What is it about?

We have blended a data-driven time series method with the metabolic theory of ecology to forecast the dynamics of organisms in environments with variable temperatures. The metabolic theory of ecology predicts that biological rates should scale with temperature. Accordingly, our method rescales time to a constant "metabolic" time step: when temperatures are high, the metabolic time step encompasses less calendar time; when temperatures are low, it encompasses more calendar time. We show that this leads to consistently better forecasts in variable temperature environments (on average 19% higher forecast accuracy). Our method is a demonstration of how we can improve data-driven methods with biological rules and theoretical understanding.

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Why is it important?

Forecasting how populations respond to climate change is an important challenge for conservation biology and natural resource managers but current methods are often complex or tailored toward particular species. Our approach is very general and should apply to many ectotherms (as long as certain assumptions hold), without a very detailed understanding of their biology.

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This page is a summary of: Constraining nonlinear time series modeling with the metabolic theory of ecology, Proceedings of the National Academy of Sciences, March 2023, Proceedings of the National Academy of Sciences, DOI: 10.1073/pnas.2211758120.
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