The total amount of DNA utilized to store hereditary information varies immensely among eukaryotic organisms. Single copy genome sizes – disregarding differences due to ploidy - differ by more than three orders of magnitude ranging from a few million nucleotides (Mb) to hundreds of billions (Gb). With the ever-increasing availability of fully sequenced genomes we now know that most of the difference is due either to whole genome duplication or to variation in the abundance of repetitive elements. Regarding repetitive elements, the evolutionary forces underlying the large variation 'allowing' more or less elements in a genome remain largely elusive. A tentative correlation between an organism's complexity (however this may be adequately measured) and genome size, the so called C-value paradox [1], has long been dismissed. Studies testing for selection on secondary phenotypic effects associated with genome size (cell size, metabolic rates, nutrient availability) have yielded mixed results. Nonadaptive theories capitalizing on a role of deleterious insertion-deletion mutations and genetic drift as the main drivers have likewise received mixed support [2-3]. Overall, most evidence was derived from analyses across broad taxonomical scales [4-6].

Lefébure and colleagues [7] take a different approach. They confine their considerations to a homogeneous, restricted taxonomical group, isopod crustaceans of the superfamily Aselloidea. This taxonomic focus allows the authors to circumvent many of the confounding factors such as phylogenetic inertia, life history divergence and mutation rate variation that tend to trouble analyses across broad taxonomic timescales. Another important feature of the chosen system is the evolutionary independent transition of habitat use that has occurred at least 11 times. One group of species inhabits subterranean ecosystems (groundwater), another group thrives on surface water. Populations of the former live in low-energy habitats and are expected to be outnumbered by their surface dwelling relatives. Interestingly – and a precondition for the study - the groundwater species have significantly larger genomes (up to 137%). With this unique set-up, the authors are able to investigate the link between genome size and evolutionary forces related to a proxy of long-term population size by removing many of the confounding factors a priori.

Upfront, we learn that the dN/dS ratio is higher in the groundwater species. This may either suggest prevalent positive selection or lower efficacy of purifying selection (relaxed constraint) in the group of species in which population sizes are expected to be low. Using a series of population genetic analyses the authors provide compelling evidence for the latter. Analyses are carefully conducted and include models for estimating the intensity and frequency of purifying and positive selection, the DoS (direction of selection) and α statistic. Next the authors also exclude the possibility that increased dN/dS of the subterranean groundwater species may be due to nonfunctionalization, which may result from the subterranean lifestyle.

Overall, these analyses suggest relaxed constraint in smaller populations as the most plausible alternative to explain increased dN/dS ratios. In addition to the efficacy of selection, the authors estimate the timing of the ecological transition under the rationale that the amount of time a species may have been exposed to the subterranean habitat may reflect long term population sizes. To calibrate the 'colonization clock' they apply a neat trick based on the degree of degeneration of the opsin gene (as vision tends to get lost in these habitats). When finally testing which parameters may explain differences in genome size all factors – ecological status, selection efficiency as measured by dN/dS and colonization time - turned out to be significant predictors. Direct estimates of the short term effective population size Ne from polymorphism data, however, did not correlate with genome size. Ruling out the effect of other co-variates such as body size and growth rate the authors conclude that genome size was overall best predicted by long-term population size change upon habitat shift. In that the authors provide convincing evidence that the increase in genome size is linked to a decrease in long-term reduction of selection efficiency of subterranean species. Assuming a bias for insertion mutations over deletion mutations (which is usually the case in eukaryotes) this result is in agreement with the theory of mutational hazard [4-6]. This theory proposed by Michael Lynch postulates that the accumulation of non-functional DNA has a weak deleterious effect that can only be efficiently opposed by natural selection in species with high Ne.

In conclusion, Lefébure and colleagues provide novel and welcome evidence supporting a 'neutralist' hypothesis of genome size evolution without the need to invoke an adaptive component. Methodologically, the study cautions against the common use of polymorphism-based estimates of Ne which are often obfuscated by transitory demographic change. Instead, alternative measures of selection efficacy linked to long-term population size may serve as better predictors of genome size. We hope that this study will stimulate additional work testing the link between Ne and genome size variation in other taxonomical groups [8-9]. Using genome sequences instead of the transcriptome approach applied here may concomitantly further our understanding of the molecular mechanisms underlying genome size change.

References

[1] Thomas, CA Jr. 1971. The genetic organization of chromosomes. Annual Review of Genetics 5: 237–256. doi: 10.1146/annurev.ge.05.120171.001321

[2] Ågren JA, Greiner S, Johnson MTJ, Wright SI. 2015. No evidence that sex and transposable elements drive genome size variation in evening primroses. Evolution 69: 1053–1062. doi: 10.1111/evo.12627

[3] Bast J, Schaefer I, Schwander T, Maraun M, Scheu S, Kraaijeveld K. 2016. No accumulation of transposable elements in asexual arthropods. Molecular Biology and Evolution 33: 697–706. doi: 10.1093/molbev/msv261

[4] Lynch M. 2007. The Origins of Genome Architecture. Sinauer Associates.

[5] Lynch M, Bobay LM, Catania F, Gout JF, Rho M. 2011. The repatterning of eukaryotic genomes by random genetic drift. Annual Review of Genomics and Human Genetics 12: 347–366. doi: 10.1146/annurev-genom-082410-101412

[6] Lynch M, Conery JS. 2003. The origins of genome complexity. Science 302: 1401–1404. doi: 10.1126/science.1089370

[7] Lefébure T, Morvan C, Malard F, François C, Konecny-Dupré L, Guéguen L, Weiss-Gayet M, Seguin-Orlando A, Ermini L, Der Sarkissian C, Charrier NP, Eme D, Mermillod-Blondin F, Duret L, Vieira C, Orlando L, and Douady CJ. 2017. Less effective selection leads to larger genomes. Genome Research 27: 1016-1028. doi: 10.1101/gr.212589.116

[8] Lower SS, Johnston JS, Stanger-Hall KF, Hjelmen CE, Hanrahan SJ, Korunes K, Hall D. 2017. Genome size in North American fireflies: Substantial variation likely driven by neutral processes. Genome Biolology and Evolution 9: 1499–1512. doi: 10.1093/gbe/evx097

[9] Sessegolo C, Burlet N, Haudry A. 2016. Strong phylogenetic inertia on genome size and transposable element content among 26 species of flies. Biology Letters 12: 20160407. doi: 10.1098/rsbl.2016.0407

It is easy to define phenotypic plasticity as a mechanism by which traits change in response to a modification of the environment. Many complex mechanisms are nevertheless involved with plastic responses, their strength, and stability (e.g., reliability of cues, type of exposure, genetic expression, epigenetics). It is rather intuitive to think that environmental cues perceived at different stages of development will logically drive different phenotypic responses (Fawcett and Frankenhuis 2015). However, it has proven challenging to try and explain, or model how and why different effects are caused by similar cues experienced at different developmental or life stages (Walasek et al. 2022). The impact of these ‘sensitive windows’ on the stability of plastic responses within or across generations remains unclear. In their paper entitled “Sensitive windows for within- and trans-generational plasticity of anti-predator defences”, Tariel-Adam (2023) address this question.

In this paper, Tariel et al. acknowledge the current state of the art, i.e., that some traits influenced by the environment at early life stages become fixed later in life (Snell-Rood et al. 2015) and that sensitive windows are therefore more likely to be observed during early stages of development. Constructive exchanges with the reviewers illustrated that Tariel et al. presented a clear picture of the knowledge on sensitive windows from a conceptual and a mechanistic perspective, thereby providing their study with a strong and elegant rationale. Tariel et al. outlined that little is known about the significance of this scenario when it comes to transgenerational plasticity. Theory predicts that exposure late in the life of parents should be more likely to drive transgenerational plasticity because the cue perceived by parents is more likely to be reliable if time between parental exposure and offspring expression is short (McNamara et al. 2016). I would argue that although sensible, this scenario is likely oversimplifying the complexity of evolutionary, ecological, and inheritance mechanisms at play (Danchin et al. 2018). Tariel-Adam et al. (2023) point out in their paper how the absence of experimental results limits our understanding of the evolutionary and adaptive significance of transgenerational plasticity and decided to address this broad question.

Tariel-Adam et al. (2023) used the context of predator-prey interactions, which is a powerful framework to evaluate the temporality of predator cues and prey responses within and across generations (Sentis et al. 2018). They conducted a very elegant experiment whereby two generations of freshwater snails Physa acuta were exposed to crayfish predator cues at different developmental windows. They triggered the within-generation phenotypic plastic response of inducible defences (e.g., shell thickness) and identified sensitive windows as to evaluate their role in within-generation phenotypic plasticity versus transgenerational plasticity. They used different linear models, which lead to constructive exchanges with reviewers, and between reviewers, well trained on these approaches, in particular on effect sizes, that improved the paper by pushing the discussion all the way towards a consensus. 

Tariel-Adam et al. (2023) results showed that the phenotypic plastic response of different traits was associated with different sensitive windows. Although early-life development was confirmed to be a sensitive window, it was far from being the only developmental stage driving within-generation plastic responses of defence traits. This finding contributes to change our views on plasticity because where theoretical models predict early- and late-life sensitive windows, empirical results gathered here present a more continuous opportunity for sensitive windows over the lifetime of freshwater snails. This is likely because multifactorial mechanisms drive the reliability and adaptive significance of predator cues. To me, this paper most original contribution lies probably in the empirical investigation of sensitive windows underlying transgenerational plasticity. Their finding implies mechanistic ties between sensitive windows driving within-generation and transgenerational plasticity for some traits, but they also shed light on the possible independence of these processes. Although one may be disheartened by these findings illustrating the ability of nature to combine complex mechanisms in order to produce somewhat unpredictable scenarios, one can only find that this unlimited range of phenotypic plasticity scenarios is a wonder to investigate because much remains to be understood. As mentioned in the conclusion of the paper, the opportunity for sensitive windows to drive such a range of plastic responses may also be an opportunity for organisms to adapt to a wide range of environmental demands. 

References

Danchin E, A Pocheville, O Rey, B Pujol, and S Blanchet (2019). Epigenetically facilitated mutational assimilation: epigenetics as a hub within the inclusive evolutionary synthesis. Biological Reviews, 94: 259-282. https://doi.org/10.1111/brv.12453

Fawcett TW, and WE Frankenhuis (2015). Adaptive Explanations for Sensitive Windows in Development. Frontiers in Zoology 12, S3. https://doi.org/10.1186/1742-9994-12-S1-S3 

McNamara JM, SRX Dall, P Hammerstein, and O Leimar (2016). Detection vs. Selection: Integration of Genetic, Epigenetic and Environmental Cues in Fluctuating Environments. Ecology Letters 19, 1267–1276. https://doi.org/10.1111/ele.12663

Sentis A, R Bertram, N Dardenne, et al. (2018). Evolution without standing genetic variation: change in transgenerational plastic response under persistent predation pressure. Heredity 121, 266–281. https://doi.org/10.1038/s41437-018-0108-8 

Snell-Rood EC, EM Swanson, and RL Young (2015). Life History as a Constraint on Plasticity: Developmental Timing Is Correlated with Phenotypic Variation in Birds. Heredity 115, 379–388. https://doi.org/10.1038/hdy.2015.47

Tariel-Adam J, E Luquet, and S Plénet (2023). Sensitive windows for within- and trans-generational plasticity of anti-predator defences. OSF preprints, ver. 4 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.31219/osf.io/mr8hu

Walasek N, WE Frankenhuis, and K Panchanathan (2022). An Evolutionary Model of Sensitive Periods When the Reliability of Cues Varies across Ontogeny. Behavioral Ecology 33, 101–114. https://doi.org/10.1093/beheco/arab113

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

References

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

Genetic variation is the raw material for selection to act upon and the amount of genetic variation present within a population is a pivotal determinant of a population’s evolutionary potential. A large effective population size, i.e., the ideal number of individuals experiencing the same amount of genetic drift and inbreeding as an actual population, Ne (Wright 1931, Crow 1954), thus increases the probability of long-term survival of a population. However, natural populations, as opposed to theoretical ones, rarely adhere to the requirements of an ideal panmictic population (Sjödin et al. 2005). A range of circumstances can reduce Ne, including the structuring of populations (through space and time, as well as age and developmental stages) and inbreeding (Charlesworth 2009). In mammals, species with a larger body mass (as a proxy for lower Ne) were found to have a higher rate of nonsynonymous nucleotide substitutions (that alter the amino acid sequence of a protein), as well as radical amino acid substitutions (altering the physicochemical properties of a protein) (Popadin et al. 2007). In general, low effective population sizes increase the chance of mutation accumulation and drift, while reducing the strength of selection (Sjödin et al. 2005).
In this paper, Weyna and Romiguier (2021) set out to test if parasitism, body size, geographic range, and/or eusociality affect the strength of selection in Hymenoptera. Hymenoptera include the bees, wasps and ants and is an extraordinarily diverse order within the insects. It was recently estimated that Hymenoptera is the most speciose order of the animal kingdom (Forbes et al. 2018). Hymenoptera are further characterized by an impressive radiation of parasitic species, mainly parasitoids, that feed in or on a single host individual to complete their own development (Godfray 1994). All hymenopterans share the same sex determination system: haplo-diploidy, where unfertilized eggs are haploid males and fertilized eggs are diploid females. Compared to other animals, Hymenoptera further contain an impressive number of clades that evolved eusociality (Rehan and Toth 2015), in which societies show a clear division of labor for reproduction (i.e., castes) and cooperative brood care. Hymenopterans thus represent a diverse and interesting group of insects to investigate potential factors affecting strength of selection and Ne.
Using a previously published phylogenomic dataset containing 3256 genes and 169 hymenopteran species (Peters et al. 2017), Weyna and Romiguier (2021) estimated mean genomic dN/dS ratios (nonsynonymous to synonymous substitution rates) for each species and compared these values between parasitic and non-parasitic species, eusocial and solitary species, and in relation to body size, parasitoid-specific traits and geographic range, thought to affect the effective population size and strength of selection. The use of a large number of species, as well as several distinct traits is a clear asset of this study. The authors found no effect of body size, geographic range or parasitism (including a range of parasitoid-specific traits). There was an effect, however, of eusociality where dN/dS increased in three out of four eusocial lineages. Future studies including more independent evolutionary transitions to eusociality can lend further support that eusocial species indeed reduces the efficiency of selection. The most intriguing result was that for solitary and social bees, with high dN/dS ratios and a strong signature of relaxed selection (i.e., the elimination or reduction of a source of selection (Lahti et al. 2009). The authors suggest that the pollen-collecting behaviors of these species can constrain Ne, as pollen availability varies at both a spatial and temporal scale, requiring a large investment in foraging that may in turn limit reproductive output. It would be interesting to see if other pollen feeders, such as certain beetles, flies, butterflies and moths, as well as mites and spiders, experience relaxed selection as a consequence of the trade-off between energy investment in pollen foraging versus fecundity.

References

Charlesworth, B. (2009). Effective population size and patterns of molecular evolution and variation. Nature Reviews Genetics, 10(3), 195-205. doi: https://doi.org/10.1038/nrg2526
Crow, J. F. (1954) Statistics and Mathematics in Biology (eds Kempthorne, O., Bancroft, T. A., Gowen, J. W. & Lush, J. L.) 543–556 (Iowa State Univ. Press, Ames, Iowa)
Forbes, A. A., Bagley, R. K., Beer, M. A., Hippee, A. C., and Widmayer, H. A. (2018). Quantifying the unquantifiable: why Hymenoptera, not Coleoptera, is the most speciose animal order. BMC ecology, 18(1), 1-11. doi: https://doi.org/10.1186/s12898-018-0176-x
Godfray, H. C. J. (1994) Parasitoids: Behavioral and Evolutionary Ecology. Vol. 67, Princeton University Press, 1994. doi: https://doi.org/10.2307/j.ctvs32rmp
Lahti et al. (2009). Relaxed selection in the wild. Trends in ecology & evolution, 24(9), 487-496. doi: https://doi.org/10.1016/j.tree.2009.03.010
Peters et al. (2017). Evolutionary history of the Hymenoptera. Current Biology, 27(7), 1013-1018. doi: https://doi.org/10.1016/j.cub.2017.01.027
Popadin, K., Polishchuk, L. V., Mamirova, L., Knorre, D., and Gunbin, K. (2007). Accumulation of slightly deleterious mutations in mitochondrial protein-coding genes of large versus small mammals. Proceedings of the National Academy of Sciences, 104(33), 13390-13395. doi: https://doi.org/10.1073/pnas.0701256104
Rehan, S. M., and Toth, A. L. (2015). Climbing the social ladder: the molecular evolution of sociality. Trends in ecology & evolution, 30(7), 426-433. doi: https://doi.org/10.1016/j.tree.2015.05.004
Sjödin, P., Kaj, I., Krone, S., Lascoux, M., and Nordborg, M. (2005). On the meaning and existence of an effective population size. Genetics, 169(2), 1061-1070. doi: https://doi.org/10.1534/genetics.104.026799
Weyna, A., and Romiguier, J. (2021) Relaxation of purifying selection suggests low effective population size in eusocial Hymenoptera and solitary pollinating bees. bioRxiv, 2020.04.14.038893, ver. 5 peer-reviewed and recommended by PCI Evol Biol. doi: https://doi.org/10.1101/2020.04.14.038893
Wright, S. (1931). Evolution in Mendelian populations. Genetics, 16(2), 97-159.

In this instructive review, Stritt and Gagneux offer a balanced perspective on the evolutionary forces shaping Mycobacterium tuberculosis and make the case that our instinct for storytelling be balanced with quantitative models. M. tuberculosis is perhaps the best-known clonal bacterial pathogen – evolving largely in the absence of horizontal gene transfer. Its genome is full of puzzling patterns, including much higher GC content than most intracellular pathogens (which suggests efficient selection to resist AT-skewed mutational bias) but a very high ratio of nonsynonymous to synonymous substitution rates (dN/dS ~ 0.5, typically interpreted as weak selection against deleterious amino acid changes). 

The authors offer alternative explanations for these patterns, framing the question: is M. tuberculosis evolution shaped mainly by drift or by efficient selection? They propose that this question can only be answered by accounting for the pathogen’s extreme clonality. A clonal lifestyle can have its advantages, for example when adaptations must arise in a particular order (Kondrashov and Kondrashov 2001). An important disadvantage highlighted by the authors are linkage effects: without recombination to shuffle them up, beneficial mutations are linked to deleterious mutations in the same genome (hitchhiking) and purging deleterious mutations also purges neutral diversity across the genome (background selection). The authors propose the latter – efficient purifying selection and strong linkage – as an explanation for the low genetic diversity observed in M. tuberculosis. This is of course not exclusive of other related explanations, such as clonal interference (Gerrish and Lenski 1998). They also champion the use of forward evolutionary simulations (Haller and Messer 2019) to model the interplay between selection, recombination, and demography as a powerful alternative to traditional backward coalescent models.

At times, Stritt and Gagneux are pessimistic about our existing methods – arguing that dN/dS and homoplasies “tell us little about the frequency and strength of selection.” Even though I favour a more optimistic view, I fully agree that our traditional population genetic metrics are sensitive to a slew of different deviations from a standard neutral evolution model and must be interpreted with caution. As I and others have argued, the extent of recombination (measured as the amount of linkage in a genome) is a key factor in determining how best to test for natural selection (Shapiro et al. 2009) and to conduct genotype-phenotype association studies (Chen and Shapiro 2021) in microbes. While this article is focused on the well-studied M. tuberculosis complex, there are many parallels with other clonal bacteria, including pathogens and symbionts. Whatever your favourite bug, we must all be careful to make sure the stories we tell about them are not “just so tales” but are supported, to the extent possible, by data and quantitative models.

References

Chen, Peter E., and B. Jesse Shapiro. 2021. "Classic Genome-Wide Association Methods Are Unlikely to Identify Causal Variants in Strongly Clonal Microbial Populations." bioRxiv. 
https://doi.org/10.1101/2021.06.30.450606
 
Gerrish, P. J., and R. E. Lenski. 1998. "The Fate of Competing Beneficial Mutations in an Asexual Population." Genetica 102-103 (1-6): 127-44.
https://doi.org/10.1023/A:1017067816551
 
Haller, Benjamin C., and Philipp W. Messer. 2019. "SLiM 3: Forward Genetic Simulations Beyond the Wright-Fisher Model." Molecular Biology and Evolution 36 (3): 632-37.
https://doi.org/10.1093/molbev/msy228
 
Kondrashov, F. A., and A. S. Kondrashov. 2001. "Multidimensional Epistasis and the Disadvantage of Sex." Proceedings of the National Academy of Sciences of the United States of America 98 (21): 12089-92.
https://doi.org/10.1073/pnas.211214298
 
Shapiro, B. Jesse, Lawrence A. David, Jonathan Friedman, and Eric J. Alm. 2009. "Looking for Darwin's Footprints in the Microbial World." Trends in Microbiology 17 (5): 196-204.
https://doi.org/10.1016/j.tim.2009.02.002 

Stritt, C., Gagneux, S. (2023). How do monomorphic bacteria evolve? The Mycobacterium tuberculosis complex and the awkward population genetics of extreme clonality. EcoEvoRxiv, ver.3 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.32942/X2GW2P