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 (microsatellitelike) loci and samples of S = 50 individuals from each of n = 4 subpopulations, power to detect departures from panmixia was very high (∼ ∼ ∼ ∼ 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 ≤ ≤ ≤ ≤ 0.5 X . 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 ≤ ≤ ≤ ≤ 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.

NeEstimator v2 is a completely revised and updated implementation of software that produces estimates of contemporary effective population size, using several different methods and a single input file. NeEstimator v2 includes three single-sample estimators (updated versions of the linkage disequilibrium and heterozygote-excess methods, and a new method based on molecular coancestry), as well as the two-sample (moment-based temporal) method. New features include the following: (i) an improved method for accounting for missing data; (ii) options for screening out rare alleles; (iii) confidence intervals for all methods; (iv) the ability to analyse data sets with large numbers of genetic markers (10 000 or more); (v) options for batch processing large numbers of different data sets, which will facilitate cross-method comparisons using simulated data; and (vi) correction for temporal estimates when individuals sampled are not removed from the population (Plan I sampling). The user is given considerable control over input data and composition, and format of output files. The freely available software has a new JAVA interface and runs under MacOS, Linux and Windows.

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ldne is a program with a Visual Basic interface that implements a recently developed bias correction for estimates of effective population size (Ne) based on linkage disequilibrium data. The program reads genotypic data in standard formats and can accommodate an arbitrary number of samples, individuals, loci, and alleles, as well as two mating systems: random and lifetime monogamy. ldne calculates separate estimates using different criteria for excluding rare alleles, which facilitates evaluation of data for highly polymorphic markers such as microsatellites. The program also introduces a jackknife method for obtaining confidence intervals that appears to perform better than parametric methods currently in use.

Genetic methods are routinely used to estimate contemporary effective population size (Ne) in natural populations, but the vast majority of applications have used only the temporal (two-sample) method. We use simulated data to evaluate how highly polymorphic molecular markers affect precision and bias in the single-sample method based on linkage disequilibrium (LD). Results of this study are as follows: (1) Low-frequency alleles upwardly bias , but a simple rule can reduce bias to

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Analysis of linkage disequilibrium (r 2 =mean squared correlation of allele frequencies at different gene loci) provides a means of estimating effective population size (N e ) from a single sample, but this method has seen much less use than the temporal method (which requires at least two samples). It is shown that for realistic numbers of loci and alleles, the linkage disequilibrium method can provide precision comparable to that of the temporal method. However, computer simulations show that estimates of N e based onr 2 for unlinked, diallelic gene loci are sharply biased downwards (N e =N<0:1 in some cases) if sample size (S) is less than true N e . The bias is shown to arise from inaccuracies in published formula for Eðr 2 Þ when S and/or N e are small. Empirically derived modifications to Eðr 2 Þ for two mating systems (random mating and lifetime monogamy) effectively eliminate the bias (residual bias inN e <5% in most cases). The modified method also performs well in estimating N e in non-ideal populations with skewed sex ratio or non-random variance in reproductive success. Recent population declines are not likely to seriously affectN e , but if N has recently increased from a bottleneckN e can be biased downwards for a few generations. These results should facilitate application of the disequilibrium method for estimating contemporary N e in natural populations. However, a comprehensive assessment of performance ofr 2 with highly polymorphic markers such as microsatellites is needed.

Times Cited: 83International audienceAssignment methods, which use genetic information to ascertain population membership of individuals or groups of individuals, have been used in recent years to study a wide range of evolutionary and ecological processes. In applied studies, the first step of articulating the biological question(s) to be addressed should be followed by selection of the method(s) best suited for the analysis. However, this first step often receives less attention than it should, and the recent proliferation of assignment methods has made the selection step challenging. Here, we review assignment methods and discuss how to match the appropriate methods with the underlying biological questions for several common problems in ecology and conservation (assessing population structure; measuring dispersal and hybridization; and forensics and mixture analysis). We also identify several topics for future research that should ensure that this field remains dynamic and productive