Habitat Connectivity for the Conservation of Small Ungulates in A Human-Dominated Landscape

Abstract

Conserving landscape connections among favorable habitats is a widely used strategy to maintain populations in an increasingly fragmented world. A species can then exist as a metapopulation consisting of several subpopulations connected by dispersal. Our study focuses on the importance of human–wildlife coexistence areas in maintaining connectivity among primary habitats of small ungulates within and outside protected areas in a large landscape in central India. We used geospatial information and species presence data to model the suitable habitats, core habitats, and connectivity corridors for four antelope species in an ~89,000 km2 landscape. We found that about 63% of the core habitats, integrated across the four species, lie outside the protected areas. We then measured connectivity in two scenarios: the present setting, and a hypothetical future setting – where habitats outside protected areas are lost. We also modelled the areas with a high risk of human-influenced antelope mortality using eco-geographical variables and wildlife mortality records. Overall, we found that the habitats in multiple-use forests play a central role in maintaining the connectivity network for antelopes. Sizable expanses of privately held farmlands and plantations also contribute to the essential movement corridors. Some perilous patches with greater mortality risk for species require mitigation measures such as underpasses, overpasses, and fences. Greater conservation efforts are needed in the spaces of human–wildlife coexistence to conserve the habitat network of small ungulates.

Habitat connectivity of three threatened ungulate species in the high plains native savanna of northern South America

“…The resistance values of each grid cell in the HPNSL connecting the core habitats and the maximum dispersal capacity of the species were used to derive the probabilistic dispersal area, where each cell was assigned a corridor value (range 0 to 100). The corridor values, together with core habitats, were used to create an integrated conservation area for the four UFS that highlighted regions with high probability corridors and core habitats (Niyogi et al 2021).…”

“…The conceptual approach considered was that of circuit theory (setting θ equal to 0 in RSPA). This method is reported byZeller et al (2018) as the most appropriate for modelling dispersal corridors when the starting data are locations of species presence (θ equal to 1 would simulate the least-cost path)(Niyogi et al 2021). RSPA estimates density values for each grid cell in the landscape, where higher values represent a higher probability of a random walker passing through the cell(McRae et al 2007).…”

“…RSPA estimates density values for each grid cell in the landscape, where higher values represent a higher probability of a random walker passing through the cell(McRae et al 2007). The data used for this analysis were (i) core habitat patches, (ii) resistance surface, and (iii) maximum dispersal distances of UFS(Niyogi et al 2021).…”

“…Habitat suitability was then used to derive the resistance surface and the core habitat patches(Niyogi et al 2021). We evaluated the performance of the MaxEnt models by calculating commission and omission errors(Anderson et al 2003), the area under the curve (We applied the speci city-sensitivity threshold using the R package presence-absence test(Freeman 2012), and binarization converted the continuous gradient maps into binary distributions of suitable and unsuitable classes(Niyogi et al 2021). Subsequently, we performed the Morphological Spatial Pattern Analysis (MSPA) on the suitable areas using the Guidos Toolbox (Soille and Vogt 2009; Vogt and Ritters 2017) to identify core habitat patches(Niyogi et al 2021).…”

“…We evaluated the performance of the MaxEnt models by calculating commission and omission errors(Anderson et al 2003), the area under the curve (We applied the speci city-sensitivity threshold using the R package presence-absence test(Freeman 2012), and binarization converted the continuous gradient maps into binary distributions of suitable and unsuitable classes(Niyogi et al 2021). Subsequently, we performed the Morphological Spatial Pattern Analysis (MSPA) on the suitable areas using the Guidos Toolbox (Soille and Vogt 2009; Vogt and Ritters 2017) to identify core habitat patches(Niyogi et al 2021). Additionally, the MSPA classi es the image into seven categories: (i) core, (ii) islet, (iii) loop, (iv) bridge, (v) perforation, (vi) edge, and (vii) branch (Soille and Vogt 2009), based on the shape and physical appearance of its features.…”