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

In experimental evolution followed by whole genome resequencing, parallel evolution, defined as the increase in frequency of identical changes in independent populations adapting to the same environment, is often considered as the product of similar selection pressures and the parallel changes are interpreted as adaptive.
However, theory predicts that heterogeneity both in mutation rate and selection intensity across the genome can trigger patterns of parallel evolution. It is thus important to evaluate and quantify the contribution of both mutation and selection in determining parallel evolution to interpret more accurately experimental evolution genomic data and also potentially improve our capacity to predict the genes that will respond to selection.
In their manuscript, Bailey, Guo and Bataillon [1] derive a framework of statistical models to partition the role of mutation and selection in determining patterns of parallel evolution at the gene level. The rationale is to use the synonymous mutations dataset as a baseline to characterize the mutation rate heterogeneity, assuming a negligible impact of selection on synonymous mutations and then analyse the non-synonymous dataset to identify additional source(s) of heterogeneity, by examining the proportion of the variation explained by a number of genomic variables.
This framework is applied to a published data set of resequencing of 40 Saccharomyces cerevisiae populations adapting to a laboratory environment [2]. The model explaining at best the synonymous mutations dataset is one of homogeneous mutation rate along the genome with a significant positive effect of gene length, likely reflecting variation in the size of the mutational target. For the non-synonymous mutations dataset, introducing heterogeneity between sites for the probability of a change to increase in frequency is improving the model fit and this heterogeneity can be partially explained by differences in gene length, recombination rate and number of functional protein domains.
The application of the framework to an experimental data set illustrates its capacity to disentangle the role of mutation and selection and to identify genomic variables explaining heterogeneity in parallel evolution probability but also points to potential limits, cautiously discussed by the authors: first, the number of mutations in the dataset analysed needs to be sufficient, in particular to establish the baseline on the synonymous dataset. Here, despite a high replication (40 populations evolved in the exact same conditions), the total number of synonymous mutations that could be analysed was not very high and there was only one case of a gene with synonymous mutation in two independent populations. Second, although the models are able to identify factors affecting the mutation counts, the proportion of the variation explained is quite low. The consequence is that the models correctly predicts the mutation count distribution but the objective of predicting on which genes the response to selection will occur still seems quite far away.
The framework developed in this manuscript [1] clearly represents a very useful tool for the analysis of large “evolve and resequence” data sets and to gain a better understanding of the determinants of parallel evolution in general. The extension of its application to mutations others than SNPs would provide the possibility to get a more complete picture of the differences in contributions of mutation and selection intensity heterogeneities depending on the mutation types.

References

[1] Bailey SF, Guo Q and Bataillon T (2018) Identifying drivers of parallel evolution: A regression model approach. bioRxiv 118695, ver. 4 peer-reviewed by Peer Community In Evolutionary Biology. doi: 10.1101/118695

[2] Lang GI, Rice DP, Hickman, MJ, Sodergren E, Weinstock GM, Botstein D, and Desai MM (2013) Pervasive genetic hitchhiking and clonal interference in forty evolving yeast populations. Nature 500: 571–574. doi: 10.1038/nature12344

Parasitoid wasps have developed different mechanisms to increase their parasitic success, usually at the expense of host survival (Fellowes and Godfray, 2000). Eggs of these insects are deposited inside the juvenile stages of their hosts, which in turn deploy several immune response strategies to eliminate or disable them (Yang et al., 2020). Drosophila melanogaster protects itself against parasitoid attacks through the production of specific elongated haemocytes called lamellocytes which form a capsule around the invading parasite (Lavine and Strand, 2002; Rizki and Rizki, 1992) and the subsequent activation of the phenol-oxidase cascade leading to the release of toxic radicals (Nappi et al., 1995). On the parasitoid side, robust responses have evolved to evade host immune defenses as for example the Drosophila-specific endoparasite Leptopilina boulardi, which releases venom during oviposition that modifies host behaviour (Varaldi et al., 2006) and inhibits encapsulation (Gueguen et al., 2011; Martinez et al., 2012).
Studies have shown that the wasp parasitic capacity is correlated to venom presence and its content (Colinet et al., 2009; Poirié et al., 2014), including that evolution of venom protein composition is driven by different levels of host susceptibility to infection (Cavigliasso et al., 2019). However, it had not been determined to this day, if and how parasitic range can affect venom protein composition and to which extent host specialization requires broad-spectrum factors or a plethora of specialized components.
These outstanding questions are now approached in a study by Cavigliasso and colleagues (Cavigliasso et al., 2021), where they perform experimental evolution of L. boulardi for 9 generations exposing it to different Drosophila host species and genetic backgrounds (two strains of D. melanogaster, D. simulans and D. yakuba). The authors tested whether the parasitic success of each selection regime was host-specific and how they influenced venom composition in parasitoids. For the first part, infection outcomes were assayed for each selection regime when cross-infecting different hosts. To get a finer measurement of the mechanisms under selection, the authors differentiated three phenotypes: overall parasitic success, encapsulation inhibition and escape from capsule. Throughout the course of experimental evolution, only encapsulation inhibition did not show an improved response upon selection on any host. Importantly, the cross-infection scenario revealed a clear specificity to the selected host for each evolved resistance.
As for venom composition, a trend of differential evolution was detected between host species, although a significant part of that was due to a larger differentiation in the D. yakuba regime, which showed a completely different directionality. Importantly, the authors could identify some of the specific proteins targeted by the several selection regimes, whether selected or counter-selected for. Interestingly, the D. yakuba regime is the only case where the key parasitoid protein LbSPNy (Colinet et al., 2009) was not counter-selected and the only regime in which the overall venom composition did not evolve towards the Ism strain, one of the two ancestral strains of L. boulardi used in the study. It is possible that these two results are correlated, since LbSPNy has been described to inhibit activation of the phenoloxidase cascade in D. yakuba and is one of the most abundant proteins in the ISy venom, making it a good target for selection (Colinet et al., 2013). The authors also discuss the possibility that this difference is related to the geographical distribution of the strains of L. boulardi, since each coincide with either D. melanogaster or D. yakuba.
This methodologically broad work by Cavigliasso and colleagues constitutes an important experimental contribution towards the understanding of how parasitoid adaptation to specific hosts is achieved at different phenotypic and mechanistic levels. It provides compelling evidence that venom composition evolves differently in response to specific parasitic ranges, particularly considering the evolutionary difference between the selective hosts. In line with this result, it is also concluded that the majority of venom proteins selected are lineage-specific, although a few broad-spectrum factors could also be detected. 
The question of whether parasitic range can affect venom composition and parasitic success is still open to more contributions. A potentially interesting long-term direction will be to use a similar setup of experimental evolution on the generalist L. heterotoma (Schlenke et al., 2007) . On a more immediate horizon, comparing the venom evolution of both L. heterotoma and L. boulardi under selection with different hosts and under cross-infection scenarios could reveal interesting patterns. The recent sequencing of the L. boulardi genome together with the vast number of studies addressing mechanisms of Drosophila resistance to parasitoid infection, will enable the thorough characterization of the genetic basis of host-parasitoid interactions and the deeper understanding of these ubiquitous and economically-relevant relationships.
 
*This recommendation text has been co-written with Tânia F. Paulo who is not a recommender of PCI Evol Biol

References

Cavigliasso, F., Mathé-Hubert, H., Gatti, J.-L., Colinet, D. and Poirié, M. (2021) Parasitic success and venom composition evolve upon specialization of parasitoid wasps to different host species. bioRxiv, 2020.10.24.353417, ver. 3 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2020.10.24.353417

Cavigliasso, F., Mathé-Hubert, H., Kremmer, L., Rebuf, C., Gatti, J.-L., Malausa, T., Colinet, D., Poiré, M. and  Léne. (2019). Rapid and Differential Evolution of the Venom Composition of a Parasitoid Wasp Depending on the Host Strain. Toxins, 11(629). https://doi.org/10.3390/toxins11110629

Colinet, D., Deleury, E., Anselme, C., Cazes, D., Poulain, J., Azema-Dossat, C., Belghazi, M., Gatti, J. L. and  Poirié, M. (2013). Extensive inter- and intraspecific venom variation in closely related parasites targeting the same host: The case of Leptopilina parasitoids of Drosophila. Insect Biochemistry and Molecular Biology, 43(7), 601–611. https://doi.org/10.1016/j.ibmb.2013.03.010

Colinet, D., Dubuffet, A., Cazes, D., Moreau, S., Drezen, J. M. and  Poirié, M. (2009). A serpin from the parasitoid wasp Leptopilina boulardi targets the Drosophila phenoloxidase cascade. Developmental and Comparative Immunology, 33(5), 681–689. https://doi.org/10.1016/j.dci.2008.11.013

Fellowes, M. D. E. and  Godfray, H. C. J. (2000). The evolutionary ecology of resistance to parasitoids by Drosophila. Heredity, 84(1), 1–8. https://doi.org/10.1046/j.1365-2540.2000.00685.x

Gueguen, G., Rajwani, R., Paddibhatla, I., Morales, J. and  Govind, S. (2011). VLPs of Leptopilina boulardi share biogenesis and overall stellate morphology with VLPs of the heterotoma clade. Virus Research, 160(1–2), 159–165. https://doi.org/10.1016/j.virusres.2011.06.005

Lavine, M. D. and  Strand, M. R. (2002). Insect hemocytes and their role in immunity. Insect Biochemistry and Molecular Biology, 32(10), 1295–1309. https://doi.org/10.1016/S0965-1748(02)00092-9

Martinez, J., Duplouy, A., Woolfit, M., Vavre, F., O’Neill, S. L. and  Varaldi, J. (2012). Influence of the virus LbFV and of Wolbachia in a host-parasitoid interaction. PloS One, 7(4), e35081. https://doi.org/10.1371/journal.pone.0035081

Nappi, A. J., Vass, E., Frey, F. and  Carton, Y. (1995). Superoxide anion generation in Drosophila during melanotic encapsulation of parasites. European Journal of Cell Biology, 68(4), 450–456.

Poirié, M., Colinet, D. and  Gatti, J. L. (2014). Insights into function and evolution of parasitoid wasp venoms. Current Opinion in Insect Science, 6, 52–60. https://doi.org/10.1016/j.cois.2014.10.004

Rizki, T. M. and  Rizki, R. M. (1992). Lamellocyte differentiation in Drosophila larvae parasitized by Leptopilina. Developmental and Comparative Immunology, 16(2–3), 103–110. https://doi.org/10.1016/0145-305X(92)90011-Z

Schlenke, T. A., Morales, J., Govind, S. and  Clark, A. G. (2007). Contrasting infection strategies in generalist and specialist wasp parasitoids of Drosophila melanogaster. PLoS Pathogens, 3(10), 1486–1501. https://doi.org/10.1371/journal.ppat.0030158

Varaldi, J., Petit, S., Boulétreau, M. and  Fleury, F. (2006). The virus infecting the parasitoid Leptopilina boulardi exerts a specific action on superparasitism behaviour. Parasitology, 132(Pt 6), 747–756. https://doi.org/10.1017/S0031182006009930

Yang, L., Qiu, L., Fang, Q., Stanley, D. W. and  Gong‐Yin, Y. (2020). Cellular and humoral immune interactions between Drosophila and its parasitoids. Insect Science. https://doi.org/10.1111/1744-7917.12863

Genome-wide data obtained from even a small number of individuals can provide unprecedented levels of detail about the evolutionary history of populations and species [1], determinants of genetic diversity [2], species boundaries and the process of speciation itself [3]. Loire and Galtier [4] present a clear example, using the emblematic Galápagos giant tortoise (Chelonoidis nigra), of how multi-species comparative population genomic approaches can provide valuable insights about population structure and species delimitation even when sample sizes are limited but the number of loci is large and distributed across the genome.

Galápagos giant tortoises are endemic to the Galápagos Islands and are currently recognized as an endangered, multi-species complex including both extant and extinct taxa. Taxonomic definitions are based on morphology, geographic isolation and population genetic evidence based on short DNA sequences of the mitochondrial genome (mtDNA) and/or a dozen or so nuclear microsatellite loci [5-8]. The species complex enjoys maximal protection. Population recoveries have been quite successful and spectacular conservation programs based on mitochondrial genes and microsatellites are ongoing. This includes for example individual translocations, breeding program, “hybrid” sterilization or removal, and resurrection of extinct lineages).

In 2013, Loire et al. [9] provided the first population genomic analyses based on genome scale data (~1000 coding loci derived from blood-transcriptomes) from five individuals, encompassing three putative “species”: Chelonnoidis becki, C. porteri and C. vandenburghi. Their results raised doubts about the validity/accuracy of the currently accepted designations of “genetic distinctiveness”. However, the implications for conservation and management have remained unnoticed.

In 2017, Loire and Galtier [4] have re-appraised this issue using an original multi-species comparative population genomic analysis of their previous data set [9]. Based on a comparison of 53 animal species, they show that both the level of genome-wide neutral diversity (πS) and level of population structure estimated using the inbreeding coefficient (F) are much lower than would be expected from a sample covering multiple species. The observed values are more comparable to those typically reported at the “among population” level within a single species such as human (Homo sapiens). The authors go to great length to assess the sensitivity of their method to detect population structure (or lack thereof) and show that their results are robust to potential issues, such as contamination and sequencing errors that can occur with Next Generation Sequencing techniques; and biases related to the small sample size and sub-sampling of individuals. They conclude that published mtDNA and microsatellite-based assessment of population structure and species designations may be biased towards over-splitting.

This manuscript is a very good read as it shows the potential of the now relatively affordable genome-wide data for helping to both resolve and clarify population and species boundaries, illuminate demographic trends, evolutionary trajectories of isolated groups, patterns of connectivity among them, and test for evidence of local adaptation and even reproductive isolation. The comprehensive information provided by genome-wide data can critically inform and assist the development of the best strategies to preserve endangered populations and species. Loire and Galtier [4] make a strong case for applying genomic data to the Galápagos giant tortoises, which is likely to redirect conservation efforts more effectively and at lower cost. The case of the Galápagos giant tortoises is certainly a very emblematic example, which will find an echo in many other endangered species conservation programs.

References

[1] Li H and Durbin R. 2011. Inference of human population history from individual whole-genome sequences. Nature, 475: 493–496. doi: 10.1038/nature10231

[2] Romiguier J, Gayral P, Ballenghien M, Bernard A, Cahais V, Chenuil A, Chiari Y, Dernat R, Duret L, Faivre N, Loire E, Lourenco JM, Nabholz B, Roux C, Tsagkogeorga G, Weber AA-T, Weinert LA, Belkhir K, Bierne N, Glémin S and Galtier N. 2014. Comparative population genomics in animals uncovers the determinants of genetic diversity. Nature, 515: 261–263. doi: 10.1038/nature13685

[3] Roux C, Fraïsse C, Romiguier J, Anciaux Y, Galtier N and Bierne N. 2016. Shedding light on the grey zone of speciation along a continuum of genomic divergence. PLoS Biology, 14: e2000234. doi: 10.1371/journal.pbio.2000234

[4] Loire E and Galtier N. 2017. Lacking conservation genomics in the giant Galápagos tortoise. bioRxiv 101980, ver. 4 of September 26, 2017. doi: 10.1101/101980

[5] Beheregaray LB, Ciofi C, Caccone A, Gibbs JP and Powell JR. 2003. Genetic divergence, phylogeography and conservation units of giant tortoises from Santa Cruz and Pinzón, Galápagos Islands. Conservation Genetics, 4: 31–46. doi: 10.1023/A:1021864214375

[6] Ciofi C, Milinkovitch MC, Gibbs JP, Caccone A and Powell JR. 2002. Microsatellite analysis of genetic divergence among populations of giant Galápagos tortoises. Molecular Ecology, 11: 2265–2283. doi: 10.1046/j.1365-294X.2002.01617.x

[7] Garrick RC, Kajdacsi B, Russello MA, Benavides E, Hyseni C, Gibbs JP, Tapia W and Caccone A. 2015. Naturally rare versus newly rare: demographic inferences on two timescales inform conservation of Galápagos giant tortoises. Ecology and Evolution, 5: 676–694. doi: 10.1002/ece3.1388

[8] Poulakakis N, Edwards DL, Chiari Y, Garrick RC, Russello MA, Benavides E, Watkins-Colwell GJ, Glaberman S, Tapia W, Gibbs JP, Cayot LJ and Caccone A. 2015. Description of a new Galápagos giant tortoise species (Chelonoidis; Testudines: Testudinidae) from Cerro Fatal on Santa Cruz island. PLoS ONE, 10: e0138779. doi: 10.1371/journal.pone.0138779

[9] Loire E, Chiari Y, Bernard A, Cahais V, Romiguier J, Nabholz B, Lourenço JM and Galtier N. 2013. Population genomics of the endangered giant Galápagos tortoise. Genome Biology, 14: R136. doi: 10.1186/gb-2013-14-12-r136

Genetic diversity in temperate and boreal forests tree species has been strongly affected by late Pleistocene climate oscillations [2,3,5], but also by anthropogenic forces. Particularly in Europe, where a long history of human intervention has re-distributed species and populations, it can be difficult to know if a given forest arose through natural regeneration and gene flow or through some combination of natural and human-mediated processes. This uncertainty can confound inferences of the causes and consequences of standing genetic variation, which may impact our interpretation of demographic events that shaped species before humans became dominant on the landscape. In their paper entitled "Genomic data provides new insights on the demographic history and the extent of recent material transfers in Norway spruce", Chen et al. [1] used a genome-wide dataset of 400k SNPs to infer the demographic history of Picea abies (Norway spruce), the most widespread and abundant spruce species in Europe, and to understand its evolutionary relationship with two other spruces (Picea obovata [Siberian spruce] and P. omorika [Serbian spruce]). Three major Norway spruce clusters were identified, corresponding to central Europe, Russia and the Baltics, and Scandinavia, which agrees with previous studies. The density of the SNP data in the present paper enabled inference of previously uncharacterized admixture between these groups, which corresponds to the timing of postglacial recolonization following the last glacial maximum (LGM). This suggests that multiple migration routes gave rise to the extant distribution of the species, and may explain why Chen et al.'s estimates of divergence times among these major Norway spruce groups were older (15mya) than those of previous studies (5-6mya) – those previous studies may have unknowingly included admixed material [4]. Treemix analysis also revealed extensive admixture between Norway and Siberian spruce over the last ~100k years, while the geographically-restricted Serbian spruce was both isolated from introgression and had a dramatically smaller effective population size (Ne) than either of the other two species. This small Ne resulted from a bottleneck associated with the onset of the iron age ~3000 years ago, which suggests that anthropogenic depletion of forest resources has severely impacted this species. Finally, ancestry of Norway spruce samples collected in Sweden and Denmark suggest their recent transfer from more southern areas of the species range. This northward movement of genotypes likely occurred because the trees performed well relative to local provenances, which is a common observation when trees from the south are planted in more northern locations (although at the potential cost of frost damage due to inappropriate phenology). While not the reason for the transfer, the incorporation of southern seed sources into the Swedish breeding and reforestation program may lead to more resilient forests under climate change. Taken together, the data and analysis presented in this paper allowed inference of the intra- and interspecific demographic histories of a tree species group at a very high resolution, and suggest caveats regarding sampling and interpretation of data from areas with a long history of occupancy by humans.

References

[1] Chen, J., Milesi, P., Jansson, G., Berlin, M., Karlsson, B., Aleksić, J. M., Vendramin, G. G., Lascoux, M. (2018). Genomic data provides new insights on the demographic history and the extent of recent material transfers in Norway spruce. BioRxiv, 402016. ver. 3 peer-reviewed and recommended by PCI Evol Biol. doi: 10.1101/402016
[2] Holliday, J. A., Yuen, M., Ritland, K., & Aitken, S. N. (2010). Postglacial history of a widespread conifer produces inverse clines in selective neutrality tests. Molecular Ecology, 19(18), 3857–3864. doi: 10.1111/j.1365-294X.2010.04767.x
[3] Ingvarsson, P. K. (2008). Multilocus patterns of nucleotide polymorphism and the demographic history of Populus tremula. Genetics, 180, 329-340. doi: 10.1534/genetics.108.090431
[4] Lockwood, J. D., Aleksić, J. M., Zou, J., Wang, J., Liu, J., & Renner, S. S. (2013). A new phylogeny for the genus Picea from plastid, mitochondrial, and nuclear sequences. Molecular Phylogenetics and Evolution, 69(3), 717–727. doi: 10.1016/j.ympev.2013.07.004
[5] Pyhäjärvi, T., Garcia-Gil, M. R., Knürr, T., Mikkonen, M., Wachowiak, W., & Savolainen, O. (2007). Demographic history has influenced nucleotide diversity in European Pinus sylvestris populations. Genetics, 177(3), 1713–1724. doi: 10.1534/genetics.107.077099 "