Recent methodological developments and increased genome sequencing efforts have introduced the tantalizing possibility of inferring ancestral phenotypes using DNA from contemporary species. One intriguing application of this idea is to exploit the apparent correlation between substitution rates and body size to infer ancestral species' body sizes using the inferred patterns of substitution rate variation among species lineages based on genomes of extant species [1].
The recommended paper by Figuet et al. [2] examines the utility of such approaches by analyzing the Cetartiodactyla, a clade of large mammals that have mostly well resolved phylogenetic relationships and a reasonably good fossil record. This combination of genomic data and fossils allows a direct comparison between body size predictions obtained from the genomic data and empirical evidence from the fossil record. If predictions seem good in groups such as the Cetartiodactyla, where there is independent evidence from the fossil record, this would increase the credibility of predictions made for species with less abundant fossils.
Figuet et al. [2] analyze transcriptome data for 41 species and report a significant effect of body mass on overall substitution rate, synonymous vs. non-synonymous rates, and the dynamics of GC-content, thus allowing a prediction of small ancestral body size in this group despite the fact that the extant species that were analyzed are nearly all large.
A comparative method based solely on morphology and phylogenetic relationships would be very unlikely to make such a prediction. There are many sources of uncertainty in the variables and parameters associated with these types of approaches: phylogenetic uncertainty (topology and branch lengths), uncertainty about inferred substitution rates, and so on. Although the authors do not account for all these sources of uncertainty the fact that their predicted body sizes appear sensible is encouraging and undoubtedly the methods will become more statistically sophisticated over time.

References

[1] Romiguier J, Ranwez V, Douzery EJP and Galtier N. 2013. Genomic evidence for large, long-lived ancestors to placental mammals. Molecular Biology and Evolution 30: 5–13. doi: 10.1093/molbev/mss211

[2] Figuet E, Ballenghien M, Lartillot N and Galtier N. 2017. Reconstruction of body mass evolution in the Cetartiodactyla and mammals using phylogenomic data. bioRxiv, ver. 3 of 4th December 2017. 139147. doi: 10.1101/139147

This is an original paper [1] addressing the question whether cultural transmission occurs in insects and studying the mechanisms of such transmission. Often, culture-like phenomena require relatively sophisticated learning mechanisms, for example imitation and/or teaching. In insects, seemingly complex processes of social information acquisition, can sometimes instead be mediated by relatively simple learning mechanisms suggesting that cultural processes may not necessarily require sophisticated learning abilities.

An important quality of this paper is to describe neatly the experimental protocols used for such typically complex behavioural analyses, providing a detailed understanding of the results while it remains a joy to read. This becomes rare in high impact journals. In a clever experimental design, individual bumblebees are trained to pull an artificial flower from under a Plexiglas table to get access to a reward, by pulling a string attached to the flower. Individuals that have learnt this task are then shown to inexperienced bees while performing this task. This results in a large proportion of the inexperienced observers learning to pull the string and getting access to the reward. Finally, the authors could then document the spread of the string pulling skill amongst other workers in the colony. Even when the originally trained individuals had died, the skill of string-pulling persisted in the colony, as long as they were challenged with the task. This shows that cultural transmission takes place within a colony. The authors provide evidence that the transmission of this behavior among individuals relies on a mix of social learning by local enhancement (bees were attracted to the location where they had observed a demonstrator) and of non-social, individual learning (pulling the string is learned by trial and errors and not by direct imitation of the conspecific). Data also show that simple associative mechanisms are enough and that stimulus enhancement was involved (bees were attracted to the string when its location was concordant with that during prior observation).

The cleverly designed experiments use a paradigm (string-pulling) which has often been used to investigate cognitive abilities in vertebrates. Comparison with such studies indicate that bees, in some aspects of their learning, may not be different from birds, dogs, or apes as they also relied on the perceptual feedback provided by their actions, resulting in target movement to learn string pulling. The results of the study suggest that the combination of relatively simple forms of social learning and trial-and-error learning can mediate the acquisition of new skills and that bumblebees possess the essential cognitive elements for cultural transmission and in a broader sense, that the capacity of culture may be present within most animals.

Can we expect behavioural innovation such as string pulling to occur in nature? Bombus terrestris colonies can reach a total of several hundreds foragers. In the experiments, foragers needed on average 5 rounds of observations with different demonstrators to learn how to pull the string. As individuals forage in a meadow full of flowers and conspecifics, transmission of behavioural innovations by repeated observations shouldn’t strike us as something impossible. Would the behavior survive through the winter? Bumblebee colonies are seasonal in northern areas and in the Mediterranean area but tropical species persists for several years. In seasonal species, all the workers die before winter and only new queens overwinter. So there is no possibility for seasonal foragers to transmit the technique overwinter. Only queens could potentially transmit it to new foragers in spring. However flowers are different in autumn and spring. Therefore, what queens have learnt about flowers in autumn would unlikely be useful in spring (providing that they can remember it). However there is no reason why the technique couldn't be transmitted from a colony to another between spring to autumn. Such transmission of new behaviour would more easily persist in perennial social insect colonies, like honeybees. Importantly, the bees used in these experiments came from a company whose rearing conditions are unknown, and only a few colonies were used for each experiment. As learning ability has a genetic basis [2-3], colonies differ in their ability to learn [4]. In this regard, the authors showed variation between individual bumblebees and between bumblebee colonies in learning ability. Hence, we would wish to know more about the level of genetic diversity in the wild, and of genetic differentiation between tested colonies (were they independent replicates?), to extrapolate the results to what may happen in the wild.

Excitingly, the authors found 2 true innovators among the >400 individuals that were tested at least once for 5 min who would solve such a task without stepwise training or observation of skilled demonstrators, showing that behavioural innovation can occur in very small numbers of individuals, provided that an ecological trigger is provided (food reward). Hence this study shows that all ingredients for the long proposed “social heredity” theory proposed by Baldwin in 1896 are available in this organism, suggesting that social transmission of behavioural innovations could technically act as an additional mechanism for adaptive evolution [5], next to genetic evolution that may take longer to produce adaptive evolution. The question remains whether the behavioural innovations are arising from standing genetic variation in the bees, or do not need a firm genetic background to appear.

References

[1] Alem S, Perry CJ, Zhu X, Loukola OJ, Ingraham T, Søvik E, Chittka L. 2016. Associative mechanisms allow for social learning and cultural transmission of string pulling in an insect. PloS Biology 14:e1002564. doi: 10.1371/journal.pbio.1002564

[2] Mery F, Kawecki TJ. 2002. Experimental evolution of learning ability in fruit flies. Proceeding of the National Academy of Science USA 99:14274-14279. doi: 10.1073/pnas.222371199

[3] Mery F, Belay AT, So AKC, Sokolowski MB, Kawecki TJ. 2007. Natural polymorphism affecting learning and memory in Drosophila. Proceeding of the National Academy of Science USA 104:13051-13055. doi: 10.1073/pnas.0702923104

[4] Raine NE, Chittka L. 2008. The correlation of learning speed and natural foraging success in bumble-bees. Proceeding of the Royal Society of London 275: 803-808. doi : 10.1098/rspb.2007.1652

[5] Baldwin JM. 1896. A New Factor in Evolution. American Naturalist 30:441-451 and 536-553. doi: 10.1086/276408

Estimating the absolute age of diversification events is challenging, because molecular sequences provide timing information in units of substitutions, not years. Additionally, the rate of molecular evolution (in substitutions per year) can vary widely across lineages. Accurate dating of speciation events traditionally relies on non-molecular data. For very fast-evolving organisms such as SARS-CoV-2, for which samples are obtained over a time span, the collection times provide this external information from which we can learn the rate of molecular evolution and date past events (Boni et al. 2020). In groups for which the fossil record is abundant, state-of-the-art dating methods use fossil information to complement molecular data, either in the form of a prior distribution on node ages (Nguyen & Ho 2020), or as data modelled with a fossilization process (Heath et al. 2014).

Dating is a challenge in groups that lack fossils or other geological evidence, such as very old lineages and microbial lineages. In these groups, horizontal gene transfer (HGT) events have been identified as informative about relative dates: the ancestor of the gene's donor must be older than the descendants of the gene's recipient. Previous work using HGTs to date phylogenies have used methodologies that are ad-hoc (Davín et al 2018) or employ a small number of HGTs only (Magnabosco et al. 2018, Wolfe & Fournier 2018).

Szöllősi et al. (2021) present and validate a Bayesian approach to estimate the age of diversification events based on relative information on these ages, such as implied by HGTs. This approach is flexible because it is modular: constraints on relative node ages can be combined with absolute age information from fossil data, and with any substitution model of molecular evolution, including complex state-of-art models. To ease the computational burden, the authors also introduce a two-step approach, in which the complexity of estimating branch lengths in substitutions per site is decoupled from the complexity of timing the tree with branch lengths in years, accounting for uncertainty in the first step. Currently, one limitation is that the tree topology needs to be known, and another limitation is that constraints need to be certain. Users of this method should be mindful of the latter when hundreds of constraints are used, as done by Szöllősi et al. (2021) to date the trees of Cyanobacteria and Archaea.

Szöllősi et al. (2021)'s method is implemented in RevBayes, a highly modular platform for phylogenetic inference, rapidly growing in popularity (Höhna et al. 2016). The RevBayes tutorial page features a step-by-step tutorial "Dating with Relative Constraints", which makes the method highly approachable.

References:

Boni MF, Lemey P, Jiang X, Lam TT-Y, Perry BW, Castoe TA, Rambaut A, Robertson DL (2020) Evolutionary origins of the SARS-CoV-2 sarbecovirus lineage responsible for the COVID-19 pandemic. Nature Microbiology, 5, 1408–1417. https://doi.org/10.1038/s41564-020-0771-4

Davín AA, Tannier E, Williams TA, Boussau B, Daubin V, Szöllősi GJ (2018) Gene transfers can date the tree of life. Nature Ecology & Evolution, 2, 904–909. https://doi.org/10.1038/s41559-018-0525-3

Heath TA, Huelsenbeck JP, Stadler T (2014) The fossilized birth–death process for coherent calibration of divergence-time estimates. Proceedings of the National Academy of Sciences, 111, E2957–E2966. https://doi.org/10.1073/pnas.1319091111

Höhna S, Landis MJ, Heath TA, Boussau B, Lartillot N, Moore BR, Huelsenbeck JP, Ronquist F (2016) RevBayes: Bayesian Phylogenetic Inference Using Graphical Models and an Interactive Model-Specification Language. Systematic Biology, 65, 726–736. https://doi.org/10.1093/sysbio/syw021

Magnabosco C, Moore KR, Wolfe JM, Fournier GP (2018) Dating phototrophic microbial lineages with reticulate gene histories. Geobiology, 16, 179–189. https://doi.org/10.1111/gbi.12273

Nguyen JMT, Ho SYW (2020) Calibrations from the Fossil Record. In: The Molecular Evolutionary Clock: Theory and Practice  (ed Ho SYW), pp. 117–133. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-030-60181-2_8

Szollosi, G.J., Hoehna, S., Williams, T.A., Schrempf, D., Daubin, V., Boussau, B. (2021) Relative time constraints improve molecular dating. bioRxiv, 2020.10.17.343889, ver. 8  recommended and peer-reviewed by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2020.10.17.343889

Wolfe JM, Fournier GP (2018) Horizontal gene transfer constrains the timing of methanogen evolution. Nature Ecology & Evolution, 2, 897–903. https://doi.org/10.1038/s41559-018-0513-7

Sexual antagonism (SA), wherein the fitness interests of the sexes do not align, is inherent to organisms with two (or more) sexes.  SA leads to intra-locus sexual conflict, where an allele that confers higher fitness in one sex reduces fitness in the other [1, 2].  This situation leads to what has been referred to as "gender load", resulting from the segregation of SA alleles in the population.  Gender load can be reduced by the evolution of sex-specific (or sex-biased) gene expression.  A specific prediction is that gene-duplication can lead to sub- or neo-functionalization, in which case the two duplicates partition the function in the different sexes.  The conditions for invasion by a SA allele differ between sex-chromosomes and autosomes, leading to the prediction that (in XY or XO systems) the X should accumulate recessive male-favored alleles and dominant female-favored alleles; similar considerations apply in ZW systems ([3, but see 4].

Aphids present an interesting special case, for several reasons: they have XO sex-determination, and three distinct reproductive morphs (sexual females, parthenogenetic females, and males).  Previous theoretical work by the lead author predict that the X should be optimized for male function, which was borne out by whole-animal transcriptome analysis [5].  

Here [6], the authors extend that work to investigate “tissue”-specific (heads, legs and gonads), sex-specific gene expression.  They argue that, if intra-locus SA is the primary driver of sex-biased gene expression, it should be generally true in all tissues.  They set up as an alternative the possibility that sex-biased gene expression could also be driven by dosage compensation.  They cite references supporting their argument that "dosage compensation (could be) stronger in the brain", although the underlying motivation for that argument appears to be based on empirical evidence rather than theoretical predictions.      

At any rate, the results are clear: all tissues investigated show masculinization of the X.  Further, X-linked copies of gene duplicates were more frequently male-biased than duplicated autosomal genes or X-linked single-copy genes.

To sum up, this is a nice empirical study with clearly interpretable (and interpreted) results, the most obvious of which is the greater sex-biased expression in sexually-dimorphic tissues.  Unfortunately, as the authors emphasize, there is no general theory by which SA, variable dosage-compensation, and meiotic sex chromosome inactivation can be integrated in a predictive framework.  It is to be hoped that empirical studies such as this one will motivate deeper and more general theoretical investigations.

References

[1] Rice WR, Chippindale AK (2001) Intersexual ontogenetic conflict. Journal of Evolutionary Biology 14: 685-693. https://doi.org/10.1046/j.1420-9101.2001.00319.x

[2] Bonduriansky R, Chenoweth SF (2009) Intralocus sexual conflict. Trends Ecol Evol 24: 280-288. https://doi.org/10.1016/j.tree.2008.12.005

[3] Rice WR. (1984) Sex chromosomes and the evolution of sexual dimorphism. Evolution 38: 735-742. https://doi.org/10.1086/595754

[4] Fry JD (2010) The genomic location of sexually antagonistic variation: some cautionary comments. Evolution 64: 1510-1516. https://doi.org/10.1111%2Fj.1558-5646.2009.00898.x

[5] Jaquiéry J, Rispe C, Roze D, Legeai F, Le Trionnaire G, Stoeckel S, et al. (2013) Masculinization of the X Chromosome in the Pea Aphid. PLoS Genetics 9. https://doi.org/10.1371/journal.pgen.1003690

[6] Jaquiéry J, Simon J-C, Robin S, Richard G, Peccoud J, Boulain H, Legeai F, Tanguy S, Prunier-Leterme N, Le Trionnaire G (2022) Masculinization of the X-chromosome in aphid soma and gonads. bioRxiv, 2021.08.13.453080, ver. 4 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2021.08.13.453080 

Heliconius butterflies are famous for their colorful wing patterns acting as a warning of their chemical defenses [1]. Most species are involved in Müllerian mimicry assemblies, as predators learn to associate common wing patterns with unpalatability and preferentially target rare variants. Such positive-frequency dependent selection homogenizes wing patterns at different localities, and in several species, all individuals within a community belong to the same morph [2]. In this respect, H. numata stands out. This species shows stable local polymorphism across multiple localities, with local populations home to up to seven distinct morphs [2]. Although a balance between migration and local positive-frequency dependent selection can allow some degree of local polymorphism, theory suggests that this occurs only when migration is within a narrow window [3].
One factor that potentially enhances local polymorphism in H. numata is disassortative mating. Mate choice assays have in fact revealed that females of this species tend to reject males with the same wing pattern [4]. However the evolution of such mating behavior and its effect on polymorphism remain unclear when selection is locally positive-frequency dependent. Using a mathematical model, Maisonneuve et al. [5] clarify the conditions that favor the evolution of disassortative mating in the complicated system of H. numata. In particular, they investigate whether the genetic basis of wing colour can favor the emergence of disassortative mating. Variation in wing pattern in H. numata is controlled by the supergene P, which is a single genomic region harboring multiple protein coding genes that have ceased to recombine due to chromosomal inversions [6]. If such remarkable genetic configuration allows for the co-adaptation of multiple loci participating to a complex phenotype such as wing color pattern, the absence of recombination can also result in the accumulation of deleterious mutations [7]. In fact, alleles at the P locus have been associated with a recessive genetic load, leading to a fitness advantage for heterozygotes at this locus [8]. Can this fitness advantage to heterozygotes lead to the evolution of disassortative mating? And if so, can such evolution lead to the maintenance of local polymorphism in spite of strong positive frequency-dependent selection?
To investigate these questions, Maisonneuve et al. [5] model evolution at two loci, one is the P locus for wing pattern, and the other influences mating behavior. The population is divided among two connected patches that differ in their butterfly communities, so that different alleles at the P locus are favored by positive frequency-dependent selection in different patches. The different alleles at the P locus are ordered in dominance relationships such that the most dominant over wing color pattern are also those with the highest load. By tracking the dynamics of haplotype frequencies in the population, the authors first show that disassortative mating readily evolves via the invasion of an allele causing females carrying it to reject males that resemble them phenotypically. Such “self-referencing” mechanism of mate choice, however, has never been reported and has been argued to be rare due to its complicated nature [9].
Maisonneuve et al. [5] then compare the evolution of disassortative mating via two alternative mechanisms: attraction and rejection. In these cases, alleles at the mating locus determine attraction to or rejection of specific phenotypes (e.g., under attraction rule, allele “B” encodes attraction to males with phenotype B). With the P and mating loci fully linked, disassortative mating can evolve under all three mechanisms (self-referencing, attraction and rejection), but tends to be less prevalent at equilibrium under attraction rule. This in turn results in the maintenance of less genetic variation under attraction compared to the other mating mechanisms. The loss of variation that occurs under attraction rules is due to a combination of dominance relationships between alleles at the P locus and the searching cost to females in finding rare types of males. When a particular wing pattern, say B, is only expressed in homozygotic form, B males are relatively rare. Females that carry the allele at the mating locus causing them to be attracted to such males then suffer a fitness cost due to lost mating opportunities. This mating allele is therefore purged, and in turn so is the recessive allele for B phenotype at the P locus. Under self-referencing and rejection rules, however, choosy females only reject males of a specific phenotype. They can therefore potentially mate with larger pool of males than females attracted to a single type. As a result, self-referencing and rejection rules are less sensitive to demographic effects and so are more conducive to disassortative mating evolution.
In their final analysis, Maisonneuve et al. [5] investigate the influence of recombination among the P and mating loci. They show that recombination has different effects on disassortative mating evolution depending on the mechanism of mate choice. Under the self-referencing rule, loose linkage leads to higher levels of disassortative mating and polymorphism than when linkage is tight. Under attraction or rejection rule, however, even very limited recombination completely inhibits the evolution of disassortative mating. This is because, with alleles at the mating locus coding for attraction/rejection to specific males, recombination breaks the association between the P and mating loci necessary for disassortative mating. By contrast, disassortative mating via a self-referencing rule does not depend on the linkage among the P and mating loci: females choose males that are different to themselves independently from the alleles they carry at the P locus.
Taken together, Maisonneuve et al.’s analyses [5] show that disassortative mating can readily evolve in a system like H. numata, but that this evolution depends on the genetic architecture of mating behavior. The architectures that are more conducive to the evolution of disassortative mating are: (1) epistatic interactions among the P and mating loci such that females are able to recognize their own phenotype and base their mating decision upon this information (self-referencing rule); and (2) full linkage among the P supergene and a mating locus that triggers rejection of a specific color pattern. While the mechanisms behind disassortative mating remain to be elucidated, assortative mating seems to rely on alleles triggering attraction to specific cues with variation in attraction and cues linked together [10]. These observations support the notion that disassortative mating is due to alleles causing rejection, in tight linkage to the P locus. If so, mating loci would in fact be part of the P supergene, thus controlling not only intricate wing color pattern but also mating behavior.
Beyond the specific system of H. numata, Maisonneuve et al.’s study [5] helps understand the evolution of disassortative mating and its association with the genetic architecture of correlated traits. In particular, Maisonneuve et al. [5] expands the role of supergenes for ecologically relevant traits to mating behavior, further bolstering the relevance of these remarkable genetic elements in the maintenance of variation in complex and elaborate phenotypes.

References

[1] Merrill, R M, K K Dasmahapatra, J W Davey, D D Dell'Aglio, J J Hanly, B Huber, C D Jiggins, et al. (2015). The Diversification of Heliconius butterflies: What Have We Learned in 150 Years? Journal of Evolutionary Biology 28 (8), 1417–38. https://doi.org/10.1111/jeb.12672.
[2] Joron M, IR Wynne, G Lamas, and J Mallet (1999). Variable selection and the coexistence of multiple mimetic forms of the butterfly Heliconius numata. Evolutionary Ecology 13, 721– 754. https://doi.org/10.1023/A:1010875213123
[3] Joron M and Y Iwasa (2005). The evolution of a Müllerian mimic in a spatially distributed community. Journal of Theoretical Biology 237, 87–103. https://doi.org/10.1016/j.jtbi.2005.04.005
[4] Chouteau M, V Llaurens, F Piron-Prunier, and M Joron (2017). Polymorphism at a mimicry su- pergene maintained by opposing frequency-dependent selection pressures. Proceedings of the National Academy of Sciences 114, 8325–8329. https://doi.org/10.1073/pnas.1702482114
[5] Maisonneuve, L, Chouteau, M, Joron, M and Llaurens, V. (2020). Evolution and genetic architecture of disassortative mating at a locus under heterozygote advantage. bioRxiv, 616409, ver. 9 peer-reviewed and recommended by PCI Evolutionary Biology. https://doi.org/10.1101/616409
[6] Joron M, L Frezal, RT Jones, NL Chamberlain, SF Lee, CR Haag, A Whibley, M Becuwe, SW Baxter, L Ferguson, et al. (2011). Chromosomal rearrangements maintain a polymorphic super- gene controlling butterfly mimicry. Nature 477, 203. https://doi.org/10.1038/nature10341
[7] Schwander T, R Libbrecht, and L Keller (2014). Supergenes and Complex Phenotypes.” Current Biology. 24 (7), 288–94. https://doi.org/10.1016/j.cub.2014.01.056.
[8] Jay P, M Chouteau, A Whibley, H Bastide, V Llaurens, H Parrinello, and M Joron (2019). Mutation accumulation in chromosomal inversions maintains wing pattern polymorphism in a butterfly. bioRxiv. https://doi.org/ 10.1101/736504.
[9] Kopp M, MR Servedio, TC Mendelson, RJ Safran, RL Rodrıguez, ME Hauber, EC Scordato, LB Symes, CN Balakrishnan, DM Zonana, et al. (2018). Mechanisms of assortative mating in speciation with gene flow: connecting theory and empirical research. The American Naturalist 191, 1–20. https://doi.org/10.1086/694889
[10] Merrill RM, P Rastas, SH Martin, MC Melo, S Barker, J Davey, WO McMillan, and CD Jiggins (2019). Genetic dissection of assortative mating behavior. PLoS biology 17, e2005902. https://doi.org/10.1371/journal.pbio.2005902