Abstract
Habitat suitability models are useful to understand species distribution and to guide management and conservation strategies. The grey wolf (Canis lupus) has been extirpated from most of its historic range in Pakistan primarily due to its impact on livestock and livelihoods. We used non-invasive survey data from camera traps and genetic sampling to develop a habitat suitability model for C. lupus in northern Pakistan and to explore the extent of connectivity among populations. We detected suitable habitat of grey wolf using a maximum entropy approach (Maxent ver. 3.4.0) and identified suitable movement corridors using the Circuitscape 4.0 tool. Our model showed high levels of predictive performances, as seen from the values of area under curve (0.971±0.002) and true skill statistics (0.886±0.021). The main predictors for habitat suitability for C. lupus were distances to road, mean temperature of the wettest quarter and distance to river. The model predicted ca. 23,129 km2 of suitable areas for wolf in Pakistan, with much of suitable habitat in remote and inaccessible areas that appeared to be well connected through vulnerable movement corridors. These movement corridors suggest that potentially the wolf range can expand in Pakistan’s Northern Areas. However, managing protected areas with stringent restrictions is challenging in northern Pakistan, in part due to heavy dependence of people on natural resources. The habitat suitability map provided by this study can inform future management strategies by helping authorities to identify key conservation areas.
Highlights
IntroductionSpecies distribution models (SDMs) are increasingly important for investigating the requirements of species and for conservation planning [1,2,3,4,5]
The distribution of species in space and time is a central topic in ecology
Our model identified areas with suitable habitat and corridors through which wolves may travel to reach new territories across northern Pakistan
Summary
Species distribution models (SDMs) are increasingly important for investigating the requirements of species and for conservation planning [1,2,3,4,5]. Such models provide valuable quantitative information on the threats, such as areas of where there is high risk from humans, or where are the required resources [6] and they help identify conservation priorities [7,8,9,10,11]. The conservation of large carnivores remains challenging, in part due to a poor understanding of the complex spatial dynamics that facilitate population persistence [12].
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