Northwest Fisheries Science Center

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Document Type: Journal Article
Center: NWFSC
Document ID: 4416
Title: Simple life history traits explain key effective population size ratios across diverse taxa
Author: Robin S. Waples, G. Luikart, James R. Faulkner, David A. Tallmon
Publication Year: 2013
Journal: Proceedings of the Royal Society of London. Series B
Volume: 280
Issue: 1768
Pages: 1339
Keywords: life history,effective populaton size,vital rates,age-at-maturity,adult lifespan,Leslie matrix
Abstract:

Effective population size (Ne) controls both the rate of random genetic drift and the effectiveness of selection and migration, but it is difficult to estimate in nature.  In particular, for species with overlapping generations, it is easier to estimate the effective number of breeders in one reproductive cycle (Nb) than Ne per generation.  We empirically evaluated the relationship between life history and ratios of Ne, Nb, and adult census size (N) using a recently-developed model (AGENE) and published vital rates for 63 iteroparous animals and plants.  Nb/Ne varied a surprising six-fold across species and, contrary to expectations, Nb was larger than Ne in over half the species.  Up to 2/3 of the variance in Nb/Ne and up to half the variance in Ne/N was explained by just two life history traits (age at maturity and adult lifespan) that have long interested both ecologists and evolutionary biologists.  These results provide novel insights into, and demonstrate a close general linkage between, demographic and evolutionary processes across diverse taxa.  For the first time, our results also make it possible to interpret rapidly accumulating estimates of Nb in the context of the rich body of evolutionary theory based on Ne per generation.

URL1: The next link will exit from NWFSC web site http://dx.doi.org/10.1098/rspb.2013.1339
Theme: Recovery, Rebuilding and Sustainability of Marine and Anadromous Species
Foci: Characterize vital rates and other demographic parameters for key species, and develop and improve methods for predicting risk and viability/sustainability from population dynamics and demographic information.
Develop methods to use physiological and biological information to predict population-level processes.