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.

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.

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.

Context

Multi-scale approaches to habitat modeling have been shown to provide more accurate understanding and predictions of species-habitat associations. It remains however unexplored how spatial and temporal variations in habitat use may affect multi-scale habitat modeling.

Objectives

We aimed at assessing how seasonal and temporal differences in species habitat use and distribution impact operational scales, variable influence, habitat suitability spatial patterns, and performance of multi-scale models.

Methods

We evaluated the environmental factors driving brown bear habitat relationships in the Cantabrian Range (Spain) based on species presence records (ground observations) for the period 2000–2010, LiDAR data on forest structure, and seasonal estimates of foraging resources. We separately developed multi-scale habitat models for (i) each season (spring, summer, fall and winter) (ii) two sub-periods with different population status: 2000–2004 (with brown bear distribution restricted to the main population nuclei) and 2005–2010 (with expanding bear population and range); and (iii) the entire 2000–2010 period.

Results

Scales of effect remained considerably stable across seasonal and temporal variations, but not the influence of certain environmental variables. The predictive ability of multi-scale models was lower in the seasons or periods in which populations used larger areas and a broader variety of environmental conditions. Seasonal estimates of foraging resources, together with LiDAR data, appeared to improve the performance of multi-scale habitat models.

Conclusions

We highlight that the understanding of multi-scale behavioral responses of species to spatial patterns that continually shift over time may be essential to unravel habitat relationships and produce reliable estimates of species distributions.

Context

In heterogeneous landscapes, local patterns of community structure are a product of the habitat size and condition within a patch interacting with adjacent habitat patches of varying composition and quantity. While evidence for local versus landscape factors have been found in terrestrial biomes, support for such multi-scale effects shaping marine ecological communities is equivocal.

Objectives

We investigated whether within-patch habitat condition can override seascape context to explain the community structure of macroalgae-associated reef fishes across a tropical seascape.

Methods

We mapped the distribution and abundance of a diverse family of reef fishes (Labridae) occupying macroalgae meadows within a tropical reef ecosystem, and using best-subsets model selection, investigated the potential for habitat structural connectivity and/or local habitat quality for predicting variations in fish community structure across the seascape.

Results

Local habitat quality (canopy structure, hard habitat complexity) and area of coral-dominated habitat within 500 m of a macroalgal meadow provided the best predictors of fish community structure. However, the specific importance of a given predictor varied with fish life history stage and functional trophic group. Interestingly, macroalgae meadow area was among the least important predictors.

Conclusions

Given the complex interplay between local habitat quality and spatial context effects on fish biodiversity, our study reveals the multi-scale predictors that should be used in spatial conservation and management approaches for tropical fish diversity. Moreover, our findings question the ubiquity of habitat area effects in patchy landscapes, and cautions against a sole reliance on habitat quantity in spatial management.

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.

Context

GPS telemetry collars and their ability to acquire accurate and consistently frequent locations have increased the use of step selection functions (SSFs) and path selection functions (PathSFs) for studying animal movement and estimating resistance. However, previously published SSFs and PathSFs often do not accommodate multiple scales or multi-scale modeling.

Objectives

We present a method that allows multiple scales to be analyzed with SSF and PathSF models. We also explore the sensitivity of model results and resistance surfaces to whether SSFs or PathSFs are used, scale, prediction framework, and GPS collar sampling interval.

Methods

We use 5-min GPS collar data from pumas (Puma concolor) in southern California to model SSFs and PathSFs at multiple scales, to predict resistance using two prediction frameworks (paired and unpaired), and to explore potential bias from GPS collar sampling intervals.

Results

Regression coefficients were extremely sensitive to scale and pumas exhibited multiple scales of selection during movement. We found PathSFs produced stronger regression coefficients, larger resistance values, and superior model performance than SSFs. We observed more heterogeneous surfaces when resistance was predicted in a paired framework compared with an unpaired framework. Lastly, we observed bias in habitat use and resistance results when using a GPS collar sampling interval longer than 5 min.

Conclusions

The methods presented provide a novel way to model multi-scale habitat selection and resistance from movement data. Due to the sensitivity of resistance surfaces to method, scale, and GPS schedule, care should be used when modeling corridors for conservation purposes using these methods.

Context

Invasive Burmese pythons are altering the ecology of southern Florida and their distribution is expanding northward. Understanding their habitat use is an important step in understanding the pathways of the invasion.

Objectives

This study identifies key landscape variables in predicting relative habitat suitability for pythons at the present stage of invasion through presence-only ecological niche modeling using geographical sampling bias correction.

Methods

We used 2014 presence-only observations from the EDDMapS database and three landscape variables to model habitat suitability: fine-scale land cover, home range-level land cover, and distance to open freshwater or wetland. Ten geographical sampling bias correction scenarios based on road presence and sampling effort were evaluated to improve the efficacy of modeling.

Results

The best performing models treated road presence as a binary factor rather than a continuous decrease in sampling effort with distance from roads. Home range-level cover contributed the most to the final prediction, followed by proximity to water and fine-scale land cover. Estuarine habitat and freshwater wetlands were the most important variables to contribute to python habitat suitability at both the home range-level and fine-scale. Suitability was highest within 30 m of open freshwater and wetlands.

Conclusions

This study provides quantifiable, predictive relationships between habitat types and python presence at the current stage of invasion. This knowledge can elucidate future targeted studies of python habitat use and behavior and help inform management efforts. Furthermore, it illustrates how estimates of relative habitat suitability derived from MaxEnt can be improved by both multi-scale perspectives on habitat and consideration of a variety of bias correction scenarios for selecting background points.

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.

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.

Context

The problem of how ecological mechanisms create and interact with patterns across different scales is fundamental not only for understanding ecological processes, but also for interpretations of ecological dynamics and the strategies that organisms adopt to cope with variability and cross-scale influences.

Objectives

Our objective was to determine the consistency of the role of individual habitat patches in pattern-process relationships (focusing on the potential for dispersal within a network of patches in a fragmented landscape) across a range of scales.

Methods

Network analysis was used to assess and compare the potential connectivity and spatial distribution of highland fynbos habitat in and between protected areas of the Western Cape of South Africa. Connectivity of fynbos patches was measured using ten maximum threshold distances, ranging from five to 50 km, based on the known average dispersal distances of fynbos endemic bird species.

Results

Network connectivity increased predictably with scale. More interestingly, however, the relative contributions of individual protected areas to network connectivity showed strong scale dependence.

Conclusions

Conservation approaches that rely on single-scale analyses of connectivity and context (e.g., based on data for a single species with a given dispersal distance) are inadequate to identify key land parcels. Landscape planning, and specifically the assessment of the value of individual areas for dispersal, must therefore be undertaken with a multi-scale approach. Developing a better understanding of scaling dependencies in fragmenting landscapes is of high importance for both ecological theory and conservation planning.

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.

Context

Animals selectively use landscapes to meet their energetic needs, and trade-offs in habitat use may depend on availability and environmental conditions. For example, habitat selection at high temperatures may favor thermal cover at the cost of reduced foraging efficiency under consistently warm conditions.

Objective

Our objective was to examine habitat selection and space use in distinct populations of moose (Alces alces). Hypothesizing that endotherm fitness is constrained by heat dissipation efficiency, we predicted that southerly populations would exhibit greater selection for thermal cover and reduced selection for foraging habitat.

Methods

We estimated individual step selection functions with shrinkage for 134 adult female moose in Minnesota, USA, and 64 in Ontario, Canada, to assess habitat selection with variation in temperature, time of day, and habitat availability. We averaged model coefficients within each site to quantify selection strength for habitats differing in forage availability and thermal cover.

Results

Moose in Ontario favored deciduous and mixedwood forest, indicating selection for foraging habitat across both diel and temperature. Habitat selection patterns of moose in Minnesota were more dynamic and indicated time- and temperature-dependent trade-offs between use of foraging habitat and thermal cover.

Conclusions

We detected a scale-dependent functional response in habitat selection driven by the trade-off between selection for foraging habitat and thermal cover. Landscape composition and internal state interact to produce complex patterns of space use, and animals exposed to increasingly high temperatures may mitigate fitness losses from reduced foraging efficiency by increasing selection for foraging habitat in sub-prime foraging landscapes.

Context

The analysis of individual movement choices can be used to better understand population-level resource selection and inform management.

Objectives

We investigated movements and habitat selection of 13 bobcats in Vermont, USA, under the assumption individuals makes choices based upon their current location. Results were used to identify “movement-defined” corridors.

Methods

We used GPS-collars and GIS to estimate bobcat movement paths, and extracted statistics on land cover proportions, topography, fine-scale vegetation, roads, and streams within “used” and “available” space surrounding each movement path. Compositional analyses were used to determine habitat preferences with respect to landcover and topography; ratio tests were used to determine if used versus available ratios for vegetation, roads, and streams differed from 1. Results were used to create travel cost maps, a primary input for corridor analysis.

Results

Forested and scrub-rock land cover were most preferred for movement, while developed land cover was least preferred. Preference depended on the composition of the “available” landscape: Bobcats moved?>?3 times more quickly through forest and scrub-rock habitat when these habitats were surrounded by agriculture or development than when the available buffer was similarly composed. Overall, forest edge, wetland edge and higher stream densities were selected, while deep forest core and high road densities were not selected. Landscape-scale connectivity maps differed depending on whether habitat suitability, preference, or selection informed the travel cost map.

Conclusions

Both local and landscape scale land cover characteristics affect habitat preferences and travel speed of bobcats, which in turn can inform management and conservation activities.

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.

Context

Golden-cheeked warblers (Setophaga chrysoparia), an endangered wood-warbler, breed exclusively in woodlands co-dominated by Ashe juniper (Juniperus ashei) in central Texas. Their breeding range is becoming increasingly urbanized and habitat loss and fragmentation are a main threat to the species’ viability.

Objectives

We investigated the effects of remotely sensed local habitat and landscape attributes on point occupancy and density of warblers in an urban preserve and produced a spatially explicit density map for the preserve using model-supported relationships.

Methods

We conducted 1507 point-count surveys during spring 2011–2014 across Balcones Canyonlands Preserve (BCP) to evaluate warbler habitat associations and predict density of males. We used hierarchical Bayesian models to estimate multiple components of detection probability and evaluate covariate effects on detection probability, point occupancy, and density.

Results

Point occupancy was positively related to landscape forest cover and local canopy cover; mean occupancy was 0.83. Density was influenced more by local than landscape factors. Density increased with greater amounts of juniper and mixed forest and decreased with more open edge. There was a weak negative relationship between density and landscape urban land cover.

Conclusions

Landscape composition and habitat structure were important determinants of warbler occupancy and density, and the large intact patches of juniper and mixed forest on BCP (>2100 ha) supported a high density of warblers. Increasing urbanization and fragmentation in the surrounding landscape will likely result in lower breeding density due to loss of juniper and mixed forest and increasing urban land cover and edge.

Context

The roosting habits of many temperate zone bats are well documented at microhabitat scales, but fewer studies have included multi-scale assessments of landscape patterns in bat roost site selection.

Objectives

To identify and assess at the landscape-scale the location of spring and early season maternity roosts of female northern long-eared bats (Myotis septentrionalis) from 2015 to 2016 at Mammoth Cave National Park (MACA), Kentucky, USA.

Methods

We used mist-nets and radiotelemetry to catch and track bats to roost trees across the landscape of MACA. Data on roosting sites were evaluated using spatial point pattern analysis to examine distributional trends of roosts. A variety of spatial covariates were used to model the effect of landscape pattern, including: forest type, elevation, and proximity to hibernacula, water, and road corridors.

Results

Data indicate that roost locations of female northern long-eared bats in MACA were typically situated within 2000 m of known winter hibernacula, occurring more often at higher elevations in mesic upland deciduous forests, and in close proximity to water sources and roads. We present hypotheses to account for the patterns observed in relation to landscape features and habitat resources in the Park.

Conclusions

Our data indicate that a more comprehensive understanding of habitat requirements which includes empirically-based, landscape-scale patterns, and not solely considerations at stand or local levels, could lead to better informed management policies targeting conservation of maternity habitat of forest-dwelling bats, including the northern long-eared bat, a species in decline throughout much of its distribution in North America.

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.

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.

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.