Abstract
Managing human-wildlife conflicts (HWCs) is an important conservation objective for the many terrestrial landscapes dominated by humans. Forecasting where future conflicts are likely to occur and assessing risks to lives and livelihoods posed by wildlife are central to informing HWC management strategies. Existing assessments of the spatial occurrence patterns of HWC are based on either understanding spatial patterns of past conflicts or patterns of species distribution. In the former case, the absence of conflicts at a site cannot be attributed to the absence of the species. In the latter case, the presence of a species may not be an accurate measure of the probability of conflict occurrence. We present a Bayesian hierarchical modeling framework that integrates conflict reporting data and species distribution data, thus allowing the estimation of the probability that conflicts with a species are reported from a site, conditional on the species being present. In doing so, our model corrects for both false-positive and false-negative conflict reporting errors. We provide study design recommendations using simulations that explore the performance of the model under a range of conflict reporting probabilities. We applied the model to data on wild boar (Sus scrofa) space use and conflicts collected from the Central Terai Landscape (CTL), an important tiger conservation landscape in India. We found that tolerance for wildlife was a predictor of the probability with which farmers report conflict with wild boars from sites not used by the species. We also discuss useful extensions of the model when conflict data are verified for potential false-positive errors and when landscapes are monitored over multiple seasons.
Highlights
World over, tropical, sub-tropical, temperate, and Arctic landscapes are experiencing increased anthropogenic pressures as a direct consequence of growth in human populations and demand for resources (Kennedy et al, 2019)
Our models are similar to integrated species distribution models (Pacifici et al, 2017; Miller et al, 2019), where diverse data are integrated by means of a shared parameter
The model with the least support was one where both true and false conflict reporting probabilities were modeled without covariates and wild boar use probabilities were modeled as a function of distance of the cell to protected areas (PA)
Summary
Tropical, sub-tropical, temperate, and Arctic landscapes are experiencing increased anthropogenic pressures as a direct consequence of growth in human populations and demand for resources (Kennedy et al, 2019). The resulting increase in human activities in wildlife habitats is occurring alongside an increased recognition of the use of human-dominated areas by many adaptable wildlife species (Gordon, 2009; Ferreira et al, 2018). As a consequence of these shifts in habitat and resource use patterns by humans and wildlife populations, the frequency with which they interact is increasing (Nyhus, 2016). Human-wildlife conflicts (HWC) are a subset of these interactions that adversely affect wildlife populations and human communities (Dickman, 2010). Persistent HWC, when unmitigated, can affect conservation efforts by diminishing human tolerance for coexisting with wildlife (Kansky et al, 2016). Where human lives are lost, or where socio-economically marginalized communities are disproportionately affected, HWC complicates the moral and ethical arguments in support of wildlife conservation. Monitoring HWC, and understanding its spatio-temporal dimensions and drivers is an integral part of most conservation programs
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