Quantifying the relationship between prey density, livestock and illegal killing of leopards

Many large mammalian carnivores are facing population declines due to illegal killing (e.g., shooting) and habitat modification (e.g., livestock farming). Illegal killing occurs cryptically and hence is difficult to detect. However, reducing illegal killing requires a solid understanding of its magnitude and underlying drivers, while accounting for the imperfect detection of illegal killing events. Despite the importance of illegal killing of large carnivores in comparison with other causes of mortality, its relationship with potential drivers such as livestock density and wild prey abundance is rarely described. Using ranger-collected data (2007-2019) of leopard killing events and data on covariates (livestock density, wild prey abundance, road length, protected area size, elevation) across Iran, we applied a single-visit N-mixture model to jointly model variation in detection probability and expected annualized number of leopard killing events. Over the study period, we estimated 428 leopard mortalities (95% CI 184–1014), which was 45% larger than the observed number. Expected intensity of leopard killing was positively related to protected area size, livestock density and wild prey abundance. Detection of leopard killing was higher in areas with more developed road networks. Synthesis and applications Ranger based monitoring data on poaching of carnivores are cost-effective, but traditional analysis does not take into account imperfect detection. We show that innovative statistics (single-visit N-mixture modeling) can reliably quantify poaching events and address their drivers, at large geographical scales. We used the example of the Persian leopard across Iran, but our approach is also applicable to understand killing dynamics of other species. Results suggest that a high frequency of leopard killing is likely to occur in areas with > 100 livestock per km2 and > 450 individuals of wild prey per km2. This highlights the need for improved management of livestock grazing and effective measures around high-risk protected areas to mitigate human-leopard conflict and reduce killing of leopards.Funding provided by: Alexander von Humboldt-StiftungCrossref Funder Registry ID: http://dx.doi.org/10.13039/100005156Award Number: DEU 1220304 FLF-P, 2021-2023Funding provided by: German Federal Ministry of Education and Research*Crossref Funder Registry ID: Award Number: 57436650Funding provided by: Alexander von Humboldt-StiftungCrossref Funder Registry ID: http://dx.doi.org/10.13039/100005156Award Number:Our dataset composed of three parts: 1. The leopard illegal killing events' data (2007-2019) were collected by rangers across Iran (provided by Iranian Department of Environment). 2. The wild prey abundance data were provided by Iran's Department of Environment (DoE). The wild prey abundance are being collected annually (November-December) by skilled rangers within a regular monitoring program, which is being managed by the DoE across the country. 3. The site covariates data were obtained from the following sources: 3.1. Elevation (mean elevation from a 30-m resolution digital elevation model, was obtained from the NASA Shuttle Radar Topography Mission (https://search.earthdata.nasa.gov) 3.2. Road length (we obtained this from http://download.geofabrik.de/236 and https://extract.bbbike.org/, 2018) 3.3. Protected area (Iran's Department of Environment) 3.4. Human density (we obtained this from Gridded Population of the World v.4 at a 1-km spatial resolution from the Socioeconomic Data and Application Center (http://sedac.ciesin.columbia.edu/data/se, 2015) 3.5. Livestock densities (data on livestock (i.e., cattle, sheep and goats) were derived from the Food and Agriculture Organization, FAO (http://www.fao.org; 2005) All these data were extracted and assigned per grid cell (20*20 km2) across Iran using ArcGIS version 10.7.1 (ESRI USA). The data were analysed within a single-visit N-mixture modelling framework (Royle 2004 Biometrics; Sólymos et al. 2012 Environmetrics; Kéry and Royle 2021 AHMbook) in 'unmarked' R package (Fiske & Chandler 2011) in R software version 3.3.6 (R Core Team 2020)