Learning from observational data to improve protected area management

从观测数据中学习以改进保护区管理

• 批准号: NE/N001370/1
• 负责人: Aidan Keane
• 金额: $ 87.63万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FN001370%2F1
• 关键词: Learning; observational; data; improve; protected

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.

人为造成的环境破坏是对地球上生命可持续性的重大挑战。为了有效的解决方案，我们需要了解有害行为，并发现如何最好地鼓励改变。在与人类行为有关的领域（如经济学和心理学），令人兴奋的发展有助于解释人们为什么会做出这样的决定。与此同时，生态学家开发了复杂的方法来分析普通人（“公民科学家”）收集的数据，并借助智能手机等新技术。到目前为止，这些发展仍然是分开的，但更密切的结合将有利于科学和实践。行为科学家将从采用生态学中强大的新分析技术中获益，这些技术使他们能够使用以新方式收集的数据来了解人类如何与环境相互作用。生态学家将受益于能够将人类行为的坚实理论模型纳入他们对人类行为如何产生生态结果的理解。管理者和政策制定者将从基于证据的理解中受益，了解如何改变真实的世界中的行为。为了说明这种方法的组合有多么强大，我们将把它们应用于全球保护面临的一个关键问题：如何管理保护区，使它们能够在面临非法偷猎和其他威胁时成为濒危物种的有效避难所。了解非法行为是困难的，因为那些参与者很少愿意公开谈论，所以“保护侦探”必须从其他信息来源进行推断。许多保护组织现在收集公园巡逻员的报告。这可能是非常有益的，但也可能是非常误导的。以诱捕为例：护林员看到陷阱是几个相互作用过程的结果（偷猎者决定在哪里放置陷阱，护林员决定巡逻，护林员是否在灌木丛中发现陷阱），移除陷阱可能会影响偷猎者未来的决定;因此，数据是在动态景观中玩猫捉老鼠游戏的产物。为了解决这个问题，我们将建立两种类型的计算机模型来探索护林员和偷猎者如何相互作用及其环境：i）基于生态和行为理论以及我们对系统的理解，以模拟巡逻记录作为结果，对导致观察到陷阱的潜在过程进行概念模型; ii）从圈套数据开始的统计模型，看看哪种因素的组合最能解释它。建立两个模型意味着每个模型都可以用来通知另一个。我们将以两种方式测试模型;首先是在一个抽象的系统中，我们可以改变巡逻人员和偷猎者的行为以及他们相互作用的环境，看看这如何影响陷阱观测的结果模式，其次是在一个真实的系统中，柬埔寨的Seima保护林。在这里，我们有大量的现有知识来帮助我们建立模型，并将收集新的信息来提高我们的理解。我们的工作也将能够直接为他们的保护战略提供信息。这将是第一次有可能准确描绘公园内的非法行为，并为管理人员提供关于如何设计巡逻的科学建议。我们还将探讨如何更广泛地使用这种新方法来解决其他环境问题。例如，每年有大量的人参加鸟类调查，当地社区越来越多地收集信息，以便他们能够管理自己的资源;我们的工作将导致如何最好地分析这些类型的数据的经验法则。这可能对广泛的生态学家和观测数据的实际用户有用。

项目成果

A Framework for Assessing Impacts of Wild Meat Hunting Practices in the Tropics

• DOI: 10.1007/s10745-019-0075-6
• 发表时间: 2019-06-01
• 期刊: HUMAN ECOLOGY
• 影响因子: 2
• 作者: Dobson, Andy D. M.;Milner-Gulland, E. J.;Keane, Aidan
• 通讯作者: Keane, Aidan

THE PROBLEM WITH CRIME PROBLEM-SOLVING: TOWARDS A SECOND GENERATION POP?

• DOI: 10.1093/bjc/azz029
• 发表时间: 2020-01-01
• 期刊: BRITISH JOURNAL OF CRIMINOLOGY
• 影响因子: 2.6
• 作者: Borrion, Herve;Ekblom, Paul;Toubaline, Sonia
• 通讯作者: Toubaline, Sonia

Detecting deterrence from patrol data.

• DOI: 10.1111/cobi.13222
• 发表时间: 2019-06
• 期刊: Conservation biology : the journal of the Society for Conservation Biology
• 作者: Dobson ADM;Milner-Gulland EJ;Beale CM;Ibbett H;Keane A
• 通讯作者: Keane A

Full methods and code for the model from Integrating models of human behaviour between the individual and population levels to inform conservation interventions

模型的完整方法和代码，来自整合个体和群体水平之间的人类行为模型，为保护干预措施提供信息

• DOI: 10.6084/m9.figshare.8268683
• 发表时间: 2019
• 作者: Dobson A

Making Messy Data Work for Conservation

• DOI: 10.1016/j.oneear.2020.04.012
• 发表时间: 2020-05-22
• 期刊: ONE EARTH
• 影响因子: 16.2
• 作者: Dobson, A. D. M.;Milner-Gulland, E. J.;Keane, Aidan
• 通讯作者: Keane, Aidan