In evolutionary biology, sexual selection is hypothesized to increase speciation rates in animals, as theory predicts that sexual selection will contribute to phenotypic diversification and affect rates of species accumulation at macro-evolutionary time scales. However, testing this hypothesis and gathering convincing evidence have proven difficult. Although some studies have shown a strong correlation between proxies of sexual selection and species diversity (mostly in birds), this relationship relies on some assumptions on the link between these proxies and the strength of sexual selection and is not detected in some other taxa, making taxonomically widespread conclusions impossible.

In a recent study published in Current Biology [1], Ellis and Oakley provide strong evidence that bioluminescent sexual displays have driven high species richness in taxonomically diverse animal lineages, providing a crucial link between sexual selection and speciation.
It was known that bioluminescence has evolved independently more than 40 times, with males often using it as a mating signal but with also some other possible adaptive functions including anti-predator defense and predation. Moreover, it has been reported that small marine lanternfishes and sharks that use bioluminescence in mate identification had a greater concentration of species than other deep-sea fishes that use bioluminescence for defensive purposes [2-4]. But no one had ever determined whether this pattern is consistent across diverse and distantly related animal groups living on sea and land.

Ellis and Oakley [1] explored the scientific literature for well-resolved evolutionary trees with branches containing bioluminescent lineages and identified lineages that use light for courtship or camouflage in a wide range of marine and terrestrial taxa including insects, crustaceans, cephalopods, segmented worms, and fishes. The researchers counted the number of species in each bioluminescent clade and found that all groups with light-courtship displays had more species and faster rates of species accumulation than their non-luminous most closely related sister lineages or ancestors. In contrast, those groups that used bioluminescence for predator avoidance had a lower than expected rate of species richness on average.

Nicely encompassing a diversity of taxa and neatly controlling for the rate of species accumulation of the encompassing clade, the results of Ellis and Oakley are clear-cut and provide the most comprehensive evidence to date for the hypothesis that sexual displays can act as drivers of speciation. One question this study incites is what is happening in terms of sexual selection in species displaying defensive bioluminescence or no bioluminescence at all: do those lineages use no mating signals at all or other mating signals that are less apparent, and will those experience lower levels of sexual selection than bioluminescent mating signals, i.e. consistent with Ellis and Oakley results? It would also be interesting to investigate the diversification rates in animal species using other modalities, such as chemical, acoustic or any other type of signals used by males, females or both sexes, to determine what types of sexual signals may be more generally drivers of speciation.

References

[1] Ellis EA, Oakley TH. 2016. High Rates of Species Accumulation in Animals with Bioluminescent Courtship Displays. Current Biology 26:1916–1921. doi: 10.1016/j.cub.2016.05.043

[2] Davis MP, Holcroft NI, Wiley EO, Sparks JS, Smith WL. 2014. Species-specific bioluminescence facilitates speciation in the deep sea. Marine Biology 161:1139­1148. doi: 10.1007/s00227-014-2406-x

[3] Davis MP, Sparks JS, Smith WL. 2016. Repeated and Widespread Evolution of Bioluminescence in Marine Fishes. PLoS One 11:e0155154. doi: 10.1371/journal.pone.0155154

[4] Claes JM, Nilsson D-E, Mallefet J, Straube N. 2015. The presence of lateral photophores correlates with increased speciation in deep-sea bioluminescent sharks. Royal Society Open Science 2:150219. doi: 10.1098/rsos.150219

The advent of genome-wide association studies (GWAS) has been a great promise for our understanding of the connection between genotype and phenotype. Today, the NHGRI-EBI GWAS catalog contains 251,401 associations from 4,961 studies (1). This wealth of studies has also generated interest to use the summary statistics beyond the few top hits in order to make predictions for individuals without known phenotype, e.g. to predict polygenic risk scores or to study polygenic selection by comparing different groups. For instance, polygenic selection acting on the most studied polygenic trait, height, has been subject to multiple studies during the past decade (e.g. 2–6). They detected north-south gradients in Europe which were consistent with expectations. However, their GWAS summary statistics were based on the GIANT consortium data set, a meta-analysis of GWAS conducted in different European cohorts (7,8). The availability of large data sets with less stratification such as the UK Biobank (9) has led to a re-evaluation of those results. The nature of the GIANT consortium data set was realized to represent a potential problem for studies of polygenic adaptation which led several of the authors of the original articles to caution against the interpretations of polygenic selection on height (10,11). This was a great example on how the scientific community assessed their own earlier results in a critical way as more data became available. At the same time it left the question whether there is detectable polygenic selection separating populations more open than ever.

Generally, recent years have seen several articles critically assessing the portability of GWAS results and risk score predictions to other populations (12–14). Refoyo-Martínez et al. (15) are now presenting a systematic assessment on the robustness of cross-population signatures of polygenic adaptation in humans. They compiled GWAS results for complex traits which have been studied in more than one cohort and then use allele frequencies from the 1000 Genomes Project data (16) set to detect signals of polygenic score overdispersion. As the source for the allele frequencies is kept the same across all tests, differences between the signals must be caused by the underlying GWAS. The results are concerning as the level of overdispersion largely depends on the choice of GWAS cohort. Cohorts with homogenous ancestries show little to no overdispersion compared to cohorts of mixed ancestries such as meta-analyses. It appears that the meta-analyses fail to fully account for stratification in their data sets.

The authors based most of their analyses on the heavily studied trait height. Additionally, they use educational attainment (measured as the number of school years of an individual) as an example. This choice was due to the potential over- or misinterpretation of results by the media, the general public and by far right hate groups. Such traits are potentially confounded by unaccounted cultural and socio-economic factors. Showing that previous results about polygenic selection on educational attainment are not robust is an important result that needs to be communicated well. This forms a great example for everyone working in human genomics. We need to be aware that our results can sometimes be misinterpreted. And we need to make an effort to write our papers and communicate our results in a way that is honest about the limitations of our research and that prevents the misuse of our results by hate groups.

This article represents an important contribution to the field. It is cruicial to be aware of potential methodological biases and technical artifacts. Future studies of polygenic adaptation need to be cautious with their interpretations of polygenic score overdispersion. A recommendation would be to use GWAS results obtained in homogenous cohorts. But even if different biobank-scale cohorts of homogeneous ancestry are employed, there will always be some remaining risk of unaccounted stratification. These conclusions may seem sobering but they are part of the scientific process. We need additional controls and new, different methods than polygenic score overdispersion for assessing polygenic selection. Last year also saw the presentation of a novel approach using sequence data and GWAS summary statistics to detect directional selection on a polygenic trait (17). This new method appears to be robust to bias stemming from stratification in the GWAS cohort as well as other confounding factors. Such new developments show light at the end of the tunnel for the use of GWAS summary statistics in the study of polygenic adaptation.

References

1. Buniello A, MacArthur JAL, Cerezo M, Harris LW, Hayhurst J, Malangone C, et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Research. 2019 Jan 8;47(D1):D1005–12. doi: https://doi.org/10.1093/nar/gky1120

2. Turchin MC, Chiang CW, Palmer CD, Sankararaman S, Reich D, Hirschhorn JN. Evidence of widespread selection on standing variation in Europe at height-associated SNPs. Nature Genetics. 2012 Sep;44(9):1015–9. doi: https://doi.org/10.1038/ng.2368

3. Berg JJ, Coop G. A Population Genetic Signal of Polygenic Adaptation. PLOS Genetics. 2014 Aug 7;10(8):e1004412. doi: https://doi.org/10.1371/journal.pgen.1004412

4. Robinson MR, Hemani G, Medina-Gomez C, Mezzavilla M, Esko T, Shakhbazov K, et al. Population genetic differentiation of height and body mass index across Europe. Nature Genetics. 2015 Nov;47(11):1357–62. doi: https://doi.org/10.1038/ng.3401

5. Mathieson I, Lazaridis I, Rohland N, Mallick S, Patterson N, Roodenberg SA, et al. Genome-wide patterns of selection in 230 ancient Eurasians. Nature. 2015 Dec;528(7583):499–503. doi: https://doi.org/10.1038/nature16152

6. Racimo F, Berg JJ, Pickrell JK. Detecting polygenic adaptation in admixture graphs. Genetics. 2018. Arp;208(4):1565–1584. doi: https://doi.org/10.1534/genetics.117.300489

7. Lango Allen H, Estrada K, Lettre G, Berndt SI, Weedon MN, Rivadeneira F, et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature. 2010 Oct;467(7317):832–8. doi: https://doi.org/10.1038/nature09410

8. Wood AR, Esko T, Yang J, Vedantam S, Pers TH, Gustafsson S, et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat Genet. 2014 Nov;46(11):1173–86. doi: https://doi.org/10.1038/ng.3097

9. Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018 Oct;562(7726):203–9. doi: https://doi.org/10.1038/s41586-018-0579-z

10. Berg JJ, Harpak A, Sinnott-Armstrong N, Joergensen AM, Mostafavi H, Field Y, et al. Reduced signal for polygenic adaptation of height in UK Biobank. eLife. 2019 Mar 21;8:e39725. doi: https://doi.org/10.7554/eLife.39725

11. Sohail M, Maier RM, Ganna A, Bloemendal A, Martin AR, Turchin MC, et al. Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies. eLife. 2019 Mar 21;8:e39702. doi: https://doi.org/10.7554/eLife.39702

12. Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ. Clinical use of current polygenic risk scores may exacerbate health disparities. Nature Genetics. 2019 Apr;51(4):584–91. doi: https://doi.org/10.1038/s41588-019-0379-x

13. Bitarello BD, Mathieson I. Polygenic Scores for Height in Admixed Populations. G3: Genes, Genomes, Genetics. 2020 Nov 1;10(11):4027–36. doi: https://doi.org/10.1534/g3.120.401658

14. Uricchio LH, Kitano HC, Gusev A, Zaitlen NA. An evolutionary compass for detecting signals of polygenic selection and mutational bias. Evolution Letters. 2019;3(1):69–79. doi: https://doi.org/10.1002/evl3.97

15. Refoyo-Martínez A, Liu S, Jørgensen AM, Jin X, Albrechtsen A, Martin AR, Racimo F. How robust are cross-population signatures of polygenic adaptation in humans? bioRxiv, 2021, 2020.07.13.200030, version 5 peer-reviewed and recommended by Peer community in Evolutionary Biology. doi: https://doi.org/10.1101/2020.07.13.200030

16. Auton A, Abecasis GR, Altshuler DM, Durbin RM, Abecasis GR, Bentley DR, et al. A global reference for human genetic variation. Nature. 2015 Sep 30;526(7571):68–74. doi: https://doi.org/10.1038/nature15393

17. Stern AJ, Speidel L, Zaitlen NA, Nielsen R. Disentangling selection on genetically correlated polygenic traits using whole-genome genealogies. bioRxiv. 2020 May 8;2020.05.07.083402. doi: https://doi.org/10.1101/2020.05.07.083402

Elevation gradients are convenient and widely used natural setups to study local adaptation, particularly in these times of rapid climate change [e.g. 1]. Marin and her collaborators [2] did not follow the mainstream, however. Instead of tackling adaptation to climate change, they used elevation gradients to address another crucial evolutionary question [3]: could adaptation to altitude lead to ecological speciation, i.e. reproductive isolation between populations in spite of gene flow? More specifically, they examined how much local adaptation to environmental variation differed among closely-related, recently diverged subspecies. They studied several populations of two subspecies of snapdragon (Antirrhinum majus), with adjacent geographical distributions. Using common garden experiments and the classical, but still useful, QST-FST comparison, they demonstrate contrasting patterns of local adaptation to altitude between the two subspecies, with several traits under divergent selection in A. majus striatum but none in A. majus pseudomajus. These differences in local adaptation may contribute to species divergence, and open many stimulating questions on the underlying mechanisms, such as the identity of environmental drivers or contribution of reproductive isolation involving flower color polymorphism.

References

[1] Anderson, J. T., and Wadgymar, S. M. (2020). Climate change disrupts local adaptation and favours upslope migration. Ecology letters, 23(1), 181-192. doi: 10.1111/ele.13427
[2] Marin, S., Gibert, A., Archambeau, J., Bonhomme, V., Lascoste, M., and Pujol, B. (2020). Potential adaptive divergence between subspecies and populations of snapdragon plants inferred from QST – FST comparisons. Zenodo, 3628168, ver. 3 peer-reviewed and recommended by Peer Community in Evolutionary Biology. doi: 10.5281/zenodo.3628168
[3] Schluter, D. (2009). Evidence for ecological speciation and its alternative. Science, 323(5915), 737-741. doi: 10.1126/science.1160006

Theoretical work, initiated by Levene (1953) [1] and Dempster (1955) [2], suggests that within a given environment, the way populations are regulated and contribute to the next generation is a key factor for the maintenance of local adaptation polymorphism. In this theoretical context, hard selection describes the situation where the genetic composition of each population affects its contribution to the next generation whereas soft selection describes the case where the contribution of each population is fixed, whatever its genetic composition. Soft selection is able to maintain polymorphism, whereas hard selection invariably leads to the fixation of one of the alleles. Although the specific conditions (e.g. of migration between populations or drift level) in which this prediction holds have been studied in details by theoreticians, experimental tests have mainly failed, usually leading to the conclusion that the allele frequency dynamics was driven by other mechanisms in the experimental systems and conditions used. Gallet, Froissart and Ravigné [3] have set up a bacterial experimental system which allowed them to convincingly demonstrate that soft selection generates the conditions for polymorphism maintenance when hard selection does not, everything else being equal. The key ingredients of their experimental system are (1) the possibility to accurately produce hard and soft selection regimes when daily transferring the populations and (2) the ability to establish artificial well-characterized reproducible trade-offs. To do so, they used two genotypes resisting each one to one antibiotic and combined, across habitats, low antibiotic doses and difference in medium productivity. The experimental approach contains two complementary parts: the first one is looking at changes in the frequencies of two genotypes, initially introduced at around 50% each, over a small number of generations (ca 40) in different environments and selection regimes (soft/hard) and the second one is convincingly showing polymorphism protection by establishing that in soft selection regimes, the lowest fitness genotype is not eliminated even when introduced at low frequency. In this manuscript, a key point is the dialog between theoretical and experimental approaches. The experiments have been thought and designed to be as close as possible to the situations analysed in theoretical work. For example, the experimental polymorphism protection test (experiment 2) closely matches the equivalent analysis classically performed in theoretical approaches. This close fit between theory and experiment is clearly a strength of this study. This said, the experimental system allowing them to realise this close match also has some limitations. For example, changes in allele frequencies could only be monitored over a quite low number of generations because a longer time-scale would have allowed the contribution of de novo mutations and the likely emergence of a generalist genotype resisting to both antibiotics used to generate the local adaptation trade-offs. These limitations, as well as the actual significance of the experimental tests, are discussed in deep details in the manuscript.

References

[1] Levene H. 1953. Genetic equilibrium when more than one niche is available. American Naturalist 87: 331–333. doi: 10.1086/281792

[2] Dempster ER. 1955. Maintenance of genetic heterogeneity. Cold Spring Harbor Symposia on Quantitative Biology. 20: 25–32. doi: 10.1101/SQB.1955.020.01.005

[3] Gallet R, Froissart R, Ravigné V. 2017. Things softly attained are long retained: dissecting the impacts of selection regimes on polymorphism maintenance in experimental spatially heterogeneous environments. bioRxiv 100743; doi: 10.1101/100743

Exciting times are afoot for those of us interested in the ecology and evolution of invasive populations. Recent years have seen evolutionary process woven firmly into our understanding of invasions (Miller et al. 2020). This integration has inspired a welter of empirical and theoretical work. We have moved from field observations and verbal models to replicate experiments and sophisticated mathematical models. Progress has been rapid, and we have seen science at its best; an intimate discussion between theory and data.
An area currently under very active development is our understanding of pushed invasions. Here a population spreads through space driven, not by dispersal and growth originating at the leading tip of the invasion, but by dispersal and growth originating deeper in the bulk of the population. These pushed invasions may be quite common – they result when per capita growth and dispersal rates are higher in the bulk of the wave than at the leading tip. They result from a range of well-known phenomena, including Allee effects and density-dependent dispersal (Gandhi et al. 2016; Bîrzu et al. 2019). Pushed invasions travel faster than we would expect given growth and dispersal rates on the leading tip, and they lose genetic diversity more slowly than classical pulled invasions (Roques et al. 2012; Haond et al. 2018; Bîrzu et al. 2019).
Well… in theory, anyway. The theory on pushed waves has momentarily streaked ahead of the empirical work, because empirical systems for studying pushed invasions are rare (though see Gandhi et al. 2016; Gandhi, Korolev, and Gore 2019). In this paper, Dahirel and colleagues (2020) make the argument that we may be able to generate pushed invasions in laboratory systems simply by reducing the connectedness of our experimental landscapes. If true, we might have a simple tool for turning many of our established experimental systems into systems for studying pushed dynamics.
It’s a nice idea, and the paper goes to careful lengths to explore the possibility in their lab system (a parasitoid wasp, Trichogramma). They run experiments on replicate wasp populations comparing strongly- v poorly-connected arrays, and estimate the resulting invasion speeds and rate of diversity loss. They also build a simulation model of the system, allowing them to explore in-silico a range of possible processes underlying their results.
As well as developing these parallel systems, Dahirel and colleagues (2020) go to careful lengths to develop statistical analyses that allow inference on key parameters, and they apply these analyses to both the experimental and simulation data. They have been motivated to apply methods that might be used in both laboratory and field settings to help classify invasions.
Ultimately, they found reasonable evidence that their poorly-connected habitat did induce a pushed dynamic. Their poorly connected invasions travelled faster than they should have if they were pulled, they lost diversity more slowly than the highly connected habitat, and replicates with a higher carrying capacity tended to have higher invasion speeds. All in line with expectations of a pushed dynamic. Interestingly, however, their simulation results suggest that they probably got this perfect result for unexpected reasons. The strong hint is that their poorly-connected habitat induced density dependent dispersal in the wasps. Without this effect, their simulations suggest they should have seen diversity decreasing much more rapidly than it did.
There is a nuanced, thoughtful, and carefully argued discussion about all this in the paper, and it is worth reading. There is much of value in this paper. Theirs is not a perfect empirical system in which all the model assumptions are met and in which huge population sizes make stochastic effects negligible. Here is a system one step closer to the messy reality of biology. The struggle to align this system with new theory has been worth the effort. Not only does it give us hope that we might usefully be able to discriminate between classes of invasions using real-world data, but it hints at a rule that Tolstoy might have expressed this way: all pulled invasions are alike, each pushed invasion is pushed in its own way.

References

Bîrzu, G., Matin, S., Hallatschek, O., and Korolev, K. S. (2019). Genetic drift in range expansions is very sensitive to density dependence in dispersal and growth. Ecology Letters, 22(11), 1817-1827. doi: https://doi.org/10.1111/ele.13364
Dahirel, M., Bertin, A., Haond, M., Blin, A., Lombaert, E., Calcagno, V., Fellous, S., Mailleret, L., Malausa, T., and Vercken, E. (2020). Shifts from pulled to pushed range expansions caused by reduction of landscape connectivity. bioRxiv, 2020.05.13.092775, ver. 4 peer-reviewed and recommended by PCI Evolutionary Biology. https://doi.org/10.1101/2020.05.13.092775
Gandhi, S. R., Korolev, K. S., and Gore, J. (2019). Cooperation mitigates diversity loss in a spatially expanding microbial population. Proceedings of the National Academy of Sciences, 116(47), 23582-23587. doi: https://doi.org/10.1073/pnas.1910075116
Gandhi, S. R., Yurtsev, E. A., Korolev, K. S., and Gore, J. (2016). Range expansions transition from pulled to pushed waves as growth becomes more cooperative in an experimental microbial population. Proceedings of the National Academy of Sciences, 113(25), 6922-6927. doi: https://doi.org/10.1073/pnas.1521056113
Haond, M., Morel-Journel, T., Lombaert, E., Vercken, E., Mailleret, L. and Roques, L. (2018). When higher carrying capacities lead to faster propagation (2018), bioRxiv, 307322, ver. 4 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/307322
Miller et al. (2020). Eco‐evolutionary dynamics of range expansion. Ecology, 101(10), e03139. doi: https://doi.org/10.1002/ecy.3139
Roques, L., Garnier, J., Hamel, F., and Klein, E. K. (2012). Allee effect promotes diversity in traveling waves of colonization. Proceedings of the National Academy of Sciences, 109(23), 8828-8833. doi: https://doi.org/10.1073/pnas.1201695109