Validating predictions with ranger insights to enhance anti-poaching patrol strategies in protected areas

This is an idea proposed in 2025 as a good starter project, and is currently being worked on by Hannah McLoone. It is co-supervised with Charles Emogor and Rob Fletcher.

Biodiversity is declining at an unprecedented rate, underscoring the critical role of protected areas (PAs) in conserving threatened species and ecosystems. Yet, many of these are increasingly dismissed as "paper parks" due to poor management. Park rangers play a vital role in PA effectiveness by detecting and potentially deterring illegal activities. However, limited funding for PA management has led to low patrol frequency and detection rates, reducing the overall deterrent effect of ranger efforts. This resource scarcity often results in non-systematic patrol strategies, which are sub-optimal given that illegal hunters tend to be selective in where and when they operate.

The situation is poised to become more challenging as countries expand PA coverage under the Kunming-Montreal Global Biodiversity Framework—aiming to increase global PA area from 123 million km2 to 153 million km2 by 2030. Without a substantial boost in enforcement capacity, both existing and newly designated PAs will remain vulnerable. Continued overexploitation of wildlife threatens not only species survival but also ecosystem integrity and the well-being of local communities who rely on wildlife for food and income.

This project aims to combine data from rangers in multiple African protected areas and hunters around a single protected area (Nigeria) to improve the deterrence effect of ranger patrols by optimising ranger efforts and provide information on the economic impacts of improved ranger patrols on community livelihoods and well-being. We plan to deploy our models to rangers in the field via SMART, which is used in > 1000 PAs globally to facilitate monitoring and data collection during patrols.

The two main aims are to:

1. develop an accessibility layer using long-term ranger-collected data
2. validate the results of this layer, as well as those from other models developed, using ranger insights.

This work involves collaborating with the Wildlife Conservation Society (WCS) Nigeria team and rangers from Cross River National Park—who are already active collaborators in this project. They have provided ranger patrol data, contributed valuable on-the-ground perspectives for interpreting the data, and engaged with preliminary model outputs.