5 research outputs found
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Cloudy with a Chance of Poaching: Adversary Behavior Modeling and Forecasting with Real-World Poaching Data
Wildlife conservation organizations task rangers to deter and capture wildlife poachers. Since rangers are responsible for patrolling vast areas, adversary behavior modeling can help more effectively direct future patrols. In this innovative application track paper, we present an adversary behavior modeling system, INTERCEPT (INTERpretable Classification Ensemble to Protect Threatened species), and provide the most extensive evaluation in the AI literature of one of the largest poaching datasets from Queen Elizabeth National Park (QENP) in Uganda, comparing INTERCEPT with its competitors; we also present results from a month-long test of INTERCEPT in the field. We present three major contributions. First, we present a paradigm shift in modeling and forecasting wildlife poacher behavior. Some of the latest work in the AI literature (and in Conservation) has relied on models similar to the Quantal Response model from Behavioral Game Theory for poacher behavior prediction. In contrast, INTERCEPT presents a behavior model based on an ensemble of decision trees (i) that more effectively predicts poacher attacks and (ii) that is more effectively interpretable and verifiable. We augment this model to account for spatial correlations and construct an ensemble of the best models, significantly improving performance. Second, we conduct an extensive evaluation on the QENP dataset, comparing 41 models in prediction performance over two years. Third, we present the results of deploying INTERCEPT for a one-month field test in QENP - a first for adversary behavior modeling applications in this domain. This field test has led to finding a poached elephant and more than a dozen snares (including a roll of elephant snares) before they were deployed, potentially saving the lives of multiple animals - including elephants.Engineering and Applied Science
Efficiently targeting resources to deter illegal activities in protected areas
In many countries, areas delineated for conservation purposes can only achieve their objectives if effective law enforcement occurs within them. However, there is no method currently available to allocate law enforcement effort in a way that protects species and habitats in a cost-effective manner. Law enforcement is expensive and effort is usually concentrated near the locations of patrol stations where rangers are based. This hampers effective conservation, particularly in large protected areas, or regions with limited enforcement capacity. Using the spatial planning tool Marxan, we demonstrate a method for prioritizing law enforcement in a globally important conservation landscape (the Greater Virunga Landscape, GVL, in central Africa) using data on the spatial distribution of illegal activities and conservation features within the landscape. Our analysis of current patrol data shows that law enforcement activity is inadequate with only 22% of the landscape being effectively patrolled and most of this activity occurring within 3km of a patrol post. We show that the current patrol effort does not deter illegal activities beyond this distance. We discover that when we account for the costs of effective patrolling and set targets for covering key species populations and habitats, we can reduce the costs of meeting all conservation targets in the landscape by 63%, to 5 center dot 9million USD for the GVL but would better target effort from both patrol posts and mobile patrol units in the landscape. Synthesis and applications. Our results demonstrate a method that can be used to plan enforcement patrolling, resulting in more cost-efficient prevention of illegal activities in a way that is targeted at halting declines in species of conservation concern