A second unveiling: Haplotig masking of the eastern oyster genome improves population-level inference

Genome assembly can be challenging for species that are characterized by high amounts of polymorphism, heterozygosity, and large effective population sizes. High levels of heterozygosity can result in genome mis-assemblies and a larger than expected genome size due to the haplotig versions of a single locus being assembled as separate loci. Here, we describe the first chromosome-level genome for the eastern oyster, Crassostrea virginica. Publicly released and annotated in 2017, the assembly has a scaffold N50 of 54 mb and is over 97.3% complete based on BUSCO analysis. The genome assembly for the eastern oyster is a critical resource for foundational research into molluscan adaptation to a changing environment and for selective breeding for the aquaculture industry. Subsequent resequencing data suggested the presence of haplotigs in the original assembly, and we developed a post hoc method to break up chimeric contigs and mask haplotigs in published heterozygous genomes and evaluated improvements to the accuracy of downstream analysis. Masking haplotigs had a large impact on SNP discovery and estimates of nucleotide diversity and had more subtle and nuanced effects on estimates of heterozygosity, population structure analysis, and outlier detection. We show that haplotig masking can be a powerful tool for improving genomic inference, and we present an open, reproducible resource for the masking of haplotigs in any published genome.

How chromosomal inversions reorient the evolutionary process

Inversions are structural mutations that reverse the sequence of a chromosome segment and reduce the effective rate of recombination in the heterozygous state. They play a major role in adaptation, as well as in other evolutionary processes such as speciation. Although inversions have been studied since the 1920s, they remain difficult to investigate because the reduced recombination conferred by them strengthens the effects of drift and hitchhiking, which in turn can obscure signatures of selection. Nonetheless, numerous inversions have been found to be under selection. Given recent advances in population genetic theory and empirical study, here we review how different mechanisms of selection affect the evolution of inversions. A key difference between inversions and other mutations, such as single nucleotide variants, is that the fitness of an inversion may be affected by a larger number of frequently interacting processes. This considerably complicates the analysis of the causes underlying the evolution of inversions. We discuss the extent to which these mechanisms can be disentangled, and by which approach.

The paradox of adaptive trait clines with nonclinal patterns in the underlying genes

Multivariate climate change presents an urgent need to understand how species adapt to complex environments. Population genetic theory predicts that loci under selection will form monotonic allele frequency clines with their selective environment, which has led to the wide use of genotype-environment associations (GEAs). This study used a set of simulations to elucidate the conditions under which allele frequency clines are more or less likely to evolve as multiple quantitative traits adapt to multivariate environments. Phenotypic clines evolved with nonmonotonic (i.e., nonclinal) patterns in allele frequencies under conditions that promoted unique combinations of mutations to achieve the multivariate optimum in different parts of the landscape. Such conditions resulted from interactions among landscape, demography, pleiotropy, and genetic architecture. GEA methods failed to accurately infer the genetic basis of adaptation under a range of scenarios due to first principles (clinal patterns did not evolve) or statistical issues (clinal patterns evolved but were not detected due to overcorrection for structure). Despite the limitations of GEAs, this study shows that a back-transformation of multivariate ordination can accurately predict individual multivariate traits from genotype and environmental data regardless of whether inference from GEAs was accurate. In addition, frameworks are introduced that can be used by empiricists to quantify the importance of clinal alleles in adaptation. This research highlights that multivariate trait prediction from genotype and environmental data can lead to accurate inference regardless of whether the underlying loci display clinal or nonmonotonic patterns.

Development and Evaluation of High-Density SNP Arrays for the Eastern Oyster Crassostrea virginica

The eastern oyster Crassostrea virginica is a major aquaculture species for the USA. The sustainable development of eastern oyster aquaculture depends upon the continued improvement of cultured stocks through advanced breeding technologies. The Eastern Oyster Breeding Consortium (EOBC) was formed to advance the genetics and breeding of the eastern oyster. To facilitate efficient genotyping needed for genomic studies and selection, the consortium developed two single-nucleotide polymorphism (SNP) arrays for the eastern oyster: one screening array with 566K SNPs and one breeders' array with 66K SNPs. The 566K screening array was developed based on whole-genome resequencing data from 292 oysters from Atlantic and Gulf of Mexico populations; it contains 566,262 SNPs including 47K from protein-coding genes with a marker conversion rate of 48.34%. The 66K array was developed using best-performing SNPs from the screening array, which contained 65,893 oyster SNPs including 22,984 genic markers with a calling rate of 99.34%, a concordance rate of 99.81%, and a much-improved marker conversion rate of 92.04%. Null alleles attributable to large indels were found in 13.1% of the SNPs, suggesting that copy number variation is pervasive. Both arrays provided easy identification and separation of selected stocks from wild progenitor populations. The arrays contain 31 mitochondrial SNPs that allowed unambiguous identification of Gulf mitochondrial genotypes in some Atlantic populations. The arrays also contain 756 probes from 13 oyster and human pathogens for possible detection. Our results show that marker conversion rate is low in high polymorphism species and that the two-step process of array development can greatly improve array performance. The two arrays will advance genomic research and accelerate genetic improvement of the eastern oyster by delineating genetic architecture of production traits and enabling genomic selection. The arrays also may be used to monitor pedigree and inbreeding, identify selected stocks and their introgression into wild populations, and assess the success of oyster restoration.

Simulation Tests of Methods in Evolution, Ecology, and Systematics: Pitfalls, Progress, and Principles

Complex statistical methods are continuously developed across the fields of ecology, evolution, and systematics (EES). These fields, however, lack standardized principles for evaluating methods, which has led to high variability in the rigor with which methods are tested, a lack of clarity regarding their limitations, and the potential for misapplication. In this review, we illustrate the common pitfalls of method evaluations in EES, the advantages of testing methods with simulated data, and best practices for method evaluations. We highlight the difference between method evaluation and validation and review how simulations, when appropriately designed, can refine the domain in which a method can be reliably applied. We also discuss the strengths and limitations of different evaluation metrics. The potential for misapplication of methods would be greatly reduced if funding agencies, reviewers, and journals required principled method evaluation.

Genomic architecture of supergenes: connecting form and function

Supergenes are tightly linked sets of loci that are inherited together and control complex phenotypes. While classical supergenes-governing traits such as wing patterns in Heliconius butterflies or heterostyly in Primula-have been studied since the Modern Synthesis, we still understand very little about how they evolve and persist in nature. The genetic architecture of supergenes is a critical factor affecting their evolutionary fate, as it can change key parameters such as recombination rate and effective population size, potentially redirecting molecular evolution of the supergene in addition to the surrounding genomic region. To understand supergene evolution, we must link genomic architecture with evolutionary patterns and processes. This is now becoming possible with recent advances in sequencing technology and powerful forward computer simulations. The present theme issue brings together theoretical and empirical papers, as well as opinion and synthesis papers, which showcase the architectural diversity of supergenes and connect this to critical processes in supergene evolution, such as polymorphism maintenance and mutation accumulation. Here, we summarize those insights to highlight new ideas and methods that illuminate the path forward for the study of supergenes in nature. This article is part of the theme issue 'Genomic architecture of supergenes: causes and evolutionary consequences'.

Inversion invasions: when the genetic basis of local adaptation is concentrated within inversions in the face of gene flow

Across many species where inversions have been implicated in local adaptation, genomes often evolve to contain multiple, large inversions that arise early in divergence. Why this occurs has yet to be resolved. To address this gap, we built forward-time simulations in which inversions have flexible characteristics and can invade a metapopulation undergoing spatially divergent selection for a highly polygenic trait. In our simulations, inversions typically arose early in divergence, captured standing genetic variation upon mutation, and then accumulated many small-effect loci over time. Under special conditions, inversions could also arise late in adaptation and capture locally adapted alleles. Polygenic inversions behaved similarly to a single supergene of large effect and were detectable by genome scans. Our results show that characteristics of adaptive inversions found in empirical studies (e.g. multiple large, old inversions that are F_ST outliers, sometimes overlapping with other inversions) are consistent with a highly polygenic architecture, and inversions do not need to contain any large-effect genes to play an important role in local adaptation. By combining a population and quantitative genetic framework, our results give a deeper understanding of the specific conditions needed for inversions to be involved in adaptation when the genetic architecture is polygenic. This article is part of the theme issue 'Genomic architecture of supergenes: causes and evolutionary consequences'.

A novel analytical framework to quantify co-gradient and countergradient variation

Spatial covariance between genotypic and environmental influences on phenotypes (Cov_GE ) can result in the nonrandom distribution of genotypes across environmental gradients and is a potentially important factor driving local adaptation. However, a framework to quantify the magnitude and significance of Cov_GE has been lacking. We develop a novel quantitative/analytical approach to estimate and test the significance of Cov_GE from reciprocal transplant or common garden experiments, which we validate using simulated data. We demonstrate how power to detect Cov_GE changes over a range of experimental designs. We confirm an inverse relationship between gene-by-environment interactions (GxE) and Cov_GE , as predicted by first principles, but show how phenotypes can be influenced by both. The metric provides a way to measure how phenotypic plasticity covaries with genetic differentiation and highlights the importance of understanding the dual influences of Cov_GE and GxE on phenotypes in studies of local adaptation and species' responses to environmental change.

Seeing the Forest for the trees: Assessing genetic offset predictions from Gradient Forest

Gradient Forest (GF) is a machine learning algorithm designed to analyze spatial patterns of biodiversity as a function of environmental gradients. An offset measure between the GF‐predicted environmental association of adapted alleles and a new environment (GF Offset) is increasingly being used to predict the loss of environmentally adapted alleles under rapid environmental change, but remains mostly untested for this purpose. Here, we explore the robustness of GF Offset to assumption violations, and its relationship to measures of fitness, using SLiM simulations with explicit genome architecture and a spatial metapopulation. We evaluate measures of GF Offset in: (1) a neutral model with no environmental adaptation; (2) a monogenic “population genetic” model with a single environmentally adapted locus; and (3) a polygenic “quantitative genetic” model with two adaptive traits, each adapting to a different environment. We found GF Offset to be broadly correlated with fitness offsets under both single locus and polygenic architectures. However, neutral demography, genomic architecture, and the nature of the adaptive environment can all confound relationships between GF Offset and fitness. GF Offset is a promising tool, but it is important to understand its limitations and underlying assumptions, especially when used in the context of predicting maladaptation.

Does a complex life cycle affect adaptation to environmental change? Genome-informed insights for characterizing selection across complex life cycle

Complex life cycles, in which discrete life stages of the same organism differ in form or function and often occupy different ecological niches, are common in nature. Because stages share the same genome, selective effects on one stage may have cascading consequences through the entire life cycle. Theoretical and empirical studies have not yet generated clear predictions about how life cycle complexity will influence patterns of adaptation in response to rapidly changing environments or tested theoretical predictions for fitness trade-offs (or lack thereof) across life stages. We discuss complex life cycle evolution and outline three hypotheses—ontogenetic decoupling, antagonistic ontogenetic pleiotropy and synergistic ontogenetic pleiotropy—for how selection may operate on organisms with complex life cycles. We suggest a within-generation experimental design that promises significant insight into composite selection across life cycle stages. As part of this design, we conducted simulations to determine the power needed to detect selection across a life cycle using a population genetic framework. This analysis demonstrated that recently published studies reporting within-generation selection were underpowered to detect small allele frequency changes (approx. 0.1). The power analysis indicates challenging but attainable sampling requirements for many systems, though plants and marine invertebrates with high fecundity are excellent systems for exploring how organisms with complex life cycles may adapt to climate change.

Invertebrate methylomes provide insight into mechanisms of environmental tolerance and reveal methodological biases

There is a growing focus on the role of DNA methylation in the ability of marine invertebrates to rapidly respond to changing environmental factors and anthropogenic impacts. However, genome-wide DNA methylation studies in nonmodel organisms are currently hampered by a limited understanding of methodological biases. Here, we compare three methods for quantifying DNA methylation at single base-pair resolution-whole genome bisulfite sequencing (WGBS), reduced representation bisulfite sequencing (RRBS), and methyl-CpG binding domain bisulfite sequencing (MBDBS)-using multiple individuals from two reef-building coral species with contrasting environmental sensitivity. All methods reveal substantially greater methylation in Montipora capitata (11.4%) than the more sensitive Pocillopora acuta (2.9%). The majority of CpG methylation in both species occurs in gene bodies and flanking regions. In both species, MBDBS has the greatest capacity for detecting CpGs in coding regions at our sequencing depth, but MBDBS may be influenced by intrasample methylation heterogeneity. RRBS yields robust information for specific loci albeit without enrichment of any particular genome feature and with significantly reduced genome coverage. Relative genome size strongly influences the number and location of CpGs detected by each method when sequencing depth is limited, illuminating nuances in cross-species comparisons. As genome-wide methylation differences, supported by data across bisulfite sequencing methods, may contribute to environmental sensitivity phenotypes in critical marine invertebrate taxa, these data provide a genomic resource for investigating the functional role of DNA methylation in environmental tolerance.

Novel and disappearing climates in the global surface ocean from 1800 to 2100

Marine ecosystems are experiencing unprecedented warming and acidification caused by anthropogenic carbon dioxide. For the global sea surface, we quantified the degree that present climates are disappearing and novel climates (without recent analogs) are emerging, spanning from 1800 through different emission scenarios to 2100. We quantified the sea surface environment based on model estimates of carbonate chemistry and temperature. Between 1800 and 2000, no gridpoints on the ocean surface were estimated to have experienced an extreme degree of global disappearance or novelty. In other words, the majority of environmental shifts since 1800 were not novel, which is consistent with evidence that marine species have been able to track shifting environments via dispersal. However, between 2000 and 2100 under Representative Concentrations Pathway (RCP) 4.5 and 8.5 projections, 10-82% of the surface ocean is estimated to experience an extreme degree of global novelty. Additionally, 35-95% of the surface ocean is estimated to experience an extreme degree of global disappearance. These upward estimates of climate novelty and disappearance are larger than those predicted for terrestrial systems. Without mitigation, many species will face rapidly disappearing or novel climates that cannot be outpaced by dispersal and may require evolutionary adaptation to keep pace.

Characterizing the multivariate physiogenomic response to environmental change

Global change is altering the climate that species have historically adapted to - in some cases at a pace not recently experienced in their evolutionary history - with cascading effects on all taxa. A central aim in global change biology is to understand how specific populations may be "primed" for global change, either through acclimation or adaptive standing genetic variation. It is therefore an important goal to link physiological measurements to the degree of stress a population experiences (Annual Review of Marine Science, 2012, 4, 39). Although "omic" approaches such as gene expression are often used as a proxy for the amount of stress experienced, we still have a poor understanding of how gene expression affects ecologically and physiologically relevant traits in non-model organisms. In a From the Cover paper in this issue of Molecular Ecology, Griffiths, Pan and Kelley (Molecular Ecology, 2019, 28) link gene expression to physiological traits in a temperate marine coral. They discover population-specific responses to ocean acidification for two populations that originated from locations with different histories of exposure to acidification. By integrating physiological and gene expression data, they were able to elucidate the mechanisms that explain these population-specific responses. Their results give insight into the physiogenomic feedbacks that may prime organisms or make them unfit for ocean global change.

Modularity of genes involved in local adaptation to climate despite physical linkage

BACKGROUND

Linkage among genes experiencing different selection pressures can make natural selection less efficient. Theory predicts that when local adaptation is driven by complex and non-covarying stresses, increased linkage is favored for alleles with similar pleiotropic effects, with increased recombination favored among alleles with contrasting pleiotropic effects. Here, we introduce a framework to test these predictions with a co-association network analysis, which clusters loci based on differing associations. We use this framework to study the genetic architecture of local adaptation to climate in lodgepole pine, Pinus contorta, based on associations with environments.

RESULTS

We identify many clusters of candidate genes and SNPs associated with distinct environments, including aspects of aridity and freezing, and discover low recombination rates among some candidate genes in different clusters. Only a few genes contain SNPs with effects on more than one distinct aspect of climate. There is limited correspondence between co-association networks and gene regulatory networks. We further show how associations with environmental principal components can lead to misinterpretation. Finally, simulations illustrate both benefits and caveats of co-association networks.

CONCLUSIONS

Our results support the prediction that different selection pressures favor the evolution of distinct groups of genes, each associating with a different aspect of climate. But our results went against the prediction that loci experiencing different sources of selection would have high recombination among them. These results give new insight into evolutionary debates about the extent of modularity, pleiotropy, and linkage in the evolution of genetic architectures.

Comment on “Genomic signals of selection predict climate-driven population declines in a migratory bird”

Bay et al . (Reports, 5 January 2018, p. 83) combine genomics, spatial modeling, and future climate scenarios to examine yellow warbler population trends in response to climate change, and they suggest that their methods can inform conservation. We discuss problems in their statistical analyses and explain why the concept of “genomic vulnerability” needs further validation before application to real-world conservation problems.

Responsible RAD: Striving for best practices in population genomic studies of adaptation

Two recent articles were written in response to our paper "Breaking RAD: An evaluation of the utility of restriction site associated DNA sequencing scans of adaptation." While we agree with some of the comments made by the authors of these two response papers, we still believe caution should be employed in RADseq studies that aim to detect loci that contribute to adaptation. In this rebuttal, we evaluate the key points made in these papers, attempt to identify a middle ground and make suggestions for responsibly conducting future studies to understand the genomewide mechanisms of adaptation.

Breaking RAD: an evaluation of the utility of restriction site-associated DNA sequencing for genome scans of adaptation

Understanding how and why populations evolve is of fundamental importance to molecular ecology. Restriction site-associated DNA sequencing (RADseq), a popular reduced representation method, has ushered in a new era of genome-scale research for assessing population structure, hybridization, demographic history, phylogeography and migration. RADseq has also been widely used to conduct genome scans to detect loci involved in adaptive divergence among natural populations. Here, we examine the capacity of those RADseq-based genome scan studies to detect loci involved in local adaptation. To understand what proportion of the genome is missed by RADseq studies, we developed a simple model using different numbers of RAD-tags, genome sizes and extents of linkage disequilibrium (length of haplotype blocks). Under the best-case modelling scenario, we found that RADseq using six- or eight-base pair cutting restriction enzymes would fail to sample many regions of the genome, especially for species with short linkage disequilibrium. We then surveyed recent studies that have used RADseq for genome scans and found that the median density of markers across these studies was 4.08 RAD-tag markers per megabase (one marker per 245 kb). The length of linkage disequilibrium for many species is one to three orders of magnitude less than density of the typical recent RADseq study. Thus, we conclude that genome scans based on RADseq data alone, while useful for studies of neutral genetic variation and genetic population structure, will likely miss many loci under selection in studies of local adaptation.

Finding the Genomic Basis of Local Adaptation: Pitfalls, Practical Solutions, and Future Directions

Uncovering the genetic and evolutionary basis of local adaptation is a major focus of evolutionary biology. The recent development of cost-effective methods for obtaining high-quality genome-scale data makes it possible to identify some of the loci responsible for adaptive differences among populations. Two basic approaches for identifying putatively locally adaptive loci have been developed and are broadly used: one that identifies loci with unusually high genetic differentiation among populations (differentiation outlier methods) and one that searches for correlations between local population allele frequencies and local environments (genetic-environment association methods). Here, we review the promises and challenges of these genome scan methods, including correcting for the confounding influence of a species' demographic history, biases caused by missing aspects of the genome, matching scales of environmental data with population structure, and other statistical considerations. In each case, we make suggestions for best practices for maximizing the accuracy and efficiency of genome scans to detect the underlying genetic basis of local adaptation. With attention to their current limitations, genome scan methods can be an important tool in finding the genetic basis of adaptive evolutionary change.

Reliable Detection of Loci Responsible for Local Adaptation: Inference of a Null Model through Trimming the Distribution of F(ST)

Loci responsible for local adaptation are likely to have more genetic differentiation among populations than neutral loci. However, neutral loci can vary widely in their amount of genetic differentiation, even over the same geographic range. Unfortunately, the distribution of differentiation--as measured by an index such as F(ST)--depends on the details of the demographic history of the populations in question, even without spatially heterogeneous selection. Many methods designed to detect F(ST) outliers assume a specific model of demographic history, which can result in extremely high false positive rates for detecting loci under selection. We develop a new method that infers the distribution of F(ST) for loci unlikely to be strongly affected by spatially diversifying selection, using data on a large set of loci with unknown selective properties. Compared to previous methods, this approach, called OutFLANK, has much lower false positive rates and comparable power, as shown by simulation.

Genome scans for the contemporary response to selection in quantitative traits

Genome scans have been an important approach for discovering historical signatures of selection in both model and nonmodel species. An exciting new experimental design for genome scans is to measure the change in allele frequency before and after contemporary selection within a generation, from a single population. The most widely-used methods, however, have two major limitations: they are based on testing one locus at a time, and they only have power to uncover loci that have evolved under relatively strong selection. On the other hand, complex quantitative traits are common in nature and are caused by several loci of small effect. Selection on a quantitative trait at the phenotypic level is predicted to be accompanied by subtle allele frequency changes in many loci that covary (a polygenic soft sweep), rather than a large, single-effect allele (a selective sweep). In this issue of Molecular Ecology, Bourret et al. (2014) measure the contemporary response to natural selection across the genome in multiple cohorts of Atlantic salmon during their first year at sea. They introduce a multilocus framework based on groups of markers that covary in their genotypic distribution. While the traditional, single-locus approach did not find evidence for repeated patterns of selection, the multivariate approach found that a group of covarying SNPs was selected for in different cohorts at one site. Their multilocus framework has potential to be a more fruitful approach for uncovering the genomic basis of adaptation in quantitative traits, although caution should be applied as the framework has yet to be validated with simulated data.

Evaluation of demographic history and neutral parameterization on the performance of FST outlier tests

F_ST outlier tests are a potentially powerful way to detect genetic loci under spatially divergent selection. Unfortunately, the extent to which these tests are robust to nonequilibrium demographic histories has been understudied. We developed a landscape genetics simulator to test the effects of isolation by distance (IBD) and range expansion on F_ST outlier methods. We evaluated the two most commonly used methods for the identification of F_ST outliers (FDIST2 and BayeScan, which assume samples are evolutionarily independent) and two recent methods (FLK and Bayenv2, which estimate and account for evolutionary nonindependence). Parameterization with a set of neutral loci (‘neutral parameterization’) always improved the performance of FLK and Bayenv2, while neutral parameterization caused FDIST2 to actually perform worse in the cases of IBD or range expansion. BayeScan was improved when the prior odds on neutrality was increased, regardless of the true odds in the data. On their best performance, however, the widely used methods had high false-positive rates for IBD and range expansion and were outperformed by methods that accounted for evolutionary nonindependence. In addition, default settings in FDIST2 and BayeScan resulted in many false positives suggesting balancing selection. However, all methods did very well if a large set of neutral loci is available to create empirical P-values. We conclude that in species that exhibit IBD or have undergone range expansion, many of the published F_ST outliers based on FDIST2 and BayeScan are probably false positives, but FLK and Bayenv2 show great promise for accurately identifying loci under spatially divergent selection.

Evaluation of rockfish conservation area networks in the United States and Canada relative to the dispersal distance for black rockfish (Sebastes melanops)

Marine reserves networks are implemented as a way to mitigate the impact of fishing on marine ecosystems. Theory suggests that a reserve network will function synergistically when connected by dispersal, but the scale of dispersal is often unknown. On the Pacific coast of the United States and Canada, both countries have recently implemented a number of rockfish conservation areas (RCAs) to protect exploited rockfish species, but no study has evaluated the connectivity within networks in each country or between the two countries. We used isolation-by-distance theory to estimate the scale of dispersal from microsatellite data in the black rockfish, Sebastes melanops, and compared this estimate with the distance between RCAs that would protect this species. Within each country, we found that the distance between RCAs was generally within the confidence intervals of mean dispersal per generation. The distance between these two RCA networks, however, was greater than the average dispersal per generation. The data were also consistent with a genetic break between southern Oregon and central Oregon. We discuss whether additional nearshore RCAs in southern Oregon and Washington would help promote connectivity between RCA's for shallow-water rockfishes.

Nonsignificant isolation by distance implies limited dispersal

Understanding the scale of dispersal is an important consideration in the conservation and management of many species. However, in species in which the high-dispersal stage is characterized by tiny gametes or offspring, it may be difficult to estimate dispersal directly. This is the case for many marine species, whose pelagic larvae are dispersed by ocean currents by several days or weeks before beginning a benthic, more sedentary, adult stage. As consequence of the high-dispersal larval stage, many marine species have low genetic structure on large spatial scales (Waples 1998; Hellberg 2007). Despite the high capacity for dispersal, some tagging studies have found that a surprising number of larvae recruit into the population they were released from (self-recruitment). However, estimates of self-recruitment are not informative about mean dispersal between subpopulations. To what extent are limited dispersal estimates from tagging studies compatible with high potential for dispersal and low genetic structure? In this issue, a study on five species of coral reef fish used isolation by distance (IBD) between individuals to estimate mean dispersal distances (Puebla et al. 2012). They found that mean dispersal was unexpectedly small (<50 km), given relatively low IBD slopes and long pelagic durations. This study demonstrates how low genetic structure is compatible with limited dispersal in marine species. A comprehensive understanding of dispersal in marine species will involve integrating methods that estimate dispersal over different spatial and temporal scales. Genomic data may increase power to resolve these issues but must be applied carefully to this question.