Generalizing clusters of similar species as a signature of coexistence under competition
| Autor: | Rafael D’Andrea, Annette Ostling, Maria A. Riolo |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
0106 biological sciences Species Delimitation Computer science Speciation 01 natural sciences 0302 clinical medicine Cluster Analysis Evolutionary Emergence Biology (General) 10. No inequality media_common Ecology Heuristic Community structure Contrast (statistics) Biological Evolution Signature (logic) Habitats 010601 ecology Order (biology) Computational Theory and Mathematics Fecundity Community Ecology Modeling and Simulation Trait Ecological Niches Research Article Evolutionary Processes Ecological Metrics QH301-705.5 media_common.quotation_subject 010603 evolutionary biology Models Biological Competition (biology) 03 medical and health sciences Cellular and Molecular Neuroscience Population Metrics Genetics Animals Cluster analysis Molecular Biology Community Structure Ecology Evolution Behavior and Systematics Ecosystem Ecological niche Evolutionary Biology Community Population Biology Ecology and Environmental Sciences Species diversity Biology and Life Sciences Computational Biology Species Diversity 15. Life on land 030104 developmental biology Evolutionary biology 030217 neurology & neurosurgery |
| Zdroj: | PLoS Computational Biology PLoS Computational Biology, Vol 15, Iss 1, p e1006688 (2019) |
| DOI: | 10.1101/264606 |
| Popis: | Patterns of trait distribution among competing species can potentially reveal the processes that allow them to coexist. It has been recently proposed that competition may drive the spontaneous emergence of niches comprising clusters of similar species, in contrast with the dominant paradigm of greater-than-chance species differences. However, current clustering theory relies largely on heuristic rather than mechanistic models. Furthermore, studies of models incorporating demographic stochasticity and immigration, two key players in community assembly, did not observe clusters. Here we demonstrate clustering under partitioning of resources, partitioning of environmental gradients, and a competition-colonization tradeoff. We show that clusters are robust to demographic stochasticity, and can persist under immigration. While immigration may sustain clusters that are otherwise transient, too much dilutes the pattern. In order to detect and quantify clusters in nature, we introduce and validate metrics which have no free parameters nor require arbitrary trait binning, and weigh species by their abundances rather than relying on a presence-absence count. By generalizing beyond the circumstances where clusters have been observed, our study contributes to establishing them as an update to classical trait patterning theory. Author summary Species traits determine how they compete with each other. As such, patterns in the distributions of traits in a community of competing species may reveal the processes responsible for coexistence. One central idea in theoretical ecology is that the strength of competition relates to similarity in species needs and strategies, and therefore if competition plays out at short timescales, coexisting species should be more different than expected by chance. However, recent theory suggests that competition may lead species to temporarily self-organize into groups with similar traits. Here we show that this clustering is a generic feature of competitive dynamics, which is robust to demographic stochasticity and can be indefinitely maintained by immigration. We show that clustering arises whether species coexist by partitioning resources, environmental preferences, or through tradeoffs in life-history strategies. We introduce and validate metrics that, given species traits and abundances, determine whether they are clustered, and if so, how many clusters occur. By showing the generality of self-organized species clusters and providing tools for their detection, our study contributes to updating classical ideas about how competition shapes communities, and motivates searches for them in nature. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|