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

University Of California-Berkeley, Berkeley CA

Investigators

Abstract

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.