Within-host parasite density is tightly linked to parasite fitness often determining both transmission success and virulence (parasite-induced harm to the host) (Alizon et al., 2009, Anderson & May, 1982). Parasite density may thus be controlled by selection balancing these conflicting pressures. Actual within-host density regulation may be under host or parasite control, or due to other environmental factors (Wale et al., 2019, Vale et al., 2011, Chrostek et al., 2013). Vertically transmitted parasites may also be more vulnerable to drift associated with bottlenecks between generations, which may also determine within-host population size (Mathe-Hubert et al., 2019, Mira & Moran, 2002).

Bénard et al. (2021) use 3 experiments to disentangle the role of drift versus host factors in the control of within-host Wolbachia growth in Drosophila melanogaster. They use the wMelPop Wolbachia strain in which virulence (fly longevity) and within-host growth correlate positively with copy number in the genomic region Octomom (Chrostek et al., 2013, Chrostek & Teixeira, 2015). Octomom copy number can be used as a marker for different genetic lineages within the wMelPop strain.

In a first experiment, they introgressed and backcrossed this Wolbachia strain into 6 different host genetic backgrounds and show striking differences in within-host symbiont densities which correlate positively with Octomom copy number. This is consistent with host genotype selecting different Wolbachia strains, but also with bottlenecks and drift between generations. To distinguish between these possibilities, they perform 2 further experiments. 

A second experiment repeated experiment 1, but this time introgression was into 3 independent lines of the Bolivia and USA Drosophila populations; those that, respectively, exhibited the lowest and highest Wolbachia density and Octomom copy number. In this experiment, growth and Octomom copy number were measured across the 3 lines, for each population, after 1, 13 and 25 generations. Although there were little differences between replicates at generation 1, there were differences at generations 13 and 25 among the replicates of both the Bolivia and USA lines. These results are indicative of parasite control, or drift being responsible for within-host growth rather than host factors. 

A third experiment tested whether Wolbachia density and copy number were under host or parasite control. This was done, again using the USA and Bolivia lines, but this time those from the first experiment, several generations following the initial introgression and backcrossing. The newly introgressed lines were again followed for 25 generations. At generation 1, Wolbachia phenotypes resembled those of the donor parasite population and not the recipient host population indicating a possible maternal effect, but a lack of host control over the parasite. Furthermore, Wolbachia densities and Octomom number differed among replicate lines through time for Bolivia populations and from the donor parasite lines for both populations. These differences among replicate lines that share both host and parasite origins suggest that drift and/or maternal effects are responsible for within-host Wolbachia density and Octomom number. 

These findings indicate that drift appears to play a role in shaping Wolbachia evolution in this system. Nevertheless, completely ruling out the role of the host or parasite in controlling densities will require further study. The findings of Bénard and coworkers (2021) should stimulate future work on the contribution of drift to the evolution of vertically transmitted parasites.

References

Alizon S, Hurford A, Mideo N, Baalen MV (2009) Virulence evolution and the trade-off hypothesis: history, current state of affairs and the future. Journal of Evolutionary Biology, 22, 245–259. https://doi.org/10.1111/j.1420-9101.2008.01658.x

Anderson RM, May RM (1982) Coevolution of hosts and parasites. Parasitology, 85, 411–426. https://doi.org/10.1017/S0031182000055360

Bénard A, Henri H, Noûs C, Vavre F, Kremer N (2021) Wolbachia load variation in Drosophila is more likely caused by drift than by host genetic factors. bioRxiv, 2020.11.29.402545, ver. 4  recommended and peer-reviewed by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2020.11.29.402545

Chrostek E, Marialva MSP, Esteves SS, Weinert LA, Martinez J, Jiggins FM, Teixeira L (2013) Wolbachia Variants Induce Differential Protection to Viruses in Drosophila melanogaster: A Phenotypic and Phylogenomic Analysis. PLOS Genetics, 9, e1003896. https://doi.org/10.1371/journal.pgen.1003896

Chrostek E, Teixeira L (2015) Mutualism Breakdown by Amplification of Wolbachia Genes. PLOS Biology, 13, e1002065. https://doi.org/10.1371/journal.pbio.1002065

Mathé‐Hubert H, Kaech H, Hertaeg C, Jaenike J, Vorburger C (2019) Nonrandom associations of maternally transmitted symbionts in insects: The roles of drift versus biased cotransmission and selection. Molecular Ecology, 28, 5330–5346. https://doi.org/10.1111/mec.15206

Mira A, Moran NA (2002) Estimating Population Size and Transmission Bottlenecks in Maternally Transmitted Endosymbiotic Bacteria. Microbial Ecology, 44, 137–143. https://doi.org/10.1007/s00248-002-0012-9

Vale PF, Wilson AJ, Best A, Boots M, Little TJ (2011) Epidemiological, Evolutionary, and Coevolutionary Implications of Context-Dependent Parasitism. The American Naturalist, 177, 510–521. https://doi.org/10.1086/659002

Wale N, Jones MJ, Sim DG, Read AF, King AA (2019) The contribution of host cell-directed vs. parasite-directed immunity to the disease and dynamics of malaria infections. Proceedings of the National Academy of Sciences, 116, 22386–22392. https://doi.org/10.1073/pnas.1908147116

The advent of genome-wide association studies (GWAS) has been a great promise for our understanding of the connection between genotype and phenotype. Today, the NHGRI-EBI GWAS catalog contains 251,401 associations from 4,961 studies (1). This wealth of studies has also generated interest to use the summary statistics beyond the few top hits in order to make predictions for individuals without known phenotype, e.g. to predict polygenic risk scores or to study polygenic selection by comparing different groups. For instance, polygenic selection acting on the most studied polygenic trait, height, has been subject to multiple studies during the past decade (e.g. 2–6). They detected north-south gradients in Europe which were consistent with expectations. However, their GWAS summary statistics were based on the GIANT consortium data set, a meta-analysis of GWAS conducted in different European cohorts (7,8). The availability of large data sets with less stratification such as the UK Biobank (9) has led to a re-evaluation of those results. The nature of the GIANT consortium data set was realized to represent a potential problem for studies of polygenic adaptation which led several of the authors of the original articles to caution against the interpretations of polygenic selection on height (10,11). This was a great example on how the scientific community assessed their own earlier results in a critical way as more data became available. At the same time it left the question whether there is detectable polygenic selection separating populations more open than ever.

Generally, recent years have seen several articles critically assessing the portability of GWAS results and risk score predictions to other populations (12–14). Refoyo-Martínez et al. (15) are now presenting a systematic assessment on the robustness of cross-population signatures of polygenic adaptation in humans. They compiled GWAS results for complex traits which have been studied in more than one cohort and then use allele frequencies from the 1000 Genomes Project data (16) set to detect signals of polygenic score overdispersion. As the source for the allele frequencies is kept the same across all tests, differences between the signals must be caused by the underlying GWAS. The results are concerning as the level of overdispersion largely depends on the choice of GWAS cohort. Cohorts with homogenous ancestries show little to no overdispersion compared to cohorts of mixed ancestries such as meta-analyses. It appears that the meta-analyses fail to fully account for stratification in their data sets.

The authors based most of their analyses on the heavily studied trait height. Additionally, they use educational attainment (measured as the number of school years of an individual) as an example. This choice was due to the potential over- or misinterpretation of results by the media, the general public and by far right hate groups. Such traits are potentially confounded by unaccounted cultural and socio-economic factors. Showing that previous results about polygenic selection on educational attainment are not robust is an important result that needs to be communicated well. This forms a great example for everyone working in human genomics. We need to be aware that our results can sometimes be misinterpreted. And we need to make an effort to write our papers and communicate our results in a way that is honest about the limitations of our research and that prevents the misuse of our results by hate groups.

This article represents an important contribution to the field. It is cruicial to be aware of potential methodological biases and technical artifacts. Future studies of polygenic adaptation need to be cautious with their interpretations of polygenic score overdispersion. A recommendation would be to use GWAS results obtained in homogenous cohorts. But even if different biobank-scale cohorts of homogeneous ancestry are employed, there will always be some remaining risk of unaccounted stratification. These conclusions may seem sobering but they are part of the scientific process. We need additional controls and new, different methods than polygenic score overdispersion for assessing polygenic selection. Last year also saw the presentation of a novel approach using sequence data and GWAS summary statistics to detect directional selection on a polygenic trait (17). This new method appears to be robust to bias stemming from stratification in the GWAS cohort as well as other confounding factors. Such new developments show light at the end of the tunnel for the use of GWAS summary statistics in the study of polygenic adaptation.

References

1. Buniello A, MacArthur JAL, Cerezo M, Harris LW, Hayhurst J, Malangone C, et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Research. 2019 Jan 8;47(D1):D1005–12. doi: https://doi.org/10.1093/nar/gky1120

2. Turchin MC, Chiang CW, Palmer CD, Sankararaman S, Reich D, Hirschhorn JN. Evidence of widespread selection on standing variation in Europe at height-associated SNPs. Nature Genetics. 2012 Sep;44(9):1015–9. doi: https://doi.org/10.1038/ng.2368

3. Berg JJ, Coop G. A Population Genetic Signal of Polygenic Adaptation. PLOS Genetics. 2014 Aug 7;10(8):e1004412. doi: https://doi.org/10.1371/journal.pgen.1004412

4. Robinson MR, Hemani G, Medina-Gomez C, Mezzavilla M, Esko T, Shakhbazov K, et al. Population genetic differentiation of height and body mass index across Europe. Nature Genetics. 2015 Nov;47(11):1357–62. doi: https://doi.org/10.1038/ng.3401

5. Mathieson I, Lazaridis I, Rohland N, Mallick S, Patterson N, Roodenberg SA, et al. Genome-wide patterns of selection in 230 ancient Eurasians. Nature. 2015 Dec;528(7583):499–503. doi: https://doi.org/10.1038/nature16152

6. Racimo F, Berg JJ, Pickrell JK. Detecting polygenic adaptation in admixture graphs. Genetics. 2018. Arp;208(4):1565–1584. doi: https://doi.org/10.1534/genetics.117.300489

7. Lango Allen H, Estrada K, Lettre G, Berndt SI, Weedon MN, Rivadeneira F, et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature. 2010 Oct;467(7317):832–8. doi: https://doi.org/10.1038/nature09410

8. Wood AR, Esko T, Yang J, Vedantam S, Pers TH, Gustafsson S, et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat Genet. 2014 Nov;46(11):1173–86. doi: https://doi.org/10.1038/ng.3097

9. Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018 Oct;562(7726):203–9. doi: https://doi.org/10.1038/s41586-018-0579-z

10. Berg JJ, Harpak A, Sinnott-Armstrong N, Joergensen AM, Mostafavi H, Field Y, et al. Reduced signal for polygenic adaptation of height in UK Biobank. eLife. 2019 Mar 21;8:e39725. doi: https://doi.org/10.7554/eLife.39725

11. Sohail M, Maier RM, Ganna A, Bloemendal A, Martin AR, Turchin MC, et al. Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies. eLife. 2019 Mar 21;8:e39702. doi: https://doi.org/10.7554/eLife.39702

12. Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ. Clinical use of current polygenic risk scores may exacerbate health disparities. Nature Genetics. 2019 Apr;51(4):584–91. doi: https://doi.org/10.1038/s41588-019-0379-x

13. Bitarello BD, Mathieson I. Polygenic Scores for Height in Admixed Populations. G3: Genes, Genomes, Genetics. 2020 Nov 1;10(11):4027–36. doi: https://doi.org/10.1534/g3.120.401658

14. Uricchio LH, Kitano HC, Gusev A, Zaitlen NA. An evolutionary compass for detecting signals of polygenic selection and mutational bias. Evolution Letters. 2019;3(1):69–79. doi: https://doi.org/10.1002/evl3.97

15. Refoyo-Martínez A, Liu S, Jørgensen AM, Jin X, Albrechtsen A, Martin AR, Racimo F. How robust are cross-population signatures of polygenic adaptation in humans? bioRxiv, 2021, 2020.07.13.200030, version 5 peer-reviewed and recommended by Peer community in Evolutionary Biology. doi: https://doi.org/10.1101/2020.07.13.200030

16. Auton A, Abecasis GR, Altshuler DM, Durbin RM, Abecasis GR, Bentley DR, et al. A global reference for human genetic variation. Nature. 2015 Sep 30;526(7571):68–74. doi: https://doi.org/10.1038/nature15393

17. Stern AJ, Speidel L, Zaitlen NA, Nielsen R. Disentangling selection on genetically correlated polygenic traits using whole-genome genealogies. bioRxiv. 2020 May 8;2020.05.07.083402. doi: https://doi.org/10.1101/2020.05.07.083402

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

Genetic variation is the raw material for selection to act upon and the amount of genetic variation present within a population is a pivotal determinant of a population’s evolutionary potential. A large effective population size, i.e., the ideal number of individuals experiencing the same amount of genetic drift and inbreeding as an actual population, Ne (Wright 1931, Crow 1954), thus increases the probability of long-term survival of a population. However, natural populations, as opposed to theoretical ones, rarely adhere to the requirements of an ideal panmictic population (Sjödin et al. 2005). A range of circumstances can reduce Ne, including the structuring of populations (through space and time, as well as age and developmental stages) and inbreeding (Charlesworth 2009). In mammals, species with a larger body mass (as a proxy for lower Ne) were found to have a higher rate of nonsynonymous nucleotide substitutions (that alter the amino acid sequence of a protein), as well as radical amino acid substitutions (altering the physicochemical properties of a protein) (Popadin et al. 2007). In general, low effective population sizes increase the chance of mutation accumulation and drift, while reducing the strength of selection (Sjödin et al. 2005).
In this paper, Weyna and Romiguier (2021) set out to test if parasitism, body size, geographic range, and/or eusociality affect the strength of selection in Hymenoptera. Hymenoptera include the bees, wasps and ants and is an extraordinarily diverse order within the insects. It was recently estimated that Hymenoptera is the most speciose order of the animal kingdom (Forbes et al. 2018). Hymenoptera are further characterized by an impressive radiation of parasitic species, mainly parasitoids, that feed in or on a single host individual to complete their own development (Godfray 1994). All hymenopterans share the same sex determination system: haplo-diploidy, where unfertilized eggs are haploid males and fertilized eggs are diploid females. Compared to other animals, Hymenoptera further contain an impressive number of clades that evolved eusociality (Rehan and Toth 2015), in which societies show a clear division of labor for reproduction (i.e., castes) and cooperative brood care. Hymenopterans thus represent a diverse and interesting group of insects to investigate potential factors affecting strength of selection and Ne.
Using a previously published phylogenomic dataset containing 3256 genes and 169 hymenopteran species (Peters et al. 2017), Weyna and Romiguier (2021) estimated mean genomic dN/dS ratios (nonsynonymous to synonymous substitution rates) for each species and compared these values between parasitic and non-parasitic species, eusocial and solitary species, and in relation to body size, parasitoid-specific traits and geographic range, thought to affect the effective population size and strength of selection. The use of a large number of species, as well as several distinct traits is a clear asset of this study. The authors found no effect of body size, geographic range or parasitism (including a range of parasitoid-specific traits). There was an effect, however, of eusociality where dN/dS increased in three out of four eusocial lineages. Future studies including more independent evolutionary transitions to eusociality can lend further support that eusocial species indeed reduces the efficiency of selection. The most intriguing result was that for solitary and social bees, with high dN/dS ratios and a strong signature of relaxed selection (i.e., the elimination or reduction of a source of selection (Lahti et al. 2009). The authors suggest that the pollen-collecting behaviors of these species can constrain Ne, as pollen availability varies at both a spatial and temporal scale, requiring a large investment in foraging that may in turn limit reproductive output. It would be interesting to see if other pollen feeders, such as certain beetles, flies, butterflies and moths, as well as mites and spiders, experience relaxed selection as a consequence of the trade-off between energy investment in pollen foraging versus fecundity.

References

Charlesworth, B. (2009). Effective population size and patterns of molecular evolution and variation. Nature Reviews Genetics, 10(3), 195-205. doi: https://doi.org/10.1038/nrg2526
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Forbes, A. A., Bagley, R. K., Beer, M. A., Hippee, A. C., and Widmayer, H. A. (2018). Quantifying the unquantifiable: why Hymenoptera, not Coleoptera, is the most speciose animal order. BMC ecology, 18(1), 1-11. doi: https://doi.org/10.1186/s12898-018-0176-x
Godfray, H. C. J. (1994) Parasitoids: Behavioral and Evolutionary Ecology. Vol. 67, Princeton University Press, 1994. doi: https://doi.org/10.2307/j.ctvs32rmp
Lahti et al. (2009). Relaxed selection in the wild. Trends in ecology & evolution, 24(9), 487-496. doi: https://doi.org/10.1016/j.tree.2009.03.010
Peters et al. (2017). Evolutionary history of the Hymenoptera. Current Biology, 27(7), 1013-1018. doi: https://doi.org/10.1016/j.cub.2017.01.027
Popadin, K., Polishchuk, L. V., Mamirova, L., Knorre, D., and Gunbin, K. (2007). Accumulation of slightly deleterious mutations in mitochondrial protein-coding genes of large versus small mammals. Proceedings of the National Academy of Sciences, 104(33), 13390-13395. doi: https://doi.org/10.1073/pnas.0701256104
Rehan, S. M., and Toth, A. L. (2015). Climbing the social ladder: the molecular evolution of sociality. Trends in ecology & evolution, 30(7), 426-433. doi: https://doi.org/10.1016/j.tree.2015.05.004
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Weyna, A., and Romiguier, J. (2021) Relaxation of purifying selection suggests low effective population size in eusocial Hymenoptera and solitary pollinating bees. bioRxiv, 2020.04.14.038893, ver. 5 peer-reviewed and recommended by PCI Evol Biol. doi: https://doi.org/10.1101/2020.04.14.038893
Wright, S. (1931). Evolution in Mendelian populations. Genetics, 16(2), 97-159.

Exciting times are afoot for those of us interested in the ecology and evolution of invasive populations. Recent years have seen evolutionary process woven firmly into our understanding of invasions (Miller et al. 2020). This integration has inspired a welter of empirical and theoretical work. We have moved from field observations and verbal models to replicate experiments and sophisticated mathematical models. Progress has been rapid, and we have seen science at its best; an intimate discussion between theory and data.
An area currently under very active development is our understanding of pushed invasions. Here a population spreads through space driven, not by dispersal and growth originating at the leading tip of the invasion, but by dispersal and growth originating deeper in the bulk of the population. These pushed invasions may be quite common – they result when per capita growth and dispersal rates are higher in the bulk of the wave than at the leading tip. They result from a range of well-known phenomena, including Allee effects and density-dependent dispersal (Gandhi et al. 2016; Bîrzu et al. 2019). Pushed invasions travel faster than we would expect given growth and dispersal rates on the leading tip, and they lose genetic diversity more slowly than classical pulled invasions (Roques et al. 2012; Haond et al. 2018; Bîrzu et al. 2019).
Well… in theory, anyway. The theory on pushed waves has momentarily streaked ahead of the empirical work, because empirical systems for studying pushed invasions are rare (though see Gandhi et al. 2016; Gandhi, Korolev, and Gore 2019). In this paper, Dahirel and colleagues (2020) make the argument that we may be able to generate pushed invasions in laboratory systems simply by reducing the connectedness of our experimental landscapes. If true, we might have a simple tool for turning many of our established experimental systems into systems for studying pushed dynamics.
It’s a nice idea, and the paper goes to careful lengths to explore the possibility in their lab system (a parasitoid wasp, Trichogramma). They run experiments on replicate wasp populations comparing strongly- v poorly-connected arrays, and estimate the resulting invasion speeds and rate of diversity loss. They also build a simulation model of the system, allowing them to explore in-silico a range of possible processes underlying their results.
As well as developing these parallel systems, Dahirel and colleagues (2020) go to careful lengths to develop statistical analyses that allow inference on key parameters, and they apply these analyses to both the experimental and simulation data. They have been motivated to apply methods that might be used in both laboratory and field settings to help classify invasions.
Ultimately, they found reasonable evidence that their poorly-connected habitat did induce a pushed dynamic. Their poorly connected invasions travelled faster than they should have if they were pulled, they lost diversity more slowly than the highly connected habitat, and replicates with a higher carrying capacity tended to have higher invasion speeds. All in line with expectations of a pushed dynamic. Interestingly, however, their simulation results suggest that they probably got this perfect result for unexpected reasons. The strong hint is that their poorly-connected habitat induced density dependent dispersal in the wasps. Without this effect, their simulations suggest they should have seen diversity decreasing much more rapidly than it did.
There is a nuanced, thoughtful, and carefully argued discussion about all this in the paper, and it is worth reading. There is much of value in this paper. Theirs is not a perfect empirical system in which all the model assumptions are met and in which huge population sizes make stochastic effects negligible. Here is a system one step closer to the messy reality of biology. The struggle to align this system with new theory has been worth the effort. Not only does it give us hope that we might usefully be able to discriminate between classes of invasions using real-world data, but it hints at a rule that Tolstoy might have expressed this way: all pulled invasions are alike, each pushed invasion is pushed in its own way.

References

Bîrzu, G., Matin, S., Hallatschek, O., and Korolev, K. S. (2019). Genetic drift in range expansions is very sensitive to density dependence in dispersal and growth. Ecology Letters, 22(11), 1817-1827. doi: https://doi.org/10.1111/ele.13364
Dahirel, M., Bertin, A., Haond, M., Blin, A., Lombaert, E., Calcagno, V., Fellous, S., Mailleret, L., Malausa, T., and Vercken, E. (2020). Shifts from pulled to pushed range expansions caused by reduction of landscape connectivity. bioRxiv, 2020.05.13.092775, ver. 4 peer-reviewed and recommended by PCI Evolutionary Biology. https://doi.org/10.1101/2020.05.13.092775
Gandhi, S. R., Korolev, K. S., and Gore, J. (2019). Cooperation mitigates diversity loss in a spatially expanding microbial population. Proceedings of the National Academy of Sciences, 116(47), 23582-23587. doi: https://doi.org/10.1073/pnas.1910075116
Gandhi, S. R., Yurtsev, E. A., Korolev, K. S., and Gore, J. (2016). Range expansions transition from pulled to pushed waves as growth becomes more cooperative in an experimental microbial population. Proceedings of the National Academy of Sciences, 113(25), 6922-6927. doi: https://doi.org/10.1073/pnas.1521056113
Haond, M., Morel-Journel, T., Lombaert, E., Vercken, E., Mailleret, L. and Roques, L. (2018). When higher carrying capacities lead to faster propagation (2018), bioRxiv, 307322, ver. 4 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/307322
Miller et al. (2020). Eco‐evolutionary dynamics of range expansion. Ecology, 101(10), e03139. doi: https://doi.org/10.1002/ecy.3139
Roques, L., Garnier, J., Hamel, F., and Klein, E. K. (2012). Allee effect promotes diversity in traveling waves of colonization. Proceedings of the National Academy of Sciences, 109(23), 8828-8833. doi: https://doi.org/10.1073/pnas.1201695109