Examining speed versus selection in connectivity models using elk migration as an example

Context

Landscape resistance is vital to connectivity modeling and frequently derived from resource selection functions (RSFs). RSFs estimate relative probability of use and tend to focus on understanding habitat preferences during slow, routine animal movements (e.g., foraging). Dispersal and migration, however, can produce rarer, faster movements, in which case models of movement speed rather than resource selection may be more realistic for identifying habitats that facilitate connectivity.

Objective

To compare two connectivity modeling approaches applied to resistance estimated from models of movement rate and resource selection.

Methods

Using movement data from migrating elk, we evaluated continuous time Markov chain (CTMC) and movement-based RSF models (i.e., step selection functions [SSFs]). We applied circuit theory and shortest random path (SRP) algorithms to CTMC, SSF and null (i.e., flat) resistance surfaces to predict corridors between elk seasonal ranges. We evaluated prediction accuracy by comparing model predictions to empirical elk movements.

Results

All connectivity models predicted elk movements well, but models applied to CTMC resistance were more accurate than models applied to SSF and null resistance. Circuit theory models were more accurate on average than SRP models.

Conclusions

CTMC can be more realistic than SSFs for estimating resistance for fast movements, though SSFs may demonstrate some predictive ability when animals also move slowly through corridors (e.g., stopover use during migration). High null model accuracy suggests seasonal range data may also be critical for predicting direct migration routes. For animals that migrate or disperse across large landscapes, we recommend incorporating CTMC into the connectivity modeling toolkit.

     

A multi-method approach to delineate and validate migratory corridors

Context

Managers are faced with numerous methods for delineating wildlife movement corridors, and often must make decisions with limited data. Delineated corridors should be robust to different data and models.

Objectives

We present a multi-method approach for delineating and validating wildlife corridors using multiple data sources, which can be used conserve landscape connectivity. We used this approach to delineate and validate migration corridors for wildebeest (Connochaetes taurinus) in the Tarangire Ecosystem of northern Tanzania.

Methods

We used two types of locational data (distance sampling detections and GPS collar locations), and three modeling methods (negative binomial regression, logistic regression, and Maxent), to generate resource selection functions (RSFs) and define resistance surfaces. We compared two corridor detection algorithms (cost-distance and circuit theory), to delineate corridors. We validated corridors by comparing random and wildebeest locations that fell within corridors, and cross-validated by data type.

Results

Both data types produced similar RSFs. Wildebeest consistently selected migration habitat in flatter terrain farther from human settlements. Validation indicated three of the combinations of data type, modeling, and corridor detection algorithms (detection data with Maxent modeling, GPS collar data with logistic regression modeling, and GPS collar data with Maxent modeling, all using cost-distance) far outperformed the other seven. We merged the predictive corridors from these three data-method combinations to reveal habitat with highest probability of use.

Conclusions

The use of multiple methods ensures that planning is able to prioritize conservation of migration corridors based on all available information.

     

Sensitivity of resource selection and connectivity models to landscape definition

Context

The definition of the geospatial landscape is the underlying basis for species-habitat models, yet sensitivity of habitat use inference, predicted probability surfaces, and connectivity models to landscape definition has received little attention.

Objectives

We evaluated the sensitivity of resource selection and connectivity models to four landscape definition choices including (1) the type of geospatial layers used, (2) layer source, (3) thematic resolution, and (4) spatial grain.

Methods

We used GPS telemetry data from pumas (Puma concolor) in southern California to create multi-scale path selection function models (PathSFs) across landscapes with 2500 unique landscape definitions. To create the landscape definitions, we identified seven geospatial layers that have been shown to influence puma habitat use. We then varied the number, sources, spatial grain, and thematic resolutions of these layers to create our suite of plausible landscape definitions. We assessed how PathSF model performance (based on AIC) was affected by landscape definition and examined variability among the predicted probability of movement surfaces, connectivity models, and road crossing locations.

Results

We found model performance was extremely sensitive to landscape definition and identified only seven top models out of our suite of definitions (<1%). Spatial grain and the number of geospatial layers selected for a landscape definition significantly affected model performance measures, with finer grains and greater numbers of layers increasing model performance.

Conclusions

Given the sensitivity of habitat use inference, predicted probability surfaces, and connectivity models to landscape definition, out results indicate the need for increased attention to landscape definition in future studies.

     

Multi-scale prediction of landscape resistance for tiger dispersal in central India

Context

Connectivity models for animal movement frequently use resistance surfaces, but rarely incorporate actual movement data and multiple scale drivers of landscape resistance.

Objectives

Using GPS data, we developed a multi-scale model of landscape resistance for tiger (Panthera tigris) dispersal in central India and evaluated the performance, interpretation and predictions against single scale models.

Methods

Six dispersing tiger paths were subjected to a path level analysis with conditional logistic regression to parameterize a resistance surface. We evaluated for 21 scales of available habitat and selected the best scale for each variable. We derived a scale-optimized multivariate path selection function and predicted landscape resistance across the landscape.

Results

The tigers preferred to move along areas with forest cover at relatively high elevations along the ridges with rugged topography at broad scale, while avoiding areas with agriculture-village matrix at fine scale. We found that the scale that was most supported by Akaike’s information criterion was not always the scale that maximized the magnitude (effect size) of the relationship. Further, the multi-scale optimized model differed substantially from the single scale models in terms of variable importance, magnitude of coefficients and predictions of connectivity.

Conclusions

Our results demonstrate that the variables in landscape resistance models produce markedly different predictions of population connectivity depending on the scales of analyses and interpretation. Thus, scale optimization in parameterization is critical for appropriate inferences and sound management strategies.

     

Resource depletion versus landscape complementation: habitat selection by a multiple central place forager

Context

The spatial distribution of non-substitutable resources implies diverging predictions for animal movement patterns. At broad scales, animals should respond to landscape complementation by selecting areas where resource patches are close-by to minimize movement costs. Yet at fine scales, central place effects lead to the depletion of patches that are close to one another and that should ultimately be avoided by consumers.

Objectives

We developed a multi-scale resource selection framework to test whether animal movement is driven by landscape complementation or resource depletion and identify at which spatial scale these processes are relevant from an animal’s perspective.

Methods

During the dry season, surface water and forage are non-substitutable resources for African elephants. Eight family herds were tracked using GPS loggers in Hwange National Park, Zimbabwe. We explained habitat selection during foraging trips by mapping surface water at two scales with gaussian kernels of varying widths placed over each waterhole.

Results

Unexpectedly, elephants select areas with low waterhole density at both fine scales (< 1 km) and broad scales (5–7 km). Selection is stronger when elephants forage far away from water, even more so as the dry season progresses.

Conclusions

Elephant selection of low waterhole density areas suggests that resource depletion around multiple central places is the main driver of their habitat selection. By identifying the scale at which animals respond to waterhole distribution we provide a template for water management in arid and semi-arid landscapes that can be tailored to match the requirements and mobility of free ranging wild or domestic species.

     

Predicting species-habitat relationships: does body size matter?

Context

Allometric scaling laws are foundational to structuring processes from cellular to ecosystem levels. The idea that allometric relationships underlie species characteristic selection scales, the spatial scales at which species respond to landscape features, has recently been investigated, however, supporting empirical evidence is scarce.

Objectives

Lack of pattern can be explained by inaccurate estimation, low power, confounding factors, or absence of a relationship. In this paper, we evaluate the relationship between body size and species characteristic selection scales after overcoming limitations of previous study designs.

Methods

We conducted 1328 avian point counts across the state of Nebraska using the robust sampling design to account for imperfect detection. We used Bayesian latent indicator scale selection with N-mixture models to estimate species’ characteristic selection scales of six habitat features for 86 species. We propagated the uncertainty associated with assigning characteristic scales to a model of the relationship between body size and characteristic spatial scales.

Results

Species characteristic scales varied across habitat predictors, and varied in the uncertainty associated with selecting single characteristic scales. After propagating uncertainty our results do not support a relationship between species’ body size and the spatial scales at which they respond to landscape features.

Conclusions

As species abundance integrates birth, death, immigration, and emigration processes, each of which are influenced by ecological processes manifesting at various scales, we question whether a general allometric relationship should be expected. Our results suggest that selection may act on responses to specific environmental features, rather than responses to spatial scale per se.

     

Multi-level,multi-scale habitat selection by a wide-ranging,federally threatened snake

Context

Although multi-scale approaches are commonly used to assess wildlife-habitat relationships, few studies have examined selection at multiple spatial scales within different hierarchical levels/orders of selection [sensu Johnson’s (1980) orders of selection]. Failure to account for multi-scale relationships within a single level of selection may lead to misleading inferences and predictions.

Objectives

We examined habitat selection of the federally threatened eastern indigo snake (Drymarchon couperi) in peninsular Florida at the level of the home range (Level II selection) and individual telemetry location (Level III selection) to identify influential habitat covariates and predict relative probability of selection.

Methods

Within each level, we identified the characteristic scale for each habitat covariate to create multi-scale resource selection functions. We used home range selection functions to model Level II selection and paired logistic regression to model Level III selection.

Results

At both levels, EIS selected undeveloped upland land covers and habitat edges while avoiding urban land covers. Selection was generally strongest at the finest scales with the exception of Level II urban edge which was avoided at a broad scale indicating avoidance of urbanized land covers rather than urban edge per se.

Conclusions

Our study illustrates how characteristic scales may vary within a single level of selection and demonstrates the utility of multi-level, scale-optimized habitat selection analyses. We emphasize the importance of maintaining large mosaics of natural habitats for eastern indigo snake conservation.

     

Influence of separating home range and dispersal movements on characterizing corridors and effective distances

Context

Corridors are usually delineated as areas of minimum cumulative resistance to movement through a resistance surface and characterized by their effective distance (accumulated resistance along the least-cost path). The results of these assessments depend on resistance values, which are typically derived from the inverse of habitat suitability models or from presence data of individuals within their home ranges, rather than from data on dispersal or exploratory movements.

Objective

Evaluate the extent to which corridor delineation and effective distance estimates may vary depending on whether home range locations or dispersal data are used to characterize species habitat selection and landscape resistance to movement.

Methods

We analyzed a large telemetry dataset (GPS collars) for the endangered Iberian lynx. We modeled corridors and effective distances three ways: (1) considering only GPS locations within home ranges, (2) considering only locations in dispersal or exploratory movements outside home ranges, and (3) considering all locations together.

Results

Delineated least-cost corridors followed similar trajectories and sometimes overlapped in the three models. The estimated effective distances were 42 % lower in the dispersal-based model than in the model based solely on home range use.

Conclusions

Models derived exclusively from locations within home ranges may provide lower connectivity estimates than models derived from dispersal locations, affecting estimates of resistance to move between habitat areas, even when the most likely movement routes are similar. Although dispersal data are costly to gather, they potentially provide more realistic assessments of the actual isolation of populations in heterogeneous landscapes.

     

Extent-dependent habitat selection in a migratory large herbivore: road avoidance across scales

Context

In southwestern Alberta, human development, including roads, is encroaching on the landscape and into the range of a partially migratory population of elk (Cervus elaphus).

Objectives

To quantify factors influencing among- and within-home-range selection of winter range in this population.

Methods

We studied individual habitat selection and road avoidance at two biologically relevant spatial scales. We outlined availability extents for 107 individual elk-years based on observed fall migration distance, and based on a minimum convex polygon around winter telemetry relocations. To model the response by elk to road disturbance, we fit a habitat-selection model to each elk-year at each of the two availability extents, and examined population-level and individual variation in space-use. We then evaluated the relationship between inferred selection at the two scales and the functional response in selection.

Results

Roads had a ubiquitous influence on elk across scales. Elk, individually and as a population, avoided roads when migrating to their winter range and within this seasonal home range. Individual elk that avoided roads more strongly relative to the population did so at both scales of analysis.Further, the avoidance of low-use roads decreased with increasing road density. These results support bottom-up habitat-selection patterns (i.e., scale-independent) and functional response in habitat selection.

Conclusions

Overall, using a multi-scale habitat selection analysis, we show that road avoidance is a major determinant of elk space-use behaviour across multiple scales. Consequently, any new road construction or increases in road-use intensity could have detrimental effects on migratory elk populations by restricting space-use.

     

Assessing differences in connectivity based on habitat versus movement models for brown bears in the Carpathians

Context

Connectivity assessments typically rely on resistance surfaces derived from habitat models, assuming that higher-quality habitat facilitates movement. This assumption remains largely untested though, and it is unlikely that the same environmental factors determine both animal movements and habitat selection, potentially biasing connectivity assessments.

Objectives

We evaluated how much connectivity assessments differ when based on resistance surfaces from habitat versus movement models. In addition, we tested how sensitive connectivity assessments are with respect to the parameterization of the movement models.

Methods

We parameterized maximum entropy models to predict habitat suitability, and step selection functions to derive movement models for brown bear (Ursus arctos) in the northeastern Carpathians. We compared spatial patterns and distributions of resistance values derived from those models, and locations and characteristics of potential movement corridors.

Results

Brown bears preferred areas with high forest cover, close to forest edges, high topographic complexity, and with low human pressure in both habitat and movement models. However, resistance surfaces derived from the habitat models based on predictors measured at broad and medium scales tended to underestimate connectivity, as they predicted substantially higher resistance values for most of the study area, including corridors.

Conclusions

Our findings highlighted that connectivity assessments should be based on movement information if available, rather than generic habitat models. However, the parameterization of movement models is important, because the type of movement events considered, and the sampling method of environmental covariates can greatly affect connectivity assessments, and hence the predicted corridors.

     

Multi-scale Mexican spotted owl (<Emphasis Type="Italic">Strix occidentalis lucida</Emphasis>) nest/roost habitat selection in Arizona and a comparison with single-scale modeling results

Context

Organisms commonly respond to their environment across a range of scales, however many habitat selection studies still conduct selection analyses using a single-scale framework. The adoption of multi-scale modeling frameworks in habitat selection studies can improve the effectiveness of these studies and provide greater insights into scale-dependent relationships between species and specific habitat components.

Objectives

Our study assessed multi-scale nest/roost habitat selection of the federally “Threatened” Mexican spotted owl (Strix occidentalis lucida) in northern Arizona, USA in an effort to provide improved conservation and management strategies for this subspecies.

Methods

We conducted multi-scale habitat modeling to assess habitat selection by Mexican spotted owls using survey data collected by the USFS. Each selected covariate was included in multi-scale models at their “characteristic scale” and we used an all-subsets approach and model selection framework to assess habitat selection.

Results

The “characteristic scale” identified for each covariate varied considerably among covariates and results from multi-scale models indicated that percent canopy cover and slope were the most important covariates with respect to habitat selection by Mexican spotted owls. Multi-scale models consistently outperformed their analogous single-scale counterparts with respect to the proportion of deviance explained and model predictive performance.

Conclusions

Efficacy of future habitat selection studies will benefit by taking a multi-scale approach. In addition to potentially providing increased explanatory power and predictive capacity, multi-scale habitat models enhance our understanding of the scales at which species respond to their environment, which is critical knowledge required to implement effective conservation and management strategies.

     

Multi-scale habitat selection modeling: a review and outlook

Context

Scale is the lens that focuses ecological relationships. Organisms select habitat at multiple hierarchical levels and at different spatial and/or temporal scales within each level. Failure to properly address scale dependence can result in incorrect inferences in multi-scale habitat selection modeling studies.

Objectives

Our goals in this review are to describe the conceptual origins of multi-scale habitat selection modeling, evaluate the current state-of-the-science, and suggest ways forward to improve analysis of scale-dependent habitat selection.

Methods

We reviewed more than 800 papers on habitat selection from 23 major ecological journals published between 2009 and 2014 and recorded a number of characteristics, such as whether they addressed habitat selection at multiple scales, what attributes of scale were evaluated, and what analytical methods were utilized.

Results

Our results show that despite widespread recognition of the importance of multi-scale analyses of habitat relationships, a large majority of published habitat ecology papers do not address multiple spatial or temporal scales. We also found that scale optimization, which is critical to assess scale dependence, is done in less than 5 % of all habitat selection modeling papers and less than 25 % of papers that address “multi-scale” habitat analysis broadly defined.

Conclusions

Our review confirms the existence of a powerful conceptual foundation for multi-scale habitat selection modeling, but that the majority of studies on wildlife habitat are still not adopting multi-scale frameworks. Most importantly, our review points to the need for wider adoption of a formal scale optimization of organism response to environmental variables.

     

One size does not fit all: European bison habitat selection across herds and spatial scales

Context

Understanding habitat selection can be challenging for species surviving in small populations, but is needed for landscape-scale conservation planning.

Objectives

We assessed how European bison (Bison bonasus) habitat selection, and particularly forest use, varies across subpopulations and spatial scales.

Methods

We gathered the most comprehensive European bison occurrence dataset to date, from five free-ranging herds in Poland. We compared these data to a high-resolution forest map and modelled the influence of environmental and human-pressure variables on habitat selection.

Results

Around 65% of European bison occurrences were in forests, with cows showing a slightly higher forest association than bulls. Forest association did not change markedly across spatial scales, yet differed strongly among herds. Modelling European bison habitat suitability confirmed forest preference, but also showed strong differences in habitat selection among herds. Some herds used open areas heavily and actively selected for them. Similarly, human-pressure variables were important in all herds, but some herds avoided human-dominated areas more than others.

Conclusions

Assessing European bison habitat across multiple herds revealed a more generalist habitat use pattern than when studying individual herds only. Our results highlight that conflicts with land use and people could be substantial if bison are released in human-dominated landscapes. Future restoration efforts should target areas with low road and human population density, regardless of the degree of forest cover. More broadly, our study highlights the importance of considering multiple subpopulations and spatial scales in conservation planning.

     

Shaken but not stirred: multiscale habitat suitability modeling of sympatric marten species (<Emphasis Type="Italic">Martes martes</Emphasis> and <Emphasis Type="Italic">Martes foina</Emphasis>) in the northern Iberian Peninsula

Context

Multispecies and multiscale habitat suitability models (HSM) are important to identify the environmental variables and scales influencing habitat selection and facilitate the comparison of closely related species with different ecological requirements.

Objectives

This study explores the multiscale relationships of habitat suitability for the pine (Martes martes) and stone marten (M. foina) in northern Spain to evaluate differences in habitat selection and scaling, and to determine if there is habitat niche displacement when both species coexist.

Methods

We combined bivariate scaling and maximum entropy modeling to compare the multiscale habitat selection of the two martens. To optimize the HSM, the performance of three sampling bias correction methods at four spatial scales was explored. HSMs were compared to explore niche differentiation between species through a niche identity test.

Results

The comparison among HSMs resulted in the detection of a significant niche divergence between species. The pine marten was positively associated with cooler mountainous areas, low levels of human disturbance, high proportion of natural forests and well-connected forestry plantations, and medium-extent agroforestry mosaics. The stone marten was positively related to the density of urban areas, the proportion and extensiveness of croplands, the existence of some scrub cover and semi-continuous grasslands.

Conclusions

This study outlines the influence of the spatial scale and the importance of the sampling bias corrections in HSM, and to our knowledge, it is the first comparing multiscale habitat selection and niche divergence of two related marten species. This study provides a useful methodological framework for multispecies and multiscale comparatives.

     

Estimating landscape resistance from habitat suitability: effects of data source and nonlinearities

Context

Conservation corridors must facilitate long-distance dispersal movements to promote gene flow, prevent inbreeding, and allow animals to shift ranges with climate change. Least-cost models are used to identify areas that support long-distance movement. These models rely on estimates of landscape resistance, which are typically derived from habitat suitability.

Objectives

We examine two key steps in estimating resistance from habitat suitability: choosing a procedure to estimate habitat suitability, and choosing a transformation function to translate habitat suitability into resistance.

Methods

We used linear and nonlinear functions to convert three types of habitat suitability estimates (from expert opinion, resource selection functions, and step selection functions) into resistances for elk (Cervus canadensis) and desert bighorn sheep (Ovis canadensis nelsoni). We evaluated the resulting resistance maps on an independent set of observed long-distance, prospecting movements.

Results

A negative exponential function best described the relationship between resistance values and habitat suitability for desert bighorn sheep indicating long-distance movers readily travel through moderately-suitable areas and avoid only the least suitable habitat. For desert bighorn sheep, all three suitability estimates performed better than chance, and resource and step selection functions outperformed expert opinion. For elk, all three suitability estimates performed the same as chance.

Conclusions

When designing corridors to facilitate long-distance movements of mobile animals, we recommend transforming habitat suitability into resistance with a negative exponential function. Use of an exponential transformation means that larger fractions of the landscape offer low resistance, allowing greater flexibility in where a corridor is located.

     

Quantifying edges as gradients at multiple scales improves habitat selection models for northern spotted owl

Context

Testing the influence of edges on animal distributions depends on our capacity to quantify ‘edge’, particularly in heterogeneous landscapes. Habitat quality is likely to differ in instances where edges are abrupt and anthropogenic in origin, versus diffuse, disturbance-created edges.

Objectives

We tested whether or not structurally distinct edge types influence northern spotted owl habitat selection and whether the relationship between edge type and use varied across spatial scales relevant to owl foraging (<3 ha) and home range selection (50–800 ha).

Methods

We used remotely sensed disturbance severity data to define two distinct edge types, ‘hard’ and ‘diffuse’, following a 11,000 ha fire and subsequent salvage logging in southern Oregon. The approach quantifies the steepness of gradients directly by measuring the ‘slope’ of change in disturbance severity. We tested the degree to which 23 radio-collared spotted owls responded to edge characteristics caused by fire and logging.

Results

Spotted owls showed a strong negative association with hard edge, even after accounting for habitat suitability and other confounding variables. However, this negative relationship was highly scale-dependent; spotted owls were resilient to hard edges at broad scales, but avoided the same feature at fine scales. On the other hand, spotted owls showed a positive association with diffuse edge, especially at broader scales.

Conclusions

Differential use of edge types indicates that owls favor disturbances that create diffuse edge habitat (e.g. low and mixed-severity fire) and rather than abrupt boundaries created by high severity disturbance.

     

Temporal shifts in landscape connectivity for an ecosystem engineer,the roe deer,across a multiple-use landscape

Context

Routine movements of large herbivores, often considered as ecosystem engineers, impact key ecological processes. Functional landscape connectivity for such species influences the spatial distribution of associated ecological services and disservices.

Objectives

We studied how spatio-temporal variation in the risk-resource trade-off, generated by fluctuations in human activities and environmental conditions, influences the routine movements of roe deer across a heterogeneous landscape, generating shifts in functional connectivity at daily and seasonal time scales.

Methods

We used GPS locations of 172 adult roe deer and step selection functions to infer landscape connectivity. In particular, we assessed the influence of six habitat features on fine scale movements across four biological seasons and three daily periods, based on variations in the risk-resource trade-off.

Results

The influence of habitat features on roe deer movements was strongly dependent on proximity to refuge habitat, i.e. woodlands. Roe deer confined their movements to safe habitats during daytime and during the hunting season, when human activity is high. However, they exploited exposed open habitats more freely during night-time. Consequently, we observed marked temporal shifts in landscape connectivity, which was highest at night in summer and lowest during daytime in autumn. In particular, the onset of the autumn hunting season induced an abrupt decrease in landscape connectivity.

Conclusions

Human disturbance had a strong impact on roe deer movements, generating pronounced spatio-temporal variation in landscape connectivity. However, high connectivity at night across all seasons implies that Europe’s most abundant and widespread large herbivore potentially plays a key role in transporting ticks, seeds and nutrients among habitats.

     

Influences of scale on bat habitat relationships in a forested landscape in Nicaragua

Context

Scale dependence of bat habitat selection is poorly known with few studies evaluating relationships among landscape metrics such as class versus landscape, or metrics that measure composition or configuration. This knowledge can inform conservation approaches to mitigate habitat loss and fragmentation.

Objectives

We evaluated scale dependence of habitat associations and scaling patterns of landscape metrics in relation to bat occurrence or capture rate in forests of southwestern Nicaragua.

Methods

We captured 1537 bats at 35 locations and measured landscape and class metrics across 10 spatial scales (100–1000 m) surrounding capture locations. We conducted univariate scaling across the 10 scales and identified scales and variables most related to bat occurrence or capture rate.

Results

Edge and patch density, at both landscape and class levels, were the most important variables across species. Feeding guilds varied in their response to metrics. Certain landscape and configuration metrics were most influential at fine (100 m) and/or broad (1000 m) spatial scales while most class and composition metrics were influential at intermediate scales.

Conclusions

These results provide insight into the scale dependence of habitat associations of bat species and the influence of fine and broad scales on habitat associations. The effects of scale, examined in our study and others from fine (100 m) to broad (5 km) indicate habitat relationships for bats may be more informative at larger scales. Our results suggest there could be general differences in scale relationships for different groups of landscape metrics, which deserves further evaluation in other taxonomic groups.

     

Grain-dependent functional responses in habitat selection

Context

Spatial scale is an important consideration for understanding how animals select habitat, and multi-scalar designs in resource selection studies have become increasingly common. Despite this, examination of functional responses in habitat selection at multiple scales is rare. The perceptual range of an animal changes as a function of vegetation association, suggesting that use, selection and functional responses may all be habitat- and scale-dependent.

Objectives

Our objective was to determine how varying grain size affects our interpretation of functional response in habitat selection and to elucidate scalar and landscape effects on habitat selection.

Methods

We quantified the functional response of GPS-collared, female white-tailed deer (Odocoileus virginianus, n = 18) in Riding Mountain National Park, Canada, to different habitat types. Functional responses were quantified at multiple spatial scales by regressing proportion of habitat used against proportion of habitat available at different buffer radii (ranging from 75–1000 m radius) surrounding used (telemetry) locations and available points within the individual’s seasonal home range. We examined how functional responses changed as a function of grain by plotting grain size against the slope of the functional response.

Results

We detected functional responses in most habitat types. As expected, functional responses tended to converge towards 1 (use proportional to availability) at large buffer sizes; however, the relationship between scale and functional response was typically non-linear and depended on habitat type.

Conclusions

We conclude that a multi-scalar approach to modelling animal functional responses in habitat selection is important for understanding patterns in animal behaviour and resource use.