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      Samples from subdivided populations yield biased estimates of effective size that overestimate the rate of loss of genetic variation

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          Abstract

          Many empirical studies estimating effective population size apply the temporal method that provides an estimate of the variance effective size through the amount of temporal allele frequency change under the assumption that the study population is completely isolated. This assumption is frequently violated, and the magnitude of the resulting bias is generally unknown. We studied how gene flow affects estimates of effective size obtained by the temporal method when sampling from a population system and provide analytical expressions for the expected estimate under an island model of migration. We show that the temporal method tends to systematically underestimate both local and global effective size when populations are connected by gene flow, and the bias is sometimes dramatic. The problem is particularly likely to occur when sampling from a subdivided population where high levels of gene flow obscure identification of subpopulation boundaries. In such situations, sampling in a manner that prevents biased estimates can be difficult. This phenomenon might partially explain the frequently reported unexpectedly low effective population sizes of marine populations that have raised concern regarding the genetic vulnerability of even exceptionally large populations.

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          What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity.

          We review commonly used population definitions under both the ecological paradigm (which emphasizes demographic cohesion) and the evolutionary paradigm (which emphasizes reproductive cohesion) and find that none are truly operational. We suggest several quantitative criteria that might be used to determine when groups of individuals are different enough to be considered 'populations'. Units for these criteria are migration rate (m) for the ecological paradigm and migrants per generation (Nm) for the evolutionary paradigm. These criteria are then evaluated by applying analytical methods to simulated genetic data for a finite island model. Under the standard parameter set that includes L = 20 High mutation (microsatellite-like) loci and samples of S = 50 individuals from each of n = 4 subpopulations, power to detect departures from panmixia was very high ( approximately 100%; P < 0.001) even with high gene flow (Nm = 25). A new method, comparing the number of correct population assignments with the random expectation, performed as well as a multilocus contingency test and warrants further consideration. Use of Low mutation (allozyme-like) markers reduced power more than did halving S or L. Under the standard parameter set, power to detect restricted gene flow below a certain level X (H(0): Nm < X) can also be high, provided that true Nm < or = 0.5X. Developing the appropriate test criterion, however, requires assumptions about several key parameters that are difficult to estimate in most natural populations. Methods that cluster individuals without using a priori sampling information detected the true number of populations only under conditions of moderate or low gene flow (Nm < or = 5), and power dropped sharply with smaller samples of loci and individuals. A simple algorithm based on a multilocus contingency test of allele frequencies in pairs of samples has high power to detect the true number of populations even with Nm = 25 but requires more rigorous statistical evaluation. The ecological paradigm remains challenging for evaluations using genetic markers, because the transition from demographic dependence to independence occurs in a region of high migration where genetic methods have relatively little power. Some recent theoretical developments and continued advances in computational power provide hope that this situation may change in the future.
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            Genetic estimates of contemporary effective population size: what can they tell us about the importance of genetic stochasticity for wild population persistence?

            Genetic stochasticity due to small population size contributes to population extinction, especially when population fragmentation disrupts gene flow. Estimates of effective population size (Ne) can therefore be informative about population persistence, but there is a need for an assessment of their consistency and informative relevance. Here we review the body of empirical estimates of Ne for wild populations obtained with the temporal genetic method and published since Frankham's (1995) review. Theoretical considerations have identified important sources of bias for this analytical approach, and we use empirical data to investigate the extent of these biases. We find that particularly model selection and sampling require more attention in future studies. We report a median unbiased Ne estimate of 260 (among 83 studies) and find that this median estimate tends to be smaller for populations of conservation concern, which may therefore be more sensitive to genetic stochasticity. Furthermore, we report a median Ne/N ratio of 0.14, and find that this ratio may actually be higher for small populations, suggesting changes in biological interactions at low population abundances. We confirm the role of gene flow in countering genetic stochasticity by finding that Ne correlates strongest with neutral genetic metrics when populations can be considered isolated. This underlines the importance of gene flow for the estimation of Ne, and of population connectivity for conservation in general. Reductions in contemporary gene flow due to ongoing habitat fragmentation will likely increase the prevalence of genetic stochasticity, which should therefore remain a focal point in the conservation of biodiversity.
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              Understanding and estimating effective population size for practical application in marine species management.

              Effective population size (N(e)) determines the strength of genetic drift in a population and has long been recognized as an important parameter for evaluating conservation status and threats to genetic health of populations. Specifically, an estimate of N(e) is crucial to management because it integrates genetic effects with the life history of the species, allowing for predictions of a population's current and future viability. Nevertheless, compared with ecological and demographic parameters, N(e) has had limited influence on species management, beyond its application in very small populations. Recent developments have substantially improved N(e) estimation; however, some obstacles remain for the practical application of N(e) estimates. For example, the need to define the spatial and temporal scale of measurement makes the concept complex and sometimes difficult to interpret. We reviewed approaches to estimation of N(e) over both long-term and contemporary time frames, clarifying their interpretations with respect to local populations and the global metapopulation. We describe multiple experimental factors affecting robustness of contemporary N(e) estimates and suggest that different sampling designs can be combined to compare largely independent measures of N(e) for improved confidence in the result. Large populations with moderate gene flow pose the greatest challenges to robust estimation of contemporary N(e) and require careful consideration of sampling and analysis to minimize estimator bias. We emphasize the practical utility of estimating N(e) by highlighting its relevance to the adaptive potential of a population and describing applications in management of marine populations, where the focus is not always on critically endangered populations. Two cases discussed include the mechanisms generating N(e) estimates many orders of magnitude lower than census N in harvested marine fishes and the predicted reduction in N(e) from hatchery-based population supplementation. ©2011 Society for Conservation Biology.
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                Author and article information

                Journal
                Mol Ecol Resour
                Mol Ecol Resour
                men
                Molecular Ecology Resources
                BlackWell Publishing Ltd (Oxford, UK )
                1755-098X
                1755-0998
                January 2014
                06 September 2013
                : 14
                : 1
                : 87-99
                Affiliations
                [* ]Division of Population Genetics, Department of Zoology, Stockholm University SE-106 91, Stockholm, Sweden
                []Division of Biological Sciences, University of Montana Missoula, MT, 59812, USA
                []Department of Biosciences, Centre for Ecological and Evolutionary Synthesis (CEES), University of Oslo N-0316, Oslo, Norway
                [§ ]Department of Mathematics, Stockholm University SE-106 91, Stockholm, Sweden
                Author notes
                Correspondence: Nils Ryman, Fax: +46-8-154041; E-mail: nils.ryman@ 123456popgen.su.se
                [1]

                Present address: Institute of Marine Research, Flødevigen Marine Research Station N-4817, His, Norway

                Article
                10.1111/1755-0998.12154
                4274017
                24034449
                15519b47-5bb4-4655-8ffb-f839c480d285
                © 2013 The Authors. Molecular Ecology Resources Published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

                History
                : 09 January 2013
                : 11 July 2013
                : 15 July 2013
                Categories
                Resource Articles

                Ecology
                effective population size,subdivided populations,temporal method
                Ecology
                effective population size, subdivided populations, temporal method

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