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Revealing Complex Ecological Dynamics via Symbolic Regression.

ABSTRACT: Understanding the dynamics of complex ecosystems is a necessary step to maintain and control them. Yet, reverse-engineering ecological dynamics remains challenging largely due to the very broad class of dynamics that ecosystems may take. Here, this challenge is tackled through symbolic regression, a machine learning method that automatically reverse-engineers both the model structure and parameters from temporal data. How combining symbolic regression with a "dictionary" of possible ecological functional responses opens the door to correctly reverse-engineering ecosystem dynamics, even in the case of poorly informative data, is shown. This strategy is validated using both synthetic and experimental data, and it is found that this strategy is promising for the systematic modeling of complex ecological systems.

SUBMITTER: Chen Y

PROVIDER: S-EPMC7339472 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Revealing Complex Ecological Dynamics via Symbolic Regression.

Chen Yize Y Angulo Marco Tulio MT Liu Yang-Yu YY

BioEssays : news and reviews in molecular, cellular and developmental biology 20191016 12

Understanding the dynamics of complex ecosystems is a necessary step to maintain and control them. Yet, reverse-engineering ecological dynamics remains challenging largely due to the very broad class of dynamics that ecosystems may take. Here, this challenge is tackled through symbolic regression, a machine learning method that automatically reverse-engineers both the model structure and parameters from temporal data. How combining symbolic regression with a "dictionary" of possible ecological f ...[more]

PMID: 31617228

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