Effective population size in a partially clonal plant is not predicted by the number of genetic individuals

Roberta Gargiulo, Robin S. Waples, Adri K. Grow, Richard P. Shefferson, Juan Viruel, Michael F. Fay, Tiiu Kull

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Estimating effective population size (N-e) is important for theoretical and practical applications in evolutionary biology and conservation. Nevertheless, estimates of N-e in organisms with complex life-history traits remain scarce because of the challenges associated with estimation methods. Partially clonal plants capable of both vegetative (clonal) growth and sexual reproduction are a common group of organisms for which the discrepancy between the apparent number of individuals (ramets) and the number of genetic individuals (genets) can be striking, and it is unclear how this discrepancy relates to N-e. In this study, we analysed two populations of the orchid Cypripedium calceolus to understand how the rate of clonal versus sexual reproduction affected N-e. We genotyped > 1000 ramets at microsatellite and SNP loci, and estimated contemporary N-e with the linkage disequilibrium method, starting from the theoretical expectation that variance in reproductive success among individuals caused by clonal reproduction and by constraints on sexual reproduction would lower N-e. We considered factors potentially affecting our estimates, including different marker types and sampling strategies, and the influence of pseudoreplication in genomic data sets on N-e confidence intervals. The magnitude of N-e/N-ramets and N-e/N-genets ratios we provide may be used as reference points for other species with similar life-history traits. Our findings demonstrate that N-e in partially clonal plants cannot be predicted based on the number of genets generated by sexual reproduction, because demographic changes over time can strongly influence N-e. This is especially relevant in species of conservation concern in which population declines may not be detected by only ascertaining the number of genets.

Original languageEnglish
Pages (from-to)750-766
Number of pages17
JournalEvolutionary Applications
Issue number3
Early online date21 Feb 2023
Publication statusPublished - Mar 2023

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