Using genetic markers to directly estimate gene flow and reproductive success parameters in plants on the basis of naturally regenerated seedlings.

Estimating seed and pollen gene flow in plants on the basis of samples of naturally regenerated seedlings can provide much needed information about "realized gene flow," but seems to be one of the greatest challenges in plant population biology. Traditional parentage methods, because of their inability to discriminate between male and female parentage of seedlings, unless supported by uniparentally inherited markers, are not capable of precisely describing seed and pollen aspects of gene flow realized in seedlings. Here, we describe a maximum-likelihood method for modeling female and male parentage in a local plant population on the basis of genotypic data from naturally established seedlings and when the location and genotypes of all potential parents within the population are known. The method models female and male reproductive success of individuals as a function of factors likely to influence reproductive success (e.g., distance of seed dispersal, distance between mates, and relative fecundity--i.e., female and male selection gradients). The method is designed to account for levels of seed and pollen gene flow into the local population from unsampled adults; therefore, it is well suited to isolated, but also wide-spread natural populations, where extensive seed and pollen dispersal complicates traditional parentage analyses. Computer simulations were performed to evaluate the utility and robustness of the model and estimation procedure and to assess how the exclusion power of genetic markers (isozymes or microsatellites) affects the accuracy of the parameter estimation. In addition, the method was applied to genotypic data collected in Scots pine (isozymes) and oak (microsatellites) populations to obtain preliminary estimates of long-distance seed and pollen gene flow and the patterns of local seed and pollen dispersal in these species.

Estimating pollen flow using SSR markers and paternity exclusion: accounting for mistyping.

Highly informative genetic markers, such as simple sequence repeats (SSRs), can be used to directly measure pollen flow by parentage analysis. However, mistyping (i.e. false inference of genotypes caused by the occurrence of null alleles, mutations, and detection errors) can lead to substantial biases in the estimates obtained. Using computer simulations, we evaluated a direct method for estimating pollen immigration using SSR markers and a paternity exclusion approach. This method accounts for mistyping and does not rely on assumptions about the distribution of male reproductive success. If ignored, even minor rates of mistyping (1.5%) resulted in overestimating pollen immigration by up to 150%. When we required at least two mismatching loci before excluding candidate fathers from paternity, the resulting pollen immigration estimates had small biases for rates of mistyping up to 4.5%. Requiring at least three mismatches for exclusion was needed to minimize the upward biases of pollen immigration caused by rates of mistyping up to 10.5%. The minimum number of highly variable SSR loci needed to minimize cryptic gene flow and obtain reliable estimates of pollen immigration varied from five to seven for a sampling scheme applicable to most conifers (i.e. when paternal haplotypes can be unambiguously determined). Between five and nine highly variable SSR loci were needed for a more general sampling scheme that is applicable to all diploid seed plants. With moderately variable SSR markers, consistently accurate estimates of pollen immigration could be obtained only for rates of mistyping up to 4.5%. We developed the POLLEN FLOW (PFL) computer program which can be used to obtain unbiased and precise estimates of pollen immigration under a wide range of conditions, including population sizes as large as 600 parents and mistyping rates as high as 10.5%.

Estimation of population pharmacokinetic parameters using destructively obtained experimental data: a simulation study of the one-compartment open model.

A simulation study of the one-compartment open pharmacokinetic model has been made. The population pharmacokinetic parameters which characterize the population of drug residues over time are assumed to be stochastic. A general theoretical model framework for parameter estimation via the method of extended least squares is presented. Formulas approximating the required mean and variance time functions are developed and subsequently used in the simulation study. The effects of four different designs in four different animal populations are presented. The simulated data are those of the single observation per animal per time point type. The characterizing population pharmacokinetic parameters have been analyzed for bias and reliability in both a naive and second-order mean model. Recommendations for choosing an appropriate sampling design are included.