Stochasticity enhances the gaining of bet-hedging strategies in contact-process-like dynamics
arXiv:1412.1066 · doi:10.1103/PhysRevE.91.032114
Abstract
In biology and ecology, individuals or communities of individuals living in unpredictable environments often alternate between different evolutionary strategies to spread and reduce risks. Such behavior is commonly referred to as "bet-hedging." Long-term survival probabilities and population sizes can be much enhanced by exploiting such hybrid strategies. Here, we study the simplest possible birth-death stochastic model in which individuals can choose among a poor but safe strategy, a better but risky alternative, or a combination of both. We show analytically and computationally that the benefits derived from bet-hedging strategies are much stronger for higher environmental variabilities (large external noise) and/or for small spatial dimensions (large intrinsic noise). These circumstances are typically encountered by living systems, thus providing us with a possible justification for the ubiquitousness of bet-hedging in nature.
arXiv comment10 pages, 8 figures
References in corpus (9)
- New paradoxical games based on Brownian ratchets
- Genetic Demixing and Evolutionary Forces in the One-Dimensional Stepping Stone Model
- Chemical warfare and survival strategies in bacterial range expansions
- Quasi-stationary distributions for stochastic processes with an absorbing state
- Temporal Griffiths Phases
- On the nature of different types of absorbing states
- Selective advantage of diffusing faster
- Range Expansion of Heterogeneous Populations
- Specialization and Bet Hedging in Heterogeneous Populations
Cited by in corpus (5)
- Bet-hedging strategies in expanding populations
- Impact of environmental colored noise in single-species population dynamics
- Bet-hedging against demographic fluctuations
- Phenotypic-dependent variability and the emergence of tolerance in bacterial populations
- Stochastic multi-scale models of competition within heterogeneous cellular populations: simulation methods and mean-field analysis