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 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 against patrol effort (CPUE‐E plots). 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 diagnosed 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‐effort 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 our understanding of deterrence in this context, especially the mechanisms by which it occurs. In view of these considerations, we provide practical recommendations for on‐the‐ground data interpretation