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16 May 2023
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A new and almost perfectly accurate approximation of the eigenvalue effective population size of a dioecious population: comparisons with other estimates and detailed proofs

All you ever wanted to know about Ne in one handy place

Recommended by based on reviews by Jesse ("Jay") Taylor and 1 anonymous reviewer

​Of the four evolutionary forces, three can be straightforwardly summarized both conceptually and mathematically in the context of an allele at a genomic locus.  Mutation (the mutation rate, μ) is simply captured by the per-site, per-generation probability that an allele mutates into a different allele. Recombination (the recombination rate, r) is captured as the probability of recombination between two sites, wherein alleles that are in different genomes in one generation come together in the same genome in the next generation.  Natural selection (the selection coefficient, s) is captured by the probability that an allele is present in the next generation, relative to some reference.  

Random genetic drift – the random fluctuation in allele frequency due to sampling in a finite population - is not so straightforwardly summarized.  The first, and most common way of characterizing evolutionary dynamics in a finite population is the Wright-Fisher model, in which the only deviation from the assumptions of Hardy-Weinberg conditions is finite population size.  Importantly, in a W-F population, mating between diploid individuals is random, which implies self-fertile monoecy, and generations are non-overlapping.  In an ideal W-F population, the probability that a gene copy leaves i descendants in the next generation is the result of binomial sampling of uniting gametes (if the locus is biallelic).  The – and the next word is meaningful – magnitude/strength/rate/power/amount of genetic drift is proportional to 1/2N, where N is the size of the population.  All of the following are affected by genetic drift: (1) the probability that a neutral allele ultimately reaches fixation, (2) the rate of loss of genetic variation within a population, (3) the rate of increase of genetic variance among populations, (4) the amount of genetic variation segregating in a population, (5) the probability of fixation/loss of a weakly selected variant.    

Presumably no real population adheres to ideal W-F conditions, which leads to the notion of "effective population size", Ne (Wright 1931), loosely defined as "the size of an ideal W-F population that experiences an equivalent strength of genetic drift".  Almost always, Ne<N, and any violation of W-F assumptions can affect Ne.  Importantly, Ne can be defined in different ways, and the specific formulation of Ne can have different implications for evolution.  Ne was initially defined in terms of the rate of decrease of heterozygosity (inbreeding effective size) and increase in variance among populations (variance effective size).  Ewens (1979) defined the Eigenvalue effective size (equivalent to the "random extinction" effective size) and elaborated on the conditions under which the various formulations of Ne differ (Ewens 1982).  Nordborg and Krone (2002) defined the effective size in terms of the coalescent, and they identified conditions in which genetic drift cannot be described in terms of a W-F model (Sjodin et al. 2005); also see Karasov et al. (2010); Neher and Shraiman (2011).

Distinct from the issue of defining Ne is the issue of calculating Ne from data, which is the focus of this paper by De Meeus and Noûs (2023).  Pudovkin et al. (1996) showed that the Eigenvalue effective size in a dioecious population can be formulated in terms of excess heterozygosity, which the current authors note is equivalent to formulating Ne in terms of Wright's FIS statistic.  As emphasized by the title, the marquee contribution of this paper is to provide a better approximation of the Eigenvalue effective size in a dioecious population.  Science marches onward, although the empirical utility of this advance is obviously limited, given the tremendous inherent sources of uncertainty in real-world estimates of Ne.  Perhaps more valuable, however, is the extensive set of appendixes, in which detailed derivations are provided for the various formulations of effective size.  By way of analogy, the material presented here can be thought of as an extension of the material presented in section 7.6 of Crow and Kimura (1970), in which the Inbreeding and Variance effective population sizes are derived and compared.  The appendixes should serve as a handy go-to source of detailed theoretical information with respect to the different formulations of effective population size.

REFERENCES

Crow, J. F. and M. Kimura. 1970. An Introduction to Population Genetics Theory. The Blackburn Press, Caldwell, NJ.

De Meeûs, T. and Noûs, C. 2023. A new and almost perfectly accurate approximation of the eigenvalue effective population size of a dioecious population: comparisons with other estimates and detailed proofs. Zenodo, ver. 6 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.5281/zenodo.7927968

Ewens, W. J. 1979. Mathematical Population Genetics. Springer-Verlag, Berlin.

Ewens, W. J. 1982. On the concept of the effective population size. Theoretical Population Biology 21:373-378. https://doi.org/10.1016/0040-5809(82)90024-7

Karasov, T., P. W. Messer, and D. A. Petrov. 2010. Evidence that adaptation in Drosophila Is not limited by mutation at single sites. Plos Genetics 6. https://doi.org/10.1371/journal.pgen.1000924

Neher, R. A. and B. I. Shraiman. 2011. Genetic Draft and Quasi-Neutrality in Large Facultatively Sexual Populations. Genetics 188:975-U370. https://doi.org/10.1534/genetics.111.128876

Nordborg, M. and S. M. Krone. 2002. Separation of time scales and convergence to the coalescent in structured populations. Pp. 194–232 in M. Slatkin, and M. Veuille, eds. Modern Developments in Theoretical Population Genetics: The Legacy of Gustave Malécot. Oxford University Press, Oxford. https://www.webpages.uidaho.edu/~krone/malecot.pdf

Pudovkin, A. I., D. V. Zaykin, and D. Hedgecock. 1996. On the potential for estimating the effective number of breeders from heterozygote-excess in progeny. Genetics 144:383-387. https://doi.org/10.1093/genetics/144.1.383

Sjodin, P., I. Kaj, S. Krone, M. Lascoux, and M. Nordborg. 2005. On the meaning and existence of an effective population size. Genetics 169:1061-1070. https://doi.org/10.1534/genetics.104.026799

Wright, S. 1931. Evolution in Mendelian populations. Genetics 16:0097-0159. https://doi.org/10.1093/genetics/16.2.97

A new and almost perfectly accurate approximation of the eigenvalue effective population size of a dioecious population: comparisons with other estimates and detailed proofsThierry de Meeûs and Camille Noûs<p>The effective population size is an important concept in population genetics. It corresponds to a measure of the speed at which genetic drift affects a given population. Moreover, this is most of the time the only kind of population size that e...Bioinformatics & Computational Biology, Evolutionary Ecology, Evolutionary Theory, Population Genetics / Genomics, Reproduction and SexCharles Baer2023-02-22 16:53:49 View
29 Nov 2023
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Does sociality affect evolutionary speed?

On the evolutionary implications of being a social animal

Recommended by based on reviews by Rafael Lucas Rodriguez and 1 anonymous reviewer

What does it mean to be highly social?  Considering the so-called four ‘pinnacles’ of animal society (Wilson, 1975) – humans, cooperative breeding as found in some non-human mammals and birds, the social insects, and colonial marine invertebrates – having inter-individual relations extending beyond the sexual pair and the parent-offspring interaction is foremost.  In many cases being social implies a high local population density, interaction with the same group of individuals over an extended time period, and an overlapping of generations.  Additional features of social species may be a wide geographical range, perhaps associated with ecological and behavioral plasticity, the latter often facilitated by cultural transmission of traditions.  

Narrowing our perspective to the domain of PCI Evolutionary Biology, we might continue our question by asking whether being social predisposes one to a special evolutionary path toward the future.  Do social species evolve faster (or slower) than their more solitary relatives such that over time they are more unlike (or similar to) those relatives (anagenesis)?  And are evolutionary changes in social species more or less likely to be accompanied by lineage splitting (cladogenesis) and ultimately speciation?  The latter question is parallel to one first posed over 40 years ago (West-Eberhard, 1979; Lande, 1981) for sexually selected traits:  Do strong mating preferences and conspicuous courtship signals generate speciation via the Fisherian process or ecological divergence?  An extensive survey of birds had found little supporting evidence (Price, 1998), but a recent one that focused on plumage complexity in tanagers did reveal a relationship, albeit a weak one (Price-Waldman et al., 2020).  Because sexual selection has been viewed as a part of the broader process of social selection (West-Eberhard, 1979), it is thus fitting to extend our surveys to the evolutionary implications of being social.

Unlike the inquiry for a sexual selection - evolutionary change connection, a social behavior counterpart has remained relatively untreated.  Diverse logistical problems might account for this oversight.  What objective proxies can be used for social behavior, and for the rate of evolutionary change within a lineage?  How many empirical studies have generated data from which appropriate proxies could be extracted?  More intractable is the conundrum arising from the connectedness between socially- and sexually-selected traits.  For example, the elevated population density found in highly social species can greatly increase the mating advantage enjoyed by an attractive male.  If anagenesis is detected, did it result from social behavior or sexual selection?  And if social behavior leads to a group structure in which male-male competition is reduced, would a modest rate of evolutionary change be support for the sexual selection - evolutionary speed connection or evidence opposing the sociality - evolution one?

Against the above odds, several biologists have begun to explore the notion that social behavior just might favor evolutionary speed in either anagenesis or cladogenesis.  In a recent analysis relying on the comparative method, Lluís Socias-Martínez and Louise Rachel Peckre (2023) combed the scientific literature archives and identified those studies with specific data on the relationships between sexual selection or social behavior and evolutionary change, either anagenesis or cladogenesis.  The authors were careful to employ fairly conservative criteria for including studies, and the number eventually retained was small.  Nonetheless, some patterns emerge:  Many more studies report anagenesis than cladogenesis, and many more report correlations with sexually-selected traits than with non-sexual social behavior ones.  And, no study indicates a potential effect of social behavior on cladogenesis.  Is this latter observation authentic or an artifact of a paucity of data?  There are some a priori reasons why cladogenesis may seldom arise.  Whereas highly social behavior could lead to fission encompassing mutually isolated population clusters within a species, social behavior may also engender counterbalancing plasticity that allows and even promotes inter-cluster migration and fusion.  And briefly – and non-systematically, as the rate of lineage splitting would need to be measured – looking at one of the pinnacles of animal social behavior, the social insects, there is little indication that diversification has been accelerated.  There are fewer than 3000 described species of termites, only ca. 16,000 ants, and the vast majority of bees and wasps are solitary.                            

Lluís Socias-Martínez and Louise Rachel Peckre provide us with a very detailed discussion of these and a myriad of other complications.  I end with a common refrain, we need more consideration of the authors’ interesting question, and much more data and analysis.  One can thank Socias-Martínez and Peckre for pointing us in that direction.

References

Lande, R. (1981). Models of speciation by sexual selection on polygenic traits. Proc. Natn. Acad. Sci. USA 78, 3721-3725. https://doi.org/10.1073/pnas.78.6.3721

Price, T. (1998). Sexual selection and natural selection in bird speciation. Phil. Trans. Roy. Soc. B, 353, 251-260.  https://doi.org/10.1098/rstb.1998.0207  

Price‐Waldman, R. M., Shultz, A. J., & Burns, K. J. (2020). Speciation rates are correlated with changes in plumage color complexity in the largest family of songbirds. Evolution, 74(6), 1155–1169. https://doi.org/10.1111/evo.13982

Socias-Martínez and Peckre. (2023). Does sociality affect evolutionary speed? Zenodo, ver. 3 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.5281/zenodo.10086186

West-Eberhard, M. J. (1979). Sexual selection, social competition, and evolution. Proceedings of the American Philosophical Society, 123(4), 222–234. http://www.jstor.org/stable/2828804

Wilson, E. O. (1975). Sociobiology. The New Synthesis. Cambridge, Mass., The Belknap Press of Harvard University

Does sociality affect evolutionary speed?Lluís Socias-Martínez, Louise Rachel Peckre<p>An overlooked source of variation in evolvability resides in the social lives of animals. In trying to foster research in this direction, we offer a critical review of previous work on the link between evolutionary speed and sociality. A first ...Behavior & Social Evolution, Evolutionary Dynamics, Evolutionary Theory, Genome Evolution, Macroevolution, Molecular Evolution, Population Genetics / Genomics, Sexual Selection, SpeciationMichael D Greenfield2023-03-03 00:10:49 View
30 Oct 2023
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Telomere length vary with sex, hatching rank and year of birth in little owls, Athene noctua

Deciphering the relative contribution of environmental and biological factors driving telomere length in nestlings

Recommended by based on reviews by Florentin Remot and 1 anonymous reviewer

The search for physiological markers of health and survival in wild animal populations is attracting a great deal of interest. At present, there is no (and may never be) consensus on such a single, robust marker but of all the proposed physiological markers, telomere length is undoubtedly the most widely studied in the field of evolutionary ecology (Monaghan et al., 2022). 

Broadly speaking, telomeres are non-coding DNA sequences located at the end of chromosomes in eukaryotes, protecting genomic DNA against oxidative stress and various detrimental processes (e.g. DNA end-joining) and thus maintaining genome stability (Blackburn et al., 2015). However, in most somatic cells from the vast majority of the species, telomere sequences are not replicated and telomere length progressively declines with increased age (Remot et al., 2022). This shortening of telomere length upon a critical level is causally linked to cellular senescence and has been invoked as one of the primary causes of the aging process (López-Otín et al., 2023). Studies performed in both captive and wild populations of animals have further demonstrated that short telomeres (or telomere sequences with a fast attrition rate) are to some extent associated with an increased risk of mortality, even if the magnitude of this association largely differs between species and populations (Wilbourn et al., 2018).

The repeated observations of associations between telomere length and mortality risk have called for studies seeking to identify the ecological and biological factors that – beyond chronological age – shape the between-individual variability in telomere length. A wide spectrum of environmental stressors such as the level of exposure to pathogens or the degree of human disturbances has been proposed as possible modulators of telomere dynamics (see Chatelain et al., 2019). However, within species, the relative contribution of various ecological and biological factors on telomere length has been rarely quantified. In that context, the study of Criscuolo and colleagues (2023) constitutes a timely attempt to decipher the relative contribution of environmental and biological factors driving telomere length in nestlings (i.e. when individuals are between 15 and 35 days of age) from a wild population of little owls, Athene noctua.

In addition to chronological age, Criscuolo and colleagues (2023) analysed the effects of two environmental variables (i.e. cohort and habitat quality) as well as three life history traits (i.e. hatching rank, sex and body condition). Among these traits, sex was found to impact nestling’s telomere length with females carrying longer telomeres than males. Traditionally, the among-individuals variability in telomere length during the juvenile period is interpreted as a direct consequence of differences in growth allocation. Fast-growing individuals are typically supposed to undergo more cell divisions and a higher exposure to oxidative stress, which ultimately shortens telomeres (Monaghan & Ozanne, 2018). Whether - despite a slightly female-biased sexual size dimorphism - male little owls display a condensed period of fast growth that could explain their shorter telomere is yet to be determined. Future studies should also explore the consequences of these sex differences in telomere length in terms of mortality risk. In birds, it has been observed that telomere length during early life can predict lifespan (see Heidinger et al., 2012 in zebra finches, Taeniopygia guttata), suggesting that females little owls might live longer than their conspecific males. Yet, adult mortality is generally female-biased in birds (Liker & Székely, 2005) and whether little owls constitute an exception to this rule - possibly mediated by sex-specific telomere dynamics - remains to be explored.   

Quite surprisingly, the present study in little owls did not evidence any clear effect of environmental conditions on nestling’s telomere length, at both temporal and special scales. While a trend for a temporal effect was detected with telomere length being slightly shorter for nestling born the last year of the study (out of 4 years analysed), habitat quality (measured by the proportion of meadow and orchards in the nest environment) had absolutely no impact on nestling telomere length. Recently published studies in wild populations of vertebrates have highlighted the detrimental effects of harsh environmental conditions on telomere length (e.g. Dupoué et al., 2022 in common lizards, Zootoca vivipara), arguing for a key role of telomere dynamics in the emerging field of conservation physiology. While we can recognize the relevance of such an integrative approach, especially in the current context of climate change, the study by Criscuolo and colleagues (2023) reminds us that the relationships between environmental conditions and telomere dynamics are far from straightforward. Depending on the species and its life history, telomere length in early life could indeed capture very different environmental signals.

References

Blackburn, E. H., Epel, E. S., & Lin, J. (2015). Human telomere biology: A contributory and interactive factor in aging, disease risks, and protection. Science, 350(6265), 1193-1198. https://doi.org/10.1126/science.aab3389
 
Chatelain, M., Drobniak, S. M., & Szulkin, M. (2019). The association between stressors and telomeres in non-human vertebrates: A meta-analysis. Ecology Letters, 23, 381-398. https://doi.org/10.1111/ele.13426
 
Criscuolo, F., Fache, I., Scaar, B., Zahn, S. & Bleu, J. (2023). Telomere length vary with sex, hatching rank and year of birth in little owls, Athene noctua. EcoEvoRxiv, ver.4, peer-reviewed and recommended by PCI Evol Biol. https://doi.org/10.32942/X2BS3S
 
Dupoué, A., Blaimont, P., Angelier, F., Ribout, C., Rozen-Rechels, D., Richard, M., & Le Galliard, J. F. (2022). Lizards from warm and declining populations are born with extremely short telomeres. Proceedings of the National Academy of Sciences, 119(33), 2201371119. https://doi.org/10.1073/pnas.2201371119
 
Heidinger, B. J., Blount, J. D., Boner, W., Griffiths, K., Metcalfe, N. B., & Monaghan, P. (2012). Telomere length in early life predicts lifespan. Proceedings of the National Academy of Sciences, 109(5), 1743-1748. https://doi.org/10.1073/pnas.1113306109
 
Liker, A., & Székely, T. (2005). Mortality costs of sexual selection and parental care in natural populations of birds. Evolution, 59(4), 890-897. https://doi.org/10.1111/j.0014-3820.2005.tb01762.x
 
López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M., & Kroemer, G. (2023). Hallmarks of aging: An expanding universe. Cell, 186(2), 243-278. https://doi.org/10.1016/j.cell.2022.11.001
 
Monaghan, P., Olsson, M., Richardson, D. S., Verhulst, S., & Rogers, S. M. (2022). Integrating telomere biology into the ecology and evolution of natural populations: Progress and prospects. Molecular Ecology, 31(23), 5909-5916. https://doi.org/10.1111/mec.16768
 
Monaghan, P., & Ozanne, S. E. (2018). Somatic growth and telomere dynamics in vertebrates: Relationships, mechanisms and consequences. Phil. Trans. R. Soc. B, 373(1741), 20160446. https://doi.org/10.1098/rstb.2016.0446
 
Remot, F., Ronget, V., Froy, H., Rey, B., Gaillard, J., Nussey, D. H., & Lemaitre, J. (2022). Decline in telomere length with increasing age across nonhuman vertebrates: A meta‐analysis. Molecular Ecology, 31(23), 5917-5932. https://doi.org/10.1111/mec.16145
 
Wilbourn, R. V., Moatt, J. P., Froy, H., Walling, C. A., Nussey, D. H., & Boonekamp, J. J. (2018). The relationship between telomere length and mortality risk in non-model vertebrate systems: A meta-analysis. Phil. Trans. R. Soc. B, 373(1741), 20160447. https://doi.org/10.1098/rstb.2016.0447

Telomere length vary with sex, hatching rank and year of birth in little owls, *Athene noctua*François Criscuolo, Inès Fache, Bertrand Scaar, Sandrine Zahn, Josefa Bleu<p>Telomeres are non-coding DNA sequences located at the end of linear chromosomes, protecting genome integrity. In numerous taxa, telomeres shorten with age and telomere length (TL) is positively correlated with longevity. Moreover, TL is also af...Evolutionary Ecology, Life HistoryJean-François Lemaitre2023-03-07 09:44:32 View
03 Oct 2023
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The evolutionary dynamics of plastic foraging and its ecological consequences: a resource-consumer model

Evolution and consequences of plastic foraging behavior in consumer-resource ecosystems

Recommended by based on reviews by 2 anonymous reviewers

Plastic responses of organisms to their environment may be maladaptive in particular when organisms are exposed to new environments. Phenotypic plasticity may also have opposite effects on the adaptive response of organisms to environmental changes: whether phenotypic plasticity favors or hinders such adaptation depends on a balance between the ability of the population to respond to the change non-genetically in the short term, and the weakened genetic response to environmental change. These topics have received continued attention, particularly in the context of climate change (e.g., Chevin et al. 2013, Duputié et al., 2015, Vinton et al . 2022).

In their work, Ledru et al. focus on the adaptive nature of plastic behavior and on its consequences in a consumer-resource ecosystem. As they emphasize, previous works have found that plastic foraging promotes community stability, but these did not consider plasticity as an evolving trait, so Ledru et al. set out to test whether this conclusion holds when both plastic foraging and niche traits of consumers and resources evolve (though ultimately, their new conclusions may not all depend on plasticity evolving). Along the way, they first seek to clarify when such plasticity will evolve, and how it affects the evolution of the niche diversity of consumers and resources, before turning to the question of consumer persistence. 

The model is rather complex, as three traits are allowed to evolve, and the resource uptake expressed through plastic behavior has its own dynamics affected by some form of social learning. Classically, in models of niche evolution, a consumer's efficiency in exploiting a resource characterized by a trait y (here, the resource's individual niche trait), has been described in terms of location-scale (typically Gaussian) kernels, with mean x (the consumer's individual niche trait) specifying the most efficiently exploited resource, and with variance characterizing individual niche breadth. The evolution of the variance has been considered in some previous models but is assumed to be fixed here.  Rather, the new model considers the evolution of the distribution of resource traits, of the consumer's individual niche trait (which is not plastic), and of a "plastic foraging trait" that controls the relative time spent foraging plastically versus foraging randomly. When foraging plastically, the consumers modify their foraging effort towards the type of resource that maximizes their energy intake. in some previous models, the effect of variation in the extent of plastic foraging was already considered, but the evolution of allocation to a plastic foraging strategy versus random foraging was not considered. The model is formulated through reaction-diffusion equations, and its dynamics is investigated by numerical integration.

Foraging plasticity readily evolves, when resources vary widely enough, competition for resources is strong, and the cost of plasticity is weak. This means in particular that a large individual niche width of consumers selects for increased plastic foraging, as the evolution of plastic foraging leads to reduced niche overlap between consumers. The evolution of plastic foraging itself generally, though not always, favors the diversification of the niche traits of consumers and of resources. There is thus a positive feedback loop between plastic foraging and resource diversity. Ledru et al. conclude that the total niche width of the consumer population should also correlate with the evolution of plastic foraging, an implication which they relate to the so-called niche variation hypothesis and to empirical tests of it. 

The joint evolution of the consumer's individual niche trait and plastic foraging trait generates a striking pattern within populations: consumers whose individual niche trait is at an edge of the resource distribution forage more plastically. The authors observe that this relatively simple prediction has not been subjected to any empirical test. 

Returning to the question of consumer persistence, Ledru et al. evaluate this persistence when consumer mortality increases, and in response to either gradual or sudden environmental changes. These different perturbations all reduce the benefits of plastic foraging. The effect of plastic foraging on stability are complex, being negative or positive effect depending on the type of disturbance, and in particular the ecosystem has a lower sustainable rate of environmental change in the presence of plastic foraging. However, allowing the evolutionary regression of plastic foraging then has a comparatively positive effect on persistence.

Despite the substantial effort devoted to analyzing this complex model, relaxing some of its assumptions would likely reveal further complexities. Notably, the overall effect of plasticity on consumer persistence depends on effects already encountered in models of the adaptive response of single species to environmental change: a fast non-genetic response in the short term versus a weakened genetic response in the longer term. The overall balance between these opposite effects on adaptation may be difficult to predict robustly. In the case of a constant rate of environmental change, the results of the present model depend on a lag load between the trait changes of consumer and resource populations, and the extent of the lag may also depend on many factors, such as the extent of genetic variation (e.g., Bürger & Lynch, 1995) for niche traits in consumers and resources. Here, the same variance of mutational effects was assumed for all three evolving traits. Further, spatial environmental variation, a central issue in studies of adaptive responses to environmental changes (e.g., Parmesan, 2006, Zhu et al., 2012), was not considered. Finally, the rate of adjustment of effort by consumers with given niche trait and plastic foraging trait values was assumed proportional to the density of consumers with such trait values. This was justified as a way of accounting for the use of social cues during foraging, but to the extent that they occur, social effects could manifest themselves through other learning dynamics. 

In conclusion, Ledru et al. have addressed a broad range of questions, suggesting new empirical tests of behavioural patterns on one side, and recovering in the context of community response to environmental changes a complexity that could be expected from earlier works on adaptive responses of organisms but that has been overlooked by previous works on community effects of phenotypic plasticity.

References

Bürger, R. and Lynch, M. (1995), Evolution and extinction in a changing environment: a quantitative-genetic analysis. Evolution, 49: 151-163. https://doi.org/10.1111/j.1558-5646.1995.tb05967.x

Chevin, L.-M., Collins, S. and Lefèvre, F. (2013), Phenotypic plasticity and evolutionary demographic responses to climate change: taking theory out to the field. Funct Ecol, 27: 967-979. https://doi.org/10.1111/j.1365-2435.2012.02043.x

Duputié, A., Rutschmann, A., Ronce, O. and Chuine, I. (2015), Phenological plasticity will not help all species adapt to climate change. Glob Change Biol, 21: 3062-3073. https://doi-org.inee.bib.cnrs.fr/10.1111/gcb.12914

Ledru, L., Garnier, J., Guillot, O., Faou, E., & Ibanez, S. (2023). The evolutionary dynamics of plastic foraging and its ecological consequences: a resource-consumer model. EcoEvoRxiv, ver. 4 peer-reviewed and recommended by Peer Community In Evolutionary Biology. https://doi.org/10.32942/X2QG7M

Parmesan, C. (2006) Ecological and evolutionary responses to recent climate change
Annual Review of Ecology, Evolution, and Systematics 2006 37:1, 637-669. https://doi.org/10.1146/annurev.ecolsys.37.091305.110100

Vinton, A.C., Gascoigne, S.J.L., Sepil, I., Salguero-Gómez, R., (2022) Plasticity’s role in adaptive evolution depends on environmental change components. Trends in Ecology & Evolution, 37: 1067-1078.
https://doi.org/10.1016/j.tree.2022.08.008

Zhu, K., Woodall, C.W. and Clark, J.S. (2012), Failure to migrate: lack of tree range expansion in response to climate change. Glob Change Biol, 18: 1042-1052. https://doi.org/10.1111/j.1365-2486.2011.02571.x

The evolutionary dynamics of plastic foraging and its ecological consequences: a resource-consumer modelLéo Ledru, Jimmy Garnier, Océane Guillot, Erwan Faou, Camille Noûs, Sébastien Ibanez<p style="text-align: justify;">Phenotypic plasticity has important ecological and evolutionary consequences. In particular, behavioural phenotypic plasticity such as plastic foraging (PF) by consumers, may enhance community stability. Yet little ...Bioinformatics & Computational Biology, Evolutionary Dynamics, Evolutionary Ecology, Phenotypic PlasticityFrançois Rousset2023-03-25 12:04:08 View
23 Feb 2024
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Exploring the effects of ecological parameters on the spatial structure of genetic tree sequences

Disentangling the impact of mating and competition on dispersal patterns

Recommended by based on reviews by Anthony Wilder Wohns, Christian Huber and 2 anonymous reviewers

Spatial population genetics is a field that studies how different evolutionary processes shape geographical patterns of genetic variation. This field is currently hampered by the lack of a deep understanding of the impact of different evolutionary processes shaping the genetic diversity observed across a continuous space (Bradburd and Ralph 2019). Luckily, the recent development of slendr (Petr et al. 2023), which uses the simulator SLiM (Haller and Messer 2023), provides a powerful tool to perform simulations to analyze the impact of different evolutionary parameters on spatial patterns of genetic variation. Here, Ianni-Ravn, Petr, and Racimo 2023 present a series of well-designed simulations to study how three evolutionary factors (dispersal distance, competition distance, and mate choice distance) shape the geographical structure of genealogies.

The authors model the dispersal distance between parents and their offspring using five different distributions. Then, the authors perform simulations and they contrast the correspondence between the distribution of observed parent-offspring distances (called DD in the paper) and the distribution used in the simulations (called DF). The authors observe a reasonable correspondence between DF and DD. The authors then show that the competition distance, which decreases the fitness of individuals due to competition for resources if the individuals are close to each other, has small effects on the differences between DD and DF. In contrast, the mate choice distance (which specifies how far away can a parent go to choose a mate) causes discrepancies between DD and DF. When the mate choice distance is small, the individuals tend to cluster close to each other. Overall, these results show that the observed distances between parents and offspring are dependent on the three parameters inspected (dispersal distance, competition distance, and mate choice distance) and make the case that further ecological knowledge of each of these parameters is important to determine the processes driving the dispersal of individuals across geographical space. Based on these results, the authors argue that an “effective dispersal distance” parameter, which takes into account the impact of mate choice distance and dispersal distance, is more prone to be inferred from genetic data.

The authors also assess our ability to estimate the dispersal distance using genealogical data in a scenario where the mating distance has small effects on the dispersal distance. Interestingly, the authors show that accurate estimates of the dispersal distance can be obtained when using information from all the parents and offspring going from the present back to the coalescence of all the individuals to the most recent common ancestor. On the other hand, the estimates of the dispersal distance are underestimated when less information from the parent-offspring relationships is used to estimate the dispersal distance.

This paper shows the importance of considering mating patterns and the competition for resources when analyzing the dispersal of individuals. The analysis performed by the authors backs up this claim with carefully designed simulations. I recommend this preprint because it makes a strong case for the consideration of ecological factors when analyzing the structure of genealogies and the dispersal of individuals. Hopefully more studies in the future will continue to use simulations and to develop analytical theory to understand the importance of various ecological processes driving spatial genetic variation changes.

References

Bradburd, Gideon S., and Peter L. Ralph. 2019. “Spatial Population Genetics: It’s About Time.” Annual Review of Ecology, Evolution, and Systematics 50 (1): 427–49. https://doi.org/10.1146/annurev-ecolsys-110316-022659.

Haller, Benjamin C., and Philipp W. Messer. 2023. “SLiM 4: Multispecies Eco-Evolutionary Modeling.” The American Naturalist 201 (5): E127–39. https://doi.org/10.1086/723601.

Ianni-Ravn, Mariadaria K., Martin Petr, and Fernando Racimo. 2023. “Exploring the Effects of Ecological Parameters on the Spatial Structure of Genealogies.” bioRxiv, ver. 3 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2023.03.27.534388.

Petr, Martin, Benjamin C. Haller, Peter L. Ralph, and Fernando Racimo. 2023. “Slendr: A Framework for Spatio-Temporal Population Genomic Simulations on Geographic Landscapes.” Peer Community Journal 3 (e121). https://doi.org/10.24072/pcjournal.354.

Exploring the effects of ecological parameters on the spatial structure of genetic tree sequencesMariadaria K. Ianni-Ravn, Martin Petr, Fernando Racimo<p>Geographic space is a fundamental dimension of evolutionary change, determining how individuals disperse and interact with each other. Consequently, space has an important influence on the structure of genealogies and the distribution of geneti...Phylogeography & Biogeography, Population Genetics / GenomicsDiego Ortega-Del Vecchyo2023-03-31 18:21:02 View
14 Dec 2023
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Genetic sex determination in three closely related hydrothermal vent gastropods, including one species with intersex individuals

A shared XY sex chromosome system with variable recombination rates

Recommended by based on reviews by Hugo Darras, Daniel Jeffries and 1 anonymous reviewer

Many species with separate sexes have evolved sex chromosomes, with the sex-limited chromosomes (i.e. the Y or W chromosomes) exhibiting a wide range of genetic divergences from their homologous X or Z chromosomes (Bachtrog et al., 2014). Variable divergences can result from the cessation of recombination between sex chromosomes that occurred at different time points, with the mechanisms of initiation and expansion of recombination suppression along sex chromosomes remaining poorly understood (Charlesworth, 2017). 

The study by Castel et al (2023) describes the serendipitous discovery of a shared XY sex chromosome system in three closely related hydrothermal vent gastropods. The X and Y chromosomes appear to still recombine but at variable rates across the three species. This variation makes the gastropod system a very promising focus for future research on sex chromosome evolution. 

An additional intriguing finding is that some females in one of three gastropod species contain male reproductive tissue in their gonads, providing a fascinating case of a mixed or transitory sexual system. Overall, the study by Castel et al (2023) offers the first insights into the reproduction and sex chromosome system of animals living in deep marine vents, which have remained poorly studied and open outstanding research perspectives on these creatures.

References

Bachtrog, D., J.E.Mank, C.L.Peichel, M.Kirkpatrick, S.P.Otto, T.L. Ashman, M.W.Hahn, J.Kitano, I.Mayrose, R.Ming, et al. 2014.Sex determination: why so many ways of doing it? PLoSBiol. 12:e1001899. https://doi.org/10.1371/journal.pbio.1001899

Charlesworth, D. Young sex chromosomes in plants and animals. 2019. New Phytologist 224: 1095–1107. https://doi.org/10.1111/nph.16002

Castel J, Pradillon F, Cueff V, Leger G, Daguin-Thiébaut C, Ruault S, Mary J, Hourdez S, Jollivet D, and Broquet T 2023. Genetic sex determination in three closely related hydrothermal vent gastropods, including one species with intersex individuals. bioRxiv, ver. 2 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2023.04.11.536409

Genetic sex determination in three closely related hydrothermal vent gastropods, including one species with intersex individualsCastel J, Pradillon F, Cueff V, Leger G, Daguin-Thiébaut C, Ruault S, Mary J, Hourdez S, Jollivet D, and Broquet T<p style="text-align: justify;">Molluscs have a wide variety of sexual systems and have undergone many transitions from separate sexes to hermaphroditism or vice versa, which is of interest for studying the evolution of sex determination and diffe...Population Genetics / Genomics, Reproduction and SexTanja Schwander2023-04-14 11:48:25 View
04 Mar 2024
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Interplay between fecundity, sexual and growth selection on the spring phenology of European beech (Fagus sylvatica L.).

Interplay between fecundity, sexual and growth selection on the spring phenology of European beech (Fagus sylvatica L.)

Recommended by ORCID_LOGO based on reviews by 2 anonymous reviewers

Starting with the seminar paper by Lande & Arnold (1983), several studies have addressed phenotypic selection in natural populations of a wide variety of organisms, with a recent renewed interest in forest trees (e.g., Oddou-Muratorio et al. 2018; Alexandre et al. 2020; Westergren et al. 2023). Because of their long generation times, long-lived organisms such as forest trees may suffer the most from maladaptation due to climate change, and whether they will be able to adapt to new environmental conditions in just one or a few generations is hotly debated.

In this study, Oddou-Muratorio and colleagues (2024) extend the current framework to add two additional selection components that may alter patterns of fecundity selection and the estimation of standard selection gradients, namely sexual selection (evaluated as differences in flowering phenology conducting to assortative mating) and growth (viability) selection. Notably, the study is conducted in two contrasted environments (low vs high altitude populations) providing information on how the environment may modulate selection patterns in spring phenology. Spring phenology is a key adaptive trait that has been shown to be already affected by climate change in forest trees (Alberto et al. 2013). While fecundity selection for early phenology has been extensively reported before (see Munguía-Rosas et al. 2011), the authors found that this kind of selection can be strongly modulated by sexual selection, depending on the environment. Moreover, they found a significant correlation between early phenology and seedling growth in a common garden, highlighting the importance of this trait for early survival in European beech.

As a conclusion, this original research puts in evidence the need for more integrative approaches for the study of natural selection in the field, as well as the importance of testing multiple environments and the relevance of common gardens to further evaluate phenotypic changes due to real-time selection.

PS: The recommender and the first author of the preprint have shared authorship in a recent paper in a similar topic (Westergren et al. 2023). Nevertheless, the recommender has not contributed in any way or was aware of the content of the current preprint before acting as recommender, and steps have been taken for a fair and unpartial evaluation.

References

Alberto, F. J., Aitken, S. N., Alía, R., González‐Martínez, S. C., Hänninen, H., Kremer, A., Lefèvre, F., Lenormand, T., Yeaman, S., Whetten, R., & Savolainen, O. (2013). Potential for evolutionary responses to climate change - evidence from tree populations. Global Change Biology, 19(6), 1645‑1661.
https://doi.org/10.1111/gcb.12181
 
Alexandre, H., Truffaut, L., Klein, E., Ducousso, A., Chancerel, E., Lesur, I., Dencausse, B., Louvet, J., Nepveu, G., Torres‐Ruiz, J. M., Lagane, F., Musch, B., Delzon, S., & Kremer, A. (2020). How does contemporary selection shape oak phenotypes? Evolutionary Applications, 13(10), 2772‑2790.
https://doi.org/10.1111/eva.13082
 
Lande, R., & Arnold, S. J. (1983). The measurement of selection on correlated characters. Evolution, 37(6), 1210-1226.
https://doi.org/10.2307/2408842
 
Munguía-Rosas, M. A., Ollerton, J., Parra-Tabla, V., & De-Nova, J. A. (2011). Meta-analysis of phenotypic selection on flowering phenology suggests that early flowering plants are favoured. Ecology Letters, 14(5), 511-521
https://doi.org/10.1111/j.1461-0248.2011.01601.x

Oddou-Muratorio S, Bontemps A, Gauzere J, Klein E (2024) Interplay between fecundity, sexual and growth selection on the spring phenology of European beech (Fagus sylvatica L.). bioRxiv, 2023.04.27.538521, ver. 2 peer-reviewed and recommended by Peer Community In Evolutionary Biology https://doi.org/10.1101/2023.04.27.538521 

Oddou-Muratorio, S., Gauzere, J., Bontemps, A., Rey, J.-F., & Klein, E. K. (2018). Tree, sex and size: Ecological determinants of male vs. female fecundity in three Fagus sylvatica stands. Molecular Ecology, 27(15), 3131‑3145.
https://doi.org/10.1111/mec.14770
 
Westergren, M., Archambeau, J., Bajc, M., Damjanić, R., Theraroz, A., Kraigher, H., Oddou‐Muratorio, S., & González‐Martínez, S.C. (2023). Low but significant evolutionary potential for growth, phenology and reproduction traits in European beech. Molecular Ecology, Early View 
https://doi.org/10.1111/mec.17196

Interplay between fecundity, sexual and growth selection on the spring phenology of European beech (*Fagus sylvatica* L.).Sylvie Oddou-Muratorio, Aurore Bontemps, Julie Gauzere, Etienne Klein<p>Background: Plant phenological traits such as the timing of budburst or flowering can evolve on ecological timescales through response to fecundity and viability selection. However, interference with sexual selection may arise from assortative ...Adaptation, Evolutionary Ecology, Quantitative Genetics, Reproduction and Sex, Sexual SelectionSantiago C. Gonzalez-Martinez2023-05-02 11:57:23 View
25 Jan 2024
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Sperm production and allocation in response to risk of sperm competition in the black soldier fly Hermetia illucens

Elevated sperm production and faster transfer: plastic responses to the risk of sperm competition in males of the black sodier fly Hermetia illuce

Recommended by ORCID_LOGO based on reviews by Rebecca Boulton, Isabel Smallegange and 1 anonymous reviewer

In this paper (Manas et al., 2023), the authors investigate male responses to risk of sperm competition in the black soldier fly Hermetia illuce, a widespread insect that has gained recent attention for its potential to be farmed for sustainable food production (Tomberlin & van Huis, 2020).

Using an experimental approach that simulated low-risk (males were kept individually) and high-risk (males were kept in groups of 10) of sperm competition, they found that males reared in groups showed a significant increase in sperm production compared with males reared individually. This shows a response to the rearing environment in sperm production that is consistent with an increase in the perceived risk of sperm competition.

These males were then used in mating experiments to determine whether sperm allocation to females during mating was influenced by the perceived risk of sperm competition. Mating experiments were initiated in groups, since mating only occurs when more than one male and one female are present, indicating strong sexual selection in the wild. Once a copulation began, the pair was moved to a new environment with no competition, with male competitors, or with other females, to test how social environment and potentially the sex of surrounding individuals influenced sperm allocation during mating. Copulation duration and the number of sperm transferred were subsequently counted.

In these mating experiments, the number of sperm stored in the female spermathecae increased under immediate risk of sperm competition. Interestingly, this was not because males copulated for longer depending on the risk of sperm competition, indicating that males respond plastically to the risk of competition by elevating their investment in sperm production and speed of sperm transfer. There was no difference between competitive environments consisting of males or females respectively, suggesting that it is the presence of other flies per se that influence sperm allocation.

The study provides an interesting new example of how males alter reproductive investment in response to social context and sexual competition in their environment. In addition, it provides new insights into the reproductive biology of the black soldier fly Hermetia illucens, which may be relevant for optimizing farming conditions.

References

Manas F, Labrousse C, Bressac C (2023) Sperm production and allocation in response to risks of sperm competition in the black soldier fly Hermetia illucens. bioRxiv, 2023.06.20.544772, ver. 5 peer-reviewed and recommended by Peer Community in Evolutionary Biology.  https://doi.org/10.1101/2023.06.20.544772

Tomberlin JK, Van Huis A (2020) Black soldier fly from pest to ‘crown jewel’ of the insects as feed industry: an historical perspective. Journal of Insects as Food and Feed, 6, 1–4. https://doi.org/10.3920/JIFF2020.0003

Sperm production and allocation in response to risk of sperm competition in the black soldier fly Hermetia illucensFrédéric Manas, Carole Labrousse, Christophe Bressac<p style="text-align: justify;">In polyandrous species, competition between males for offspring paternity goes on after copulation through the competition of their ejaculates for the fertilisation of female's oocytes. Given that males allocating m...Reproduction and Sex, Sexual SelectionTrine Bilde2023-06-26 09:41:07 View
24 Sep 2024
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Spatial autocorrelation and host anemone species drive variation in local components of fitness in a wild clownfish population

Is our best measure of fitness correlated with environment? A study in an orange clownfish population.

Recommended by ORCID_LOGO based on reviews by Stefan Vriend and 2 anonymous reviewers

Getting a clear definition of fitness for a particular evolutionary biology question is a complex challenge, fraught with pitfalls and misconceptions (Orr, 2009; Walsh & Lynch, 2018). In longitudinal surveys of wild populations, lifetime reproductive success (LRS) is generally considered the best measure of individual fitness (Bonnet, 2022). However, it is important to bear in mind that LRS is only a (noisy) measure of the realised success, relying on a substantial amount of assumptions (e.g. with regard to generation overlap, Walsh & Lynch, 2018), not a direct measure of fitness.

In a study on the clownfish, Marrot et al. (2024) studied the spatial and ecological drivers of lifetime reproductive success. To do so, they analysed a 10-year long survey on over 300 anemones harbouring clownfishes, and used a genetics-based pedigree to infer the LRS of each individual. Using a characterisation of the micro-habitat provided by each anemone, they used the anemone species, density and depth as ecological drivers and spatial-autocorrelated models to study more general (and undefined) spatial drivers.

The authors found that LRS was influenced by a significant amount by the spatial structure of the population, and, to some extent, by the anemone species harbouring the clownfish individuals. Together, they explain a substantial proportion of the individual variation in LRS.

While the actual determinants of spatial variation of LRS in this (and other) species remain understood, this study highlights an important aspect of measuring fitness in wild populations using LRS: it is particularly noisy and subject to environmental variation. This certainly does not mean that LRS is a bad proxy for fitness, it is still among the best measure of it we can have access to. However, it highlights how carefully we should thread when analysing it. Especially, spatial auto-correlation of LRS, combined with population structure within a population, would lead to genotype-environment correlation for fitness, which is likely to bias predictions of response to natural selection and would be extremely difficult to estimate (Falconer & Mackay, 1996).

References

Pascal Marrot, Cécile Fauvelot, Michael L. Berumen, Maya Srinivasan, Geoffrey P. Jones, Serge Planes, and Benoit Pujol (2024) Spatial autocorrelation and host anemone species drive variation in local components of fitness in a wild clownfish population. Zenodo, ver.3 peer-reviewed and recommended by PCI Evol Biol https://doi.org/10.5281/zenodo.13806778

Bonnet, T., Morrissey, M. B., de Villemereuil, P., Alberts, S. C., Arcese, P., Bailey, L. D., Boutin, S., Brekke, P., Brent, L. J. N., Camenisch, G., Charmantier, A., Clutton-Brock, T. H., Cockburn, A., Coltman, D. W., Courtiol, A., Davidian, E., Evans, S. R., Ewen, J. G., Festa-Bianchet, M., … Kruuk, L. E. B. (2022). Genetic variance in fitness indicates rapid contemporary adaptive evolution in wild animals. Science, 376(6596), 1012–1016. https://doi.org/10.1126/science.abk0853

Falconer, D. S. and Mackay, T. F. C. (1996). Introduction to quantitative genetics (4th ed.). Benjamin Cummings.

Orr, H. A. (2009). Fitness and its role in evolutionary genetics. Nature Reviews Genetics, 10(8), 531–539. https://doi.org/10.1038/nrg2603

Walsh, B. and Lynch, M. (2018). Evolution and selection of quantitative traits. Oxford University Press.

Spatial autocorrelation and host anemone species drive variation in local components of fitness in a wild clownfish populationPascal Marrot, Cécile Fauvelot, Michael L. Berumen, Maya Srinivasan, Geoffrey P. Jones, Serge Planes, and Benoit Pujol<p style="text-align: justify;">The susceptibility of species to habitat changes depends on which ecological drivers shape individual fitness components. To date, only a few studies have quantified fitness components such as the Lifetime Reproduct...Adaptation, Evolutionary Ecology, Quantitative GeneticsPierre de Villemereuil2023-07-31 11:42:58 View
04 Mar 2024
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Simultaneous Inference of Past Demography and Selection from the Ancestral Recombination Graph under the Beta Coalescent

Beyond the standard coalescent: demographic inference with complete genomes and graph neural networks under the beta coalescent

Recommended by ORCID_LOGO based on reviews by 2 anonymous reviewers

Modelling the evolution of complete genome sequences in populations requires accounting for the recombination process, as a single tree can no longer describe the underlying genealogy. The sequentially Markov coalescent (SMC, McVean and Cardin 2005; Marjoram and Wall 2006) approximates the standard coalescent with recombination process and permits estimating population genetic parameters (e.g., population sizes, recombination rates) using population genomic datasets. As such datasets become available for an increasing number of species, more fine-tuned models are needed to encompass the diversity of life cycles of organisms beyond the model species on which most methods have been benchmarked.

The work by Korfmann et al. (Korfmann et al. 2024) represents a significant step forward as it accounts for multiple mergers in SMC models. Multiple merger models account for simultaneous coalescence events so that more than two lineages find a common ancestor in a given generation. This feature is not allowed in standard coalescent models and may result from selection or skewed offspring distributions, conditions likely met by a broad range of species, particularly microbial.

Yet, this work goes beyond extending the SMC, as it introduces several methodological innovations. The "classical" SMC-based inference approaches rely on hidden Markov models to compute the likelihood of the data while efficiently integrating over the possible ancestral recombination graphs (ARG). Following other recent works (e.g. Gattepaille et al. 2016), Korfmann et al. propose to separate the ARG inference from model parameter estimation under maximum likelihood (ML). They introduce a procedure where the ARG is first reconstructed from the data and then taken as input in the model fitting step. While this approach does not permit accounting for the uncertainty in the ARG reconstruction (which is typically large), it potentially allows for the extraction of more information from the ARG, such as the occurrence of multiple merging events. Going away from maximum likelihood inference, the authors trained a graph neural network (GNN) on simulated ARGs, introducing a new, flexible way to estimate population genomic parameters.

The authors used simulations under a beta-coalescent model with diverse demographic scenarios and showed that the ML and GNN approaches introduced can reliably recover the simulated parameter values. They further show that when the true ARG is given as input, the GNN outperforms the ML approach, demonstrating its promising power as ARG reconstruction methods improve. In particular, they showed that trained GNNs can disentangle the effects of selective sweeps and skewed offspring distributions while inferring past population size changes.

This work paves the way for new, exciting applications, though many questions must be answered. How frequent are multiple mergers? As the authors showed that these events "erase" the record of past demographic events, how many genomes are needed to conduct reliable inference, and can the methods computationally cope with the resulting (potentially large) amounts of required data? This is particularly intriguing as micro-organisms, prone to strong selection and skewed offspring distributions, also tend to carry smaller genomes.

References

Gattepaille L, Günther T, Jakobsson M. 2016. Inferring Past Effective Population Size from Distributions of Coalescent Times. Genetics 204:1191-1206.
https://doi.org/10.1534/genetics.115.185058
 
Korfmann K, Sellinger T, Freund F, Fumagalli M, Tellier A. 2024. Simultaneous Inference of Past Demography and Selection from the Ancestral Recombination Graph under the Beta Coalescent. bioRxiv, 2022.09.28.508873. ver. 5 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2022.09.28.508873
 
Marjoram P, Wall JD. 2006. Fast "coalescent" simulation. BMC Genet. 7:16.
https://doi.org/10.1186/1471-2156-7-16
 
McVean GAT, Cardin NJ. 2005. Approximating the coalescent with recombination. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 360:1387-1393.
https://doi.org/10.1098/rstb.2005.1673

Simultaneous Inference of Past Demography and Selection from the Ancestral Recombination Graph under the Beta CoalescentKevin Korfmann, Thibaut Sellinger, Fabian Freund, Matteo Fumagalli, Aurélien Tellier<p style="text-align: justify;">The reproductive mechanism of a species is a key driver of genome evolution. The standard Wright-Fisher model for the reproduction of individuals in a population assumes that each individual produces a number of off...Adaptation, Bioinformatics & Computational Biology, Evolutionary Applications, Evolutionary Theory, Life History, Population Genetics / GenomicsJulien Yann Dutheil2023-07-31 13:11:22 View