The evolution of mutualistic symbiosis is a puzzle that has fascinated evolutionary ecologist for quite a while. Data on transitions between symbiotic bacterial ways of life has evidenced shifts from mutualism towards parasitism and vice versa (Sachs et al., 2011), so there does not seem to be a strong determinism on those transitions. From the host’s perspective, mutualistic symbiosis implies at the very least some form of immune tolerance, which can be costly (e.g. Sorci, 2013). Empirical approaches thus raise very important questions: How can symbiosis turn from parasitism into mutualism when it seemingly needs such a strong alignment of selective pressures on both the host and the symbiont? And yet why is mutualistic symbiosis so widespread and so important to the evolution of macro-organisms (Margulis, 1998)?

While much of the theoretical literature on the evolution of symbiosis and mutualism has focused on either the stability of such relationships when non-mutualists can invade the host-symbiont system (e.g. Ferrière et al., 2007) or the effect of the mode of symbiont transmission on the evolutionary dynamics of mutualism (e.g. Genkai-Kato and Yamamura, 1999), the question remains whether and under which conditions parasitic symbiosis can turn into mutualism in the first place. Earlier results suggested that spatial demographic heterogeneity between host populations could be the leading determinant of evolution towards mutualism or parasitism (Hochberg et al., 2000). Here, Ledru et al. (2022) investigate this question in an innovative way by simulating host-symbiont evolutionary dynamics in a spatially explicit context. Their hypothesis is intuitive but its plausibility is difficult to gauge without a model: Does the evolution towards mutualism depend on the ability of the host and symbiont to evolve towards close-range dispersal in order to maintain clusters of efficient host-symbiont associations, thus outcompeting non-mutualists?

I strongly recommend reading this paper as the results obtained by the authors are very clear: competition strength and the cost of dispersal both affect the likelihood of the transition from parasitism to mutualism, and once mutualism has set in, symbiont trait values clearly segregate between highly dispersive parasites and philopatric mutualists. The demonstration of the plausibility of their hypothesis is accomplished with brio and thoroughness as the authors also examine the conditions under which the transition can be reversed, the impact of the spatial range of competition and the effect of mortality. Since high dispersal cost and strong, long-range competition appear to be the main factors driving the evolutionary transition towards mutualistic symbiosis, now is the time for empiricists to start investigating this question with spatial structure in mind.

References

Ferrière, R., Gauduchon, M. and Bronstein, J. L. (2007) Evolution and persistence of obligate mutualists and exploiters: competition for partners and evolutionary immunization. Ecology Letters, 10, 115-126. https://doi.org/10.1111/j.1461-0248.2006.01008.x

Genkai-Kato, M. and Yamamura, N. (1999) Evolution of mutualistic symbiosis without vertical transmission. Theoretical Population Biology, 55, 309-323. https://doi.org/10.1006/tpbi.1998.1407

Hochberg, M. E., Gomulkiewicz, R., Holt, R. D. and Thompson, J. N. (2000) Weak sinks could cradle mutualistic symbioses - strong sources should harbour parasitic symbioses. Journal of Evolutionary Biology, 13, 213-222. https://doi.org/10.1046/j.1420-9101.2000.00157.x

Ledru L, Garnier J, Rohr M, Noûs C and Ibanez S (2022) Mutualists construct the ecological conditions that trigger the transition from parasitism. bioRxiv, 2021.08.18.456759, ver. 5 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2021.08.18.456759

Margulis, L. (1998) Symbiotic planet: a new look at evolution, Basic Books, Amherst.

Sachs, J. L., Skophammer, R. G. and Regus, J. U. (2011) Evolutionary transitions in bacterial symbiosis. Proceedings of the National Academy of Sciences, 108, 10800-10807. https://doi.org/10.1073/pnas.1100304108

Sorci, G. (2013) Immunity, resistance and tolerance in bird–parasite interactions. Parasite Immunology, 35, 350-361. https://doi.org/10.1111/pim.12047

Understanding the factors that shape the virome of a host is key to understanding virus ecology and evolution (Obbard, 2018; French & Holmes, 2020). There is still much to learn about the diversity and distribution of viruses in a host community (Wille et al., 2019; Chen et al., 2023). The viruses of parasitoid wasps are well studied, and their viruses, or integrated viral genes, are known to suppress their insect host’s immune response to enhance parasitoid survival (Herniou et al., 2013; Coffman et al., 2022). Likewise, the insect virome is being increasingly well studied (Shi et al., 2016), with the virome of Drosophila species being particularly well characterised over the best part of the last century (L'Heritier & Teissier, 1937; L'Heritier, 1970; Brun & Plus, 1980; Longdon et al., 2010; Longdon et al., 2011; Longdon et al., 2012; Webster et al., 2015; Webster et al., 2016; Medd et al., 2018; Wallace et al., 2021). However, the viromes of parasitoids and their insect host communities have been less well studied (Leigh et al., 2018; Caldas-Garcia et al., 2023), and the inherent connectivity between parasitoids and their hosts provides an interesting system to study virus host range and cross-species transmission.

Here, Varaldi et al (Varaldi et al., 2024) have examined the viruses associated with a community of nine Drosophilidae hosts and six parasitoids. Using both RNA and DNA sequencing of insects reared for two generations, they selected viruses that are maintained in the lab either via vertical transmission or contamination of rearing medium. From 55 pools of insects they found 53 virus-like sequences, 37 of which were novel. Parasitoids were host to nearly twice as many viruses as their Drosophila hosts, although they note this could be due to differences in the rearing temperatures of the hosts.  

They next quantified if species, year, season, or location played a role in structuring the virome, finding only a significant effect of host species, which explained just over 50% of the variation in virus distribution. No evidence was found of related species sharing more similar virus communities. Although looking at a limited number of species, this suggests that these viruses are not co-speciating or preferentially host switching between closely related species.

Finally, they carried out crosses between lines of the parasitoid Leptopilina heterotoma that were infected and uninfected for a novel Iflavirus found in their sequencing data.  They found evidence of high levels of maternal transmission and lower level horizontal transmission between wasp larvae parasitising the same host. No evidence of changes in parasitoid-induced mortality, developmental success or the sex ratio was found in iflavirus-infected parasitoids. Interestingly individuals infected with this RNA virus also contained viral DNA, but this did not appear to be integrated into the wasp genome.

Overall, this work has taken the first steps in examining the community structure of the virome of parasitoids together with their Drosophilidae hosts. This work will not doubt stimulate follow-up studies to explore the evolution and ecology of these novel virus communities.

References

Brun G, Plus N (1980) The viruses of Drosophila. In: The genetics and biology of Drosophila eds Ashburner M & Wright TRF), pp. 625-702. Academic Press, New York.
 
Caldas-Garcia GB, Santos VC, Fonseca PLC, de Almeida JPP, Costa MA, Aguiar ERGR (2023) The Viromes of Six Ecosystem Service Provider Parasitoid Wasps. Viruses, 15. https://doi.org/10.3390/v15122448
 
Chen YM, Hu SJ, Lin XD, Tian JH, Lv JX, Wang MR, Luo XQ, Pei YY, Hu RX, Song ZG, Holmes EC, Zhang YZ (2023) Host traits shape virome composition and virus transmission in wild small mammals. Cell, 186, 4662-4675 e4612. https://doi.org/10.1016/j.cell.2023.08.029
 
Coffman KA, Hankinson QM, Burke GR (2022) A viral mutualist employs posthatch transmission for vertical and horizontal spread among parasitoid wasps. Proceedings of the National Academy of Sciences of the United States of America, 119. https://doi.org/10.1073/pnas.2120048119
 
French RK, Holmes EC (2020) An Ecosystems Perspective on Virus Evolution and Emergence. Trends in Microbiology, 28, 165-175. https://doi.org/10.1016/j.tim.2019.10.010
 
Herniou EA, Huguet E, Thézé J, Bézier A, Periquet G, Drezen JM (2013) When parasitic wasps hijacked viruses: genomic and functional evolution of polydnaviruses. Philosophical Transactions of the Royal Society B-Biological Sciences, 368. https://doi.org/10.1098/rstb.2013.0051
 
L'Heritier PH (1970) Drosophila viruses and their role as evolutionary factors. Evolutionary Biology, 4, 185-209
 
L'Heritier PH, Teissier G (1937) Une anomalie physiologique héréditaire chez la Drosophile. C.R. Acad. Sci. Paris, 231, 192-194
 
Leigh BA, Bordenstein SR, Brooks AW, Mikaelyan A, Bordenstein SR (2018) Finer-Scale Phylosymbiosis: Insights from Insect Viromes. Msystems, 3. https://doi.org/10.1128/mSystems.00131-18
 
Longdon B, Obbard DJ, Jiggins FM (2010) Sigma viruses from three species of Drosophila form a major new clade in the rhabdovirus phylogeny. Proceedings of the Royal Society B, 277, 35-44. 
https://doi.org/10.1098/rspb.2009.1472
 
Longdon B, Wilfert L, Jiggins FM (2012) The Sigma Viruses of Drosophila. Caister Academic Press, Norfolk, UK.
 
Longdon B, Wilfert L, Osei-Poku J, Cagney H, Obbard DJ, Jiggins FM (2011) Host switching by a vertically-transmitted rhabdovirus in Drosophila. Biology Letters, 7, 747-750. 
https://doi.org/10.1098/rsbl.2011.0160
 
Medd NC, Fellous S, Waldron FM, Xuereb A, Nakai M, Cross JV, Obbard DJ (2018) The virome of Drosophila suzukii, an invasive pest of soft fruit. Virus Evol, 4, vey009. 
https://doi.org/10.1093/ve/vey009
 
Obbard DJ (2018) Expansion of the metazoan virosphere: progress, pitfalls, and prospects. Curr Opin Virol, 31, 17-23. https://doi.org/10.1016/j.coviro.2018.08.008
 
Shi M, Lin XD, Tian JH, Chen LJ, Chen X, Li CX, Qin XC, Li J, Cao JP, Eden JS, Buchmann J, Wang W, Xu J, Holmes EC, Zhang YZ (2016) Redefining the invertebrate RNA virosphere. Nature. https://doi.org/10.1038/nature20167
 
Varaldi J, Lepetit D, Burlet N, Faber C, Baretje B, Allemand R (2024) Community structure of heritable viruses in a Drosophila-parasitoids complex. bioRxiv, 2023.2007.2029.551099, ver.3, peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2023.07.29.551099
 
Wallace MA, Coffman KA, Gilbert C, Ravindran S, Albery GF, Abbott J, Argyridou E, Bellosta P, Betancourt AJ, Colinet H, Eric K, Glaser-Schmitt A, Grath S, Jelic M, Kankare M, Kozeretska I, Loeschcke V, Montchamp-Moreau C, Ometto L, Onder BS, Orengo DJ, Parsch J, Pascual M, Patenkovic A, Puerma E, Ritchie MG, Rota-Stabelli O, Schou MF, Serga SV, Stamenkovic-Radak M, Tanaskovic M, Veselinovic MS, Vieira J, Vieira CP, Kapun M, Flatt T, Gonzalez J, Staubach F, Obbard DJ (2021) The discovery, distribution, and diversity of DNA viruses associated with Drosophila melanogaster in Europe. Virus Evol, 7, veab031. https://doi.org/10.1093/ve/veab031
 
Webster CL, Longdon B, Lewis SH, Obbard DJ (2016) Twenty-Five New Viruses Associated with the Drosophilidae (Diptera). Evol Bioinform Online, 12, 13-25. https://doi.org/10.4137/EBO.S39454
 
Webster CL, Waldron FM, Robertson S, Crowson D, Ferrai G, Quintana JF, Brouqui JM, Bayne EH, Longdon B, Buck AH, Lazzaro BP, Akorli J, Haddrill PR, Obbard DJ (2015) The discovery, distribution and evolution of viruses associated with Drosophila melanogaster. Plos Biology, 13(7): e1002210. https://doi.org/10.1371/journal.pbio.1002210
 
Wille M, Shi M, Klaassen M, Hurt AC, Holmes EC (2019) Virome heterogeneity and connectivity in waterfowl and shorebird communities. ISME J, 13, 2603-2616. https://doi.org/10.1038/s41396-019-0458-0

Sequencing and analyzing SARS-Cov-2 genomes in nearly real time has the potential to quickly confirm (and inform) our knowledge of, and response to, the current pandemic [1,2]. In this manuscript [3], Danesh and colleagues use the earliest set of available SARS-Cov-2 genome sequences available from France to make inferences about the timing of the major epidemic wave, the duration of infections, and the efficacy of lockdown measures. Their phylodynamic estimates -- based on fitting genomic data to molecular clock and transmission models -- are reassuringly close to estimates based on 'traditional' epidemiological methods: the French epidemic likely began in mid-January or early February 2020, and spread relatively rapidly (doubling every 3-5 days), with people remaining infectious for a median of 5 days [4,5]. These transmission parameters are broadly in line with estimates from China [6,7], but are currently unknown in France (in the absence of contact tracing data). By estimating the temporal reproductive number (Rt), the authors detected a slowing down of the epidemic in the most recent period of the study, after mid-March, supporting the efficacy of lockdown measures.
Along with the three other reviewers of this manuscript, I was impressed with the careful and exhaustive phylodynamic analyses reported by Danesh et al. [3]. Notably, they take care to show that the major results are robust to the choice of priors and to sampling. The authors are also careful to note that the results are based on a limited sample size of SARS-Cov-2 genomes, which may not be representative of all regions in France. Their analysis also focused on the dominant SARS-Cov-2 lineage circulating in France, which is also circulating in other countries. The variations they inferred in epidemic growth in France could therefore be reflective on broader control policies in Europe, not only those in France. Clearly more work is needed to fully unravel which control policies (and where) were most effective in slowing the spread of SARS-Cov-2, but Danesh et al. [3] set a solid foundation to build upon with more data. Overall this is an exemplary study, enabled by rapid and open sharing of sequencing data, which provides a template to be replicated and expanded in other countries and regions as they deal with their own localized instances of this pandemic.

References

[1] Grubaugh, N. D., Ladner, J. T., Lemey, P., Pybus, O. G., Rambaut, A., Holmes, E. C., & Andersen, K. G. (2019). Tracking virus outbreaks in the twenty-first century. Nature microbiology, 4(1), 10-19. doi: 10.1038/s41564-018-0296-2
[2] Fauver et al. (2020) Coast-to-Coast Spread of SARS-CoV-2 during the Early Epidemic in the United States. Cell, 181(5), 990-996.e5. doi: 10.1016/j.cell.2020.04.021
[3] Danesh, G., Elie, B., Michalakis, Y., Sofonea, M. T., Bal, A., Behillil, S., Destras, G., Boutolleau, D., Burrel, S., Marcelin, A.-G., Plantier, J.-C., Thibault, V., Simon-Loriere, E., van der Werf, S., Lina, B., Josset, L., Enouf, V. and Alizon, S. and the COVID SMIT PSL group (2020) Early phylodynamics analysis of the COVID-19 epidemic in France. medRxiv, 2020.06.03.20119925, ver. 3 peer-reviewed and recommended by PCI Evolutionary Biology. doi: 10.1101/2020.06.03.20119925
[4] Salje et al. (2020) Estimating the burden of SARS-CoV-2 in France. hal-pasteur.archives-ouvertes.fr/pasteur-02548181
[5] Sofonea, M. T., Reyné, B., Elie, B., Djidjou-Demasse, R., Selinger, C., Michalakis, Y. and Samuel Alizon, S. (2020) Epidemiological monitoring and control perspectives: application of a parsimonious modelling framework to the COVID-19 dynamics in France. medRxiv, 2020.05.22.20110593. doi: 10.1101/2020.05.22.20110593
[6] Rambaut, A. (2020) Phylogenetic analysis of nCoV-2019 genomes. virological.org/t/phylodynamic-analysis-176-genomes-6-mar-2020/356
[7] Li et al. (2020) Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia. N Engl J Med, 382: 1199-1207. doi: 10.1056/NEJMoa2001316

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

Flowering plants display a staggering diversity of both mating systems and life histories, ranging from almost exclusively selfers to obligate outcrossers, very short-lived annual herbs to super long lived trees. One pervasive pattern that has attracted considerable attention is the tight correlation that is found between mating systems and lifespan [1]. Until recently, theoretical explanations for these patterns have relied on static models exploring the consequences of several non-mutually exclusive important process: levels of inbreeding depression and ability to successfully were center stage. This make sense: successful colonization after long‐distance dispersal is far more likely to happen for self‐compatible than for self‐incompatible individuals in a sexually reproducing species. Furthermore, inbreeding depression (essentially a genetically driven phenomenon) and reproductive insurance are expected to shape the evolution of both mating system and lifespan.
But modelling jointly several processes and how their interplay to shape the evolution of a trait is challenging enough so models for the evolution of mating system tend invariably – for mathematical convenience and tractability – to fix lifespan [2].
However, comparative analysis of between species variations that map traits transitions among sister species in phylogenetic trees reveals a pervasive pattern: frequent transitions from a state outcrossing perennial to selfing annuals. This beg the question: is one transition triggering the other and if so, what comes first or are these transitions happening together? In this work, Lesaffre and Billiard use a very sophisticated machinery developed by Kirkpatrick et al. [3] and consider a general class of so-called modifiers models [4]. They study jointly the evolution of life span and mating system. They do so by using models where different life stages are tracked with life stage having some (fixed for once) amount of inbreeding depression. Their paper is technically demanding, mixing analytics and computer simulations, and along the way generates several important findings that are expected to stimulate further empirical and theoretical studies: (1) pure selfing versus pure outcrossing is the expected stable evolutionary outcomes (despite observation that mixed mating systems can be regularly met in nature), (2) increasing life-span drastically reduces the scope for the evolution of selfing, conversely (3) transition to selfing will also select for shorter life span as a way to mitigate the cumulative effects of inbreeding depression on adult life stages.
As usual there is room for future work, in particular the authors’ model assumes fixed inbreeding depression in the different life stages and this highlights the need for models that explore how inbreeding depression, a pivotal quantity in these models, can itself be molded by both mating system and lifespan. A third-generation of models should be “soon” on the way!

References
[1] Grossenbacher D, Briscoe Runquist R, Goldberg EE, and Brandvain Y. (2015) Geographic range size is predicted by plant mating system. Ecology Letters 18, 706–713. doi: 10.1111/ele.12449
[2] Morgan MT, Schoen DJ, and Bataillon T. (1997) The evolution of self-fertilization in perennials. The American Naturalist 150, 618–638. doi: 10.1086/286085
[3] Kirkpatrick M, Johnson T, and Barton N. (2002) General models of multilocus evolution. Genetics 161, 1727–1750.
[4] Lesaffre, T, and Billiard S. (2019) The joint evolution of lifespan and self-fertilisation. bioRxiv, 420877, ver. 3 peer-reviewed and recommended by PCI Evol Biol. doi: 10.1101/420877