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

Krishnamurthy, R.; Cushman, Samuel A.; Sarkar, Mriganka Shekhar; Malviya, Manjari; Naveen, Moorthy; Johnson, Jeyaraj Antony; Sen, Subharanjan

doi:10.1007/s10980-016-0363-0

Cited by 55 publications

(50 citation statements)

References 48 publications

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“…Krishnamurthy et al. () found that relocated tigers that were naïve to their new landscapes did indeed select forest cover and avoid human‐dominated landscapes to a high degree, while also selecting topographically rough terrain and ridge tops for movement, in agreement with this study. That study found a relatively much larger effect of land cover than we found here, probably due to the newness of the terrain to the translocated animals.…”

Section: Discussionsupporting

confidence: 89%

“…A possible explanation of the weaker land cover effect in this study than seen in Krishnamurthy et al. () could be related to time‐lag effects. Specifically, the Krishnamurthy et al.…”

Section: Discussioncontrasting

confidence: 62%

“…In addition, Krishnamurthy et al. () recently identified forest and scrub‐forest cover (using the same classifications as in this paper) as the strongest predictors of tiger dispersal movements using conditional logistic regression analysis of telemetry data. The weak effect of land cover and human footprint observed here could be the result of several not mutually exclusive processes.…”

Section: Discussionmentioning

confidence: 87%

“…Expert opinionbased estimates of landscape resistance are of unknown quality, and when tested have often been found to be inferior to predictions from empirical data (Mateo-Sánchez, Cushman, & Saura, 2014;Shirk, Wallin, Cushman, Rice, & Warheit, 2010). Thus, it is advantageous to use empirical data directly related to movement (Cushman & Lewis, 2010;Elliot, Cushman, Loveridge, Mtare, & MacDonald, 2014;Krishnamurthy et al, 2016) or genetic differentiation (Castillo, Epps, Davis, & Cushman, 2014;Cushman, McKelvey, Hayden, & Schwartz, 2006;Shirk et al, 2010;Wasserman, Cushman, Schwartz, & Wallin, 2010) to optimize resistance to movement or gene flow as a function of landscape features.…”

Section: Introductionmentioning

confidence: 99%

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

Reddy

Cushman

Srivastava

et al. 2017

Diversity and Distributions

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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.

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Section: Discussionsupporting

confidence: 89%

“…A possible explanation of the weaker land cover effect in this study than seen in Krishnamurthy et al. () could be related to time‐lag effects. Specifically, the Krishnamurthy et al.…”

Section: Discussioncontrasting

confidence: 62%

Section: Discussionmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

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

Reddy

Cushman

Srivastava

et al. 2017

Diversity and Distributions

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“…There are new ways to empirically validate the predicted corridors, including monitoring movements of a large number of individuals using camera‐trapping and satellite tacking (e.g., Cushman & Lewis, ; Elliot et al., ), using genetic mark–recapture and landscape genetic analyses (e.g., Shirk, Wallin, Cushman, Rice, & Warheit, ). We recommend the implementation of telemetry studies in the study area to validate and optimize predictions of functional connectivity between the protected areas (e.g., Elliot et al., ; Krishnamurthy et al., ). In recent years, radio tracking of carnivores has been started in Iran and used for leopard and Persian Wild Ass.…”

Section: Discussionmentioning

confidence: 99%

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

Khosravi

Hemami

Cushman

2017

Diversity and Distributions

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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.

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Multiscale landscape genetic analysis identifies major waterways as a barrier to dispersal of feral pigs in north Queensland, Australia

Ryan,

Prentis,

Fuller

2023

Ecology and Evolution

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Feral pigs (Sus scrofa) are a destructive and widespread invasive pest in Australia. An understanding of feral pig movement is required to develop management strategies to control feral pigs in Australia. Because landscape structure can have a strong influence on animal movement, it is important to determine how landscape features facilitate or impede the movement of feral pigs. Consequently, we conducted a landscape genetic analysis of feral pig populations in the Herbert region of far north Queensland, Australia, to determine management units and provide recommendations to better inform feral pig population control strategies. Using microsatellite data obtained from 256 feral pig samples from 44 sites, we examined feral pig population structure at multiple spatial scales for univariate and multivariate landscape resistance surfaces to determine the optimal spatial scale and to identify which of the nine landscape features tested impede or facilitate feral pig gene flow. Only weak genetic structure was found among the 44 sampling sites, but major waterways were identified as a minor barrier to gene flow, and an isolation by distance model was supported. We also found that highways facilitated gene flow across the study area, and this suggests that they may act as movement corridors or indicate translocation of feral pigs. Additionally, incorporating a second spatial scale enhanced the ability of our landscape genetics analysis to detect the influence of landscape structure on gene flow. We identified three management units based on natural barriers to gene flow and future targeted control should be undertaken in these management units to deliver sustained reduction of feral pig populations in the Herbert region. This study demonstrates how a landscape genetic approach can be used to gain insight into the ecology of an invasive pest species and be used to develop population control strategies which utilise natural barriers to movement.

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

Cited by 55 publications

References 48 publications

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

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

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

Multiscale landscape genetic analysis identifies major waterways as a barrier to dispersal of feral pigs in north Queensland, Australia

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