Human footprint differentially impacts genetic connectivity of four wide-ranging mammals in a fragmented landscape

Abstract

AimMaintaining connectivity is critical for long-term persistence of wild carnivores in landscapes fragmented due to anthropogenic activity. We examined spatial genetic structure and the impact of landscape features on connectivity in four wide-spread species- jungle cat (Felis chaus), leopard (Panthera pardus), sloth bear (Melursus ursinus) and tiger (Panthera tigris). Location Our study was carried out in the central Indian landscape, a stronghold in terms of distribution and abundance of large mammals. The landscape comprises fragmented forests embedded in a heterogeneous matrix of multiple land-use types.MethodsMicrosatellite data from non-invasively sampled individuals (90 jungle cats, 82 leopards, 104 sloth bears and 117 tigers) were used to investigate genetic differentiation. Impact of landscape features on gene flow was inferred using a multi-model landscape resistance optimization approach.ResultsAll four study species revealed significant isolation by distance (IBD). The correlation between genetic and geographic distance was significant only over a short distance for jungle cat, followed by longer distances for sloth bear, leopard and tiger. Overall, human footprint had a high negative impact on geneflow in tigers, followed by leopards, sloth bears and the least on jungle cats. Individual landscape variables- land-use, human population density, density of linear features and roads- impacted the study species differently. Although land-use was found to be an important variable explaining genetic connectivity for all four species, the amount of variation explained, the optimum spatial resolution and the resistance offered by different land-use classes varied.Main conclusionsAs expected from theory, but rarely demonstrated using empirical data, the pattern of spatial autocorrelation of genetic variation scaled with dispersal ability and density of the study species. Landscape genetic analyses revealed species-specific impact of landscape features and provided insights into interactions between species biology and landscape structure. Our results emphasize the need for incorporating functional connectivity data from multiple species for landscape-level conservation planning.

Closely related species show species-specific environmental responses and different spatial conservation needs: Prionailurus cats in the Indian subcontinent

“…SM1.2 ; full data in Table SM1.2 ). Although finer resolution environmental data is available, analyses were performed at 10 km resolution based on home-range estimates for the target species 37 , 38 , 45 , 46 , except for P. rubiginous for which estimates are not available. This resolution is therefore likely to minimize potential spatial autocorrelation while accounting for inaccuracies in the geographical positioning of records.…”

Habitat specificity drives differences in space‐use patterns of multiple mesocarnivores in an agroecosystem

“…The Indian fox is endemic to the Indian subcontinent and is generally found in areas with native savannah grasslands (Punjabi et al, 2013;Vanak & Gompper, 2010). The jungle cat, despite being known to occur in a wide variety of landscapes across India (Kalle et al, 2013;Mukherjee et al, 2004;Thatte et al, 2020), has a strong affinity toward water, and is often found near swamps, wetlands, and riparian zones, even in drier habitats (Duckworth et al, 2005;Mukherjee et al, 2010). These three mesocarnivore species are also sympatric in our study area.…”

Synthesizing habitat connectivity analyses of a globally important human‐dominated tiger‐conservation landscape

“…The finding that a majority of pixels showed two or three of five studies agreeing on a mode value (Figure 3a) can be attributed in large part to the distributions of the five studies' resistance layers (Appendix S6). Dutta et al (2018) and Thatte et al (2020) classified the landscape as predominantly low resistance, with few high resistance areas. Yumnam et al (2014), Mondal et al (2016), andReddy et al's (2017) layers show the Nonvillage areas include some villages without accurate boundaries (points or very small circular shapes) (e.g., east and south of Kanha-Phen PA).…”