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1,847 results for “vertebrates”
Drought experiment on aquatic vertebrate populations in McRae Creek, HJ Andrews Experimental Forest, 2022
Three distinct reaches in McRae Creek west tributary (MCTW) within the HJ Andrews Experimental Forest in western Oregon were designated for manipulation and data collection. Manipulations included increasing the temperature (T), reducing streamflow (Q), and a reference (R reach). Population estimates of vertebrates, specifically Coastal Cutthroat Trout and Coastal Giant Salamander, were obtained using three-pass depletion methods in each reach. A Before-After-Control-Impact (BACI) design was implemented, distinguishing between the "Before" and "After" periods. "Before" surveys were conducted from July 18th to 20th, 2022, while "After" surveys occurred from September 8th to 9th, 2022. During the surveys, each species was identified, noting life stage, and relevant measurements were taken. For trout, these included the length from the snout to the tail fork (Length_Fork_Vent), the snout to the tail (Length_Tail), and weight. In the "Before" survey, all trout were tagged with elastomer tags: red for the T reach, yellow for the Q reach, and orange for the R reach. Trout larger than 80 mm also received PIT tags in their abdominal cavities. Salamanders were measured similarly, not elastomer or PIT tags were applied. During the "After" survey, no new elastomer or PIT tags were inserted; only previously tagged fish were recorded. Additionally, stream cross-sections were surveyed every 5 meters to document stream dimensions. Recorded data included the location, reach, sample date, BACI status, and distance downstream from the upstream cross-section (0 meters). Measurements at each cross-section included wetted width, bankfull width, and depths at five evenly spaced points. Furthermore, pools were identified and measured in each reach, noting the maximum pool depth, depth at the outflow, width, and length. Temperature sensors were installed in each reach, recording stream temperature every 15 minutes. Sensor locations were recorded as the distance downstream from the top of e
Aquatic Vertebrate Population Study in Mack Creek, Andrews Experimental Forest, 1987 to present
Populations of Coastal Cutthroat trout (Oncorhynchus clarkii clarkii) in two standard reaches of Mack Creek in the H.J. Andrews Experimental Forest have been monitored since 1987. Monitoring of Coastal Giant Salamanders, Dicamptodon tenebrosus began in 1993. The two standard reaches are in a section of clearcut forest (ca. 1963) and an upstream 500 year old coniferous forest. Sub-reaches are sampled with 2-pass electrofishing, and all captured vertebrates are measured and weighed. Additionally, a set of channel measurements are taken with each sampling. This study constitutes one of the longest continuous records of salmonid populations on record.
Aquatic vertebrate populations in streams throughout the HJ Andrews Experimental Forest, 2013 to present
This multi-year study quantifies the distribution and density of aquatic vertebrates throughout Lookout Creek stream network, including Mack, McRae and Upper Lookout Creeks. We are examining how predictable and consistent the densities and sizes of cutthroat trout and coastal giant salamanders are throughout the network and whether they fluctuate synchronously with vertebrates sizes and densities in Mack Creek that have been measured since 1987 (AS006). We are also examining biomass and condition of individuals across sites. Density and developmental stage of coastal tailed frog tadpoles are noted. We use electroshocking in discrete blocked reaches (depletion and mark-recapture methodologies) to collect individuals that are then weighed, measured and released. Site specific characteristics are measured at each sampling event, including wetted widths and water depths. Broader landscape metrics, including stream gradient and watershed area, were calculated for sites.
NUT01 Nutrient Network: Investigating the roles of nutrient availability and vertebrate herbivory on grassland structure and function at Konza Prairie
The goals and focal research questions are copied below from the Nutrient Network website. More information can be found at nutnet.org. NutNet focal research questions: (1) How general is our current understanding of productivity-diversity relationships? (2) To what extent are plant production and diversity co-limited by multiple nutrients in herbacoues-dominated communities? (3) Under what conditions do grazers or fertilization control plant biomass, diversity, and composition? NutNet goals: (1) To collect data from a broad range of sites in a consistent manner to allow direct comparisons of environment-productivity-diversity relationships among systems around the world. This is currently occurring at each site in the network and, when these data are compiled, will allow us to provide new insights into several important, unanswered questions in ecology. (2) To implement a cross-site experiment requiring only nominal investment of time and resources by each investigator, but quantifying community and ecosystem responses in a wide range of herbaceous-dominated ecosystems (i.e., desert grasslands to arctic tundra).
VIR01 Effects of invertebate and vertebrate herbivory on tallgrass prairie plant community composition and biomass, Konza Prairie LTER
The effects of herbivores and their interactions with nutrient availability on primary production and plant community composition in grassland systems is expected to vary with herbivore type. Although nutrient additions are known to affect plant species diversity and primary productivity, the role of herbivores in mediating the strength of these effects also remains unclear. Herbivores may alter plant responses to nutrient additions in several ways. First, herbivores can alter the plant community response to nutrient additions by either selectively feeding on particular groups of species (e.g. grasses versus forbs) or by generally opening up space, allowing for species turnover and immigration. Second, feeding by herbivores may reduce the production response to nutrient additions if the plants cannot compensate for tissue lost to herbivory. As the functional effects of vertebrate and invertebrate herbivores on plant community composition and production may vary, the interactive effects of vertebrate versus invertebrate herbivores with nutrient additions may also vary. Here we are experimentally assessing the independent and interactive effects of removing vertebrate and invertebrate herbivores on aboveground biomass and plant community composition in native tallgrass prairie. Further, we are examining whether the removal of vertebrate and invertebrate herbivores interacts with nutrient availability. By doing this, we address three related questions: 1) what is the relative strength of the effects of invertebrate versus vertebrate herbivory in a grassland system; 2) how does herbivory (invertebrate and/or vertebrate) affect the relative abundances of grasses and forbs, the two dominant plant functional types within the ecosystem; and 3) what are the consequences of these changes in composition for aboveground net primary productivity, an important ecosystem function?
Data from: The genetic legacy of extreme exploitation in a polar vertebrate
<p>Microsatellite data (39 loci) from Antarctic fur seals and Subantarctic fur seals, used in the paper: "The genetic legacy of extreme exploitation in a polar vertebrate"</p> <p> </p> <p><strong>Abstract</strong></p> <p>Understanding the effects of human exploitation on the genetic composition of wild populations is important for predicting species persistence and adaptive potential. We therefore investigated the genetic legacy of large-scale commercial harvesting by reconstructing on a global scale the recent demographic history of the Antarctic fur seal (<em>Arctocephalus gazella</em>), a species that was hunted to the brink of extinction by 18<sup>th</sup> and 19<sup>th</sup> century sealers. Molecular genetic data from over 2,000 individuals, sampled from all eight major breeding colonies across the species᾿ circumpolar geographic distribution, show that at least four relict populations around Antarctica survived commercial hunting. Coalescent simulations suggest that all of these populations experienced severe bottlenecks down to effective population sizes of around 150–200. Nevertheless, comparably high levels of neutral genetic variability were retained as these declines are unlikely to have been strong enough to deplete allelic richness by more than around 15%. These findings suggest that even dramatic short-term declines need not necessarily result in major losses of diversity, and explain the apparent contradiction between the high genetic diversity of this species and its extreme exploitation history.</p> <p> </p> <p><strong>Funding</strong></p> <p>This research was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) in<br> the framework of a Sonderforschungsbereich (project numbers 316099922 and 396774617–TRR 212) and the<br> priority programme "Antarctic Research with Comparative Investigations in Arctic Ice Areas" SPP 1158 (project<br> number 424119118). It was also funded by Norwegian Antarctic Research Expeditions (NARE) programme.<br> This work contributes to the Ecosystems project of the British Antarctic Survey, Natural Environmental Research<br> Council, and is part of the Polar Science for Planet Earth Programme. The Department of Environmental Affairs<br> provided logistical support for research at Marion Island and the Department of Science and Technology of<br> South Africa provided funding through the National Research Foundation (NRF). We are grateful to Caroline<br> Bonin, Debbie Baird-Bower and Iain Staniland together with the seal biologists working within the Marion<br> Island Marine Mammal Programme for sample collection and logistics. We acknowledge support for the Article<br> Processing Charge by the Deutsche Forschungsgemeinschaft and the Open Access Publication Fund of Bielefeld<br> University.</p>
CamTrapAsia: a dataset of tropical forest vertebrate communities from 239 camera trapping studies
<p>Dataset containing data from 239 camera trap studies conducted in tropical Asia. A total of 278,260 independent records were compiled, from 371 distinct species, comprising 232 mammals, 132 birds, and 7 reptiles. The accumulated trapping effort was 876,606 trap nights, distributed among Indonesia, Singapore, Malaysia, Bhutan, Thailand, Myanmar, Cambodia, Laos, Vietnam, Nepal and far-eastern India.</p>
Supporting data for "Unveiling Vertebrate Development Dynamics in Frog Xenopus laevis using Micro-CT Imaging"
<p>The dataset contains X-ray Micro Computed Tomography data of Xenopus laevis frog. There are twenty datasets of ten individual animals. Each animal was CT scanned twice – once as a native scan to visualize the hard tissues, and once contrast-stained to visualize the soft tissues. The datasets include nine developmental stages (NF44-45, NF52, NF53, NF54, NF57, NF59, NF62, NF66 and adult). There are two adults, one male and one female. The CT data (in 8bit .tiff format compressed as .tar.gz files) are supported by .stl files created from each dataset. The database also includes .stl files of selected structures of interest (body, skeleton, skull, brain and guts of individual animals).</p>
Species diversity and extinction risk of vertebrate pollinators in India
<p>This repository includes the data compiled and used for the study of <strong>‘Species diversity and extinction risk of vertebrate</strong><br><strong>pollinators in India’</strong>. If you use these data, please cite them along with our manuscript:</p> <blockquote> <p>Kallivalappil R., Grattarola F., de Alwis Pitts D., Cotter S.C. & Pincheira-Donoso D. (2024). Species diversity and extinction risk of vertebrate<br>pollinators in India. <em>Biodiversity and Conservation</em>. https://doi.org/10.1007/s10531-024-02848-3</p> </blockquote> <p> </p> <h2>Abstract</h2> <p>Animal pollinators underpin the functioning and persistence of ecosystems globally. However, the vital role of pollination is being progressively eroded by the worldwide decline of pollinator species caused by human-induced environmental degradation, resulting in rising costs to biodiversity, agriculture, and economy. Most studies quantifying pollinator diversity and declines have focused on insects, whereas vertebrate pollinators remain comparatively neglected. Here, we<br>present the first comprehensive study quantifying the macroecological patterns of species richness and extinction risk of bird and mammal pollinators in India, a region of extremely high biodiversity and increasing anthropogenic pressure. Our results reveal that hotspots of mammal pollinator diversity are restricted to the south of the Western Ghats, whereas bird pollinator diversity hotspots are scattered throughout the country. Analyses of hotspots of threatened species<br>(based on the IUCN Red List) show that only mammal pollinators are currently classified as threatened in India, whereas multiple hotspots of population declines were observed for birds, and primarily in the Southwest for mammal pollinators. Our analyses failed to identify a role for species traits as drivers of these patterns, whereas most pollinators appear to be threatened by agriculture, logging and hunting for food, and medicinal purposes. Pollinator endangerment has widescale<br>ecological and economic implications such as reduced food production, plant extinction, loss of functional and genetic diversity, and economic damage. We suggest protection of vertebrate pollinators should be emphasised in active conservation agendas in India.</p> <p> </p> <h2>Files</h2> <h3>Spatial</h3> <ul> <li><code>india.gpkg</code></li> <li><code>birds.gpkg</code></li> <li><code>mammals.gpkg</code></li> <li><code>how_to_read_gpkg_data.R</code></li> </ul> <h3>Phylogenetic</h3> <ul> <li><code>PGLS_phylogeny_birds.nex</code></li> <li><code>PGLS_phylogeny_mammals.nex</code></li> </ul> <h3>Tables</h3> <ul> <li><code>all_bird_traits.csv</code></li> <li><code>all_mammals_traits.csv</code></li> <li><code>threatened_mammals_traits.csv</code></li> <li><code>plant_pollinator_dataset.csv</code></li> <li><code>pollinator_plant_dataset.csv</code></li> <li><code>references.txt</code></li> </ul>
Vertebrate DNA damage tolerance requires the C-terminus but not BRCT or transferase domains of REV1: Additional Files
<p>Additional information for Ross et al. (2005) Vertebrate DNA damage tolerance requires the C-terminus but not BRCT or transferase domains of REV1 published in Nucleic Acids Research Volume 33, Issue 4 doi:10.1093/nar/gki279, requested by an anonymous poster on PubPeer.</p> <p>A description of the individual files can be found in the README.txt file</p>
Vertebrate traits: Vertebrate trait records, 5 November 2018
<p>This zip archive records all of the current trait records in EOL__s graph database, for vertebrate taxa. It contains four .csv files: pages.csv listing taxa and their names, traits.csv with trait records, metadata.csv with auxiliary records referred to by trait records, and terms.csv listing all of the relationship URIs in the database. For a description of the schema, see</p> <p>https://github.com/EOL/eol_website/blob/master/doc/trait-schema.md</p> <p>See dataset description. Generated 5 November 2018 from the live graph database. Uploaded 10 December 2018.</p>
D-PLACE dataset derived from Jenkins et al. 2013 'Global patterns of terrestrial vertebrate diversity and conservation'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Jenkins CN, Pimm SL, Joppa LN. Global patterns of terrestrial vertebrate diversity and conservation. Proc Natl Acad Sci. 2013;110: E2602–E2610.</p> </blockquote>
Alignments from "Caecilian genomes reveal molecular basis of adaptation and convergent evolution of limblessness in vertebrates"
<p>Compressed file containing the alignments at both nucleotide and amino acid level for the manuscript "Caecilian genomes reveal molecular basis of adaptation and convergent evolution of limblessness in vertebrates" </p>
Vertebrate gene family trees
<p>This is a dataset of phylogenies for 118 vertebrate gene families, used in two papers published by James Cotton and Roderic Page in 2002. The folder named "genes.zip" contains FASTA and NEXUS sequence files, and Newick-format tree files for each gene family. There is a PDF file "suppl.pdf" listing the names of each family. The file "final_dataset.gml" contains a graph of the taxonomic overlap in the gene trees. All 118 gene trees have been combined into the file "final_dataset.gtr" which is a NEXUS file with custom blocks recognised by GeneTree.</p> <table> <tbody> <tr> <th>HOVERGEN FAMILY CODE</th> <th>GENE FAMILY NAME</th> </tr> <tr> <td>FAM000030A</td> <td>wnt 5</td> </tr> <tr> <td>FAM000030B</td> <td>wnt 7</td> </tr> <tr> <td>FAM000030C</td> <td>wnt 11</td> </tr> <tr> <td>FAM000030D</td> <td>wnt/int 1</td> </tr> <tr> <td>FAM000030E</td> <td>wnt 4</td> </tr> <tr> <td>FAM000030F</td> <td>wnt 10/12</td> </tr> <tr> <td>FAM000030G</td> <td>wnt 3</td> </tr> <tr> <td>FAM000030H</td> <td>wnt 2</td> </tr> <tr> <td>FAM000030I</td> <td>wnt 8</td> </tr> <tr> <td>FAM000105</td> <td>rhodopsin</td> </tr> <tr> <td>FAM000214</td> <td>beta-B globin</td> </tr> <tr> <td>FAM000033</td> <td>protamine 1</td> </tr> <tr> <td>FAM000370</td> <td>PrP a prion-protein</td> </tr> <tr> <td>FAM000014</td> <td>growth hormone</td> </tr> <tr> <td>FAM000556</td> <td>Rag-1 recombination activation gene</td> </tr> <tr> <td>FAM001493</td> <td>c-mos proto-oncogene</td> </tr> <tr> <td>FAM001462</td> <td>tyrosine kinase / yes / fyn / src / lck</td> </tr> <tr> <td>FAM001041</td> <td>metallothionein</td> </tr> <tr> <td>FAM000364</td> <td>Ldh-2 lactate dehydrogenase-B (EC</td> </tr> <tr> <td>FAM000215</td> <td>alpha globin</td> </tr> <tr> <td>FAM000016</td> <td>placental lactogen - prolactin</td> </tr> <tr> <td>FAM000008</td> <td>insulin</td> </tr> <tr> <td>FAM000824</td> <td>phosphoglycerate kinase</td> </tr> <tr> <td>FAM000550</td> <td>neurotrophin-4 (NT-4)</td> </tr> <tr> <td>FAM001478</td> <td>tyrosine kinase receptor, c-fms oncogene</td> </tr> <tr> <td>FAM000173A</td> <td>guanine nucleotide-binding protein</td> </tr> <tr> <td>FAM000173B</td> <td>transducin alpha</td> </tr> <tr> <td>FAM000502</td> <td>cytochrome P-450 aromatase</td> </tr> <tr> <td>FAM000192</td> <td>alpha-fetoprotein / serum albumin</td> </tr> <tr> <td>FAM000627</td> <td>neurone-specific enolase</td> </tr> <tr> <td>FAM001232</td> <td>preprotrypsin (ta)</td> </tr> <tr> <td>FAM000664</td> <td>complement component 3 (C3)</td> </tr> <tr> <td>FAM000175</td> <td>Ras</td> </tr> <tr> <td>FAM000248</td> <td>alpha B-crystallin</td> </tr> <tr> <td>FAM000006</td> <td>insulin-like growth factor II</td> </tr> <tr> <td>FAM000639</td> <td>transthyretin (prealbumin)</td> </tr> <tr> <td>FAM001303</td> <td>butylcholinesterase (BCHE)</td> </tr> <tr> <td>FAM000242</td> <td>connexin / gap junction protein</td> </tr> <tr> <td>FAM000058</td> <td>dopamine D1 receptor</td> </tr> <tr> <td>FAM000055</td> <td>beta-3-adrenergic receptor .</td> </tr> <tr> <td>FAM000131</td> <td>ATPase (Na+K+, H+K+)</td> </tr> <tr> <td>FAM003983</td> <td>preprogastrin</td> </tr> <tr> <td>FAM000353</td> <td>vasopressin</td> </tr> <tr> <td>FAM000152</td> <td>acetylcholine receptor</td> </tr> <tr> <td>FAM001327</td> <td>peripherin, desmin, vimentin, GFAP</td> </tr> <tr> <td>FAM003199</td> <td>(C57BL/6J)</td> </tr> <tr> <td>FAM002881</td> <td>cytochrome P-450, 17a-hydroxylase (CYP17)</td> </tr> <tr> <td>FAM002789</td> <td>(MUAHRB-1) Ah-receptor (Ah)</td> </tr> <tr> <td>FAM001461</td> <td>tropomyosin</td> </tr> <tr> <td>FAM000385</td> <td>Y3 peptide supply factor</td> </tr> <tr> <td>FAM000378</td> <td>pancreatic polypeptide, neuropeptide Y</td> </tr> <tr> <td>FAM001329</td> <td>cytokeratin</td> </tr> <tr> <td>FAM001619</td> <td>amelogenin (enamel-specific protein)</td> </tr> <tr> <td>FAM000286</td> <td>glucagon</td> </tr> <tr> <td>FAM000330</td> <td>tissue inhibitor of</td> </tr> <tr> <td>FAM000475</td> <td>lipophilin</td> </tr> <tr> <td>FAM001060</td> <td>ornithine carbamoyltransferase</td> </tr> <tr> <td>FAM001328</td> <td>neurofilament</td> </tr> <tr> <td>FAM001370</td> <td>peroxisome proliferator</td> </tr> <tr> <td>FAM000371</td> <td>somatostatin</td> </tr> <tr> <td>FAM001607</td> <td>Wilms tumor assocated protein (WT1)</td> </tr> <tr> <td>FAM000495</td> <td>aldolase A, B, C</td> </tr> <tr> <td>FAM001365</td> <td>liver receptor homologous protein (LRH-1)</td> </tr> <tr> <td>FAM000672</td> <td>ribosomal protein S4,</td> </tr> <tr> <td>FAM000135</td> <td>Na, K-ATPase beta-1 subunit</td> </tr> <tr> <td>FAM000271</td> <td>enkephalin : 1 2.</td> </tr> <tr> <td>FAM000799</td> <td>RING10</td> </tr> <tr> <td>FAM001664</td> <td>glutamate decarboxylase</td> </tr> <tr> <td>FAM000617</td> <td>creatine kinase</td> </tr> <tr> <td>FAM001337</td> <td>amyloid beta protein precursor</td> </tr> <tr> <td>FAM000274</td> <td>basic fibroblast growth factor (bFGF)</td> </tr> <tr> <td>FAM001108</td> <td>Sl-d mutant allele kit ligand (KL)</td> </tr> <tr> <td>FAM000504</td> <td>anion exchange protein 3</td> </tr> <tr> <td>FAM001239</td> <td>prothrombin</td> </tr> <tr> <td>FAM001360</td> <td>high mobility group proteins HMG1 and HMG2</td> </tr> <tr> <td>FAM000534</td> <td>glutamine synthetase</td> </tr> <tr> <td>FAM001053</td> <td>nucleoside diphosphate kinase</td> </tr> <tr> <td>FAM001390</td> <td>low density lipoprotein receptor LDLR</td> </tr> <tr> <td>FAM001464</td> <td>Cek6 receptor tyrosine kinase</td> </tr> <tr> <td>FAM000801</td> <td>manganese-containing superoxide</td> </tr> <tr> <td>FAM003946</td> <td>Six2 / Six1 mRNA</td> </tr> <tr> <td>FAM001606</td> <td>Ikaros binding protein (Ikaros)</td> </tr> <tr> <td>FAM000350</td> <td>SPARC protein</td> </tr> <tr> <td>FAM001339</td> <td>calcium-binding protein</td> </tr> <tr> <td>FAM000553</td> <td>pyruvate kinase</td> </tr> <tr> <td>FAM000604</td> <td>t complex polypeptide 1 (Tcp-1-a)</td> </tr> <tr> <td>FAM002988</td> <td>mSlo</td> </tr> <tr> <td>FAM001266</td> <td>transcription factor / hepatocyte nuclear factor</td> </tr> <tr> <td>FAM000453</td> <td>terminal deoxynucleotidyltransferase</td> </tr> <tr> <td>FAM001642</td> <td>transformation associated protein p53</td> </tr> <tr> <td>FAM000300A</td> <td>glucose-regulated protein 78 / HSP70 PART A</td> </tr> <tr> <td>FAM000300B</td> <td>glucose-regulated protein 78 / HSP70 PART B</td> </tr> <tr> <td>FAM000492A</td> <td>alpha actin etc</td> </tr> <tr> <td>FAM000492B</td> <td>beta actin etc</td> </tr> <tr> <td>FAM000649</td> <td>Myelin Basic Protein</td> </tr> <tr> <td>FAM000843</td> <td>ribosomal protein S4</td> </tr> <tr> <td>FAM000170</td> <td>atrial natriuretic protein</td> </tr> <tr> <td>FAM000871A</td> <td>tyrosinase</td> </tr> <tr> <td>FAM000871B</td> <td>tyrosinase related protein 1</td> </tr> <tr> <td>FAM001605</td> <td>ZFX put. transcription activator</td> </tr> <tr> <td>FAM001479</td> <td>fibroblast growth factor</td> </tr> <tr> <td>FAM000160</td> <td>pro-opiomelanocortin (POMC)</td> </tr> <tr> <td>FAM001644</td> <td>fibrinogen alpha subunit</td> </tr> <tr> <td>FAM000564</td> <td>SNAP-25</td> </tr> <tr> <td>FAM000266</td> <td>nitric oxide synthase</td> </tr> <tr> <td>FAM004159</td> <td>chondroitin-6 sulfotransferase</td> </tr> <tr> <td>FAM000800</td> <td>Lmp-2 (LMPq) proteasome subunit</td> </tr> <tr> <td>FAM001595</td> <td>factor B</td> </tr> <tr> <td>FAM001134</td> <td>zona pellucida (ZP)</td> </tr> <tr> <td>FAM001632</td> <td>stromelysin-3</td> </tr> <tr> <td>FAM001366</td> <td>steroid receptor (TR2-9)</td> </tr> <tr> <td>FAM000526</td> <td>c-ski protein</td> </tr> <tr> <td>FAM002463</td> <td>thymosin beta 4 peptide</td> </tr> <tr> <td>FAM000904</td> <td>sequence-specific DNA-binding protein (AP-2)</td> </tr> <tr> <td>FAM001465</td> <td>T-cell specific tyrosine kinase (ltk)</td> </tr> <tr> <td>FAM000567</td> <td>triosephosphate isomerase</td> </tr> <tr> <td>FAM006113</td> <td>DNA-dependent RNA polymerase III, large subunit</td> </tr> <tr> <td>FAM001733</td> <td>DNA-dependent RNA polymerase II</td> </tr> </tbody> </table>
Joint embedding of vertebrate brain single-cell RNA-Seq using sequence or structure
<p>Embeddings of single-cell RNA-Seq data from three adult vertebrate brain datasets into Orthogroup feature space or Structural cluster feature space. Orthogroups were generated using OrthoFinder v5.5.0; Structural clusters were assigned by using FoldSeek to cluster AlphaFold-v4 structural predictions.<br> <br> The three datasets used as the basis for these embeddings were:</p> <ul> <li>sample <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM3768152">"Brain8"</a> from the <a href="https://www.frontiersin.org/articles/10.3389/fcell.2021.743421/full">Jiang et al. 2021</a> zebrafish cell atlas (files beginning with GSM3768152)</li> <li>sample <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM2906405">"Brain1"</a> from the <a href="https://www.sciencedirect.com/science/article/pii/S0092867418301168#sec4">Han et al. 2018</a> mouse cell atlas (files beginning with GSM2906405)</li> <li>sample <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM6214268">"Xenopus_brain_COL65"</a> from the <a href="https://www.nature.com/articles/s41467-022-31949-2">Liao et al. 2022</a> Xenopus laevis adult cell atlas (files beginning with GSM6214268)</li> </ul> <p>For each dataset, we also generated a standardized cell type annotation file based on the author's originally provided cell type annotation data. The first column is the cell barcode for that species and the second column is the original study's cell type annotation for that cell.</p> <p>For the Xenopus brain data, we removed around ~18k cells that were not annotated in the original data to simplify data analyses - these are reflected in the files with the "subsampled" suffix. Subsampled versions of the data are also available for the joint embedding space (prefixed with "DrerMmusXlae").</p> <p>For the final datasets used in our analyses, we also provide features x cell matrices as .h5ad files for smaller file sizes and faster loading using Scanpy. </p> <p>For visualizing our UMAP plots of our top200 embedding space, we provide ".tsv" files with a variety of metrics and the x and y positions of each cell in the UMAP. See "DrerMmusXlae_adultbrain_FoldSeek_plotlydata.tsv" and "DrerMmusXlae_adultbrain_OrthoFinder_plotlydata.tsv"</p> <p>These data are part of the Arcadia Science Pub titled <a href="https://doi.org/10.57844/arcadia-vw5e-2670">"Comparing gene expression across species based on protein structure instead of sequence"</a>.</p>
First-order vertebrated mortality due the 2020 wildfires in the Pantanal wetland, Brazil
We conducted ground surveys along line transects to estimate the first-order impact of the 2020 wildfires on vertebrates in the Pantanal wetlands, Brazil. We adopted the distance sampling technique (Burnham et al 1980) to estimate the densities and the number of dead vertebrates in the 39,030 square kilometers affected by fire. We covered 123 transects scattered in the floodplain, up to 72 hours after the fires, mostly within 24 or 48 hours. We recorded the perpendicular distance between each carcass found in the field and the line transect. The carcasses were identified at least at Order level, down to species level when possible. The surveys were conducted from August to November 2020.
Community structure of aquatic vertebrates and macroinvertebrates in relation to hydrologic variables in the Shark River Slough, Everglades National Park, Florida USA: Ongoing since 2012
The purpose of this project is to characterize the community structure of aquatic fauna (macroinvertebrates and fishes) and floating mats (including periphyton) across the heterogenous landscape of the NE Shark River Slough in Everglades NP. The community structure (abundance, biomass, and species composition) of floating mat, emergent vascular plants, fishes, and macroinvertebrates, and the spatial distribution of the latter in relation to hydrology, are among the key performance measures targeted for monitoring. The aim of our study is to provide a baseline characterization of these parameters so that the impact of future management activities (e.g., Modified Water Deliveries and Comprehensive Everglades Restoration Plan) can be assessed. The study area is sampled during the late wet season (September through November) to capture data when all sites are innundated with water and aquatic animal standing stocks are typically at their maximum. We document landscape-scale patterns in the NE Shark River Slough from this comprehensive sampling.
Fig. 8 in A new vertebrate for Europe: the discovery of a range-restricted relict viper in the western Italian Alps
Fig. 8. Currently known extent of occurrence of Vipera walser sp. nov. (in blue) and V. berus (in red) in north western Italy
Fig. 3 in A new vertebrate for Europe: the discovery of a range-restricted relict viper in the western Italian Alps
Fig. 3. Results of the non-metric multidimensional scaling (nMDS, Bray – Curtis with similarity index), for the females (left) and males (right) conducted separately. The following variables were considered: subcaudals, crown scales, apicals, perioculars, parietals and loreals (only on the right side because V. walser show a much higher degree of asymmetry on loreal scales count, compared to V. berus). The analysis was carried out on V. walser and three groups of V. berus having the same number of samples, in order to evaluate intraspecific variability. V. walser are in red. The graphs show V. walser to be well differentiated in respect to the three groups of V. berus, which are mostly overlapping.
Fig. 10 in A new vertebrate for Europe: the discovery of a range-restricted relict viper in the western Italian Alps
Fig. 10. Current and projected (2035; CMIP 5 [IPPC Fifth Assessment]) mean annual rainfall and mean minimum temperatures (° C) for the months May – October within the current range of Vipera walser sp. nov.
ScienceDex guides
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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.