Resource Selection Functions Based on Use–Availability Data: Theoretical Motivation and Evaluation Methods

Location‐only and use‐availability data: analysis methods converge

Studies of resource selection form the basis for much of our understanding of wildlife habitat requirements, and resource selection functions (RSFs), which predict relative probability of use, have been proposed as a unifying concept for analysis and interpretation of wildlife habitat data. Logistic regression that contrasts used and available or unused resource units is one of the most common analyses for developing RSFs. Recently, resource utilization functions (RUFs) have been developed, which also predict probability of use. Unlike RSFs, however, RUFs are based on a continuous metric of space use summarized by a utilization distribution. Although both RSFs and RUFs predict space use, a direct comparison of these 2 modeling approaches is lacking. We compared performance of RSFs and RUFs by applying both approaches to location data for 75 Rocky Mountain elk ( Cervus elaphus ) and 39 mule deer ( Odocoileus hemionus ) collected at the Starkey Experimental Forest and Range in northeastern Oregon, USA. We evaluated differences in maps of predicted probability of use, relative ranking of habitat variables, and predictive power between the 2 models. For elk, 3 habitat variables were statistically significant ( P < 0.05) in the RSF, whereas 7 variables were significant in the RUF. Maps of predicted probability of use differed substantially between the 2 models for elk, as did the relative ranking of habitat variables. For mule deer, 4 variables were significant in the RSF, whereas 6 were significant in the RUF, and maps of predicted probability of use were similar between models. In addition, distance to water was the top‐ranked variable in both models for mule deer. Although space use by both species was predicted most accurately by the RSF based on cross‐validation, differences in predictive power between models were more substantial for elk than mule deer. To maximize accuracy and utility of predictive wildlife‐habitat models, managers must be aware of the relative strengths and weaknesses of different modeling techniques. We conclude that although RUFs represent a substantial advance in resource selection theory, techniques available for generating RUFs remain underdeveloped and, as a result, RUFs sometimes predict less accurately than models derived using more conventional techniques.

Conservation planning for species of concern or importance can be aided by resource selection functions (RSFs) that identify important areas or attributes. Models that can be interpreted biologically and provide reasonable predictive capacity may best be based on data from individuals grouped into seasonal selection strategies for particular geographical areas or similarities in topographical and vegetative associations. We used logistic regression, the information–theoretic approach, satellite imagery, and locational data (n = 31 females; 16 803 locations) from global positioning system (GPS) collared woodland caribou ( Rangifer tarandus caribou (Gmelin, 1788)) to model resource selection by animals during calving, summer, fall, breeding, winter, and late-winter seasons. Higher variation in resource use corresponded to times when caribou and their young were most susceptible to predation or when food resources were limited. Even with multiple selection strategies, caribou followed a general progression from higher to lower elevation habitats from calving and summer to late winter. Caribou selected against or completely avoided the burned–disturbed vegetation class in every season except summer. We incorporated RSFs with a raster geographic information system to create selection landscapes. We validated selection landscapes using withheld GPS data (n = 6077), 50 known calving sites, and Spearman’s rank correlation coefficient. Selection models and final selection landscapes performed well in validating use locations of woodland caribou in all seasons (all P < 0.003) and in predicting known calving sites (P < 0.001). When seasonal selection strategies are identified and models are coupled with validation, RSFs are effective tools to assist in conservation planning.

To assess the wolf‐habitat relationship on the home range scale (i.e., third order selection), we developed a resource selection probability function (RSPF) through a multiple logistic regression model based on the winter travel routes of a wolf pack in the northern Apennines, Italy (1991–95). Both travel routes (240 km) and habitat variables were mapped at 1:10 000 scale, digitised as Geographical Information System (GIS) layers, and overlaid with a 100×100 m pixel grid to census all used and unused resource units. Out of 15 covariates, the full model included 10 variables and 4 interaction terms. According to the model, travel routes by wolves were not randomly located within the home range but were clearly associated with selected bio‐physical factors, including human‐related habitat modifications (i.e., roads), which appeared to affect the wolves' resource selection and, ultimately, habitat quality. Using a jackknife procedure, the model correctly classified 73.1% of used resource units and 63.2% of unused resource units. A Monte Carlo test showed a non‐significant effect on the model coefficients of 4 increasing sub‐sampling levels of used resource units, suggesting that autocorrelation of snow‐tracking data exerted little influence on the point estimates of the coefficients. However, given the increased standard error at higher sub‐sampling levels, autocorrelation might have caused an underestimation of the theoretical variance. Although wolves are generally considered habitat generalists, this study shows that patterns of habitat selection are disclosed at finer scales of analysis. In this perspective, resource selection probability functions at finer scales offer different and complementary insight with respect to regional landscape applications, and provide a useful management tool for assessment of habitat quality at the local scale.

Quantifying landscape characteristics that wildlife select is essential for conservation and management action. Models that map wildlife resource selection tend to be informed by telemetry technology which is costly to acquire/maintain and potentially risky to deploy. Therefore, there is value in pursuing alternative data collection protocols, such as citizen scientist approaches to ascertain whether they can reveal results comparable to those derived from telemetry studies. The conservation of African wild dogs (Lycaon pictus) presents an interesting case study to examine this topic. The species is rare and wideranging, hence data collection is both challenging and costly. They are, however, a groupliving species with unique and conspicuous coat markings, making them potentially well-suited to citizen science data collection strategies. Here, we fitted resource selection functions (RSFs) built from Global Position System (GPS) telemetry data, and from citizen scientist data, collected in and around Hwange National Park, Zimbabwe. We assessed comparability of these RSFs by evaluating the relative importance of parameters, parameter coefficients (direction and magnitude of effect), and the spatial predictions of relative probability of use by African wild dogs. The most important predictors in both models were proportion of woodland and bushland, the number of habitat types, and distance to waterhole. Furthermore, spatial predictions from both models displayed a high degree of overlap (r = 0.74), indicating similarities in selected and avoided habitat patches. Our analysis demonstrates that sufficient citizen science data can be a valuable alternative to telemetry data for African wild dogs. We thus encourage the collection and use of citizen science data for similar analyses, particularly when funding is limited. Our work also highlights areas in and around Hwange National Park with the highest probability of being used by African wild dogs, which is where conservation efforts should be intensified.

We analyzed the relative influence of foraging substrate characteristics as predictors of the probability of use by the pileated woodpecker (Dryocopus pileatus L.) and determined threshold values for significant predictors. We sampled used and available substrates around 126 stations distributed in an intensively managed forest in northwestern New Brunswick, Canada. We developed a resource selection function (RSF), validated by a resampling procedure, and compared selection ratios for significant predictors. Diameter at breast height (DBH) of trees and snags was the most significant predictor, probably reflecting nesting selection by its main prey, carpenter ants (Camponotus spp.). The pileated woodpecker preferred deciduous substrates with DBH >35 cm and coniferous substrates with DBH >30 cm. Among deciduous substrates, it preferred snags over living trees, but there was no such preference for coniferous substrates. American beech (Fagus grandifolia Ehrh.) was clearly preferred over all other species. The RSF we developed and the thresholds we obtained should help forest managers and conservation planners assess habitat quality for this keystone species.

Ungulates often alter behavior and space use in response to interspecific competition. Despite observable changes in behavior caused by competitive interactions, research describing the effects of competition on survival or growth is lacking. We used spatial modeling to determine if habitat use by female mule deer (Odocoileus hemionus) was affected by other ungulate species prior to, during, and after parturition. We conducted our study in the Book Cliffs region of eastern Utah, USA, during 2019 and 2020. We used resource selection function (RSF) analysis to model space use of 4 ungulate species that potentially competed with mule deer: bison (Bos bison), cattle, elk (Cervus canadensis), and feral horses. We incorporated RSF models for competing species into a random forest analysis to determine if space use by mule deer was influenced by these other ungulate species. We used survival and growth data from neonate mule deer to directly assess potential negative effects of other ungulates. Habitat use by elk was an important variable in predicting use locations of mule deer during birthing and rearing. The relationship was positive, suggesting interference competition was not occurring. Survival of neonate mule deer increased as the probability of use by elk increased (hazard ratio = 0.185 ± 0.497 [SE]). Further, probability of use by elk in rearing habitat had no influence on growth of neonate mule deer from birth to 6 months of age, suggesting that exploitative competition was not occurring.

ABSTRACTResource selection functions (RSFs) are statistical models defined to be proportional to the probability of use of a resource unit. My objective with this review is to identify how RSFs can be used to unravel the influence of scale in habitat selection. In wildlife habitat studies, including radiotelemetry, RSFs can be estimated using a variety of statistical methods, all of which can be used to explore the role of scale. All RSFs are bounded by the resolution of data and the spatial extent of the study area, but also allow predictor covariates to be measured at a variety of scales. Conditional logistic regression permits designs (e.g. matched case) that relate the process of habitat selection to a limited domain of resource units that might better characterize what is truly ‘available’ to the animal. Scale influences the process of habitat selection, e.g. food resources are often selected at fine spatial scales, whereas landscape patterns at much larger scales typically influence the location of home ranges. Scale also influences appropriate sampling in many ways: (1) heterogeneity might be obliterated (transmutation) if resolution or grain size is too large, (2) variance of habitat characteristics might be undersampled if extent or domain is too small, (3) timing and duration of observations can influence RSF models, and (d) both spatial and temporal autocorrelations can vary directly with the intensity of sampling. Using RSFs, researchers can examine habitat selection at multiple scales, and predictive models that bridge scales can be estimated. Using Geographical Information Systems, predictor covariates in RSF models can be measured at different scales easily so that the predictive ability of models at alternative spatial and temporal domains can be explored by the investigator. Identification of the scale that best explains the data can be evaluated by comparing alternative models using information‐theoretic metrics such as Akaike Information Criteria, and predictive capability of the models can be assessed using k‐fold cross validation.

Summary Corridors are commonly used to connect fragments of wildlife habitat, yet the identification of conservation corridors typically neglects processes of habitat selection and movement for target organisms. New technologies and analytical tools make it possible to better integrate landscape patterns with behavioural processes. We illustrate the integration of resource selection functions (RSFs) and least‐cost path (LCP) analyses for the purpose of corridor planning for two large carnivores. We used RSFs developed from Global Positioning System telemetry data to predict the seasonal distribution of two large carnivores: grizzly bears Ursus arctos and cougars Puma concolor. We then applied LCP analyses to identify potential corridors in two fragmented montane landscapes – Canmore and Crowsnest Pass – in Alberta, Canada. Grizzly bear habitat selection in both areas positively correlated with greenness in all seasons and soil wetness and proximity to water in the summer when both variables were associated with bear forage. During spring, grizzly bear occurrence in Canmore inversely correlated with road density. For cougars, habitat selection varied by region: it negatively correlated with road density in Canmore during non‐winter and positively correlated with terrain ruggedness in Crowsnest Pass. Cougar occurrence during the non‐winter season in Canmore positively correlated with greenness. For each species, seasonal RSFs were used to develop a cost surface for LCP analyses to identify potential corridor locations in each study area. Overlaying the paths for the two species highlighted where the landscape could support corridors for both species and potential highway crossing zones. The telemetry data supported some of these modelled crossings. Synthesis and applications. We show how to integrate RSFs and least‐cost modelling to identify corridors for conservation. We focus on two large carnivores in the Canadian Rocky Mountains to identify potential corridors in Canmore and provide a framework for corridor planning in Crowsnest. We suggest that our approach is applicable to many other target species in addition to large carnivores in human‐dominated landscapes.

Plastic contamination extends across all Great Lakes ecosystems, including in wildlife, with the potential for risk based on laboratory experiments and risk assessment. Due to widespread contamination, and based on evidence suggesting measurable risk, it is time for policy-makers to develop and implement monitoring programs to guide management . Here, we discuss the need for a monitoring strategy with clear guidelines. We synthesize the research that has been published across the Great Lakes, reporting on contamination, regions that have been the focus of study, and the methods used across matrices. Based on our findings, we suggest how research may inform guidelines and next steps – especially if microplastics are to be considered as a Toxic Chemicals sub-indicator under the Great Lakes Water Quality Agreement. Future monitoring, using standard and/or harmonized protocols for sampling and analysis, should build baselines across the basin and begin tracking how contamination changes to assess the health of the Great Lakes, to inform source-reduction, and to measure the effectiveness of policies aimed at reducing emissions of plastics to freshwater.

Wildlife managers delineate priority areas for species to define critical habitat and to prioritize management efforts. Each method used to identify priority areas involves data that can be unavailable or expensive to obtain. Therefore, it is of interest to compare spatial efficiency between methods used for defining priority areas. We compared priority areas created using different methods and data types. We used resource selection function (RSF) models to predict areas of high use and generated a map depicting ≥ 90% predicted use in three seasons; it was 1,143 km2, encompassed 91% of nests, 68% of summer locations, and 71% of winter locations. We compared the RSF priority area to priority areas developed using two alternative methods: (1) modified conservation buffer, and (2) utilization distribution (UD) models. The modified conservation buffer method was used by South Dakota Game, Fish and Parks in 2014 to delineate a priority area by buffering active lek sites by 6.4 km, including connectivity corridors defined via expert opinion, and known high use areas. The priority area generated by the modified conservation buffer method was 3,977 km2, encompassed 95% of nest locations, 92% of spring/summer locations, and 99% of winter locations. Lastly, we developed a priority area using combined UDs from radio-tracking data gathered during spring/summer, and winter and included a lek buffer encompassing 90% of known nest-sites. This priority area was 3,498 km2, encompassed 99% of nests, 98% of spring/summer locations, and 97% of winter locations. The priority area generated by RSF models was the smallest and encompassed the least number of nests and spring/summer and winter locations but was considered the most spatially efficient; it had the most nests, spring/summer locations, and winter locations per 100 km2. The UD and modified conservation buffer methods created priority areas that were similar in size and spatial efficiency. The modified conservation buffer method encompassed >90% of known sage-grouse locations and nests, indicating that in the absence of detailed movement data and more sophisticated modeling, the method can be sufficient in developing an adequate priority area.

The wild water deer (Hydropotes inermis) population has declined rapidly over recent decades and has reached an endangered status in China. Therefore, it is important to understand their habitat selection to effectively protect both existing and emerging populations. This paper used the data of 11 habitat factors in Baishan Musk Deer National Nature Reserve in the autumn and winter from 2018 to 2019 to conduct a habitat selection study of water deer by resource selection function analysis. The results indicated that in both the autumn and winter, water deer preferred grasslands at sunny and middle slopes, dominated by Artemisia carvifolia and A. argyi, respectively. In addition, the resource selection function showed that the height of dominant herbage, hiding cover, distance from water, and distance to human settlements greatly contribute to the habitat selection of water deer in the cold season. The correct prediction rate of the resource selection function model exceeded 80%, highlighting its suitability for predicting the habitat selection of water deer. The outcomes of this study provide an effective scientific basis for the conservation and restoration of water deer, and valuable enlightenment for implementing a sustainable development strategy in northeast China.

Prescribed fire effects on resource selection by cattle in mesic sagebrush steppe. Part 1: Spring grazing

Abiotic and biotic factors influence sheep and goat landscape distribution. Resource selection functions allow us to determine which factors influence distribution the most. This study tested distance to water, distance to trees, tree count, wind direction, and vegetation metrics as resources influencing the distribution of sheep and goats on extensive pastures in Queensland, Australia. Vegetation metrics were computed from remotely sensed data, and are measured as green vegetation, nongreen vegetation, bare ground, and total standing dry matter. We found the location of water, trees, and the prevailing wind direction were the most influential factors affecting sheep and goat distribution. Both sheep and goats selected for areas close to water, trees, and in the direction of the prevailing wind. At one site, goats showed a preference for green vegetation mainly in treed areas, while sheep showed a preference for nongreen vegetation which in the drought conditions of this study were found in less treed areas and can be interpreted as a selection for herbaceous material. At the other sites, the influence of water, trees, and wind was too strong to see a direct influence of vegetation on resource selection. Further research in nondrought conditions would help to better explain vegetation influence on sheep and goat landscape utilization.

Site-specific variation in relative habitat abundance and disturbance regimes may produce differences in habitat preferences of associated populations. An evaluation of the predictive power of habitat selection models across space would benefit our understanding of the reliability of models of selection and space use in predicting animal occurrence. We used presence–absence data from winter surveys of moose (Alces alces (L., 1758)) to estimate resource selection functions (RSFs) across two study sites using Far North Land Cover updated with recent disturbance from fire and timber harvest. Moose selected foraging habitat (e.g., deciduous land cover) and for increasing deciduous foliage cover (ΔNDVI, i.e., the difference in the normalized difference vegetation index). Snow depth negatively influenced habitat selection, likely due to increased predation risk and reduced movement and foraging efficiency. Models lost little predictive power when applied to another site based on comparison of receiver operating characteristic (ROC) curves. Our results corroborated the current body of knowledge concerning moose habitat selection, i.e., moose preferentially use forest stands dominated by deciduous species, but suggested that moose strongly avoided very recently disturbed areas. Minimal site-specific variation and ROC comparison suggests that RSFs may be extended into novel systems, given adequate consideration for habitat quality and abundance, thereby simplifying management needs of this important species.