The social amoeba Dictyostelium discoideum is an important model system for the study of cooperation and multicellularity as is has both unicellular and aggregative life phases. In the aggregative phase, which typically occurs when nutrients are limiting, individual cells eventually gather together to form a fruiting bodies whose spores may be dispersed to another, better, location and whose stalk cells, which support the spores, die. This extreme form of cooperation has been the focus of numerous studies that have revealed the importance genetic relatedness and kin selection (Hamilton 1964; Lehmann and Rousset 2014) in explaining the maintenance of this cooperative collective behavior (Strassmann et al. 2000; Kuzdzal-Fick et al. 2011; Strassmann and Queller 2011). However, much remains unknown with respect to how the interactions between individual cells, their neighbors, and their environment produce cooperative behavior at the scale of whole groups or collectives. In this preprint, Forget et al. (2021) describe how the D. discoideum system is crucial in this respect because it allows these cellular-level interactions to be studied in a systematic and tractable manner.
Spore bias, which is the tendency of a particular genotype or strain to disproportionately migrate to the spore instead of the stalk, is often used to define which strains are "cheaters" (positive spore bias) and which are "cooperative" (negative spore bias). Forget et al. (2021) note that spore bias depends on a number of stochastic factors including external drivers such as variation in environmental (or nutrient) quality and internal drivers like cell-cycle phase at the time of starvation. Spore bias is also affected by the social environment where the fraction of cheater strains in a spore may be limited by the ability of the remaining stalk cells to support the spore. The social environment can also affect cells through their differential responsiveness to the chemical factors that induce differentiation into stalk cells; responsiveness is partly a function of nutrient quality (Thompson and Kay 2000), which in turn can be a function of cell density. Thus, Forget et al. (2021) highlight a number of mechanisms that could generate frequency-dependent selection that would lead to the stable maintenance of multiple strains with different spore biases; in other words, both cheater and cooperative strains might stably coexist due to these cellular-level interactions.
The cellular-level interactions that Forget et al. (2021) highlight are particularly important because they pose a challenge evolutionary theory: some evolutionary models of social and collective behavior neglect or simplify these interactions. For example, Forget et al. (2021) note that the developmental, behavior, and environmental timescales relevant for Dictyostelium fruiting body formation all overlap. Evolutionary analyses often assume some of these timescales, for example developmental and behavior, are separate in order to simplify the analysis of any interactions. Thus, new theoretical work that allows these timescales to overlap may shed light on how cellular-level interactions can produce environmental, physiological, and behavioral feedbacks that drive the evolution of cooperation and other collective behaviors.

References

Forget, M., Adiba, S. and De Monte, S.(2021) Social conflicts in *Dictyostelium discoideum *: a matter of scales. HAL, hal-03088868, ver. 2 peer-reviewed and recommended by PCI Evolutionary Biology. https://hal.archives-ouvertes.fr/hal-03088868/

Hamilton, W. D. (1964). The genetical evolution of social behaviour. II. Journal of theoretical biology, 7(1), 17-52. doi: https://doi.org/10.1016/0022-5193(64)90039-6

Kuzdzal-Fick, J. J., Fox, S. A., Strassmann, J. E., and Queller, D. C. (2011). High relatedness is necessary and sufficient to maintain multicellularity in Dictyostelium. Science, 334(6062), 1548-1551. doi: https://doi.org/10.1126/science.1213272

Lehmann, L., and Rousset, F. (2014). The genetical theory of social behaviour. Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1642), 20130357. doi: https://doi.org/10.1098/rstb.2013.0357

Strassmann, J. E., and Queller, D. C. (2011). Evolution of cooperation and control of cheating in a social microbe. Proceedings of the National Academy of Sciences, 108(Supplement 2), 10855-10862. doi: https://doi.org/10.1073/pnas.1102451108

Strassmann, J. E., Zhu, Y., & Queller, D. C. (2000). Altruism and social cheating in the social amoeba Dictyostelium discoideum. Nature, 408(6815), 965-967. doi: https://doi.org/10.1038/35050087

Thompson, C. R., & Kay, R. R. (2000). Cell-fate choice in Dictyostelium: intrinsic biases modulate sensitivity to DIF signaling. Developmental biology, 227(1), 56-64. doi: https://doi.org/10.1006/dbio.2000.9877

Tropical biodiversity is immense, under enormous threat, and yet still poorly understood. Global climatic breakdown and habitat destruction are impacting on and removing this diversity before we can understand how the biota responds to such changes, or even fully appreciate what we are losing [1]. This is particularly the case for woody shrubs and trees [2] and for the flora of tropical Africa [3].  

Helmstetter et al. [4] have taken a significant step to improve our understanding of African tropical tree diversity in the context of past climatic change. They have done so by means of a remarkably in-depth analysis of one species of the tropical plant family Annonaceae: Annickia affinis [5]. A. affinis shows a distribution pattern in Africa found in various plant (but interestingly not animal) groups: a discontinuity between north and south of the equator [6]. There is no obvious physical barrier to cause this discontinuity, but it does correspond with present day distinct northern and southern rainy seasons. Various explanations have been proposed for this discontinuity, set out as hypotheses to be tested in this paper: climatic fluctuations resulting in changes in plant distributions in the Pleistocene, or differences in flowering times or in ecological niche between northerly and southerly populations. These explanations are not mutually exclusive, but they can be tested using phylogenetic inference – if you can sample variable enough sequence data from enough individuals – complemented with analysis of ecological niches and traits.  

Using targeted sequence capture, the authors amassed a dataset representing 351 nuclear markers for 112 individuals of A. affinis. This dataset is impressive for a number of reasons: First, sampling such a species across such a wide range in tropical Africa presents numerous challenges of itself. Second, the technical achievement of using this still relatively new sequencing technique with a custom set of baits designed specifically for this plant family [7] is also considerable. The result is a volume of data that just a few years ago would not have been feasible to collect, and which now offers the possibility to meaningfully analyse DNA sequence variation within a species across numerous independent loci of the nuclear genome. This is the future of our research field, and the authors have ably demonstrated some of its possibilities.  

Using this data, they performed on the one hand different population genetic clustering approaches, and on the other, different phylogenetic inference methods. I would draw attention to their use and comparison of coalescence and network-based approaches, which can account for the differences between gene trees that might be expected between populations of a single species. The results revealed four clades and a consistent sequence of divergences between them. The authors inferred past shifts in geographic range (using a continuous state phylogeographic model), depicting a biogeographic scenario involving a dispersal north over the north/south discontinuity; and demographic history, inferring in some (but not all) lineages increases in effective population size around the time of the last glacial maximum, suggestive of expansion from refugia. Using georeferenced specimen data, they compared ecological niches between populations, discovering that overlap was indeed smallest comparing north to south. Just the phenology results were effectively inconclusive: far better data on flowering times is needed than can currently be harvested from digitised herbarium specimens.  

Overall, the results add to the body of evidence for the impact of Pleistocene climatic changes on population structure, and for niche differences contributing to the present day north/south discontinuity. However, they also paint a complex picture of idiosyncratic lineage-specific responses, even within a single species. With the increasing accessibility of the techniques used here we can look forward to more such detailed analyses of independent clades necessary to test and to expand on these conclusions, better to understand the nature of our tropical plant diversity while there is still opportunity to preserve it for future generations.  

References

[1] Mace, G. M., Gittleman, J. L., and Purvis, A. (2003). Preserving the Tree of Life. Science, 300(5626), 1707–1709. doi: 10.1126/science.1085510
[2] Humphreys, A. M., Govaerts, R., Ficinski, S. Z., Nic Lughadha, E., and Vorontsova, M. S. (2019). Global dataset shows geography and life form predict modern plant extinction and rediscovery. Nature Ecology and Evolution, 3(7), 1043–1047. doi: 10.1038/s41559-019-0906-2
[3] Stévart, T., Dauby, G., Lowry, P. P., Blach-Overgaard, A., Droissart, V., Harris, D. J., Mackinder, B. A., Schatz, G. E., Sonké, B., Sosef, M. S. M., Svenning, J. C., Wieringa, J. J., and Couvreur, T. L. P. (2019). A third of the tropical African flora is potentially threatened with extinction. Science Advances, 5(11), eaax9444. doi: 10.1126/sciadv.aax9444
[4] Helmstetter, A. J., Amoussou, B. E. N., Bethune, K., Kandem, N. G., Kakaï, R. G., Sonké, B., and Couvreur, T. L. P. (2020). Phylogenomic approaches reveal how a climatic inversion and glacial refugia shape patterns of diversity in an African rain forest tree species. BioRxiv, 807727, ver. 3 peer-reviewed and recommended by PCI Evolutionary Biology. doi: 10.1101/807727
[5] Versteegh, C. P. C., and Sosef, M. S. M. (2007). Revision of the African genus Annickia (Annonaceae). Systematics and Geography of Plants, 77, 91–118.
[6] Hardy, O. J., Born, C., Budde, K., Daïnou, K., Dauby, G., Duminil, J., Ewédjé, E.-E. B. K., Gomez, C., Heuertz, M., Koffi, G. K., Lowe, A. J., Micheneau, C., Ndiade-Bourobou, D., Piñeiro, R., and Poncet, V. (2013). Comparative phylogeography of African rain forest trees: A review of genetic signatures of vegetation history in the Guineo-Congolian region. Comptes Rendus Geoscience, 345(7), 284-296. doi: 10.1016/j.crte.2013.05.001
[7] Couvreur, T. L. P., Helmstetter, A. J., Koenen, E. J. M., Bethune, K., Brandão, R. D., Little, S. A., Sauquet, H., and Erkens, R. H. J. (2019). Phylogenomics of the Major Tropical Plant Family Annonaceae Using Targeted Enrichment of Nuclear Genes. Frontiers in Plant Science, 9. doi: 10.3389/fpls.2018.01941

Inbreeding, or mating between relatives, generally lowers fitness [1]. Mating between genetically similar individuals can result in higher levels of homozygosity and consequently a higher frequency with which recessive disease alleles may be expressed within a population. Reduced fitness as a consequence of inbreeding, or inbreeding depression, can vary between individuals, sexes, populations and species [2], but remains a pervasive challenge for many organisms with small local population sizes, including humans [3]. But all is not lost for individuals within small populations, because an array of mechanisms can be employed to evade the negative effects of inbreeding [4], including sib-mating avoidance and dispersal [5, 6].

Despite thorough investigation of inbreeding and sib-mating avoidance in the laboratory, only very few studies have ventured into the field besides studies on vertebrates and eusocial insects. The study of Collet et al. [7] is a surprising exception, where the effect of male density and frequency of relatives on inbreeding avoidance was tested in the laboratory, after which robust field collections and microsatellite genotyping were used to infer relatedness and dispersal in natural populations. The parasitic wasp Venturia canescens is an excellent model system to study inbreeding, because mating success was previously found to decrease with increasing relatedness between mates in the laboratory [8] and this species thus suffers from inbreeding depression [9–11]. The authors used an elegant design combining population genetics and model simulations to estimate relatedness of mating partners in the field and compared that with a theoretical distribution of potential mate encounters when random mating is assumed. One of the most important findings of this study is that mating between siblings is not avoided in this species in the wild, despite negative fitness effects when inbreeding does occur. Similar findings were obtained for another insect species, the field cricket Gryllus campestris [12], which leaves us to wonder whether inbreeding tolerance could be more common in nature than currently appreciated.

The authors further looked into sex-specific dispersal patterns between two patches located a few hundred meters apart. Females were indeed shown to be more related within a patch, but no genetic differences were observed between males, suggesting that V. canescens males more readily disperse. Moreover, microsatellite data at 18 different loci did not reveal genetic differentiation between populations approximately 300 kilometers apart. Gene flow is thus occurring over considerable distances, which could play an important role in the ability of this species to avoid negative fitness consequences of inbreeding in nature.

Another interesting aspect of this work is that discrepancies were found between laboratory- and field-based data. What is the relevance of laboratory-based experiments if they cannot predict what is happening in the wild? Many, if not most, biologists (including us) bring our model system into the laboratory to control, at least to some extent, the plethora of environmental factors that could potentially affect our system (in ways that we do not want). Most behavioral studies on mating patterns and sexual selection are conducted in standardized laboratory conditions, but sexual selection is in essence social selection, because an individual’s fitness is partly determined by the phenotype of its social partners (i.e. the social environment) [13]. The social environment may actually dictate the expression of female mate choice and it is unclear how potential laboratory-induced social biases affect mating outcome. In V. canescens, findings using field-caught individuals paint a completely opposite picture of what was previously shown in the laboratory, i.e. sib-avoidance is not taking place in the field. It is likely that density, level of relatedness, sex ratio in the field, and/or the size of experimental arenas in the lab are all factors affecting mate selectivity, as we have previously shown in a butterfly [14–16]. If females, for example, typically only encounter a few males in sequence in the wild, it may be problematic for them to express choosiness when confronted simultaneously with two or more males in the laboratory. A recent study showed that, in the wild, female moths take advantage of staying in groups to blur male choosiness [17]. It is becoming more and more clear that what we observe in the laboratory may not actually reflect what is happening in nature [18]. Instead of ignoring the species-specific life history and ecological features of our favorite species when conducting lab experiments, we suggest that it is time to accept that we now have the theoretical foundations to tease apart what in this “environmental noise” actually shapes sexual selection in nature. Explicitly including ecology in studies on sexual selection will allow us to make more meaningful conclusions, i.e. rather than “this is what may happen in the wild”, we would be able to state “this is what often happens in nature”.

References

[1] Charlesworth D & Willis JH. 2009. The genetics of inbreeding depression. Nat. Rev. Genet. 10: 783–796. doi: 10.1038/nrg2664
[2] Hedrick PW & Garcia-dorado A. 2016. Understanding inbreeding depression, purging, and genetic rescue. Trends Ecol. Evol. 31: 940–952. doi: 10.1016/j.tree.2016.09.005
[3] Bittles AH & Black ML. 2010. Consanguinity, human evolution, and complex diseases. Proc. Natl. Acad. Sci. United States Am. 107: 1779–1786. doi: 10.1073/pnas.0906079106
[4] Pusey A & Wolf M. 1996. Inbreeding avoidance in animals. Trends Ecol. Evol. 11: 201–206. doi: 10.1016/0169-5347(96)10028-8
[5] Greenwood PJ & Harvey PH. 1978. Inbreeding and dispersal in the great tit. Nature 271: 52–54. doi: 10.1038/271052a0
[6] Szulkin M & Sheldon BC. 2008. Dispersal as a means of inbreeding avoidance in a wild bird population. Proc. R. Soc. B 275: 703–711. doi: 10.1098/rspb.2007.0989
[7] Collet M, Amat I, Sauzet S, Auguste A, Fauvergue X, Mouton L, Desouhant E. 2018. Insects and incest: sib-mating tolerance in natural populations of a parasitoid wasp. bioRxiv 169268, ver. 4 peer-reviewed by Peer Community In Evolutionary Biology. doi: 10.1101/169268
[8] Metzger M, Bernstein C, Hoffmeister TS & Desouhant E. 2010. Does kin recognition and sib-mating avoidance limit the risk of genetic incompatibility in a parasitic wasp ? PLoS One 5: e13505. doi: 10.1371/journal.pone.0013505
[9] Beukeboom LW. 2001. Single-locus complementary sex determination in the Ichneumonid Venturia canescens. Netherlands J. Zool. 51: 1–15. doi: 10.1163/156854201X00017
[10] Vayssade C, de Fazio C, Quaglietti B, Auguste A, Ris N, Fauvergue X. 2014. Inbreeding depression in a parasitoid wasp with single- locus complementary sex determination. PLoS One 9: 1–8. doi: 10.1371/journal.pone.0097733
[11] Chuine A, Sauzet S, Debias F & Desouhant E. 2015. Consequences of genetic incompatibility on fitness and mate choice: the male point of view. Biol. J. Linn. Soc. 114: 279–286. doi: 10.1111/bij.12421
[12] Bretman A, Rodri R & Tregenza T. 2011. Fine-scale population structure , inbreeding risk and avoidance in a wild insect population. Mol. Ecol. 20: 3045–3055. doi: 10.1111/j.1365-294X.2011.05140.x
[13] West-Eberhard MJ. 2014. Darwin’s forgotten idea: The social essence of sexual selection. Neurosci. Biobehav. Rev. 46: 501–508. doi: 10.1016/j.neubiorev.2014.06.015
[14] Holveck M-J, Gauthier A-L & Nieberding CM 2015. Dense, small and male-biased cages exacerbate male-male competition and reduce female choosiness in Bicyclus anynana. Anim. Behav. 104: 229–245. doi: 10.1016/j.anbehav.2015.03.025
[15] Nieberding, CM & Holveck M-J 2017. Laboratory social environment biases mating outcome: a first quantitative synthesis in a butterfly. Behav. Ecol. Sociobiol. 71: 117. doi: 10.1007/s00265-017-2346-9
[16] Nieberding CM & Holveck M-J. (In prep). Comentary on Kehl et al. 2018: "Young male mating success is associated with sperm number but not with male sex pheromone titres". Front. Ecol. Evol.
[17] Wijk M Van, Heath J, Lievers R, Schal C & Groot AT. 2017. Proximity of signallers can maintain sexual signal variation under stabilizing selection. Sci. Rep. 7: 18101. doi: 10.1038/s41598-017-17327-9
[18] Miller CW & Svensson EI. 2014. Sexual selection in complex environments. Annu. Rev. Entomol. 59: 427–445. doi: 10.1146/annurev-ento-011613-162044

Two evolutionary processes have been shown in theory to enhance the effects of natural selection in purging deleterious mutations from a population (here ""natural"" selection is defined as ""selection other than sexual selection""). First, inbreeding, especially self-fertilization, facilitates the removal of deleterious recessive alleles, the effects of which are largely hidden from selection in heterozygotes when mating is random. Second, sexual selection can facilitate the removal of deleterious alleles of arbitrary dominance, with little or no demographic cost, provided that deleterious effects are greater in males than in females (""genic capture""). Inbreeding (especially selfing) and sexual selection are often negatively correlated in nature. Empirical tests of the role of sexual selection in purging deleterious mutations have been inconsistent, potentially due to the positive relationship between sexual selection and intersexual genetic conflict.
In their preprint, Noël et al. [1] report a cleverly designed, and impressively long-term, experimental evolution study designed to tease apart the relative contributions of selfing and sexual selection in purging deleterious mutations, using the self-compatible hermaphroditic snail Physa acuta. Hermaphroditism relieves at least some of the potential conflict between males and females because each individual expresses traits of each sex. The authors report a 50-generation (ten years!) evolution experiment with four experimental treatments: Control (C), in which snails reproduced by mass mating (allowing sexual selection) and the next generation was sampled randomly from offspring in proportion to maternal family size; Male-selection (M) in which snails reproduced by mass mating but maternal family size was held constant, removing the opportunity for fertility selection; Female fertility selection (F) in which snails mated monogamously but fertility selection was imposed, and selfing (S), in which snails reproduced by selfing every other generation, alternating with monogamy + fertility selection. Juvenile survival was taken as the proxy for fitness and was measured for offspring of self-fertilization and of outcross matings. Each line type (C, M, F, S) was replicated twice.
The results are enviably clear-cut: after 50 generations of evolution, outcross fitness dropped precipitously in the F treatment (monogamy+female fertility selection) and remained at ancestral levels in the other three treatments. Clearly, sexual selection in males is more efficient at purging deleterious alleles than is female fertility selection. Similarly, inbreeding depression was reduced in the S lines relative to the other treatments, indicating that, unsurprisingly, deleterious recessive mutations of large effect are purged under strong inbreeding. Outcross fitness in the S lines did not decline, in contrast to the F lines, which indicates that deleterious mutations are on average slightly recessive.
Taken as a whole, this study by Noël et al. [1] provides a compelling empirical demonstration of the efficacy of both sexual selection and strong inbreeding as mechanisms of purging, and implicates sexual conflict as a potentially important factor in studies in which relaxation of sexual selection fails to result in purging.

References

[1] Noël, E., Fruitet, E., Lelaurin, D., Bonel, N., Segard, A., Sarda, V., Jarne, P., & David P. (2018). Sexual selection and inbreeding: two efficient ways to limit the accumulation of deleterious mutations. bioRxiv, 273367, ver. 3 recommended and peer-reviewed by PCI Evol Biol. doi: 10.1101/273367

The observed levels of genomic diversity in contemporary populations are the result of changes imposed by several evolutionary processes. Among them, natural selection is known to dramatically shape the genetic diversity of loci associated with phenotypes which affect the fitness of carriers. As such, many efforts have been dedicated towards developing methods to detect signatures of natural selection from genomes of contemporary samples [1].
Recent technological advances made the generation of large-scale genomic data from temporal samples, either from experimental populations or historical or ancient samples, accessible to a wide scientific community [2]. Notably, temporal population genomic data allow for a direct observation and study of how, for instance, allele frequencies change through time in response to evolutionary stimuli. Such information can be exploited to detect loci under natural selection, either via mathematical modelling or by investigating empirical distributions [3].
However, most of current methods to detect selection from temporal genomic data have largely ignored selfing populations, despite the latter comprising a significant proportion of species with social and economic importance. Selfing changes genomic patterns by reducing the effective recombination rate, which makes distinguishing between neutral evolution and natural selection even more challenging than for the case of outcrossing populations [4]. Nevertheless, an outlier-approach based on temporal genomic data for the selfing Arabidopsis thaliana population revealed loci under selection [5].
This study suggested the promise of detecting selection for selfing populations and encouraged further investigations to test the power of selection scans under different mating systems.
To address this question, Navascués et al. [6] extended a previously proposed approach for temporal genome scan [7] to incorporate partial self-fertilization. In the original implementation [7], it is assumed that, under neutrality, all loci provide levels of genetic differentiation drawn from the same distribution. If some of the loci are under selection, such distribution should show heterogeneity. Navascués et al. [6] proposed a test for the homogeneity between loci-specific and genome-wide differentiation by deriving a null distribution of FST via simulations using SLiM [8]. After filtering for low-frequency variants and correct for multiple tests, authors derived a statistical test for selection and assess its power under a wide range of scenarios of selfing rate, selection coefficient, duration and type of selection [6].
The newly proposed test achieved good performance to distinguish between neutral and selected loci in most tested scenarios.
As expected, the test's performance significantly drops for scenarios of high selfing rates and selection from standing variation. Additionally, the probability to correctly detect selection decreases with increasing distance from the causal variant. Intriguingly, the test showed high power when the selected ancestral allele had an initial low frequency, and when the selected derived allele had a high initial frequency. When applied to a data set of around 1,000 SNPs from the highly selfing Medicago truncatula population, an annual plant of the legume family [9], the test did not provide any candidate loci under selection [6].
In summary, the detection of loci under selection in selfing populations is and largely remains a challenging task even when explictly account for the different mating system. However, recombination events that occurred before the selective pressure allow ancestral beneficial alleles to exhibit a detectable pattern of non-neutrality. As such, in partially selfing populations, the strength of the footprint of selection depends on several factors, mostly on the selfing rate, the time of onset and type of selection.
One major assumption of this study is that the model implies unstructured population and continuity between samples obtained from the same geographical location over time. As such assumptions are typically violated in real populations, further research into the effect of more complex demographic scenarios is desired to fully understand the power to detect selection in selfing populations. Furthermore, more power could be gained by including additional genomic information at each time point. In this context, recent approaches that make full use of genomic data based on deep learning [10] may contribute significantly towards this goal. Similarly, the effect of data filtering on the power to detect selection should be further explored, especially in the context of DNA resequencing experiments. These analyses will help elucidate the power offered by selection scans from temporal genomic data in selfing populations.

References

[1] Stern AJ, Nielsen R (2019) Detecting Natural Selection. In: Handbook of Statistical Genomics , pp. 397–40. John Wiley and Sons, Ltd. https://doi.org/10.1002/9781119487845.ch14
[2] Leonardi M, Librado P, Der Sarkissian C, Schubert M, Alfarhan AH, Alquraishi SA, Al-Rasheid KAS, Gamba C, Willerslev E, Orlando L (2017) Evolutionary Patterns and Processes: Lessons from Ancient DNA. Systematic Biology, 66, e1–e29. https://doi.org/10.1093/sysbio/syw059
[3] Dehasque M, Ávila‐Arcos MC, Díez‐del‐Molino D, Fumagalli M, Guschanski K, Lorenzen ED, Malaspinas A-S, Marques‐Bonet T, Martin MD, Murray GGR, Papadopulos AST, Therkildsen NO, Wegmann D, Dalén L, Foote AD (2020) Inference of natural selection from ancient DNA. Evolution Letters, 4, 94–108. https://doi.org/10.1002/evl3.165
[4] Vitalis R, Couvet D (2001) Two-locus identity probabilities and identity disequilibrium in a partially selfing subdivided population. Genetics Research, 77, 67–81. https://doi.org/10.1017/S0016672300004833
[5] Frachon L, Libourel C, Villoutreix R, Carrère S, Glorieux C, Huard-Chauveau C, Navascués M, Gay L, Vitalis R, Baron E, Amsellem L, Bouchez O, Vidal M, Le Corre V, Roby D, Bergelson J, Roux F (2017) Intermediate degrees of synergistic pleiotropy drive adaptive evolution in ecological time. Nature Ecology and Evolution, 1, 1551–1561. https://doi.org/10.1038/s41559-017-0297-1
[6] Navascués M, Becheler A, Gay L, Ronfort J, Loridon K, Vitalis R (2020) Power and limits of selection genome scans on temporal data from a selfing population. bioRxiv, 2020.05.06.080895, ver. 4 peer-reviewed and recommended by PCI Evol Biol. https://doi.org/10.1101/2020.05.06.080895
[7] Goldringer I, Bataillon T (2004) On the Distribution of Temporal Variations in Allele Frequency: Consequences for the Estimation of Effective Population Size and the Detection of Loci Undergoing Selection. Genetics, 168, 563–568. https://doi.org/10.1534/genetics.103.025908
[8] Messer PW (2013) SLiM: Simulating Evolution with Selection and Linkage. Genetics, 194, 1037–1039. https://doi.org/10.1534/genetics.113.152181
[9] Siol M, Prosperi JM, Bonnin I, Ronfort J (2008) How multilocus genotypic pattern helps to understand the history of selfing populations: a case study in Medicago truncatula. Heredity, 100, 517–525. https://doi.org/10.1038/hdy.2008.5
[10] Sanchez T, Cury J, Charpiat G, Jay F Deep learning for population size history inference: Design, comparison and combination with approximate Bayesian computation. Molecular Ecology Resources, n/a. https://doi.org/10.1111/1755-0998.13224