Inferring Fitness in Finite Populations with Moran-like dynamics
This provides a robust inference tool for evolutionary biologists studying fitness in finite populations, though it appears incremental as it extends existing Bayesian methods to more complex scenarios.
The authors tackled the problem of inferring unobservable biological fitness from population dynamics by developing a Bayesian inference method for Moran-like processes, which can estimate fitness from birth events in evolving populations on dynamic networks and with changing sizes.
Biological fitness is not an observable quantity and must be inferred from population dynamics. Bayesian inference applied to the Moran process and variants yields a robust inference method that can infer fitness in populations evolving via a Moran dynamic and generalizations. Information about fitness is derived solely from birth-events in birth-death and death-birth processes in which selection acts proportionally to fitness, which allows the method to be applied to populations on a network where the network itself may be changing in time. Populations may also be allowed to change size while still allowing estimates for fitness to be inferred.