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APPENDIX III. Bayesian inference topology of COI gene with posterior probability values. Numbers at the nodes represent posterior probability support, other nodes with red circles has> 95%. Terminals with locality specification are species prior for understand the description of I. crassa sp. nov. (★). in A new species of Ischnocnema (Anura: Brachycephalidae) from the mountainous region of Atlantic Forest, southeastern Brazil, with a new phylogeny and diagnose for Ischnocnema parva series
APPENDIX III. Bayesian inference topology of COI gene with posterior probability values. Numbers at the nodes represent posterior probability support, other nodes with red circles has> 95%. Terminals with locality specification are species prior for understand the description of I. crassa sp. nov. (★).
Supplements for Inferring Long-Term Tectonic Uplift Patterns from Bayesian Inversion of Fluvially-Incised Landscapes paper
<p><strong>Data and File Organization:</strong></p> <ol> <li><strong>Natural Landscapes (DEM):</strong> <ul> <li>Look for <code>.tif</code> files containing DEMs of natural landscapes. These files are in latitude-longitude coordinates; convert them to UTM if needed.</li> </ul> </li> <li><strong>Synthetic Landscapes (DEM):</strong> <ul> <li>DEM files for synthetic landscapes, ready for use in inversion schemes, are labeled with a <code>syn_</code> prefix.</li> </ul> </li> <li><strong>Climatic Data:</strong> <ul> <li>Climatic data for the Himalayas is available in <code>climate_data_him.zip</code>.</li> </ul> </li> </ol> <p><strong>Running the Code:</strong></p> <ol> <li> <p><strong>Loading DEMs:</strong></p> <ul> <li>Use the <code>loadDEM</code> package to load your DEM file.</li> <li>Specify <code>Z0</code> and <code>A0</code> values, then plot the landscape and <code>basinID</code> for reference.</li> </ul> </li> <li> <p><strong>Identifying Basins of Interest:</strong></p> <ul> <li>Determine which <code>basinID</code>s are of interest, then save them as forward objects. The functions for this process are available within the relevant packages.</li> </ul> </li> <li> <p><strong>Loading the Forward Model:</strong></p> <ul> <li>Load the forward model from the saved file using the appropriate function in the <code>frd</code> package.</li> <li>Choose the number of knots and specify if you prefer a 1D or 2D inversion.</li> </ul> </li> <li> <p><strong>Running and Plotting Inversion Results:</strong></p> <ul> <li>After running the inversion, view results in <code>inversion.step</code>.</li> <li>Plot these results using the plotting functions in the <code>frdplotting</code> package.</li> </ul> </li> </ol> <p> </p> <p> </p> <p><strong>Setup and Installation:</strong></p> <ul> <li>Install the package <code>scabbard</code> with: <div> <div> </div> <div><code>pip install pyscabbard </code></div> </div> </li> <li>All other Python dependencies are standard and can be installed via <code>pip</code> or <code>conda</code> as needed.</li> </ul>
Data from: Bayesian inference reveals positive but subtle effects of experimental fishery closures on marine predator demographics
Global forage-fish landings are increasing, with potentially grave consequences for marine ecosystems. Predators of forage fish may be influenced by this harvest, but the nature of these effects is contentious. Experimental fishery manipulations offer the best solution to quantify population-level impacts, but are rare. We used Bayesian inference to examine changes in chick survival, body condition and population growth rate of endangered African penguins Spheniscus demersus in response to eight years of alternating time-area closures around two pairs of colonies. Our results demonstrate that fishing closures improved chick survival and condition, after controlling for changing prey availability. However, this effect was inconsistent across sites and years, highlighting the difficultly of assessing management interventions in marine ecosystems. Nevertheless, modelled increases in population growth rates exceeded 1% at one colony; i.e. the threshold considered biologically meaningful by fisheries management in South Africa. Fishing closures evidently can improve the population trend of a forage-fish dependent predator – we therefore recommend they continue in South Africa and support their application elsewhere. However, detecting demographic gains for mobile marine predators from small no-take zones requires experimental time-frames and scales that will often exceed those desired by decision-makers.
FIGURE 1. Bayesian phylogenetic tree inferred from 621 in Description of two new species of Rhamphus related to R. oxyacanthae (Curculionidae, Curculioninae, Rhamphini) from Italy based on a morphological study supported by molecular data
FIGURE 1. Bayesian phylogenetic tree inferred from 621 bp of the mitochondrial DNA (mtDNA) cytochrome oxidase subunit I (COI) gene sampled from the Rhamphus specimens originated from Italy. Bayesian a posteriori probabilities are shown above/below branches (values below 0.7 are omitted). Abbreviation: oxy = R. oxyacanthae; bav = R.bavierai n. sp.; ham = R. hampsicora n. sp.; mon = R. monzinii.
FIGURE 2. Bayesian Inference phylogram showing relationships among 21 in Goniurosaurus chengzheng sp. nov., a new species of Leopard Gecko from Guangxi China (Squamata: Eublepharidae)
FIGURE 2. Bayesian Inference phylogram showing relationships among 21 Goniurosaurus species and two outgroup taxa. Support values in the form posterior probabilities (Bayesian Inference)/ bootstrap values (maximum likelihood) are shown above branches. The tree is a Bayesian topology. The position of G. chengzheng sp. nov. is highlighted in the tree.
FIGURE 2 in A reassessment of apheloriine millipede phylogeny: additional taxa, Bayesian inference, and direct optimization (Polydesmida: Xystodesmidae)
FIGURE 2. Preferred phylogram reconstructed using Bayesian inference from the new data set comprising an additional 29 species. Names in bold are new taxa sequenced for this study—15 of which are new species, indicated by "n. sp." Harmonic mean likelihood ≥ -18315.52. Thickened, black branches denote posterior clade probability values ≥ 0.95. Boxes above branches indicate clades supported by different analyses: "Circle B", Bayesian inference, posterior clade probability ≥ 0.95; "Circle P", Direct optimization, node recovered in ≥ 95% of trees generated under the preferred '121' gap-opening and extension parameters summarized in a 95% majority rule consensus; and "Circle M", Maximum likelihood, nonparametric bootstrap clade support values ≥ 95%. Numbers in boxes indicate support values <95%. Light gray box = Southern clade; medium gray box = Appalachioria species; dark gray box = Brachoria species. Exemplar species names followed by specimen numbers in parentheses. Gyalostethus monticolens branch abbreviated with a break for ease of illustration (branch length = 1.047, about 1.6X greater than shown).
Figure 5. Bayesian inference tree using the TIM2 in Complete mitogenome of Chinese shrew mole Uropsilus soricipes (Milne- Edwards, 1871) (Mammalia: Talpidae) and genetic structure of the species in the Jiajin Mountains (China)
Figure 5. Bayesian inference tree using the TIM2 + I + G model depicting the relationship of Uropsilus soricipes. The phylogenetic tree was rooted using Rattus rattus and Neotetracus sinensis. Numbers represent node supports inferred from Bayesian posterior probabilities. Clade A. Jiajin Mountains (JM); Dujiangyan (DJY); Tianquan (TQ); Maoxian (MX); Lixian (LX); Jiuzhaigou (JZG); Clade B. Yuexi (YX).
FIGURE 2. Bayesian tree inferred from LSU gene DNA sequences. Posterior probabilities exceeding 50 in A new species of the genus Tripylina Brzeski, 1963 (Nematoda: Enoplida: Trischistomatidae) from Shanxi province, China
FIGURE 2. Bayesian tree inferred from LSU gene DNA sequences. Posterior probabilities exceeding 50% are given on appropriate clades. Nematode species and GenBank numbers are listed for each taxon.
FIGURE 1. Bayesian tree inferred from SSU gene DNA sequences. Posterior probabilities exceeding 50 in A new species of the genus Tripylina Brzeski, 1963 (Nematoda: Enoplida: Trischistomatidae) from Shanxi province, China
FIGURE 1. Bayesian tree inferred from SSU gene DNA sequences. Posterior probabilities exceeding 50% are given on appropriate clades. Nematode species and GenBank numbers are listed for each taxon.
FIGURE 3a. Bayesian phylogeny inferred from cyt b in Specific limits and emerging diversity patterns in East African populations of laminate-toothed rats, genus Otomys (Muridae: Murinae: Otomyini): Revision of the Otomys typus complex 3024
FIGURE 3a. Bayesian phylogeny inferred from cyt b sequences partitioned by codon position for 34 East African specimens of O. typus s.l. and O. tropicalis s.l. in relation to representatives of seven Otomys species and Parotomys brantsii from eastern, western and southern Africa. Representatives of nine murine taxa served as outgroups (see Appendix 1). Nodal support indices for the main clades (annotated 1 to 8) and subclades (a, b, c, d and e), as well as supported deeper nodes (A, H, I, M, N, O) are given beside the phylogram in the following order: Bayesian posterior probabilities (BPP)/ bootstrap support from 1000 replicates for maximum likelihood (MLbs), under an unpartitioned GTR+I+G model/ unweighted parsimony (UwPbs), six-parameter-weighted parsimony partitioned by codon positions (6PPbs)/ neighbour-joining using uncorrected p-distances (Njpbs)/ neighbour-joining using GTR+I+G-corrected distances (Njcbs). Estimated divergence dates (Myr) for the major lineages identified in the cyt b phylogeny are indicated to the left of each node. Bayesian estimation of divergence times was conducted under a relaxed clock model using BEAST v.1.4. Four well established murine fossil calibration points were specified (Pocock 1987; Rowe et al. 2008, see text).
Figure 15. Bayesian tree inferred from D2-D3 in Phylogenetic relationships within the superfamily Desmodoroidea (Nematoda: Desmodorida), with descriptions of two new and one known species
Figure 15. Bayesian tree inferred from D2-D3 of LSU sequences under the general time-reversible (GTR) + proportion of invariable sites (I) + gamma distribution (G) model. Posterior probability (left) and bootstrap values (right) greater than or equal to 75% are given on appropriate clades. Taxa belonging to the Draconematidae and Epsilonematidae are shown in blue and red, respectively. The scale stands for substitutions per site.
Figure 11. Phylogenetic relationships within the Xiphinema americanum-group complex. Bayesian 50 in Cryptic diversity and species delimitation in the Xiphinema americanum-group complex (Nematoda: Longidoridae) as inferred from morphometrics and molecular markers
Figure 11. Phylogenetic relationships within the Xiphinema americanum-group complex. Bayesian 50% majority rule consensus tree as inferred from partial cytochrome c oxidase subunit I (coxI) sequence alignment under a transversional of invariable sites and gamma-shaped distribution model TVM + I + G model. Posterior probabilities more than 65% are given for appropriate clades; bootstrap values greater than 50% are given on appropriate clades in the maximum likelihood analysis. Sequences newly obtained in this study in this study are in bold. Scale bar = expected changes per site.
Figure 10. Phylogenetic relationships within the Xiphinema americanum-group complex. Bayesian 50 in Cryptic diversity and species delimitation in the Xiphinema americanum-group complex (Nematoda: Longidoridae) as inferred from morphometrics and molecular markers
Figure 10. Phylogenetic relationships within the Xiphinema americanum-group complex. Bayesian 50% majority rule consensus tree as inferred from internal transcribed spacer 1 (ITS1) rRNA sequence alignment under the general timereversible and gamma-shaped distribution model. Posterior probabilities more than 65% are given for appropriate clades; bootstrap values greater than 50% are given on appropriate clades in the maximum likelihood analysis. Sequences newly obtained in this study are in bold. Scale bar = expected changes per site.
Figure 1. Bayesian tree inferred from the 18S in A molecular analysis of the phylogenetic position of the suborder Cavernicola within the Tricladida (Platyhelminthes), with the description of a new species of stygobiont flatworm from Benin
Figure 1. Bayesian tree inferred from the 18S rDNA sequences showing the relationship of the new Novomitchellia species to other Tricladida species included in this analysis. Maximum likelihood (ML) yielded the same topology. Asterisks at nodes indicate posterior probabilities = 1/bootstrap values> 75% obtained respectively in the ML and Bayesian inference analyses. Scale bar: number of substitutions per nucleotide position.
FIGURE 6 in A new species of Paralaophonte Lang 1948 (Harpacticoida: Laophontidae), with notes on the phylogeny of the genus and its relationships with Loureirophonte Jakobi 1953 using Bayesian inference
FIGURE 6. Paralaophonte (Pa.) ullama sp. nov., female. A, P3, anterior; B, P4, anterior. Scale bars represent: A and B, 50 µm.
FIGURE 4 in A new species of Paralaophonte Lang 1948 (Harpacticoida: Laophontidae), with notes on the phylogeny of the genus and its relationships with Loureirophonte Jakobi 1953 using Bayesian inference
FIGURE 4. Paralaophonte (Pa.) ullama sp. nov., female. A, mandible; B, maxillule; C, maxilla; D, maxilliped. Scale bars represent: A–D, 25 µm.
FIGURE 1 in A new species of Paralaophonte Lang 1948 (Harpacticoida: Laophontidae), with notes on the phylogeny of the genus and its relationships with Loureirophonte Jakobi 1953 using Bayesian inference
FIGURE 1. Paralaophonte (Pa.) ullama sp. nov., female. A, habitus, dorsal; B, anal somite and caudal rami, dorsal; C, habitus, lateral; D, anal somite and left caudal ramus, lateral. Scale bars represent: A and C, 200 µm; B and D, 50 µm.
FIGURE 5 in A new species of Paralaophonte Lang 1948 (Harpacticoida: Laophontidae), with notes on the phylogeny of the genus and its relationships with Loureirophonte Jakobi 1953 using Bayesian inference
FIGURE 5. Paralaophonte (Pa.) ullama sp. nov., female. A, P1, anterior; B, P2, anterior. Scale bars represent: A and B, 50 µm.
FIGURE 1. The Bayesian consensus tree inferred from D2 in Nematodes from galls on Myrtaceae. III. Fergusobia from flower bud and stigma galls on Eucalyptus, with descriptions of four new species
FIGURE 1. The Bayesian consensus tree inferred from D2/D3 under TVM+I+G model (lnL=4001.6121; freqA=0.2903; freqC=0.1443; freqG=0.2394; freqT=0.3259; R(a)=0.8475; R(b)=3.4236; R(c)=1.9889; R(d)=0.455; R(e)=3.4236; R(f)=1; Pinva=0.522; Shape=0.5933). Posterior probability values exceeding 50% are given on appropriate clades. (Tree labels comprise nematode species, location (Australia state code), gall type, host plant species and GenBank accession number.
Monte Carlo Simulations results for estimating an offshore structure fatigue life with a Fracture Mechanics based crack growth model, after additional information was considered through Bayesian inference at t=13 years
<p>Monte Carlo Simulations results for estimating an offshore structure fatigue life with a Fracture Mechanics based crack growth model, after additional information was considered through Bayesian inference at t=13 years</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.