Literature DB >> 28674928

Stochastic population growth in spatially heterogeneous environments: the density-dependent case.

Alexandru Hening1,2, Dang H Nguyen3, George Yin3.   

Abstract

This work is devoted to studying the dynamics of a structured population that is subject to the combined effects of environmental stochasticity, competition for resources, spatio-temporal heterogeneity and dispersal. The population is spread throughout n patches whose population abundances are modeled as the solutions of a system of nonlinear stochastic differential equations living on [Formula: see text]. We prove that r, the stochastic growth rate of the total population in the absence of competition, determines the long-term behaviour of the population. The parameter r can be expressed as the Lyapunov exponent of an associated linearized system of stochastic differential equations. Detailed analysis shows that if [Formula: see text], the population abundances converge polynomially fast to a unique invariant probability measure on [Formula: see text], while when [Formula: see text], the population abundances of the patches converge almost surely to 0 exponentially fast. This generalizes and extends the results of Evans et al. (J Math Biol 66(3):423-476, 2013) and proves one of their conjectures. Compared to recent developments, our model incorporates very general density-dependent growth rates and competition terms. Furthermore, we prove that persistence is robust to small, possibly density dependent, perturbations of the growth rates, dispersal matrix and covariance matrix of the environmental noise. We also show that the stochastic growth rate depends continuously on the coefficients. Our work allows the environmental noise driving our system to be degenerate. This is relevant from a biological point of view since, for example, the environments of the different patches can be perfectly correlated. We show how one can adapt the nondegenerate results to the degenerate setting. As an example we fully analyze the two-patch case, [Formula: see text], and show that the stochastic growth rate is a decreasing function of the dispersion rate. In particular, coupling two sink patches can never yield persistence, in contrast to the results from the non-degenerate setting treated by Evans et al. which show that sometimes coupling by dispersal can make the system persistent.

Entities:  

Keywords:  Density-dependence; Dispersion; Ergodicity; Habitat fragmentation; Lotka–Volterra model; Lyapunov exponent; Spatial and temporal heterogeneity; Stochastic environment; Stochastic population growth

Mesh:

Year:  2017        PMID: 28674928      PMCID: PMC5772867          DOI: 10.1007/s00285-017-1153-2

Source DB:  PubMed          Journal:  J Math Biol        ISSN: 0303-6812            Impact factor:   2.259


  21 in total

1.  Dispersal, Environmental Correlation, and Spatial Synchrony in Population Dynamics.

Authors:  Bruce E Kendall; Ottar N Bjørnstad; Jordi Bascompte; Timothy H Keitt; William F Fagan
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2.  The inflationary effects of environmental fluctuations in source-sink systems.

Authors:  Andrew Gonzalez; Robert D Holt
Journal:  Proc Natl Acad Sci U S A       Date:  2002-11-04       Impact factor: 11.205

Review 3.  Permanence and the dynamics of biological systems.

Authors:  V Hutson; K Schmitt
Journal:  Math Biosci       Date:  1992-09       Impact factor: 2.144

4.  Evolutionary stability of ideal free dispersal strategies in patchy environments.

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Journal:  J Math Biol       Date:  2011-11-03       Impact factor: 2.259

5.  Temporal autocorrelation can enhance the persistence and abundance of metapopulations comprised of coupled sinks.

Authors:  Manojit Roy; Robert D Holt; Michael Barfield
Journal:  Am Nat       Date:  2005-05-26       Impact factor: 3.926

6.  Patchy populations in stochastic environments: critical number of patches for persistence.

Authors:  Jordi Bascompte; Hugh Possingham; Joan Roughgarden
Journal:  Am Nat       Date:  2002-02       Impact factor: 3.926

7.  Invasion dynamics in spatially heterogeneous environments.

Authors:  Sebastian J Schreiber; James O Lloyd-Smith
Journal:  Am Nat       Date:  2009-10       Impact factor: 3.926

8.  Evolution of dispersal distance.

Authors:  Rick Durrett; Daniel Remenik
Journal:  J Math Biol       Date:  2011-06-17       Impact factor: 2.259

9.  Populations can persist in an environment consisting of sink habitats only.

Authors:  V A Jansen; J Yoshimura
Journal:  Proc Natl Acad Sci U S A       Date:  1998-03-31       Impact factor: 11.205

10.  Robust permanence for ecological equations with internal and external feedbacks.

Authors:  Swati Patel; Sebastian J Schreiber
Journal:  J Math Biol       Date:  2017-10-26       Impact factor: 2.259

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  5 in total

1.  Persistence and extinction for stochastic ecological models with internal and external variables.

Authors:  Michel Benaïm; Sebastian J Schreiber
Journal:  J Math Biol       Date:  2019-05-03       Impact factor: 2.259

2.  Asymptotic harvesting of populations in random environments.

Authors:  Alexandru Hening; Dang H Nguyen; Sergiu C Ungureanu; Tak Kwong Wong
Journal:  J Math Biol       Date:  2018-08-04       Impact factor: 2.259

3.  Stochastic Lotka-Volterra food chains.

Authors:  Alexandru Hening; Dang H Nguyen
Journal:  J Math Biol       Date:  2017-11-17       Impact factor: 2.259

4.  The competitive exclusion principle in stochastic environments.

Authors:  Alexandru Hening; Dang H Nguyen
Journal:  J Math Biol       Date:  2020-01-10       Impact factor: 2.259

5.  Tipping Cascades in a Multi-patch System with Noise and Spatial Coupling.

Authors:  Abhishek Mallela; Alan Hastings
Journal:  Bull Math Biol       Date:  2021-09-30       Impact factor: 1.758

  5 in total

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