Facilitating human-wildlife cohabitation through conflict prediction

TL;DR

This paper presents a predictive analysis approach for human-wildlife conflicts in unprotected forest areas, aiming to enable targeted interventions that reduce conflicts and save lives, especially in resource-limited settings.

Contribution

It introduces the first predictive model for human-wildlife conflicts in unprotected regions, addressing feature selection and data sparsity challenges.

Findings

Successful identification of conflict hotspots in the case study

Demonstrated potential for targeted interventions based on predictions

Improved understanding of conflict drivers in low-resource settings

Abstract

With increasing world population and expanded use of forests as cohabited regions, interactions and conflicts with wildlife are increasing, leading to large-scale loss of lives (animal and human) and livelihoods (economic). While community knowledge is valuable, forest officials and conservation organisations can greatly benefit from predictive analysis of human-wildlife conflict, leading to targeted interventions that can potentially help save lives and livelihoods. However, the problem of prediction is a complex socio-technical problem in the context of limited data in low-resource regions. Identifying the "right" features to make accurate predictions of conflicts at the required spatial granularity using a sparse conflict training dataset} is the key challenge that we address in this paper. Specifically, we do an illustrative case study on human-wildlife conflicts in the Bramhapuri Forest Division in Chandrapur, Maharashtra, India. Most existing work has considered human-wildlife conflicts in protected areas and to the best of our knowledge, this is the first effort at prediction of human-wildlife conflicts in unprotected areas and using those predictions for deploying interventions on the ground.