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Correction: A combinatorial analysis using observational data identifies species that govern ecosystem functioning
- Source :
- PLoS ONE, PLoS ONE, Vol 13, Iss 9, p e0203681 (2018), PLoS ONE, Public Library of Science, 2018, 13 (8), ⟨10.1371/journal.pone.0203681⟩, PLoS ONE, Public Library of Science, 2018, PLoS ONE, 2018, 13 (8), ⟨10.1371/journal.pone.0203681⟩
- Publication Year :
- 2018
-
Abstract
- Understanding the relationship between biodiversity and ecosystem functioning has so far resulted from two main approaches: the analysis of species' functional traits, and the analysis of species interaction networks. Here we propose a third approach, based on the association between combinations of species or of functional groups, which we term assembly motifs, and observed ecosystem functioning. Each assembly motif describes a biotic environment in which species interactions have particular effects on a given ecosystem function. Clustering species in functional groups generates a classification of ecosystems based on their assembly motif. We evaluate the quality of each species clustering, that is its ability to predict an ecosystem function, by the coefficient of determination of the ecosystem classification. An iterative process then enables identifying the species clustering in functional groups that best accounts for the functioning of the observed ecosystems. We test this approach using experimental and simulated datasets. We show that our combinatorial analysis makes it possible to identify the combinations of functional groups of species whose interactions govern ecosystem functioning without any a priori knowledge of the species themselves or their interactions. Our combinatorial approach reproduces the associative learning of empirical ecologists, and proves to be powerful and parsimonious.
- Subjects :
- Ecological Metrics
Computer science
Biomass (Ecology)
Biodiversity
lcsh:Medicine
Social Sciences
Sample (statistics)
computer.software_genre
Ecosystems
Combinatorial analysis
03 medical and health sciences
Human Learning
0302 clinical medicine
Microbial Ecosystems
Learning and Memory
Learning
Psychology
Ecosystem
lcsh:Science
ComputingMilieux_MISCELLANEOUS
Multidisciplinary
Ecology
business.industry
lcsh:R
Ecology and Environmental Sciences
technology, industry, and agriculture
Cognitive Psychology
Biology and Life Sciences
Species Diversity
Field (geography)
Species Interactions
Section (archaeology)
030220 oncology & carcinogenesis
[SDE]Environmental Sciences
Cognitive Science
Observational study
lcsh:Q
Artificial intelligence
[SDE.BE]Environmental Sciences/Biodiversity and Ecology
business
computer
030217 neurology & neurosurgery
Natural language processing
Ecosystem Functioning
Research Article
Neuroscience
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 13
- Issue :
- 9
- Database :
- OpenAIRE
- Journal :
- PloS one
- Accession number :
- edsair.doi.dedup.....c5dac1e1a420fa92810052794f9aa433