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Detecting past population bottlenecks using temporal genetic data
Author(s) -
RAMAKRISHNAN UMA,
HADLY ELIZABETH A.,
MOUNTAIN JOANNA L.
Publication year - 2005
Publication title -
molecular ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.619
H-Index - 225
eISSN - 1365-294X
pISSN - 0962-1083
DOI - 10.1111/j.1365-294x.2005.02586.x
Subject(s) - coalescent theory , bottleneck , biology , population bottleneck , population , population size , effective population size , evolutionary biology , mutation rate , genetic drift , demographic history , sampling (signal processing) , population growth , genetic data , genetic variation , demography , computer science , genetics , microsatellite , allele , filter (signal processing) , sociology , gene , computer vision , embedded system , phylogenetic tree
Population bottlenecks wield a powerful influence on the evolution of species and populations by reducing the repertoire of responses available for stochastic environmental events. Although modern contractions of wild populations due to human‐related impacts have been documented globally, discerning historic bottlenecks for all but the most recent and severe events remains a serious challenge. Genetic samples dating to different points in time may provide a solution in some cases. We conducted serial coalescent simulations to assess the extent to which temporal genetic data are informative regarding population bottlenecks. These simulations demonstrated that the power to reject a constant population size hypothesis using both ancient and modern genetic data is almost always higher than that based solely on modern data. The difference in power between the modern and temporal DNA approaches depends significantly on effective population size and bottleneck intensity and less significantly on sample size. The temporal approach provides more power in cases of genetic recovery (via migration) from a bottleneck than in cases of demographic recovery (via population growth). Choice of genetic region is critical, as mutation rate heavily influences the extent to which temporal sampling yields novel information regarding the demographic history of populations.