Why Did the Bear Cross the Road? Comparing the Performance of Multiple Resistance Surfaces and Connectivity Modeling Methods

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Aim There is enormous interest in applying connectivity modelling to resistance surfaces for identifying corridors for conservation action. However, the multiple analytical approaches used to estimate resistance surfaces and predict connectivity across resistance surfaces have not been rigorously compared, and it is unclear what methods provide the best inferences about population connectivity. Using a large empirical data set on puma (Puma concolor), we are the first to compare several of the most common approaches for estimating resistance and modelling connectivity and validate them with dispersal data. Location Southern California, USA. Methods We estimate resistance using presence‐only data, GPS telemetry data from puma home ranges and genetic data using a variety of analytical methods. We model connectivity with cost distance and circuit theory algorithms. We then measure the ability of each data type and connectivity algorithm to capture GPS telemetry points of dispersing pumas. Results We found that resource selection functions based on GPS telemetry points and paths outperformed species distribution models when applied using cost distance connectivity algorithms. Point and path selection functions were not statistically different in their performance, but point selection functions were more sensitive to the transformation used to convert relative probability of use to resistance. Point and path selection functions and landscape genetics outperformed other methods when applied with cost distance; no methods outperformed one another with circuit theory. Main conclusions We conclude that path or point selection functions, or landscape genetic models, should be used to estimate landscape resistance for wildlife. In cases where resource limitations prohibit the collection of GPS collar or genetic data, our results suggest that species distribution models, while weaker, may still be sufficient for resistance estimation. We recommend the use of cost distance‐based approaches, such as least‐cost corridors and resistant kernels, for estimating connectivity and identifying functional corridors for terrestrial wildlife.

Section: Discussionmentioning

confidence: 91%

Are all data types and connectivity models created equal? Validating common connectivity approaches with dispersal data

Zeller

Jennings

Vickers

et al. 2018

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171

“…Cushman et al. () found that cumulative resistant kernel value was a very strong predictor of locations of American black bear movement in a forested landscape in North America. The strong difference in the patterns of resistant kernel connectivity and predicted effective population size indicate that dispersal and local abundance of tigers in Central India are driven by different factors.…”

Section: Discussionmentioning

confidence: 99%

“…A priori we hypothesized that genetic effective population size could be related to several factors, including the following: (1) proportion of the local landscape covered by protected areas, (2) local extent of forest cover, (3) local average human footprint, (4) local landscape resistance as predicted by the landscape genetic optimization, and (5) cumulative resistant kernel value of connectivity across the resistance layer produced by landscape genetic optimization. Following Cushman, Lewis, and Landguth (), we expected that cumulative resistant kernel value would out‐predict other measures, because it measures functional connectivity of the region surrounding each population centre and is an index of rates of movement across the local landscape, which is the primary driver of genetic diversity within populations. We also predicted that effective population size would be positively related to extent of the local landscape covered by protected areas and forest cover, given that these provide habitat and lower anthropogenic mortality risk.…”

Section: Methodsmentioning

confidence: 99%

Tiger abundance and gene flow in Central India are driven by disparate combinations of topography and land cover

Reddy

Cushman

Srivastava

et al. 2017

Aim Effective conservation of the endangered tiger depends upon reliable knowledge of factors driving genetic differentiation and population connectivity. Connectivity models frequently use resistance surfaces not optimized with actual movement or genetic data which limits reliability. Our aim is to use empirical data on genetic diversity of tiger populations to optimize landscape resistance to gene flow and identify factors that predict local population abundance across Central India. Location The study area covers 697,000 km2 across Madhya Pradesh and parts of Rajasthan, Jharkhand and Maharashtra. Methods We used genetic data of 309 tigers and restricted multivariate optimization of correlation between landscape variables and genetic distance in a reciprocal causal modelling framework to parameterize a resistance surface for gene flow. We further evaluated the association between effective population size and landscape connectivity using all‐subsets logistic regression with model averaging based on AICc. Results Gene flow is primarily related to topographic roughness and slope position and secondarily to human footprint and land cover. It is much higher in areas of rough topography and ridge tops and is facilitated by forest cover in areas with low human footprint. In contrast, effective population size in protected areas is primarily driven by extent of protected areas and surrounding forest cover, and is not significantly related to resistant kernel connectivity value. Main Conclusions This is the first study to use a rigorous multivariate optimization approach to identify factors which limit gene flow of tigers. Tiger movement is highly affected by landscape features, and dispersing tigers move through rough terrain along forested ridges, avoiding non‐forest areas with high human footprint, while local tiger population density is driven primarily by the extent of protected forested habitat. These results have important implications for tiger conservation and can be used to develop empirically supported prioritization of core areas and corridors.

“…This method estimates the expected number of dispersing individuals traversing each cell of a grid‐based landscape based on landscape resistance, and the species dispersal ability using a specific dispersal function (Cushman, Landguth, et al., ; Cushman, McRae, et al., ). Importantly, the method is spatially synoptic (Cushman et al., ), producing spatially explicit predictions of movement rates for every location across the landscape, rather than for a few source or destination patches. In addition, factorial least‐cost path analysis provides useful information to complement results of resistant kernel modelling and provides localization of the highest importance and usage corridors between source points (e.g., Cushman et al., ).…”

Section: Introductionmentioning

confidence: 99%

“…Importantly, the method is spatially synoptic (Cushman et al., ), producing spatially explicit predictions of movement rates for every location across the landscape, rather than for a few source or destination patches. In addition, factorial least‐cost path analysis provides useful information to complement results of resistant kernel modelling and provides localization of the highest importance and usage corridors between source points (e.g., Cushman et al., ).…”

Section: Introductionmentioning

confidence: 99%

Multispecies assessment of core areas and connectivity of desert carnivores in central Iran

Khosravi

Hemami

Cushman

2017

Aim Central Iran is a priority area for biodiversity conservation, which is threatened by encroachment on core habitats and fragmentation by roads. The goal of this study was to identify core areas and connectivity corridors for a set of desert carnivores by predicting habitat suitability and calculating resistant kernel, factorial least‐cost path modelling and graph network indices. Location Iran. Methods We used an ensemble model (EM) of habitat suitability methods to predict the potential habitats of leopard, cheetah, caracal, wild cat, sand cat and grey wolf and used resistant kernel and factorial least‐cost path modelling to identify important core habitats and corridors between patches. We also used a graph network analysis to quantify the importance of each core patch to landscape connectivity. Results Potential habitats of the studied carnivores appeared to be strongly influenced by prey density, annual precipitation, topographical roughness, shrubland density and anthropogenic factors. Most of the core patches were covered by protected areas and no‐hunting areas. This may be attributed to the relatively high resistance outside protected areas leading to isolated occupied patches. Patch importance to connectivity was significantly correlated with patch extent, density of dispersing individuals and probability of occurrence in the core patch. Main conclusions Our findings revealed that prey abundance in core habitat is critically important, and has higher influence than habitat area per se. In addition, our analysis provided the first map of landscape connectivity for multiple species in Iran and revealed that conserving these species requires integrated landscape‐level management to reduce mortality risk and protect core areas and linkages among them. These results will assist the development of multispecies conservation strategies to protect core areas for carnivores.