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Age structure, changing demography and effective population size in Atlantic salmon (Salmo salar).


ABSTRACT: Effective population size (N(e)) is a central evolutionary concept, but its genetic estimation can be significantly complicated by age structure. Here we investigate N(e) in Atlantic salmon (Salmo salar) populations that have undergone changes in demography and population dynamics, applying four different genetic estimators. For this purpose we use genetic data (14 microsatellite markers) from archived scale samples collected between 1951 and 2004. Through life table simulations we assess the genetic consequences of life history variation on N(e). Although variation in reproductive contribution by mature parr affects age structure, we find that its effect on N(e) estimation may be relatively minor. A comparison of estimator models suggests that even low iteroparity may upwardly bias N(e) estimates when ignored (semelparity assumed) and should thus empirically be accounted for. Our results indicate that N(e) may have changed over time in relatively small populations, but otherwise remained stable. Our ability to detect changes in N(e) in larger populations was, however, likely hindered by sampling limitations. An evaluation of N(e) estimates in a demographic context suggests that life history diversity, density-dependent factors, and metapopulation dynamics may all affect the genetic stability of these populations.

SUBMITTER: Palstra FP 

PROVIDER: S-EPMC2728862 | biostudies-other | 2009 Aug

REPOSITORIES: biostudies-other

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Age structure, changing demography and effective population size in Atlantic salmon (Salmo salar).

Palstra Friso P FP   O'Connell Michael F MF   Ruzzante Daniel E DE  

Genetics 20090615 4


Effective population size (N(e)) is a central evolutionary concept, but its genetic estimation can be significantly complicated by age structure. Here we investigate N(e) in Atlantic salmon (Salmo salar) populations that have undergone changes in demography and population dynamics, applying four different genetic estimators. For this purpose we use genetic data (14 microsatellite markers) from archived scale samples collected between 1951 and 2004. Through life table simulations we assess the ge  ...[more]

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