Beyond FST: Analysis of population genetic data for conservation

Abstract

Both the ability to generate DNA data and the variety of analytical methods for conservation genetics are expanding at an ever-increasing pace. Analytical approaches are now possible that were unthinkable even five years ago due to limitations in computational power or the availability of DNA data, and this has vastly expanded the accuracy and types of information that may be gained from population genetic data.Here we provide a guide to recently developed methods for population genetic analysis, including identification of population structure, quantification of gene flow, and inference of demographic history. We cover both allele-frequency and sequence-based approaches, with a special focus on methods relevant to conservation genetic applications. Although classical population genetic approaches such as F S T S T (and its derivatives) have carried the field thus far, newer, more powerful, methods can infer much more from the data, rely on fewer assumptions, and are appropriate for conservation genetic management when precise estimates are needed.

Effective population size is strongly correlated with breeding pond size in the endangered California tiger salamander, Ambystoma californiense

“…We used BOTTLENECK V1.2.02 Piry et al 1999) to investigate deviations from expected heterozygote excess relative to allelic diversity. During population bottlenecks, rare alleles are lost due to drift at a faster rate than the overall loss of heterozygosity, and BOTTLENECK utilizes this disparity to detect past bottlenecks (Spencer et al 2000;Pearse and Crandall 2004;Williamson-Natesan 2005). We performed the analysis under all three microsatellite mutational models available: the infinite alleles model (IAM), stepwise mutational model (SMM), and two phase mutational model (TPM) with 80% single-step mutations and 20% multiple-step mutations, based on empirical estimates of microsatellite mutation rates (Goldstein and Schlotterer 1999).…”

“…In this test, M is the ratio between the number of alleles at a locus and the total range in allele sizes. Unless all rare alleles are at the ends of the allele size distribution, bottlenecks will tend to eliminate rare alleles but leave the allele size distribution intact, and M will be reduced in populations that have experienced a significant population size reduction (Pearse and Crandall 2004;WilliamsonNatesan 2005). We parameterized the TPM used in M-RATIO by assigning an 80% rate of single-step mutations and a mean of 2.8 repeats to the size change of multiplestep mutations, based upon empirical estimates of microsatellite evolution (Garza and Williamson 2001), and used a mutation rate of 5 9 10 -4 (Goldstein and Schlotterer 1999).…”

Fauna crime: elucidating the potential source and introduction history of European smelt (Osmerus eperlanus L.) into Lake Storsjøen, Norway

“…The software GENECLASS2 (Piry et al 2004) was used to exclude or assign reference groups as possible sources, i.e. to determine which groups are likely source populations, and to significantly exclude unlikely sources (Pearse and Crandall 2004). This was done by using all the different criteria available for calculation; Bayesian, allele frequency, and distance based.…”

“…Bayesian and frequency based approaches in this software have the advantage that they do not assume that the source population is among the sampled populations. This gives the possibility of asking if the ''true'' source population is among the sampled populations, rather than asking which population has the highest likelihood as a potential source, and to significantly exclude unlikely sources (Pearse and Crandall 2004). Initial tests, using all criteria, were performed with GENECLASS2 by testing all spawning populations with known origin against all the potential source populations to determine the power, or consistency of this analysis.…”

“…microsatellites provide an effective tool for natural scientists (Beaumont et al 2002;Stauffer, 2008;Stephens and Balding 2009) allowing for statistical genetic assignment and identification of a given individual to Electronic supplementary material The online version of this article (doi:10.1007/s10592-015-0724-2) contains supplementary material, which is available to authorized users. putative source populations (Pearse and Crandall 2004). Such methods are useful in identifying indigenous and introduced individuals (Primmer et al 2000), and have been extensively used in a number of convictions, e.g.…”