Detecting wildlife poaching: a rigorous method for comparing patrol strategies using an experimental design

Many studies of wildlife poaching acknowledge the challenges of detecting poaching activities, but few address the issue. Data on poaching may be an inaccurate reflection of the true spatial distribution of events because of low detection rates. The deployment of conservation and law enforcement resources based on biased data could be ineffective or lead to unintended outcomes. Here, we present a rigorous method for estimating the probabilities of detecting poaching and for evaluating different patrol strategies. We illustrate the method with a case study in which imitation snares were set in a private nature reserve in South Africa. By using an experimental design with a known spatial distribution of imitation snares, we estimated the detection probability of the current patrol strategy used in the reserve and compared it to three alternative patrol strategies: spatially focused patrols, patrols with independent observers, and systematic search patterns. Although detection probabilities were generally low, the highest proportion of imitation snares was detected with systematic search strategies. Our study provides baseline data on the probability of detecting snares used for poaching, and presents a method that can be modified for use in other regions and for other types of wildlife poaching.

Modeling illegal logging in Brazil

Deforestation is a major threat to global environmental wellness, with illegal logging as one of the major causes. Recently, there has been increased effort to model environmental crime, with the goal of assisting law enforcement agencies in deterring these activities. We present a continuous model for illegal logging applicable to arbitrary domains. We model the practice of criminals under influence of law enforcement agencies using tools from multiobjective optimal control theory and consider non-instantaneous logging events and load-dependent travel velocity. We calibrate our model using real deforestation data from the Brazilian rainforest and demonstrate the importance of geographically targeted patrol strategies.

When to Follow the Tip: Security Games with Strategic Informants

Although security games have attracted intensive research attention over the past years, few existing works consider how information from local communities would affect the game. In this paper, we introduce a new player -- a strategic informant, who can observe and report upcoming attacks -- to the defender-attacker security game setting. Characterized by a private type, the informant has his utility structure that leads to his strategic behaviors. We model the game as a 3-player extensive-form game and propose a novel solution concept of Strong Stackelberg-perfect Bayesian equilibrium. To compute the optimal defender strategy, we first show that although the informant can have infinitely many types in general, the optimal defense plan can only include a finite (exponential) number of different patrol strategies. We then prove that there exists a defense plan with only a linear number of patrol strategies that achieve the optimal defender's utility, which significantly reduces the computational burden and allows us to solve the game in polynomial time using linear programming. Finally, we conduct extensive experiments to show the effect of the strategic informant and demonstrate the effectiveness of our algorithm.

AI for Conservation: Aerial Monitoring to Learn and Plan against Illegal Actors

Conservation of our planet’s natural resources is of the utmost importance and requires constant innovation. This project focuses on innovation for one aspect of conservation: the reduction of wildlife poaching. Park rangers patrol parks to decrease poaching by searching for poachers and animal snares left by poachers. Multiple strategies exist to aid in these patrols, including adversary behavior prediction and planning optimal ranger patrol strategies. These research efforts suffer from a key shortcoming: they fail to integrate real-time data, and rely on historical data collected during ranger patrols. With the recent advances in unmanned aerial vehicle (UAV) technology, UAVs have become viable tools to aid in park ranger patrols. There is now an opportunity to augment the input for these strategies in real time. Detection is done on real-time data collected from UAVs. Detection will then be used to learn adversaries’ behaviors, or where poaching may occur in the future, in future work. This will then be used to plan where to fly in the long term, such as the next mission. Finally, planning where to fly next during the current flight will depend on the long term plan and the real-time detections in case a poacher is spotted. Through our collaboration with Air Shepherd, a program of the Charles A. and Anne Morrow Lindbergh Foundation, we have already begun deploying poacher detection prototypes in Africa and will be able to deploy further advances there in the future.