Modelling the habitat requirements of leopard Panthera pardus in west and central Asia

Abstract

Summary 1.Top predators are seen as keystone species of ecosystems. Knowledge of their habitat requirements is important for their conservation and the stability of the wildlife communities that depend on them. The goal of our study was to model the habitat of leopard Panthera pardus in west and central Asia, where it is endangered, and analyse the connectivity between different known populations in the Caucasus to enable more effective conservation management strategies to be implemented. 2. Presence and absence data for the species were evaluated from the Caucasus, Middle East and central Asia. Habitat variables related to climate, terrain, land cover and human disturbance were used to construct a predictive model of leopard habitat selection by employing a geographic information system (GIS) and logistic regression. 3. Our model suggested that leopards in west and central Asia avoid deserts, areas with long-duration snow cover and areas that are near urban development. Our research also provides an algorithm for sample data management, which could be used in modelling habitats for similar species. 4. Synthesis and applications . This model provides a tool to improve search effectiveness for leopard in the Caucasus, Middle East and central Asia as well as for the conservation and management of the species. The model can predict the probable distribution of leopards and the corridors between various known populations. Connectivity patterns can be used to facilitate corridor planning for leopard conservation, especially in the Caucasus, where the leopard is a top priority conservation species. Also, as top predators are often associated with high biodiversity, the leopard habitat model could help to identify biodiversity hotspots. The protection of biodiversity hotspots is seen as the most effective way to conserve biodiversity globally.

Assessing the relative importance of landscape and husbandry factors in determining large carnivore depredation risk in Tanzania’s Ruaha landscape

“…Data on distance to rivers was generated using the Euclidean Distance function in ArcGIS v. 10.1 (Esri, 2012) based on the presence of water bodies in the study site. A vegetation continuous field (VCF) image was used to determine percentage of tree cover, whereas a Normalized Difference Vegetation Index (NDVI) image, derived from a compilation and processing of NDVI rasters from Terra (EOS AM) satellite images generated between March 2011 and December 2012, was considered as a proxy for wild prey availability (Gavashelishvili and Lukarevskiy, 2008). EGVs were converted in a 1 km  1 km habitat-grid cells raster file according to their original resolution size (Table 1).…”

Habitat features influencing jaguar Panthera onca (Carnivora: Felidae) occupancy in Tortuguero National Park, Costa Rica

“…Humans commonly hunt large carnivores and an increase in human presence can therefore be considered to equate to an increased predation risk (Wolf & Ale, 2009). Globally, large predators have been shown to demonstrate human avoidance, not only due to the threat of being hunted but also as a result of habitat degradation and a depletion of natural prey species (Whittington, Clair, & Mercer, 2005;Johnson, Vongkhamheng, Hedemark, & Saithongdam, 2006;Gavashelishvili & Lukarevskiy, 2008;Chávez, 2010;Conde et al, 2010).…”

Habitat suitability models of mountain ungulates: identifying potential areas for conservation

“…In our study, we first tested whether habitat variables can be used to predict the presence and absence of species using logistic regression and derived suitability indices for particular habitat variables based on Jacob's index (D). Furthermore, the multiplication approach used to combine habitat variables with suitability results in values of 0 (not suitable) and 1 (poorly suitable) and reduces the incidence of overoptimistic predictions (Dayton and Fitzgerald 2006;Gavashelishvili and Lukarevskiy 2008). Another possible criticism may be that too few variables are included in the model.…”

“…Prior knowledge of environmental determinants of species distribution is crucial to improve the quality and reliability of predictions (Hirzel and Le Lay 2008). There is therefore a need for an alternative modelling approach that does not require sophisticated tools and can be implemented using limited environmental data based on documented species-habitat associations (Gavashelishvili and Lukarevskiy 2008). A multiplicative operation for combining important environmental variables is one of the widely used techniques of model construction (e.g.…”