ArticleJournal of mathematical biology2021
A general theory of coexistence and extinction for stochastic ecological communities.
Article in Journal of mathematical biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Article
- Threshold-awareness in adaptive cancer therapy.PLoS computational biology · 2024Article
- Strong stochastic persistence of some Lévy-driven Lotka-Volterra systems.Journal of mathematical biology · 2022Article
- Stationary distributions of persistent ecological systems.Journal of mathematical biology · 2021Article
- Harvesting and seeding of stochastic populations: analysis and numerical approximation.Journal of mathematical biology · 2020Article
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3 authors.
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Abstract
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.
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