Learning from observational data to improve protected area management

GSTDTAP

项目编号	NE/N001370/1
	Learning from observational data to improve protected area management
	[unavailable]
主持机构	University of Edinburgh
项目开始年	2016
	2016
项目结束日期	2018-12-31
资助机构	UK-NERC
项目类别	Research Grant
国家	英国
语种	英语
英文摘要	Human-caused environmental destruction is a major challenge to the sustainability of life on earth. For effective solutions, we need to learn about damaging behaviours and discover how best to encourage change. Exciting developments in fields concerned with human behaviour (such as economics and psychology) are helping to explain why people make the decisions they do. In parallel, ecologists have developed sophisticated methods for analysing data collected by ordinary people ("citizen scientists"), aided by new technologies such as smart phones. Up to now, these developments have remained separate, but closer integration would benefit both science and practice. Behavioural scientists would gain from the adoption of powerful new analytical techniques from ecology, which enable them to use data collected in new ways to understand how humans interact with the environment. Ecologists would benefit from being able to include a solid theoretical model of human behaviour into their understanding of how ecological outcomes arise from human actions. Managers and policy-makers will benefit from evidence-based understanding of how to change behaviour in the real world. To illustrate how powerful this combination of approaches can be, we will apply them to a key problem facing global conservation: how to manage protected areas so that they can act as effective refuges for endangered species in the face of illegal poaching and other threats. Learning about illegal behaviour is difficult because those involved are rarely willing to talk openly, so the 'conservation detective' must make deductions from other sources of information. Many conservation organisations now collect reports made by the rangers who patrol parks. This is potentially very informative, but also potentially very misleading. Consider snaring as an example: a ranger seeing a snare is the outcome of several interacting processes (where the poacher decides to lay their snare, where the ranger decides to patrol, and whether the ranger spots it in the undergrowth), and removing that snare may affect the future decisions of the poacher; so the data are the product of a game of cat-and-mouse played out in a dynamic landscape. This makes patrol data very hard to interpret. 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 Seima Protection Forest 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. For the first time it will be possible to paint an accurate picture of illegal behaviour within parks and to give managers scientific advice about how to design their patrols. We will also explore how this novel approach can be used more widely to tackle other environmental issues. For example, large numbers of people participate in bird surveys each year, and local communities are increasingly collecting information so that they can manage their own resources; our work will lead to rules of thumb for how best to analyse these types of data. This could be useful to a wide range of ecologists and practical users of observational data.
来源学科分类	Natural Environment Research
文献类型	项目
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/85947
专题	环境与发展全球科技态势
推荐引用方式 GB/T 7714	[unavailable].Learning from observational data to improve protected area management.2016.