Generalism drives abundance: A computational causal discovery approach.
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 gener...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-09-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1010302 |
| _version_ | 1797996799585157120 |
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| author | Chuliang Song Benno I Simmons Marie-Josée Fortin Andrew Gonzalez |
| author_facet | Chuliang Song Benno I Simmons Marie-Josée Fortin Andrew Gonzalez |
| author_sort | Chuliang Song |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T10:23:04Z |
| format | Article |
| id | doaj.art-cd2054b35a4049e6b556aa416e7733a5 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-11T10:23:04Z |
| publishDate | 2022-09-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-cd2054b35a4049e6b556aa416e7733a52022-12-22T04:29:41ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582022-09-01189e101030210.1371/journal.pcbi.1010302Generalism drives abundance: A computational causal discovery approach.Chuliang SongBenno I SimmonsMarie-Josée FortinAndrew GonzalezA 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.https://doi.org/10.1371/journal.pcbi.1010302 |
| spellingShingle | Chuliang Song Benno I Simmons Marie-Josée Fortin Andrew Gonzalez Generalism drives abundance: A computational causal discovery approach. PLoS Computational Biology |
| title | Generalism drives abundance: A computational causal discovery approach. |
| title_full | Generalism drives abundance: A computational causal discovery approach. |
| title_fullStr | Generalism drives abundance: A computational causal discovery approach. |
| title_full_unstemmed | Generalism drives abundance: A computational causal discovery approach. |
| title_short | Generalism drives abundance: A computational causal discovery approach. |
| title_sort | generalism drives abundance a computational causal discovery approach |
| url | https://doi.org/10.1371/journal.pcbi.1010302 |
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