Learning From Observational Data To Improve Protected Area Management
Period: January 2016-January 2019
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.
On patrol with the snare team in Keo Seima Wildlife Sanctuary. Photo credit: Harriet Ibbett[/caption] 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.
Regardless of whether SMART is used, patrol teams need to be deployed strategically, which requires reliable information on poacher identities, poacher behaviour, and interventions that might change poacher behaviour. Another necessity is the need to validate patrol findings against expectations, and to respond adaptively to change. Differences between patrol observations and prior expectations need to be understood, and biases or inaccuracies separated out from genuine changes in poacher behaviour.
This project aims to address some of these challenges and provide greater understanding of the utility of observational datasets for protected area management.
Summary of planned activities
The project has a two components:
- Field based data collection - Quantitative and qualitative approaches will be used to understand how both patrol teams and poachers behave and why.
- Statistical and mechanistic modelling - Agent-Based Modelling will be used to simulate interactions between poachers and patrols under different scenarios and Bayesian Hierarchical Modelling will be used to interpret datasets to produce robust estimates of the prevalence of illegal activities, and to identify the factors associated with higher or lower levels of these activities.
Field work will take place in Keo Seima Wildlife Sanctuary, a unique mountainous forest complex comprising of deciduous, dipterocarp and lush evergreen forests. KSWS is located in the eastern plains of Cambodia and sits on the border of Vietnam. We will focus our field work to assess the interactions between hunters and law enforcement agencies, with a specific focus on understanding:
- Who hunts, where do people hunt and why do people hunt?
- What factors motivate and affect the effectiveness of law enforcement teams?
- How effective are patrol teams at detecting snares?
Our aim in this project is to support the developers and users of ranger-generated observational datasets, so that they can get more from this increasingly important information source. To this end, we aim to interact with end-users from the beginning of the project, and throughout, both at KSWS and internationally. This will help us to understand how we can contribute most usefully to their ongoing work.
Project Outputs A full project brief is available here