Using landscape connectivity to predict human wildlife conflict
Abstract Human wildlife conflict has serious conservation consequences, both for populations of wildlife and for the people who live alongside them. Connectivity analyses can incorporate species specific landscape resistance, and therefore have the potential to be used to understand where wildlife moves and causes conflict with people. We used circuit theory to develop connectivity models for the African savanna elephant in northwestern Botswana based on step selection functions of movement data for 15 elephants and tested whether areas of high connectivity were correlated with occurrences of crop raiding. We used government records and field assessments of crop raiding incidents between 2010 and 2016 to quantify conflict, which we predicted would correlate with landscape connectivity. The step selection model revealed that linear boundaries such as rivers, fences, and dune crests were barriers to movement that impacted connectivity, while high vegetation index values and distance from villages were strong positive predictors of movement. Connectivity values were positively and significantly correlated with frequency of conflict incidents (p