Reverse-engineering ecological theory from data. Issue 1878 (16th May 2018)

• Record Type: Journal Article
• Authors: Martin, Benjamin T.; Munch, Stephan B.; Hein, Andrew M.
• Is Part Of: Proceedings. Volume 285:Issue 1878(2018)
• Publication Date: 2018-05-16
• Subjects: theoretical ecology -- population dynamics -- ecological prediction -- forecasting -- time-series analysis -- multi-model inference; Biology -- Periodicals; 570.5
• DOI: 10.1098/rspb.2018.0422
• Languages: English
• ISSNs: 0962-8452

Abstract

Abstract : Ecologists have long sought to understand the dynamics of populations and communities by deriving mathematical theory from first principles. Theoretical models often take the form of dynamical equations that comprise the ecological processes (e.g. competition, predation) believed to govern system dynamics. The inverse of this approach—inferring which processes and ecological interactions drive observed dynamics—remains an open problem in ecology. Here, we propose a way to attack this problem using a machine learning method known as symbolic regression, which seeks to discover relationships in time-series data and to express those relationships using dynamical equations. We found that this method could rapidly discover models that explained most of the variance in three classic demographic time series. More importantly, it reverse-engineered the models previously proposed by theoretical ecologists to describe these time series, capturing the core ecological processes these models describe and their functional forms. Our findings suggest a potentially powerful new way to merge theory development and data analysis.