Many weak interactions and few strong; food-web feasibility depends on the combination of the strength of species’ interactions and their correct arrangement
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Ecological communities consist of generalists who interact with proportionally many species, and specialists who interact with proportionally few. The strength of these interactions also varies, with communities typically exhibiting a few strong links embedded within many weak links. Historically, it has been argued that generalists should interact more weakly with their partners than specialists and, since weak interactions are thought to increase community stability, that this pattern increases the stability of diverse communities. Here, we studied model-generated predator-prey communities to explicitly investigate the validity of this argument. In feasible communities—those which were both locally stable and all species had positive biomass—we indeed found that species with many predators or prey are affected by them more weakly than species with few. This relationship, however, is only part of the story. While species with many predators (or prey) tend to be only weakly affected by each of them, these many weak interactions are balanced by a few strong interactions with prey (or predators). These few strong interactions are large enough that, when the effect of predator and prey interactions are combined, it seems that species with many interactions actually interact more strongly than species with few interactions. Though past research has tended to focus on either the arrangement of species interactions or the strength of those interactions, we show here that the two are in fact inextricably linked. This observation has implications for both the realistic design of theoretical models, and the conservation of ecological communities, especially those in which the strength and arrangement of species’ interactions are impacted by biodiversity-loss disturbances such as habitat alteration.
KeywordsInteraction strength Stability Food-web structure Predator-prey Community matrix
We thank Alyssa Cirtwill, Camille Coux, Guilio Dalla Riva, Nick Baker, Carla Gomez Creutzberg, Melissa Broussard, Johanna Voinopol-Sassu, Michelle Lambert, Karen Adair, Nixie Boddy, Sophie Hunt, Katie Bowron, Liezl Thalwitzer, Maggie Olsen and Josh Van Lier for comments on the manuscript.
We thank Stefano Allesina and Si Tang for discussions about solving for growth rate and biomass directly from the community matrix.
KLW was supported by a University of Canterbury Master’s Scholarship, a William Georgetti Scholarship, a Freemason’s University Scholarship, a Sadie Balkind Scholarship, administered by the Canterbury Branch of the New Zealand Federation of Graduate Women, a University of Canterbury Summer Research Scholarship, a University of Canterbury Alumni Association Scholarship and a University of Canterbury Senior Scholarship, and DBS by a Marsden Fund Fast-Start grant (UOC-1101) and a Rutherford Discovery Fellowship, both administered by the Royal Society of New Zealand. We are thankful to the BlueFern University of Canterbury Super Computer for computing facilities.
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