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dc.contributor.authorDobson, Andrew
dc.contributor.authorMilner-Gulland, EJ
dc.contributor.authorBeale, Colin
dc.contributor.authorIbbett, Harriet
dc.contributor.authorKeane, Aidan
dc.identifier.citationDobson, A., Milner-Gulland, E. J., Beale, C., Ibbett, H. and Keane, A. (2018). Detecting deterrence from patrol data. 5th European Congress of Conservation Biology. doi: 10.17011/conference/eccb2018/107778
dc.description.abstractThe threat posed to protected areas (PAs) by the illegal killing of wildlife is countered principally by ranger patrols that aim to detect and deter potential offenders. Deterring poaching is a fundamental conservation objective [1]. However, deterrence is difficult to identify, especially when the prime source of information comes in the form of the patrols’ own records, which inevitably contain biases [2]. Sophisticated statistical techniques for the analysis of patrol data have been developed which considerable promise [3], but there is also a need for simple, widely-applicable metrics which can reliably detect deterrence. Here, we present a mechanistic model of law-breaking and law enforcement in which we simulate deterrence alongside exogenous changes in the frequency of offences, under different temporal patterns of enforcement effort. We use this to compare the performance of a set of candidate metrics that can be derived from patrol data alone. We find that plots of infractions detected per unit of patrol effort against patrol effort (IPUE-E plots) are not reliable indicators of deterrence. However, plots of change in IPUE over change in effort (ΔIPUE-ΔE) reliably diagnose deterrence, regardless of the temporal distribution of effort or any exogenous change in illegal activity levels, as long as data are collected such that detection probability does not saturate with increased effort, and when the time-lag between patrol effort and subsequent behavioural change among offenders is approximately known. ΔIPUE-ΔE plots are no more conceptually complicated than the basic IPUE-effort plots, and require no specialist knowledge or software to produce. ΔIPUE-ΔE offers a robust, simple, metric for monitoring patrol effectiveness where detection is proportional to effort. This work provides key insights into the nature of patrol data, as well as practical recommendations for on-the-ground data interpretation. 1. Wright EM, Bhammar HM, Gonzalez Velosa AM, Sobrevila C (2016) Analysis of international funding to tackle illegal wildlife trade. (Washington, D.C.: World Bank Group. Accessed 26/9/17. 2. Keane A, Jones JP, Milner‐Gulland EJ (2011) Encounter data in resource management and ecology: pitfalls and possibilities. J Appl Ecol 48(5):1164-1173. 3. Critchlow R, Plumptre AJ, Driciru M, Rwetsiba A, Stokes EJ, Tumwesigye C, Wanyama F, Beale CM (2015) Spatiotemporal trends of illegal activities from ranger‐collected data in a Ugandan national park. Conserv Biol 29(5):1458-1470.
dc.publisherOpen Science Centre, University of Jyväskylä
dc.rightsCC BY 4.0
dc.titleDetecting deterrence from patrol data
dc.type.coarconference paper not in proceedings
dc.rights.copyright© the Authors, 2018
dc.relation.conferenceECCB2018: 5th European Congress of Conservation Biology. 12th - 15th of June 2018, Jyväskylä, Finland

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  • ECCB 2018 [712]
    5th European Congress of Conservation Biology. 12th - 15th of June 2018, Jyväskylä, Finland

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CC BY 4.0
Except where otherwise noted, this item's license is described as CC BY 4.0