Learning From Observational Data To Improve Protected Area Management

Law enforcement is an essential part of Protected Area (PA) management. This project aims to determine how patrol data can best be analysed by PA managers to monitor enforcement effort, inform future patrolling strategies, and motivate rangers.

Photo credit: Harriet Ibbett

Key facts

Period: January 2016-January 2019

Funder: National Environment Research Council (NERC)

Researchers: Aidan Keane, E.J. Milner-Gulland, Colin Beale, Andy Dobson, Harriet Ibbett

Collaborators: University of Oxford, University of York, WCS Cambodia, Hannah O’Kelly, Henry Travers

Project Overview

Law enforcement is an essential part of Protected Area (PA) management. Over the past decade, the effectiveness of law enforcement has been enhanced by the development of tools such as MIST and SMART, which enable rangers to record important information such as patrol routes, observations and incidences of illegal behaviour. These data can be analysed by PA managers to monitor enforcement effort, inform future patrolling strategies, and motivate rangers.

Whilst observations made by rangers provide important spatial information for PA managers, their overall utility for monitoring, strategic planning and evaluation is limited by inherent bias in the data collection process. Recording of illegal activities is influenced by a multitude of ecological and social factors, and unlike ecological studies which only aim to observe behaviour, the fundamental purpose of patrols is to change the behaviour of offenders. These conditions make the analysis of ranger-generated data extremely complex.

As yet there is neither a clear strategy for how informative analysis can be achieved, nor practical tools to implement them. If this gap is not adequately filled, conclusions drawn from SMART patrol data may be biased and seriously misleading, with significant implications for biodiversity.

To tackle this issue we will build two types of computer model to explore how rangers and poachers interact with one another and their environment: i) conceptual models of the underlying processes that lead to the observation of a snare, based on ecological and behavioural theory and our understanding of our system, with simulated patrol records as their outcome; ii) statistical models that start with the snare data, and see which combination of factors best explains it. Building both models means that each can be used to inform the other.

We will test the models in two ways; firstly in an abstract system, where we can vary the behaviour of the patrollers and poachers and the environment in which they interact, and see how this affects the resultant patterns of snare observations, and secondly in a real-world system, the Keo Seima Wildlife Sanctuary in Cambodia. Here we have substantial existing knowledge to help us to build our models, and will collect new information to improve our understanding. Our work will also be able directly to inform their conservation strategy.

  1. […] I am working on a NERC funded collaborative project with the University of Edinburgh entitled “Learning from observational data to improve protected area management”. My role is primarily field-based and focuses on understanding hunting in a tropical forest […]

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