Studying citizen science through adaptive management and learning feedbacks as mechanisms for improving conservation
Studying citizen science through adaptive management and learning feedbacks as mechanisms for improving conservation
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通过适应性管理和学习反馈来研究公民科学，作为改善保护的机制
DOI：
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发表时间：
2016
影响因子：
6.3
通讯作者：
Alycia Crall
中科院分区：
文献类型：
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作者：
R. Jordan;Steven Gray;A. Sorensen;Greg Newman;D. Mellor;Greg Newman;C. Hmelo‐Silver;S. LaDeau;D. Biehler;Alycia Crall
公民科学在科学家和社区团体中产生了越来越大的兴趣，公民科学计划已经专门为保护而创建。我们研究了合作科学，一种高度互动的公民科学形式，我们在理论上知情的框架内开发。在这篇文章中，我们集中在我们的框架的两个方面：社会学习和适应性管理。与基于个人的学习相比，社会学习强调协作和生成性洞察力，非常适合适应性管理。自适应管理集成了反馈回路，这些反馈回路由所学到的知识提供信息，并由迭代决策指导。参与公民科学的参与者能够通过原始数据收集增加他们正在学习的内容，这可以产生保护所需的真实的实时信息。我们的工作特别及时，因为研究出版物一直报告缺乏既定的框架和评估计划，以解决公民科学保护成果的程度。为了说明我们的框架如何通过公民科学来支持保护，我们研究了2个项目如何制定我们的合作科学框架。此外，我们还检查了案例研究项目的初步保护成果。这些方案尽管最近才实施，但在积极的保护成果方面显示出了希望。到目前为止，他们独立地赚取资金来支持研究，从当地合作伙伴那里获得资金来进行实验，并且在没有领先科学家的情况下，正在收集数据来测试想法。我们认为，这一成功是由于公民科学家围绕当地问题组织起来，并参与迭代、协作和适应性学习。
Citizen science has generated a growing interest among scientists and community groups, and citizen science programs have been created specifically for conservation. We examined collaborative science, a highly interactive form of citizen science, which we developed within a theoretically informed framework. In this essay, we focused on 2 aspects of our framework: social learning and adaptive management. Social learning, in contrast to individual‐based learning, stresses collaborative and generative insight making and is well‐suited for adaptive management. Adaptive‐management integrates feedback loops that are informed by what is learned and is guided by iterative decision making. Participants engaged in citizen science are able to add to what they are learning through primary data collection, which can result in the real‐time information that is often necessary for conservation. Our work is particularly timely because research publications consistently report a lack of established frameworks and evaluation plans to address the extent of conservation outcomes in citizen science. To illustrate how our framework supports conservation through citizen science, we examined how 2 programs enacted our collaborative science framework. Further, we inspected preliminary conservation outcomes of our case‐study programs. These programs, despite their recent implementation, are demonstrating promise with regard to positive conservation outcomes. To date, they are independently earning funds to support research, earning buy‐in from local partners to engage in experimentation, and, in the absence of leading scientists, are collecting data to test ideas. We argue that this success is due to citizen scientists being organized around local issues and engaging in iterative, collaborative, and adaptive learning.