We demonstrate that the mean ratio of the number of alleles to the range in allele size, which we term M, calculated from a population sample of microsatellite loci, can be used to detect reductions in population size. Using simulations, we show that, for a general class of mutation models, the value of M decreases when a population is reduced in size. The magnitude of the decrease is positively correlated with the severity and duration of the reduction in size. We also find that the rate of recovery of M following a reduction in size is positively correlated with post-reduction population size, but that recovery occurs in both small and large populations. This indicates that M can distinguish between populations that have been recently reduced in size and those which have been small for a long time. We employ M to develop a statistical test for recent reductions in population size that can detect such changes for more than 100 generations with the post-reduction demographic scenarios we examine. We also compute M for a variety of populations and species using microsatellite data collected from the literature. We find that the value of M consistently predicts the reported demographic history for these populations. This method, and others like it, promises to be an important tool for the conservation and management of populations that are in need of intervention or recovery.

We develop a maximum-likelihood framework for using temporal changes in allele frequencies to estimate the number of breeding individuals in a population. We use simulations to compare the performance of this estimator to an F-statistic estimator of variance effective population size. The maximum-likelihood estimator had a lower variance and smaller bias. Taking advantage of the likelihood framework, we extend the model to include exponential growth and show that temporal allele frequency data from three or more sampling events can be used to test for population growth.

A population’s effective size is an important quantity for conservation and management. The effective size may be estimated from the change of allele frequencies observed in temporally spaced genetic samples taken from the population. Though moment-based estimators exist, recently Williamson and Slatkin demonstrated the advantages of a maximum-likelihood approach that they applied to data on diallelic genetic markers. Their computational methods, however, do not extend to data on multiallelic markers, because in such cases exact evaluation of the likelihood is impossible, requiring an intractable sum over latent variables. We present a Monte Carlo approach to compute the likelihood with data on multiallelic markers. So as to be computationally efficient, our approach relies on an importance-sampling distribution constructed by a forward-backward method. We describe the Monte Carlo formulation and the importance-sampling function and then demonstrate their use on both simulated and real datasets.