Exploring systematic biases, rooting methods and morphological evidence to unravel the evolutionary history of the genus <i>Ficus</i> (Moraceae)

Rasplus, Jean‐Yves; Rodriguez, Lillian Jennifer; Sauné, Laure; Peng, Yang-Qiong; Bain, Anthony; Kjellberg, Finn; Harrison, Rhett D.; Pereira, Rodrigo Augusto Santinelo; Ubaidillah, Rosichon; Tollon-Cordet, Christine; Gautier, Mathieu; Rossi, Jean‐Pierre; Cruaud, Astrid

doi:10.1111/cla.12443

Cited by 13 publications

(19 citation statements)

References 93 publications

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“…However, a recent study used genome-wide RAD loci and morphological features to infer the phylogenetic relationships in Ficus revealed that long-branch attraction led to section Pharmacosycea was the basal clade ( Rasplus et al, 2021 ). Their results also strongly support the subgenera Sycidium , Sycomorus , and Urostigma as monophyletic clades, and suggest that the polyphyletic nature of these chloroplast genome-based clades may be due to heterogeneity ( Rasplus et al, 2021 ). Thus, more research is needed to determine the reasons for nuclear and plastid discordance in figs, as it is unrealistic to rely on single genomes or small sample sizes to resolve these questions.…”

Section: Discussionmentioning

confidence: 99%

“…Our placement of section Pharmacosycea ( F. maxima and F. adhatodifolia ) is consistent with previous study based on a nuclear genomic data, providing strong support that this section is sister to all other figs ( Bruun-Lund et al, 2017 ; Wang et al, 2021 ). However, a recent study used genome-wide RAD loci and morphological features to infer the phylogenetic relationships in Ficus revealed that long-branch attraction led to section Pharmacosycea was the basal clade ( Rasplus et al, 2021 ). Their results also strongly support the subgenera Sycidium , Sycomorus , and Urostigma as monophyletic clades, and suggest that the polyphyletic nature of these chloroplast genome-based clades may be due to heterogeneity ( Rasplus et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

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Comparative chloroplast genome analysis of Ficus (Moraceae): Insight into adaptive evolution and mutational hotspot regions

Zhang

Yang²,

Li³

et al. 2022

Front. Plant Sci.

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As the largest genus in Moraceae, Ficus is widely distributed across tropical and subtropical regions and exhibits a high degree of adaptability to different environments. At present, however, the phylogenetic relationships of this genus are not well resolved, and chloroplast evolution in Ficus remains poorly understood. Here, we sequenced, assembled, and annotated the chloroplast genomes of 10 species of Ficus, downloaded and assembled 13 additional species based on next-generation sequencing data, and compared them to 46 previously published chloroplast genomes. We found a highly conserved genomic structure across the genus, with plastid genome sizes ranging from 159,929 bp (Ficus langkokensis) to 160,657 bp (Ficus religiosa). Most chloroplasts encoded 113 unique genes, including a set of 78 protein-coding genes, 30 transfer RNA (tRNA) genes, four ribosomal RNA (rRNA) genes, and one pseudogene (infA). The number of simple sequence repeats (SSRs) ranged from 67 (Ficus sagittata) to 89 (Ficus microdictya) and generally increased linearly with plastid size. Among the plastomes, comparative analysis revealed eight intergenic spacers that were hotspot regions for divergence. Additionally, the clpP, rbcL, and ccsA genes showed evidence of positive selection. Phylogenetic analysis indicated that none of the six traditionally recognized subgenera of Ficus were monophyletic. Divergence time analysis based on the complete chloroplast genome sequences showed that Ficus species diverged rapidly during the early to middle Miocene. This research provides basic resources for further evolutionary studies of Ficus.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Comparative chloroplast genome analysis of Ficus (Moraceae): Insight into adaptive evolution and mutational hotspot regions

Zhang

Yang²,

Li³

et al. 2022

Front. Plant Sci.

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“…While genome-scale data can often help resolving phylogenetic relationships, they have proven frustrating when addressing difficult nodes within the Saturniidae, presumably because of the lack of signal associated with an old, rapid radiation (Hamilton et al 2021) and of the increasing probability of observing conflicting signals between markers (Kumar et al 2012). In particular, although these properties are still rarely thoroughly explored, heterogeneity in base composition and evolutionary rates of taxa and markers (e.g., Boussau et al 2014, Romiguier et al 2016, Bossert et al 2017, Borowiec et al 2019, Cruaud et al 2021, Rasplus et al 2021) as well as saturation (Borowiec 2019, Duchêne et al 2021) were identified as major causes of analytical bias in phylogenomic analyses. These factors, in concert with limited taxon sampling, may, for instance, explain the poorly resolved placement of Agliinae within the Saturniidae (Hamilton et al 2019) and constrain our understanding of the early evolutionary history of this family.…”

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confidence: 99%

Phylogenomics Illuminates the Evolutionary History of Wild Silkmoths in Space and Time (Lepidoptera: Saturniidae)

Rougerie

Cruaud

Arnal

et al. 2022

Preprint

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Wild silkmoths (Saturniidae) are one of the most emblematic and most studied families of moths. Yet, the absence of a robust phylogenetic framework based on a comprehensive taxonomic sampling impedes our understanding of their evolutionary history. We analyzed 1,024 ultraconserved elements (UCEs) and their flanking regions to infer the relationships among 338 species of Saturniidae representing all described subfamilies, tribes, and genera. We investigated systematic biases in genomic data and performed dating and historical biogeographic analyses to reconstruct the evolutionary history of wild silkmoths in space and time. Using Gene Genealogy Interrogation, we showed that saturation of nucleotide sequence data blurred our understanding of early divergences and first biogeographic events. Our analyses support a Neotropical origin of saturniids, shortly after the Cretaceous-Paleogene extinction event (ca 64.0 [stem] - 52.0 [crown] Ma), and two independent colonization events of the Old World during the Eocene, presumably through the Bering Land Bridge. Early divergences strongly shaped the distribution of extant subfamilies as they showed very limited mobility across biogeographical regions, except for Saturniinae, a subfamily now present on all continents but Antarctica. Overall, our results provide a framework for in-depth investigations into the spatial and temporal dynamics of all saturniid lineages and for the integration of their evolutionary history into further global studies of biodiversity and conservation. Rather unexpectedly for a taxonomically well-known family such as Saturniidae, the proper alignment of taxonomic divisions and ranks with our phylogenetic results leads us to propose substantial rearrangements of the family classification, including the description of one new subfamily and two new tribes.

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“…The program is intended for fast bootstrap calculations. Three papers published in Cladistics during 2019–2020 performed analyses using MPBoot; the MPBoot results in two of those (Kallal et al., 2020; Rasplus et al., 2020) seem unremarkable, but those in the third one (Inoue et al., 2019) are unusual in that they display a relatively strong cladistic structure within a group of over 50 virtually identical specimens of Lampsilis hydiana (for which the likelihood bootstrap tree exhibited virtually no resolution). As discussed below, this may have been an artefact produced by MPBoot’s approximate algorithms, and it seems likely that proper parsimony results would have been more similar to those of maximum‐likelihood.…”

Section: Introductionmentioning

confidence: 99%

Parsimony analysis of phylogenomic datasets (II): evaluation of PAUP*, MEGA and MPBoot

2021

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This paper examines the implementation of parsimony methods in the programs PAUP*, MEGA and MPBoot, and compares them with TNT. PAUP* implements standard, well-tested algorithms, and flexible search strategies and options for handling trees; its main drawback is the lack of advanced search algorithms, which makes it difficult to find most parsimonious trees for large and complex datasets. In addition, branch-swapping can be much slower than in TNT for datasets with large numbers of taxa, although this is only occasionally a problem for phylogenomic datasets given that they typically have small numbers of taxa. The parsimony implementation of MEGA has major drawbacks. MEGA often fails to find parsimonious trees because it does not perform all possible branch swapping subtree pruning regrafting (SPR)/tree bisection-reconnection (TBR) rearrangements. It furthermore fails to properly handle ambiguity or multiple equally parsimonious trees, and it uses the same addition sequence for all bootstrap replicates. The latter yields values of group support that depend on the order in which taxa are listed in the dataset. In addition, tree searches are very slow and do not facilitate the exploration of different starting points (as random seed is fixed). MPBoot searches for optimal trees using the ratchet, but it is based on SPR instead of TBR (and only evaluates by default a subset of the SPR rearrangements). MPBoot approximates bootstrap frequencies by first finding a sample of trees and then selecting from those trees for every replicate, without performing a tree-search. The approximation is too rough in many cases, producing serious under-or overestimations of the correct support values and, for most kinds of datasets, slower estimations than can be obtained with TNT. In addition, bootstrapping with PAUP*, MEGA or MPBoot can attribute strong supports to groups that have no support at all under any meaningful concept of support, such as likelihood ratios or Bremer supports. In TNT, this problem is decreased by using the strict consensus tree to represent each replicate, or eliminated entirely by using different approximations of the Bremer support.

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Exploring systematic biases, rooting methods and morphological evidence to unravel the evolutionary history of the genus Ficus (Moraceae)

Cited by 13 publications

References 93 publications

Comparative chloroplast genome analysis of Ficus (Moraceae): Insight into adaptive evolution and mutational hotspot regions

Comparative chloroplast genome analysis of Ficus (Moraceae): Insight into adaptive evolution and mutational hotspot regions

Phylogenomics Illuminates the Evolutionary History of Wild Silkmoths in Space and Time (Lepidoptera: Saturniidae)

Parsimony analysis of phylogenomic datasets (II): evaluation of PAUP*, MEGA and MPBoot

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