Mechanistic understanding of human–wildlife conflict through a novel application of dynamic occupancy models

Abstract

Crop and livestock depredation by wildlife is a primary driver of human-wildlife conflict, a problem that threatens the coexistence of people and wildlife globally. Understanding mechanisms that underlie depredation patterns holds the key to mitigating conflicts across time and space. However, most studies do not consider imperfect detection and reporting of conflicts, which may lead to incorrect inference regarding its spatiotemporal drivers. We applied dynamic occupancy models to elephant crop depredation data from India between 2005 and 2011 to estimate crop depredation occurrence and model its underlying dynamics as a function of spatiotemporal covariates while accounting for imperfect detection of conflicts. The probability of detecting conflicts was consistently <1.0 and was negatively influenced by distance to roads and elevation gradient, averaging 0.08-0.56 across primary periods (distinct agricultural seasons within each year). The probability of crop depredation occurrence ranged from 0.29 (SE 0.09) to 0.96 (SE 0.04). The probability that sites raided by elephants in primary period t would not be raided in primary period t + 1 varied with elevation gradient in different seasons and was influenced negatively by mean rainfall and village density and positively by distance to forests. Negative effects of rainfall variation and distance to forests best explained variation in the probability that sites not raided by elephants in primary period t would be raided in primary period t + 1. With our novel application of occupancy models, we teased apart the spatiotemporal drivers of conflicts from factors that influence how they are observed, thereby allowing more reliable inference on mechanisms underlying observed conflict patterns. We found that factors associated with increased crop accessibility and availability (e.g., distance to forests and rainfall patterns) were key drivers of elephant crop depredation dynamics. Such an understanding is essential for rigorous prediction of future conflicts, a critical requirement for effective conflict management in the context of increasing human-wildlife interactions.

Determinants of elephant foraging behaviour in a coupled human‐natural system: Is brown the new green?

“…Indeed, collared elephants spent nearly 30% of their time in croplands between September and November. This contrasts with the results of some previous studies that have reported peaks in crop raiding by elephants associated with higher rainfall (e.g., Goswami, Medhi, Nichols, & Oli, ; Osborn, ; Sukumar, ). This discrepancy likely is at least partially related to ease of access to irrigation water in our study area.…”

Are ranger patrols effective in reducing poaching‐related threats within protected areas?

“…Thus, we also sought to (4) evaluate the efficacy of ranger patrols in reducing poaching‐related threats in NNP. We used dynamic occupancy models to analyse the data (MacKenzie et al., , ) because they provide a rigorous statistical framework for estimating relevant parameters while also accounting for imperfect detection of threats by rangers (Goswami et al., ; Linkie et al., ; MacKenzie et al., ; Sharma, Wright, Joseph, & Desai, ).…”

A Bayesian hierarchical approach to quantifying stakeholder attitudes toward conservation in the presence of reporting error

“…Hierarchical models have been extensively used in the field of applied conservation research to parse processes of interest (here, attitudes of respondents) from observational processes (in our case, misreporting) (Williams et al 2002;Royle & Dorazio 2008;Lachish et al 2012;Goswami et al 2015). These models have been used previously on data obtained from questionnaire surveys as well, accounting simultaneously for probabilities of falsepositive and false-negative reporting (e.g., Pillay et al 2014).…”