Natural selection on feralization genes contributed to the invasive spread of wild pigs throughout the United States.

Despite a long presence in the contiguous United States (US), the distribution of invasive wild pigs (Sus scrofa × domesticus) has expanded rapidly since the 1980s, suggesting a more recent evolutionary shift towards greater invasiveness. Contemporary populations of wild pigs represent exoferal hybrid descendants of domestic pigs and European wild boar, with such hybridization expected to enrich genetic diversity and increase the adaptive potential of populations. Our objective was to characterize how genetic enrichment through hybridization increases the invasiveness of populations by identifying signals of selection and the ancestral origins of selected loci. Our study focused on invasive wild pigs within Great Smoky Mountains National Park, which represents a hybrid population descendent from the admixture of established populations of feral pigs and an introduction of European wild boar to North America. Accordingly, we genotyped 881 wild pigs with multiple high-density single-nucleotide polymorphism (SNP) arrays. We found 233 markers under putative selection spread over 79 regions across 16 out of 18 autosomes, which contained genes involved in traits affecting feralization. Among these, genes were found to be related to skull formation and neurogenesis, with two genes, TYRP1 and TYR, also encoding for crucial melanogenesis enzymes. The most common haplotypes associated with regions under selection for the Great Smoky Mountains population were also common among other populations throughout the region, indicating a key role of putatively selective variants in the fitness of invasive populations. Interestingly, many of these haplotypes were absent among European wild boar reference genotypes, indicating feralization through genetic adaptation.

Analyzing Autopolyploid Genetic Data Using GenoDive.

Analyzing autopolyploid genetic data still presents numerous challenges due to, e.g., missing dosage information of genotypes and the presence of multiple ploidy levels within species or populations, but also because the choice of software is limited when compared to what is available for diploid data. However, over the last years, the number of software programs that can deal with polyploid data is slowly increasing. The software GENODIVE is one of the most widely used programs for the analysis of polyploid genetic data, presenting a wide array of different methods. In this chapter, I outline several frequently used types of population genetic analyses and explain how these apply to polyploid data, including possible pitfalls and biases. I then explain how GENODIVE approaches these analyses and whether and how it can overcome possible biases. Specifically, I focus on analyses of genetic diversity, Hardy-Weinberg equilibrium, quantifying population differentiation, clustering, and calculation of genetic distances. GENODIVE can be downloaded freely from http://www.patrickmeirmans.com/software .

Biogeographic Population Structure of Chimeric Blades of Porphyra in the Northeast Atlantic Reveals Southern Rich Gene Pools, Introgression and Cryptic Plasticity.

The genus Porphyra sensu lato (Bangiaceae, Rhodophyta), an important seaweed grown in aquaculture, is the most genetically diverse group of the Class Bangiophyceae, but has poorly understood genetic variability linked to complex evolutionary processes. Genetic studies in the last decades have largely focused on resolving gene phylogenies; however, there is little information on historical population biogeography, structure and gene flow in the Bangiaceae, probably due to their cryptic nature, chimerism and polyploidy, which render analyses challenging. This study aims to understand biogeographic population structure in the two abundant Porphyra species in the Northeast Atlantic: Porphyra dioica (a dioecious annual) and Porphyra linearis (protandrous hermaphroditic winter annual), occupying distinct niches (seasonality and position on the shore). Here, we present a large-scale biogeographic genetic analysis across their distribution in the Northeast Atlantic, using 10 microsatellites and cpDNA as genetic markers and integrating chimerism and polyploidy, including simulations considering alleles derived from different ploidy levels and/or from different genotypes within the chimeric blade. For P. linearis, both markers revealed strong genetic differentiation of north-central eastern Atlantic populations (from Iceland to the Basque region of Northeast Iberia) vs. southern populations (Galicia in Northwest Iberia, and Portugal), with higher genetic diversity in the south vs. a northern homogenous low diversity. For. P. dioica, microsatellite analyses also revealed two genetic regions, but with weaker differentiation, and cpDNA revealed little structure with all the haplotypes mixed across its distribution. The southern cluster in P. linearis also included introgressed individuals with cpDNA from P. dioica and a winter form of P. dioica occurred spatially intermixed with P. linearis. This third entity had a similar morphology and seasonality as P. linearis but genomes (either nuclear or chloroplast) from P. dioica. We hypothesize a northward colonization from southern Europe (where the ancestral populations reside and host most of the gene pool of these species). In P. linearis recently established populations colonized the north resulting in homogeneous low diversity, whereas for P. dioica the signature of this colonization is not as obvious due to hypothetical higher gene flow among populations, possibly linked to its reproductive biology and annual life history.

History or demography? Determining the drivers of genetic variation in North American plants.

Understanding the impact of historical and demographic processes on genetic variation is essential for devising conservation strategies and predicting responses to climate change. Recolonization after Pleistocene glaciations is expected to leave distinct genetic signatures, characterised by lower genetic diversity in previously glaciated regions. Populations' positions within species ranges also shape genetic variation, following the central-marginal paradigm dictating that peripheral populations are depauperate, sparse and isolated. However, the general applicability of these patterns and relative importance of historical and demographic factors remains unknown. Here, we analysed the distribution of genetic variation in 91 native species of North American plants by coupling microsatellite data and species distribution modelling. We tested the contributions of historical climatic shifts and the central-marginal hypothesis on genetic diversity and structure on the whole data set and across subsets based on taxonomic groups and growth forms. Decreased diversity was found with increased distance from potential glacial refugia, coinciding with the expected make-up of postglacially colonised localities. At the range periphery, lower genetic diversity, higher inbreeding levels and genetic differentiation were reported, following the assumptions of the central-marginal hypothesis. History and demography were found to have approximately equal importance in shaping genetic variation.

Niche divergence contributes to geographical parthenogenesis in two dandelion taxa.

Many sexual-asexual complexes show a distinct pattern where the asexuals have larger and more northerly ranges than closely related sexuals. A prime candidate to explain this so-called "geographical parthenogenesis" is ecological niche divergence between the sexuals and asexuals. Modern niche modelling techniques allow testing niche divergence by directly comparing the niches of sexuals and asexuals. In this study, I use such techniques to perform range-wide tests of whether nine bioclimatic variables, including annual mean temperature and annual precipitation, contribute to geographical parthenogenesis in two dandelion taxa: Taraxacum section Ruderalia and Taraxacum section Erythrosperma, which are both comprised of sexual diploids and asexual triploids. For both sections, I found evidence of niche divergence, though the exact nature of this divergence was different for the two sections. In section Ruderalia, the sexuals preferred warmer and wetter conditions, whereas in section Erythrosperma, the sexuals preferred dryer conditions. Using Species Distribution Modelling, consistent differences between the sexuals and asexuals were found when looking at the niche determinants: the variables that are most important for modelling the distribution. Furthermore, and in contrast with theoretical expectations that predict that the sexuals should have a wider niche, in section Erythrosperma the asexuals were found to have a wider niche than the sexuals. In conclusion, differences in niche optima, niche determinants, and niche width all contribute to the pattern of geographical parthenogenesis of these two dandelion taxa. However, the results also indicate that the exact causation of geographical parthenogenesis is not uniform across taxa.

genodive version 3.0: Easy-to-use software for the analysis of genetic data of diploids and polyploids.

genodive version 3.0 is a user-friendly program for the analysis of population genetic data. This version presents a major update from the previous version and now offers a wide spectrum of different types of analyses. genodive has an intuitive graphical user interface that allows direct manipulation of the data through transformation, imputation of missing data, and exclusion and inclusion of individuals, population and/or loci. Furthermore, genodive seamlessly supports 15 different file formats for importing or exporting data from or to other programs. One major feature of genodive is that it supports both diploid and polyploid data, up to octaploidy (2n = 8x) for some analyses, but up to hexadecaploidy (2n = 16x) for other analyses. The different types of analyses offered by genodive include multiple statistics for estimating population differentiation (φST , FST , F'ST , GST , G'ST , G''ST , Dest , RST , ρ), analysis of molecular variance-based K-means clustering, Hardy-Weinberg equilibrium, hybrid index, population assignment, clone assignment, Mantel test, Spatial Autocorrelation, 23 ways of calculating genetic distances, and both principal components and principal coordinates analyses. A unique feature of genodive is that it can also open data sets with nongenetic variables, for example environmental data or geographical coordinates that can be included in the analysis. In addition, genodive makes it possible to run several external programs (lfmm, structure, instruct and vegan) directly from its own user interface, avoiding the need for data reformatting and use of the command line. genodive is available for computers running Mac OS X 10.7 or higher and can be downloaded freely from: http://www.patrickmeirmans.com/software.

STRUCTURE is more robust than other clustering methods in simulated mixed-ploidy populations.

Analysis of population genetic structure has become a standard approach in population genetics. In polyploid complexes, clustering analyses can elucidate the origin of polyploid populations and patterns of admixture between different cytotypes. However, combining diploid and polyploid data can theoretically lead to biased inference with (artefactual) clustering by ploidy. We used simulated mixed-ploidy (diploid-autotetraploid) data to systematically compare the performance of k-means clustering and the model-based clustering methods implemented in STRUCTURE, ADMIXTURE, FASTSTRUCTURE and INSTRUCT under different scenarios of differentiation and with different marker types. Under scenarios of strong population differentiation, the tested applications performed equally well. However, when population differentiation was weak, STRUCTURE was the only method that allowed unbiased inference with markers with limited genotypic information (co-dominant markers with unknown dosage or dominant markers). Still, since STRUCTURE was comparatively slow, the much faster but less powerful FASTSTRUCTURE provides a reasonable alternative for large datasets. Finally, although bias makes k-means clustering unsuitable for markers with incomplete genotype information, for large numbers of loci (>1000) with known dosage k-means clustering was superior to FASTSTRUCTURE in terms of power and speed. We conclude that STRUCTURE is the most robust method for the analysis of genetic structure in mixed-ploidy populations, although alternative methods should be considered under some specific conditions.

Subsampling reveals that unbalanced sampling affects STRUCTURE results in a multi-species dataset.

Studying the genetic population structure of species can reveal important insights into several key evolutionary, historical, demographic, and anthropogenic processes. One of the most important statistical tools for inferring genetic clusters is the program STRUCTURE. Recently, several papers have pointed out that STRUCTURE may show a bias when the sampling design is unbalanced, resulting in spurious joining of underrepresented populations and spurious separation of overrepresented populations. Suggestions to overcome this bias include subsampling and changing the ancestry model, but the performance of these two methods has not yet been tested on actual data. Here, I use a data set of 12 high-alpine plant species to test whether unbalanced sampling affects the STRUCTURE inference of population differentiation between the European Alps and the Carpathians. For four of the 12 species, subsampling of the Alpine populations-to match the sample size between the Alps and the Carpathians-resulted in a drastically different clustering than the full data set. On the other hand, STRUCTURE results with the alternative ancestry model were indistinguishable from the results with the default model. Based on these results, the subsampling strategy seems a more viable approach to overcome the bias than the alternative ancestry model. However, subsampling is only possible when there is an a priori expectation of what constitute the main clusters. Though these results do not mean that the use of STRUCTURE should be discarded, it does indicate that users of the software should be cautious about the interpretation of the results when sampling is unbalanced.

Sex in the wild: How and why field-based studies contribute to solving the problem of sex.

Why and how sexual reproduction is maintained in natural populations, the so-called "queen of problems," is a key unanswered question in evolutionary biology. Recent efforts to solve the problem of sex have often emphasized results generated from laboratory settings. Here, we use a survey of representative "sex in the wild" literature to review and synthesize the outcomes of empirical studies focused on natural populations. Especially notable results included relatively strong support for mechanisms involving niche differentiation and a near absence of attention to adaptive evolution. Support for a major role of parasites is largely confined to a single study system, and only three systems contribute most of the support for mutation accumulation hypotheses. This evidence for taxon specificity suggests that outcomes of particular studies should not be more broadly extrapolated without extreme caution. We conclude by suggesting steps forward, highlighting tests of niche differentiation mechanisms in both laboratory and nature, and empirical evaluation of adaptive evolution-focused hypotheses in the wild. We also emphasize the value of leveraging the growing body of genomic resources for nonmodel taxa to address whether the clearance of harmful mutations and spread of beneficial variants in natural populations proceeds as expected under various hypotheses for sex.

The Lesser Antillean Iguana (Iguana delicatissima) on St. Eustatius: Genetically Depauperate and Threatened by Ongoing Hybridization.

The Lesser Antillean Iguana (Iguana delicatissima) is an endangered species threatened by habitat loss and hybridization with non-native Green Iguanas (Iguana iguana). Iguana delicatissima has been extirpated on several islands, and the Green Iguana has invaded most islands with extant populations. Information is essential to protect this species from extinction. We collected data on 293 iguanas including 17 juveniles from St. Eustasius, one of the few remaining I. delicatissima strongholds. Genetic data were leveraged to test for hybridization presence with the Green Iguana using both mitochondrial and nuclear genes, including 16 microsatellite loci. The microsatellites were also analyzed to estimate genetic diversity, population structure, and effective population size. Using molecular and morphological data, we identified 286 I. delicatissima individuals captured during our first fieldwork effort, and 7 non-native iguanas captured during a second effort, showing hybridization occurs within this population. Comparing homologous microsatellites used in studies on Dominica and Chancel, the I. delicatissima population on St. Eustatius has extremely low genetic diversity (HO = 0.051; HE = 0.057), suggesting this population is genetically depauperate. Furthermore, there is significant evidence for inbreeding (FIS = 0.12) and weak spatial genetic structure (FST = 0.021, P = 0.002) within this population. Besides immediate threats including hybridization, this population's low genetic diversity, presence of physiological abnormalities and low recruitment could indicate presence of inbreeding depression that threatens its long-term survival. We conclude there is a continued region-wide threat to I. delicatissima and highlight the need for immediate conservation action to stop the continuing spread of Green Iguanas and to eliminate hybridization from St. Eustatius.

The Analysis of Polyploid Genetic Data.

Though polyploidy is an important aspect of the evolutionary genetics of both plants and animals, the development of population genetic theory of polyploids has seriously lagged behind that of diploids. This is unfortunate since the analysis of polyploid genetic data-and the interpretation of the results-requires even more scrutiny than with diploid data. This is because of several polyploidy-specific complications in segregation and genotyping such as tetrasomy, double reduction, and missing dosage information. Here, we review the theoretical and statistical aspects of the population genetics of polyploids. We discuss several widely used types of inferences, including genetic diversity, Hardy-Weinberg equilibrium, population differentiation, genetic distance, and detecting population structure. For each, we point out how the statistical approach, expected result, and interpretation differ between different ploidy levels. We also discuss for each type of inference what biases may arise from the polyploid-specific complications and how these biases can be overcome. From our overview, it is clear that the statistical toolbox that is available for the analysis of genetic data is flexible and still expanding. Modern sequencing techniques will soon be able to overcome some of the current limitations to the analysis of polyploid data, though the techniques are lagging behind those available for diploids. Furthermore, the availability of more data may aggravate the biases that can arise, and increase the risk of false inferences. Therefore, simulations such as we used throughout this review are an important tool to verify the results of analyses of polyploid genetic data.

Outbreeding depression and breeding system evolution in small, remnant populations of Primula vulgaris: consequences for genetic rescue.

Many species suffer from anthropogenic habitat fragmentation. The resulting small and isolated populations are more prone to extinction due to, amongst others, genetic erosion, inbreeding depression and Allee-effects. Genetic rescue can help mitigate such problems, but might result in outbreeding depression. We evaluated offspring fitness after selfing and outcrossing within and among three very small and isolated remnant populations of the heterostylous plant Primula vulgaris. We used greenhouse-grown offspring from these populations to test several fitness components. One population was fixed for the pin-morph, and was outcrossed with another population in the field to obtain seeds. Genetic diversity of parent and offspring populations was studied using microsatellites. Morph and population-specific heterosis, inbreeding and outbreeding depression were observed for fruit and seed set, seed weight and cumulative fitness. Highest fitness was observed in the field-outcrossed F1-population, which also showed outbreeding depression following subsequent between-population (back)crossing. Despite outbreeding depression, fitness was still relatively high. Inbreeding coefficients indicated that the offspring were more inbred than their parent populations. Offspring heterozygosity and inbreeding coefficients correlated with observed fitness. One population is evolving homostyly, showing a thrum morph with an elongated style and high autonomous fruit and seed set. This has important implications for conservation strategies such as genetic rescue, as the mating system will be altered by the introduction of homostyles.

The challenge of separating signatures of local adaptation from those of isolation by distance and colonization history: The case of two white pines.

Accurately detecting signatures of local adaptation using genetic-environment associations (GEAs) requires controlling for neutral patterns of population structure to reduce the risk of false positives. However, a high degree of collinearity between climatic gradients and neutral population structure can greatly reduce power, and the performance of GEA methods in such case is rarely evaluated in empirical studies. In this study, we attempted to disentangle the effects of local adaptation and isolation by environment (IBE) from those of isolation by distance (IBD) and isolation by colonization from glacial refugia (IBC) using range-wide samples in two white pine species. For this, SNPs from 168 genes, including 52 candidate genes for growth and phenology, were genotyped in 133 and 61 populations of Pinus strobus and P. monticola, respectively. For P. strobus and using all 153 SNPs, climate (IBE) did not significantly explained among-population variation when controlling for IBD and IBC in redundancy analyses (RDAs). However, 26 SNPs were significantly associated with climate in single-locus GEA analyses (Bayenv2 and LFMM), suggesting that local adaptation took place in the presence of high gene flow. For P. monticola, we found no evidence of IBE using RDAs and weaker signatures of local adaptation using GEA and FST outlier tests, consistent with adaptation via phenotypic plasticity. In both species, the majority of the explained among-population variation (69 to 96%) could not be partitioned between the effects of IBE, IBD, and IBC. GEA methods can account differently for this confounded variation, and this could explain the small overlap of SNPs detected between Bayenv2 and LFMM. Our study illustrates the inherent difficulty of taking into account neutral structure in natural populations and the importance of sampling designs that maximize climatic variation, while minimizing collinearity between climatic gradients and neutral structure.

Seven common mistakes in population genetics and how to avoid them.

As the data resulting from modern genotyping tools are astoundingly complex, genotyping studies require great care in the sampling design, genotyping, data analysis and interpretation. Such care is necessary because, with data sets containing thousands of loci, small biases can easily become strongly significant patterns. Such biases may already be present in routine tasks that are present in almost every genotyping study. Here, I discuss seven common mistakes that can be frequently encountered in the genotyping literature: (i) giving more attention to genotyping than to sampling, (ii) failing to perform or report experimental randomization in the laboratory, (iii) equating geopolitical borders with biological borders, (iv) testing significance of clustering output, (v) misinterpreting Mantel's r statistic, (vi) only interpreting a single value of k and (vii) forgetting that only a small portion of the genome will be associated with climate. For every of those issues, I give some suggestions how to avoid the mistake. Overall, I argue that genotyping studies would benefit from establishing a more rigorous experimental design, involving proper sampling design, randomization and better distinction of a priori hypotheses and exploratory analyses.

Nonconvergence in Bayesian estimation of migration rates.

The estimation of migration rates using molecular markers is an important aspect of many population genetic studies. Several different methods are available for estimating migration, but most of these make multiple limiting assumptions. One method that is relatively free from assumptions is BAYESASS, which uses assignment methods in a Bayesian framework. However, when tested using simulated data, this method was found to have problems with the convergence of the Markov chain Monte Carlo. Here, I perform a literature study to test whether these convergence problems are also present when BAYESASS is used to estimate migration rates from empirical data. A review of 100 studies that have used BAYESASS shows that this is indeed the case. The estimated proportions of nonmigrants were mostly either close to 2/3 or 1, indicating that the MCMC tends to get trapped near the bounds of the prior distribution. In addition, I found that the quality of the inference was negatively affected by the number of sampled populations, but increased with increasing numbers of sampled individuals and with the strength of the population structure as measured by FST . Based on these results, I give several recommendations that should help to reduce problems when using BAYESASS with empirical data. Most importantly, I argue that researchers should be more realistic about inferences of migration rates and that BAYESASS will give optimal results only when the experiment has been especially designed around its use.

Dissimilarity of contemporary and historical gene flow in a wild carrot (Daucus carota) metapopulation under contrasting levels of human disturbance: implications for risk assessment and management of transgene introgression.

BACKGROUND AND AIMS: Transgene introgression from crops into wild relatives may increase the resistance of wild plants to herbicides, insects, etc. The chance of transgene introgression depends not only on the rate of hybridization and the establishment of hybrids in local wild populations, but also on the metapopulation dynamics of the wild relative. The aim of the study was to estimate gene flow in a metapopulation for assessing and managing the risks of transgene introgression. METHODS: Wild carrots (Daucus carota) were sampled from 12 patches in a metapopulation. Eleven microsatellites were used to genotype wild carrots. Genetic structure was estimated based on the FST statistic. Contemporary (over the last several generations) and historical (over many generations) gene flow was estimated with assignment and coalescent methods, respectively. KEY RESULTS: The genetic structure in the wild carrot metapopulation was moderate (FST = 0·082) and most of the genetic variation resided within patches. A pattern of isolation by distance was detected, suggesting that most of the gene flow occurred between neighbouring patches (≤1 km). The mean contemporary gene flow was 5 times higher than the historical estimate, and the correlation between them was very low. Moreover, the contemporary gene flow in roadsides was twice that in a nature reserve, and the correlation between contemporary and historical estimates was much higher in the nature reserve. Mowing of roadsides may contribute to the increase in contemporary gene flow. Simulations demonstrated that the higher contemporary gene flow could accelerate the process of transgene introgression in the metapopulation. CONCLUSIONS: Human disturbance such as mowing may alter gene flow patterns in wild populations, affecting the metapopulation dynamics of wild plants and the processes of transgene introgression in the metapopulation. The risk assessment and management of transgene introgression and the control of weeds need to take metapopulation dynamics into consideration.

Quantifying introgression risk with realistic population genetics.

Introgression is the permanent incorporation of genes from the genome of one population into another. This can have severe consequences, such as extinction of endemic species, or the spread of transgenes. Quantification of the risk of introgression is an important component of genetically modified crop regulation. Most theoretical introgression studies aimed at such quantification disregard one or more of the most important factors concerning introgression: realistic genetical mechanisms, repeated invasions and stochasticity. In addition, the use of linkage as a risk mitigation strategy has not been studied properly yet with genetic introgression models. Current genetic introgression studies fail to take repeated invasions and demographic stochasticity into account properly, and use incorrect measures of introgression risk that can be manipulated by arbitrary choices. In this study, we present proper methods for risk quantification that overcome these difficulties. We generalize a probabilistic risk measure, the so-called hazard rate of introgression, for application to introgression models with complex genetics and small natural population sizes. We illustrate the method by studying the effects of linkage and recombination on transgene introgression risk at different population sizes.

AMOVA-based clustering of population genetic data.

Determining the genetic structure of populations is becoming an increasingly important aspect of genetic studies. One of the most frequently used methods is the calculation of F-statistics using an Analysis of Molecular Variance (AMOVA). However, this has the drawback that the population hierarchy has to be known a priori. Therefore, the population structure is often based on the results of a clustering analysis. Here I show how these two steps, clustering and calculation of F-statistics, can be combined in a single analysis. I do this by showing how the AMOVA framework is theoretically related to the widely used method of K-means clustering and can be used for the clustering of populations into groups. Simulations were used to show that the method performed very well both under random mating and under nonrandom mating. However, when the migration rates were high, the results were better under random mating than under predominant selfing or clonal reproduction. Two summary statistics were tested for estimating the number of clusters. Overall, pseudo-F showed the better performance, but BIC is better for detecting whether any significant structure is present. The results show that the AMOVA-based K-means clustering is useful for clustering population genetic data. Programs to perform the clustering can be downloaded from www.patrickmeirmans.com/software.

The trouble with isolation by distance.

The genetic population structure of many species is characterised by a pattern of isolation by distance (IBD): due to limited dispersal, individuals that are geographically close tend to be genetically more similar than individuals that are far apart. Despite the ubiquity of IBD in nature, many commonly used statistical tests are based on a null model that is completely non-spatial, the Island model. Here, I argue that patterns of spatial autocorrelation deriving from IBD present a problem for such tests as it can severely bias their outcome. I use simulated data to illustrate this problem for two widely used types of tests: tests of hierarchical population structure and the detection of loci under selection. My results show that for both types of tests the presence of IBD can indeed lead to a large number of false positives. I therefore argue that all analyses in a study should take the spatial dependence in the data into account, unless it can be shown that there is no spatial autocorrelation in the allele frequency distribution that is under investigation. Thus, it is urgent to develop additional statistical approaches that are based on a spatially explicit null model instead of the non-spatial Island model.

The costs of sex: facing real-world complexities.

Understanding the maintenance of sexual reproduction constitutes a difficult problem for evolutionary biologists because of the immediate costs that sex seems to incur. Typically, general benefits to sex and recombination are investigated that might outweigh these costs. However, several factors can strongly influence the complex balance between costs and benefits of sex; these include constraints on the evolution of asexuality, ecological differentiation, and certain lif-history traits. We review these factors and their empirical support for the first time in a unified framework and find that they can reduce the costs of sex, circumvent them, or make them inapplicable. These factors can even tip the scales to a net benefit for sex. The reviewed factors affect species and species groups differently, and we conclude consequently that understanding the maintenance of sex could turn out to be more species-specific than commonly assumed. Interestingly, our study suggests that, in some species, no general benefits to sex and recombination might be needed to understand the maintenance of sex, as in our case study of dandelions.