The unique challenges associated with living on an island often result in organisms displaying a specific suite of traits commonly referred to as “island syndrome” (Adler and Levins, 1994; Burns, 2019; Baeckens and Van Damme, 2020). Large phenotypic shifts such as changes in size (e.g. shifts to gigantism or dwarfism, Lomolino, 2005) or coloration (Doutrelant et al., 2016) abound in the literature. However, less obvious phenotypes may also play a key role in adaptation to islands.

One such trait, reduced immune function, has important implications for the future of island populations in the face of anthropogenic-induced changes. Due to lower parasite pressure caused by a less diverse and less virulent parasite population, island hosts may show a decrease in immune defenses (Beadell et al., 2006; Pérez‐Rodríguez et al., 2013). However, this hypothesis has been challenged, as many studies have found ambiguous or conflicting results (Matson, 2006; Illera et al., 2015).

While most previous work has examined various immunological parameters (e.g., antibody concentrations), here, Barthe et al. (2022) take the novel approach of examining molecular signatures of immune genes. Using comparative genomic data from 34 different species of birds the authors examine the ratio of synonymous substitutions (i.e., not changing an amino acid) to non-synonymous substitutions (i.e., changing an amino acid) in innate and acquired immune genes (Pn/Ps ratio). Because population sizes on islands are lower which will affect molecular evolution, they compare these results to data from 97 control genes.  Assuming relaxed selection on islands predicts that the difference between the Pn/Ps ratio of immune genes and of control genes (ΔPn/Ps) is greater in island species compared to mainland ones.

As with previous work the authors found that the results differ depending on the category of immune genes. Both forms of innate defense: beta-defensins and Toll-like receptors did not show higher ΔPn/Ps for island populations. As these genes still have a higher Pn/Ps than control genes, the authors argue these results are in line with these genes being under purifying selection but lacking an “island effect”. Instead, the authors argue that demographic effects (i.e., reductions in Ne) may lead to the decreased immunity documented in other studies. In contrast, there was a reduction in Pn/Ps in MHC II genes, known to be under balancing selection. This reduction was stronger in island species and thus the authors argue that this is the only class of genes where a role for relaxed selection can be invoked. 

Together these results demonstrate that the changes in immunity experienced by island species are complex and that different categories of immune genes can experience different selective pressures. By including control genes in their study, they particularly highlight the importance of accounting for shifts in Ne when examining patterns of island species evolution. Hopefully, this kind of framework will be applied to other taxa to determine if these results are widespread or more specific to birds. 

References

Adler GH, Levins R (1994) The Island Syndrome in Rodent Populations. The Quarterly Review of Biology, 69, 473–490. https://doi.org/10.1086/418744

Baeckens S, Van Damme R (2020) The island syndrome. Current Biology, 30, R338–R339. https://doi.org/10.1016/j.cub.2020.03.029

Barthe M, Doutrelant C, Covas R, Melo M, Illera JC, Tilak M-K, Colombier C, Leroy T, Loiseau C, Nabholz B (2022) Evolution of immune genes in island birds: reduction in population sizes can explain island syndrome. bioRxiv, 2021.11.21.469450, ver. 4 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2021.11.21.469450

Beadell JS, Ishtiaq F, Covas R, Melo M, Warren BH, Atkinson CT, Bensch S, Graves GR, Jhala YV, Peirce MA, Rahmani AR, Fonseca DM, Fleischer RC (2006) Global phylogeographic limits of Hawaii’s avian malaria. Proceedings of the Royal Society B: Biological Sciences, 273, 2935–2944. https://doi.org/10.1098/rspb.2006.3671

Burns KC (2019) Evolution in Isolation: The Search for an Island Syndrome in Plants. Cambridge University Press, Cambridge. https://doi.org/10.1017/9781108379953

Doutrelant C, Paquet M, Renoult JP, Grégoire A, Crochet P-A, Covas R (2016) Worldwide patterns of bird colouration on islands. Ecology Letters, 19, 537–545. https://doi.org/10.1111/ele.12588

Illera JC, Fernández-Álvarez Á, Hernández-Flores CN, Foronda P (2015) Unforeseen biogeographical patterns in a multiple parasite system in Macaronesia. Journal of Biogeography, 42, 1858–1870. https://doi.org/10.1111/jbi.12548

Lomolino MV (2005) Body size evolution in insular vertebrates: generality of the island rule. Journal of Biogeography, 32, 1683–1699. https://doi.org/10.1111/j.1365-2699.2005.01314.x

Matson KD (2006) Are there differences in immune function between continental and insular birds? Proceedings of the Royal Society B: Biological Sciences, 273, 2267–2274. https://doi.org/10.1098/rspb.2006.3590

Pérez-Rodríguez A, Ramírez Á, Richardson DS, Pérez-Tris J (2013) Evolution of parasite island syndromes without long-term host population isolation: parasite dynamics in Macaronesian blackcaps Sylvia atricapilla. Global Ecology and Biogeography, 22, 1272–1281. https://doi.org/10.1111/geb.12084

Artificial selection experiments are key experiments in evolutionary biology. The demonstration that application of selective pressure across multiple generations results in heritable phenotypic changes is a tangible and reproducible proof of the evolution by natural selection.
Artificial selection experiments are used to evaluate the joint effects of selection on multiple traits, their genetic covariances and differences in responses in different environments. Most studies on artificial selection experiments report and base their analyses on phenotypic changes [1]. More recently, changes in allele frequency and other patterns of molecular genetic diversity have been used to identify genomic locations where selection has had an effect. However, so far the changes in gene expression have not been in the focus of artificial selection experiment studies (see [2] for an example though).
In plants, one of the most famous artificial selection experiments is the Illinois Corn Experiment where maize (Zea mays) is selected for oil and protein content [3], but in addition, similar experiments have been conducted also for other traits in maize. In Saclay divergent selection experiment [4] two maize inbred lines (F252 and MBS847) have been selected for early and late flowering for 13 generations, resulting in two week difference in flowering time.
In ”Transcriptomic response to divergent selection for flowering time in maize reveals convergence and key players of the underlying gene regulatory network ” [5] Maud Tenaillon and her coworkers study the gene expression differences among these two independently selected maize populations. Their experiments cover two years in field conditions and they use samples of shoot apical meristem at three different developmental stages: vegetative, transitioning and reproductive. They use RNA-seq transcriptome level differences and qRT-PCR for gene expression pattern investigation. The work is continuation to earlier genetic and phenotypic studies on the same material [4, 6].
The reviewers and I agree that dataset is unique and its major benefit is that it has been obtained from field conditions similar to those that species may face under natural setting during selection. Their tissue sampling is supported by flowering time phenotypic observations and covers the developmental transition stage, making a good effort to identify key transcriptional and phenotypic changes and their timing affected by selection.
Tenaillon et al. [5] identify more than 2000 genes that are differentially expressed among early and late flowering populations. Expectedly, they are enriched for known flowering time genes. As they point out, differential expression of thousands of genes does not mean that they all were independently affected by selection, but rather that the whole transcriptional network has shifted, possibly due to just few upstream or hub-genes. Also, the year-to-year variation had smaller effect in gene expression compared to developmental stage or genetic background, possibly indicating selection for stability across environmental fluctuation for such an important phenotype as flowering time.
Another noteworthy observation is that they find convergent patterns of transcriptional changes among the two selected lines. 115 genes expression patterns are shifted due to selection in both genetic backgrounds. This convergent pattern can be a result of either selection on standing variation or de novo mutations. The data does not allow testing which process is underlying the observed convergence. However, their results show that this is an interesting future question that can be addressed using genotype and gene expression data from the same ancestral and derived material and possibly their hybrids.

References

[1] Hill, W. G., & Caballero, A. (1992). Artificial selection experiments. Annual Review of Ecology and Systematics, 23(1), 287-310. doi: 10.1146/annurev.es.23.110192.001443
[2] Konczal, M., Babik, W., Radwan, J., Sadowska, E. T., & Koteja, P. (2015). Initial molecular-level response to artificial selection for increased aerobic metabolism occurs primarily through changes in gene expression. Molecular biology and evolution, 32(6), 1461-1473. doi: 10.1093/molbev/msv038
[3] Moose, S. P., Dudley, J. W., & Rocheford, T. R. (2004). Maize selection passes the century mark: a unique resource for 21st century genomics. Trends in plant science, 9(7), 358-364. doi: 10.1016/j.tplants.2004.05.005
[4] Durand, E., Tenaillon, M. I., Ridel, C., Coubriche, D., Jamin, P., Jouanne, S., Ressayre, A., Charcosset, A. and Dillmann, C. (2010). Standing variation and new mutations both contribute to a fast response to selection for flowering time in maize inbreds. BMC evolutionary biology, 10(1), 2. doi: 10.1186/1471-2148-10-2
[5] Tenaillon, M. I., Seddiki, K., Mollion, M., Le Guilloux, M., Marchadier, E., Ressayre, A. and Dillmann C. (2019). Transcriptomic response to divergent selection for flowering time in maize reveals convergence and key players of the underlying gene regulatory network. BioRxiv, 461947 ver. 5 peer-reviewed and recommended by PCI Evolutionary Biology. doi: 10.1101/461947
[6] Durand, E., Tenaillon, M. I., Raffoux, X., Thépot, S., Falque, M., Jamin, P., Bourgais A., Ressayre, A. and Dillmann, C. (2015). Dearth of polymorphism associated with a sustained response to selection for flowering time in maize. BMC evolutionary biology, 15(1), 103. doi: 10.1186/s12862-015-0382-5

Understanding the mechanisms underlying range expansions is key for predicting species distributions in response to environmental changes (such as global warming) and managing the global expansion of invasive species (Parmesan 2006; Suarez & Tsutsui 2008). Traditionally, two types of ecological processes were studied as essential in shaping range expansion: dispersal and population growth. However, ecology and evolution are intertwined in range expansions, as phenotypic evolution of traits involved in demographic and dispersal patterns and processes can affect and be affected by ecological dynamics, representing a full eco-evolutionary loop (Williams et al. 2019; Miller et al. 2020).

Range expansions can be characterized by the type of population growth and dispersal, divided into pushed or pulled range expansions. Species that have high dispersal and high population growth at low densities present pulled range expansions (pulled by individuals from the edge populations). In contrast, populations presenting increased growth rate at intermediate densities (due to Allee effects - Allee & Bowen 1932; i.e. where growth rate decreases at lower densities) and high dispersal at high densities present pushed range expansions (driven by individuals from core and intermediate populations) (Gandhi et al. 2016). Importantly, the type of expansion is expected to have very different consequences on the genetic (and therefore) phenotypic composition of core and edge populations. Specifically, genetic variability is expected to be lower in populations experiencing pulled expansions and higher in populations involved in pushed expansions (Gandhi et al. 2016; Miller et al. 2020). However, it is not always possible to distinguish between pulled and pushed expansions, as variation in speed and shape can overlap between the two types. In addition, it is difficult to experimentally manipulate the strength of the Allee effect to create pushed versus pulled expansions. Thus, several critical predictions regarding the genetic and phenotypic composition of pulled and pushed expansions are lacking empirical tests (but see Gandhi et al. 2016).

In a previous study, Dahirel et al. (2021a) combined simulations and experimental evolution of the small wasps Trichogramma brassicae to show that low connectivity led to more pushed expansions, and higher connectivity generated more pulled expansions. In accordance with theoretical predictions, this led to reduced genetic diversity in pulled expansions, and the reverse pattern in pushed expansions. However, the question of how pulled and pushed expansions affect trait evolution remained unanswered.

In this follow-up study, Dahirel et al. (2021b) tackled this issue and linked the changes in connectivity and type of expansion with the phenotypic evolution of several traits using individuals from their previous experiment. Namely, the authors compared core and edge populations with founder strains to test how evolution in pushed vs. pulled expansions affected wasp size, short movement, fecundity, dispersal, and density dependent dispersal. When density dependence was not accounted for, phenotypic changes in edge populations did not match the expectations from changes in expansion dynamics. This could be due to genetic trade-offs between traits that limit phenotypic evolution (Urquhart & Williams 2021). 

However, when accounting for density dependent dispersal, Dahirel et al. (2021b) observed that more connected landscapes (with pulled expansions) showed positive density dispersal in core populations and negative density dispersal in edge populations, similarly to other studies (e.g. Fronhofer et al. 2017). Interestingly, in pushed (with lower connectivity) landscapes, such shift was not observed. Instead, edge populations maintained positive density dispersal even after 14 generations of expansion, whereas core populations showed higher dispersal at lower density. The authors suggest that this seemingly contradictory result is due to a combination of three processes: 1) the expansion reduced positive density dispersal in edge populations; 2) reduced connectivity directly increased dispersal costs, increasing high density dispersal; and 3) reduced connectivity indirectly caused demographic stochasticity (and reduced temporal variability in patches) leading to higher dispersal at low density in core populations. However, these results must be taken with a grain of salt, since only one of the four experimental replicates were used in the density dependent dispersal experiment. In range expansions experiments, replication is fundamental, since stochastic processes (such as gene surfing, where alleles maybe rise in frequency due by chance) are prevalent (Miller et al. 2020), and results are highly dependent on sample size, or number of replicate populations analysed. 

Having said that, results from Dahirel et al. (2021b) highlight the importance to contextualize the management of invasions and species distribution, since it is thought that pulled expansions are more prevalent in nature, but pushed expansions can be more important in scenarios where patchiness is high, such as urban landscapes. Moreover, Dahirel's et al. (2021b) study is a first step showing that accounting for trait density dependence is crucial when following phenotypic evolution during range expansion, and that evolution of density dependent traits may be constrained by landscape conditions. This highlights the need to account for both connectivity and density dependence to draw more accurate predictions on the evolutionary and ecological outcomes of range expansions. 
 
References

Allee WC, Bowen ES (1932) Studies in animal aggregations: Mass protection against colloidal silver among goldfishes. Journal of Experimental Zoology, 61, 185–207. https://doi.org/10.1002/jez.1400610202

Dahirel M, Bertin A, Calcagno V, Duraj C, Fellous S, Groussier G, Lombaert E, Mailleret L, Marchand A, Vercken E (2021a) Landscape connectivity alters the evolution of density-dependent dispersal during pushed range expansions. bioRxiv, 2021.03.03.433752, ver. 4 peer-reviewed and recommended by Peer Community in Evolutionary Biology. https://doi.org/10.1101/2021.03.03.433752

Dahirel M, Bertin A, Haond M, Blin A, Lombaert E, Calcagno V, Fellous S, Mailleret L, Malausa T, Vercken E (2021b) Shifts from pulled to pushed range expansions caused by reduction of landscape connectivity. Oikos, 130, 708–724. https://doi.org/10.1111/oik.08278

Fronhofer EA, Gut S, Altermatt F (2017) Evolution of density-dependent movement during experimental range expansions. Journal of Evolutionary Biology, 30, 2165–2176. https://doi.org/10.1111/jeb.13182

Gandhi SR, Yurtsev EA, Korolev KS, 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, 6922–6927. https://doi.org/10.1073/pnas.1521056113

Miller TEX, Angert AL, Brown CD, Lee-Yaw JA, Lewis M, Lutscher F, Marculis NG, Melbourne BA, Shaw AK, Szűcs M, Tabares O, Usui T, Weiss-Lehman C, Williams JL (2020) Eco-evolutionary dynamics of range expansion. Ecology, 101, e03139. https://doi.org/10.1002/ecy.3139

Parmesan C (2006) Ecological and Evolutionary Responses to Recent Climate Change. Annual Review of Ecology, Evolution, and Systematics, 37, 637–669.  https://doi.org/10.1146/annurev.ecolsys.37.091305.110100

Suarez AV, Tsutsui ND (2008) The evolutionary consequences of biological invasions. Molecular Ecology, 17, 351–360. https://doi.org/10.1111/j.1365-294X.2007.03456.x

Urquhart CA, Williams JL (2021) Trait correlations and landscape fragmentation jointly alter expansion speed via evolution at the leading edge in simulated range expansions. Theoretical Ecology. https://doi.org/10.1007/s12080-021-00503-z

Williams JL, Hufbauer RA, Miller TEX (2019) How Evolution Modifies the Variability of Range Expansion. Trends in Ecology & Evolution, 34, 903–913. https://doi.org/10.1016/j.tree.2019.05.012

According to the Red Queen theory of antagonistic host-parasite coevolution, adaptation of parasites to the most common host genotype results in negative frequency-dependent selection whereby rare host genotypes are favoured. Assuming that host resistance relies on a genetic host-parasite (mis)match involving several linked loci, then recombination appears as much more efficient than parthenogenesis in generating new resistant host genotypes. This has long been proposed to explain one of the biggest so-called paradoxes in evolutionary biology, i.e. the maintenance of recombination despite its twofold cost.

Evidence from various study systems indicates that successful infection (and hence host resistance) depends on a genetic match between the parasite’s and the host’s genotype via molecular interactions involving elicitor/receptor mechanisms. However the key assumption of epistasis, i.e. that this genetic host-parasite match involves several linked resistance loci, remained unsupported so far. Metzger and coauthors [1] now provide empirical support for it.

Daphnia magna can reproduce both sexually and clonally and their well-studied interaction with Pasteuria ramosa makes them an excellent model system to investigate the genetics of host resistance. D. magna hosts were found to be either resistant (complete lack of attachment of parasite spores to the host’s foregut) or susceptible (full attachment). In this study the authors carried out an elegant Mendelian genetic investigation by performing multiple crosses between four host genotypes differing in their resistance to two different parasite isolates [1].

Their results show that resistance of D. magna to each of the two P. ramosa isolates relies on Mendelian inheritance at two loci that are linked (A and B), each of them having two alleles with dominant resistance; furthermore resistance to one parasite isolate confers susceptibility to the other. They also show that a third locus appears to confer double resistance (C), but that even double resistant hosts remain susceptible to other parasite isolates, and hence that universal host resistance is lacking – all of this supporting the Red Queen theory.

This paper demonstrates with a high level of clarity that host resistance is governed by multiple linked loci. The assumption of epistasis between resistance loci is supported, which makes it possible for sexual recombination to be maintained by antagonistic host-parasite coevolution.

Reference

[1] Metzger CMJA, Luijckx P, Bento G, Mariadassou M, Ebert D. 2016. The Red Queen lives: epistasis between linked resistance loci. Evolution 70:480-487. doi: 10.1111/evo.12854

South American rainforests, particularly the Tropical Andes, have been recognized as the hottest spot of plant biodiversity on Earth, while facing unprecedented threats from human impact [1,2]. Considerable research efforts have recently focused on unravelling the complex geological, bioclimatic, and biogeographic history of the region [3,4]. While many studies have addressed the question of Neotropical plant diversification using parametric methods to reconstruct ancestral areas and patterns of dispersal, Pirie et al. [5] take a distinct, complementary approach. Based on a new, near-complete molecular phylogeny of two Neotropical genera of the flowering plant family Annonaceae, the authors modelled the ecological niche of each species and reconstructed the history of niche differentiation across the region. The main conclusion is that, despite similar current distributions and close phylogenetic distance, the two genera experienced rather distinct processes of diversification, responding differently to the major geological events marking the history of the region in the last 20 million years (Andean uplift, drainage of Lake Pebas, and closure of the Panama Isthmus).

As a researcher who has not personally worked on Neotropical biogeography, I found this paper captivating and especially enjoyed very much reading the Introduction, which sets out the questions very clearly. The strength of this paper is the near-complete diversity of species the authors were able to sample in each clade and the high-quality data compiled for the niche models. I would recommend this paper as a nice example of a phylogenetic study aimed at unravelling the detailed history of Neotropical plant diversification. While large, synthetic meta-analyses of many clades should continue to seek general patterns [4,6], careful studies restricted on smaller, but well controlled and sampled datasets such as this one are essential to really understand tropical plant diversification in all its complexity.

References

[1] Antonelli A, and Sanmartín I. 2011. Why are there so many plant species in the Neotropics? Taxon 60, 403–414.

[2] Mittermeier RA, Robles-Gil P, Hoffmann M, Pilgrim JD, Brooks TB, Mittermeier CG, Lamoreux JL and Fonseca GAB. 2004. Hotspots revisited: Earths biologically richest and most endangered ecoregions. CEMEX, Mexico City, Mexico 390pp

[3] Antonelli A, Nylander JAA, Persson C and Sanmartín I. 2009. Tracing the impact of the Andean uplift on Neotropical plant evolution. Proceedings of the National Academy of Science of the USA 106, 9749–9754. doi: 10.1073/pnas.0811421106

[4] Hoorn C, Wesselingh FP, ter Steege H, Bermudez MA, Mora A, Sevink J, Sanmartín I, Sanchez-Meseguer A, Anderson CL, Figueiredo JP, Jaramillo C, Riff D, Negri FR, Hooghiemstra H, Lundberg J, Stadler T, Särkinen T and Antonelli A. 2010. Amazonia through time: Andean uplift, climate change, landscape evolution, and biodiversity. Science 330, 927–931. doi: 10.1126/science.1194585

[5] Pirie MD, Maas PJM, Wilschut R, Melchers-Sharrott H and Chatrou L. 2017. Parallel diversifications of Cremastosperma and Mosannona (Annonaceae), tropical rainforest trees tracking Neogene upheaval of the South American continent. bioRxiv, 141127, ver. 3 of 28th Sept 2017. doi: 10.1101/141127

[6] Bacon CD, Silvestro D, Jaramillo C, Tilston Smith B, Chakrabartye P and Antonelli A. 2015. Biological evidence supports an early and complex emergence of the Isthmus of Panama. Proceedings of the National Academy of Science of the USA 112, 6110–6115. doi: 10.1073/pnas.1423853112