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Ensemble modelling predicts Human Carnivore Conflict for a community adjacent to a protected area in Zimbabwe
Author(s) -
Mpakairi Kudzai,
Ndaimani Henry,
Vingi Knowledge,
Madiri Tinaapi Hilary,
Nekatambe Tendai
Publication year - 2018
Publication title -
african journal of ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.499
H-Index - 54
eISSN - 1365-2028
pISSN - 0141-6707
DOI - 10.1111/aje.12526
Subject(s) - carnivore , livestock , predation , geography , wildlife , statistic , population , normalized difference vegetation index , environmental resource management , ecology , statistics , biology , environmental science , forestry , climate change , mathematics , demography , sociology
Overlapping interests between humankind and nature have resulted in humans and wildlife interacting. These interactions usually emanate from the absence of fences or hard boundaries to restrict animal movement and when this happens, it results in Human Carnivore Conflict ( HCC ). People residing in areas adjacent to protected areas are the ones mostly at risk of HCC . In this study, we used ensemble modelling to explain predation risk and indicate the key drivers of HCC in Matetsi Communal Area, Zimbabwe. Ensemble modelling involves building a single consensus model from several candidate models. We used seven environmental variables for the modelling process, and these were distance from the park boundary, distance from rivers, Normalised Difference Vegetation Index ( NDVI ), human population density and livestock density for cattle, goats and sheep. Livestock kill sites were used as the presence data. Our results illustrate that ensemble modelling explains predation risk with a true skill statistic (TSS) of 0.9 for Matetsi Communal Area. This study provides the potential application of ensemble modelling in HCC management through identifying predation risk areas. In identifying predation risk areas, proactive and cost‐efficient management strategies for dealing with HCC in specific high‐risk areas are plausible.