Sexual selection occurs in flowering plants too. However it tends to be understudied in comparison to animal sexual selection, in part because the minuscule size and long dispersal distances of the individuals producing male gametes (pollen grains) seriously complicate the estimation of male siring success and thereby the measurement of sexual selection. Dorken and Perry [1] introduce a novel and clever approach to estimate sexual selection in plants, which bypasses the need for a direct quantification of absolute male mating success. This approach builds on the fact that the strength of sexual selection is directly related to the ability of individuals to monopolize mates [2]. In plants, mate monopolization can be assessed by examining the proportion of seeds produced by a given plant that are full-sibs, i.e. that share the same father. A nice feature of this proportion of full-sib seeds per maternal parent is it equals the coefficient of correlated paternity of Ritland [3], which can be readily obtained from the hundreds of plant mating system studies using genetic markers. A less desirable feature of the proportion of full sibs per maternal plant is that it is inversely related to population size, an effect that should be corrected for. The resulting index of mate monopolization is a simple product: (coefficient of correlated paternity)x(population size – 1). The authors test whether their index of mate monopolization is a good correlate of sexual selection, measured more traditionally as the selection differential on a trait influencing mating success, using a combination of theoretical and experimental approaches. Both approaches confirm that the two quantities are positively correlated, which suggests that the index of mate monopolization could be a convenient way to estimate the relative strength of sexual selection in flowering plants. These results call for further investigation, e.g. to verify that the effect of population size is well controlled for, or to assess the effects of non-random mating and inbreeding depression; however, this work paves the way for an expansion of sexual selection studies in flowering plants.

References

[1] Dorken ME and Perry LE. 2017. Correlated paternity measures mate monopolization and scales with the magnitude of sexual selection. Journal of Evolutionary Biology 30: 377-387 doi: 10.1111/jeb.13013

[2] Klug H, Heuschele J, Jennions M and Kokko H. 2010. The mismeasurement of sexual selection. Journal of Evolutionary Biology 23:447-462. doi: 10.1111/j.1420-9101.2009.01921.x

[3] Ritland K. 1989. Correlated matings in the partial selfer Mimulus guttatus. Evolution 43:848-859. doi: 10.2307/2409312

Despite a vast array of ways in which organisms can reproduce (Bell, 1982), most animals engage in sexual reproduction (Otto & Lenormand, 2002). A fascinating alternative to sex is parthenogenesis, where offspring are produced asexually from a gamete, typically the egg, without receiving genetic material from another gamete (Simon, Delmotte, Rispe, & Crease, 2003). One of the long-standing questions in the field is why parthenogenesis is not more widespread, given the costs associated with sex (Otto & Lenormand, 2002).  Natural populations of most species appear to be reproducing either sexually or parthenogenetically, even if a species can employ both reproductive modes (Larose et al 2023). Larose et al (2023) highlight the conundrum in this pattern, as organisms that are capable of employing parthenogenesis facultatively would be able to gain the benefits of both modes of reproduction. Why then, is facultative parthenogenesis not more common?

Larose et al (2023) propose that constraints on being efficient in both sexual and asexual reproduction could cause a trade-off between reproductive modes that favours an obligate strategy of either sex or no sex. This would provide an explanation for why facultative parthenogenesis is rare. Timema stick insects provide an excellent system to investigate reproductive strategies, as some species have parthenogenetic females, while other species are sexual, and they show repeated transitions from sexual reproduction to obligate parthenogenesis (Schwander & Crespi, 2009). The authors performed comprehensive and complementary studies in a recently discovered species T. douglasi, in which populations show both modes of reproduction, with some populations consisting only of females and others showing equal proportions of males and females. The sex ratio varied significantly, with the proportion of females ranging between 43-100% across 29 populations. These populations form a monophyletic clade with clustering into three genetic lineages and only a few cases of admixture. Females from all populations were capable of producing unfertilized eggs, but the hatching success varied hugely among populations and lineages (3-100%). Parthenogenetically produced offspring were homozygous, showing that parthenogenesis causes a complete loss of heterozygosity in a single generation. After producing eggs as virgins, females were mated to assess the capacity to also reproduce sexually, and fertilization increased the hatching success of eggs in two lineages. In one lineage, in which the hatching success of unfertilized eggs is similar to that of other sexually reproducing Timema species, fertilization reduced egg-hatching success, indicating a trade-off between reproductive modes with parthenogenetic reproduction performing best. Approximately 58% of the offspring produced after mating were fertilized, demonstrating the capacity of females to reproduce parthenogenetically also after mating has occurred, however with huge variation among individuals.

This wonderful and meticulously performed study produces strong and complementary evidence for facultative parthenogenesis in T. douglasi populations. The study shows large variation in how reproductive mode is employed, supporting the existence of a trade-off between sexual and parthenogenetic reproduction. This might be an example of an ongoing transition from sexual to asexual reproduction, which indicates that obligate parthenogenesis may derive via transient facultative parthenogenesis. 

REFERENCES

Bell, G. (1982) The Masterpiece of Nature: The Evolution and Genetics of Sexuality. University of California Press. 635 p.

Otto, S. P., & Lenormand, T. (2002). Resolving the paradox of sex and recombination. Nature Reviews Genetics, 3(4), 252-261. https://doi.org/10.1038/nrg761

Schwander, T., & Crespi, B. J. (2009). Multiple direct transitions from sexual reproduction to apomictic parthenogenesis in Timema stick insects. Evolution, 63(1), 84-103. 
https://doi.org/10.1111/j.1558-5646.2008.00524.x

Simon, J.-C., Delmotte, F., Rispe, C., & Crease, T. (2003). Phylogenetic relationships between parthenogens and their sexual relatives: the possible routes to parthenogenesis in animals. Biological Journal of the Linnean Society, 79(1), 151-163. https://doi.org/10.1046/j.1095-8312.2003.00175.x

Larose, C., Lavanchy,  G., Freitas, S., Parker, D.J., Schwander, T. (2023) Facultative parthenogenesis: a transient state in transitions between sex and obligate asexuality in stick insects? bioRxiv, 2022.03.25.485836, ver. 4 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2022.03.25.485836

Population geneticists frequently use the genetic and genotypic information of a population sample of individuals to make inferences on the reproductive system of a species. The detection of clones, i.e. individuals with the same genotype, can give information on whether there is clonal (vegetative) reproduction in the species. If clonality is detected, population geneticists typically use genotypic richness R, the number of distinct genotypes relative to the sample size, to estimate the rate of clonality c, which can be defined as the proportion of reproductive events that are clonal. Estimating the rate of clonality based on genotypic richness is however problematic because, to date, there is no analytical, nor simulation-based, characterization of this relationship. Furthermore, the effect of sampling on this relationship has never been critically examined.
The paper by Stoeckel, Porro and Arnaud-Haond [1] contributes significantly to the characterization of the relationship between rate of clonality and genetic and genotypic parameters in a population. The authors use an extensive individual-based simulation approach to assess the effects of rate of clonality (fully sexual, fully clonal and a range of intermediate levels of clonality, i.e., partial clonality) on genetic and genotypic parameters, considering variable population size, sample size, and numbers of generations elapsed since population initiation. Based on their simulations, they derive empirical formulae that link for the first time the rate of clonality to the genotypic richness and to the size distribution of clones (genotypic parameters), as well as to the population inbreeding coefficient and to a metric of linkage disequilibrium (genetic parameters). They then use the simulated data to assess the accuracy of their predictions. In a second phase, the authors use a Bayesian supervised learning algorithm to estimate rates of clonality from the simulated data.
The authors show that the relationship between rate of clonality and genotypic richness is not linear: genotypic richness decreases slowly with increasing clonality, a large drop in genotypic richness is only seen for rates of clonality ≥ 0.90. Genetic parameters are only sensitive to high rates of clonality. The practical implications of these results are that genotypic and genetic parameters can complement each other for the estimation of rates of clonality, with genotypic parameters most useful throughout most of the range of clonality values and with genetic parameters complementing them meaningfully at higher values. The most meaningful practical result of the paper is the demonstration of sampling bias on the estimation of genotypic richness. Commonly used population sample sizes in population genetics studies (n ≤ 50) lead to great overestimation of genotypic richness, which consequently leads to a severe underestimation of the rate of clonality in most systems, irrespectively of whether they have reached stationary equilibrium. Only in small populations, these effects are attenuated.
Biologists interested in the estimation of the rate of clonality will find this paper highly useful to design their sampling, and to choose their statistics for inference in a meaningful way. This paper also calls for a careful reappraisal of previously published works that infer rates of clonality from genetic data, and highlights the prime importance of complementary information on species life history data for a correct understanding of partial clonality.

References

[1] Stoeckel, S., Porro, B., and Arnaud-Haond, S. (2019). The discernible and hidden effects of clonality on the genotypic and genetic states of populations: improving our estimation of clonal rates. ArXiv:1902.09365 [q-Bio] v4 peer-reviewed and recommended by Peer Community in Evolutionary Biology. Retrieved from http://arxiv.org/abs/1902.09365v4

The human genome not only encodes for biological functions and for what makes us human, it also encodes the population history of our ancestors. Changes in past population sizes, for example, affect the distribution of times to the most recent common ancestor (tMRCA) of genomic segments, which in turn can be inferred by sophisticated modelling along the genome.
A key framework for such modelling of local tMRCA tracts along genomes is the Sequentially Markovian Coalescent (SMC) (McVean and Cardin 2005, Marjoram and Wall 2006) . The problem that the SMC solves is that the mosaic of local tMRCAs along the genome is unknown, both in their actual ages and in their positions along the genome. The SMC allows to effectively sum across all possibilities and handle the uncertainty probabilistically. Several important tools for inferring the demographic history of a population have been developed built on top of the SMC, including PSMC (Li and Durbin 2011), diCal (Sheehan et al 2013), MSMC (Schiffels and Durbin 2014), SMC++ (Terhorst et al 2017), eSMC (Sellinger et al. 2020) and others.
In this paper, Sellinger, Abu Awad and Tellier (2020) review these SMC-based methods and provide a coherent simulation design to comparatively assess their strengths and weaknesses in a variety of demographic scenarios (Sellinger, Abu Awad and Tellier 2020). In addition, they used these simulations to test how breaking various key assumptions in SMC methods affects estimates, such as constant recombination rates, or absence of false positive SNP calls.
As a result of this assessment, the authors not only provide practical guidance for researchers who want to use these methods, but also insights into how these methods work. For example, the paper carefully separates sources of error in these methods by observing what they call “Best-case convergence” of each method if the data behaves perfectly and separating that from how the method applies with actual data. This approach provides a deeper insight into the methods than what we could learn from application to genomic data alone.
In the age of genomics, computational tools and their development are key for researchers in this field. All the more important is it to provide the community with overviews, reviews and independent assessments of such tools. This is particularly important as sometimes the development of new methods lacks primary visibility due to relevant testing material being pushed to Supplementary Sections in papers due to space constraints. As SMC-based methods have become so widely used tools in genomics, I think the detailed assessment by Sellinger et al. (2020) is timely and relevant.
In conclusion, I recommend this paper because it bridges from a mere review of the different methods to an in-depth assessment of performance, thereby addressing both beginners in the field who just seek an initial overview, as well as experienced researchers who are interested in theoretical boundaries and assumptions of the different methods.

References

[1] Li, H., and Durbin, R. (2011). Inference of human population history from individual whole-genome sequences. Nature, 475(7357), 493-496. doi: https://doi.org/10.1038/nature10231
[2] Marjoram, P., and Wall, J. D. (2006). Fast"" coalescent"" simulation. BMC genetics, 7(1), 16. doi: https://doi.org/10.1186/1471-2156-7-16
[3] McVean, G. A., and Cardin, N. J. (2005). Approximating the coalescent with recombination. Philosophical Transactions of the Royal Society B: Biological Sciences, 360(1459), 1387-1393. doi: https://doi.org/10.1098/rstb.2005.1673
[4] Schiffels, S., and Durbin, R. (2014). Inferring human population size and separation history from multiple genome sequences. Nature genetics, 46(8), 919-925. doi: https://doi.org/10.1038/ng.3015
[5] Sellinger, T. P. P., Awad, D. A., Moest, M., and Tellier, A. (2020). Inference of past demography, dormancy and self-fertilization rates from whole genome sequence data. PLoS Genetics, 16(4), e1008698. doi: https://doi.org/10.1371/journal.pgen.1008698
[6] Sellinger, T. P. P., Awad, D. A. and Tellier, A. (2020) Limits and Convergence properties of the Sequentially Markovian Coalescent. bioRxiv, 2020.07.23.217091, ver. 3 peer-reviewed and recommended by PCI Evolutionary Biology. doi: https://doi.org/10.1101/2020.07.23.217091
[7] Sheehan, S., Harris, K., and Song, Y. S. (2013). Estimating variable effective population sizes from multiple genomes: a sequentially Markov conditional sampling distribution approach. Genetics, 194(3), 647-662. doi: https://doi.org/10.1534/genetics.112.149096
[8] Terhorst, J., Kamm, J. A., and Song, Y. S. (2017). Robust and scalable inference of population history from hundreds of unphased whole genomes. Nature genetics, 49(2), 303-309. doi: https://doi.org/10.1038/ng.3748

Each individual multicellular organism, each of our bodies, is a small universe. Every living surface -skin, cuticle, bark, mucosa- is the home place to milliards of bacteria, fungi and viruses. They constitute our microbiota. Some of them are essential for certain organisms. Other could not live without their hosts. For many species, the relationship between host and microbiota is so close that their histories are inseparable. The recognition of this biological inextricability has led to the notion of holobiont as the organism ensemble of host and microbiota. When individuals of a particular animal or plant species expand their geographical range, it is the holobiont that expands. And these processes of migration, expansion and colonization are often accompanied by evolutionary and ecological innovations in the interspecies relationships, at the macroscopic level (e.g. novel predator-prey or host-parasite interactions) and at the microscopic level (e.g. changes in the microbiota composition). From the human point of view, these novel interactions can be economically disastrous if they involve and threaten important crop or cattle species. And this is especially worrying in the present context of genetic standardization and intensification for mass-production on the one hand, and of climate change on the other.

With this perspective, the international team led by Amandine Cornille presents a study aiming at understanding the evolutionary history of the rosy apple aphid Dysaphis plantaginea Passerini, a major pest of the cultivated apple tree Malus domestica Borkh (1). The apple tree was probably domesticated in Central Asia, and later disseminated by humans over the world in different waves, and it was probably introduced in Europe by the Greeks. It is however unclear when and where D. plantaginea started parasitizing the cultivated apple tree. The ancestral D. plantaginea could have already infected the wild ancestor of current cultivated apple trees, but the aphid is not common in Central Asia. Alternatively, it may have gained access only later to the plant, possibly via a host jump, from Pyrus to Malus that may have occurred in Asia Minor or in the Caucasus. In the present preprint, Olvera-Vázquez and coworkers have analysed over 650 D. plantaginea colonies from 52 orchards in 13 countries, in Western, Central and Eastern Europe as well as in Morocco and the USA. The authors have analysed the genetic diversity in the sampled aphids, and have characterized as well the composition of the associated endosymbiont bacteria. The analyses detect substantial recent admixture, but allow to identify aphid subpopulations slightly but significantly differentiated and isolated by distance, especially those in Morocco and the USA, as well as to determine the presence of significant gene flow. This process of colonization associated to gene flow is most likely indirectly driven by human interactions. Very interestingly, the data show that this genetic diversity in the aphids is not reflected by a corresponding diversity in the associated microbiota, largely dominated by a few Buchnera aphidicola variants. In order to determine polarity in the evolutionary history of the aphid-tree association, the authors have applied approximate Bayesian computing and machine learning approaches. Albeit promising, the results are not sufficiently robust to assess directionality nor to confidently assess the origin of the crop pest. Despite the large effort here communicated, the authors point to the lack of sufficient data (in terms of aphid isolates), especially originating from Central Asia. Such increased sampling will need to be implemented in the future in order to elucidate not only the origin and the demographic history of the interaction between the cultivated apple tree and the rosy apple aphid. This knowledge is needed to understand how this crop pest struggles with the different seasonal and geographical selection pressures while maintaining high genetic diversity, conspicuous gene flow, differentiated populations and low endosymbiontic diversity.

References

1. Olvera-Vazquez SG, Remoué C, Venon A, Rousselet A, Grandcolas O, Azrine M, Momont L, Galan M, Benoit L, David GM, Alhmedi A, Beliën T, Alins G, Franck P, Haddioui A, Jacobsen SK, Andreev R, Simon S, Sigsgaard L, Guibert E, Tournant L, Gazel F, Mody K, Khachtib Y, Roman A, Ursu TM, Zakharov IA, Belcram H, Harry M, Roth M, Simon JC, Oram S, Ricard JM, Agnello A, Beers EH, Engelman J, Balti I, Salhi-Hannachi A, Zhang H, Tu H, Mottet C, Barrès B, Degrave A, Razmjou J, Giraud T, Falque M, Dapena E, Miñarro M, Jardillier L, Deschamps P, Jousselin E, Cornille A (2021) Large-scale geographic survey provides insights into the colonization history of a major aphid pest on its cultivated apple host in Europe, North America and North Africa. bioRxiv, 2020.12.11.421644, ver. 3 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2020.12.11.421644