Robin S. Waples

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 (∼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 (H0: Nm < X) can also be high, provided that true Nm ≤ 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 ≤ 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.

ldne: a program for estimating effective population size from data on linkage disequilibrium

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.

NeEstimator v2: re-implementation of software for the estimation of contemporary effective population size (Ne ) from genetic data.

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.

A MULTISPECIES APPROACH TO THE ANALYSIS OF GENE FLOW IN MARINE SHORE FISHES

Ten species of marine shore fishes with a wide range of life‐history strategies were collected from four areas in southern California, U.S.A., and Baja California, Mexico, and examined for patterns of genetic differentiation. Multilocus D and FST values (based on 32–42 presumptive gene loci in each species) were both negatively correlated with estimated dispersal capability. These results were robust to variations in the number and type of loci used in the analysis and are compatible with the hypothesis that levels of genetic differentiation in these shore fishes are determined primarily by gene flow and genetic drift. There is no a priori reason to expect the observed correlation to result from natural selection or historical factors. The findings thus suggest that populations of these shore fishes are in at least a quasi‐equilibrium with respect to migration, mutation, and genetic drift.

Linkage disequilibrium estimates of contemporary Ne using highly variable genetic markers: a largely untapped resource for applied conservation and evolution

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

Compromising genetic diversity in the wild: unmonitored large-scale release of plants and animals

Large-scale exploitation of wild animals and plants through fishing, hunting and logging often depends on augmentation through releases of translocated or captively raised individuals. Such releases are performed worldwide in vast numbers. Augmentation can be demographically and economically beneficial but can also cause four types of adverse genetic change to wild populations: (1) loss of genetic variation, (2) loss of adaptations, (3) change of population composition, and (4) change of population structure. While adverse genetic impacts are recognized and documented in fisheries, little effort is devoted to actually monitoring them. In forestry and wildlife management, genetic risks associated with releases are largely neglected. We outline key features of programs to effectively monitor consequences of such releases on natural populations.

Evaluating the performance of a multilocus Bayesian method for the estimation of migration rates

Bayesian methods have become extremely popular in molecular ecology studies because they allow us to estimate demographic parameters of complex demographic scenarios using genetic data. Articles presenting new methods generally include sensitivity studies that evaluate their performance, but they tend to be limited and need to be followed by a more thorough evaluation. Here we evaluate the performance of a recent method, bayesass, which allows the estimation of recent migration rates among populations, as well as the inbreeding coefficient of each local population. We expand the simulation study of the original publication by considering multi‐allelic markers and scenarios with varying number of populations. We also investigate the effect of varying migration rates and FST more thoroughly in order to identify the region of parameter space where the method is and is not able to provide accurate estimates of migration rate. Results indicate that if the demographic history of the species being studied fits the assumptions of the inference model, and if genetic differentiation is not too low (FST ≥ 0.05), then the method can give fairly accurate estimates of migration rates even when they are fairly high (about 0.1). However, when the assumptions of the inference model are violated, accurate estimates are obtained only if migration rates are very low (m = 0.01) and genetic differentiation is high (FST ≥ 0.10). Our results also show that using posterior assignment probabilities as an indication of how much confidence we can place on the assignments is problematical since the posterior probability of assignment can be very high even when the individual assignments are very inaccurate.

Dispelling Some Myths about Hatcheries

Abstract Contributing to the controversies that have surrounded fish hatcheries in recent years are a number of misconceptions or myths about hatcheries and their effects on natural populations. These myths impede productive dialogue among those with differing views about hatcheries. Most of the myths include a measure of truth, which makes it difficult to recognize the elements that are not true. Consideration of these myths leads to the following conclusions: (1) Hatcheries are intrinsically neither good nor bad—their value can be determined only in the context of clearly defined goals; (2) genetic changes in cultured populations can be reduced but not eliminated entirely; (3) empirical evidence exists of many adverse effects of hatcheries, but some risks have been overstated; and (4) monitoring and evaluation programs are important but should not be used as a substitute for developing risk-averse hatchery programs in the first place. A key step in resolving some of the controversies will be moving towa...

LIFE-HISTORY DIVERGENCE IN CHINOOK SALMON: HISTORIC CONTINGENCY AND PARALLEL EVOLUTION

Abstract By jointly considering patterns of genetic and life‐history diversity in over 100 populations of Chinook salmon from California to British Columbia, we demonstrate the importance of two different mechanisms for life‐history evolution. Mapping adult run timing (the life‐history trait most commonly used to characterize salmon populations) onto a tree based on the genetic data shows that the same run‐time phenotypes exist in many different genetic lineages. In a hierarchical gene diversity analysis, differences among major geographic and ecological provinces explained the majority (62%) of the overall GST, whereas run‐time differences explained only 10%. Collectively, these results indicate that run‐timing diversity has developed independently by a process of parallel evolution in many different coastal areas. However, genetic differences between coastal populations with different run timing from the same basin are very modest (GST < 0.02), indicating that evolutionary divergence of this trait linked to reproductive isolation has not led to parallel speciation, probably because of ongoing gene flow. A strikingly different pattern is seen in the interior Columbia River Basin, where run timing and other correlated life‐history traits map cleanly onto two divergent genetic lineages (GST˜ 0.15), indicating that some patterns of life‐history diversity have a much older origin. Indeed, genetic data indicate that in the interior Columbia Basin, the two divergent lineages behave essentially as separate biological species, showing little evidence of genetic contact in spite of the fact that they comigrate through large areas of the river and ocean and in some locations spawn in nearly adjacent areas.

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 populations 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.