aerial surveys for the dugong (Dugong dugon) allow for variation in environmental conditions but use the average time dugongs spend near the surface (i.e., constant availability corrections). To improve availability estimates, we examined location and dive data from nine dugongs fitted with satellite telemetry units and time‐depth recorders (TDRs) in eastern Australia. The effects of water depth, tidal conditions, and habitat types on dugong surfacing time were examined using generalized linear mixed models (GLMMs). We found that availability for detection differed with water depth, and depth‐specific availability estimates were often lower than the constant estimates. The habitat effect was less influential, and there was no tidal effect. The number of dugongs estimated using depth‐specific availabilities were higher than those obtained using constant availabilities across water depth. Hence, information on water depth can refine availability estimates and subsequent abundance estimates from dugong aerial surveys. The methodology may be applicable to other aquatic wildlife.     

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.     

Fix Success and Accuracy of Global Positioning System Collars in Old-Growth Temperate Coniferous Forests

Abstract: Global Positioning System (GPS) telemetry is used extensively to study animal distribution and resource selection patterns but is susceptible to biases resulting from data omission and spatial inaccuracies. These data errors may cause misinterpretation of wildlife habitat selection or spatial use patterns. We used both stationary test collars and collared free-ranging American black bears (Ursus americanus) to quantify systemic data loss and location error of GPS telemetry in mountainous, old-growth temperate forests of Olympic National Park, Washington, USA. We developed predictive models of environmental factors that influence the probability of obtaining GPS locations and evaluated the ability of weighting factors derived from these models to mitigate data omission biases from collared bears. We also examined the effects of microhabitat on collar fix success rate and examined collar accuracy as related to elevation changes between successive fixes. The probability of collars successfully obtaining location fixes was positively associated with elevation and unobstructed satellite view and was negatively affected by the interaction of overstory canopy and satellite view. Test collars were 33% more successful at acquiring fixes than those on bears. Fix success rates of collared bears varied seasonally and diurnally. Application of weighting factors to individual collared bear fixes recouped only 6% of lost data and failed to reduce seasonal or diurnal variation in fix success, suggesting that variables not included in our model contributed to data loss. Test collars placed to mimic bear bedding sites received 16% fewer fixes than randomly placed collars, indicating that microhabitat selection may contribute to data loss for wildlife equipped with GPS collars. Horizontal collar errors of >800 m occurred when elevation changes between successive fixes were >400 m. We conclude that significant limitations remain in accounting for data loss and error inherent in using GPS telemetry in coniferous forest ecosystems and that, at present, resource selection patterns of large mammals derived from GPS telemetry should be interpreted cautiously.     

Using broad-scale, community survey data to compare species conservation strategies across regions: A case study of the Koala in a set of adjacent 'catchments'

Summary   Managers of wildlife populations with a wide geographical range are understandably interested in the question of whether they can manage a broader population with a single conservation strategy (e.g. covering a set of adjacent management regions, referred to as 'catchments' in Australia) or whether separate strategies are required for individual catchments. We addressed this question using data from a statewide, community wildlife survey to quantify Koala ( Phascolarctos cinereus ) habitat relationships in the catchments of four adjacent Catchment Management Authorities or CMA (>10 000 km²) of New South Wales, Australia and then tested whether these habitat relationships were similar across catchments. Although the results were constrained by the coarse resolution of the community survey and environmental data, we were able to model broad-scale patterns of habitat use. Model explanatory power and cross-regional predictability was low, but consistent with Koala ecology. Two environmental variables emerged as having a strong relationship with Koala presence – mean elevation and percentage of fertile soils – the importance of which varied among catchments depending on land-use patterns. The results highlight the need for local wildlife management plans, not a single plan covering multiple catchments.     

Measuring ecological niche overlap from occurrence and spatial environmental data

Aim Concerns over how global change will influence species distributions, in conjunction with increased emphasis on understanding niche dynamics in evolutionary and community contexts, highlight the growing need for robust methods to quantify niche differences between or within taxa. We propose a statistical framework to describe and compare environmental niches from occurrence and spatial environmental data. Location Europe, North America and South America. Methods The framework applies kernel smoothers to densities of species occurrence in gridded environmental space to calculate metrics of niche overlap and test hypotheses regarding niche conservatism. We use this framework and simulated species with pre‐defined distributions and amounts of niche overlap to evaluate several ordination and species distribution modelling techniques for quantifying niche overlap. We illustrate the approach with data on two well‐studied invasive species. Results We show that niche overlap can be accurately detected with the framework when variables driving the distributions are known. The method is robust to known and previously undocumented biases related to the dependence of species occurrences on the frequency of environmental conditions that occur across geographical space. The use of a kernel smoother makes the process of moving from geographical space to multivariate environmental space independent of both sampling effort and arbitrary choice of resolution in environmental space. However, the use of ordination and species distribution model techniques for selecting, combining and weighting variables on which niche overlap is calculated provide contrasting results. Main conclusions The framework meets the increasing need for robust methods to quantify niche differences. It is appropriate for studying niche differences between species, subspecies or intra‐specific lineages that differ in their geographical distributions. Alternatively, it can be used to measure the degree to which the environmental niche of a species or intra‐specific lineage has changed over time.     

An inter-regional approach to intraspecific variation in habitat association: Rock Buntings Emberiza cia as a case study

The habitat association approach has been increasingly used in ecology to resolve problems in wildlife conservation and management. One problem related to habitat association studies is that they are restricted to small geographical areas within a species' range, and thus they are applicable to only a limited set of environmental conditions utilized by the species. In addition, very few studies address why the preference for specific habitat components may be adaptive for the species in question. The objective of this study was to examine how consideration of populations of a species from two dramatically different environments affects the results of habitat association modelling for a ground-nesting passerine, the Rock Bunting Emberiza cia . At a regional scale, a trend to defending breeding habitat patches with relatively higher stone cover was confined to birds from a temperate region in Slovakia. In contrast, in a semi-arid region in southeastern Spain, Rock Buntings preferred to use breeding habitat patches that had relatively higher grass cover. Combining data from both regions, breeding Rock Buntings showed a general pattern of using habitat patches close to hedges, with low bush cover, high ditch density and a steep slope. Whereas regional habitat association models appear to be sensitive to the particularities of the breeding environment, our study suggests that Rock Bunting breeding habitat association is constrained by the adults' tactics to protect themselves against predators. Although the birds prefer to nest in patches of low vegetation, the better to see nearby predators, these patches are ideally close to taller vegetation that can be used to provide cover when evading predators, and they are also of a rugged profile that helps the birds to approach and leave the nest stealthily.     

Teaching animal habitat selection using wildlife tracking equipment

We present a hands-on outdoor activity coupled with classroom discussion to teach students about wildlife habitat selection, the process by which animals choose where to live. By selecting locations or habitats with many benefits (e.g., food, shelter, mates) and few costs (e.g., predators), animals improve their ability to survive and reproduce. Biologists track animal movement using radio telemetry technology to study habitat selection so they can better provide species with habitats that promote population growth. We present a curriculum in which students locate “animals” (transmitters) using radio telemetry equipment and apply math skills (use of fractions and percentages) to assess their “animal's” habitat selection by comparing the availability of habitat types with the proportion of “animals” they find in each habitat type.     

What's your move? Movement as a link between personality and spatial dynamics in animal populations

Recent studies have established the ecological and evolutionary importance of animal personalities. Individual differences in movement and space‐use, fundamental to many personality traits (e.g. activity, boldness and exploratory behaviour) have been documented across many species and contexts, for instance personality‐dependent dispersal syndromes. Yet, insights from the concurrently developing movement ecology paradigm are rarely considered and recent evidence for other personality‐dependent movements and space‐use lack a general unifying framework. We propose a conceptual framework for personality‐dependent spatial ecology. We link expectations derived from the movement ecology paradigm with behavioural reaction‐norms to offer specific predictions on the interactions between environmental factors, such as resource distribution or landscape structure, and intrinsic behavioural variation. We consider how environmental heterogeneity and individual consistency in movements that carry‐over across spatial scales can lead to personality‐dependent: (1) foraging search performance; (2) habitat preference; (3) home range utilization patterns; (4) social network structure and (5) emergence of assortative population structure with spatial clusters of personalities. We support our conceptual model with spatially explicit simulations of behavioural variation in space‐use, demonstrating the emergence of complex population‐level patterns from differences in simple individual‐level behaviours. Consideration of consistent individual variation in space‐use will facilitate mechanistic understanding of processes that drive social, spatial, ecological and evolutionary dynamics in heterogeneous environments.     

Landscapes for Energy and Wildlife: Conservation Prioritization for Golden Eagles across Large Spatial Scales

Proactive conservation planning for species requires the identification of important spatial attributes across ecologically relevant scales in a model-based framework. However, it is often difficult to develop predictive models, as the explanatory data required for model development across regional management scales is rarely available. Golden eagles are a large-ranging predator of conservation concern in the United States that may be negatively affected by wind energy development. Thus, identifying landscapes least likely to pose conflict between eagles and wind development via shared space prior to development will be critical for conserving populations in the face of imposing development. We used publically available data on golden eagle nests to generate predictive models of golden eagle nesting sites in Wyoming, USA, using a suite of environmental and anthropogenic variables. By overlaying predictive models of golden eagle nesting habitat with wind energy resource maps, we highlight areas of potential conflict among eagle nesting habitat and wind development. However, our results suggest that wind potential and the relative probability of golden eagle nesting are not necessarily spatially correlated. Indeed, the majority of our sample frame includes areas with disparate predictions between suitable nesting habitat and potential for developing wind energy resources. Map predictions cannot replace on-the-ground monitoring for potential risk of wind turbines on wildlife populations, though they provide industry and managers a useful framework to first assess potential development.     

Identifying Habitat Linkages for American Black Bears in North Carolina,USA

ABSTRACT Understanding landscape structure and the role of habitat linkages is important to managing wildlife populations in fragmented landscapes. We present a data-based method for identifying local- and regional-scale habitat linkages for American black bears (Ursus americanus) on the Albemarle-Pamlico Peninsula of North Carolina, USA. We used weights-of-evidence, a discrete multivariate technique for combining spatial data, to make predictions about bear habitat use from 1,771 telemetry locations on 2 study areas (n = 35 bears). The model included 3 variables measured at a 0.2-km² scale: forest cohesion, forest diversity, and forest-agriculture edge density, adequately describing important habitat characteristics for bears on our study area. We used 2 categories of unique habitat conditions to delineate favorable bear habitat, which correctly classified 79.5% of the bear locations in a 10-fold model validation. Forest cohesion and forest-agriculture edge density were the most powerful predictors of black bear habitat use. We used predicted probabilities of bear occurrence from the model to delineate habitat linkages among local and regional areas where bear densities were relatively high. Our models clearly identified 2 of the 3 sites previously recommended for wildlife underpasses on a new, 4-lane highway in the study area. Our approach yielded insights into how landscape metrics can be integrated to identify linkages suitable as habitat and dispersal routes.     

Markov models and network analysis reveal sex‐specific differences in the space‐use of a coastal apex predator

Understanding the links between external variables such as habitat and interactions with conspecifics and animal space‐use is fundamental to developing effective management measures. In the marine realm, automated acoustic tracking has become a widely used method for monitoring the movement of free‐ranging animals, yet researchers generally lack robust methods for analysing the resulting spatial‐usage data. In this study, acoustic tracking data from male and female broadnose sevengill sharks Notorynchus cepedianus, collected in a system of coastal embayments in southeast Tasmania were analyzed to examine sex‐specific differences in the sharks’ coastal space‐use and test novel methods for the analysis of acoustic telemetry data. Sex‐specific space‐use of the broadnose sevengill shark from acoustic telemetry data was analysed in two ways: The recently proposed spatial network analysis of between‐receiver movements was employed to identify sex‐specific space‐use patterns. To include the full breadth of temporal information held in the data, movements between receivers were furthermore considered as transitions between states of a Markov chain, with the resulting transition probability matrix allowing the ranking of the relative importance of different parts of the study area. Both spatial network and Markov chain analysis revealed sex‐specific preferences of different sites within the study area. The identification of priority areas differed for the methods, due to the fact that in contrast to network analysis, our Markov chain approach preserves the chronological sequence of detections and accounts for both residency periods and movements. In addition to adding to our knowledge of the ecology of a globally distributed apex predator, this study presents a promising new step towards condensing the vast amounts of information collected with acoustic tracking technology into straightforward results which are directly applicable to the management and conservation of any species that meet the assumptions of our model.