DOI: 10.5061/dryad.4xgxd2583
title: Optimal allocation of law enforcement patrol effort to mitigate poaching activities

description: Poaching is a global problem causing the decline of species worldwide.
 Optimizing the efficiency of ranger patrols to deter poaching activity at
 the lowest possible cost is crucial for protecting species with limited
 resources. We applied decision analysis and spatial optimization
 algorithms to allocate efforts of ranger patrols throughout a national
 park. Our objective was to mitigate poaching activity at or below
 management risk targets for the lowest monetary cost. We examined this
 tradeoff by constructing a Pareto efficiency frontier using integer linear
 programing. We used data from a ranger-based monitoring program in Nyungwe
 National Park, Rwanda. Our measure of poaching risk is based on dynamic
 occupancy models which account for imperfect detection of poaching
 activities. We found that in order to achieve a 5% reduction in poaching
 risk, 622 ranger patrol events (each corresponding to patrolling 1 km2
 sites) were needed within a year at a cost of $49,760 USD. In order to
 attain a 60% reduction in poaching risk, 15,560 patrol events were needed
 at a cost of $1,244,800 USD. We evaluated the tradeoff between patrol cost
 and poaching risk based on our model by constructing a Pareto efficiency
 frontier and park managers found the solution for a 50% risk reduction to
 be a practical tradeoff based on funding constraints (comparable to recent
 years) and the diminishing returns between risk mitigation and cost. This
 expected reduction in risk required 8,558 patrol events per year at a cost
 of $684,640 USD. Our results suggest that optimal solutions could increase
 efficiency compared to the actual effort allocations from 2006 – 2016 in
 Nyungwe National Park (e.g., risk reductions of ~30% under recent budgets
 compared to ~50% reduction in risk under the optimal strategy). The
 modeling framework in this study took into account imperfect detection of
 poaching risk as well as the directional and conditional nature of ranger
 patrol events given the spatial adjacency relationships of neighboring
 sites and access points. Our analyses can help to improve the efficiency
 of ranger patrols, and the modeling framework can be broadly applied to
 other spatial conservation planning problems with conditional,
 multi-level, site selection.
description: Data on poaching risk and ranger patrol cost were originally
 collected from the ranger-based monitoring program in Nyungwe National
 Park (NNP), Rwanda. These data are published and discussed in:  Moore, J.
 F., Mulindahabi, F., Masozera, M. K., Nichols, J. D., Hines, J. E.,
 Turikunkiko, E., & Oli, M. K. (2017). Are ranger patrols effective
 in reducing poaching-related threats within protected areas?
 <i>Journal of Applied Ecology</i>, 99–107.
 https://doi.org/10.1111/1365-2664.1296. We used the fitted dynamic
 occupancy model and data from this publication to predict the probability
 of poaching (poaching risk) in each grid cell in the park under different
 levels of ranger patrol effort.  The grid cell
 neighbors, and upstream/downstream relationships of grid cells along each
 straight line path between terminal grid cells and their nearest access
 point (e.g. ranger station, road) were calculated using ArcMap 10.1 (see
 corresponding manuscript for more details). 
description: This dataset contains the data and code required to recreate the
 spatial optimization analysis from: Jennifer F. Moore,
 Bradley J. Udell, Julien Martin, Ezechiel Turikunkiko, and Michel K.
 Masozera (2021). Optimal allocation of law enforcement patrol effort to
 mitigate poaching activities. Ecological Applications (Full citation
 forthcoming).  In addition to recreating the ranger
 patrol optimization analysis from the manuscript, this code also provides
 a general method for incorporating directed network connectivity
 constraints in spatial conservation planning problems using integer linear
 programming.  The code requires an input for the list of upstream and
 downstream neighbor pairs, from which it encodes the directional network
 constraints, so these relationships must be determined a
 beforehand.  Metadata is provided in the .xml file.
 Description files are also provided for each .csv. to define the column
 headers.  The R script recreates spatial optimization analysis from the
 manuscript.  It requires the software Gurobi and a liscense, which can
 currently be obtained freely for academic purposes. However, because the R
 code structures all the componets of the integer linear programing
 problem, the code could be readily adapted to use
 different solvers. If you use this code in your work,
 please cite both the manuscript and code. Also please refer to the
 manuscript for detailed methods, and for annotated code in the supporting
 materials.