One can only hope that one day, we will be able to evaluate how the ecological complexity surrounding natural populations affects their ability to adapt. This is more like a long term quest than a simple scientific aim. Many steps are heading in the right direction. This paper by Monroe and colleagues (2021) is one of them.
Many ecological and genetic mechanisms shape the evolutionary potential of phenotypic trait variation and many of them involve environmental heterogeneity (Pujol et al 2018). To date, we cannot look into these ecological and genetic mechanisms without oversimplifying their effects. We often look into trait variation one trait at a time albeit the variation of multiple phenotypic traits is often linked at the genetic or environmental level. As a consequence, we put our conclusions at risk by not accounting for the reciprocal impacts of trait changes upon each other (Teplitsky et al 2014). We also usually restrict the study of a continuous gradient of environmental conditions to a few conditions because it would otherwise be impossible to model its environmental effect. As a consequence, we miss the full picture of the continuous often nonlinear phenotypic plastic response. Whether the trait undergo threshold effect changes thereby remains obscured to us. Collectively, these issues impede our ability to understand how selection shapes the ecological strategy of organisms under variable environments.
In this paper, Monroe and colleagues (2021) propose an original approach that raised to these two challenges. They analysed phenotypic plastic changes in response to a continuous environment in a multidimensional trait space, namely the response of Brachypodium plant developmental and physiological traits to a continuous gradient of soil moisture. They used dry down experimental treatments to produce the continuous soil moisture gradient and compared the plant capacity to use water between annual B. distachyon and perennial B. sylvaticum. Their results revealed the best mathematical functions that model the nonlinear curvature of the continuous plastic response of Brachypodium plants. This work reinforces our view that nonlinear plastic responses can result in greater or lesser trait values at any stage of the environmental gradient that were unexpected on the basis of linear predictors (Gienapp and Brommer 2014). Their findings also imply that different threshold responses characterize different genotypes. These could otherwise have been missed by a classical approach. By shedding light on unforeseen interactions between traits that make their correlation vary along the nonlinear response, they were able to describe more accurately Brachypodium ecological strategies and the changes in evolutionary constraints along the soil moisture gradient.
Their empirical approach allows to test what environmental conditions maximises the opportunity for selection to shape trait variation. For example, it revealed unforeseen divergence in potentially adaptive mechanisms or life history strategies – and not just trait values – between annual and perennial species of Brachypodium. Behind every environmental variation of the constraints to the future evolutionary change of multiple traits, we can expect that the evolutionary history of the populations shaped their trait genetic correlations. Investigating the nonlinear signature of adaptive evolution across continuous environments will get us into uncharted territory.
Our ability to predict the adaptive potential of species is limited. With their approach of continuous environmental gradients beyond linearity, Monroe and collaborators (2021) improve our understanding of plant phenotypic responses and open a brand new range of exciting developments. As they mention: "the opportunity for scaling up" their approach is big. To illustrate this prospect, I can easily think of an example: the quantitative genetic random regression model. This model allows to use any degree of genetic relatedness in a wild population to estimate the genetic variation of phenotypic plastic reaction norms (Nussey et al 2007, Pujol and Galaud 2013). However, in this approach, only a few modalities of the environmental gradient are used to model nonlinear phenotypic plastic responses. From there, it is rather intuitive. Combining the best of these two approaches (continuity of genetic relatedness in the wild & continuity of environmental gradient in experiments) could open ground breaking new perspectives in research.

References

Gienapp P. & J.E. Brommer. 2014. Evolutionary dynamics in response to climate change. In: Charmentier A, Garant D, Kruuk LEB, editors. Quantitative genetics in the wild. Oxford: Oxford University Press, Oxford. pp. 254–273. doi: https://doi.org/10.1093/acprof:oso/9780199674237.003.0015
Monroe, J. G., Cai, H., and Des Marais, D. L. (2020). Trait plasticity and covariance along a continuous soil moisture gradient. bioRxiv, 2020.02.17.952853, ver. 5 peer-reviewed and recommended by PCI Evol Biol. doi: https://doi.org/10.1101/2020.02.17.952853
Pujol et al. (2018). The missing response to selection in the wild. Trends in ecology & evolution, 33(5), 337-346. doi: https://doi.org/10.1016/j.tree.2018.02.007
Pujol, B., and Galaud, J. P. (2013). A practical guide to quantifying the effect of genes underlying adaptation in a mixed genomics and evolutionary ecology approach. Botany Letters, 160(3-4), 197-204. doi: https://doi.org/10.1080/12538078.2013.799045
Nussey, D. H., Wilson, A. J., and Brommer, J. E. (2007). The evolutionary ecology of individual phenotypic plasticity in wild populations. Journal of evolutionary biology, 20(3), 831-844. doi: https://doi.org/10.1111/j.1420-9101.2007.01300.x
Teplitsky et al. (2014). Assessing multivariate constraints to evolution across ten long-term avian studies. PLoS One, 9(3), e90444. doi: https://doi.org/10.1371/journal.pone.0090444

Perhaps because of its deadliness, the 2013-2016 Ebola Virus (EBOV) epidemics in West-Africa has led to unprecedented publication and sharing of full virus genome sequences. This was both rapid (90 full genomes were shared within weeks [1]) and important (more than 1500 full genomes have been released overall [2]). Furthermore, the availability of the metadata (especially GPS location) has led to depth analyses of the geographical spread of the epidemics [3].
In their work, Dellicour et al. [4] pursue earlier phylogeographical investigations in an original and yet simple approach to address questions of key public health importance. The originality of the approach is dual. First, from a technical standpoint, they capture the spread of infectious diseases in a continuous framework using a novel model that allows for rare long-distance dispersal events. Second, in a more classical discrete meta-population framework, they simulate the effect of public health interventions by pruning the phylogenetic tree and assessing how this affects key parameters. For instance, to simulate the effect of closing borders they remove subsets of the phylogeny that involved dispersal between countries and to simulate the effect of protecting a region by quarantine they remove all the leaves (i.e. the infections sampled) from this region. This phylogeny pruning is both original and simple. It is however limited because it currently assumes that policies are 100% effective and earlier modelling work on human influenza showed that long distance travel bans had to be implemented with >99% efficiency in order to slow epidemic growth from a time scale of days to weeks [5].
From a biological standpoint, Dellicour et al. [4] corroborate earlier findings that highly populated locations (>1,000,000 inhabitants) were crucial in explaining the magnitude of the epidemics but also show the importance of the transmission between the three capital cities. They also show that rare long-distance dispersing events of the virus are not key to explaining the magnitude of the epidemics (even though they assume 100% efficiency of suppressing long-distance event). Finally, thanks to their continuous model they estimate the speed of spread of the epidemics and are able to detect the effect of border closing on this speed.
Overall, this study [4], which involves state-of-the-art Bayesian inference methods of infection phylogenies using MCMC, stands out because of its effort to simulate public health interventions. It stands as an encouragement for the development of intervention models with increased realism and for even faster and larger virus sequence data sharing.

References

[1] Gire et al. 2014. Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreak. Science 345: 1369–1372. doi: 10.1126/science.1259657.
[2] Holmes EC, Dudas G, Rambaut A and Andersen KG. 2016. The evolution of Ebola virus: insights from the 2013-2016 epidemic. Nature 538: 193–200. doi: 10.1038/nature19790.
[3] Dudas et al. 2017. Virus genomes reveal factors that spread and sustained the Ebola epidemic. Nature 544: 309–315 (2017). doi: 10.1038/nature22040.
[4] Dellicour S, Baele G, Dudas G, Faria NR, Pybus OG, Suchard MA, Rambaud A and Lemey P. 2018. Phylodynamic assessment of intervention strategies for the West African Ebola virus outbreak. bioRxiv, 163691, ver. 3 peer-reviewed by Peer Community In Evolutionary Biology. doi: 10.1101/163691.
[5] Hollingsworth TD, Ferguson NM and Anderson RM. 2006. Will travel restrictions control the international spread of pandemic influenza? Nature Medicine 12, 497–499. doi: 10.1038/nm0506-497.

Biodiversity is unevenly distributed over time, space and the tree of life [1]. The fact that regions are richer than others as exemplified by the latitudinal diversity gradient has fascinated biologists as early as the first explorers travelled around the world [2]. Provincialism was one of the first general features of land biotic distributions noted by famous nineteenth century biologists like the phytogeographers J.D. Hooker and A. de Candolle, and the zoogeographers P.L. Sclater and A.R. Wallace [3]. When these explorers travelled among different places, they were struck by the differences in their biotas (e.g. [4]). The limited distributions of distinctive endemic forms suggested a history of local origin and constrained dispersal. Much biogeographic research has been devoted to identifying areas where groups of organisms originated and began their initial diversification [3]. Complementary efforts found evidence of both historical barriers that blocked the exchange of organisms between adjacent regions and historical corridors that allowed dispersal between currently isolated regions. The result has been a division of the Earth into a hierarchy of regions reflecting patterns of faunal and floral similarities (e.g. regions, subregions, provinces). Therefore a first ensuing question is: “how regional species pools have been assembled through time and space?”, which can be followed by a second question: “what are the ecological and evolutionary processes leading to differences in species richness among species pools?”.

To address these questions, the study of Calatayud et al. [5] developed and performed an interesting approach relying on phylogenetic data to identify regional and sub-regional pools of European beetles (using the iconic ground beetle genus Carabus). Specifically, they analysed the processes responsible for the assembly of species pools, by comparing the effects of dispersal barriers, niche similarities and phylogenetic history. They found that Europe could be divided in seven modules that group zoogeographically distinct regions with their associated faunas, and identified a transition zone matching the limit of the ice sheets at Last Glacial Maximum (19k years ago). Deviance of species co-occurrences across regions, across sub-regions and within each region was significantly explained, primarily by environmental niche similarity, and secondarily by spatial connectivity, except for northern regions. Interestingly, southern species pools are mostly separated by dispersal barriers, whereas northern species pools are mainly sorted by their environmental niches. Another important finding of Calatayud et al. [5] is that most phylogenetic structuration occurred during the Pleistocene, and they show how extreme recent historical events (Quaternary glaciations) can profoundly modify the composition and structure of geographic species pools, as opposed to studies showing the role of deep-time evolutionary processes.

The study of biogeographic assembly of species pools using phylogenies has never been more exciting and promising than today. Catalayud et al. [5] brings a nice study on the importance of Pleistocene glaciations along with geographical barriers and niche-based processes in structuring the regional faunas of European beetles. The successful development of powerful analytical tools in recent years, in conjunction with the rapid and massive increase in the availability of biological data (including molecular phylogenies, fossils, georeferrenced occurrences and ecological traits), will allow us to disentangle complex evolutionary histories. Although we still face important limitations in data availability and methodological shortcomings, the last decade has witnessed an improvement of our understanding of how historical and biotic triggers are intertwined on shaping the Earth’s stupendous biological diversity. I hope that the Catalayud et al.’s approach (and analytical framework) will help movement in that direction, and that it will provide interesting perspectives for future investigations of other regions. Applied to a European beetle radiation, they were able to tease apart the relative contributions of biotic (niche-based processes) versus abiotic (geographic barriers and climate change) factors.

References

[1] Rosenzweig ML. 1995. Species diversity in space and time. Cambridge: Cambridge University Press.
[2] Mittelbach GG, Schemske DW, Cornell HV, Allen AP, Brown JM et al. 2007. Evolution and the latitudinal diversity gradient: speciation, extinction and biogeography. Ecology Letters. 10: 315–331. doi: 10.1111/j.1461-0248.2007.01020.x
[3] Lomolino MV, Riddle BR, Whittaker RJ and Brown JH. 2010. Biogeography, 4th edn. Sinauer Associates, Inc., Sunderland, MA.
[4] Wallace AR. 1876. The geographical distribution of animals: with a study of the relations of living and extinct faunas as elucidating the past changes of the earth's surface. New York: Harper and Brothers, Publishers.
[5] Calatayud J, Rodríguez MÁ, Molina-Venegas R, Leo M, Hórreo JL and Hortal J. 2018. Pleistocene climate change and the formation of regional species pools. bioRxiv 149617 ver. 4 peer-reviewed by Peer Community In Evolutionary Biology. doi: 10.1101/149617

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.

Each individual multicellular organism, each of our bodies, is a small universe. Every living surface -skin, cuticle, bark, mucosa- is the home place to milliards of bacteria, fungi and viruses. They constitute our microbiota. Some of them are essential for certain organisms. Other could not live without their hosts. For many species, the relationship between host and microbiota is so close that their histories are inseparable. The recognition of this biological inextricability has led to the notion of holobiont as the organism ensemble of host and microbiota. When individuals of a particular animal or plant species expand their geographical range, it is the holobiont that expands. And these processes of migration, expansion and colonization are often accompanied by evolutionary and ecological innovations in the interspecies relationships, at the macroscopic level (e.g. novel predator-prey or host-parasite interactions) and at the microscopic level (e.g. changes in the microbiota composition). From the human point of view, these novel interactions can be economically disastrous if they involve and threaten important crop or cattle species. And this is especially worrying in the present context of genetic standardization and intensification for mass-production on the one hand, and of climate change on the other.

With this perspective, the international team led by Amandine Cornille presents a study aiming at understanding the evolutionary history of the rosy apple aphid Dysaphis plantaginea Passerini, a major pest of the cultivated apple tree Malus domestica Borkh (1). The apple tree was probably domesticated in Central Asia, and later disseminated by humans over the world in different waves, and it was probably introduced in Europe by the Greeks. It is however unclear when and where D. plantaginea started parasitizing the cultivated apple tree. The ancestral D. plantaginea could have already infected the wild ancestor of current cultivated apple trees, but the aphid is not common in Central Asia. Alternatively, it may have gained access only later to the plant, possibly via a host jump, from Pyrus to Malus that may have occurred in Asia Minor or in the Caucasus. In the present preprint, Olvera-Vázquez and coworkers have analysed over 650 D. plantaginea colonies from 52 orchards in 13 countries, in Western, Central and Eastern Europe as well as in Morocco and the USA. The authors have analysed the genetic diversity in the sampled aphids, and have characterized as well the composition of the associated endosymbiont bacteria. The analyses detect substantial recent admixture, but allow to identify aphid subpopulations slightly but significantly differentiated and isolated by distance, especially those in Morocco and the USA, as well as to determine the presence of significant gene flow. This process of colonization associated to gene flow is most likely indirectly driven by human interactions. Very interestingly, the data show that this genetic diversity in the aphids is not reflected by a corresponding diversity in the associated microbiota, largely dominated by a few Buchnera aphidicola variants. In order to determine polarity in the evolutionary history of the aphid-tree association, the authors have applied approximate Bayesian computing and machine learning approaches. Albeit promising, the results are not sufficiently robust to assess directionality nor to confidently assess the origin of the crop pest. Despite the large effort here communicated, the authors point to the lack of sufficient data (in terms of aphid isolates), especially originating from Central Asia. Such increased sampling will need to be implemented in the future in order to elucidate not only the origin and the demographic history of the interaction between the cultivated apple tree and the rosy apple aphid. This knowledge is needed to understand how this crop pest struggles with the different seasonal and geographical selection pressures while maintaining high genetic diversity, conspicuous gene flow, differentiated populations and low endosymbiontic diversity.

References

1. Olvera-Vazquez SG, Remoué C, Venon A, Rousselet A, Grandcolas O, Azrine M, Momont L, Galan M, Benoit L, David GM, Alhmedi A, Beliën T, Alins G, Franck P, Haddioui A, Jacobsen SK, Andreev R, Simon S, Sigsgaard L, Guibert E, Tournant L, Gazel F, Mody K, Khachtib Y, Roman A, Ursu TM, Zakharov IA, Belcram H, Harry M, Roth M, Simon JC, Oram S, Ricard JM, Agnello A, Beers EH, Engelman J, Balti I, Salhi-Hannachi A, Zhang H, Tu H, Mottet C, Barrès B, Degrave A, Razmjou J, Giraud T, Falque M, Dapena E, Miñarro M, Jardillier L, Deschamps P, Jousselin E, Cornille A (2021) Large-scale geographic survey provides insights into the colonization history of a major aphid pest on its cultivated apple host in Europe, North America and North Africa. bioRxiv, 2020.12.11.421644, ver. 3 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2020.12.11.421644