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Revealing Complex Ecological Dynamics via Symbolic Regression
Author(s) -
Chen Yize,
Angulo Marco Tulio,
Liu YangYu
Publication year - 2019
Publication title -
bioessays
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.175
H-Index - 184
eISSN - 1521-1878
pISSN - 0265-9247
DOI - 10.1002/bies.201900069
Subject(s) - computer science , dynamics (music) , regression , symbolic regression , reverse engineering , ecology , ecosystem , artificial intelligence , machine learning , regression analysis , mathematics , biology , psychology , statistics , genetic programming , pedagogy , programming language
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.