TY - JOUR
T1 - A general theory of coexistence and extinction for stochastic ecological communities
AU - Hening, Alexandru
AU - Nguyen, Dang H.
AU - Chesson, Peter
N1 - Funding Information:
The authors acknowledge generous support from the NSF through the Grants DMS-1853463 for Alexandru Hening, DMS-1853467 for Dang Nguyen, and DEB-1353715 for Peter Chesson.
Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
PY - 2021/5
Y1 - 2021/5
N2 - We analyze a general theory for coexistence and extinction of ecological communities that are influenced by stochastic temporal environmental fluctuations. The results apply to discrete time (stochastic difference equations), continuous time (stochastic differential equations), compact and non-compact state spaces and degenerate or non-degenerate noise. In addition, we can also include in the dynamics auxiliary variables that model environmental fluctuations, population structure, eco-environmental feedbacks or other internal or external factors. We are able to significantly generalize the recent discrete time results by Benaim and Schreiber (J Math Biol 79:393–431, 2019) to non-compact state spaces, and we provide stronger persistence and extinction results. The continuous time results by Hening and Nguyen (Ann Appl Probab 28(3):1893–1942, 2018a) are strengthened to include degenerate noise and auxiliary variables. Using the general theory, we work out several examples. In discrete time, we classify the dynamics when there are one or two species, and look at the Ricker model, Log-normally distributed offspring models, lottery models, discrete Lotka–Volterra models as well as models of perennial and annual organisms. For the continuous time setting we explore models with a resource variable, stochastic replicator models, and three dimensional Lotka–Volterra models.
AB - We analyze a general theory for coexistence and extinction of ecological communities that are influenced by stochastic temporal environmental fluctuations. The results apply to discrete time (stochastic difference equations), continuous time (stochastic differential equations), compact and non-compact state spaces and degenerate or non-degenerate noise. In addition, we can also include in the dynamics auxiliary variables that model environmental fluctuations, population structure, eco-environmental feedbacks or other internal or external factors. We are able to significantly generalize the recent discrete time results by Benaim and Schreiber (J Math Biol 79:393–431, 2019) to non-compact state spaces, and we provide stronger persistence and extinction results. The continuous time results by Hening and Nguyen (Ann Appl Probab 28(3):1893–1942, 2018a) are strengthened to include degenerate noise and auxiliary variables. Using the general theory, we work out several examples. In discrete time, we classify the dynamics when there are one or two species, and look at the Ricker model, Log-normally distributed offspring models, lottery models, discrete Lotka–Volterra models as well as models of perennial and annual organisms. For the continuous time setting we explore models with a resource variable, stochastic replicator models, and three dimensional Lotka–Volterra models.
KW - Auxiliary variables
KW - Coexistence
KW - Environmental fluctuations
KW - Environmental fluctuations
KW - Extinction
KW - Population dynamics
KW - Stochastic difference equations
KW - Stochastic differential equations
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U2 - 10.1007/s00285-021-01606-1
DO - 10.1007/s00285-021-01606-1
M3 - Article
C2 - 33963448
AN - SCOPUS:85105547812
SN - 0303-6812
VL - 82
JO - Journal of Mathematical Biology
JF - Journal of Mathematical Biology
IS - 6
M1 - 56
ER -