CAREER: Harnessing the data revolution for predicting and managing ecosystem regime shifts

职业：利用数据革命来预测和管理生态系统格局的转变

• 批准号: 1942280
• 负责人: Carl Boettiger
• 金额: $ 59.46万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1942280&HistoricalAwards=false
• 关键词: CAREER; Harnessing; data; revolution; predicting

Abrupt ecosystem shifts represent not only some of the most complex and impactful changes in our environment, but also the most difficult to predict and manage. Forest devastation by beetles and fire, the collapse of the Atlantic cod fishery, or the outbreak of a disease may all be examples of such sudden changes. Effective management of oceans and forests is impaired by abrupt shifts between productivity and crisis. A revolution in how we collect data, from satellites and micro-sensors to large-scale observatories will make new data-hungry machine learning approaches to forecasting and management across these scales feasible yet the net gain in clarity remains unknown. This research seeks to evaluate how the tools of machine learning and artificial intelligence can improve the ability to predict and manage sudden ecosystem change, and understand the limits where it cannot. Empowering the next generation of ecologists and environmental scientists to understand these tools sufficiently to make informed decisions is key to realizing this vision. An integrated research and education program will tackle these questions through an innovative pedagogical approach that seeks to promote diversity at this interface between data science and ecological and environmental issues.This research seeks to advance current knowledge in ecological forecasting and decision-making by adapting and combining machine-learning algorithms with mechanistically motivated models and emerging ecological data sources. The first phase of the project assesses the effectiveness of recurrent neural network architectures to predict dynamics in ecological systems that are capable of sudden regime shifts – a setting where theory suggests existing machine learning approaches are likely to fail. This research then seeks more robust forecast design by combining machine learning approaches with mechanistic models guided by ecological theory. The second phase of the project seeks to draw on emerging methods in reinforcement learning to address common optimization problems in conservation, such as determining sustainable harvest levels or the location of protected areas. Here, research will blend recent developments in “deep” reinforcement learning with process-based approaches to ecological management. Both phases of this research will be supported by the development of open source software tools for implementing these approaches in a wide variety of contexts. Results of the project, including updates and links to resulting scientific publications and software products can be found at https://carlboettiger.info.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

生态系统的突然变化不仅是我们环境中最复杂和最具影响力的变化，也是最难以预测和管理的变化。森林被甲虫和大火破坏，大西洋鳕鱼渔业的崩溃，或者疾病的爆发都可能是这种突然变化的例子。海洋和森林的有效管理因生产力和危机之间的突然转变而受到损害。从卫星和微型传感器到大型观测站，我们收集数据的方式发生了革命，这将使新的数据饥渴机器学习方法在这些规模上进行预测和管理变得可行，但清晰度的净收益仍然未知。这项研究旨在评估机器学习和人工智能工具如何提高预测和管理突然生态系统变化的能力，并了解无法实现的限制。赋予下一代生态学家和环境科学家充分理解这些工具以做出明智决策的能力是实现这一愿景的关键。一个综合的研究和教育计划将通过创新的教学方法来解决这些问题，该方法旨在促进数据科学与生态和环境问题之间的这种界面的多样性。这项研究旨在通过调整和结合机器学习算法与机械动机模型和新兴生态数据源来推进生态预测和决策的现有知识。该项目的第一阶段评估了递归神经网络架构在预测能够突然发生政权转移的生态系统动态方面的有效性-理论表明现有机器学习方法可能会失败。然后，本研究通过将机器学习方法与生态理论指导的机械模型相结合，寻求更稳健的预测设计。该项目的第二阶段旨在利用强化学习中的新兴方法来解决保护中常见的优化问题，例如确定可持续的收获水平或保护区的位置。在这里，研究将把“深度”强化学习的最新发展与基于过程的生态管理方法相结合。这两个阶段的研究将支持开发的开源软件工具，在各种各样的情况下实施这些方法。该项目的结果，包括更新和链接到所产生的科学出版物和软件产品可以在www.example.com上找到https://carlboettiger.info.This奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

项目成果

Containers for computational reproducibility

• DOI: 10.1038/s43586-023-00236-9
• 发表时间: 2023-07
• 期刊: Nature Reviews Methods Primers
• 作者: David Moreau;K. Wiebels;C. Boettiger

The forecast trap

• DOI: 10.1111/ele.14024
• 发表时间: 2022-05-30
• 期刊: ECOLOGY LETTERS
• 影响因子: 8.8
• 作者: Boettiger, Carl

Power and accountability in reinforcement learning applications to environmental policy

强化学习在环境政策中的应用中的权力和责任

• 发表时间: 2021
• 作者: Chapman, Melissa;Scoville, Caleb;Lapeyrolerie, Marcus;Boettiger, Carl
• 通讯作者: Boettiger, Carl

Leveraging private lands to meet 2030 biodiversity targets in the United States

• DOI: 10.1111/csp2.12897
• 发表时间: 2023-03-09
• 期刊: CONSERVATION SCIENCE AND PRACTICE
• 影响因子: 3.1
• 作者: Chapman, Melissa;Boettiger, Carl;Brashares, Justin S.
• 通讯作者: Brashares, Justin S.

A community convention for ecological forecasting: Output files and metadata version 1.0

生态预测社区公约：输出文件和元数据版本 1.0

• DOI: 10.1002/ecs2.4686
• 发表时间: 2023
• 期刊: Ecosphere
• 影响因子: 2.7
• 作者: Dietze, Michael C.;Thomas, R. Quinn;Peters, Jody;Boettiger, Carl;Koren, Gerbrand;Shiklomanov, Alexey N.;Ashander, Jaime
• 通讯作者: Ashander, Jaime