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Data for: The structure of evolutionary model space for proteins across the tree of life
<p>Supporting data for "The structure of evolutionary model space for proteins across the tree of life," submitted by GE Scolaro and EL Braun. The data files correspond to three gzipped tarballs including protein multiple sequence alignments, PAML format models of protein evolution, and model fit data; see included README for details.</p>
FIGURE 3 Majority-rule consensus tree from a in Evolutionary history of species of the fireFly subgenus Hotaria (Coleoptera, Lampyridae, Luciolinae, Luciola) inferred from DNA barcoding data
FIGURE 3 Majority-rule consensus tree from a Bayesian analysis (BI) of 128 samples of 14 morphospecies based on COI barcode sequences. The numbers at each node indicate Downloadedposteriorfrom Brill. probabilities com 12. /12/ Weakly 2023 03 sup-:05:57PM ported nodes (posterior via probabilityOpen below Access. 0.95) Thisareis an shownopenin red. access article distributed under the terms of the CC-BY 4.0 License. https://creativecommons.org/licenses/by/4.0/
Fig. 2. Topologiesof the Bayesian trees with branchlengths. Posteriorprobability andbootstrapsupport valuesare given. A. Vachellia and B. Senegalias.l in Evolutionary and taxonomic relationships of Acacia s.l. (Leguminosae: Mimosoideae)
Fig. 2. Topologiesof the Bayesian trees with branchlengths. Posteriorprobability andbootstrapsupport valuesare given. A. Vachellia and B. Senegalias.l.
Polymorphism-aware estimation of species trees and evolutionary forces from genomic sequences with RevBayes
<p>Supplementary files of Polymorphism-aware estimation of species trees and evolutionary forces from genomic sequences with RevBayes by Borges, Boussau, Höhna, Pereira and Kosiol<br> </p>
Figure 2 in Middle Miocene origins for tough-browse dietary specialisations in the koala (Marsupialia, Phascolarctidae) evolutionary tree: description of a new genus and species from the Riversleigh World Heritage Area
Figure 2. Stelakoala riversleighensis gen. et sp. nov. holotype (QM F57737) from Jim's Jaw Site, Riversleigh World Heritage Area, Qld. A-A', Occlusal stereopair; B, labelled occlusal view; C, lingual view; D, buccal view. Abbreviations: co, cristid obliqua; end, entoconid; esd, entostylid; er, entostylid ridge; hyd, hypoconid; lr, lingual ribs; med, metaconid; msd, metastylid; pad, paraconid; ppsd, preprotostylid cristid; prd, protoconid; psd, protostylid.
Figure 1 in Middle Miocene origins for tough-browse dietary specialisations in the koala (Marsupialia, Phascolarctidae) evolutionary tree: description of a new genus and species from the Riversleigh World Heritage Area
Figure 1. Map and regional schematic of the Riversleigh World Heritage Area (After Arena, 2005, and Megirian, 1992). The Type locality of Stelakoala riversleighensis gen. et sp. nov., Jim's Jaw Site, is located on the northern Gag Plateau (highlighted red).
Figure 3 in Middle Miocene origins for tough-browse dietary specialisations in the koala (Marsupialia, Phascolarctidae) evolutionary tree: description of a new genus and species from the Riversleigh World Heritage Area
Figure 3. The evolution of primary M1 trigonid cuspids (metaconid, protoconid and protostylid) in phascolarctids. A, Schematic diagram of a phascolarctid right M 1 illustrating how dimensions and percentages represented in Table 1 were obtained (adapted from Black et al., 2014a); B, Graphical representation of the relative positions and distance between the primary M 1 trigonid cuspids as a percentage of trigonid width; C, phylogenetic relationships of phascolarctids (from Black et al., 2012a). Abbreviations: end, entoconid; hyd, hypoconid; med, metaconid, prd, protoconid; psd, protostylid. Data for Madakoala, Perikoala, Litokoala, Nimiokoala and Phascolarctos, is based on M. devisi, Pe. robustus, L. kutjamarpensis, N. greystanesi and P. cinereus, respectively, with mean values used for the latter two species (Table 1).
Fig. 3. Evolutionary analysis conducted through a neighbor-joining phylogenetic tree between the cox1 in Detection of maize bushy stunt phytoplasma in leafoppers collected in native corn crops grown at high elevations in southeast Mexico
Fig. 3. Evolutionary analysis conducted through a neighbor-joining phylogenetic tree between the cox1 mini-barcode sequences obtained for the red speckled nymphs and Idiodonus wickhami (Hemiptera: Cicadellidae) (both marked with a circle) with reference sequences from GenBank. Bar 5 substitution in 100 positions.
Fig. 4. Evolutionary analysis conducted through a neighbor-joining phylogenetic tree between the 16S in Detection of maize bushy stunt phytoplasma in leafoppers collected in native corn crops grown at high elevations in southeast Mexico
Fig. 4. Evolutionary analysis conducted through a neighbor-joining phylogenetic tree between the 16S rRNA sequences amplified in this study from phytoplasma DNA, bar 1 substitution in 100 positions. Sequences in the grey square belong to the subgroup 16SrI-B. Sequences amplified from leafoppers (Hemiptera: Cicadellidae) Dalbulus elimatus marked with a circle and from Idiodonus wickhami marked with a square.
Figure 3. Synthetic tree derived from the 12S in Evolutionary systematics of the Indian mouse Mus famulus Bonhote, 1898: molecular (DNA/DNA hybridization and 12S rRNA sequences) and morphological evidence
Figure 3. Synthetic tree derived from the 12S rRNA datasets with the inclusion of all substitutions (TV + TI). The thin lines indicate nodes that are not robustly supported by all kinds of analysis. The robustness of the different nodes are indicated as follows: [BP(BPweighted analysis)/BSI (Parsimony)]/[BP(NJ)/Reliability Percentage (ML)].
Figure 2. A, Tree resulting from maximum likelihood analysis from the 12S in A molecular perspective on the evolutionary affinities of an enigmatic neotropical frog, Allophryne ruthveni
Figure 2. A, Tree resulting from maximum likelihood analysis from the 12S data set using the Hasegawa–Kishino–Yano two parameter model (Hasegawa et al., 1985). Included for comparative purposes are bootstrap support from NJ analyses. Below each resolved branch are indicated percentage bootstrap support in excess of 50% for: ML (100 pseudoreplicates), NJ (1000 pseudoreplicates; Kimura 2-parameter), NJ (1000 pseudoreplicates; Tamura–Nei). B, Strict consensus of three most parsimonious trees (all substitutions weighted equally; tree length = 164 steps). Below each branch are indicated percentage bootstrap support (1000 pseudreplicates) in excess of 50% for: MP (unweighted), MP (stems weighted twice loops), MP (transversions weighted four times transitions). Bremer decay indices (DI) are the final value shown below each resolved branch (for MP unweighted only). A dash indicates bootstrap support of less than 50%.
Figure 3. A, Tree resulting from a in A molecular perspective on the evolutionary affinities of an enigmatic neotropical frog, Allophryne ruthveni
Figure 3. A, Tree resulting from a maximum likelihood analysis from the combined data set using the Hasegawa–Kishino–Yano two-parameter model (Hasegawa et al., 1985). Included are bootstrap support values for NJ analyses. Below each resolved branch are indicated percentage bootstrap support in excess of 50% for: ML (100 pseudoreplicates), NJ (1000 pseudoreplicates; Kimura 2-parameter), NJ (1000 pseudoreplicates; Tamura–Nei). B, Strict consensus of eight most parsimonious trees (tree length = 441 steps) derived from a heuristic search of combined data set (unweighted). Below each supported branch are indicated percentage bootstrap values (1000 pseudreplicates) in excess of 50% for: MP (unweighted), MP (stems positions weighted twice loops), MP (transversions weighted twice transitions). Bremer decay indices (DI) are the final value shown below each resolved branch (for MP unweighted only).
Data from: Whole genomes reveal evolutionary relationships and mechanisms underlying gene-tree discordance in Neodiprion sawflies
<p>Rapidly evolving taxa are excellent models for understanding the mechanisms that give rise to biodiversity. However, developing an accurate historical framework for comparative analysis of such lineages remains a challenge due to ubiquitous incomplete lineage sorting and introgression. Here, we use a whole-genome alignment, multiple locus-sampling strategies, and locus-based and SNP-based species-tree methods to infer a species tree for eastern North American <em>Neodiprion</em> species, a clade of pine-feeding sawflies (Order: Hymenopteran; Family: Diprionidae). We recovered a well-supported species tree that—except for three uncertain relationships—is robust to different strategies for analyzing whole-genome data. Despite this consistency, underlying gene-tree discordance is high. To understand this discordance, we use multiple regression to model topological discordance as a function of several genomic features. We find that gene-tree discordance tends to be higher in regions of the genome that may be more prone to gene-tree estimation error, as indicated by a lower density of parsimony-informative sites, a higher density of genes, a higher average pairwise genetic distance, and gene trees with lower average bootstrap support. Also, contrary to the expectation that discordance via incomplete lineage sorting is reduced in low-recombination regions of the genome, we find a <em>negative</em> correlation between recombination rate and topological discordance. We offer potential explanations for this pattern and hypothesize that it may be unique to lineages that have diverged with gene flow. Our analysis also reveals an unexpected discordance hotspot on Chromosome 1, which contains several genes potentially involved in mitochondrial-nuclear interactions and produces a gene-tree that resembles a highly discordant mitochondrial tree. Based on these observations, we hypothesize that our genome-wide scan for topological discordance has identified a nuclear locus involved in a mito-nuclear incompatibility. Together, these results demonstrate how phylogenomic analysis coupled with high-quality, annotated genomes can generate novel hypotheses about the mechanisms that drive divergence and produce variable genealogical histories across genomes.</p>
Machine learning can be as good as maximum likelihood when reconstructing phylogenetic trees and determining the best evolutionary model on four taxon alignments
<p><span>Machine learning can be as good as maximum likelihood when reconstructing phylogenetic topologies and determining the best evolutionary model on four taxon alignments.</span></p> <p><span>Phylogenetic tree reconstruction with molecular data is important in many fields of life science research. The gold standard in this discipline is the Maximum Likelihood tree reconstruction method. Here we show that for quartet trees, Machine Learning using neural networks can be as good as the Maximum Likelihood method to infer the best tree topology and the best model of sequence evolution for nucleotide as well as amino acid sequences. For this purpose we simulated data sets for a wide range of branch lengths, evolutionary models and model parameters and compared the topologies and inferred models obtained with Machine learning with those obtained with the Maximum Likelihood and the Neighbour Joining method. Our results show that neural networks are a promising avenue for determining relatedness between taxa, which is likely to accelerate the construction of phylogenetic trees in the future, while maintaining a high accuracy.</span></p>
Data from: Whole genomes reveal evolutionary relationships and mechanisms underlying gene-tree discordance in Neodiprion sawflies
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Machine learning can be as good as maximum likelihood when reconstructing phylogenetic trees and determining the best evolutionary model on four taxon alignments
Open the record for dataset details and reuse information.
Data from: Evolutionary innovation through fusion of sequences from across the tree of life
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data sets and trees for the Rasplus et al paper "Exploring systematic biases, rooting methods and morphological evidence to unravel the evolutionary history of the genus Ficus (Moraceae)" Cladistics (2020)
<p>Data sets and trees for the Rasplus <em>et al</em>. paper "Exploring systematic biases, rooting methods and morphological evidence to unravel the evolutionary history of the genus <em>Ficus</em> (Moraceae)" Cladistics (2020). Preprint = https://www.biorxiv.org/content/10.1101/2020.04.15.042259v1</p> <p><strong>*.phy = Data sets (phylip format) [see Table 2 of the paper for more details].</strong></p> <p>- <strong>mergeR1R2.phy</strong> : complete data set (530 RAD loci shared by 75% of the samples + assembly of forward & reverse reads)<br> - <strong>mergeR1R2_GCinfmean.phy</strong> : loci with GC content inferior or equal to mean GC content<br> - <strong>mergeR1R2_GCsupmean.phy</strong> : loci with GC content strictly superior to mean GC content<br> - <strong>mergeR1R2_LS3.phy</strong> : loci that evolve at a homogeneous rate across clades of interest (Clade1= sect. Pharmacosycea; Clade2=subg. Urostigma, Clade3=sect. Oreosycea, Clade4= "gynodioecious clade")<br> - <strong>mergeR1R2_PCA.phy</strong> : loci for which difference between Long Branch (LB) scores for sect. Pharmacosycea and other ingroups was not significant according to our custom iterative PCA approach</p> <p> <br> <strong>Fig*.nwk : Trees (newick format) that were obtained for the different data sets.</strong></p> <p>Trees are also included as Figures or Supplementary Figures of the paper. Note that you may visualize these nwk trees in FigTree (open FigTree. Upload the FigS1A_RAxML_mergeR1R2_inclfigtreeannot.nex first and then open the other trees - do not close FigTree in between !- Annotations included in the first file will be automatically used to annotate other trees). </p> <p><strong>Appendix S2 : Morphological matrix + morphological tree + 4 competing molecular trees. </strong></p> <p>This file can be opened in Mesquite to get reconstruction of ancestral character states</p>
Data from: Evolutionary constraints on tree size and aboveground biomass in tropical dry forests
<p>1. The extent (or lack) of phylogenetic signal for key ecological traits reveals the role of evolutionary processes on present-day ecosystem function and hints on future ecological trends under climate change scenarios. This approach has been applied to South American tropical moist forests, but not to the highly threatened seasonally dry tropical forests (SDTF), despite acknowledgement of their unique evolutionary history. To fill this knowledge gap, we investigated the legacy of evolutionary processes on vital ecological characteristics among SDTF trees: regional dominance, tree size and soil preference.</p> <p>2. We used tree community data on 313 plots of SDTF (12.52 hectares) and locally collected soil data in central-eastern Brazil. For each assessed trait (three for regional dominance, three for tree size and nine for soil preference), we investigated the legacy of evolution using two different approaches: calculating the extent of phylogenetic signal and comparing the fit of four different models of evolution.</p> <p>3. Aboveground woody biomass and tree size showed strong phylogenetic signal. Most of the SDTF biomass stock was concentrated in a few large-sized and closely related tree genera. Among the soil preference variables, only phosphorus displayed significant, albeit weak, phylogenetic signal.</p> <p>4. Synthesis. Our study is the first to show that evolutionary constraints related to tree size significantly determine regional biomass stocks of SDTF in a few closely related tree lineages. This suggests that even isolated SDTF fragments with low taxonomic and phylogenetic diversity can play an important role in the global carbon cycle, storing disproportionally large amounts of carbon in trees that belong to high-biomass lineages. Whether these lineages also share climate change-induced mortality risk deserves future investigation, as they are largely responsible for the maintenance of regional SDTF biomass stocks.</p>
Data from: Evolutionary diversity in tropical tree communities peaks at intermediate precipitation
<p>Global patterns of species and evolutionary diversity in plants are primarily determined by a temperature gradient, but precipitation gradients may be more important within the tropics, where plant species richness is positively associated with the amount of rainfall. The impact of precipitation on the distribution of evolutionary diversity, however, is largely unexplored. Here we detail how evolutionary diversity varies along precipitation gradients by bringing together a comprehensive database on the composition of angiosperm tree communities across lowland tropical South America (2,025 inventories from wet to arid biomes), and a new, large-scale phylogenetic hypothesis for the genera that occur in these ecosystems. We find a marked reduction in the evolutionary diversity of communities at low precipitation. However, unlike species richness, evolutionary diversity does not continually increase with rainfall. Rather, our results show that the greatest evolutionary diversity is found in intermediate precipitation regimes, and that there is a decline in evolutionary diversity above 1,490 mm of mean annual rainfall. If conservation is to prioritise evolutionary diversity, areas of intermediate precipitation that are found in the South American 'arc of deforestation', but which have been neglected in the design of protected area networks in the tropics, merit increased conservation attention.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.