Will a small randomly assembled community be feasible and stable?
Author(s)
Song, Chuliang; Saavedra Sanchez, Serguei
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How likely is it that few species can randomly assemble into a feasible and stable community? Some studies have answered that as long as the community is feasible, it will nearly always be stable. In contrast, other studies have answered that the likelihood is almost null. Here, we show that the origin of this debate has been the underestimation of the association of the parameter space of intrinsic growth rates with the feasibility and stability properties of small randomly‐assembled communities. In particular, we demonstrate that not all parameterizations and sampling distributions of intrinsic growth rates lead to the same probabilities of stability and feasibility, which could mistakenly lead to under‐ or overestimate the stability properties of feasible communities. Additionally, we find that stability imposes a filtering of species abundances “towards” more even distributions in small feasible randomly‐assembled communities. This indicates that the stability of feasible communities is inherently linked to the starting distribution of species abundances, a characteristic that many times has been ignored, but should be incorporated in manageable lab and field experiments. Overall, the return to this debate is a central reminder that a more systematic exploration of the feasible parameter space is necessary to derive general conclusions about the stability properties of ecological communities. Keywords: feasibility; intrinsic growth rates; random matrices; small communities; species abundances; stability
Date issued
2017-12Department
Massachusetts Institute of Technology. Department of Civil and Environmental EngineeringJournal
Ecology
Publisher
Ecological Society of America
Citation
Song, Chuliang and Serguei Saavedra. “Will a Small Randomly Assembled Community Be Feasible and Stable?” Ecology 99, 3 (January 2018): 743–751 © 2018 Ecological Society of America
Version: Final published version
ISSN
0012-9658