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

生态系统的突变不仅代表了我们环境中一些最复杂和最具影响力的变化，而且也是最难预测和管理的变化。甲虫和火灾对森林的破坏，大西洋鳕鱼捕鱼业的崩溃，或者某种疾病的爆发，都可能是这种突然变化的例子。生产力与危机之间的突然转变损害了对海洋和森林的有效管理。从卫星和微型传感器到大型天文台，我们收集数据的方式发生了一场革命，这将使新的数据饥渴型机器学习方法在这些尺度上进行预测和管理成为可能，但净收益的清晰度仍然未知。本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，并了解它无法做到的限制。让下一代生态学家和环境科学家充分了解这些工具，从而做出明智的决定，是实现这一愿景的关键。综合研究和教育计划将通过创新的教学方法解决这些问题，旨在促进数据科学与生态和环境问题之间的多样性。本研究旨在通过将机器学习算法与机械动机模型和新兴生态数据源相适应和结合，推进生态预测和决策方面的现有知识。该项目的第一阶段评估了循环神经网络架构的有效性，以预测能够突然转变的生态系统中的动态-理论表明现有的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制模型相结合，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结合起来。这项研究的两个阶段都将得到开源软件工具开发的支持，这些工具用于在各种各样的环境中实现这些方法。该项目的结果，包括更新和由此产生的科学出版物和软件产品的链接，可以在https://carlboettiger.info.This上找到。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.