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Generalism drives abundance: A computational causal discovery approach

Song, Chuliang ; Simmons, Benno I. ; Fortin, Marie-Josée ; Gonzalez, Andrew Pascual, Mercedes

PLoS computational biology, 2022-09, Vol.18 (9), p.e1010302-e1010302 [同儕審閱期刊]

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題名:
Generalism drives abundance: A computational causal discovery approach
著者: Song, Chuliang ; Simmons, Benno I. ; Fortin, Marie-Josée ; Gonzalez, Andrew
Pascual, Mercedes
主題: Abundance ; Animal ecology ; Animal populations ; Asymmetry ; Biology and Life Sciences ; Community ecology ; Computer and Information Sciences ; Computer applications ; Datasets ; Drift ; Earth Sciences ; Ecological effects ; Ecological research ; Ecology and Environmental Sciences ; Information theory ; Mathematical logic ; Methods ; Physical Sciences ; Plant communities ; Skewed distributions ; Wildlife conservation
所屬期刊: PLoS computational biology, 2022-09, Vol.18 (9), p.e1010302-e1010302
描述: 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.
出版者: San Francisco: Public Library of Science
語言: 英文
識別號: ISSN: 1553-7358
ISSN: 1553-734X
EISSN: 1553-7358
DOI: 10.1371/journal.pcbi.1010302
資源來源: Publicly Available Content Database
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Generalism drives abundance: A computational causal discovery approach

Song, Chuliang ; Simmons, Benno I. ; Fortin, Marie-Josée ; Gonzalez, Andrew Pascual, Mercedes

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