Environmental vs. demographic variability in two-species predator-prey models

Abstract

We investigate the competing effects and relative importance of intrinsic demographic and environmental variability on the evolutionary dynamics of a stochastic two-species Lotka-Volterra model by means of Monte Carlo simulations on a two-dimensional lattice. Individuals are assigned inheritable predation efficiencies; quenched randomness in the spatially varying reaction rates serves as environmental noise. We find that environmental variability enhances the population densities of both predators and prey while demographic variability leads to essentially neutral optimization.

5 pages, 4 figures included; to appear in Phys. Rev. Lett. (2013)

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• Population Dynamics in a Changing Environment: Random versus Periodic Switching
• Species coexistence in a neutral dynamics with environmental noise
• Eco-Evolutionary Dynamics of a Population with Randomly Switching Carrying Capacity
• Global attractors and extinction dynamics of cyclically competing species
• The effect of habitats and fitness on species coexistence in systems with cyclic dominance
• Evolutionary dynamics and competition stabilize three-species predator-prey communities
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• Phase diagram of the symbiotic two-species contact process
• Survival behavior in the cyclic Lotka-Volterra model with a randomly switching reaction rate
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• Boundary Effects on Population Dynamics in Stochastic Lattice Lotka-Volterra Models
• Specialization and Bet Hedging in Heterogeneous Populations
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• Environmental vs demographic variability in stochastic predator-prey models
• Heterogeneous mean-field theory for two-species symbiotic processes on networks
• Dynamical correlations and pairwise theory for the symbiotic contact process on networks
• Perturbative Field-Theoretical Analysis of Three-Species Cyclic Predator-Prey Models
• Selfish punishment with avoiding mechanism can alleviate both first-order and second-order social dilemma
• Critical fluctuations of noisy period-doubling maps
• Agent-based Monte Carlo simulations for reaction-diffusion models, population dynamics, and epidemic spreading