The present-day distribution of extant species is the result of the interplay between their past population demography (e.g., expansion, contraction, isolation, and migration) and adaptation to the environment. Shedding light on the timing and magnitude of key demographic events helps identify potential drivers of such events and interaction of those drivers, such as life history traits and past episodes of environmental shifts.

The understanding of the key factors driving species evolution gives important insights into how the species may respond to changing conditions, which can be particularly relevant for the management of harmful species, such as agricultural pests (e.g. [1]). Meaningful demographic inferences present major challenges. These include formulating evolutionary scenarios fitting species biology and the eco-geographical context and choosing informative molecular markers and accurate quantitative approaches to statistically compare multiple demographic scenarios and estimate the parameters of interest. A further issue comes with result interpretation. Accurately dating the inferred events is far from straightforward since reliable calibration points are necessary to translate the molecular estimates of the evolutionary time into absolute time units (i.e. years). This can be attempted in different ways, such as by using fossil and archaeological records, heterochronous samples (e.g. ancient DNA), and/or mutation rate estimated from independent data (e.g. [2], [3] for review). Nonetheless, most experimental systems rarely meet these conditions, hindering the comprehensive interpretation of results.

The contribution of Chapuis et al. [4] addresses these issues to investigate the recent history of the African insect pest Schistocerca gregaria (desert locust). They apply Approximate Bayesian Computation-Random Forest (ABC-RF) approaches to microsatellite markers. Owing to their fast mutation rate microsatellite markers offer at least two advantages: i) suitability for analyzing recently diverged populations, and ii) direct estimate of the germline mutation rate in pedigree samples. The work of Chapuis et al. [4] benefits of both these advantages, since they have estimates of mutation rate and allele size constraints derived from germline mutations in the species [5].

The main aim of the study is to infer the history of divergence of the two subspecies of the desert locust, which have spatially disjoint distribution corresponding to the dry regions of North and West-South Africa. They first use paleo-vegetation maps to formulate hypotheses about changes in species range since the last glacial maximum. Based on them, they generate 12 divergence models. For the selection of the demographic model and parameter estimation, they apply the recently developed ABC-RF approach, a powerful inferential tool that allows optimizing the use of summary statistics information content, among other advantages [6]. Some methodological novelties are also introduced in this work, such as the computation of the error associated with the posterior parameter estimates under the best scenario. The accuracy of timing estimate is assured in two ways: i) by the use of microsatellite markers with known evolutionary dynamics, as underlined above, and ii) by assessing the divergence time threshold above which posterior estimates are likely to be biased by size homoplasy and limits in allele size range [7]. The best-supported model suggests a recent divergence event of the subspecies of S. gregaria (around 2.6 kya) and a reduction of populations size in one of the subspecies (S. g. flaviventris) that colonized the southern distribution area. As such, results did not support the hypothesis that the southward colonization was driven by the expansion of African dry environments associated with the last glacial maximum, as it has been postulated for other arid-adapted species with similar African disjoint distributions [8]. The estimated time of divergence points at a much more recent origin for the two subspecies, during the late Holocene, in a period corresponding to fairly stable arid conditions similar to current ones [9,10].

Although the authors cannot exclude that their microsatellite data bear limited information on older colonization events than the last one, they bring arguments in favour of alternative explanations. The hypothesis privileged does not involve climatic drivers, but the particularly efficient dispersal behaviour of the species, whose individuals are able to fly over long distances (up to thousands of kilometers) under favourable windy conditions. A single long-distance dispersal event by a few individuals would explain the genetic signature of the bottleneck. There is a growing number of studies in phylogeography in arid regions in the Southern hemisphere, but the impact of past climate changes on the species distribution in this region remains understudied relative to the Northern hemisphere [11,12].

The study presented by Chapuis et al. [4] offers several important insights into demographic changes and the evolutionary history of an agriculturally important pest species in Africa, which could also mirror the history of other organisms in the continent. As the authors point out, there are necessarily some uncertainties associated with the models of past ecosystems and climate, especially for Africa. Interestingly, the authors argue that the information on paleo-vegetation turnover was more informative than climatic niche modeling for the purpose of their study since it made them consider a wider range of bio-geographical changes and in turn a wider range of evolutionary scenarios (see discussion in Supplementary Material). Microsatellite markers have been offering a useful tool in population genetics and phylogeography for decades, but their popularity is perhaps being taken over by single nucleotide polymorphism (SNP) genotyping and whole-genome sequencing (WGS) (the peak year of the number of the publication with “microsatellite” is in 2012 according to PubMed).

This study reaffirms the usefulness of these classic molecular markers to estimate past demographic events, especially when species- and locus-specific microsatellite mutation features are available and a powerful inferential approach is adopted. Nonetheless, there are still hurdles to overcome, such as the limitations in scenario choice associated with the simulation software used (e.g. not allowing for continuous gene flow in this particular case), which calls for further improvement of simulation tools allowing for more flexible modeling of demographic events and mutation patterns. In sum, this work not only contributes to our understanding of the makeup of the African biodiversity but also offers a useful statistical framework, which can be applied to a wide array of species and molecular markers (microsatellites, SNPs, and WGS).

References

[1] Lehmann, P. et al. (2018). Complex responses of global insect pests to climate change. bioRxiv, 425488. doi: https://dx.doi.org/10.1101/425488

[2] Donoghue, P. C., & Benton, M. J. (2007). Rocks and clocks: calibrating the Tree of Life using fossils and molecules. Trends in Ecology & Evolution, 22(8), 424-431. doi: https://dx.doi.org/10.1016/j.tree.2007.05.005

[3] Ho, S. Y., Lanfear, R., Bromham, L., Phillips, M. J., Soubrier, J., Rodrigo, A. G., & Cooper, A. (2011). Time‐dependent rates of molecular evolution. Molecular ecology, 20(15), 3087-3101. doi: https://dx.doi.org/10.1111/j.1365-294X.2011.05178.x

[4] Chapuis, M.-P., Raynal, L., Plantamp, C., Meynard, C. N., Blondin, L., Marin, J.-M. and Estoup, A. (2020). A young age of subspecific divergence in the desert locust Schistocerca gregaria, inferred by ABC Random Forest. bioRxiv, 671867, ver. 4 peer-reviewed and recommended by PCI Evolutionary Biology. doi: https://dx.doi.org/10.1101/671867

5] Chapuis, M.-P., Plantamp, C., Streiff, R., Blondin, L., & Piou, C. (2015). Microsatellite evolutionary rate and pattern in Schistocerca gregaria inferred from direct observation of germline mutations. Molecular ecology, 24(24), 6107-6119. doi: https://dx.doi.org/10.1111/mec.13465

[6] Raynal, L., Marin, J. M., Pudlo, P., Ribatet, M., Robert, C. P., & Estoup, A. (2018). ABC random forests for Bayesian parameter inference. Bioinformatics, 35(10), 1720-1728. doi: https://dx.doi.org/10.1093/bioinformatics/bty867

[7] Estoup, A., Jarne, P., & Cornuet, J. M. (2002). Homoplasy and mutation model at microsatellite loci and their consequences for population genetics analysis. Molecular ecology, 11(9), 1591-1604. doi: https://dx.doi.org/10.1046/j.1365-294X.2002.01576.x

[8] Moodley, Y. et al. (2018). Contrasting evolutionary history, anthropogenic declines and genetic contact in the northern and southern white rhinoceros (Ceratotherium simum). Proceedings of the Royal Society B, 285(1890), 20181567. doi: https://dx.doi.org/10.1098/rspb.2018.1567

[9] Kröpelin, S. et al. (2008). Climate-driven ecosystem succession in the Sahara: the past 6000 years. science, 320(5877), 765-768. doi: https://dx.doi.org/10.1126/science.1154913

[10] Maley, J. et al. (2018). Late Holocene forest contraction and fragmentation in central Africa. Quaternary Research, 89(1), 43-59. doi: https://dx.doi.org/10.1017/qua.2017.97

[11] Beheregaray, L. B. (2008). Twenty years of phylogeography: the state of the field and the challenges for the Southern Hemisphere. Molecular Ecology, 17(17), 3754-3774. doi: https://dx.doi.org/10.1111/j.1365-294X.2008.03857.x

[12] Dubey, S., & Shine, R. (2012). Are reptile and amphibian species younger in the Northern Hemisphere than in the Southern Hemisphere?. Journal of evolutionary biology, 25(1), 220-226. doi: https://dx.doi.org/10.1111/j.1420-9101.2011.02417.x

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A video about this preprint is available here:

Adaptations to a new ecological niche allow species to access new resources and circumvent competitors and are hence obvious pathways of evolutionary success. The evolution of agricultural pest species represents an important case to study how a species adapts, on various timescales, to a novel ecological niche. Among the numerous insects that are agricultural pests, the ability to lay eggs (or oviposit) in ripe fruit appears to be a recurrent scenario. Fruit flies (family Tephritidae) employ this strategy, and include amongst their members some of the most destructive pests (e.g., the olive fruit fly Bactrocera olea or the medfly Ceratitis capitata). In their ms, Karageorgi et al. [1] studied how Drosophila suzukii, a new major agricultural pest species that recently invaded Europe and North America, evolved the novel behavior of laying eggs into undamaged fresh fruit. The close relatives of D. suzukii lay their eggs on decaying plant substrates, and thus this represents a marked change in host use that links to substantial economic losses to the fruit industry. Although a handful of studies have identified genetic changes causing new behaviors in various species, the question of the evolution of behavior remains a largely uncharted territory. The study by Karageorgi et al. [1] represents an original and most welcome contribution in this domain for a non-model species. Using clever behavioral experiments to compare D. suzukii to several related Drosophila species, and complementing those results with neurogenetics and mutant analyses using D. suzukii, the authors nicely dissect the sensory changes at the origin of the new egg-laying behavior. The experiments they describe are easy to follow, richly illustrate through figures and images, and particularly well designed to progressively decipher the sensory bases driving oviposition of D. suzukii on ripe fruit. Altogether, Karageorgi et al.’s [1] results show that the egg-laying substrate preference of D. suzukii has considerably evolved in concert with its morphology (especially its enlarged, serrated ovipositor that enables females to pierce the skin of many ripe fruits). Their observations clearly support the view that the evolution of traits that make D. suzukii an agricultural pest included the modification of several sensory systems (i.e. mechanosensation, gustation and olfaction). These differences between D. suzukii and its close relatives collectively underlie a radical change in oviposition behavior, and were presumably instrumental in the expansion of the ecological niche of the species. The authors tentatively propose a multi-step evolutionary scenario from their results with the emergence of D. suzukii as a pest species as final outcome. Such formalization represents an interesting evolutionary model-framework that obviously would rely upon further data and experiments to confirm and refine some of the evolutionary steps proposed, especially the final and recent transition of D. suzukii from non-invasive to invasive species.

References

[1] Karageorgi M, Bräcker LB, Lebreton S, Minervino C, Cavey M, Siju KP, Grunwald Kadow IC, Gompel N, Prud’homme B. 2017. Evolution of multiple sensory systems drives novel egg-laying behavior in the fruit pest Drosophila suzukii. Current Biology, 27: 1-7. doi: 10.1016/j.cub.2017.01.055

The butterfly species Heliconius numata has a remarkable wing pattern polymorphism, with multiple pattern morphs all controlled by a single genetic locus, which harbours multiple inversions. Each morph is a near-perfect mimic of a species in the fairly distantly related genus of butterflies, Melinaea.

The article by Rodríguez de Cara et al (2023) argues that the balanced polymorphism at this single wing patterning locus actually has a major effect on genetic diversity across the whole genome. First, polymorphic populations within H. numata are more dioverse than those without polymorphism. Second, H. numata is more genetically diverse than other related species and finally reconstruction of historical demography suggests that there has been a recent increase in effective population size, putatively associated with the acquisition of the supergene polymorphism. The supergene itself generates disassortative mating, such that morphs prefer to mate with others dissimilar to themselves - in this way it is similar to mechanisms for preventing inbreeding such as self-incompatibility loci in plants. This provides a potential mechanism whereby non-random mating patterns could increase effective population size. The authors also explore this mechanism using forward simulations, and show that mating patterns at a single locus can influence linked genetic diversity over a large scale.

Overall, this is an intriguing study, which suggests a far more widespread genetic impact of a single locus than might be expected. There are interesting parallels with mechanisms of inbreeding prevention in plants, such as the Pin/Thrum polymorphism in Primula, which also rely on mating patterns determined by a single locus but presumably also influence genetic diversity genome-wide by promoting outbreeding.

REFERENCES

Rodríguez de Cara MÁ, Jay P, Rougemont Q, Chouteau M, Whibley A, Huber B, Piron-Prunier F, Ramos RR, Freitas AVL, Salazar C, Silva-Brandão KL, Torres TT, Joron M (2023) Balancing selection at a wing pattern locus is associated with major shifts in genome-wide patterns of diversity and gene flow. bioRxiv, 2021.09.29.462348, ver. 3 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2021.09.29.462348

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

It is my pleasure to recommend the paper by Raynal et al. [1] about using random forest for parameter inference. There are two reviews about the paper, one review written by Dennis Prangle and another review written by myself. Both reviews were positive and included comments that have been addressed in the current version of the preprint.

The paper nicely shows that modern machine learning approaches are useful for Approximate Bayesian Computation (ABC) and more generally for simulation-driven parameter inference in ecology and evolution.

The authors propose to consider the random forest approach, proposed by Meinshausen [2] to perform quantile regression. The numerical implementation of ABC with random forest, available in the abcrf package, is based on the RANGER R package that provides a fast implementation of random forest for high-dimensional data.

According to my reading of the manuscript, there are 3 main advantages when using random forest (RF) for parameter inference with ABC. The first advantage is that RF can handle many summary statistics and that dimension reduction is not needed when using RF.

The second advantage is very nicely displayed in Figure 5, which shows the main result of the paper. If correct, 95% posterior credibility intervals (C.I.) should contain 95% of the parameter values used in simulations. Figure 5 shows that posterior C.I. obtained with rejection are too large compared to other methods. By contrast, C.I. obtained with regression methods have been shrunken. However, the shrinkage can be excessive for the smallest tolerance rates, with coverage values that can be equal to 85% instead of the expected 95% value. The attractive property of RF is that C.I. have been shrunken but the coverage is of 100% resulting in a conservative decision about parameter values.

The last advantage is that no hyperparameter should be chosen. It is a parameter free approach, which is desirable because of the potential difficulty of choosing an appropriate acceptance rate.

The main drawback of the proposed approach concerns joint parameter inference. There are many settings where the joint parameter distribution is of interest and the proposed RF approach cannot handle that. In population genetics for example, estimation of the severity and of the duration of the bottleneck should be estimated jointly because of identifiability issues. The challenge of performing joint parameter inference with RF might constitute a useful research perspective.
 

References
 

[1] Raynal L, Marin J-M, Pudlo P, Ribatet M, Robert CP, Estoup A. 2017. ABC random forests for Bayesian parameter inference. arXiv 1605.05537v4, https://arxiv.org/pdf/1605.05537
[2] Meinshausen N. 2006. Quantile regression forests. Journal of Machine Learning Research 7: 983-999. http://www.jmlr.org/papers/v7/meinshausen06a.html