Automated Discovery of Relationships, Models, and Principles in Ecology
| Autor: | Stefano Mammola, François Rigal, José Carvalho, Rosalina Gabriel, Paulo A. V. Borges, Luis M. Correia, José Cascalho, Vasco Veiga Branco, Pedro Cardoso |
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| Přispěvatelé: | Zoology, Finnish Museum of Natural History |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0106 biological sciences
PREDICTION Computer science Ecology (disciplines) Symbolic Regression DIVERSITY Complex system lcsh:Evolution Genetic Programming Inference Genetic programming Species Richness Estimation species distribution modeling Ecological systems theory 010603 evolutionary biology 01 natural sciences 03 medical and health sciences species-area relationship Artificial Intelligence ecological complexity lcsh:QH540-549.5 Species-area Relationship REGRESSION lcsh:QH359-425 BIODIVERSITY ASSESSMENT SOUTHERN YUCATAN Ecology Evolution Behavior and Systematics 030304 developmental biology Flexibility (engineering) 0303 health sciences NEURAL-NETWORK CHALLENGES Ecology Ecological Complexity Statistical model 15. Life on land Species Distribution Modeling artificial intelligence evolutionary computation species richness estimation 1181 Ecology evolutionary biology genetic programming SPIDERS lcsh:Ecology Symbolic regression symbolic regression Evolutionary Computation RESPONSES |
| Zdroj: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) Agência para a Sociedade do Conhecimento (UMIC)-FCT-Sociedade da Informação instacron:RCAAP Frontiers in Ecology and Evolution, Vol 8 (2020) Frontiers in Ecology and Evolution 8 (2020). doi:10.3389/fevo.2020.530135 info:cnr-pdr/source/autori:Cardoso, Pedro; Branco, Vasco V.; Borges, Paulo A.V.; Carvalho, José C.; Rigal, François; Gabriel, Rosalina; Mammola, Stefano; Cascalho, José; Correia, Luís/titolo:Automated Discovery of Relationships, Models, and Principles in Ecology/doi:10.3389%2Ffevo.2020.530135/rivista:Frontiers in Ecology and Evolution/anno:2020/pagina_da:/pagina_a:/intervallo_pagine:/volume:8 |
| DOI: | 10.3389/fevo.2020.530135 |
| Popis: | 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. PC and VB were supported by Kone Foundation. PB and FR were partly funded by the project FCT-PTDC/BIABIC/119255/2010 - Biodiversity on oceanic islands: toward a unified theory. LC was supported by FCT through LASIGE Research Unit, ref. UIDB, UIDP/00408/2020. SM acknowledges support from the European Commission through Horizon 2020 Marie Sklodowska-Curie Actions (MSCA) individual fellowships (Grant no. 882221). info:eu-repo/semantics/publishedVersion |
| Databáze: | OpenAIRE |
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