Rangers and modellers collaborate to build and evaluate spatial models of African elephant poaching. (March 2020)

• Record Type: Journal Article
• Authors: Kuiper, Timothy; Kavhu, Blessing; Ngwenya, Nobesuthu A.; Mandisodza-Chikerema, Roseline; Milner-Gulland, E.J.
• Is Part Of: Biological conservation. Volume 243(2020)
• Publication Date: 2020-03
• Subjects: Ranger-based monitoring -- Encounter data -- Ranger patrols -- Elephant poaching -- Ensemble models -- Biomod -- Spatial bias -- Loxodonta Africana -- Patrol bias; Conservation of natural resources -- Periodicals; Nature conservation -- Periodicals; Ecology -- Periodicals; Environment -- Periodicals; Environmental Pollution -- Periodicals; Electronic journals; 333.9516
• DOI: 10.1016/j.biocon.2020.108486
• Languages: English
• ISSNs: 0006-3207

Abstract

Abstract: Globally, tens of thousands of wildlife rangers patrol wide areas and record evidence of poaching activity such as elephant carcasses and snares. Such data have significant potential to inform conservation, but patrols are non-random in space and time, so conclusions from raw patrol data may be biased. Here we model spatial patterns of elephant poaching based on detections of carcasses by ranger patrols in the Zambezi Valley, Zimbabwe (201 carcasses, 2000–2017), using different methodological scenarios to correct for patrol bias. We follow a participatory modelling framework, using interviews with practitioners (rangers and managers) to help build and evaluate these models. We found that poaching patterns in the bias-corrected scenarios differed among themselves and from the uncorrected scenario. Practitioners interrogated the credibility of the predictions in each scenario and thus helped discern true poaching patterns from those explained by patrol bias. We uncovered proximity to water as the strongest driver of poaching, likely reflecting both poacher and elephant behaviour. Our results show that it is essential to account for observer bias before developing management actions (such as ranger patrol strategies) from raw observational data. We further demonstrate the value of combining multiple lines of evidence (statistical models and interview responses) for more robust inference in the face of uncertainty.