Within protected areas, biodiversity loss is often a consequence of illegal resource use. Understanding the patterns and extent of illegal activities is therefore essential for effective law enforcement and prevention of biodiversity declines. We used extensive data, commonly collected by ranger patrols in many protected areas, and Bayesian hierarchical models to identify drivers, trends, and distribution of multiple illegal activities within the Queen Elizabeth Conservation Area (QECA), Uganda. Encroachment (e.g., by pastoralists with cattle) and poaching of noncommercial animals (e.g., snaring bushmeat) were the most prevalent illegal activities within the QECA. Illegal activities occurred in different areas of the QECA. Poaching of noncommercial animals was most widely distributed within the national park. Overall, ecological covariates, although significant, were not useful predictors for occurrence of illegal activities. Instead, the location of illegal activities in previous years was more important. There were significant increases in encroachment and noncommercial plant harvesting (nontimber products) during the study period (1999-2012). We also found significant spatiotemporal variation in the occurrence of all activities. Our results show the need to explicitly model ranger patrol effort to reduce biases from existing uncorrected or capture per unit effort analyses. Prioritization of ranger patrol strategies is needed to target illegal activities; these strategies are determined by protected area managers, and therefore changes at a site-level can be implemented quickly. These strategies should also be informed by the location of past occurrences of illegal activity: the most useful predictor of future events. However, because spatial and temporal changes in illegal activities occurred, regular patrols throughout the protected area, even in areas of low occurrence, are also required.

Summary1. 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. 2. 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. 3. 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 3 km of a patrol post. We show that the current patrol effort does not deter illegal activities beyond this distance. 4. 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 $2Á2-3Á0 million USD, relative to the cost of patrolling the entire landscape. This cost is well within the current expenditure of approximately $5Á9 million USD for the GVL but would better target effort from both patrol posts and mobile patrol units in the landscape. 5. 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.

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In conservation understanding the drivers of behavior and developing robust interventions to promote behavioral change is challenging and requires a multifaceted approach. This is particularly true for efforts to address illegal wildlife use, where pervasive—and sometimes simplistic—narratives often obscure complex realities. We used an indirect questioning approach, the unmatched count technique, to investigate the drivers and prevalence of wildlife crime in communities surrounding 2 national parks in Uganda and combined scenario interviews and a choice experiment to predict the performance of potential interventions designed to tackle these crimes. Although poverty is often assumed to be a key driver of wildlife crime, we found that better‐off households and those subject to human–wildlife conflict and those that do not receive any benefits from the parks’ tourism revenue sharing were more likely to be involved in certain types of wildlife crime, especially illegal hunting. The interventions predicted to have the greatest impact on reducing local participation in wildlife crime were those that directly addressed the drivers including, mitigating damage caused by wildlife and generating financial benefits for park‐adjacent households. Our triangulated approach provided insights into complex and hard‐to‐access behaviors and highlighted the importance of going beyond single‐driver narratives.

Protected areas are fundamental for conservation, yet are constantly threatened by illegal activities, such as cattle encroachment and wildlife poaching, which reduce biodiversity. Law enforcement is an essential component of reducing illegal activities. Although necessary, law enforcement is costly and its effectiveness in the field is rarely monitored. Improving ranger patrol efficiency is likely to decrease illegal activity occurrence and benefit biodiversity conservation, without additional resource implications. Using ranger-collected data, we develop a method to improve ranger patrol allocation, targeting different combinations of conservation priorities, and predict that detections of illegal activities can be greatly improved. In a field test in Queen Elizabeth Protected Area, Uganda, we increased detections of illegal activities in some cases by over 250% without a change in ranger resources. This easily implemented method can be used in any protected area where data on the distribution of illegal activities are collected, and improve law-enforcement efficiency in resource-limited settings.