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Fig. 2. Bayesian tree reconstructed from a in Molecular systematics and biogeography of the Hemigalinae civets (Mammalia, Carnivora)
Fig. 2. Bayesian tree reconstructed from a combined dataset of Cytb + ND2 + FGB + IRBP (3342 bp). The values on the branches are bayesian posterior probabilities for the partitioned analysis (see text for models) and bootstrap proportions obtained from ML analysis (model: GTR + I + G).
Fig. 1. Bayesian majority rule consensus tree reconstructed for 90 in Phylogenetic analysis and systematic position of two new species of the ant genus Crematogaster (Hymenoptera, Formicidae) from Southeast Asia
Fig. 1. Bayesian majority rule consensus tree reconstructed for 90 taxa using five genes (ArgK, CAD, LWRh, Top1, Wg) in a MrBayes analysis. Above node numbers indicate posterior probability. Data were partitioned by PartitionFinder v.1.1.1 and analyzed using a best fit model for each gene and codon position, with 10 million generations and a burn-in of 25 %. Area enclosed by dashed lines is enlarged on Fig. 2.
Data from: Effects of taxon sampling and tree reconstruction methods on phylodiversity metrics
1. The amount and patterns of phylodiversity in a community are often used to draw inferences about the local and historical factors affecting community assembly and can be used to prioritize communities and locations for conservation. Because measures of phylodiversity are based on the topology and branch lengths of phylogenetic trees, which are affected by the number and diversity of taxa in the tree, these analyses may be sensitive to changes in taxon sampling and tree reconstruction methods. 2. To investigate the effects of taxon sampling and tree reconstruction methods on measures of phylodiversity, we investigated the community phylogenetics of the Ordway-Swisher Biological Station (Florida), which is home to over 600 species of vascular plants. We studied the effects of 1) the number of taxa included in the regional phylogeny; 2) random vs. targeted sampling of species to assemble the regional species pool; 3) including only species from specific clades rather than broad sampling; 4) using trees reconstructed directly for the taxa under study compared to trees pruned from a larger reconstructed tree; and 5) using phylograms compared to chronograms. 3. We found that including more taxa in a study increases the likelihood of observing significantly non-random phylogenetic patterns. However, there were no consistent trends in the phylodiversity patterns based on random taxon sampling compared to targeted sampling, or within individual clades compared to the complete dataset. Using pruned and reconstructed phylogenies resulted in similar patterns of phylodiversity, while chronograms in some cases led to significantly different results from phylograms. 4. The methods commonly used in community phylogenetic studies can significantly impact the results, potentially influencing both inferences of community assembly and conservation decisions. We highlight the need for both careful selection of methods in community phylogenetic studies and appropriate interpretation of results, depending on the specific questions to be addressed.
LegacyVegetation: Northern Hemisphere reconstruction of past plant cover and total tree cover from pollen archives of the last 14 ka
Open the record for dataset details and reuse information.
Text-fig. 6. a: Reconstruction of the fertile shoot Archaeopteris from the Late Devonian; b: Reconstruction of tree that bore Archaeopteris shoots; c: Juvenile form ("sapling") of Archaeopteris-bearing tree. Redrawn from Cleal and Thomas (2019). in Naming Of Parts: The Use Of Fossil-Taxa In Palaeobotany
Text-fig. 6. a: Reconstruction of the fertile shoot Archaeopteris from the Late Devonian; b: Reconstruction of tree that bore Archaeopteris shoots; c: Juvenile form ("sapling") of Archaeopteris-bearing tree. Redrawn from Cleal and Thomas (2019).
Text-fig. 2. The distinction between fossil plants (a) and plant fossils (b). a: Reconstruction of a late Carboniferous arborescent lycopsid, often referred to as the Lepidodendron-tree; artwork by A. Townsend (formerly of National Museum Wales, Cardiff, UK; see Townsend et al. 1998); b: Lepidodendron aculeatum STERNB.; Middle Coal Measures Formation (Duckmantian – upper Bashkirian), Brymbo, near Wrexham, UK (see Thomas et al. 2020: fig. 16b); National Museum Wales specimen 2013.43G.88. in Naming Of Parts: The Use Of Fossil-Taxa In Palaeobotany
Text-fig. 2. The distinction between fossil plants (a) and plant fossils (b). a: Reconstruction of a late Carboniferous arborescent lycopsid, often referred to as the Lepidodendron-tree; artwork by A. Townsend (formerly of National Museum Wales, Cardiff, UK; see Townsend et al. 1998); b: Lepidodendron aculeatum STERNB.; Middle Coal Measures Formation (Duckmantian – upper Bashkirian), Brymbo, near Wrexham, UK (see Thomas et al. 2020: fig. 16b); National Museum Wales specimen 2013.43G.88.
Data from: Reconstructing 120 years of climate change impacts on Joshua tree flowering
<p>Quantifying how global change impacts wild populations remains challenging, especially for species poorly represented by systematic datasets. Here, we infer climate change effects on masting by Joshua trees (<em>Yucca brevifolia</em> and <em>Y. jaegeriana</em>), keystone perennials of the Mojave Desert, from 15 years of crowdsourced observations. We annotated phenophase in 10,212 geo-referenced images of Joshua trees on the iNaturalist crowdsourcing platform, and used them to train machine learning models predicting flowering from annual weather records. Hindcasting to 1900 with a trained model successfully recovers flowering events in independent historical records, and reveals slightly rising frequency of conditions supporting flowering since the early 20th Century. This reflects increased variation in annual precipitation, which drives masting events in wet years — but also increasing temperatures and drought stress, which may have net negative impacts on recruitment. Our findings reaffirm the value of crowdsourcing for understanding climate change impacts on biodiversity.</p>
APPENDIX 4. — Ultrametric Bayesian tree reconstructed with the 5P in Molecular data reveal the presence of three Plocamium Lamouroux species with complex patterns of distribution in Southern Chile
APPENDIX 4. — Ultrametric Bayesian tree reconstructed with the 5P-COI marker. The dotted vertical red line indicates the maximum likelihood transition point of the switch in branching rates, as estimated by a General Mixed Yule-Coalescent (GMYC) model. The GMYC analysis was performed using a single threshold. Haplotype code as in Appendix 5.
Fig. 2. Simplified neighbor-joining tree reconstructed from partial cox1 in Lurking in the dark: Cryptic Strongyloides in a Bornean slow loris
Fig. 2. Simplified neighbor-joining tree reconstructed from partial cox1 gene (716 bp) sequences of Strongyloides spp. S. fuelleborni sequences for Bornean primates cluster within the S. fuelleborni group, together with previously described sequences for the parasite found in African and Japanese primates. The S. stercoralis cluster includes sequences from humans from Laos, Africa and Japan, captive chimpanzees, and dogs. The Strongyloides sp. cluster corresponds to sequences from the slow loris. An alternative hypothesis is presented next to the tree, where instead of representing a different species, Strongyloides sp. would be part of a cryptic assemblage within the S. stercoralis group.
Fig. 4. Neighbor-joining phylogenetic tree reconstructed from a in Isolation and characterization of four unrecorded wild yeasts from the soils of Republic of Korea in winter
Fig. 4. Neighbor-joining phylogenetic tree reconstructed from a comparative analysis of 26S rRNA gene sequences showing the relationships of strain NH33 with closely related species. Bootstrap values (>50%) based on neighbor-joining methods are shown at the branch nodes. Bar, 0.01 substitutions per nucleotide position.
Fig. 3. Neighbor-joining phylogenetic tree reconstructed from a in Isolation and characterization of four unrecorded wild yeasts from the soils of Republic of Korea in winter
Fig. 3. Neighbor-joining phylogenetic tree reconstructed from a comparative analysis of 26S rRNA gene sequences showing the relationships of strain NH19 with closely related species. Bootstrap values (>50%) based on neighbor-joining methods are shown at the branch nodes. Bar, 0.01 substitutions per nucleotide position.
Fig. 2. Neighbor-joining phylogenetic tree reconstructed from a in Isolation and characterization of four unrecorded wild yeasts from the soils of Republic of Korea in winter
Fig. 2. Neighbor-joining phylogenetic tree reconstructed from a comparative analysis of 26S rRNA gene sequences showing the relationships of strains NH20 and YP416 with closely related species. Bootstrap values (>50%) based on neighbor-joining methods are shown at the branch nodes. Bar, 0.01 substitutions per nucleotide position.
Fig. 3. A Neighbor-joining phylogenetic tree reconstructed from a in Isolation and characterization of two unrecorded yeast species in the order Filobasidiales
Fig. 3. A Neighbor-joining phylogenetic tree reconstructed from a comparative analysis of 26S rRNA gene sequences showing the rela- tionships of strain PG1-1-10C with closely related species. Bootstrap values (>70%) based on neighbor-joining methods are shown at the branch nodes. Bar, 0.01 substitutions per nucleotide position.
Fig. 2. A Neighbor-joining phylogenetic tree reconstructed from a in Isolation and characterization of two unrecorded yeast species in the order Filobasidiales
Fig. 2. A Neighbor-joining phylogenetic tree reconstructed from a comparative analysis of 26S rRNA gene sequences showing the relation- ships of strains GW1-3 with closely related species. Bootstrap values (>70%) based on neighbor-joining methods are shown at the branch nodes. Bar, 0.01 substitutions per nucleotide position.
Fig. 3. A neighbor-joining phylogenetic tree reconstructed from a in Description of unrecorded wild yeasts from soil in Republic of Korea under cold conditions
Fig. 3. A neighbor-joining phylogenetic tree reconstructed from a comparative analysis of 26S rRNA gene sequences showing the relationships of strain PG3-4-10C with closely related species. Bootstrap values (>70%) based on neighbor-joining methods are shown at the branch nodes. Bar, 0.01 substitutions per nucleotide position.
Fig. 2. A neighbor-joining phylogenetic tree reconstructed from a in Description of unrecorded wild yeasts from soil in Republic of Korea under cold conditions
Fig. 2. A neighbor-joining phylogenetic tree reconstructed from a comparative analysis of 26S rRNA gene sequences showing the relationships of strain CY-9-10C with closely related species. Bootstrap values (>70%) based on neighbor-joining methods are shown at the branch nodes. Bar, 0.02 substitutions per nucleotide position.
Text-fig. 1. D&E tree of Endress and Doyle (2009), from the combined morphological and molecular analysis of Doyle and Endress (2000), with modifications based on more recent data, showing the inferred evolution of the reticulum grading character (39). Boxes under names of taxa indicate their character state; shading of branches indicates their reconstructed state based on parsimony optimization with MacClade (Maddison and Maddison 2003). Nymph = Nymphaeales, Aust = Austrobaileyales, Chlor = Chloranthaceae, Piper = Piperales, Ca = Canellales, Magnol = Magnoliales. in Early Cretaceous Monocots: A Phylogenetic Evaluation
Text-fig. 1. D&E tree of Endress and Doyle (2009), from the combined morphological and molecular analysis of Doyle and Endress (2000), with modifications based on more recent data, showing the inferred evolution of the reticulum grading character (39). Boxes under names of taxa indicate their character state; shading of branches indicates their reconstructed state based on parsimony optimization with MacClade (Maddison and Maddison 2003). Nymph = Nymphaeales, Aust = Austrobaileyales, Chlor = Chloranthaceae, Piper = Piperales, Ca = Canellales, Magnol = Magnoliales.
Fig. 2. Bayesian tree reconstructed using 478 in Detection of haemosporidian parasites in wild and domestic birds in northern and central provinces of Iran: Introduction of new lineages and hosts
Fig. 2. Bayesian tree reconstructed using 478-bp mitochondrial cytb gene for avian blood parasites lineages. The amplified sequences in the current study are highlighted in bold. Posterior probability support of>0.8 is displayed for each branch. Schematic tree is summarized in section A and separated clade for each genus is given in sections of B (Plasmodium), C (Haemoproteus), and D (Leucocytozoon).
Fig. 1. Species tree reconstruction inferred from ASTRAL-II using 89 specimens and 787 in One in, one out: Generic circumscription within subtribe Manilkarinae (Sapotaceae)
Fig. 1. Species tree reconstruction inferred from ASTRAL-II using 89 specimens and 787 individual gene trees obtained using RAxML. The node labels represent ASTRAL support values. Note that ASTRAL only calculates internal branch length and that tip lines are artificially fixed with the same length for all the specimens. Tip labels include the species names and the collector codes. Branch colors represent the traditional classification: Labramia (dark green), Manilkara (orange), Faucherea (yellow) and Labourdonnaisia (pink). The revised four major genetic clades are highlighted by a colored bar as follows: Labramia (dark green), Manilkara s.str. (orange), Faucherea and Labourdonnaisia (pink), and the Abebaia clade (blue). The main regions are indicated as follows: Afr: Africa; Ame: Americas; Com: Comoros; Ind: Indonesia; Mad: Madagascar; Msc: Mascarenes; Pac: Pacific Asia. RN: Réserves Naturelles; SF: Service Forestier.
Data for: PickMe: sample selection for species tree reconstruction using coalescent weighted quartets
<p>After collecting large data sets of many genes for many species for phylogenomics studies, researchers may make ad hoc decisions about which genes or samples to include in a species tree reconstruction analysis based on various parameters, including the amount of missing data. Optimally, sampling would be maximized, but it can be difficult for empiricists to determine where to draw the line for sample inclusion when data sets are incomplete. Under the multispecies coalescent model, in which the dominant quartet topology displayed across gene trees matches the topology of that quartet on the species tree, we propose a Bayesian framework to select samples for which there is support for inclusion in a species tree analysis. Given a collection of gene trees, a posterior probability is assigned to each quartet topology, describing the likelihood that the species tree displays this topology. From this, individual samples are assigned reliability scores computed as the average of a rescaling of the posterior probabilities. These weights are used in a Bayesian framework in an algorithm called PickM}, which determines which individuals should be included in a species tree analysis. To illustrate the efficacy of this tool, PickMe is applied to gene trees generated from target capture data from milkweeds. PickMe indicates that more samples could have reliably been included in a previous milkweed phylogenomic analysis than the authors analyzed, without access to a formal decision-making procedure. Thus, PickMe will be a valuable addition to data analysis pipelines for phylogenomics studies.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.