AUTHOR=Cardoso Pedro , Branco Vasco V. , Borges Paulo A. V. , Carvalho José C. , Rigal François , Gabriel Rosalina , Mammola Stefano , Cascalho José , Correia Luís TITLE=Automated Discovery of Relationships, Models, and Principles in Ecology JOURNAL=Frontiers in Ecology and Evolution VOLUME=8 YEAR=2020 URL=https://www.frontiersin.org/journals/ecology-and-evolution/articles/10.3389/fevo.2020.530135 DOI=10.3389/fevo.2020.530135 ISSN=2296-701X ABSTRACT=

Ecological systems are the quintessential complex systems, involving numerous high-order interactions and non-linear relationships. The most used statistical modeling techniques can hardly accommodate the complexity of ecological patterns and processes. Finding hidden relationships in complex data is now possible using massive computational power, particularly by means of artificial intelligence and machine learning methods. Here we explored the potential of symbolic regression (SR), commonly used in other areas, in the field of ecology. Symbolic regression searches for both the formal structure of equations and the fitting parameters simultaneously, hence providing the required flexibility to characterize complex ecological systems. Although the method here presented is automated, it is part of a collaborative human–machine effort and we demonstrate ways to do it. First, we test the robustness of SR to extreme levels of noise when searching for the species-area relationship. Second, we demonstrate how SR can model species richness and spatial distributions. Third, we illustrate how SR can be used to find general models in ecology, namely new formulas for species richness estimators and the general dynamic model of oceanic island biogeography. We propose that evolving free-form equations purely from data, often without prior human inference or hypotheses, may represent a very powerful tool for ecologists and biogeographers to become aware of hidden relationships and suggest general theoretical models and principles.