150 years after Darwin published ‘Descent of man and selection in relation to sex’ (Darwin, 1871), the evolutionary mechanism that he laid out in his treatise continues to fascinate us. Sexual selection is responsible for some of the most spectacular traits among animals, and plants, and it appeals to our interest in all things reproductive and sexual (Bell, 1982). In addition, sexual selection poses some of the more intractable problems in evolutionary biology: Its realm encompasses traits that are subject to markedly different selection pressures, particularly when distinct, yet associated, traits tend to be associated with males, e.g. courtship signals, and with females, e.g. preferences (cf. Ah-King & Ahnesjo, 2013). While separate, such traits cannot evolve independently of each other (Arnqvist & Rowe, 2005), and complex feedback loops and correlations between them are predicted (Greenfield et al., 2014). Traditionally, sexual selection has been modelled under simplifying assumptions, and quantitative genetic approaches that avoided evolutionary dynamics have prevailed. New computing methods may be able to free the field from these constraints, and a trio of theoreticians (Chevalier, De Coligny & Labonne 2020) describe here a novel application of a ‘demo-genetic agent (or individual) based model’, a mouthful hereafter termed DG-ABM, for arriving at a holistic picture of the sexual selection trajectory. The application is built on the premise that traits, e.g. courtship, preference, gamete investment, competitiveness for mates, can influence the genetic architecture, e.g. correlations, of those traits. In turn, the genetic architecture can influence the expression and evolvability of the traits. Much of this influence occurs via demographic features, i.e. social environment, generated by behavioral interactions during sexual advertisement, courtship, mate guarding, parental care, post-mating dispersal, etc.
The authors provide a lengthy verbal description of their model, specifying the genomic and behavioral parameters that can be set and how a ‘run’ may be initialized. There is a link to an internet site where users can then enter their own parameter values and begin exploring hypotheses. Back in the article several simulations illustrate simple tests; e.g. how gamete investment and preference jointly evolve given certain survival costs. One obvious test would have been the preference – courtship genetic correlation that represents the core of Fisherian runaway selection, and it is regrettable that it was not examined under a range of demographic parameters. As presented the author’s DG-ABM appears particularly geared toward mating systems in ‘higher’ vertebrates, where couples form during a discrete mating season and are responsible for most reproduction. It is not clear how applicable the model could be to a full range of mating systems and nuances, including those in arthropods and other invertebrates as well as plants.
What is the likely value of the DG-ABM for sexual selection researchers? We will not be able to evaluate its potential impact until readers with specialized understanding of a question and taxon begin exploring and comparing their results with prior expectations. Of course, lack of congruence with earlier predictions would not invalidate the model. Hopefully, some of these specialists will have opportunities for comparing results with pertinent empirical data.

References

Ah-King, M. and Ahnesjo, I. 2013. The ‘sex role’ concept: An overview and evaluation Evolutionary Biology, 40, 461-470. doi: https://doi.org/10.1007/s11692-013-9226-7
Arnqvist, G. and Rowe, L. 2005. Sexual Conflict. Princeton University Press, Princeton. doi: https://doi.org/10.1515/9781400850600
Bell, G. 1982. The Masterpiece of Nature: The Evolution and Genetics of Sexuality. University of California Press, Berkeley.
Chevalier, L., De Coligny, F. and Labonne, J. (2020) A demogenetic individual based model for the evolution of traits and genome architecture under sexual selection. bioRxiv, 2020.04.01.014514, ver. 4 peer-reviewed and recommended by PCI Evol Biol. doi: https://doi.org/10.1101/2020.04.01.014514
Darwin, C. 1871. The Descent of Man and Selection in Relation to Sex. J. Murray, London.
Greenfield, M.D., Alem, S., Limousin, D. and Bailey, N.W. 2014. The dilemma of Fisherian sexual selection: Mate choice for indirect benefits despite rarity and overall weakness of trait-preference genetic correlation. Evolution, 68, 3524-3536. doi: https://doi.org/10.1111/evo.12542

Whether speciation through ecological specialization occurs has been a thriving research area ever since Mayr (1942) stated this to play a central role. In herbivorous insects, ecological specialization is most likely to happen through host plant differentiation (Funk et al. 2002). Therefore, after Dorothy Pashley had identified two host strains in the Fall armyworm (FAW), Spodoptera frugiperda, in 1988 (Pashley 1988), researchers have been trying to decipher the evolutionary history of these strains, as this seems to be a model species in which speciation is currently occurring through host plant specialization. Even though FAW is a generalist, feeding on many different host plant species (Pogue 2002) and a devastating pest in many crops, Pashley identified a so-called corn strain and a so-called rice strain in Puerto Rico. Genetically, these strains were found to differ mostly in an esterase, although later studies showed additional genetic differences and markers, mostly in the mitochondrial COI and the nuclear TPI. Recent genomic studies showed that the two strains are overall so genetically different (2% of their genome being different) that these two strains could better be called different species (Kergoat et al. 2012). So far, the most consistent differences between the strains have been their timing of mating activities at night (Schoefl et al. 2009, 2011; Haenniger et al. 2019) and hybrid incompatibilities (Dumas et al. 2015; Kost et al. 2016). Whether and to what extent host plant preference or performance contributed to the differentiation of these sympatrically occurring strains has remained unclear.
In the current study, Orsucci et al. (2020) performed oviposition assays and reciprocal transplant experiments with both strains to measure fitness effects, in combination with a comprehensive RNAseq experiment, in which not only lab reared larvae were analysed, but also field-collected larvae. When testing preference and performance on the two host plants corn and rice, the authors did not find consistent fitness differences between the two strains, with both strains performing less on rice plants, although larvae from the corn strain survived more on corn plants than those from the rice strain. These results mostly confirm findings of a number of investigations over the past 30 years, where no consistent differences on the two host plants were observed (reviewed in Groot et al. 2016). However, the RNAseq experiments did show some striking differences between the two strains, especially in the reciprocally transplanted larvae, where both strains had been reared on rice or on corn plants for one generation: both strains showed transcriptional responses that correspond to their respective putative host plants, i.e. overexpression of genes involved in digestion and metabolic activity, and underexpression of genes involved in detoxification, in the corn strain on corn and in the rice strain on rice. Interestingly, similar sets of genes were found to be overexpressed in the field-collected larvae with which a RNAseq experiment was conducted as well.
The most interesting result of the study performed by Orsucci et al. (2020) is the underexpression in the corn strain of so-called numts, small genomic sequences that corresponded to fragments of the mitochondrial COI and COIII. These two numts were differentially expressed in the two strains in all RNAseq experiments analysed. This result coincides with the fact that the COI is one of the main diagnostic markers to distinguish these two strains. The authors suggestion that a difference in energy production between these two strains may be linked to a shift in host plant preference matches their finding that rice plants seem to be less suitable host plants than corn plants. However, as the lower suitability of rice plants was true for both strains, it remains unclear whether and how this difference could be linked to possible host plant differentiation between the strains. The authors also suggest that COI and potentially other mitochondrial genes may be the original target of selection between these two strains. This is especially interesting in light of the fact that field-collected larvae have frequently been found to have a rice strain mitochondrial genotype and a corn strain nuclear genotype, also in this study, while in the lab (female rice strain x male corn strain) hybrid females (i.e. females with a rice strain mitochondrial genotype and a corn strain nuclear genotype) are behaviorally sterile (Kost et al. 2016). Whether and how selection on mitochondrial genes or on mitonuclear interactions has indeed affected the evolution of these strains in the New world, and will affect the evolution of FAW in newly invaded habitats in the Old world, including Asia and Australia – where, so far, only corn strain and (female rice strain x male corn strain) hybrids have been found (Nagoshi 2019), will be a challenging research question for the coming years.

References

[1] Dumas, P. et al. (2015). Spodoptera frugiperda (Lepidoptera: Noctuidae) host-plant variants: two host strains or two distinct species?. Genetica, 143(3), 305-316. doi: 10.1007/s10709-015-9829-2
[2] Funk, D. J., Filchak, K. E. and Feder J. L. (2002) Herbivorous insects: model systems for the comparative study of speciation ecology. In: Etges W.J., Noor M.A.F. (eds) Genetics of Mate Choice: From Sexual Selection to Sexual Isolation. Contemporary Issues in Genetics and Evolution, vol 9. Springer, Dordrecht. doi: 10.1007/978-94-010-0265-3_10
[3] Groot, A. T., Unbehend, M., Hänniger, S., Juárez, M. L., Kost, S. and Heckel D. G.(2016) Evolution of reproductive isolation of Spodoptera frugiperda. In Allison, J. and Cardé, R. (eds) Sexual communication in moths. Chapter 20: 291-300.
[4] Hänniger, S. et al. (2017). Genetic basis of allochronic differentiation in the fall armyworm. BMC evolutionary biology, 17(1), 68. doi: 10.1186/s12862-017-0911-5
[5] Kost, S., Heckel, D. G., Yoshido, A., Marec, F., and Groot, A. T. (2016). AZ‐linked sterility locus causes sexual abstinence in hybrid females and facilitates speciation in Spodoptera frugiperda. Evolution, 70(6), 1418-1427. doi: 10.1111/evo.12940
[6] Mayr, E. (1942) Systematics and the origin of species. Columbia University Press, New York.
[7] Nagoshi, R. N. (2019). Evidence that a major subpopulation of fall armyworm found in the Western Hemisphere is rare or absent in Africa, which may limit the range of crops at risk of infestation. PloS one, 14(4). doi: 10.1371/journal.pone.0208966
[8] Orsucci, M., Moné, Y., Audiot, P., Gimenez, S., Nhim, S., Naït-Saïdi, R., Frayssinet, M., Dumont, G., Boudon, J.-P., Vabre, M., Rialle, S., Koual, R., Kergoat, G. J., Nagoshi, R. N., Meagher, R. L., d’Alençon, E. and Nègre N. (2020) Transcriptional differences between the two host strains of Spodoptera frugiperda (Lepidoptera: Noctuidae). bioRxiv, 263186, ver. 2 peer-reviewed and recommended by PCI Evol Biol. doi: 10.1101/263186
[9] Pashley, D. P. (1988) Current Status of Fall Armyworm Host Strains. Florida Entomologist 71 (3): 227–34. doi: 10.2307/3495425
[10] Pogue, M. (2002). A World Revision of the Genus Spodoptera Guenée (Lepidoptera: Noctuidae). American Entomological Society.
[11] Schöfl, G., Heckel, D. G., and Groot, A. T. (2009). Time‐shifted reproductive behaviours among fall armyworm (Noctuidae: Spodoptera frugiperda) host strains: evidence for differing modes of inheritance. Journal of Evolutionary Biology, 22(7), 1447-1459. doi: 10.1111/j.1420-9101.2009.01759.x
[12] Schöfl, G., Dill, A., Heckel, D. G., and Groot, A. T. (2011). Allochronic separation versus mate choice: nonrandom patterns of mating between fall armyworm host strains. The American Naturalist, 177(4), 470-485. doi: 10.1086/658904

In this preprint, Rimbaud et al. (2023) examine whether Adult Plant Resistance (APR), where plants delay their response to pathogens, is a viable alternative when the solution to evolve complete resistance from the seedling stage exists. At first glance, delaying resistance seems like a counter-intuitive strategy, unless it can result in a weaker selection of the pathogen, and therefore slow down its adaption to plant resistance.

The approach of Rimbaud et al. is to incorporate as much of the mechanisms as possible into a model. By accounting for explicit spatio-temporal dynamics, stochasticity, and the coupling between demography and population genetics, to simulate an agricultural landscape, they reach a nuanced conclusion.

Weaker and delayed activation of genes that confer APR does indeed reduce the selection pressure acting on the pathogen, at the cost of overall less effective protection. The alternative strategy of rapid or complete activation of these genes, although it results in better results in defending against the pathogen, is at risk of being overcome because it introduces a stronger selection pressure.

One important feature of this work is that it accounts for agricultural practices. The landscape that is simulated can account for monoculture, mosaic cultures, mixed cultures, and rotations of crops (with different strategies for resistance). This introduces an interesting element to the conclusion: that human practices will have an impact on the selection pressures acting within the system.

Perhaps the most striking result is that, for the plants, it might be more beneficial to bear the cost of a wild-type pathogen that can benefit from delayed activation of resistance, and therefore exclude the more virulent strains by simply being there first, and essentially buying the plant some time before it activates its resistance more completely. When the landscape is aggregated, even wild-type pathogens can cause severe epidemics; increasing fragmentation, because it enables connectivity between patches of plants with different strategies, allows pathogens to move across cultivars, and reduces the epidemic risk on susceptible plants.

These results should encourage scaling up the perspective on APR, and indeed Rimbaud et al. adopt a landscape-scale perspective, to show that APR genes and genes conferring more complete resistance early on can have synergistic effects. This is, again, both an interesting result for evolutionary biologists, but also a useful way to prioritize different crop management strategies over large spatial scales.

References

Rimbaud, Loup, et al. Durable Resistance or Efficient Disease Control? Adult Plant Resistance (APR) at the Heart of the Dilemma. 2023. bioRxiv, ver. 2 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2022.08.30.505787

Understanding the mechanisms underlying range expansions is key for predicting species distributions in response to environmental changes (such as global warming) and managing the global expansion of invasive species (Parmesan 2006; Suarez & Tsutsui 2008). Traditionally, two types of ecological processes were studied as essential in shaping range expansion: dispersal and population growth. However, ecology and evolution are intertwined in range expansions, as phenotypic evolution of traits involved in demographic and dispersal patterns and processes can affect and be affected by ecological dynamics, representing a full eco-evolutionary loop (Williams et al. 2019; Miller et al. 2020).

Range expansions can be characterized by the type of population growth and dispersal, divided into pushed or pulled range expansions. Species that have high dispersal and high population growth at low densities present pulled range expansions (pulled by individuals from the edge populations). In contrast, populations presenting increased growth rate at intermediate densities (due to Allee effects - Allee & Bowen 1932; i.e. where growth rate decreases at lower densities) and high dispersal at high densities present pushed range expansions (driven by individuals from core and intermediate populations) (Gandhi et al. 2016). Importantly, the type of expansion is expected to have very different consequences on the genetic (and therefore) phenotypic composition of core and edge populations. Specifically, genetic variability is expected to be lower in populations experiencing pulled expansions and higher in populations involved in pushed expansions (Gandhi et al. 2016; Miller et al. 2020). However, it is not always possible to distinguish between pulled and pushed expansions, as variation in speed and shape can overlap between the two types. In addition, it is difficult to experimentally manipulate the strength of the Allee effect to create pushed versus pulled expansions. Thus, several critical predictions regarding the genetic and phenotypic composition of pulled and pushed expansions are lacking empirical tests (but see Gandhi et al. 2016).

In a previous study, Dahirel et al. (2021a) combined simulations and experimental evolution of the small wasps Trichogramma brassicae to show that low connectivity led to more pushed expansions, and higher connectivity generated more pulled expansions. In accordance with theoretical predictions, this led to reduced genetic diversity in pulled expansions, and the reverse pattern in pushed expansions. However, the question of how pulled and pushed expansions affect trait evolution remained unanswered.

In this follow-up study, Dahirel et al. (2021b) tackled this issue and linked the changes in connectivity and type of expansion with the phenotypic evolution of several traits using individuals from their previous experiment. Namely, the authors compared core and edge populations with founder strains to test how evolution in pushed vs. pulled expansions affected wasp size, short movement, fecundity, dispersal, and density dependent dispersal. When density dependence was not accounted for, phenotypic changes in edge populations did not match the expectations from changes in expansion dynamics. This could be due to genetic trade-offs between traits that limit phenotypic evolution (Urquhart & Williams 2021). 

However, when accounting for density dependent dispersal, Dahirel et al. (2021b) observed that more connected landscapes (with pulled expansions) showed positive density dispersal in core populations and negative density dispersal in edge populations, similarly to other studies (e.g. Fronhofer et al. 2017). Interestingly, in pushed (with lower connectivity) landscapes, such shift was not observed. Instead, edge populations maintained positive density dispersal even after 14 generations of expansion, whereas core populations showed higher dispersal at lower density. The authors suggest that this seemingly contradictory result is due to a combination of three processes: 1) the expansion reduced positive density dispersal in edge populations; 2) reduced connectivity directly increased dispersal costs, increasing high density dispersal; and 3) reduced connectivity indirectly caused demographic stochasticity (and reduced temporal variability in patches) leading to higher dispersal at low density in core populations. However, these results must be taken with a grain of salt, since only one of the four experimental replicates were used in the density dependent dispersal experiment. In range expansions experiments, replication is fundamental, since stochastic processes (such as gene surfing, where alleles maybe rise in frequency due by chance) are prevalent (Miller et al. 2020), and results are highly dependent on sample size, or number of replicate populations analysed. 

Having said that, results from Dahirel et al. (2021b) highlight the importance to contextualize the management of invasions and species distribution, since it is thought that pulled expansions are more prevalent in nature, but pushed expansions can be more important in scenarios where patchiness is high, such as urban landscapes. Moreover, Dahirel's et al. (2021b) study is a first step showing that accounting for trait density dependence is crucial when following phenotypic evolution during range expansion, and that evolution of density dependent traits may be constrained by landscape conditions. This highlights the need to account for both connectivity and density dependence to draw more accurate predictions on the evolutionary and ecological outcomes of range expansions. 
 
References

Allee WC, Bowen ES (1932) Studies in animal aggregations: Mass protection against colloidal silver among goldfishes. Journal of Experimental Zoology, 61, 185–207. https://doi.org/10.1002/jez.1400610202

Dahirel M, Bertin A, Calcagno V, Duraj C, Fellous S, Groussier G, Lombaert E, Mailleret L, Marchand A, Vercken E (2021a) Landscape connectivity alters the evolution of density-dependent dispersal during pushed range expansions. bioRxiv, 2021.03.03.433752, ver. 4 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2021.03.03.433752

Dahirel M, Bertin A, Haond M, Blin A, Lombaert E, Calcagno V, Fellous S, Mailleret L, Malausa T, Vercken E (2021b) Shifts from pulled to pushed range expansions caused by reduction of landscape connectivity. Oikos, 130, 708–724. https://doi.org/10.1111/oik.08278

Fronhofer EA, Gut S, Altermatt F (2017) Evolution of density-dependent movement during experimental range expansions. Journal of Evolutionary Biology, 30, 2165–2176. https://doi.org/10.1111/jeb.13182

Gandhi SR, Yurtsev EA, Korolev KS, 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, 6922–6927. https://doi.org/10.1073/pnas.1521056113

Miller TEX, Angert AL, Brown CD, Lee-Yaw JA, Lewis M, Lutscher F, Marculis NG, Melbourne BA, Shaw AK, Szűcs M, Tabares O, Usui T, Weiss-Lehman C, Williams JL (2020) Eco-evolutionary dynamics of range expansion. Ecology, 101, e03139. https://doi.org/10.1002/ecy.3139

Parmesan C (2006) Ecological and Evolutionary Responses to Recent Climate Change. Annual Review of Ecology, Evolution, and Systematics, 37, 637–669.  https://doi.org/10.1146/annurev.ecolsys.37.091305.110100

Suarez AV, Tsutsui ND (2008) The evolutionary consequences of biological invasions. Molecular Ecology, 17, 351–360. https://doi.org/10.1111/j.1365-294X.2007.03456.x

Urquhart CA, Williams JL (2021) Trait correlations and landscape fragmentation jointly alter expansion speed via evolution at the leading edge in simulated range expansions. Theoretical Ecology. https://doi.org/10.1007/s12080-021-00503-z

Williams JL, Hufbauer RA, Miller TEX (2019) How Evolution Modifies the Variability of Range Expansion. Trends in Ecology & Evolution, 34, 903–913. https://doi.org/10.1016/j.tree.2019.05.012

Most mutations affecting fitness are deleterious and they have many evolutionary consequences. The dynamics and consequences of deleterious mutations are a long-standing question in evolutionary biology and a strong theoretical background has already been developed, for example, to predict the mutation load, inbreeding depression or background selection. One of the classical results is that inbreeding helps purge partially recessive deleterious mutations by exposing them to selection in homozygotes. However, this mainly results from single-locus considerations. When interactions among several, more or less linked, deleterious mutations are taken into account, peculiar dynamics can emerge. One of them, called pseudo-overdominance (POD), corresponds to the maintenance in a population of two (or more) haplotype blocks composed of several recessive deleterious mutations in repulsion that mimics overdominance. Indeed, homozygote individuals for one of the haplotype blocks expose many deleterious mutations to selection whereas they are reciprocally masked in heterozygotes, leading to higher fitness of heterozygotes compared to both homozygotes. A related process, called associative overdominance (AOD) is the effect of such deleterious alleles in repulsion on the linked neutral variation that can be increased by AOD. Although this possibility has been recognized for a long time (Otha and Kimura 1969), it has been mainly considered an anecdotal process. Recently, both theoretical (Zhao and Charlesworth 2016) and genomic analyses (Gilbert et al. 2020) have renewed interest in such a process, suggesting that it could be important in weakly recombining regions of a genome. Donald Waller (2021) - one of the co-authors of the current work - also recently proposed that POD could be quantitatively important with broad implications, and could resolve some unexplained observations such as the maintenance of inbreeding depression in highly selfing species. Yet, a proper theoretical framework analysing the effect of inbreeding on POD was lacking.

In this theoretical work, Diala Abu Awad and Donald Waller (2022) addressed this question through an elegant combination of analytical predictions and intensive multilocus simulations. They determined the conditions under which POD can be maintained and how long it could resist erosion by recombination, which removes the negative association between deleterious alleles (repulsion) at the core of the mechanism. They showed that under tight linkage, POD regions can persist for a long time and generate substantial segregating load and inbreeding depression, even under inbreeding, so opposing (for a while) to the purging effect. They also showed that background selection can affect the genomic structure of POD regions by rapidly erasing weak POD regions but maintaining strong POD regions (i.e with many tightly linked deleterious alleles).

These results have several implications. They can explain the maintenance of inbreeding depression despite inbreeding (as anticipated by Waller 2021), which has implications for the evolution of mating systems. If POD can hardly emerge under high selfing, it can persist from an outcrossing ancestor long after the transition towards a higher selfing rate and could explain the maintenance of mixed mating systems(which is possible with true overdominance, see Uyenoyama and Waller 1991). The results also have implications for genomic analyses, pointing to regions of low or no recombination where POD could be maintained, generating both higher diversity and heterozygosity than expected and variance in fitness. As structural variations are likely widespread in genomes with possible effects on suppressing recombination (Mérot et al. 2020), POD regions should be checked more carefully in genomic analyses (see also Gilbert et al. 2020).

Overall, this work should stimulate new theoretical and empirical studies, especially to assess how quantitatively strong and widespread POD can be. It also stresses the importance of properly considering genetic linkage genome-wide, and so the role of recombination landscapes in determining patterns of diversity and fitness effects.

Awad DA, Waller D (2022) Conditions for maintaining and eroding pseudo-overdominance and its contribution to inbreeding depression. bioRxiv, 2021.12.16.473022, ver. 3 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2021.12.16.473022

Gilbert KJ, Pouyet F, Excoffier L, Peischl S (2020) Transition from Background Selection to Associative Overdominance Promotes Diversity in Regions of Low Recombination. Current Biology, 30, 101-107.e3. https://doi.org/10.1016/j.cub.2019.11.063

Mérot C, Oomen RA, Tigano A, Wellenreuther M (2020) A Roadmap for Understanding the Evolutionary Significance of Structural Genomic Variation. Trends in Ecology & Evolution, 35, 561–572. https://doi.org/10.1016/j.tree.2020.03.002

Ohta T, Kimura M (1969) Linkage disequilibrium at steady state determined by random genetic drift and recurrent mutation. Genetics, 63, 229–238. https://doi.org/10.1093/genetics/63.1.229

Uyenoyama MK, Waller DM (1991) Coevolution of self-fertilization and inbreeding depression II. Symmetric overdominance in viability. Theoretical Population Biology, 40, 47–77. https://doi.org/10.1016/0040-5809(91)90046-I

Waller DM (2021) Addressing Darwin’s dilemma: Can pseudo-overdominance explain persistent inbreeding depression and load? Evolution, 75, 779–793. https://doi.org/10.1111/evo.14189

Zhao L, Charlesworth B (2016) Resolving the Conflict Between Associative Overdominance and Background Selection. Genetics, 203, 1315–1334. https://doi.org/10.1534/genetics.116.188912