Our research uses state-of-the-art machine learning algorithms to identify coupled species, quantify species interactions, and develop structurally robust harvest policies from time series, laying the groundwork for ecosystems-based management of marine fisheries.
We use these methods to develop predictive models that identify causal interactions between ecosystem members, relevant climatic drivers and the cumulative effects of fishing on marine ecosystems, and multi-species harvest policies that quantify trade-offs between sustainable fish catches and conservation of protected species. We apply these methods to study the dynamics of several systems including coastal California, the Gulf of Mexico, and George’s bank. Learn more.