Models explaining the evolutionary processes operating in living beings are often impossible to test in the real world. This is mainly because of the long time (i.e., the number of generations) which is necessary for evolution to unfold. In addition, any such experiment would require a large number of individuals and, more importantly, many replicates to account for the inherent variance of the evolutionary processes under investigation. Only organisms with fast generation times and favourable rearing conditions can be used to explicitly test for specific evolutionary hypotheses.

Computer simulations have filled this gap, revolutionising experimental testing in evolutionary biology by integrating genetic models into complex population dynamics, which can be run for (potentially) any length of time. Without going into an extensive description of the many available approaches for population genetics simulations (an exhaustive review can be found in Hoban et al 2012), three main aspects are, in my opinion, important for categorising and choosing one simulation approach over another. The first concerns the basic distinction between coalescent-based and individual-based simulators: the former being an efficient approach, which simulates back in time the coalescence events of a sample of homologous DNA fragments, while the latter is a more computationally intensive approach where all of the individuals (and their underlying genetic/genomic features) in the population are simulated forward-in-time, generation after generation. The second aspect concerns the simulation of natural selection. Although natural selection can be integrated into backward-in-time simulations, it is more realistically implemented as individual-based fitness in forward-in-time simulators. The third point, which has been often overlooked in evolutionary simulations, is about the possibility to design a simulation scenario where individuals and populations can exploit a physical (geographical) space.

Amongst the coalescent-based simulators, SPLATCHE (Currat et al 2004), and its derivatives, is one of the few simulation tools deploying the coalescence process in sub-demes which are all connected by migration, thus getting as close as possible to a spatially-explicit population. On the other hand, individual-based simulators, whose development followed the increasing power of computational machines, offer a great opportunity to include spatio-temporal dynamics within a genomic simulation model. One of the most realistic and efficient individual-based forward-in-time simulators available is SLiM (Haller and Messer 2017), which allows users to implement simulations in arbitrarily complex spaces. Here, the more challenging part is encoding the spatially-explicit scenarios using the SLiM-specific EIDOS language. 

The new R package slendr (Petr et al 2022) offers a practical solution to this issue. By wrapping different tools into a well-known scripting language, slendr allows the design of spatiotemporal simulation scenarios which can be directly executed in the individual-based SLiM simulator, and the output stored with modern tree-sequence analysis tools (tskit; Kellerer et al 2018). Alternatively, simulations of non-spatial models can be run using a coalescent-based algorithm (msprime; Baumdicker et al 2022). The main advantage of slendr is that the whole simulative experiment can be performed entirely in the R environment, taking advantage of the many libraries available for geospatial and genomic data analysis, statistics, and visualisation. The open-source nature of this package, whose main aim is to make complex population genomics modelling more accessible, and the vibrant community of SLiM and tskit users will very likely make slendr widely used amongst the molecular ecology and evolutionary biology communities. 

Slendr handles real Earth cartographic data where users can design realistic demographic processes which characterise natural populations (i.e., expansions, displacement of large populations, interactions among populations, migrations, population splits, etc.) by changing spatial population boundaries across time and space. All in all, slendr is a very flexible and scalable framework to test the accuracy of spatial models, hypotheses about demography and selection, and interactions between organisms across space and time. 

REFERENCES

Baumdicker, F., Bisschop, G., Goldstein, D., Gower, G., Ragsdale, A. P., Tsambos, G., ... & Kelleher, J. (2022). Efficient ancestry and mutation simulation with msprime 1.0. Genetics, 220(3), iyab229. https://doi.org/10.1093/genetics/iyab229

Currat, M., Ray, N., & Excoffier, L. (2004). SPLATCHE: a program to simulate genetic diversity taking into account environmental heterogeneity. Molecular Ecology Notes, 4(1), 139-142. https://doi.org/10.1046/j.1471-8286.2003.00582.x

Haller, B. C., & Messer, P. W. (2017). SLiM 2: flexible, interactive forward genetic simulations. Molecular biology and evolution, 34(1), 230-240. https://doi.org/10.1093/molbev/msw211

Hoban, S., Bertorelle, G., & Gaggiotti, O. E. (2012). Computer simulations: tools for population and evolutionary genetics. Nature Reviews Genetics, 13(2), 110-122. https://doi.org/10.1038/nrg3130

Kelleher, J., Thornton, K. R., Ashander, J., & Ralph, P. L. (2018). Efficient pedigree recording for fast population genetics simulation. PLoS computational biology, 14(11), e1006581. https://doi.org/10.1371/journal.pcbi.1006581

Petr, M., Haller, B. C., Ralph, P. L., & Racimo, F. (2023). slendr: a framework for spatio-temporal population genomic simulations on geographic landscapes. bioRxiv, 2022.03.20.485041, ver. 5 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2022.03.20.485041

​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

Interactions between species are a driving force in evolution. Many organisms host symbiotic partners that live all or part of their life in or on their host. Whether they are mutualistic or parasitic, these symbiotic associations impose strong selective pressures on both partners and affect their evolutionary trajectories. In fine, they can have a significant impact on the diversification patterns of both host and symbiont lineages, with symbiotic lineages sometimes speciating simultaneously with their hosts and/or switching from one host species to another. Long-term associations between species can also result in gene transfers between the involved organisms. Those lateral gene transfers are a source of ecological innovation but can obscure the phylogenetic signals and render the process of phylogenetic reconstructions complex (Lerat et al. 2003).

Methods known as reconciliations explore similarities and differences between phylogenetic trees. They have been widely used to both compare the diversification patterns of hosts and symbionts and identify lateral gene transfers between species. Though the reconciliation approaches used in host/ symbiont and species/ gene phylogenetic studies are identical, they are always applied separately to solve either molecular evolution questions or investigate the evolution of ecological interactions. However, the two questions are often intimately linked and the current interest in multi-level systems (e.g. the holobiont concept) calls for a unique model that will take into account three-level nested organization (gene/symbiont/ host) where both symbiont and genes can transfer among hosts. 

Here Menet and collaborators (2023) provide such a model to produce three-level reconciliations. In order to do so, they extend the two-level reconciliation model implemented in “ALE” software (Szöllősi et al. 2013), one of the most used and proven reconciliation methods. Briefly, given a symbiont gene tree, a symbiont tree and a host tree, as in previous reconciliation models, the symbiont tree is mapped onto the host tree by mixing three types of events: Duplication, Transfer or Loss (DTL), with a possibility of the symbiont evolving temporarily outside the host phylogeny (in a “ghost” host lineage). The gene tree evolves similarly inside the symbiont tree, but horizontal transfers are constrained to symbionts co-occurring within the same host. Joint reconciliation scenarios are reconstructed and DTL event rates and likelihoods are estimated according to the model. As a nice addition, the authors propose a method to infer the symbiont phylogeny through amalgamation from gene trees and a host tree.

The authors then explore the diverse possibilities offered by this method by testing it on both simulated datasets and biological datasets in order to check whether considering three nested levels is worthwhile. They convincingly show that three-level reconciliation has a better capacity to retrieve the symbiont donors and receivers of horizontal gene transfers, probably because transfers are constrained by additional elements relevant to the biological systems. Using, aphids, their obligate endosymbionts, and the symbiont genes involved in their nutritional functions, they identify horizontal gene transfers between aphid symbionts that are missed by two-level reconciliations but detected by expertise (Manzano-Marín et al. 2020). The other dataset presented here is on the human pathogen Helicobacter pylori, which history is supposed to reflect human migration. They use more than 1000 H. pylori gene families, and four populations, and use likelihood computations to compare different hypotheses on the diversification of the host.

In summary, this study is a proof-of-concept of a 3-level reconciliation, where the authors manage to convey the applicability of their framework to many biological systems. Reported complexities, confirmed by reported running times, show that the method is computationally efficient. Without a doubt, the tool presented here will be very useful to evolutionary biologists who want to investigate multi-scale cophylogenies and it will move forward the study of associations between host and symbionts when symbiont genomic data are available.

REFERENCES

Lerat, E., Daubin, V., & Moran, N. A. (2003). From gene trees to organismal phylogeny in prokaryotes: the case of the γ-Proteobacteria. PLoS biology, 1(1), e19.
https://doi.org/10.1371/journal.pbio.0000019
 
Manzano-Marın, A., Coeur d'acier, A., Clamens, A. L., Orvain, C., Cruaud, C., Barbe, V., & Jousselin, E. (2020). Serial horizontal transfer of vitamin-biosynthetic genes enables the establishment of new nutritional symbionts in aphids' di-symbiotic systems. The ISME Journal, 14(1), 259-273.
https://doi.org/10.1038/s41396-019-0533-6

Menet H, Trung AN, Daubin V, Tannier E (2023) Host-symbiont-gene phylogenetic reconciliation. bioRxiv, 2022.07.01.498457, ver. 2 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2022.07.01.498457

Szöllősi, G. J., Rosikiewicz, W., Boussau, B., Tannier, E., & Daubin, V. (2013). Efficient exploration of the space of reconciled gene trees. Systematic biology, 62(6), 901-912.
https://doi.org/10.1093/sysbio/syt054

Sometimes, important factors for explaining biological processes fall through the cracks, and it is only through careful modeling that their importance eventually comes out to light. In this study, Barroso and Dutheil introduce a new method based on the sequentially Markovian coalescent (SMC, Marjoran and Wall 2006) for jointly estimating local recombination and coalescent rates along a genome. Unlike previous SMC-based methods, however, their method can also co-estimate local patterns of variation in mutation rates. 

This is a powerful improvement which allows them to tackle questions about the reasons for the extensive variation in nucleotide diversity across the chromosomes of a species - a problem that has plagued the minds of population geneticists for decades (Begun and Aquadro 1992, Andolfatto 2007, McVicker et al., 2009, Pouyet and Gilbert 2021). The authors find that variation in de novo mutation rates appears to be the most important factor in determining nucleotide diversity in Drosophila melanogaster. Though seemingly contradicting previous attempts at addressing this problem (Comeron 2014), they take care to investigate and explain why that might be the case.

Barroso and Dutheil have also taken care to carefully explain the details of their new approach and have carried a very thorough set of analyses comparing competing explanations for patterns of nucleotide variation via causal modeling. The reviewers raised several issues involving choices made by the authors in their analysis of variance partitioning, the proper evaluation of the role of linked selection and the recombination rate estimates emerging from their model. These issues have all been extensively addressed by the authors, and their conclusions seem to remain robust. The study illustrates why the mutation landscape should not be ignored as an important determinant of local variation in genetic diversity, and opens up questions about the generalizability of these results to other organisms.

REFERENCES

Andolfatto, P. (2007). Hitchhiking effects of recurrent beneficial amino acid substitutions in the Drosophila melanogaster genome. Genome research, 17(12), 1755-1762. https://doi.org/10.1101/gr.6691007

Barroso, G. V., & Dutheil, J. Y. (2021). The landscape of nucleotide diversity in Drosophila melanogaster is shaped by mutation rate variation. bioRxiv, 2021.09.16.460667, ver. 3 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2021.09.16.460667

Begun, D. J., & Aquadro, C. F. (1992). Levels of naturally occurring DNA polymorphism correlate with recombination rates in D. melanogaster. Nature, 356(6369), 519-520. https://doi.org/10.1038/356519a0

Comeron, J. M. (2014). Background selection as baseline for nucleotide variation across the Drosophila genome. PLoS Genetics, 10(6), e1004434. https://doi.org/10.1371/journal.pgen.1004434

Marjoram, P., & Wall, J. D. (2006). Fast" coalescent" simulation. BMC genetics, 7, 1-9. https://doi.org/10.1186/1471-2156-7-16

McVicker, G., Gordon, D., Davis, C., & Green, P. (2009). Widespread genomic signatures of natural selection in hominid evolution. PLoS genetics, 5(5), e1000471. https://doi.org/10.1371/journal.pgen.1000471

Pouyet, F., & Gilbert, K. J. (2021). Towards an improved understanding of molecular evolution: the relative roles of selection, drift, and everything in between. Peer Community Journal, 1, e27. https://doi.org/10.24072/pcjournal.16

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

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

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

REFERENCES

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