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Destabilizing evolutionary and eco-evolutionary feedbacks drive empirical eco-evolutionary cycles
Author(s) -
Michael H. Cortez,
Swati Patel,
Sebastian J. Schreiber
Publication year - 2020
Publication title -
proceedings of the royal society b biological sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.342
H-Index - 253
eISSN - 1471-2954
pISSN - 0962-8452
DOI - 10.1098/rspb.2019.2298
Subject(s) - ecology , evolutionary dynamics , stability (learning theory) , evolutionary ecology , evolutionary algorithm , evolutionary physiology , biology , computer science , artificial intelligence , population , machine learning , demography , sociology , host (biology)
We develop a method to identify how ecological, evolutionary, and eco-evolutionary feedbacks influence system stability. We apply our method to nine empirically parametrized eco-evolutionary models of exploiter–victim systems from the literature and identify which particular feedbacks cause some systems to converge to a steady state or to exhibit sustained oscillations. We find that ecological feedbacks involving the interactions between all species and evolutionary and eco-evolutionary feedbacks involving only the interactions between exploiter species (predators or pathogens) are typically stabilizing. In contrast, evolutionary and eco-evolutionary feedbacks involving the interactions between victim species (prey or hosts) are destabilizing more often than not. We also find that while eco-evolutionary feedbacks rarely altered system stability from what would be predicted from just ecological and evolutionary feedbacks, eco-evolutionary feedbacks have the potential to alter system stability at faster or slower speeds of evolution. As the number of empirical studies demonstrating eco-evolutionary feedbacks increases, we can continue to apply these methods to determine whether the patterns we observe are common in other empirical communities.

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