Generalism drives abundance: A computational causal discovery approach
dc.contributor.author | Song, C | |
dc.contributor.author | Simmons, BI | |
dc.contributor.author | Fortin, M-J | |
dc.contributor.author | Gonzalez, A | |
dc.date.accessioned | 2022-11-03T09:31:59Z | |
dc.date.issued | 2022-09-29 | |
dc.date.updated | 2022-11-02T17:40:09Z | |
dc.description.abstract | A ubiquitous pattern in ecological systems is that more abundant species tend to be more generalist; that is, they interact with more species or can occur in wider range of habitats. However, there is no consensus on whether generalism drives abundance (a selection process) or abundance drives generalism (a drift process). As it is difficult to conduct direct experiments to solve this chicken-and-egg dilemma, previous studies have used a causal discovery method based on formal logic and have found that abundance drives generalism. Here, we refine this method by correcting its bias regarding skewed distributions, and employ two other independent causal discovery methods based on nonparametric regression and on information theory, respectively. Contrary to previous work, all three independent methods strongly indicate that generalism drives abundance when applied to datasets on plant-hummingbird communities and reef fishes. Furthermore, we find that selection processes are more important than drift processes in structuring multispecies systems when the environment is variable. Our results showcase the power of the computational causal discovery approach to aid ecological research. | en_GB |
dc.description.sponsorship | Royal Commission for the Exhibition of 1851 Research Fellowship | en_GB |
dc.description.sponsorship | CRC in Spatial Ecology | en_GB |
dc.description.sponsorship | Liber Ero Chair in Biodiversity Conservation | en_GB |
dc.format.extent | e1010302- | |
dc.format.medium | Electronic-eCollection | |
dc.identifier.citation | Vol. 18 (9), article e1010302 | en_GB |
dc.identifier.doi | https://doi.org/10.1371/journal.pcbi.1010302 | |
dc.identifier.uri | http://hdl.handle.net/10871/131580 | |
dc.identifier | ORCID: 0000-0002-2751-9430 (Simmons, Benno I) | |
dc.language.iso | en | en_GB |
dc.publisher | Public Library of Science (PLoS) | en_GB |
dc.relation.url | https://www.ncbi.nlm.nih.gov/pubmed/36173959 | en_GB |
dc.relation.url | http://doi.org/10.5061/dryad.c270ft8 | en_GB |
dc.relation.url | http://doi.org/10.1038/s41559-020-01342-7 | en_GB |
dc.relation.url | https://github.com/clsong/ReproduceChickenEgg | en_GB |
dc.rights | © 2022 Song et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | en_GB |
dc.subject | Causality | en_GB |
dc.subject | Consensus | en_GB |
dc.subject | Ecosystem | en_GB |
dc.subject | Information Theory | en_GB |
dc.title | Generalism drives abundance: A computational causal discovery approach | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2022-11-03T09:31:59Z | |
dc.identifier.issn | 1553-734X | |
exeter.place-of-publication | United States | |
dc.description | This is the final version. Available from Public Library of Science (PLoS) via the DOI in this record. | en_GB |
dc.description | All the datasets analyzed in this study are publicly available. The dataset of plant-hummingbird communities is available from Dryad Digital Repository dx.doi.org/10.5061/dryad.c270ft8. The dataset of coral reef fishes is available from the Reef Life Survey website dx.doi.org/10.1038/s41559-020-01342-7. The source code to produce the results is available on GitHub at https://github.com/clsong/ReproduceChickenEgg. | en_GB |
dc.identifier.eissn | 1553-7358 | |
dc.identifier.journal | PLoS Computational Biology | en_GB |
dc.relation.ispartof | PLoS Comput Biol, 18(9) | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2022-06-14 | |
dc.rights.license | CC BY | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2022-06-14 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2022-11-03T09:24:58Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2022-11-03T09:32:51Z | |
refterms.panel | A | en_GB |
refterms.depositException | publishedGoldOA | |
refterms.dateFirstOnline | 2022-09-29 |
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Except where otherwise noted, this item's licence is described as © 2022 Song et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.