Holocentric Repeat Landscapes: From Micro-evolutionary Patterns to Macro-evolutionary Associations with Karyotype Evolution

Molecular Ecology. 2023;00:1–19. | 1wileyonlinelibrary.com/journal/mec Received: 2 May 2023 | Revised: 13 July 2023 | Accepted: 28 July 2023 DOI: 10.1111/mec.17100 ORIGINAL ARTICLE Holocentric repeat landscapes: From microevolutionary patterns to macroevolutionary associations with karyotype evolution Camille Cornet1 | Pablo Mora2,3 | Hannah Augustijnen4 | Petr Nguyen3 | Marcial Escudero5 | Kay Lucek1 This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. © 2023 The Authors. Molecular Ecology published by John Wiley & Sons Ltd. 1Biodiversity Genomics Laboratory, Institute of Biology, University of Neuchâtel, Neuchâtel, Switzerland 2Department of Experimental Biology, Genetics Area, University of Jaén, Jaén, Spain 3University of South Bohemia, Faculty of Science, České Budějovice, Czech Republic 4Department of Environmental Sciences, University of Basel, Basel, Switzerland 5Department of Plant Biology and Ecology, University of Seville, Seville, Spain Correspondence Camille Cornet and Kay Lucek, Biodiversity Genomics Laboratory, Institute of Biology, University of Neuchâtel, Rue EmileArgand 11, 2000 Neuchâtel, Switzerland. Email: ca[email protected] and kay.lu[email protected] Funding information Agencia Estatal de Investigación, Grant/ Award Number: PID2021122715NBI00; Grantová Agentura České Republiky, Grant/Award Number: 2306455S; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Grant/Award Number: 310030_184934 and PCEFP3_202869; Spanish Ministry of Universities - European Union's NextGenerationEU, Grant/Award Number: UJAR10MS Handling Editor: Cristina AriasSardá Abstract Repetitive elements can cause largescale chromosomal rearrangements, for example through ectopic recombination, potentially promoting reproductive isolation and speciation. Species with holocentric chromosomes, that lack a localized centromere, might be more likely to retain chromosomal rearrangements that lead to karyotype changes such as fusions and fissions. This is because chromosome segregation during cell division should be less affected than in organisms with a localized centromere. The relationships between repetitive elements and chromosomal rearrangements and how they may translate to patterns of speciation in holocentric organisms are though poorly understood. Here, we use a referencefree approach based on lowcoverage shortread sequencing data to characterize the repeat landscape of two independently evolved holocentric groups: Erebia butterflies and Carex sedges. We consider both microand macroevolutionary scales to investigate the repeat landscape differentiation between Erebia populations and the association between repeats and karyotype changes in a phylogenetic framework for both Erebia and Carex. At a microevolutionary scale, we found population differentiation in repeat landscape that increases with overall intraspecific genetic differentiation among four Erebia species. At a macroevolutionary scale, we found indications for an association between repetitive elements and karyotype changes along both Erebia and Carex phylogenies. Altogether, our results suggest that repetitive elements are associated with the level of population differentiation and chromosomal rearrangements in holocentric clades and therefore likely play a role in adaptation and potentially species diversification. KEYWORDS Carex, Erebia, Lepidoptera, speciation, transposable elements 2 | CORNET et al. 1 | INTRODUCTION Repetitive DNA elements (repeats) are major components of most eukaryotic genomes and are increasingly recognized as important drivers of adaptation and speciation (Biémont & Vieira, 2006). Transposable elements (TEs), for example, are mobile DNA sequences propagating within genomes, occasionally generating adaptive variation (Schrader & Schmitz, 2019). For instance, the industrial melanism of the peppered moth Biston betularia, a textbook example of adaptation, is caused by the insertion of a TE in the wing patterning gene, which resulted in its altered expression (Van't Hof et al., 2016). Repeats may also contribute to reproductive isolation between populations and even promote speciation. This has been suggested for the adaptive radiation of Anolis lizards, where TEs accumulated in clusters of developmental genes may have facilitated morphological adaptations (Feiner, 2016). The insertion density of TEs is similarly correlated with speciation rates in mammals (Ricci et al., 2018). Repetitive elements are also important drivers of genome evolution through chromosomal rearrangements, such as inversions, fusions and fissions of chromosomes (Fedoroff, 2012; Lönnig & Saedler, 2002). These largescale rearrangements can be caused by ectopic recombination, that is recombination events between repeat copies from different genomic regions (Cáceres et al., 1999; Delprat et al., 2009). Repeatmediated chromosomal rearrangements could promote speciation through different processes. First, by reducing fitness of heterokaryotypes, that is hybrids that are heterozygous for chromosomal rearrangements (Faria & Navarro, 2010; Rieseberg, 2001). However, this mode of chromosomal speciation suffers from the socalled ‘underdominance paradox’, whereby highly deleterious rearrangements resulting in unfit hybrids are unlikely to become fixed in a population (Spirito, 1998). Second, novel rearrangements could suppress recombination between rearranged parts of the genome (Faria & Navarro, 2010; Rieseberg, 2001) or change gene expression (Li et al., 2023). The suppressed recombination scenario relies on reduced gene flow in rearranged genomic regions and on linkage disequilibrium between genes involved in reproductive isolation (Rieseberg, 2001). Chromosomal speciation research has primarily focussed on organisms with monocentric chromosomes that have a single localized centromere per chromosome (Coyne & Orr, 2004; White, 1978). However, the centromeric activity can be distributed along a large portion of the socalled holocentric chromosomes (Mandrioli & Manicardi, 2020). Holocentricity has evolved independently at least 19 times in various clades across the tree of life including Lepidoptera (i.e. butterflies and moths), some Angiosperms (e.g. sedges including the genus Carex) or nematodes (including the model species Caenorhabditis elegans) (Escudero, MárquezCorro, & Hipp, 2016; Melters et al., 2012). Chromosomal rearrangements such as fusions and fissions are supposedly less deleterious in holocentric than in monocentric organisms (Lucek et al., 2022; Melters et al., 2012) and meiotic adaptations to holocentricity can further mitigate the underdominance of the chromosomal rearrangements (Lukhtanov et al., 2018). Interestingly, karyotype diversity is correlated with diversification rates in angiosperms (Carta & Escudero, 2023) and in butterflies (de Vos et al., 2020), especially in some clades such as Erebia (Augustijnen et al., 2023) and Polyommatinae butterflies (Talavera et al., 2013). Chromosomal rearrangements can act as partial reproductive barriers in holocentric taxa as distant as butterflies (Lukhtanov et al., 2018; Mackintosh et al., 2023) and sedges of the genus Carex (Escudero, Hahn, et al., 2016). This suggests that chromosomal rearrangements might commonly be involved in speciation of holocentric groups. The genetic features and molecular mechanisms underlying fusion and fission of holocentric chromosomes are mostly unknown, although different types of repeats have recently been implicated in Rhynchospora (Hofstatter et al., 2022) and Carex sedges (Escudero et al., 2023), butterflies (Ahola et al., 2014; Höök et al., 2023), aphids (Mathers et al., 2021) and nematodes (Yoshida et al., 2023). Holocentric species constitute some of the most karyotypically diverse taxonomic groups, including several insect clades (e.g. Lepidoptera, n = 5– 223; de Vos et al., 2020) and plants (e.g. Carex sedges, n = 5– 66; Hipp et al., 2009; MárquezCorro et al., 2021). However, this diversity in chromosome numbers is not equally represented among holocentric clades, and the reasons for these disparities are still unknown. For example, most butterfly genera have retained the putative ancestral chromosome number of n = 31 (de Vos et al., 2020), while others show increased karyotypic diversity (e.g. chromosomes numbers ranging from n = 7 to n = 52 in the genus Erebia; Augustijnen et al., 2023). An explanation to the unequal representation of karyotypic diversity among holocentric clades could be that the karyotypically conserved clades lack the repeats involved in chromosomal rearrangements. Using a referencefree approach on lowcoverage shortread sequencing data, we first aimed to uncover the diversity of repeats and test how these may differ among species with different degrees of intraspecific differentiation. This could inform us on the potential association between repeats and population differentiation. For this, we compared individuals from different populations of four Erebia species and tested to which extent divergent populations also differ in their repeat landscape. We expected a positive correlation between the overall genetic differentiation among populations and level of differentiation of their repeat landscape (Bourgeois & Boissinot, 2019). We then employed macroevolutionary inferences at the genus level to determine whether repeats could be associated with karyotype changes along the phylogeny of Erebia, with an emphasis on the most speciesrich and karyotypically variable subclade Tyndarus (Augustijnen et al., 2023). Finally, we performed the same phylogenetic analyses for a subset of Carex species to test whether similar macroevolutionary patterns would occur in independently evolved holocentric groups. 2 | MATERIALS AND METHODS 2.1 | Study species and sample collection The main focus of our study was on the Palearctic genus Erebia, which comprises ~100 species of primarily alpine butterflies with closely related species often forming narrow contact zones in the 1365294x, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/mec.17100 by Universidad De Sevilla, Wiley Online Library on [20/12/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | 3 CORNET et al. Alps (Augustijnen et al., 2022; Cupedo, 2014; Peña et al., 2015). For the specieslevel analyses of Erebia, we focussed on four species that fall along a gradient of intraspecific genetic differentiation: (i) E. cassioides colonized part of the Western Alps since the last glaciation, likely from a refugia in Southwestern Europe (Lucek et al., 2020), and shows little genetic differentiation to populations from the Pyrenees or the French Massive Central (Schmitt et al., 2016); (ii) Erebia tyndarus is endemic to the central Alps and shows a higher population structure than E. cassioides in the Alps (Gratton et al., 2016; Schmitt et al., 2016), potentially because E. tyndarus is less affected by range expansion. For (iii) E. nivalis, we included samples from their disjunct populations in the Alps, that is from Switzerland and Austria, that are considered distinct subspecies (Table S1; Figure S1; Schmitt et al., 2016; Sonderegger, 2005). Finally, for iv) E. pronoe, we included individuals of two nominal, ecologically differentiated subspecies, that is E. pronoe psathura (n = 6) and E. pronoe vergy (n = 5; Table S1; Figure S1). We wondered whether the levels of population differentiation in those four species are also mirrored in the repeat landscape. Erebia cassioides, E. tyndarus and E. nivalis are sibling species and belong to the karyotypically diverse Tyndarus clade within Erebia. For each of these three species, we sampled 23 individuals from 47 sites (Table S1; Figure S1). Erebia cassioides and E. tyndarus form secondary contact zones in populations GRW and GRF (Sonderegger, 2005), with very few F1 hybrids and almost no introgression (Augustijnen et al., 2022; Lucek et al., 2020). To ensure that this would not cause a bias in our analyses, we selected individuals as far away as possible from the point of secondary contact in those populations. For the Erebia genuslevel (i.e. macroevolutionary) analyses, we took advantage of a shortread resequencing data set (NCBI BioProject PRJNA1000734) that was used to establish a dated phylogeny of Erebia (Augustijnen et al., 2023). We used species for which chromosome number information was available from the literature. As Erebia is one of the best karyotyped butterfly genera (de Vos et al., 2020; Robinson, 1971), this resulted in 47 species in total (Table S2). Finally, to assess whether there could also be a macroevolutionary association between repeats and karyotype changes in an independently evolved holocentric clade with high karyotypic diversity, we extended our study to the plant genus Carex (Cyperaceae). The genus Carex with ca. 2000 species is the largest genus in the family Cyperaceae and one of the largest angiosperm genera, with the highest species richness in the temperate areas of the northern hemisphere (Escudero et al., 2012). For the Carex genuslevel analyses, we included 25 samples from 14 species, two individuals per species (exceptionally only one individual for three of the species; Table S3). These 14 species are distributed across the whole genus Carex, represent all major Carex lineages (with the exception of Carex subgenus Siderosticta; Global Carex Group et al., 2021) and differ in their karyotype (n = 17– 42): eight species from Carex subgenus Carex representing six different sections (Phacocystis, Limosae, Spirostachyae, Ceratocystis, Aulocystis and Mitratae), five species from Carex subgenus Vignea representing four sections (Glareosae, Stellutae, Ovales and Foetida) and one species from Carex subgenus Eutyceras (sect. Capituligerae). 2.2 | DNA extraction and sequencing All Erebia butterflies for the population level analyses were collected between 2006 and 2022 (Table S1) using hand nets and stored at −20°C. We obtained DNA from the thorax of each individual using the standard protocol of the Qiagen Blood & Tissue Kit (Qiagen AG). We outsourced Illumina library preparation, which included a PCR amplification step, to the Department of Biosystems Science and Engineering (DBSSE) of ETH Zürich in Basel, where they carried out the subsequent pairedend wholegenome resequencing on an Illumina NovaSeq 6000 platform. The samples used for E. tyndarus and E. cassioides were previously used in Augustijnen et al. (2022). Carex specimens were collected between 2005 and 2021 and stored in silica gel (Table S3). DNA was extracted using the Qiagen Dneasy Plant Pro Kit following the manufacturer's protocol. Sequencing was carried out at the same time and using the same protocol as the Erebia specimens. 2.3 | Repetitive elements identification and quantification To detect, identify and quantify repeats in both Erebia and Carex, we used the graphbased clustering software RepeatexploReR2 (Novák et al., 2010, 2020). Briefly, RepeatexploReR2 uses lowcoverage raw shortread data to cluster sequences based on their similarities (i.e. an alltoall sequence similarity search). Read clusters correspond mostly to repeat families, which are subsequently identified based on the graph structure and on the consensus sequence of each cluster. The use of lowcoverage data (<0.5X) ensures that sequences retained in the clusters are repeats rather than duplicated gene families. This allows for repeat identification and comparison between different individuals of the same species or even across species, without the need for reference genomes. RepeatexploReR2 includes an implementation of TAREAN (TAndem REpeat ANalyzer), a graphbased automated satellite DNA (satDNA) identification pipeline (Novák et al., 2017). This approach has been used extensively to characterize the repeat landscape of various organisms, including plants (e.g. Macas et al., 2015) and insects (e.g. Silva et al., 2019). We trimmed raw reads based on quality (per base quality score > 30) retaining only reads longer than 120 bp and removed adaptors and polyG tails using fastp 0.22.0 (Chen et al., 2018). We then subsampled trimmed reads to obtain a coverage of 0.1X using seqtk 1.3 (https://github.com/lh3/seqtk) assuming genome sizes of 500 Mbps for all Erebia species (Lohse, Hayward, et al., 2022; Lohse, Lohse, et al., 2022) and using flow cytometry genome size estimates for Carex (Pellicer & Leitch, 2020; https://cvalu es.scien ce.kew.org/; Table S3). We performed individualbased repeat detection and classification for each individual in the specieslevel 1365294x, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/mec.17100 by Universidad De Sevilla, Wiley Online Library on [20/12/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 4 | CORNET et al. analyses and for each species in the genuslevel analyses, using the command line version of RepeatexploReR2 with default settings and the builtin REXdb databases (METAZOA3.0 for Erebia and VIRIDIPLANTAE3.0 for Carex). We then used the comparative mode of RepeatexploReR2 to compare the repeat landscape of all individuals for each of the four Erebia species in the specieslevel analyses, and of all species for Erebia or Carex in the genuslevel analyses. We then merged the top repeat clusters by annotation, corresponding to at least 0.01% of the analysed reads, excluding reads corresponding to organellar DNA sequences. We subsequently used the number of reads in each cluster as a proxy for repeat abundance of the respective genomes (Novák et al., 2010). For the comparative genuslevel analysis of Erebia, comprising 47 species, we were restricted to using 0.04X coverage per species due to computational limitations. Therefore, we ran the pipeline twice, on two different read subsamples, to ensure reliability and repeatability of the results. To ensure that an entirely automated annotation would not bias our results, we also performed manual curation following Goubert et al. (2022) on the 250 most abundant repeat clusters (corresponding to 89% of the analysed reads) for the comparative genuslevel analysis of Erebia. 2.4 | Specieslevel analyses To compare the repeat patterns with individual genetic diversity and differentiation, we generated SNP data sets by aligning all raw reads of E. cassioides, E. tyndarus and E. nivalis against a reference assembly of E. cassioides (NCBI BioProject PRJNA941023) and for E. pronoe against the more closely related reference genome of E. ligea (Lohse, Hayward, et al., 2022). Filtering and SNP calling followed Augustijnen et al. (2023) for each dataset. To characterize the diversity and differentiation in repeats between individuals and populations, we employed established community ecology approaches implemented in the R package vegan 2.6– 4 (Oksanen et al., 2022). For this, we considered distinct repeat annotations as ‘species’ and individual genomes as ‘sampling sites’ (Haley & Mueller, 2022; Venner et al., 2009). First, to estimate the overall differentiation in repeat landscape between individuals in each of the four Erebia species, we conducted principal coordinate analyses (PCoA) using the R package ape 5.7– 1 (Paradis & Schliep, 2019). The PCoA was performed on the Bray– Curtis dissimilarity matrix based on the repeat clusters of each individual calculated in vegan. We similarly performed a PCoA based on Euclidean distances among individuals for each SNP dataset, calculated with adegenet 2.1.10 (Jombart, 2008) in R. Second, we performed Permutational Multivariate Analyses of Variance (PERMANOVAs) for each species, with the function adonis2 implemented in vegan and 9′999 permutations, to test for differences between populations in terms of repeat landscape. While performing these analyses, we noticed the presence of two pairs of putative halfsiblings in our data sets (one in E. tyndarus and one in E. nivalis), confirmed by kinship coefficient analysis (0.29 and 0.31, respectively) in plink 1.9 (Chang et al., 2015; Table S4). Therefore, we randomly removed one individual per pair of halfsiblings from all subsequent analyses. We calculated Simpson's diversity index based on the abundances of each repeat type for each individual with the function diversity in vegan, and assessed whether this diversity would be correlated with the level of genetic diversity. For the latter, we used mlRho 2.9 (Haubold et al., 2010) which estimates individualbased expected zygosity (θ), using genomewide SNP data. For mlRho, we only included sites with a minimum quality of 28, a maximum depth of 80X, and a minimum depth of 4X. We subsequently correlated θ and Simpson's diversity index for each of the four species with a Spearman correlation using the function cor.test in R. For each species, we further compared the Bray– Curtis dissimilarity matrix based on repeat clusters of each individual against the matrix of individualbased pairwise Euclidean distances using the SNP data. The comparison between both distance matrices was performed using a distancebased Redundancy Analysis (dbRDA) using the package vegan following Benestan et al. (2021). We performed all analyses in R 4.2.2 (R Core Team, 2022) using Rstudio 2023.3.0.386 (Posit team, 2023). 2.5 | Genuslevel analyses We performed the genuslevel analyses within a phylogenetic framework to test for an association between the evolution of the repeat landscape in Erebia and Carex, respectively, and changes in karyotypes, used as a proxy for largescale chromosomal rearrangements (chromosomal fusions and fissions). The phylogeny of Erebia was taken from Augustijnen et al. (2023). In short, the Erebia species tree was established from 2′920 individual gene trees inferred with iqtRee (Nguyen et al., 2015) using a coalescent model implemented in astRal (Zhang et al., 2018). For Carex, we used a dated phylogeny that represents the 70% of extant species and is based on three DNA regions (ITS, ETS and matK) and a HybSeq phylogeny backbone (MartínBravo et al., 2019). We pruned both phylogenies to the species used in this study using the function drop.tip in the R package ape, resulting in 47 Erebia species and 14 Carex species. To determine whether the overall differentiation in repeat landscape between species shows a phylogenetic signal, we constructed a Bray– Curtis dissimilarity matrix based on repeat clusters for each species, for Erebia and Carex separately in vegan (Oksanen et al., 2022). We then contrasted the dissimilarity matrix with the phylogeny of Erebia or Carex using the R package dendextend 1.17.1 (Galili, 2015). To formally test for the presence of a phylogenetic signal in particular types of repeats, we used the R package phylosignal 1.3 (Keck et al., 2016) to compute Pagel's λ (Pagel, 1999) along with its statistical significance. If λ equals zero, there is no phylogenetic signal, that is closely related species are not more similar than distant ones. If λ equals one, the phylogenetic signal observed corresponds to Brownian motion (MolinaVenegas & Rodríguez, 2017). To assess whether there could be a macroevolutionary impact of repeats on chromosome numbers and therefore on chromosomal 1365294x, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/mec.17100 by Universidad De Sevilla, Wiley Online Library on [20/12/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | 5 CORNET et al. rearrangements, for both Erebia and Carex, we fitted phylogenetic generalized least square (PGLS) models with diploid chromosome number as a dependent variable and each repeat abundance as explanatory variables, using the gls function in nlme 3.1– 162 (Pinheiro et al., 2023). We used Pagel's λ coefficient, computed using the function corPagel in ape to take phylogenetic correlation into account. Because we had more information available for Carex, we fitted additional models including the rate of chromosomal evolution (calculated with ChRomohisse, Tribble et al., in preparation) and number of different karyotypes per species (Table S3) as explanatory variables. As some Carex species show intraspecific variation in chromosome numbers (Hipp et al., 2009), we used the mean chromosome number for each species. No intraspecific chromosome number variation has been reported in Erebia (Augustijnen et al., 2023; Robinson, 1971). We used a Bayesian reversiblejump multiregime Ornstein– Uhlenbeck (OU) approach as implemented in the R package bayou 2.0 (Uyeda & Harmon, 2014) to infer major shifts in chromosome number and abundance of each repeat type in Erebia. The OU model has two components: the stochastic and the deterministic components. The stochastic component is a Brownian Motion model with a single parameter, sigma, which quantifies the rate of stochastic evolution of a given trait. The deterministic component has two parameters: theta and alpha. Theta is the optimum towards which the trait evolves, and alpha is the rate of evolution towards the optimum. Using the bayou approach, we estimated the overall sigma, overall alpha, a theta for each inferred optimum shift and an additional theta value for the root of the phylogeny for each trait (the diploid chromosome number and abundance of each repeat type). Shifts were considered significant if they showed a posterior probability (PP) higher than 0.30 (Uyeda & Harmon, 2014). We set up the analyses following Larridon et al. (2021). Because bayou implements a Bayesian approach, we ran at least two independent Markov Chain Monte Carlo (MCMC) analyses of 1– 3 million generations for each of the variables (chromosome number and abundance of each repeat type) to test for convergence, with a burnin of 25%– 30% to consider parameters estimates only after reaching stationarity. Finally, we used the slouch.fit function in the R package slouCh 2.1.4 (Kopperud et al., 2020) to test the effect of repeat landscape on chromosome number. In slouCh, chromosome number is modelled as evolving towards an optimum following an OU process that is a linear function of the predictors (abundance of each repeat type). The predictors are modelled as evolving on the tree according to a Brownian Motion process (Hansen et al., 2008). RepeatmaskeR identifies and masks repetitive elements in genomic DNA sequences. We ran RepeatmaskeR 4.0.7. (Smit et al., 2013) using the Illumina shortreads from all the species with the default settings and the ‘- a’ option to keep the alignment in order to quantify the divergence of repeats and to plot the overall repeat landscape. As custom library, we used a custommade database combining three different datasets, coming from (i) RepBase2018, (ii) the Dfam 3.7 repeat libraries and (iii) RepeatexploReR2 contigs using the scripts id_rmasker_rexp.py and annot_to_rexp.py (https://github.com/ f j r u i z r u a n o / n g s - p r o t o c o l s ; accessed 10.04.2023). This combined database was used to improve the detection of novel and divergent repeat elements, and the RepeatexploReR2 database was included to improve the detection of lineagespecific and novel repeat elements (including satDNAs) that may not be present in the RepBase2018 and Dfam 3.7 databases. Using the RepeatmaskeR output and the Perl script calcDivergenceFromAlign.pl, we calculated the divergence for each repeat type. Then, using a custom script in R and ggplot2 3.3.6 (Wickham, 2016), we generated a repeat landscape for all the species included in our study. The repeat landscape allowed us to visualize the distribution and abundance of different repeat elements across the genomes of each species and infer the divergence for each repeat type. 3 | RESULTS 3.1 | Specieslevel analyses The proportion of repeats in the individual genomes ranged between 26.4% and 28.6% for E. cassioides, 24.7% and 26.0% for E. tyndarus, and 21.8% and 25.9% for E. nivalis (Figure S2). The proportion of repeats in E. pronoe was consistently higher in the subspecies vergy (25.7%– 34.7%) than in the subspecies psathura (24.1%– 25.3%). The most abundant repeats in all four species were LINE retrotransposons (genome proportion ranging from 1.2% to 3.0%). Among the detected repetitive sequences, the proportion of unidentified repeats ranged between 18.7% and 32.7%. In all four species, some individuals showed an interestingly high amount of satDNA (e.g. PDM_2 in E. cassioides, GRO_2 in E. nivalis; Figure S2). Satellite DNA can accumulate on a nonrecombining femalespecific W sex chromosome (e.g. CabraldeMello et al., 2021), but this is not the cause of the observed differences between individuals, as the only female in the data set is GRI_3 in E. nivalis (Table S1). The PCoA revealed increasing levels of differentiation between populations in terms of repeat landscape, corresponding to the increasing genetic differentiation from E. cassioides populations to E. pronoe subspecies. Indeed, E. cassioides populations showed similar repeat landscapes (Figure 1a), although there were significant differences between populations (PERMANOVA: F6,14 = 4.49, p < .001). Erebia tyndarus populations were more strongly separated (PERMANOVA: F6,13 = 6.83, p < .001), especially along the first axis (i.e. the axis explaining most of the differences) but also along the second axis for population ARO (Figure 1b). Erebia nivalis populations appeared even more distinct (Figure 1c), even though this differentiation between populations in terms of repeat landscape was less significant (PERMANOVA: F3,6 = 3.38, p = .036). The two Austrian populations GRO and SAJ formed a distinct cluster to the Swiss populations, suggesting distinct repeat landscapes between these geographically isolated populations. Finally, E. pronoe subspecies differed greatly in repeat landscape (PERMANOVA: F1,9 = 8.75, p = .002; Figure 1d), such a pattern being likely due to the higher overall repeat density in E. pronoe vergy, especially in the two individuals VER_2 and VER_4 (Figure S2). This increase in 1365294x, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/mec.17100 by Universidad De Sevilla, Wiley Online Library on [20/12/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 6 | CORNET et al. the levels of differentiation in repeat landscape from E. cassioides populations to E. pronoe subspecies was also noticeable by the increasing variation in repeat landscape accounted for by the two leading PCoA axes (46.7% and 9.1% for E. cassioides to 74.9% and 17.2% for E. pronoe; Figure 1). PCoAs on the Euclidean distances between individuals based on the SNP data set recovered similar patterns of differentiation among individuals (Figure S3). There was no significant correlation between individualbased estimates of genetic diversity θ and Simpson's diversity index in repeat landscape for E. cassioides, E. tyndarus and E. nivalis (p > .3; Figure 2a), when it was significant for E. pronoe (rho = 0.74, p = .013; Figure 2a). The RDA revealed a significant positive association between Euclidean genetic distance between individuals and Bray– Curtis distance in repeat landscape for all four species (E. cassioides: F4,16 = 1.19, p = .002; E. tyndarus: F3,16 = 1.44, p < .001; E. nivalis: F1,8 = 1.99, p < .001; and E. pronoe: F1,9 = 2.63, p = .015; Figure 2b). 3.2 | Genuslevel analyses The proportion of repeats in the genomes of Erebia species (Figure 3a) ranged between 18.3% (E. triarius) and 33.2% (E. pharte). Concordantly with the species level analyses of Erebia, the most abundant identified repeats were LINE retrotransposons in all species (genome proportion ranging from 0.7% to 5.2%). More generally, retrotransposons (i.e. Class I TEs characterized by a ‘copypaste’ replication mechanism with an RNA intermediate; Wells & Feschotte, 2020) were much more common in Erebia (genome proportion ranging from 0.9% to 9.3%) than DNA transposons (i.e. Class II TEs characterized by a ‘cutandpaste’ replication mechanism with a DNA intermediate (Wells & Feschotte, 2020); genome proportion ranging from 0.01% to 0.6%). Among the identified repetitive sequences, the proportion of unidentified repeats ranged between 15.0% and 27.8%. As the genuslevel analyses for Erebia were performed only on 0.04X genomic coverage, proportions of repeats in the genomes were computed twice on different runs of subsampling. The results were largely congruent, differing by less than 0.1% of the genome for each identified repeat type, and by 0.3% for unidentified repeats (Table S5a). The results were also largely congruent between the automated and manually curated repeat annotation for the 250 most abundant clusters in the Erebia dataset (Table S5b), and no wrongly annotated clusters were detected. The proportion of repeats for the Carex species (Figures 3b and S4) was more variable, ranging between 13.7% (C. capitata) and FIGURE 1 Principal Coordinate (PCo) analysis based on Bray– Curtis dissimilarity matrices of repetitive elements abundance and diversity in populations of four Erebia species in increasing order of genetic differentiation between populations: (a) E. cassioides, (b) E. tyndarus, (c) E. nivalis and (d) E. pronoe. −0.050 −0.025 0.000 0.025 PCo2 (9.1%) −0.050 −0.025 0.000 0.025 PCo2 (15.1%) −0.025 0.000 0.025 PCo2 (15.4%) −0.10 −0.05 0.00 0.05 PCo2 (17.2%) (a) Erebia cassioides (b) Erebia tyndarus (c) Erebia nivalis (d) Erebia pronoe GRF_1 GRF_2 GRF_3 GRW_1 GRW_2 GRW_3 KAN_1 KAN_2 KAN_3 PDM_1 PDM_2 PDM_3 ROU_1 ROU_2 ROU_3 SCH_1 SCH_2 SCH_3 STO_1 STO_2 STO_3 ARO_1 ARO_2 ARO_3 COM_1 COM_2COM_3 GOT_1 GOT_2 GRF_1 GRF_2 GRF_3 GRW_1 GRW_2 LAU_1 LAU_2 LAU_3 SAN_1 SAN_2 SAN_3 GRI_1 GRI_2 GRI_3 GRO_1 GRO_2 GRO_3 SAJ_1 SAJ_2 SCH_2 SCH_1 PSA_4 PSA_1 PSA_5 PSA_2 PSA_3 VER_3 VER_4 VER_5 VER_6 VER_2 VER_1 −0.075 −0.050 −0.025 0.000 0.025 0.050 0.075 PCo1 (46.7%) Populations GRF GRW KAN PDM ROU SCH STO −0.075 −0.050 −0.025 0.0000.025 0.0500.075 PCo1 (47.9%) Populations AROCOM GOTGRF GRWLAU SAN −0.075 −0.050 −0.025 0.000 0.025 0.050 0.075 PCo1 (56%) Populations GRI GRO SAJ SCH −0.10−0.05 0.00 0.05 0.10 0.15 0.20 PCo1 (74.9%) Subspecies psathura vergy 1365294x, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/mec.17100 by Universidad De Sevilla, Wiley Online Library on [20/12/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | 7 CORNET et al. 48.8% (C. sempervirens). The most abundant repeat types were LTR retrotransposons, particularly the Ty1/copia and Ty3/gypsy families (genome proportion ranging from 0.2% to 9.9% and from 0.03% to 20.0%, respectively). Interestingly, DNA transposons (especially the TIR family) were more abundant in Carex than in Erebia (tt e s t : t1,24.8 = 5.90, p < .001), with values higher than those of retrotransposons in some species (genome proportion ranging from 0.3% to 32.0% for retrotransposons and from 0.2% to 4.7% for DNA transposons). The proportion of identified rDNA was higher for Carex than for Erebia (t1,13 = 5.88, p < .001), while no statistical difference occurred for satDNA (t1,26 = 0.53, p = .599). Among the identified repetitive sequences, the proportion of unidentified repeats ranged between 10.1% and 24.8%. It was expected that this proportion would be lower for Carex than for Erebia as the RepeatexploReR2 pipeline was initially designed for plants (Novák et al., 2020). Genome size was positively correlated with total repeats proportion in the Carex genomes (PGLS: t = 11.76, p < .001). For 11 Carex species, two individuals were analysed per species, showing some intraspecific variation (Figure S5). The overall repeat landscape showed a global concordance between the phylogeny and the clustering of the species based on the repeat landscape, both for Erebia (Figure 4a) and Carex (Figure 4b), although several notable discrepancies occurred. For example, the Tyndarus clade in Erebia clustered together based on the repeat landscape, except for E. cassioides and E. rondui. For Carex, the position of C. sempervirens (the species with the highest repeat proportion in the genome) was very different in the phylogeny and in the clustering based on the repeat landscape. It is important to note that, both for Erebia and Carex, the topology of the two trees differed at several scales (i.e. sister species, subclades and deeper nodes), suggesting that the repeat landscape as characterized here is insufficient for phylogenetic inference. When statistically investigating the phylogenetic signal of specific repeat types, contrasting results were observed for Erebia (Table 1a) and Carex (Table 1b). For Erebia, LTR retrotransposons that could be identified down to repeat family level (i.e. BelPao, Ty1/copia and Ty3/gypsy), Penelope retrotransposons and Helitron DNA transposons showed phylogenetic signals corresponding to Brownian motion (i.e. Pagel's λ close to one) that remained significant following a False Discovery Rate (FDR) correction (p < .05). On the contrary, retrotransposons of the families DIRS, LINE and LTR that could not be identified at a finer scale, and Maverick DNA transposons did not show a significant phylogenetic signal (i.e. closely related species were not significantly more similar in their repeat abundance than distantly related species; Pagel's λ close to zero; p > .6). For Carex, LINE, Ty3/gypsy, LTR retrotransposons that could not be identified to repeat family level showed a significant phylogenetic signal (Pagel's λ close to one; p < .05 following FDR). The same relationship for TIR DNA transposons was not significant following the FDR. Interestingly, both for Erebia and Carex, rDNA and satDNA did not show an overall phylogenetic signal (Pagel's λ close to zero; p > .05), suggesting that rDNA and satDNA evolved independently to the phylogeny and that other factors such as selection could be involved in the evolution of these two types of repetitive elements. None of the associations between specific repeat types and chromosome number were significant following FDR (Table 1; FIGURE 2 Correlations between genomewide SNPbased estimates and estimates based on repetitive elements abundance and diversity for Erebia cassioides, E. tyndarus, E. nivalis and E. pronoe. (a) Correlations between individualbased θ estimates of genetic diversity and Simpson diversity index based on repeat landscape. (b) Correlations between Euclidean genetic distance between individuals and BrayCurtis distance in repeat landscape. Significance was assessed with a distancebased Redundancy Analysis, even though straight lines and shaded grey areas represent a linear regression and the 95% confidence interval. 0.008 0.008 0.008 0.009 0.010 estimate 0.010 0.011 0.012 estimate 0.008 0.008 0.009 0.009 estimate 0.005 0.006 0.006 estimate 1000 1100 1200 Individual based genetic distance 1100 1150 1200 1250 1300 Individual based genetic distance 900 1000 1100 1200 1300 Individual based genetic distance 600 800 1000 Individual based genetic distance (a) E. cassioides E. tyndarus E. nivalis E. pronoe (b) E. cassioides E. tyndarusE. nivalis E. pronoe 0.60 0.62 0.64 Simpson diversity 0.56 0.57 0.58 0.59 0.60 Simpson diversity 0.67 0.68 0.680.680.69 Simpson diversity 0.50 0.52 0.54 0.56 0.58 Simpson diversity 0.05 0.08 0.10 0.12 Distance in repeats 0.05 0.08 0.10 Distance in repeats 0.040.060.08 Distance in repeats 0.10 0.20 Distance in repeats rho = 0.23 p = 0.323 rho = 0.17 p = 0.466 rho = -0.07 p = 0.841 rho = 0.74 p = 0.013 F4,16 = 1.19 p = 0.002 F3,16 = 1.44 p < 0.001 F1,8 = 1.99 p < 0.001 F1,9 = 2.63 p = 0.015 1365294x, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/mec.17100 by Universidad De Sevilla, Wiley Online Library on [20/12/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 8 | CORNET et al. Figure S6), which could also indicate limited statistical power given our sample size. We summarize the relationships that were significant before applying the FDR as they could hint towards potential associations that would be ideally tested at a larger scale in future. First, there could be an association between the abundance of LTR retrotransposons and chromosome number for both Erebia and Carex (Table 1). The association was positive for Erebia, suggesting that a higher LTR abundance in the genome could corresponds to a higher chromosome number, contrarily to Carex for which this association was negative, suggesting that a higher LTR abundance in the genome corresponds to a lower chromosome number. Second, in Erebia, DNA transposons were negatively associated with chromosome number for Helitrons and positively for Mavericks. Lastly, in Carex, Ty3/Gypsy retrotransposons were negatively associated with chromosome number. When adding other variables in the model for Carex (Table S6), rates of chromosome evolution were positively associated with the abundance of LINEs (t = 5.68, p < .001), LTRs (t = 2.99, p = .014) and Helitrons (t = 3.60, p = .005) following an FDR, when the same relationship for Ty1/copia and Ty3/gypsy did not remain significant following the FDR. Similarly, the negative FIGURE 3 Proportion of repetitive elements in the genomes of (a) Erebia and (b) Carex species, calculated by the proportion of reads in clusters corresponding to repetitive elements in RepeatexploReR2. Unrooted phylogenies are represented on the left. TE— Transposable Element, rDNA— ribosomal DNA, satDNA— satellite DNA. (a) (b) 1365294x, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/mec.17100 by Universidad De Sevilla, Wiley Online Library on [20/12/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | 9 CORNET et al. association between chromosome number with the abundance of LTR and Ty3/gypsy were nonsignificant following the FDR. Number of different karyotypes per species was never significantly associated with repeats (all p > .074, Table S6). The estimated sample sizes (ESSs) for all parameters estimated in bayou were > 200, which indicate that the Bayesian analyses reached convergence and stationarity. The bayou results displayed no significant shift in optima for some of the repeat landscape variables (LINEs, Ty3/gypsy, Penelope, and Maverick; Table S7, Figure S7). However, chromosome number and most of the repeat variables (DIRS, BelPao, LTRs, Ty1/copia, Helitrons, 45S rDNA and satDNA) showed significant shifts in optima. Chromosome number showed seven shifts (eight optima), three of the four with the highest PPs occurred within the karyotypically diverse and species rich Tyndarus group (Table S7, Figure 5a). DIRS, BelPao, LTRs, Ty1/copia and satDNA showed 1– 4 shifts but related to terminal branches or small clades (2– 3 species; Table S7, Figure S7). Exceptionally, one of the shifts inferred for satDNA corresponded to a bigger clade, the Pronoe group (Table S7; Figure 5d). Helitrons (Table S7; Figure 5b) and 45S rDNA (Table S7; Figure 5c) showed four shifts each, 3 and 4 of them, respectively, related to the Tyndarus group. FIGURE 4 Correspondence between the unrooted phylogeny (left side) and Bray— Curtis distance between species based on the repeat landscape (right side), for (a) Erebia and (b) Carex. Lines for Erebia are coloured according to the different subgenera defined in Augustijnen et al. (2023). Tyndarus is the most speciesrich and karyotypically variable clade. (a) Erebia disa discoidalis magdalena callias calcaria nivalis tyndarus neleus cassioides hispania rondoui graucasica iranica ottomana melampus aethiopella mnestra gorgone gorge pluto albergana triarius medusa polaris epipsoidea meolans aethiops niphonica epiphron pharte claudina oeme melas pronoe scipio neoridas lefebvrei montana stirius styx manto euryale eriphyle ligea epistygne pandrose sthennyo disa discoidalis magdalena nivalis calcaria tyndarus callias neleus hispania claudina graucasica iranica ottomana albergana gorge aethiopella mnestra pronoe gorgone triarius medusa polaris epiphron melampus aethiops niphonica oeme melas pandrose sthennyo cassioides rondoui epipsoidea pharte meolans neoridas pluto scipio lefebvrei montana stirius styx manto eriphyle euryale ligea epistygne Phylogen yR epeat-based distance (b) Carex caryophyllea nigra magellanica lepidocarpa helodes laevigata extensa sempervirens capitata leporina maritima echinata lucennoiberica furva caryophyllea nigra magellanica lepidocarpa helodes laevigata extensa capitata leporina maritima echinata lucennoiberica furva sempervirens Phylogen yR epeat-based distance Subclades Ligea Pronoe Medusa Pluto Epiphron Tyndarus Magdalena Embla 1365294x, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/mec.17100 by Universidad De Sevilla, Wiley Online Library on [20/12/2023]. 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Molecular Ecology, 00, 1–19. https://doi.org/10.1111/ mec.17100 1365294x, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/mec.17100 by Universidad De Sevilla, Wiley Online Library on [20/12/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License