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.

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

Spatial genetic structure, i.e. the non-random spatial distribution of genotypes, arises in populations because of different processes including spatially limited dispersal and selection. Knowledge on the spatial genetic structure of plant populations is important to assess biological parameters such as gene dispersal distances and the potential for local adaptations, as well as for applications in conservation management and breeding. In their work, Niskanen and colleagues demonstrate a multifaceted approach to characterise the spatial genetic structure in two replicate sites of a continuously distributed Scots pine population in South-Eastern Finland. They mapped and assessed the ages of 469 naturally regenerated adults and genotyped them using a SNP array which resulted in 157 325 filtered polymorphic SNPs. Their dataset is remarkably powerful because of the large numbers of both individuals and SNPs genotyped. This made it possible to characterise precisely the decay of genetic relatedness between individuals with spatial distance despite the extensive dispersal capacity of Scots pine through pollen, and ensuing expectations of an almost panmictic population.

The authors’ data analysis was particularly thorough. They demonstrated that two metrics of pairwise relatedness, the genomic relationship matrix (GRM, Yang et al. 2011) and the kinship coefficient (Loiselle et al. 1995) were strongly correlated and produced very similar inference of family relationships: >99% of pairs of individuals were unrelated, and the remainder exhibited 2nd (e.g., half-siblings) to 4th degree relatedness. Pairwise relatedness decayed with spatial distance which resulted in extremely weak but statistically significant spatial genetic structure in both sites, quantified as Sp=0.0005 and Sp=0.0008. These estimates are at least an order of magnitude lower than estimates in the literature obtained in more fragmented populations of the same species or in other conifers. Estimates of the neighbourhood size, the effective number of potentially mating individuals belonging to a within-population neighbourhood (Wright 1946), were relatively large with Nb=1680-3210 despite relatively short gene dispersal distances, σg = 36.5–71.3m, which illustrates the high effective density of the population. 

The authors showed the implications of their findings for selection. The capacity for local adaptation depends on dispersal distances and the strength of the selection coefficient. In the study population, the authors inferred that local adaptation can only occur if environmental heterogeneity occurs over a distance larger than approximately one kilometre (or larger, if considering long-distance dispersal). Interestingly, in Scots pine, no local adaptation has been described on similar geographic scales, in contrast to some other European or Mediterranean conifers (Scotti et al. 2023).

The authors’ results are relevant for the management of conservation and breeding. They showed that related individuals occurred within sites only and that they shared a higher number of rare alleles than unrelated ones. Since rare alleles are enriched in new and recessive deleterious variants, selecting related individuals could have negative consequences in breeding programmes. The authors also showed, in their response to reviewers, that their powerful dataset was not suitable to obtain a robust estimate of effective population size, Ne, based on the linkage disequilibrium method (Do et al. 2014). This illustrated that the estimation of Ne used for genetic indicators supported in international conservation policy (Hoban et al. 2020, CBD 2022) remains challenging in large and continuous populations (see also Santo-del-Blanco et al. 2023, Gargiulo et al. 2024).

References

CBD (2022) Kunming-Montreal Global Biodiversity Framework. https://www.cbd.int/doc/decisions/cop-15/cop-15-dec-04-en.pdf

Do C, Waples RS, Peel D, Macbeth GM, Tillett BJ, Ovenden JR (2014). NeEstimator v2: re-implementation of software for the estimation of contemporary effective population size (Ne ) from genetic data. Molecular Ecology Resources 14: 209–214. https://doi.org/10.1111/1755-0998.12157

Gargiulo R, Decroocq V, González-Martínez SC, Paz-Vinas I, Aury JM, Kupin IL, Plomion C, Schmitt S, Scotti I, Heuertz M (2024) Estimation of contemporary effective population size in plant populations: limitations of genomic datasets. Evolutionary Applications, in press, https://doi.org/10.1101/2023.07.18.549323

Hoban S, Bruford M, D’Urban Jackson J, Lopes-Fernandes M, Heuertz M, Hohenlohe PA, Paz-Vinas I, et al. (2020) Genetic diversity targets and indicators in the CBD post-2020 Global Biodiversity Framework must be improved. Biological Conservation 248: 108654. https://doi.org/10.1016/j.biocon.2020.108654

Loiselle BA, Sork VL, Nason J & Graham C (1995) Spatial genetic structure of a tropical understorey shrub, Psychotria officinalis (Rubiaceae). American Journal of Botany 82: 1420–1425. https://doi.org/10.1002/j.1537-2197.1995.tb12679.x

Santos-del-Blanco L, Olsson S, Budde KB, Grivet D, González-Martínez SC, Alía R, Robledo-Arnuncio JJ (2022). On the feasibility of estimating contemporary effective population size (Ne) for genetic conservation and monitoring of forest trees. Biological Conservation 273: 109704. https://doi.org/10.1016/j.biocon.2022.109704

Scotti I, Lalagüe H, Oddou-Muratorio S, Scotti-Saintagne C, Ruiz Daniels R, Grivet D, et al. (2023) Common microgeographical selection patterns revealed in four European conifers. Molecular Ecology 32: 393-411. https://doi.org/10.1111/mec.16750

Wright S (1946) Isolation by distance under diverse systems of mating. Genetics 31: 39–59. https://doi.org/10.1093/genetics/31.1.39

Yang J, Lee SH, Goddard ME & Visscher PM (2011) GCTA: a tool for genome-wide complex trait analysis. The American Journal of Human Genetics 88: 76–82. https://www.cell.com/ajhg/pdf/S0002-9297(10)00598-7.pdf

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

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

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

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

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

References

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

Given the general importance of protein expression levels, in cells it is widely accepted that gene expression levels are often a target of natural selection and that most mutations affecting gene expression levels are therefore likely to be deleterious [1]. However, it is perhaps less obvious that the strength of selection on the regulated genes themselves may be influenced by their expression levels. This might be due to harmful effects of misfolded proteins, for example, when higher protein concentrations exist in cells [2]. Recent studies have suggested that highly expressed genes accumulate fewer deleterious mutations; thus a positive relationship appears to exist between gene expression levels and the relative strength of purifying selection [3].

The recommended paper by Trucchi et al. [4] examines the relationship between gene expression, purifying selection and a third variable -- effective population size -- in populations of two species of penguin with different population sizes, the Emperor penguin (Aptenodytes forsteri) and the King penguin (A. patagonicus). Using transcriptomic data and computer simulations modeling selection, they examine patterns of nonsynonymous and synonymous segregating polymorphisms (p) across genes in the two populations, concluding that even in relatively small populations purifying selection has an important effect in eliminating deleterious mutations. 

References

1] Gilad Y, Oshlack A, and Rifkin SA. 2006. Natural selection on gene expression. Trends in Genetics 22: 456-461. https://doi.org/10.1016/j.tig.2006.06.002
 
[2] Yang JR, Liao BY, Zhuang SM, and Zhang J. 2012. Protein misinteraction avoidance causes highly expressed proteins to evolve slowly. Proceedings of the National Academy of Sciences 109: E831-E840. https://doi.org/10.1073/pnas.1117408109
 
[3] Duret L, and Mouchiroud D (2000). Determinants of substitution rates in mammalian genes: expression pattern affects selection intensity but not mutation rate. Molecular Biology and Evolution 17; 68-070. https://doi.org/10.1093/oxfordjournals.molbev.a026239

[4] Trucchi E, Massa P, Giannelli F, Latrille T, Fernandes FAN, Ancona L, Stenseth NC, Obiol JF, Paris J, Bertorelle G, and Le Bohec, C. 2023. Gene expression is the main driver of purifying selection in large penguin populations. bioRxiv 2023.08.08.552445, ver. 2 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2023.08.08.552445