If machine learning were around a hundred years ago would the Lotka-Volterra equations have been created to describe predator-prey interactions? Or, would scientists have relied on black box regression models to make their predictions, and missed the simple rules underlying nature? Recent advances in computer science have made it possible for machines to learn the processes underpinning the complex dynamics that we observe in ecosystems. The “automated discovery” of the laws of nature through machine learning is an exciting and new area of growth in ecology and environmental science, but there are still many lessons to be learned. Here, I will discuss how recent advances in machine learning, techniques such as symbolic regression, Neural Ordinary Differential Equations and Hybrid Modeling among others – can be used to learn about and predict non-linear dynamics in ocean and lake ecosystems. Particular focus will be placed on predicting freshwater harmful algal blooms, to help inform water quality stakeholders tasked with minimizing the impacts of harmful algal blooms, and climate change driven regime shifts in coral ecosystems.

Primary Presenter: Emerson Arehart, University of Pennsylvania (eejjaa@gmail.com)

Authors:

James Watson, Oregon State University  (watsjame@oregonstate.edu)

Lisa McManus, University of Hawaii  (mcmanusl@hawaii.edu)

Jorge Arroyo-Esquivel, Carnigie Institute for Science  (jarroyoesquivel@carnegiescience.edu)

Emerson Arehart, University of Pennsylvania  (eejjaa@gmail.com)