Cargando…
Detecting deterrence from patrol data
The threat posed to protected areas 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, but its achievement is difficult to identify, especially when the prime sour...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7379181/ https://www.ncbi.nlm.nih.gov/pubmed/30238502 http://dx.doi.org/10.1111/cobi.13222 |
_version_ | 1783562582538321920 |
---|---|
author | Dobson, Andrew D. M. Milner‐Gulland, E. J. Beale, Colin M. Ibbett, Harriet Keane, Aidan |
author_facet | Dobson, Andrew D. M. Milner‐Gulland, E. J. Beale, Colin M. Ibbett, Harriet Keane, Aidan |
author_sort | Dobson, Andrew D. M. |
collection | PubMed |
description | The threat posed to protected areas 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, but its achievement is difficult to identify, especially when the prime source of information comes in the form of the patrols’ own records, which inevitably contain biases. The most common metric of deterrence is a plot of illegal activities detected per unit of patrol effort (CPUE) against patrol effort (CPUE‐E). We devised a simple, mechanistic model of law breaking and law enforcement in which we simulated deterrence alongside exogenous changes in the frequency of offences under different temporal patterns of enforcement effort. The CPUE‐E plots were not reliable indicators of deterrence. However, plots of change in CPUE over change in effort (ΔCPUE‐ΔE) reliably identified deterrence, regardless of the temporal distribution of effort or any exogenous change in illegal activity levels as long as the time lag between patrol effort and subsequent behavioral change among offenders was approximately known. The ΔCPUE‐ΔE plots offered a robust, simple metric for monitoring patrol effectiveness; were no more conceptually complicated than the basic CPUE‐E plots; and required no specialist knowledge or software to produce. Our findings demonstrate the need to account for temporal autocorrelation in patrol data and to consider appropriate (and poaching‐activity‐specific) intervals for aggregation. They also reveal important gaps in understanding of deterrence in this context, especially the mechanisms by which it occurs. In practical applications, we recommend the use of ΔCPUE‐ΔE plots in preference to other basic metrics and advise that deterrence should be suspected only if there is a clear negative slope. Distinct types of illegal activity should not be grouped together for analysis, especially if the signs of their occurrence have different persistence times in the environment. |
format | Online Article Text |
id | pubmed-7379181 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-73791812020-07-24 Detecting deterrence from patrol data Dobson, Andrew D. M. Milner‐Gulland, E. J. Beale, Colin M. Ibbett, Harriet Keane, Aidan Conserv Biol Conservation Methods The threat posed to protected areas 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, but its achievement is difficult to identify, especially when the prime source of information comes in the form of the patrols’ own records, which inevitably contain biases. The most common metric of deterrence is a plot of illegal activities detected per unit of patrol effort (CPUE) against patrol effort (CPUE‐E). We devised a simple, mechanistic model of law breaking and law enforcement in which we simulated deterrence alongside exogenous changes in the frequency of offences under different temporal patterns of enforcement effort. The CPUE‐E plots were not reliable indicators of deterrence. However, plots of change in CPUE over change in effort (ΔCPUE‐ΔE) reliably identified deterrence, regardless of the temporal distribution of effort or any exogenous change in illegal activity levels as long as the time lag between patrol effort and subsequent behavioral change among offenders was approximately known. The ΔCPUE‐ΔE plots offered a robust, simple metric for monitoring patrol effectiveness; were no more conceptually complicated than the basic CPUE‐E plots; and required no specialist knowledge or software to produce. Our findings demonstrate the need to account for temporal autocorrelation in patrol data and to consider appropriate (and poaching‐activity‐specific) intervals for aggregation. They also reveal important gaps in understanding of deterrence in this context, especially the mechanisms by which it occurs. In practical applications, we recommend the use of ΔCPUE‐ΔE plots in preference to other basic metrics and advise that deterrence should be suspected only if there is a clear negative slope. Distinct types of illegal activity should not be grouped together for analysis, especially if the signs of their occurrence have different persistence times in the environment. John Wiley and Sons Inc. 2018-11-28 2019-06 /pmc/articles/PMC7379181/ /pubmed/30238502 http://dx.doi.org/10.1111/cobi.13222 Text en © 2018 The Authors. Conservation Biology published by Wiley Periodicals, Inc. on behalf of Society for Conservation Biology. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Conservation Methods Dobson, Andrew D. M. Milner‐Gulland, E. J. Beale, Colin M. Ibbett, Harriet Keane, Aidan Detecting deterrence from patrol data |
title | Detecting deterrence from patrol data |
title_full | Detecting deterrence from patrol data |
title_fullStr | Detecting deterrence from patrol data |
title_full_unstemmed | Detecting deterrence from patrol data |
title_short | Detecting deterrence from patrol data |
title_sort | detecting deterrence from patrol data |
topic | Conservation Methods |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7379181/ https://www.ncbi.nlm.nih.gov/pubmed/30238502 http://dx.doi.org/10.1111/cobi.13222 |
work_keys_str_mv | AT dobsonandrewdm detectingdeterrencefrompatroldata AT milnergullandej detectingdeterrencefrompatroldata AT bealecolinm detectingdeterrencefrompatroldata AT ibbettharriet detectingdeterrencefrompatroldata AT keaneaidan detectingdeterrencefrompatroldata |