Biological invasions are natural experiments, and often show that evolution can affect dynamics in important ways [1-3]. While we often think of invasions as a conservation problem stemming from anthropogenic introductions [4,5], biological invasions are much more commonplace than this, including phenomena as diverse as natural range shifts, the spread of novel pathogens, and the growth of tumors. A major question across all these settings is which set of traits determine the ability of a population to invade new space [6,7]. Traits such as: increased growth or reproductive rate, dispersal ability and ability to defend from predation often show large evolutionary shifts across invasion history [1,6,8]. Are such multi-trait shifts driven by selection on multiple traits, or a correlated response by multiple traits to selection on one? Resolving this question is important for both theoretical and practical reasons [9,10]. But despite the importance of this issue, it is not easy to perform the necessary manipulative experiments [9].
Foucaud et al. [11] tackled this issue by performing experimental evolution on source populations of the invasive ladybug Harmonia axyridis. The authors tested if selection on a single trait could generate correlated responses in other life history traits. Specifically, they used experimental evolution to impose divergent selection on female mass, and reproductive timing. After ten generations, they found that selection for weight did not affect almost any other life history trait. However, nine generations of selection for faster reproduction led to correlated phenotypic changes in developmental, reproduction and survival rate of populations, although not always in the direction we might have expected. Despite this correlated response, none of their selected lines were able to fully recapitulate the trait shifts seen in natural invasions of this species. This implies that selection during natural invasions is operating on multiple traits; a finding in agreement with our growing understanding of how selection acts during introduction and invasion [12,13].
Populations undergoing a colonization process may also be subject to a multitude of different selective pressures [14,15]. The authors expanded their work in this direction by testing whether food availability alters the observed correlations between life history traits. The pervasiveness of genotype by environment interactions observed also points to a role for multiple selective pressures in shaping the suite of life-history shifts observed in wild ladybug populations. The work from Foucaud and colleagues [11] adds to a small but growing list of important studies that use experimental evolution to investigate how life-history traits evolve, and how they evolve during invasions in particular.

References

[1] Sakai, A.K., Allendorf, F.W., Holt, J.S. et al. (2001). The population biology of invasive species. Annual review of ecology and systematics, 32(1), 305-332. doi: 10.1146/annurev.ecolsys.32.081501.114037
[2] Hairston Jr, N. G., Ellner, S. P., Geber, M. A., Yoshida, T. and Fox, J. A. (2005). Rapid evolution and the convergence of ecological and evolutionary time. Ecology letters, 8(10), 1114-1127. doi: 10.1111/j.1461-0248.2005.00812.x
[3] Chuang, A. and Peterson, C. R. (2016). Expanding population edges: theories, traits, and trade‐offs. Global change biology, 22(2), 494-512. doi: 10.1111/gcb.13107
[4] Whitney, K. D. and Gabler, C. A. (2008). Rapid evolution in introduced species,‘invasive traits’ and recipient communities: challenges for predicting invasive potential. Diversity and Distributions, 14(4), 569-580. doi: 10.1111/j.1472-4642.2008.00473.x
[5] Catullo, R. A., Llewelyn, J., Phillips, B. L. and Moritz, C. C. (2019). The Potential for Rapid Evolution under Anthropogenic Climate Change. Current Biology, 29(19), R996-R1007. doi: 10.1016/j.cub.2019.08.028
[6] Suarez, A. V. and Tsutsui, N. D. (2008). The evolutionary consequences of biological invasions. Molecular Ecology, 17(1), 351-360. doi: 10.1111/j.1365-294X.2007.03456.x
[7] Deforet, M., Carmona-Fontaine, C., Korolev, K. S. and Xavier, J. B. (2019). Evolution at the edge of expanding populations. The American Naturalist, 194(3), 291-305. doi: 10.1086/704594
[8] Phillips, B. L., Brown, G. P., and Shine, R. (2010). Life‐history evolution in range‐shifting populations. Ecology, 91(6), 1617-1627. doi: 10.1890/09-0910.1
[9] Colautti, R. I. and Lau, J. A. (2015). Contemporary evolution during invasion: evidence for differentiation, natural selection, and local adaptation. Molecular ecology, 24(9), 1999-2017. doi: 10.1111/mec.13162
[10] Szűcs, M., Melbourne, B. A., Tuff, T., Weiss‐Lehman, C. and Hufbauer, R. A. (2017). Genetic and demographic founder effects have long‐term fitness consequences for colonising populations. Ecology Letters, 20(4), 436-444. doi: 10.1111/ele.12743
[11] Foucaud, J., Hufbauer, R. A., Ravigné, V., Olazcuaga, L., Loiseau, A., Ausset, A., Wang, S., Zang, L.-S., Lemenager, N., Tayeh, A., Weyna, A., Gneux, P., Bonnet, E., Dreuilhe, V., Poutout, B., Estoup, A. and Facon, B. (2020). How do invasion syndromes evolve? An experimental evolution approach using the ladybird Harmonia axyridis. bioRxiv, 849968 ver. 4 peer-reviewed and recommended by PCI Evolutionary Biology. doi: 10.1101/849968
[12] Simons, A. M. (2003). Invasive aliens and sampling bias. Ecology Letters, 6(4), 278-280. doi: 10.1046/j.1461-0248.2003.00430.x
[13] Phillips, B. L. and Perkins, T. A. (2019). Spatial sorting as the spatial analogue of natural selection. Theoretical Ecology, 12(2), 155-163. doi: 10.1007/s12080-019-0412-9
[14] Lavergne, S. and Molofsky, J. (2007). Increased genetic variation and evolutionary potential drive the success of an invasive grass. Proceedings of the National Academy of Sciences, 104(10), 3883-3888. doi: 10.1073/pnas.0607324104
[15] Moran, E. V. and Alexander, J. M. (2014). Evolutionary responses to global change: lessons from invasive species. Ecology Letters, 17(5), 637-649. doi: 10.1111/ele.12262

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

The reliability of published scientific papers has been the topic of much recent discussion, notably in the biomedical sciences [1]. Although small sample size is regularly pointed as one of the culprits, big data can also be a concern. The advent of high-throughput sequencing, and the processing of sequence data by opaque bioinformatics workflows, mean that sequences with often high error rates are produced, and that exact but slow analyses are not feasible.
The troubles with bioinformatics arise from the increased complexity of the tools used by scientists, and from the lack of incentives and/or skills from authors (but also reviewers and editors) to make sure of the quality of those tools. As a much discussed example, a bug in the widely used PLINK software [2] has been pointed as the explanation [3] for incorrect inference of selection for increased height in European Human populations [4].
High-throughput sequencing often generates high rates of genotyping errors, so that the development of bioinformatics tools to assess the quality of data and correct them is a major issue. The work of Gillingham et al. [5] contributes to the latter goal. In this work, the authors propose a new bioinformatics workflow (ACACIA) for performing genotyping analysis of multigene complexes, such as self-incompatibility genes in plants, major histocompatibility genes (MHC) in vertebrates, and homeobox genes in animals, which are particularly challenging to genotype in non-model organisms. PCR and sequencing of multigene families generate artefacts, hence spurious alleles. A key to Gillingham et al.‘ s method is to call candidate genes based on Oligotyping, a software pipeline originally conceived for identifying variants from microbiome 16S rRNA amplicons [6]. This allows to reduce the number of false positives and the number of dropout alleles, compared to previous workflows.
This method is not based on an explicit probability model, and thus it is not conceived to provide a control of the rate of errors as, say, a valid confidence interval should (a confidence interval with coverage c for a parameter should contain the parameter with probability c, so the error rate 1- c is known and controlled by the user who selects the value of c). However, the authors suggest a method to adapt the settings of ACACIA to each application.
To compare and validate the new workflow, the authors have constructed new sets of genotypes representing different extents copy number variation, using already known genotypes from chicken MHC. In such conditions, it was possible to assess how many alleles are not detected and what is the rate of false positives. Gillingham et al. additionally investigated the effect of using non-optimal primers. They found better performance of ACACIA compared to a preexisting pipeline, AmpliSAS [7], for optimal settings of both methods. However, they do not claim that ACACIA will always be better than AmpliSAS. Rather, they warn against the common practice of using the default settings of the latter pipeline. Altogether, this work and the ACACIA workflow should allow for better ascertainment of genotypes from multigene families.

References

[1] Ioannidis, J. P. A, Greenland, S., Hlatky, M. A., Khoury, M. J., Macleod, M. R., Moher, D., Schulz, K. F. and Tibshirani, R. (2014) Increasing value and reducing waste in research design, conduct, and analysis. The Lancet, 383, 166-175. doi: 10.1016/S0140-6736(13)62227-8
[2] Chang, C. C., Chow, C. C., Tellier, L. C. A. M., Vattikuti, S., Purcell, S. M. and Lee, J. J. (2015) Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience, 4, 7, s13742-015-0047-8. doi: 10.1186/s13742-015-0047-8
[3] Robinson, M. R. and Visscher, P. (2018) Corrected sibling GWAS data release from Robinson et al. http://cnsgenomics.com/data.html
[4] Field, Y., Boyle, E. A., Telis, N., Gao, Z., Gaulton, K. J., Golan, D., Yengo, L., Rocheleau, G., Froguel, P., McCarthy, M.I . and Pritchard J. K. (2016) Detection of human adaptation during the past 2000 years. Science, 354(6313), 760-764. doi: 10.1126/science.aag0776
[5] Gillingham, M. A. F., Montero, B. K., Wihelm, K., Grudzus, K., Sommer, S. and Santos P. S. C. (2020) A novel workflow to improve multi-locus genotyping of wildlife species: an experimental set-up with a known model system. bioRxiv 638288, ver. 3 peer-reviewed and recommended by Peer Community In Evolutionary Biology. doi: 10.1101/638288
[6] Eren, A. M., Maignien, L., Sul, W. J., Murphy, L. G., Grim, S. L., Morrison, H. G., and Sogin, M.L. (2013) Oligotyping: differentiating between closely related microbial taxa using 16S rRNA gene data. Methods in Ecology and Evolution 4(12), 1111-1119. doi: 10.1111/2041-210X.12114
[7] Sebastian, A., Herdegen, M., Migalska, M. and Radwan, J. (2016) AMPLISAS: a web server for multilocus genotyping using next‐generation amplicon sequencing data. Mol Ecol Resour, 16, 498-510. doi: 10.1111/1755-0998.12453

Phylogenetic trees can be used to extract information about the process of diversification that has generated them. The most common approach to conduct this inference is to rely on a likelihood, defined here as the probability of generating a dated tree T given a diversification model (e.g. a birth-death model), and then use standard maximum likelihood. This idea has been explored extensively in the context of the so-called diversification studies, with many variants for the models and for the questions being asked (diversification rates shifting at certain time points or in the ancestors of particular subclades, trait-dependent diversification rates, etc).
However, all this assumes that the dated tree T is known without error. In practice, trees (that is, both the tree topology and the divergence times) are inferred based on DNA sequences, possibly combined with fossil information for calibrating and informing the divergence times. Molecular dating is a delicate exercise, however, and much more so in fact than reconstructing the tree topology. In particular, a mis-specificied model for the relaxed molecular clock, or a mis-specifiied prior, can have a substantial impact on the estimation of divergence dates - which in turn could severely mislead the inference about the underlying diversification process. This thus raises the following question: would that be possible to conduct inference and testing of diversification models without having to go through the dangerous step of molecular dating?
In his article ""Probabilities of tree topologies with temporal constraints and diversification shifts"" [1], Gilles Didier introduces a recursive method for computing the probability of a tree topology under some diversification model of interest, without knowledge of the exact dates, but only interval constraints on the dates of some of the nodes of the tree. Such interval constraints, which are derived from fossil knowledge, are typically used for molecular dating: they provide the calibrations for the relaxed clock analysis. Thus, what is essentially proposed by Gilles Didier is to use them in combination with the tree topology only, thus bypassing the need to estimates divergence times first, before fitting a diversification model to a phylogenetic tree.
This article, which is primarily a mathematical and algorithmic contribution, is then complemented with several applications: testing for a diversification shift in a given subclade of the phylogeny, just based on the (undated) tree topology, with interval constraints on some of its internal nodes; but also, computing the age distribution of each node and sampling on the joint distribution on node ages, conditional on the interval constraints. The test for the presence of a diversification shift is particularly interesting: an application to simulated data (and without any interval constraint in that case) suggests that the method based on the undated tree performs about as well as the classical method based on a dated tree, and this, even granting the classical approach a perfect knowledge of the dates - given that, in practice, one in fact relies on potentially biased estimates. Finally, an application to a well-known example (rate shifts in cetacean phylogeny) is presented.
This article thus represents a particularly meaningful contribution to the methodology for diversification studies; but also, for molecular dating itself: it is a well known problem in molecular dating that computing and sampling from the conditional distributions on node ages, given fossil constraints, and more generally understanding and visualizing how interval constraints on some nodes of the tree impact the distribution at other nodes, is a particularly difficult exercise. For that reason, the algorithmic routines presented in the present article will be useful in this context as well.

References

[1] Didier, G. (2020) Probabilities of tree topologies with temporal constraints and diversification shifts. bioRxiv, 376756, ver. 4 peer-reviewed and recommended by PCI Evolutionary Biology. doi: 10.1101/376756

Ecological specialisation, especially among parasites infecting a set of host species, is ubiquitous in nature. Host specialisation can be understood as resulting from trade-offs in parasite infectivity, virulence and growth. However, it is not well understood how variation in these trade-offs shapes the overall fitness trade-off a parasite faces when adapting to multiple hosts. For instance, it is not clear whether a strong trade-off in one fitness component may sufficiently constrain the evolution of a generalist parasite despite weak trade-offs in other components. A second mechanism explaining variation in specialisation among species is habitat availability and quality. Rare habitats or habitats that act as ecological sinks will not allow a species to persist and adapt, preventing a generalist phenotype to evolve. Understanding the prevalence of those mechanisms in natural systems is crucial to understand the emergence and maintenance of host specialisation, and biodiversity in general.
In their study "Trait-specific trade-offs prevent niche expansion in two parasites", Lievens et al. [1] report the results of an evolution experiment involving two parasitic microsporidians, Anostracospora rigaudi and Enterocytospora artemiae, infecting two sympatric species of brine shrimp, Artemia franciscana and Artemia parthenogenetica. The two parasites were originally specialised on their primary host: A. rigaudi on A. parthenogenetica and E. artemiae on A. franciscana, although they encounter both species in the wild but at different rates. After passaging each parasite on each single host and on both hosts alternatively, Lievens et al. asked how host specialisation evolved. They found no change in specialisation at the fitness level in A. rigaudi in either treatment, while E. artemiae became more of a generalist after having been exposed to its secondary host, A. parthenogenetica. The most interesting part of the study is the decomposition of the fitness trade-off into its underlying trade-offs in spore production, infectivity and virulence. Both species remained specialised for spore production on their primary host, interpreted as caused by a strong trade-off between hosts preventing improvements on the secondary host. A. rigaudi evolved reduced virulence on its primary host without changes in the overall fitness trad-off, while E. artemiae evolved higher infectivity on its secondary host making it a more generalist parasite and revealing a weak trade-off for this trait and for fitness. Nevertheless, both parasites retained higher fitness on their primary host because of the lack of an evolutionary response in spore production.
This study made two important points. First, it showed that despite apparent strong trade-off in spore production, a weak trade-off in infectivity allowed E. artemiae to become less specialised. In contrast, A. rigaudi remained specialised, presumably because the strong trade-off in spore production was the overriding factor. The fitness trade-off that results from the superposition of multiple underlying trade-offs is thus difficult to predict, yet crucial to understand potential evolutionary outcomes. A second insight is related to the ecological context of the evolution of specialisation. The results showed that E. artemiae should be less specialised than observed, which points to a role played by source-sink dynamics on A. parthenogenetica in the wild. The experimental approach of Lievens et al. thus allowed them to nicely disentangle the various sources of constraints on the evolution of host adaptation in the Artemia system.

References

[1] Lievens, E.J.P., Michalakis, Y. and Lenormand, T. (2019). Trait-specific trade-offs prevent niche expansion in two parasites. bioRxiv, 621581, ver. 4 peer-reviewed and recommended by PCI Evolutionary Biology. doi: 10.1101/621581