Submitted to: Crop Science
Publication Type: Peer reviewed journal
Publication Acceptance Date: 6/5/2006
Publication Date: 4/1/2007
Citation: Lockwood, D., Richards, C.M., Volk, G.M. Probabilistic models for collecting genetic diversity: comparisons, caveats and limitations. Crop Science 47:859-866. Interpretive Summary: A critical part of establishing any ex situ collection is assembling a genetically representative sample of individuals. An important step in this process is defining an adequate sample size from each collection site. This paper examines two prominent sampling models that are used to determine the number of individuals needed to capture genetic variation in wild populations. We examine both models in a common simulation framework and directly compare their assumptions and performance at capturing allelic variation at multiple loci and among sampling sites.
Technical Abstract: Methods for collecting genetic diversity are important tools for plant conservation. Many quantitative collection strategies have been proposed, but their different assumptions regarding the collection scale and the basis for diversity often make them difficult to compare. Understanding the limitations of the different strategies enables collectors to make more informed choices when implementing conservation and restoration projects or collecting for germplasm improvement. We compare two genetically based strategies under a common set of assumptions and extend the probabilistic arguments of the strategies to accommodate rare alleles, multiple loci and multiple populations. Larger numbers of individuals must be collected to assure with a high probability (>0.95) the acquisition of alleles at multiple loci within a population. Sampling from multiple populations linked by gene flow may offset this increase. Additionally, the likelihood of capturing rare alleles remains high when sampling for multiple loci or across multiple populations. Comparison of the models provides germplasm collectors with a basis to evaluate risks of over and undersampling to capture genetic diversity within a species.