Keyword search (4,163 papers available)

"probability" Keyword-tagged Publications:

Title Authors PubMed ID
1 The infimum values of two probability functions for the Gamma distribution Sun P; Hu ZC; Sun W; 38261930
MATHSTATS
2 Diffusion dynamics on the coexistence subspace in a stochastic evolutionary game Popovic L; Peuckert L; 32025789
MATHSTATS
3 How do landscape context and fences influence roadkill locations of small and medium-sized mammals? Plante J, Jaeger JAG, Desrochers A 30711836
GEOGRAPHY
4 Aegilops tauschii Genome Sequence: A Framework for Meta-analysis of Wheat QTLs. Xu J, Dai X, Ramasamy RK, Wang L, Zhu T, McGuire PE, Jorgensen CM, Dehghani H, Gulick PJ, Luo MC, Müller HG, Dvorak J 30670607
BIOLOGY

 

Title:Diffusion dynamics on the coexistence subspace in a stochastic evolutionary game
Authors:Popovic LPeuckert L
Link:https://pubmed.ncbi.nlm.nih.gov/32025789/
DOI:10.1007/s00285-020-01476-z
Publication:Journal of mathematical biology
Keywords:CoexistenceDegenerate diffusionDiffusion approximationExtinction probabilityExtinction timeRandom environmentStochastic evolutionary game
PMID:32025789 Category:J Math Biol Date Added:2020-02-07
Dept Affiliation: MATHSTATS
1 Department of Mathematics and Statistics, Concordia University, Montreal, QC, H3G 1M8, Canada. lpopovic@mathstat.concordia.ca.
2 Department of Mathematics and Statistics, Concordia University, Montreal, QC, H3G 1M8, Canada.

Description:

Frequency-dependent selection reflects the interaction between different species as they battle for limited resources in their environment. In a stochastic evolutionary game the species relative fitnesses guides the evolutionary dynamics with fluctuations due to random drift. A selection advantage which depends on a changing environment will introduce additional possibilities for the dynamics. We analyse a simple model in which a random environment allows competing species to coexist for a long time before a fixation of a single species happens. In our analysis we use stability in a linear combination of competing species to approximate the stochastic dynamics of the system by a diffusion on a one dimensional co-existence region. Our method significantly simplifies approximating the probability of first extinction and its expected time, and demonstrates a rigorous model reduction technique for evaluating quasistationary properties of stochastic evolutionary dynamics.





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