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Constraining nonlinear time series modeling with the metabolic theory of ecology

S. Munch,Tanya L. Rogers,C. C. Symons,David F. Anderson,Frank Pennekamp

Published 2023 in Proceedings of the National Academy of Sciences of the United States of America

ABSTRACT

Significance Forecasting how populations respond to climate change is an important challenge for natural resource managers. Forecasting approaches range from machine learning that is agnostic about underlying biological mechanisms to process-based models that incorporate mechanisms but are often complex and tailored toward specific species. Here we blend these approaches by constraining empirical dynamic modeling, a machine learning approach, with the metabolic theory of ecology (MTE). Focusing on short-lived ectotherms with high-frequency sampling, the conditions under which our methodology is likely to be most effective, we obtained improved forecasts for most time series. This lends support to the MTE as a general predictive theory and provides a new tool with which to forecast abundances in environments with seasonal and/or interannual temperature change.

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