A comprehensive assessment of the determinants of effective population size (Ne) requires estimates of variance in lifetime reproductive success and past changes in census numbers. For natural populations, such information can be best obtained by combining longitudinal data on individual life histories and genetic marker-based inferences of demographic history. Independent estimates of the variance effective size (NeV, obtained from life-history data) and the inbreeding effective size (NeI, obtained from genetic data) provide a means of disentangling the effects of current and historical demography. The purpose of this study was to assess the demographic determinants of Ne in one of the most intensively studied natural populations of a vertebrate species: the population of savannah baboons (Papio cynocephalus) in the Amboseli Basin, southern Kenya. We tested the hypotheses that NeV < N < NeI (where N = population census number) due to a recent demographic bottleneck. NeV was estimated using a stochastic demographic model based on detailed life-history data spanning a 28-year period. Using empirical estimates of age-specific rates of survival and fertility for both sexes, individual-based simulations were used to estimate the variance in lifetime reproductive success. The resultant values translated into an NeV/N estimate of 0.329 (SD = 0.116, 95% CI = 0.172–0.537). Historical NeI was estimated from 14-locus microsatellite genotypes using a coalescent-based simulation model. Estimates of NeI were 2.2 to 7.2 times higher than the contemporary census number of the Amboseli baboon population. In addition to the effects of immigration, the disparity between historical NeI and contemporary N is likely attributable to the time lag between the recent drop in census numbers and the rate of increase in the average probability of allelic identity-by-descent. Thus, observed levels of genetic diversity may primarily reflect the population's prebottleneck history rather than its current demography.
Corresponding Editor: H. L. Gibbs