Phylogeny and origin of diversification of Amblyomma (Acari: Ixodidae)

doi:10.21203/rs.3.rs-3404165/v1

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Phylogeny and origin of diversification of Amblyomma (Acari: Ixodidae)

https://doi.org/10.21203/rs.3.rs-3404165/v1

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published 18 Mar, 2024

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Background

Amblyomma is the second most diversified genus of Ixodidae that is distributed across the Indomalayan, Afrotropical, Australasian (IAA), Nearctic, and Neotropical biogeographic ecoregions, reaching in the Neotropic its higher diversity. There have been hints in previously published phylogenetic trees from mitochondrial (mt) genome, nuclear rRNA, from combinations of both and morphology that the Australasian Amblyomma or the Australasian Amblyomma plus the Amblyomma species from the southern cone of South America, might be the sister-group to the Amblyomma of the rest of the world. However, a stable phylogenetic framework of Amblyommafor a better understanding of the biogeographic patterns underpinning its diversification is lacking.

Methods

We used genomic techniques to sequence complete and nearly complete mt genomes –ca. 15 kbp– as well as the ribosomal operons –ca. 8 kbp– for 17 Amblyomma ticks in order to study the phylogeny and biogeographic pattern of the genus Amblyomma, with particular emphasis on the Neotropical region. The new genomic information generated here together with genomic information available of 43 ticks (22 other Amblyommaspecies and 21 other hard ticks –as outgroup–) were used to perform probabilistic methods of phylogenetic and biogeographic inferences and time-tree estimation using biogeographic dates.

Results

In the present paper, we present the strongest evidence yet that Australasian Amblyomma may indeed be the sister group to the Amblyomma of the rest of the world (species that occur mainly in the Neotropical and Afrotropical zoogeographic regions). Our results showed that all Amblyomma subgenera included, but Walkeriana and Amblyomma, are not monophyletic, as in the cases of Cernyomma, Anastosiella, Xiphiastor, Adenopleura, Aponomma, and Dermiomma. Likewise, our best biogeographic scenario supports the origin of Amblyomma and its posterior diversification in the southern hemisphere at 47.8 and 36.8 Mya, respectively. This diversification could be associated with the end of the connection of Australasia and Neotropical ecoregions by the Antarctic land bridge. Also, the biogeographic analyses let us see the colonization patterns of some neotropical Amblyomma species to the Nearctic.

Conclusions

We found strong evidence that the main theatre of diversification of Amblyomma was the southern hemisphere, potentially driven by the Antarctic Bridge's intermittent connection in the late Eocene. In addition, the subgeneric classification of Amblyomma lacks evolutionary support. Future studies using denser taxonomic sampling may take us to new findings on the phylogenetic relationships and biogeographic history of Amblyommagenus.

Ixodidae

Metastriata

Hard ticks

Pathogen vectors

Mitogenomics

Time-tree

Ticks (Arachnida: Ixodida) are highly specialized, blood-sucking ectoparasites of vertebrates [1]. They are one of the main pathogen vectors (bacteria, viruses, and protozoa) in the world, causing a huge range of infections and diseases (such as rickettsiosis, Lyme disease, relapsing fever borreliosis, Crimean-Congo hemorrhagic fever, babesiosis, among others), and thus, of maximal economic and health importance [2–5]. The order Ixodida comprises ~ 970 living species that are classified into three families Nutalliellidae (monotypic), Argasidae, and Ixodidae [6, 7]. Ixodidae (hard ticks) is the most diversified one with 771 valid species that are classified into two groups, Prostriata (with a single genus, Ixodes) and Metastriata (including 14 extant genera plus two additional fossil genera, Compluriscutula and Cornupalpatum; [6]). Within Metastriata, Amblyomma is the second most diversified genus (after Haemaphysalis) comprising 136 living species, which are widely distributed across the Indomalayan, Afrotropical, Australasian, Nearctic and Neotropical biogeographic regions, reaching in the Neotropics its higher diversity (67 spp.) [8]. The wide distribution, high diversity, and economic and health importance make genus Amblyomma a priority group for evolutionary and biogeographical studies.

For many years, the genera Amblyomma and Aponomma were grouped together in the subfamily Amblyomminae or in the subtribe Amblyommini [9, 10]. However, Dobson and Barker [11] with the use of DNA sequences determined that the genus Aponomma was paraphyletic, and consequently also the subfamily Amblyomminae. “Indigenous” and “primitive” Aponomma species sensu Kaufman [12] were recovered as independent lineages and assigned to their own genera, Bothriocroton [13], Robertsicus, and Archaeocroton [14], while “typical” Aponomma species sensu Kaufman [12] were transferred to the Amblyomma genus [13]. Recently, Amblyomma transversale emerged as an early and independent branch in the mitogenomic tree of Metastriata, and was elevated to the genus level, namely Africaniella [15]. As a consequence of the numerous taxonomic changes in the group, the current Amblyomma diversity seems to retain plesiomorphic characters that make the taxonomic boundaries diffuse. This also hinders the assignation of fossils to a given cladogenetic event for estimation of their ages of diversification. Likewise, the most robust evolutionary framework for these lineages (based on mt genomes) is neither complete enough to establish potential diversification events, nor to explain the current wide tropical distribution of the Metastriata group [16].

Various studies aimed to understand the evolutionary patterns of the Amblyomma species and the potential historical events that have driven their diversification [16, 17–28]. However, a robust phylogeny of the genus is still lacking. Additionally, the subgeneric classification of Amblyomma sensu Santos Dias [29] and Camicas et al, [30] (nine subgenera: Adenopleura, Amblyomma, Anastosiella, Aponomma, Cernyomma, Dermiomma, Haemalastor, Walkeriana, and Xiphiastor) has shown to be particularly weak from a phylogenetic perspective [16–18, 32].

Hence, the evolutionary studies of Amblyomma remain inconclusive. Although weakly supported available phylogenetic frameworks of the genus show that early divergences are restricted to species endemic of Australian and the southern Neotropical regions [23–24, 26]. Furthermore, IAA species are recovered in a derivate phylogenetic position, together with a wide polytomy across main American lineages [26]. Thus far, genomic sequence data and taxon sampling are sparse, precluding the existence of a robust biogeographical hypothesis for hard ticks, especially in the Neotropical region, where the highest diversity of Amblyomma (67 species) is found.

At present, 316 mt genomes are available for the order Ixodida (at August 2023; [16, 32–33]), but only cover 20 species of Amblyomma, far from representing the diversity of the genus, or at least its main lineages. Therefore, the aim of this study was to generate a balanced mitogenomic dataset to reconstruct, using probabilistic methods, a robust and stable phylogenetic framework of Amblyomma for a better understanding of the evolutionary history and the biogeographic patterns underpinning its diversification, with particular emphasis on the Neotropical region.

Sample collection, DNA extraction, and sequencing

A total of 17 Amblyomma species were collected and processed in this study. See the complete list, localities, collector, and sampling date of each in Table S1. All samples were fixed in ethanol absolute and stored at -20ºC. After morphological identification, total DNA was extracted using the Qiagen DNeasy Blood & Tissue kit (Qiagen, Valencia, CA, USA) following the manufacturer’s spin-column protocol.

The partial fragment of mitochondrial cytochrome c oxidase I (COXI) using universal primers [34] was PCR-amplified and Sanger-sequenced with the aim of molecular re-confirmation. The mt genomes of five species were long-range PCR amplified using specific primers based on the partial COX1 and conserved regions using the reaction contents and thermocycler conditions described in Cotes-Perdomo et al., [28]. The long-PCR products were purified by ethanol precipitation. The cleaned fragments from the same mt genome were pooled together in equimolar concentrations and sheared to an average fragment size of 450 bp using the Covaris ME220. The library preparation and massive parallel sequencing protocol were performed at the Laboratories of Analytical Biology (LAB-NMNH) as was described in Uribe et al., [25]. For each remaining sample, a TruSeq Nano DNA Library was constructed and Whole Genome Sequenced (WGS) through NovaSeq 6000 platform (150PE, 18Gb/ea; at Macrogen®, Seoul, Korea). See the sequencing methods of the samples in Table S1, and the primers and amplification strategy in Table S2.

Data curation and ortholog search

Three kinds of massive sequencing were processed in this study, amplicons, WGS, and RNA-seq (available in NCBI; see Table S1) for the configuration of the matrixes. The quality of the massive sequencing was assessed using FastQC version 0.10.1 [35]. All raw reads were trimmed and cropped, and adapters removed using Trimmomatic version 0.39 [36]. Paired-end cleaned reads were de novo assembled with SPAdes version 3.14.0, using spades.py [37], and rnaspades.py [38]. For amplicons and WGS sequencings, the assembled mt genomes for each species was filtered by BLAST version 2.6.0 [39] searching against a custom database created using all Amblyomma mt genomes available. Then, previously sequenced COX1 of each sample was used to corroborate the identifications. All individualized mt genomes were reference mapped using the paired-end cleaned reads with Geneious® in order to visually check the assemblies and get the mean depth coverage. Mitochondrial elements were annotated using MITOS [40] and the ORFs were manually checked. The boundaries of ribosomal genes were determined following Boore et al., [41].

Construction of matrixes and phylogenetic inferences

60 mt genomes (including 17 new mt genomes of Amblyomma) were used for matrixes construction, where three Ixodes species were used as outgroup of Metastriata. See complete list of matrixes and their features in Table S3. The protein-coding genes were aligned using nucleotide sequences guided by the deduced amino acids in Translator X server [42] with MAFFT v5 [43]. The nucleotide sequences of both rRNA genes were aligned using MAFFT version 7 [44] with default parameters. Additionally, a nuclear matrix was contracted using the ribosomal operon for twelve Amblyomma species that represent all biogeographic regions.

Phylogenetic relationships were inferred using maximum likelihood (ML; [45]) and Bayesian inference (BI; [46–47]). BI analyses were performed in two software: under MrBayes v.3.1.2. [48], four simultaneous MCMC chains for 20 million generations were run, sampling every 1000 and discarding the first 25% as burn-in to prevent sampling before reaching stationarity; and under PhyloBayes [49], in this case the site-heterogeneous model of evolution CAT-GTR model was selected [50], and two independent MCMC chains were run, sampling every cycle, until convergence (checked a posteriori using the tools implemented in PhyloBayes; max-diff < 0.1, maximum discrepancy < 0.1, and effective sample size > 100). Consensus trees and the posterior probabilities (PP) of the nodes were obtained after discarding the first 10% cycles. The convergence of MCMC chains was checked and determined in Tracer v.1.7 [51]. ML analyses were carried out in IQ-TREE v.1.6.1 using a combination of rapid hill-climbing and stochastic perturbation methods. A total of 1,000 pseudoreplicates of ultrafast bootstrap were performed to assess robustness of the inferred trees, including the percentage of bootstrap (PB) of the nodes.

Two matrixes were constructed, at the amino acid and nucleotide codification. For the concatenated amino acids matrix, the best-fit amino acid replacement matrices (Rmatrix) were selected under Bayesian Information Criterion (BIC; [52]) using the command –m TEST in ModelFinder [53]; the fit of adding empirical frequency profiles (mixture model) to the selected amino acid replacement matrices was then tested using the BIC and the command –mset Rmatrix + C10, Rmatrix + C20, Rmatrix + C30, Rmatrix + C40, Rmatrix + C50, Rmatrix + C60 in ModelFinder. The nucleotide matrix was analyzed concatenated and partitioned (by codon position + 12S and 16S rRNA). The best evolutionary model to the concatenated matrix was selected with ModelFinder under BIC, and -m TESTNEW command, while for the partitioned matrixes, the command line was -m TESTMERGEONLY (which also infers the best scheme partition). See all models and the scheme selected in Table S3.

Time-tree estimation

BEAST 2.6.6 [54] was used to perform a Bayesian estimation of divergence times among Amblyomma species-based on mitochondrial genome using protein coding + ribosomal genes at nucleotide codification (subset of Matrix BI-NT-GBlock-Partitioned; see Table S3). An uncorrelated relaxed molecular clock was used to infer branch lengths and nodal ages. The tree topology was set based on topology 2 (see Figure S1b) using Dermacentor, Hyalomma, Rhipicephalus, and Rhipicentor as outgroup. For the clock model, the lognormal relaxed-clock model was selected, which allows rates to vary among branches without any a priori assumption of autocorrelation between adjacent branches. For the tree prior, a Yule process of speciation was employed. ModelFinder as is indicated above was used to select the best-fit partition and evolutionary models (Table S3). The final Markov chain was run twice for 100 million generations, sampling every 10,000 generations and the first 10 million was discarded as part of the burn-in process, according to the convergence of chains checked with Tracer v. 1.7.1. The effective sample size of all the parameters was above 200.

For the time estimations, we used a phylogenetic scenario previously reconstructed (here inferred too) as a scaffold to include dates of biogeographic events [20, 22, 28]. The posterior distribution of the estimated divergence times was obtained by specifying four calibration points as priors, which are highlighted as follows: for the divergence of Am. americanum + Am. cajennense group, a 1st calibration point was set between 25 − 15,2 million years ago (Mya) (normal distribution, 20 Mean, 3 Sigma, 0.01 Offset; 95% height posterior density –HPD–) [20]. 2nd calibration point was set to Am. cajennense group radiation with a mean of 17 Mya (normal distribution, 0.3 Sigma, 0.001 Offset; 95% HPD) [22]. 3rd calibration point was set for Am. mixtum (Am. cajennense + Am. patinoi) cladogetic event with a mean of 6 Mya (normal distribution, 0.6 Sigma, 0.001 Offset; 95% HPD between 5,01–6,99 Mya) [20]. 4th calibration point was set for the divergences of Am. maculatum + Am. tigrinum cladogenetic event between 3.3-0,899 Mya (normal distribution, 2.1 Mean, 0.73 Sigma; 95% HPD) [22].

Biogeographical analyses

The biogeographical history of the genus Amblyomma were reconstructed using BioGeoBEARS [55] implemented in R. Four biogeographical models implemented in BioGeoBears were evaluated: DEC (Dispersal-Extinction-Cladogenesis; [56]) and DIVA + like, plus these two models considering the “j” parameter (founder-event speciation), DEC + J and DIVALIKE + J [55]. Accounting for founder-event speciation is relevant when considering organisms such as Amblyomma due to their great relevance as vectors and their potential distribution changes by climate change and anthropogenic pressure (but see Ree and Sanmartin, [57] for j parameter criticism). Biogeographic models were compared using corrected Akaike information criterion (AICc) to determine the best-fit biogeographic model on data and the effect of adding the “j” parameter on model performance. For biogeographic analyses the distribution range were divided in five areas that were defined based on the distribution of the species included (Neartic –NE–, Neotropic –NO–, Australia –AU–, Afrotropic –AF–, and Indomalaya –IN–). Given that the evolutionary relationships of Metastriate are not resolved, no area was assigned to the outgroup (see Table S4). The broad geographic scope of this study justifies the consideration of such large areas for computational efficiency and to achieve the reconstruction of the biogeographical history of the whole genus Amblyomma.

Ticks are the second vector disease globally and potentially one of the most critical organisms to be studied in every concept. Establishing a solid evolutionary framework of Amblyomma within a Metastriata context may be key to understanding the origin of the diversification of the genus. We used the leafier tree (in number of species) published recently [26], to select the species representatives of each main lineage. Thus, the molecular dataset generated here is described and, for the first time at the mitogenomic level, used to reconstruct the Amblyomma phylogenetic relationships to assess previous hypotheses related to the origin and patterns of diversification of the genus.

Mitogenomic features

A total of 17 mt genomes (three partial) were newly sequenced in this study, increasing the mt genome catalog available for this tick group, from 22 to 39. The sequencing methods applied here to determine the mt genomes (amplicon and WGS) allowed us to obtain a similar sequencing depth reached in related articles recently published ([16, 28]; see depth coverage in Table S1). The features of the new complete mt genomes described here fit with those already reported for other mt genomes of Metastriata [58], including the gene order (except Africaniella tranversale [27]), length of the genomes/genes, and the two characteristic control regions. Also, a general feature of the 37 genes (13 protein-coding, 22 tRNAs, and two ribosomal RNAs), fit with the most metazoan mt genomes [41].

Phylogeny of Amblyomma

To reconstruct the internal relationships of Amblyomma, twelve phylogenetic trees were inferred in this study, six at the nucleotide and six at the amino-acid codification. All trees were summarized in Fig. 1. where the nodes that were not recovered at least in half of the trees with significative statistical support (node ≥ 0.95 PP/80 PB) were collapsed (see all topologies in Figure S1a–l). Also, data set features, best-fit partition schemes and models, and log-likelihood values of all phylogenetic analyses are provided in (Table S3). Amblyomma was recovered as monophyletic in all analyses (Fig. 1, node 1), and sister to a clade that includes (Dermacentor (Rhipicentor (Hyalomma + Rhipicephalus), as has been widely reported in previous phylogenies [27, 58].

All topologies (but two, Topology 7 and 11) are congruent in the early cladogenetic events of Amblyomma. Topology 7 and 11 (see Figures S1g and k) recovered the Am. maculatum complex as a sister clade of Amblyomma boeroi with significative and moderated support. The rest of the trees recovered endemic Australian species as a sister clade of Am. boeroi (southern cone of South America) + the rest of Amblyomma lineages. No compatible results have been reached so far in the phylogenetic analyses (Fig. 1, node 2). Although Seabolt, [23], Beati and Klompen. [24], and Santodomingo et al., [26] recovered three endemic species from the southern cone of South America (Am. boeroi, Am. parvitarsum, and Am. neumanni) and Australian endemic species as the first divergences of Amblyomma, these relationships differ between them. Beati and Klompen, [24] recovered a sister-relationship between Am. boeroi and Am. parvitarsum, while Santodomigo et al., [26] and Seabolt, [23] recovered Am. neumanni + Am. parvitarsum as a sister lineage of Am. boeroi and endemic Australian clade in an early phylogenetic position, but without statistical support. In our analyses, Am. neumanni and Am. parvitarsum are recovered as sister species but in a derivate position with strong support (Fig. 1, node 4). The close phylogenetic relationship between Am. parvitarsum and Am. neumanni was previously reported [59]. Amblyomma boeroi was recovered as an independent phylogenetic lineage within the genus Amblyomma, but it shares some morphological characters with both Am. parvitarsum and Am. neumanni [19]. On the other hand, some endemic species of Australia were recovered as the first divergence, sister clade of all other species of Amblyomma in all phylogenetic trees (Fig. 1). Our results are compatible with previous phylogenetic analyses (although they differ in some included endemic species of Australian) recovering Am. fimbriatum and Am. postoculatum as the sister clade that includes Am. moreliae, Am. albolimbatum, and Am. limbatum [24]. The species clustered in this Australian clade are mostly associated with reptile hosts, except Am. postoculatum, which parasitize the wallaby banded hare [60].

In the node 3 (Fig. 1) a tripartite polytomy exist between: i. Am. maculatum group that does not appear to be closely related to any other species included in our analyses (except in Topology 8, see above). ii. (Am. parvum + Am. auricularium) + (Am. cajennense group + Am. americanum) relationships are well supported. These relationships have been recovered in previous studies (clades G, H, I in [26]); the internal relationships within Am. cajennense complex as well as its sister relationships with Am. americanum are congruent with previous phylogenetic analyses [20, 26, 28]; however, the related species to Am. americanum should be included in a mitogenomic framework to be discussed in futures studies. iii. and, (Am. neumanni + Am. parvitarsum) + the remaining species, including Neotropical, Neotropical-Nearctic (as Am. dissimile), and IAA species.

In the clade that includes the remaining species of Amblyomma, successive relationships with strong support were recovered between (nodes 5, 6, 7, Fig. 1), i. ((Am. aureolatum, Am. ovale), Am. naponense) (Fig. 1). The close relationship of Am. aureolatum and Am. ovale was obtained in previous works [17, 25], as well as these with Am. naponense (clade E, [26, 61]). Amblyomma aureolatum and Am. ovale normally have Carnivora as hosts for adult stages (despite both species have a more widespread range of hosts) and are morphologically similar, which have caused several confusions between them, being Am. ovale more widely distributed (Nearctic and Neotropic) than Am. aureolatum (Neotropic) [8].

ii. ((Am. calcaratum, Am. nodosum), Am. dubitatum) (Fig. 1). These relationships were previously recovered [26, 62–63], however, in our analyses the relationship of Am. calcaratum and Am. nodosum is not recovered in four out of 12 trees. It happens likely because of insufficient taxon sampling of the species related to this clade, as Am. yucumense (normally associated to Am. dubitatum), Am. hadanii, and Am. coelebs [26, 63], which are missing in our analyses. Note that adults of Am. nodosum and Am. calcaratum have been often confused because they are found normally in Pilosa hosts, regardless both have other hosts throughout their life cycles [8].

iii. ((Am. calcaratum, Am. nodosum), Am. dubitatum) was recovered as sister linage of ((Am. argentinae + Am. dissimile) plus the IAA species) with confident support in all topologies (node 6 and 7, Fig. 1). But, (Am. argentinae + Am. dissimile) as a sister clade of the IAA species was recovered just in the analyses with nucleotide codification (six of out 12, Fig. 1). While in amino acids codification, (Am. argentinae + Am. dissimile) appears in a wide polytomy with IAA species (see inset, Fig. 1). Am. argentinae and Am. dissimile were recovered closely related to IAA species before but without statistical support [26]. The lack of taxonomic sampling may be the cause of the loss of the phylogenetic signal in this phylogenetic assembly.

In relation of the IAA species, three well supported clades are recovered in our analyses, i. (Am. javanense + Am. testudinarium), as was also recovered in Uribe et al., [25]. ii. ((Am. breviscutatum + Am. geoemydae) Am. nitidum) clade recovered for the first time with strong support in all topologies. Am. geoemydae and Am. nitidum have a Indomalayan distribution associate mostly to reptiles [64–65], while Am. breviscutatum is endemic of Australia, mostly found in the southwestern Pacific on feral pigs and rats (as nymphs) [60, 66]. iii. ((((Am. nutallli + Am. sparsum) Am. marmoreum) Am. hebraeum) Am. tholloni) clade that is well supported in seven out of 12 topologies (Fig. 1). This clade has also been reported in mitogenomic analyses [27, 67], it has an Afrotropical distribution and includes ticks with public health importance, as are the species of the marmoreum complex [67]. Finally, the increasing taxon sampling of the “typical” Aponomma forms (Am. gervaisi, Am. latum, and Am. fimbriatum) corroborates the inclusion of these species in Amblyomma (see [13]) with strong statistical support, despite that their phylogenetic positions remained unclear so far (Fig. 1).

The current work, in agreement with previous analysis [16–18, 31], shows that most of the Amblyomma subgenera sensu Camicas et al, [31] and Santos Dias, [29] are not monophyletic, as in the cases of Cernyomma, Anastosiella, Xiphiastor, Adenopleura, Aponomma, and Dermiomma. Amblyomma (as in [26, 28]) and Walkeriana were the only monophyletic subgenera.

Finally, the nuclear tree (Figure S2) recovered an Amblyomma monophyletic but a polytomy in its early relationships, in which are involved: Am. boeroi, Am. postoculatum, Am. neumanni + Am. parvitarsum, and a clade with the remaining Amblyomma species.

Origin of diversification and biogeographical history

Within Ixodidae, the recent incorporation to molecular phylogenetic trees of Af. transversale and some species associated to previous “Aponomma” have been demonstrated that the diversity of Amblyomma is retaining plesiomorphic characters that mislead the circumscription of its living fauna [13–15] and potentially those extinct [69]. Thus, with the aim to evaluate an independent hypothesis of the origin and diversification of Amblyomma, not linked to a potential bias related to the assignation of not phylogenetic corroborated lineages, we use geographic dating from well-established phylogenetic frameworks of Am. cajennense complex [20, 28] and Am. parvum [22] as an alternative to the taxonomic and phylogenetic unresolved questions of the genus.

The higher diversity of Amblyomma is displayed in the Neotropic, which constitutes a biogeographic ecoregion extending from the center of Mexico to the southernmost point of South America, including the Caribbean Islands [70]. This region has a geological and paleoclimatic complex history given that mountain ranges with different origins converge with several climatic fluctuations plus associated glaciations that have occurred [71]. These circumstances have given rise to intricate scenarios that could explain the hyper diversity of the Neotropical region [72]. Additionally, this diversity could have been boosted by geological events through time, such as i. continental drift [73–74], ii. the intermittent connection through bridges, such as the Antarctic bridge that connected the southern cone of South America with the Australian Region [75–76] and Berigian bridge with a northern connection to with Palearctic Region [77], iii. and the complex geological dynamics in the northern Neotropical, e.g., formation of the Isthmus of Tehuantepec and Panama [78–80].

Our new reconstructed time-calibrated tree dated the origin of Amblyomma at about 47.8 Mya, with a relatively short credibility interval (95% HPD, 57–39.6 Mya). This is the youngest origin estimated for Amblyomma so far, which controverts older estimations for the origin of the genus at 91 Mya [24], between late Jurassic and Early Cretaceous [81] or 89 − 77 Mya [23] as well as the Burmese amber fossils assigned to the genus (at 100 Mya; [69, 82]). The inconsistencies of our results with previous works could be related to the representativeness of taxa and the analysis exclusive of the mt genome. The absence of fossil calibrations may also result in an underestimation of divergence dates. Further work is needed to evaluate the time divergences of Amblyomma using solid topologies, perhaps including nuclear information and calibrations of endemic species of Islands (e.g., Galapagos, Hispaniola).

The origin of diversification of Amblyomma was estimated to have occurred 36.8 Mya (95% HPD, 43–31,6), an age that match with the end of the Antarctic bridge connection of the southern hemisphere, in the Late Eocene, at about 35 Mya [75–76]. The origin age of the diversification of the genus has been estimated at 74–60 Mya by Seabolt, [23], close ages to the starting connection of the southern hemisphere at 65 Mya [75–76]. Both scenarios do not rule out a Southern Hemisphere fauna connection. Additionally, our time-calibrated tree suggests an origin and posterior diversification of IAA lineages (positioned derivatives in Fig. 1) between 23,3 and 22,5 Mya (95% HPD, 27–19).

With the time divergence framework, we reconstructed the biogeographic patterns of Amblyomma. The ancestral area reconstructions under each evaluated biogeographical model were highly consistent (Fig. 3; Figure S3a-d; Table S5). Nevertheless, models with the founder-event speciation parameter (j parameter, alternative hypothesis) outperformed those without it (null hypothesis) (DEC vs DEC + J, p = 0.0011; DIVALIKE vs DIVALIKE + J, p = 0.007). According LnL and AICc values, the DEC + J model was the best selected model (Table S5) and therefore, we focus on reporting the results under this model. Our best biogeographic scenario supports the origin of the diversification of Amblyomma in the southern hemisphere at the end of the Eocene (Fig. 3), potentially associated with the faunistic flow in the final Antarctic Bridge connection [75–76]. This agrees with the South America and Australia lineages (here well represented) as the early divergent event in Amblyomma (Fig. 1) reached with high statistical support. This does not discard the hypothesis that argues that the origin of Amblyomma is in the Neotropic with a posterior colonization to Australia [24]. However, a solid phylogenomic framework of Metastriate (including main lineages of all genera) is necessary to evaluate the biogeographic patterns and ancestral distribution of the related lineages to Amblyomma, which could explain the Burmese amber fossil with a widespread ancestral distribution of the closest ancestors of Amblyomma [23, 69, 82]. Likewise, this could give sense also to older origin of diversification of other lineages restricted to the early divergences of Metastriata, which have a notorious gondwanic distribution, as is the case of Archaeocroton sphenodonti (New Zealand), Robertsicus elaphansis (Neartic), Bothriocroton (Australia) and Africaniella transversale (Afrotropic) [24].

While it is necessary to increase the representation of Amblyomma species in a phylogenomic framework, the biogeographic hypotheses here proposed evidence independent expansions along the Neocene to Quaternary era, as is the case of Am. dissimile, Am. ovale, Am. auricularium lineages. Also, our scenario supports a colonization event of Am. americanum and its related species (Fig. 3), despite that these are not included here.

There were hints in phylogenetic trees from mt genomes ([25] Fig. 4; [16]), from nuclear rRNA (eg., [16] Fig. 5; [26]; [27] Fig. 8;) and from combinations of mt and nuclear rRNA genes and morphology [83] that the Australasian Amblyomma or the Australasian Amblyomma plus the Amblyomma species from South America, might be the sister-group to the Amblyomma of the rest of the world. In the present paper, however, we found the strongest evidence yet that Australasian Amblyomma may indeed be the sister-group to the Amblyomma of the rest of the world (Figs. 1 & 3). The position of the Argentinian tick Am. boeroi, hints that the most recent common ancestor to the Amblyomma evolved in a region between South America and Australia, which would concur with the out-of-Antarctica hypothesis [24, 84]. And thus, on the one hand, the ancestor of the Australasian Amblyomma then dispersed from Antarctica into that part of Gondwana that became Australasia whereas on the other hand, the ancestor of the South American Amblyomma dispersed from Antarctic into that part of Gondwana that became South America. Our tree hypothesis also reveals that all Amblyomma subgenera included, but Walkeriana and Amblyomma, are not monophyletic. Our findings suggest an origin of Amblyomma and its posterior diversification more recent than the previous hypotheses (47.8 and 36.8 Mya, respectively). Finally, the biogeographic analyses let us see the colonization patterns of some neotropical Amblyomma species to the Nearctic.

Acknowledgments

All laboratory work was conducted in and with the support of the Molecular Systematics Lab (https://www.mncn.csic.es/es/investigación/servicios-cientifico-tecnicos/laboratorio-de-sistematica-molecular) of MNCN-CSIC. Computing was conducted on the Smithsonian High Performance Cluster (https://doi.org/10.25572/SIHPC). Many thanks to the Comunidad de Madrid for Atracción de Talento contract (REFF 2019-T2/AMB-13166) of JEU.

Author Contributions Statement

JEU, LRC, SN, and SCB contribute to the conceptualization and main investigation. SN, FRP, and SCB provided biological material. JEU, ACP, and SK generated the molecular data. JEU and SP did the formal analyses. JEU, SN, and SCB wrote—the original draft. The project administration/supervision was made by JEU. All authors have written—reviewed, edited, and agreed to publish the final version of the manuscript.

Funding

This study was supported by Atracción Talento de la Comunidad de Madrid Fellowship Program (REFF 2019- T2/ AMB-13,166).

Competing interests

The authors declare that they have no competing interests.

Sonenshine DE, Roe RM. External and internal anatomy of ticks. Biology of ticks. 2014;1:74–98.
Jongejan F, Uilenberg G. The global importance of ticks. Parasitology. 2014;129(S1):S3–S14.
Piesman J, Eisen L. Prevention of tick-borne diseases. Annu. Rev. Entomol. 2008;53:323–343.
Magnarelli LA. Global importance of ticks and associated infectious disease agents. Clin. Microbiol. Newsl. 2009;31(5):33–37.
Gulia-Nuss M, Nuss AB, Meyer JM, Sonenshine DE, Roe RM, Waterhouse RM et al. Genomic insights into the Ixodes scapularis tick vector of Lyme disease. Nat. Commun. 2016;7(1):10507.
Guglielmone AA, Sánchez ME, Franco LG, Nava S, Rueda LM, Robbins RG. Hard ticks (Acari: Ixodida: Ixodidae): a non-profit open-access web portal for original descriptions of tick species (valid and invalid), dubious and uncertain names, and selected nomina nuda. 2015, continuously updated; Available in http://rafaela.inta.gob.ar/nombresgarrapatas.
Mans BJ, Featherson J, Kvas M, Pillay KA, de Klerk D, Pienaar R, et al. Argasid and ixodid systematics: implications for soft tick evolution and systematics, with a new argasid species list. Ticks Tick-borne Dis. 2019;10(1):219–240.
Guglielmone AA, Nava S, Robbins RG. Neotropical Hard Ticks (Acari: Ixodida: Ixodidae). Berlin/Heidelberg, Germany: Springer International Publishing. 2021.
Hoogstraal H, Aeschlimann A. Tick-host specificity. Bulletin de la société entomologique suisse. 1982;55:5–32.
Filippova NA. Classification of the subfamily Amblyomminae (Ixodidae) in connection with reinvestigation of chaetotaxy of the anal valve. Parazitologiâ. 1994;28(1):3–12.
Dobson SJ, Barker SC. Phylogeny of the hard ticks (Ixodidae) inferred from 18S rRNA indicates that the genus Aponomma is paraphyletic. Mol. Phylogenetics Evol. 1999;11:288–295.
Kaufman TS. A revision of the genus Aponomma Neumann, 1899 (Acarina: Ixodidae). Ph.D. Dissertation, University of Maryland, 390 pp. 1972.
Klompen JSH, Dobson SJ, Barker SC. A new subfamily, Bothriocrotninae n. subfam., for the genus Bothriocroton Keirans, King & Sharrad, 1994 status amend. (Ixodida: Ixodidae), and the synonymy of Aponomma Neumann, 1899 with Amblyomma Koch, 1844. Syst. Parasitol. 2002;53:101–107.
Barker SC, Burger TD. Two new genera of hard ticks, Robertsicus n. gen. and Archaeocroton n. gen., and the solution to the mystery of Hoogstraal’s and Kaufman’s “primitive” tick from the Carpathian Mountains. Zootaxa. 2018;4500(4):543–552.
Hornok S, Kontschán J, Takács N, Chaber AL, Halajian A, Abichu G, et al. Molecular phylogeny of Amblyomma exornatum and Amblyomma transversale, with reinstatement of the genus Africaniella (Acari: Ixodidae) for the latter. Ticks Tick-borne Dis. 2020;11:101494.
Kelava S, Mans BJ, Shao R, Barker D, Teo EJ, Chatanga E, et al. Seventy-eight entire mitochondrial genomes and nuclear rRNA genes provide insight into the phylogeny of the hard ticks, particularly the Haemaphysalis species, Africaniellatransversale and Robertsicuselaphensis. Ticks Tick-borne Dis. 2023;14(2):102070.
Estrada-Peña A, Venzal JM, Mangold AJ, Cafrune MM, Guglielmone AA. The Amblyomma maculatum Koch, 1844 (Acari: Ixodidae: Amblyomminae) tick group: diagnostic characters, description of the larva of A. parvitarsum Neumann, 1901, 16S rDNA sequences, distribution and hosts. Syst. Parasitol. 2005;60:99–112.
Marrelli MT, Souza LF, Marques RC, Labruna MB, Matioli SR, Tonon AP, et al. Taxonomic and phylogenetic relationships between neotropical species of ticks from genus Amblyomma (Acari: Ixodidae) inferred from second internal transcribed spacer sequences of rDNA. J. Med. Entomol. 2007;44(2):222–228.
Nava S, Mangold AJ, Mastropaolo M, Venzal JM, Oscherov EB, Guglielmone AA. Amblyomma boeroi n.sp. (Acari: Ixodidae), a parasite of the Chacoan peccary Catagonus wagneri (Rusconi) (Artiodactyla: Tayassuidae) in Argentina. Syst. Parasitol. 2009;73:161–174
Beati L, Nava S, Burkman EJ, Barros-Battesti DM, Labruna MB, Guglielmone AA, et al. Amblyomma cajennense (Fabricius, 1787)(Acari: Ixodidae), the Cayenne tick: phylogeography and evidence for allopatric speciation. BMC Evol. Biol. 2013;13:1–20.
Lado P, Nava S, Labruna MB, Szabo MP, Durden LA, Bermudez S. et al. Amblyomma parvum Aragão, 1908 (Acari: Ixodidae): phylogeography and systematic considerations. Ticks Tick-borne Dis. 2016;7(5):817–827.
Lado P, Nava S, Mendoza-Uribe L, Caceres AG, Delgado-De La Mora J, Licona-Enriquez JD, et al. The Amblyomma maculatum Koch, 1844 (Acari: Ixodidae) group of ticks: phenotypic plasticity or incipient speciation?. Parasit Vectors. 2018;11:1–22.
Seabolt MH. Biogeographical patterns in the hard-tick genus Amblyomma Koch 1844 (Acari: Ixodidae). 2016.
Beati L, Klompen H. Phylogeography of ticks (Acari: Ixodida). Annu. Rev. Entomol. 2019;64:379–397.
Uribe JE, Nava S, Murphy KR, Tarragona EL, Castro LR. Characterization of the complete mitochondrial genome of Amblyomma ovale, comparative analyses and phylogenetic considerations. Exp. Appl. Acarol. 2020;81(3):421–439.
Santodomingo A, Uribe JE, Lopez G, Castro LR. Phylogenetic insights into the genus Amblyomma in America, including the endangered species Amblyommaalbopictum, Amblyommamacfarlandi, and Amblyommausingeri. Int. J. Acarol. 2021;47(5):427–435.
Kelava S, Mans BJ, Shao R, Moustafa MAM, Matsuno K, Takano A, et al. Phylogenies from mitochondrial genomes of 120 species of ticks: Insights into the evolution of the families of ticks and of the genus Amblyomma. Ticks Tick-borne Dis. 2021;12(1):101577.
Cotes-Perdomo AP, Nava S, Castro LR, Rivera-Paéz FA, Cortés-Vecino JA, Uribe JE. Phylogenetic relationships of the Amblyomma cajennense complex (Acari: Ixodidae) at mitogenomic resolution. Ticks Tick-borne Dis. 2023;14(3):102125.
Santos Dias jat. Nova contribucao para o estudo da sistemática do género Amblyomma Koch, 1844 (Acarina-Ixodoidea). Garcia de Orta, Ser. Bot. 1993;9:11–19
Camicas JL, Hervy JP, Adam F, Morel PC. Les tiques du monde (Acarida, Ixodida). Nomenclature, stades décrits, hôtes, répartition. ORSTOM. 1998;233.
Nava S, Guglielmone AA, Mangold AJ. An overview of systematics and evolution of ticks. Front. Biosci. 2009;14(8):2857–2877.
Barker SC, Kelava S. What have we learned from the first 600 mitochondrial genomes of Acari? Zoosymposia, 2002;22:33–34.
Kneubehl AR, Muñoz-Leal S, Filatov S, de Klerk DG, Pienaar R, Lohmeyer KH, et al. Amplification and sequencing of entire tick mitochondrial genomes for a phylogenomic analysis. Sci. Rep. 2022;12(1):19310.
Folmer O, Black M, Hoeh W, Lutz R, Vrijenhoek R. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol. Biotechnol. 1994;3:294–299.
Andrews S. FastQC. 2010. http://www.bioinformatics.babraham.ac.uk/projects/ fastqc/.
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinform. 2014;30:2114–2120.
Prjibelski A, Antipov D, Meleshko D, Lapidus A, Korobeynikov A. Using SPAdes de novo assembler. Curr. Protoc. Bioinformatics. 2020;70(1):e102.
Bushmanova E, Antipov D, Lapidus A, Prjibelski AD. rnaSPAdes: a de novo transcriptome assembler and its application to RNA-Seq data. GigaScience. 2019;8(9):giz100.
Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25(17):3389–3402.
Bernt M, Donath A, Jühling F, Externbrink F, Florentz C, Fritzsch G, et al. MITOS: improved de novo metazoan mitochondrial genome annotation. Mol. Phylogenetics Evol. 2013;69:313–319.
Boore JL, Macey JR, Medina M. Sequencing and comparing whole mitochondrial genomes of animals. Meth. Enzymol. 2005;395:311–348.
Abascal F, Zardoya R, Telford MJ. TranslatorX: multiple alignment of nucleotide sequences guided by amino acid translations. Nucleic Acids. Res. 2010;38:7–13.
Katoh K, Kuma KI, Toh H, Miyata T. MAFFT version 5: improvement in accuracy of multiple sequence alignment. Nucleic Acids Res. 2005;33:511–518.
Katoh K, Rozewicki J, Yamada KD. MAFFT online service: multiple sequence alignment, interactive sequence choice and visualization. Brief. Bioinformatics. 2019;20:1160–1166.
Felsenstein J. Journal of molecular evolution evolutionary trees from DNA sequences: a maximum likelihood approach. J. Mol. Evol. 1981;17:368–376.
Rannala B, Yang Z. Probability distribution of molecular evolutionary trees: a new method of phylogenetic inference. J. Mol. Evol. 1996;43:304–311.
Yang Z, Rannala B. Bayesian phylogenetic inference using DNA sequences: a Markov Chain Monte Carlo method. Mol. Biol. Evol. 1997;14:717–724.
Ronquist F, Huelsenbeck JP. MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinform. 2003;19(12):1572–1574.
Lartillot N, Lepage T, Blanquart S. PhyloBayes 3: a Bayesian software package for phylogenetic reconstruction and molecular dating. Bioinform. 2009;25:2286–2288.
Lartillot N, Philippe H. A Bayesian mixture model for across-site heterogeneities in the amino-acid replacement process. Mol. Biol. Evol. 2004;21:1095–1109.
Rambaut A, Drummond AJ, Xie D, Baele G, Suchard MA. Posterior Summarization in Bayesian Phylogenetics Using Tracer 1.7. Syst. Biol. 2018;67(5):901–904.
Schwarz D. Estimating the dimension of a model. Ann. Stat. 1978;6(6):461–464.
Kalyaanamoorthy S, Minh BQ, Wong TKF, von Haeseler A, Jermiin LS. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat. Methods. 2017;14:587–589.
Bouckaert R, Heled J, Kühnert D, Vaughan T, Wu CH, Xie D, et al. BEAST 2: a software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 2014;10(4):e1003537.
Matzke NJ. BioGeoBEARS: BioGeography with Bayesian (and likelihood) evolutionary analysis in R Scripts. R package, version 0.2, 1, 2013.
Ree RH, Smith SA. Maximum likelihood inference of geographic range evolution by dispersal, local extinction, and cladogenesis. Syst. Biol. 2008;57(1):4–14.
Ree RH, Sanmartín I. Conceptual and statistical problems with the DEC+ J model of founder‐event speciation and its comparison with DEC via model selection. J. Biogeogr. 2018;45(4):741–749.
Wang T, Zhang S, Pei T, Yu Z, Liu J. Tick mitochondrial genomes: structural characteristics and phylogenetic implications. Parasites Vectors. 2019;12:451.
Soares, J. F., Labruna, M. B., de Amorim, D. B., Baggio-Souza, V., Fagundes-Moreira, R, et al. Description of Amblyomma monteiroae n. sp.(Acari: Ixodidae), a parasite of the great horned owl (Strigiformes: Strigidae) in southern Brazil. Ticks Tick-borne Dis. 2023;14(6):102239.
Barker SC, Walker AR, Campelo D. A list of the 70 species of Australian ticks; diagnostic guides to and species accounts of Ixodesholocyclus (paralysis tick), Ixodes cornuatus (southern paralysis tick) and Rhipicephalus australis (Australian cattle tick); and consideration of the place of Australia in the evolution of ticks with comments on four controversial ideas. International J. Parasitol. 2014;44(12):941–953.
Medlin JS, Cohen JI, Beck DL. Vector potential and population dynamics for Amblyomma inornatum. Ticks Tick-borne Dis. 2015;6:463–472.
Nava S, Venzal JM, González-Acuña D, Martins TF, Guglielmone AA. Ticks of the Southern Cone of America: Diagnosis, Distribution and Hosts with Taxonomy, Ecology and Sanitary Importance. Elsevier, Academic Press, London. 2017.
Krawczak FS, Martins TF, Oliveira CS, Binder LC, Costa FB, Nunes PH, Gregori F, Labruna MB. Amblyomma yucumense n. sp. (Acari: Ixodidae), a parasite of wild mammals in Southern Brazil. J. Med. Entomol. 2015;52(1):28-37.
Amarga AKS, Supsup CE, Tseng HY, Kwak ML, Lin SM. The Asian turtle tick Amblyomma geoemydae Cantor, 1847 (Acari: Ixodidae) in the Philippines: first confirmed local host and locality with a complete host index. Ticks Tick-borne Dis. 2022;13(4):101958.
Kwak ML, Kuo CC, Chu HT.2020). First record of the sea snake tick Amblyomma nitidum Hirst and Hirst, 1910 (Acari: Ixodidae) from Taiwan. Ticks Tick-borne Dis. 2020;11(3):101383.
Kemp DH, Wilson N. The occurrence of Amblyomma cyprium cyprium (Acari: Ixodidae) in Australia, with additional records from the southwest Pacific. Pac. Insects. 1979;21(2-3):224–226.
Cotes-Perdomo AP, Sánchez-Vialas A, Thomas R, Jenkins A, Uribe JE. New insights into the systematics of the Afrotropical Amblyomma marmoreum complex (Acari, Ixodidae) and a novel Rickettsia africae strain using morphological and metagenomic approaches. bioRxiv. 2023;08.
Binetruy F, Buysse M, Lejarre Q, Barosi R, Villa M, Rahola N. et al. Microbial community structure reveals instability of nutritional symbiosis during the evolutionary radiation of Amblyomma ticks. Mol. Ecol. 2020;29(5):1016–1029.
Grimaldi DA, Engel MS, Nascimbene PC. Fossiliferous cretaceous amber from Myanmar (Burma): Its rediscovery, biotic diversity, and paleontological significance. Am. Mus. Novit. 2002;3361:1-71.
Morrone JJ. Biogeographical regionalisation of the Neotropical region. Zootaxa. 2014;3782(1):1–110.
Hewitt G. The genetic legacy of the Quaternary ice ages. Nature. 2000;405(6789):907–913.
Raven PH, Gereau RE, Phillipson PB, Chatelain C, Jenkins CN, Ulloa-Ulloa C. The distribution of biodiversity richness in the tropics. Sci. Adv. 2020;6(37):eabc6228.
Sanmartíin I, Ronquist F. Southern hemisphere biogeography inferred by event-based models: plant versus animal patterns. Syst. Biol. 2004;216-243.
Veevers JJ. Gondwanaland from 650–500 Ma assembly through 320 Ma merger in Pangea to 185–100 Ma breakup: supercontinental tectonics via stratigraphy and radiometric dating. Earth-Sci. Rev. 2004;68(1-2):1–132.
Hill ED. Salticidae of the Antarctic land bridge. Peckhamia. 2009;76(1):1–14.
Yanbin S. A paleoisthmus linking southern South America with the Antarctic Peninsula during Late Cretaceous and Early Tertiary. Sci. China Earth Sci. 1998;41(3):225–229.
Sanmartín I, Enghoff H, Ronquist F. Patterns of animal dispersal, vicariance and diversification in the Holarctic. Biol. J. Linn. Soc. 2001;73(4):345-390.
de Porta J. La formación del istmo de Panamá. Su incidencia en Colombia. Rev. Acad. Colomb. Cienc. 2003;27(103):191–216.
Marshall JS. Geomorphology and physiographic provinces of Central America. In J. Bundschuh, & G. Alvarado (Eds.), Central America: Geology, resources, and natural hazards. Balkema. 2007.
Morrone JJ. Halffter's Mexican transition zone (1962–2014), cenocrons and evolutionary biogeography. Zoolog. Syst. Evol. Res. 2015;53(3), 249-257.
Jeyaprakash J, Hoy MA. First divergence time estimate of spiders, scorpions, mites and ticks (subphylum: Chelicerata) inferred from mitochondrial phylogeny. Exp. Appl. Acarol. 2009;47:1–18.
Chitimia-Dobler L, De Araujo BC, Ruthensteiner B, Pfeffer T, Dunlop JA. Amblyomma birmitum a new species of hard tick in Burmese amber. Parasitology, 2017;144(11):1441–1448.
Klompen JSH, Black IV WC, Keirans JE, Norris DE. Systematics and biogeography of hard ticks, a total evidence approach. Cladistics. 2000;16(1):70–102.
Barker D, Seeman OD, Barker SC. The development of tick taxonomy and systematics in Australia and contributors and with comments on the place of Australasia in the study of the phylogeny and evolution of the ticks. Syst. Appl. Acarol. 2021;26:1793–1832.

No competing interests reported.

TableS1.pdf
Table S1. Complete list of the specimens used in this study with the respective GenBank accession number (ID), localities, mt genome length, and author reference. With asterisk are indicated the partial mt genomes.
TableS2.pdf
Table S2. Amplification strategy for mt genomes. The gene order of the mt genomes are indicated, where grey color indicating the amplified and sequenced fragment of each specie. In yellow and red are highlighted the general and specific primers used, respectively. The sequence of each primer is displayed in 5’ to 3’ direction.
TableS3.pdf
Table S3. Data set features, genetic codification, taxa included, best-fit partition schemes and models, and log-likelihood (– Ln likelihood) values of all phylogenetic analyses.
TableS45.pdf
Table S4. Presence (1) and absence (0) of the species included in the five biogeographic ecoregions assessed: Nearctic, Neotropic, Austral, Afrotropic, Indomalaya. Table S5. Report of statistical results across the four models implemented in BioGeoBear, including log-likelihood (LnL), number of parameters, f, e, j parameters, and the Akaike information criterion (AICc) values.
FigureS1al.pdf
Figure S1a. Phylogenetic tree based on mt genome data: Topology 1 with 15 concatenated genes at nucleotide codification (protein-coding and ribosomal genes). Bayesian tree was reconstructed using the best fit evolutionary model and partition scheme. Numbers at nodes are statistical support values for bootstrap proportions. The scale bar is in expected substitutions/site. Figure S1b. Phylogenetic tree based on mt genome data: Topology 2 with 15 concatenated genes at nucleotide codification (protein-coding and ribosomal genes) implemented GBlock to trim the alignment. Bayesian tree was reconstructed using the best fit evolutionary model and partition scheme. Numbers at nodes are statistical support values for posterior probability. The scale bar is in expected substitutions/site. Figure S1c. Phylogenetic tree based on mt genome data: Topology 3 with 15 concatenated genes at nucleotide codification (protein-coding and ribosomal genes). Maximum-Likelihood tree was reconstructed using the best fit evolutionary model and partition scheme. Numbers at nodes are statistical support values for bootstrap proportions. The scale bar is in expected substitutions/site. Figure S1d. Phylogenetic tree based on mt genome data: Topology 4 with 15 concatenated genes at nucleotide codification (protein-coding and ribosomal genes) implemented GBlock to trim the alignment. Maximum-Likelihood tree was reconstructed using the best fit evolutionary model and partition scheme. Numbers at nodes are statistical support values for bootstrap proportions. The scale bar is in expected substitutions/site. Figure S1e. Phylogenetic tree based on mt genome data: Topology 5 with 15 concatenated genes at nucleotide codification (protein-coding and ribosomal genes). Maximum-Likelihood tree was reconstructed using the best fit evolutionary model for the concatenated matrix. Numbers at nodes are statistical support values for bootstrap proportions. The scale bar is in expected substitutions/site. Figure S1f. Phylogenetic tree based on mt genome data: Topology 6 with 15 concatenated genes at nucleotide codification (protein-coding and ribosomal genes) implemented GBlock to trim the alignment. Maximum-Likelihood tree was reconstructed using the best fit evolutionary model for the concatenated matrix. Numbers at nodes are statistical support values for bootstrap proportions. The scale bar is in expected substitutions/site. Figure S1g. Phylogenetic tree based on mt genome data: Topology 7 with 13 concatenated genes at amino acid codification (protein-coding genes). Maximum-Likelihood tree was reconstructed using the best fit evolutionary model for the concatenated matrix. Numbers at nodes are statistical support values for bootstrap proportions. The scale bar is in expected substitutions/site. Figure S1h. Phylogenetic tree based on mt genome data: Topology 8 with 13 concatenated genes at amino acid codification (protein-coding genes) implemented GBlock to trim the alignment. Maximum-Likelihood tree was reconstructed using the best fit evolutionary model for the concatenated matrix. Numbers at nodes are statistical support values for bootstrap proportions. The scale bar is in expected substitutions/site. Figure S1i. Phylogenetic tree based on mt genome data: Topology 9 with 13 concatenated genes at amino acid codification (protein-coding genes). Bayesian tree was reconstructed using CAT-GTR evolutionary model for concatenated matrix. Numbers at nodes are statistical support values for bootstrap proportions. The scale bar is in expected substitutions/site. Figure S1j. Phylogenetic tree based on mt genome data: Topology 10 with 13 concatenated genes at amino acid codification (protein-coding genes) implemented GBlock to trim the alignment. Bayesian tree was reconstructed using CAT-GTR evolutionary model for concatenated matrix. Numbers at nodes are statistical support values for bootstrap proportions. The scale bar is in expected substitutions/site. Figure S1k. Phylogenetic tree based on mt genome data: Topology 11 with 13 concatenated genes at amino acid codification (protein-coding genes). Maximum-Likelihood tree was reconstructed using mixture evolutionary model for concatenated matrix. Numbers at nodes are statistical support values for bootstrap proportions. The scale bar is in expected substitutions/site. Figure S1l. Phylogenetic tree based on mt genome data: Topology 12 with 13 concatenated genes at amino acid codification (protein-coding genes) implemented GBlock to trim the alignment. Maximum-Likelihood tree was reconstructed using mixture evolutionary model for concatenated matrix. Numbers at nodes are statistical support values for bootstrap proportions. The scale bar is in expected substitutions/site.
FigureS2.pdf
Figure S2. Phylogenetic tree based on ribosomal operon. Maximum-Likelihood tree was reconstructed using the best fit evolutionary model for the concatenated matrix. Numbers at nodes are statistical support values for bootstrap proportions. The scale bar is in expected substitutions/site.
FigureS3ad.pdf
Figure S3a. Biogeographic hypothesis using Biogeographic hypothesis using BioGeoBEARS DEC on garrapatas M0_unconstrained ancstates: global optim, 5 areas max. d=0.0024; e=0; j=0; LnL=−71.43. Five stages were assessed, each of them indicated with its respectively abbreviation as follow: Nearctic: NE; Neotropic: NO; Australia: AU; Afrotropic: AF; Indomalaya: IN. Figure S3b. Biogeographic hypothesis using BioGeoBEARS DEC+J on garrapatas M0_unconstrained ancstates: global optim, 5 areas max. d=0.0018; e=0; j=0.0116; LnL=−66.07. Five stages were assessed, each of them indicated with its respectively abbreviation as follow: Nearctic: NE; Neotropic: NO; Australia: AU; Afrotropic: AF; Indomalaya: IN. Figure S3c. Biogeographic hypothesis using BioGeoBEARS DIVALIKE on Psychotria M0_unconstrained ancstates: global optim, 5 areas max. d=0.0028; e=0; j=0; LnL=−69.79. Five stages were assessed, each of them indicated with its respectively abbreviation as follow: Nearctic: NE; Neotropic: NO; Australia: AU; Afrotropic: AF; Indomalaya: IN. Figure S3d. Biogeographic hypothesis using BioGeoBEARS DIVALIKE+J on Psychotria M0_unconstrained ancstates: global optim, 5 areas max. d=0.0019; e=0; j=0.0104; LnL=−66.16. Five stages were assessed, each of them indicated with its respectively abbreviation as follow: Nearctic: NE; Neotropic: NO; Australia: AU; Afrotropic: AF; Indomalaya: IN.

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Phylogeny and origin of diversification of Amblyomma (Acari: Ixodidae)

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Background

Methods

Sample collection, DNA extraction, and sequencing

Data curation and ortholog search

Construction of matrixes and phylogenetic inferences

Time-tree estimation

Biogeographical analyses

Results and Discussion

Mitogenomic features

Phylogeny of Amblyomma

Origin of diversification and biogeographical history

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References

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Supplementary Files

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