IUCN / SSC Cat Specialist Group - Digital Cat Library |
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| Krishnamurthy, R.; Cushman, S.A.; Sarkar, M.S.; Malviya, M.; Naveen, M.; Johnson, J.A.; Sen, S. | |
| Multi-scale prediction of landscape resistance for tiger dispersal in central India | |
| 2016 Landscape Ecology (31): 1355-1368 | |
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 multiscale 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 scaleoptimized 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. |
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