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Ancient mitogenomes reveal the evolutionary history and biogeography of sloths
<p><strong>Supplementary Material for:</strong></p> <p>Delsuc F., Kuch M., Gibb G.C., Karpinski E., Hackenberger D., Szpak P., Martínez J.G., Mead J.I., McDonald H.G., MacPhee R.D.E., Billet G., Hautier L., and Poinar H.N. (2019). Ancient mitogenomes reveal the evolutionary history and biogeography of sloths. Current Biology. doi:10.1016/j.cub.2019.05.043.</p> <p> </p> <p><strong>Delsuc-CurrBiol-2019_capture_baits.fasta: </strong>Sequence baits designed from living xenarthran mitogenomes and reconstructed ancestral sequences used to capture ancient sloth mitogenomes. </p> <p><strong>Delsuc-CurrBiol-2019_dataset.fasta:</strong> Mitogenomic dataset used for phylogenetic reconstruction and molecular dating in fasta format.</p> <p><strong>Delsuc-CurrBiol-2019_dataset.phylip:</strong> Mitogenomic dataset used for phylogenetic reconstruction and molecular dating in phylip format.</p> <p><strong>Delsuc-CurrBiol-2019_dataset_partitions.nex:</strong> Mitogenomic dataset used for phylogenetic reconstruction and molecular dating in nexus format with partitions.</p> <p><strong>Delsuc-CurrBiol-2019_FigS2_RAxML_MLtree_100BP_nexus_for_FigTree.tree: </strong>Maximum likelihood mitogenomic tree inferred under the best-fitting partitioned model using RAxML. Related to Figure 1.<strong> </strong>Maximum-likelihood bootstrap percentages are indicating at nodes (100 replicates). Tree is rooted on midpoint. Scale is in mean number of substitutions per site. Tree in nexus format viewable with FigTree.</p> <p><strong>Delsuc-CurrBiol-2019_FigS3_IQ-TREE_MLtree_100BP_nexus_for_FigTree.tree</strong><strong>:</strong> Maximum likelihood mitogenomic tree inferred under the best-fitting partitioned model using IQ-TREE. Related to Figure 1. Maximum-likelihood bootstrap percentages are indicating at nodes (100 replicates). Tree is rooted on midpoint. Scale is in mean number of substitutions per site. Tree in nexus format viewable with FigTree.</p> <p><strong>Delsuc-CurrBiol-2019_FigS4_MrBayes_consensus_nexus_for_FigTree.tree: </strong>Bayesian consensus mitogenomic tree inferred under the best-fitting partitioned model using MrBayes. Related to Figure 1. Clade posterior probabilities (PP) are indicated at nodes. Tree is rooted on midpoint. Scale is in mean number of substitutions per site. Tree in nexus format viewable with FigTree. </p> <p><strong>Delsuc-CurrBiol-2019_FigS5_PhyloBayes_consensus_nexus_for_FigTree.tree: </strong>Bayesian consensus mitogenomic tree inferred under the CAT-GTR+G<sub>4</sub> mixture model using PhyloBayes. Related to Figure 1. Clade posterior probabilities (PP) are indicated at nodes. Tree is rooted on midpoint. Scale is in mean number of substitutions per site. Tree in nexus format viewable with FigTree.</p> <p><strong>Delsuc-CurrBiol-2019_FigS6_PhyloBayes_chronogram_nexus_for_FigTree.tree</strong><strong>: </strong>Bayesian mitogenomic chronogram. Related to Figure 2. This chronogram was inferred under the CAT-GTR+G<sub>4</sub> mixture model and an autocorrelated lognormal model of clock relaxation using PhyloBayes. Tree in nexus format viewable with FigTree.</p> <p><strong>Delsuc-CurrBiol-2019_Megatherium_bone_extraction_protocol.pdf: </strong>Detailed protocol for <em>Megatherium americanum</em> MAPB4R 3965 bone sample preparation.</p> <p><strong>Delsuc-CurrBiol-2019_ML_ancestral_reconstruction_MOL_constraint.pdf: </strong>Maximum likelihood ancestral character state reconstruction.<strong> </strong>Related to Figure 3. Maximum likelihood estimation of ancestral states for six dental characters from Varela et al. (2019) under the Mk model on the maximum likelihood topology obtained using the molecular topology as a backbone constraint. </p> <p><strong>Delsuc-CurrBiol-2019_ML_ancestral_reconstruction_MORPH_constraint.pdf: </strong>Maximum likelihood ancestral character state reconstruction.<strong> </strong>Related to Figure 3. Maximum likelihood estimation of ancestral states for six dental characters from Varela et al. (2019) under the Mk model on the maximum likelihood topology obtained using the same topological constraint that these authors used in their Bayesian phylogenetic reconstructions. </p> <p><strong>Delsuc-CurrBiol-2019_MP_ancestral_reconstruction_MOL_constraint.pdf: </strong>Maximum parsimony ancestral character state reconstruction.<strong> </strong>Related to Figure 3. Maximum parsimony estimation of ancestral states for six dental characters from Varela et al. obtained using the molecular topology as a backbone constraint.</p> <p><strong>Delsuc-CurrBiol-2019_MP_ancestral_reconstruction_MORPHO_constraint.pdf: </strong>Maximum parsimony ancestral character state reconstruction.<strong> </strong>Related to Figure 3. Maximum parsimony estimation of ancestral states for six dental characters from Varela et al. (2019) on the maximum parsimony topology obtained using the same topological constraint that these authors used in their Bayesian phylogenetic reconstructions. </p> <p><strong>Delsuc-CurrBiol-2019_TableS1_PartitionFinder_RAxML_best_partition_scheme.txt: </strong>Detailed results of the PartitionFinder analysis for RAxML.</p> <p><strong>Delsuc-CurrBiol-2019_TableS2_ModelFinder_IQ-TREE_best_partition_scheme.txt: </strong>Detailed results of the ModelFinder analysis for IQ-TREE.</p> <p><strong>Delsuc-CurrBiol-2019_TableS3_PartitionFinder_MrBayes_best_partition_scheme.txt: </strong>Detailed results of the PartitionFinder analysis for MrBayes.</p> <p> </p>
Phlorest phylogeny derived from Honkola et al. 2013 'Cultural and climatic changes shape the evolutionary history of the Uralic languages'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Honkola T, Vesakoski O, Korhonen K, Lehtinen J, Syrjänen K & Wahlberg N. 2013. Cultural and climatic changes shape the evolutionary history of the Uralic languages. Journal of Evolutionary Biology, 26(6):1244–1253.</p> </blockquote>
Single-cell atlases of two lophotrochozoan larvae highlight their complex evolutionary histories
<p>This archive contains all the code and data to reproduce the results of the associated manuscript: Piovani <em>et al</em>, "Single-cell atlases of two lophotrochozoan larvae highlight their complex evolutionary histories". We provide filtered scRNA-seq matrices, protein fasta files for each specie used to run SAMap and GenERA as well as the R-code used to generate the datasets and the jupyter notebook to generate SAMap results. In addition we provide the final Seurat objects and all analysis results which can be consulted without re-running the code.</p>
Evolutionary history of the Galápagos Rail revealed by ancient mitogenomes and modern samples
<p>Beast v. 2.6.3 input (<em>.xml</em>) files and output (<em>.log</em> and <em>.trees</em>) files for phylogenetic analyses of rails, used to determined the evolutionary history of the Galápagos Rail <em>Laterallus spilonota</em>. There are two main datasets: coding sequences of the mitochondrial genome ('mtCDS'), partitioned per codon position, and a two mitochondrial/one nuclear marker dataset ('2mt1nc'). For each of the datasets, separate runs have been made in which the fossil calibration of Rallidae is applied to the stem of the present-day family ('calRallidaeStem') or the crown node ('calRallidaeCrown), and finally all runs have been replicated with three different starting seeds ('seed_NNNNNNNNN', with the different seeds 123456789, 456789123, and 789123456).</p> <p>We provide raw output (<em>.log</em> and <em>.raw.trees</em>) as well as maximum clade credibility ('mcc') trees (<em>.mcc.trees</em>), calculated after discarding 10% of the trees as burn-in, using median ('heights_median') or mean ('heights_mean') node heights as estimated node age.</p> <p>The runs used for Table 1 (and Figure 2) in the accompanying paper are:</p> <ul> <li>Dataset mtCDS, Rallidae calibration of stem: seed 123456789</li> <li>Dataset mtCDS, Rallidae calibration of crown: seed 456789123 </li> <li>Dataset 2mt1nc, Rallidae calibration of stem: seed 789123456</li> <li>Dataset 2mt1nc, Rallidae calibration of crown: seed 123456789</li> </ul> <p>This version of the data includes <em>Pellornis mikkelseni</em> among the fossils making up the calibration distribution for crown Gruiformes. In a previous version of this data deposit, that data point was represented by <em>Messelornis cristata </em>(see accompanying paper).</p>
Data from: Evolutionary and demographic history of the Californian scrub white oak species complex: an integrative approach
<p>Understanding the factors promoting species formation is a major task in evolutionary research. Here, we employ an integrative approach to study the evolutionary history of the Californian scrub white oak species complex (genus <em>Quercus</em>). To infer the relative importance of geographical isolation and ecological divergence in driving the speciation process, we (i) analyzed inter- and intra-specific patterns of genetic differentiation and employed an approximate Bayesian computation (ABC) framework to evaluate different plausible scenarios of species divergence. In a second step, we (ii) linked the inferred divergence pathways with current and past species distribution models, and (iii) tested for niche differentiation and phylogenetic niche conservatism across taxa. ABC analyses showed that the most plausible scenario is the one considering the divergence of two main lineages followed by a more recent pulse of speciation. Genotypic data in conjunction with species distribution models and niche differentiation analyses support that different factors (geography vs. environment) and modes of speciation (parapatry, allopatry and maybe sympatry) have played a role in the divergence process within this complex. We found no significant relationship between genetic differentiation and niche overlap, which probably reflects niche lability and/or that multiple factors have contributed to speciation. Our study shows that different mechanisms can drive divergence even among closely related taxa representing early stages of species formation and exemplifies the importance of adopting integrative approaches to get a better understanding of the speciation process.</p>
Taxon-specific or universal? Using target capture to study the evolutionary history of a rapid radiation
<p>Target capture emerged as an important tool for phylogenetics and population genetics in non-model taxa. Whereas developing taxon-specific capture probes requires sustained efforts, available universal kits may have a lower power to reconstruct relationships at shallow phylogenetic scales and within rapidly radiating clades. We present here a newly-developed target capture set for Bromeliaceae, a large and ecologically-diverse plant family with highly variable diversification rates. The set targets 1,776 coding regions, including genes putatively involved in key innovations, with the aim to empower testing of a wide range of evolutionary hypotheses. We compare the relative power of this taxon-specific set, Bromeliad1776, to the universal Angiosperms353 kit. The taxon-specific set results in higher enrichment success across the entire family, however, the overall performance of both kits to reconstruct phylogenetic trees is relatively comparable, highlighting the vast potential of universal kits for resolving evolutionary relationships. For more detailed phylogenetic or population genetic analyses, e.g. the exploration of gene tree concordance, nucleotide diversity or population structure, the taxon-specific capture set presents clear benefits. We discuss the potential lessons that this comparative study provides for future phylogenetic and population genetic investigations, in particular for the study of evolutionary radiations.</p>
Variation in personality shaped by evolutionary history, genotype, and developmental plasticity in response to feeding modalities in the Arctic charr
<p>Animal personality has been shown to be influenced by both genetic and environmental factors and shaped by natural selection. Currently, little is known about mechanisms influencing the development of personality traits. This study examines the extent to which personality development is genetically influenced and/or environmentally responsive (plastic). We also investigated the role of evolutionary history, assessing whether personality traits could be canalized along a genetic and ecological divergence gradient. We tested the plastic potential of boldness in juveniles of five Icelandic Arctic charr morphs (<em>Salvelinus</em> <em>alpinus</em>), including two pairs of sympatric morphs, displaying various degrees of genetic and ecological divergence from the ancestral anadromous charr, split between treatments mimicking benthic vs. pelagic feeding modalities. We show that differences in mean boldness are mostly affected by genetics. While the benthic treatment led to bolder individuals overall, the environmental effect was rather weak, suggesting that boldness lies under strong genetic influence with reduced plastic potential. Finally, we found hints of differences by morphs in boldness canalization through reduced variance and plasticity, and higher consistency in boldness within morphs. These findings provide new insights into how behavioural development may impact adaptive diversification.</p>
Dataset for: Climate and shared evolutionary history drive trait variation among species of Neotropical understory monocots
<p>Dataset and p-values form phylogenetically generalized least square models accompanying the manuscript "Climate and shared evolutionary history drive trait variation among species of Neotropical understory monocots".</p>
Distinguishing cophylogenetic signal from phylogenetic congruence clarifies the interplay between evolutionary history and species interactions
<p>Interspecific interactions, including host-symbiont associations, can profoundly affect the evolution of the interacting species. Given the phylogenies of host and symbiont clades and knowledge of which host species interact with which symbiont, two questions are often asked: "Do closely related hosts interact with closely related symbionts?" and "Do host and symbiont phylogenies mirror one another?". These questions are intertwined and can even collapse under specific situations, such that they are often confused one with the other. However, in most situations, a positive answer to the first question, hereafter referred to as "cophylogenetic signal", does not imply a close match between the host and symbiont phylogenies. It suggests only that past evolutionary history has contributed to shaping present-day interactions, which can arise, for example, through present-day trait matching, or from a single ancient vicariance event that increases the probability that closely related species overlap geographically. A positive answer to the second, referred to as "phylogenetic congruence", is more restrictive as it suggests a close match between the two phylogenies, which may happen, for example, if symbiont diversification tracks host diversification or if the diversifications of the two clades were subject to the same succession of vicariance events. Here we apply a set of methods (ParaFit, PACo, and eMPRess), which significance is often interpreted as evidence for phylogenetic congruence, to simulations under three biologically realistic scenarios of trait matching, a single ancient vicariance event, and phylogenetic tracking. The latter is the only scenario that generates phylogenetic congruence, whereas the first two generate a cophylogenetic signal in the absence of phylogenetic congruence. We find that tests of global-fit methods (ParaFit and PACo) are significant under the three scenarios, whereas tests of event-based methods (eMPRess) are only significant under the scenario of phylogenetic tracking. Therefore, significant results from global-fit methods should be interpreted in terms of cophylogenetic signal and not phylogenetic congruence; such significant results can arise under scenarios when hosts and symbionts had independent evolutionary histories. Conversely, significant results from event-based methods suggest a strong form of dependency between hosts and symbionts evolutionary histories. Clarifying the patterns detected by different cophylogenetic methods is key to understanding how interspecific interactions shape and are shaped by evolution.</p>
FIGURE 1 in Evolutionary history of species of the fireFly subgenus Hotaria (Coleoptera, Lampyridae, Luciolinae, Luciola) inferred from DNA barcoding data
FIGURE 1 Neighbor-joining (NJ) tree of 128 samples of 14 morphospecies based on COI barcode sequences. The percentages at terminal taxa indicate intraspecific genetic divergence. The percentages at each node indicate genetic divergence for the split. Weakly supported nodes (bootstrap values below 70%) are Downloaded from Brill.com 12/12/2023 03:05:57PM shown in red. via Open Access. This is an open access article distributed under the terms of the CC-BY 4.0 License. https://creativecommons.org/licenses/by/4.0/
FIGURE 4 in Evolutionary history of species of the fireFly subgenus Hotaria (Coleoptera, Lampyridae, Luciolinae, Luciola) inferred from DNA barcoding data
FIGURE 4 Time-calibrated phylogram calculated using BEAST based on the COI dataset for 128 samples of 14 morphospecies. Blue numbers below nodes are estimated diversification dates with confidence intervals (blue bars). Posterior probabilities (PP) are marked on nodes with an asterisk (PP = 1.00). Pli = Pliocene, Ple = Pleistocene
FIGURE 5 in Evolutionary history of species of the fireFly subgenus Hotaria (Coleoptera, Lampyridae, Luciolinae, Luciola) inferred from DNA barcoding data
FIGURE 5 Distributional patterns (collection sites) for terminal taxa of Luciola unmunsana (LU), L. papariensis (LP), and L. tsushimana (LT) constructed using BEAST. Red dotted line indicates the approximate location of the "Bekdudaegan" mountains. Yellow dotted line indicates the approximate location of the "Hannam-Geumbuk Jeongmaeck" mountains. Green dotted line denotes the approximate location of the "Nakdong Jeongmaeck" mountains. The map was extracted from Google Earth.
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/
FIGURE 2 in Evolutionary history of species of the fireFly subgenus Hotaria (Coleoptera, Lampyridae, Luciolinae, Luciola) inferred from DNA barcoding data
FIGURE 2 Maximum likelihood (ML) tree of 128 samples of 14 morphospecies based on COI barcode sequences. The numbers at each node indicate support (%). Weakly supported nodes (below 70%) are shown in red. Downloaded from Brill.com 12/12/2023 03:05:57PM via Open Access. This is an open access article distributed under the terms of the CC-BY 4.0 License. https://creativecommons.org/licenses/by/4.0/
FIG. 6 in A synthesis of the evolutionary history of erymoid lobsters (Crustacea, Decapoda, Erymoidea)
FIG. 6. — Distribution of Erymoidea during the Jurassic: A, palaeobiogeography of Middle Jurassic; B, palaeobiogeography of Late Jurassic. Colors: blue, Erymidae; orange, Enoploclytiidae. Abbreviations: En., Enoploclytia; Er., Eryma; Pal., Palaeastacus; Pu., Pustulina; S., Stenodactylina. Source of maps: Scotese 2014c.
FIG. 12 in A synthesis of the evolutionary history of erymoid lobsters (Crustacea, Decapoda, Erymoidea)
FIG. 12. — Early Cretaceous erymoid fauna from the extreme south: A, holotype BAS KG.50.4 of Palaeastacus uranusiensis Devillez & Charbonnier, 2019, from the Aptian of Alexander Island (Antarctica); B, Specimen BAS KG.103.134 of Palaeastacus sussexiensis (Mantell, 1824), from the Aptian of Alexander Island (Antarctica); C, specimen BAS KG.11.4 of Palaeastacus terraereginae (Etheridge Jr, 1914), from the Barremian of Antarctica; D, specimens BAS KG-2-214 of Eryma sp. from the Aptian of Alexander Island (Antarctica); E-H, Australian specimens of P. terraereginae: specimen QM F3235, from the Barremian of Currane (E), specimen UQ F13417 from the Aptian of Boomers (F), specimen QM F3235 from the Barremian of Currane (G), specimen QM F3236 from the Barremian of Currane (H). Scale bars: 1 cm. Photographs: H. Blagbrough (A-D), P. Waddington (E-H).
FIG. 14 in A synthesis of the evolutionary history of erymoid lobsters (Crustacea, Decapoda, Erymoidea)
FIG. 14. — Erymoid fauna from the Chalk Sea (Late Cretaceous): A, specimen NHMUK 5918 of Enoploclytia seitzi Glaessner, 1932, from the Cenomanian of Dover (United Kingdom); B-D, Enoploclytia leachii (Mantell, 1822), from United Kingdom: specimen NHMUK 34404 from Arundel (B), specimen BM 016987 (C), reconstruction (D); E-J, Palaeastacus sussexiensis (Mantell, 1824): specimen BM 007757, from Glynde (United Kingdom) (E), specimen NHMUK unregistered, from Maidstone (United Kingdom) (F), specimen NHMUK 59824, from Lewes (United Kingdom) (G), specimen MNHN.F.S07674, from Couvrot (France) (H), specimen BM 016988, from United Kingdom (I), reconstruction (J); K, specimen MNHN.F.A66891 of Stenodactylina cf. armata, from the Santonian of Cognac (France). Scale bars: 1 cm. Preparation: Y. Despres (H, K). Photographs: L. Cazes (H), J. Devillez (A-C, E-G, I), P. Loubry (K). Drawings: J. Devillez.
FIG. 1 in A synthesis of the evolutionary history of erymoid lobsters (Crustacea, Decapoda, Erymoidea)
FIG. 1. — Palaeastacus terraereginae (Etheridge Jr, 1914) from Australia: A, B, holotype QM 3234 from the Barremian of the Barcoo river: general view (A), schema (B); C, D, specimen QM F3236: dorsal view (C), ventral view (D); E, F, holotype UQ F13410 of Enoploclytia tenuidigitata Woods, 1957 from the Aptian of Boomers: P1 chela (E), dorsal view of the carapace (F); G, H, specimen UQ F13417 from the Aptian of Boomer: carapace (G), schema (H). Abbreviations: a, branchiocardiac groove; b, antennal groove; b1, hepatic groove; c, postcervical groove; d, gastro-orbital groove; e1e, cervical groove; i, inferior groove; POA, post-orbital area; χ, attachment site of adductor testis muscle; ω, attachment site of mandibular muscle. Scale bars: 1 cm. Photographs: P. Waddington. Line drawings: J. Devillez.
FIG. 3 in A synthesis of the evolutionary history of erymoid lobsters (Crustacea, Decapoda, Erymoidea)
FIG. 3. — Erymoid lobsters from the Palaeozoic and the Paleogene: A, holotype PIN 1453 of Eryma antiquum (Birshtein, 1958) from the Changhsingian of Ust-Jenisseisk (Russia); B, C, Enoploclytia gardnerae (Rathbun, 1935) from the Selandian of Coahuila (Mexico): specimen CPC 1982 (B), specimen IGM-9095 (C). Scale bars: 1 cm. Photographs: F. Schram (A), F. Vega (B, C).
FIG. 2 in A synthesis of the evolutionary history of erymoid lobsters (Crustacea, Decapoda, Erymoidea)
FIG. 2. — Enoploclytia minor Woodward, 1900 from the Upper Cretaceous of Hornby Island (Canada): A-C, holotype GSC 5971: general view of the specimen (A), line drawing (B), counterpart (C); D, E, holotype of Eryma dawsoni GSC 5969: general view of the specimen (D), counterpart (E), line drawing (F). Abbreviations: a, branchiocardiac groove; b, antennal groove; b1, hepatic groove; c, postcervical groove; d, gastro-orbital groove; e1e, cervical groove; i, inferior groove. Scale bars: 1 cm. Photographs: M. Coyne. Line drawings: J. Devillez.
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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.