期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 108, 期 41, 页码 17052-17057出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1111266108
关键词
Bayesian analysis; bet-hedging; coalescent theory
资金
- German Research Foundation [STE 325/9, STE 325/13]
- Volkswagen Foundation [I/82752, I/82770]
Seed and egg dormancy is a prevalent life-history trait in plants and invertebrates whose storage effect buffers against environmental variability, modulates species extinction in fragmented habitats, and increases genetic variation. Experimental evidence for reliable differences in dormancy over evolutionary scales (e. g., differences in seed banks between sister species) is scarce because complex ecological experiments in the field are needed to measure them. To cope with these difficulties, we developed an approximate Bayesian computation (ABC) framework that integrates ecological information on population census sizes in the priors of the parameters, along with a coalescent model accounting simultaneously for seed banks and spatial genetic structuring of populations. We collected SNP data at seven nuclear loci (over 300 SNPs) using a combination of three spatial sampling schemes: population, pooled, and species-wide samples. We provide evidence for the existence of a seed bank in two wild tomato species (Solanum chilense and Solanum peruvianum) found in western South America. Although accounting for uncertainties in ecological data, we infer for each species (i) the past demography and (ii) ecological parameters, such as the germination rate, migration rates, and minimum number of demes in the metapopulation. The inferred difference in germination rate between the two species may reflect divergent seed dormancy adaptations, in agreement with previous population genetic analyses and the ecology of these two sister species: Seeds spend, on average, a shorter time in the soil in the specialist species (S. chilense) than in the generalist species (S. peruvianum).
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据