{"data":[{"id":"10.5061/dryad.tx95x6bd0","type":"dois","attributes":{"doi":"10.5061/dryad.tx95x6bd0","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["University of Sheffield"],"name":"Pereira, Lara","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-5184-8587"}]},{"nameType":"Personal","affiliation":["University of Sheffield"],"name":"Bailes, Emily J.","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-6486-7058"}]},{"nameType":"Personal","affiliation":["University of Sheffield"],"name":"Bourne, Noah G.","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0008-7941-8069"}]},{"nameType":"Personal","affiliation":["University of Sheffield"],"name":"Collins, Catherine F.","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0002-5721-9129"}]},{"nameType":"Personal","affiliation":["Royal Botanic Gardens, Kew"],"name":"Mian, Sahr","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-7828-0629"}]},{"nameType":"Personal","affiliation":["Royal Botanic Gardens, Kew"],"name":"Leitch, Ilia J.","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-3837-8186"}]},{"nameType":"Personal","affiliation":["University of York"],"name":"Lichman, Benjamin R.","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-0033-1120"}]},{"nameType":"Personal","affiliation":["University of Sheffield"],"name":"Dunning, Luke T.","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-4776-9568"}]}],"titles":[{"title":"Data from: Horizontal gene transfer fuels metabolic innovation in the grass \u003cem\u003eZuloagaea bulbosa\u003c/em\u003e"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Natural sciences","subjectScheme":"fos"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Horizontal gene transfer","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Grasses","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Evolutionary biology","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[],"dates":[{"date":"2026-06-26T11:29:09Z","dateType":"Created"},{"date":"2026-06-26T11:30:22Z","dateType":"Submitted"},{"date":"2026-07-07T00:00:00Z","dateType":"Issued"},{"date":"2026-07-07T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.64898/2026.06.09.731121","relatedIdentifierType":"DOI"},{"relationType":"IsDerivedFrom","relatedIdentifier":"\n      https://github.com/Sheffield-Plant-Evolutionary-Genomics/Zbulbosa_genome_analyses\n    ","relatedIdentifierType":"URL"},{"relationType":"IsSupplementedBy","relatedIdentifier":"https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1481957","relatedIdentifierType":"URL"}],"relatedItems":[],"sizes":["1912495510 bytes"],"formats":[],"version":"5","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Horizontal gene transfer (HGT) allows the movement of DNA across broad\n evolutionary distances without sexual reproduction. In grasses, HGT is\n widespread and although a few horizontally transferred genes (HTG) are\n adaptive, most are purged over time. Within the adaptive HTG, biosynthetic\n genes encoding enzymes that act together in the same pathway and\n physically co-localise in clusters have been reported multiple times. The\n aims of this study are to test whether HGT is bidirectional in a pair of\n grass species, maize and Zuloagaea bulbosa, and if HTGs are found more\n than expected by chance in biosynthetic genes organised in clusters. To\n achieve this, we firstly generated a phased reference genome for Z.\n bulbosa. Then we identified 56 candidate horizontally transferred genes,\n of which 45% were from Andropogoneae, including two likely to be of maize\n origin. Since transfers from Z. bulbosa to maize were previously\n described, our results show that HGT is bidirectional, although the\n balance might not be even. After predicting all biosynthetic gene clusters\n in the Z. bulbosa genome, we found that HTGs are enriched in biosynthetic\n genes organised in clusters. This correlation between HGT and gene\n clustering is likely to be a consequence of selection due to the immediate\n adaptive benefit a whole pathway can provide. Two of the HTGs from\n Andropogoneae belong to the benzoxazinoid BGC, which previously underwent\n an ancestral transfer from Panicoideae into Pooideae. The dynamism of\n biosynthetic gene clusters, including recurrent horizontal gene transfers,\n contributes to the extraordinary metabolic diversity present in plants."},{"descriptionType":"TechnicalInfo","description":"# Horizontal gene transfer fuels metabolic innovation in the grass\n *Zuloagaea bulbosa* Dataset DOI:\n [10.5061/dryad.tx95x6bd0](https://doi.org/10.5061/dryad.tx95x6bd0) ##\n Description of the data and file structure This repository contains the\n following supplementary data: **Dataset S1.** InterProScan functional\n annotation of *Z. bulbosa* predicted genes. **Dataset S2.**\n Maximum-likelihood phylogenetic trees for all candidate HTGs, Bx and\n strigolactone genes. **Dataset S3.** Biosynthetic gene cluster prediction\n from PlantiSmash. The files included in the dataset are the summary\n spreadsheet containing information about all clusters and summary GenBank\n file that contains the information for all clusters. ### Files and\n variables **Dataset S1** File DatasetS1.tsv consists of one text file that\n includes the functional annotations for all the predicted genes in the\n *Zuloagaea bulbosa* genome. This information facilitates a rapid\n interpretation of functional consequences of genomic changes. This dataset\n may be used by other scientists to predict phenotypic effects of genetic\n variants, or to expland genomic analyses such as gene family expansions\n and contractions. **Dataset S2** We used a phylogenetic approach to\n identify horizontal gene transfer in *Z. bulbosa*. The gene trees used to\n evaluate gene tree - species tree discordances, as well as larger\n phylogenies with expanded sampling for a small subset of the horizontally\n transferred genes, are included here. Dataset S2 is contained in a\n compressed file DatasetS2.zip. It includes three subfolders (HTGs, Bx, and\n Strigolactones). Each subfolder contains the gene trees produced in our\n work, in newick format. All the details about the methods used to build\n these trees can be found in the associated pre-print article. **Dataset\n S3** Some of the horizontally transferred genes were located in\n biosynthetic gene clusters, so we predicted all the clusters in *Z.\n bulbosa* genome. Dataset S3 is contained in a compressed file\n DatasetS3.zip and contains all the information about the biosynthetic gene\n clusters in *Zuloagaea bulbosa* genome predicted by the software\n PlantiSmash 2.0. This compressed folder contains two files: a spreadsheet\n with information about all clusters, and a GenBank file with all the\n details for each cluster, including sequences. This file can be used for\n visualisation and downstream analyses. ## Code/software All the code and\n software used to generate these datasets is detailed in the associated\n pre-print article and in the corresponding GitHub repository."}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"Natural Environment Research Council","funderIdentifier":"https://ror.org/02b5d8509","awardNumber":"NE/V000012/1"},{"funderIdentifierType":"ROR","funderName":"Natural Environment Research Council","funderIdentifier":"https://ror.org/02b5d8509","awardNumber":"UKRI2660"},{"funderIdentifierType":"ROR","funderName":"Natural Environment Research Council","funderIdentifier":"https://ror.org/02b5d8509","awardNumber":"NE/T011025/1"},{"funderIdentifierType":"ROR","funderName":"NERC Environmental Omics Facility","funderIdentifier":"https://ror.org/036g3b009","awardNumber":"NEOF1545"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.tx95x6bd0","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-07-07T17:54:09Z","registered":"2026-07-07T17:54:10Z","published":null,"updated":"2026-07-07T17:54:10Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.h44j0zq04","type":"dois","attributes":{"doi":"10.5061/dryad.h44j0zq04","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Liverpool John Moores University"],"name":"Hinchcliffe, Danielle","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-5204-5161"}]}],"titles":[{"title":"Data from: Fifty years of bird ringing reveal opposing seasonal responses of migration timing to temperature and rainfall on Hilbre Island"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Earth and related environmental sciences","subjectScheme":"fos"},{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Natural sciences","subjectScheme":"fos"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Ornithology","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Climate change","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Behavioral ecology","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[],"dates":[{"date":"2025-09-17T05:19:44Z","dateType":"Created"},{"date":"2026-05-03T12:49:00Z","dateType":"Submitted"},{"date":"2026-07-07T00:00:00Z","dateType":"Issued"},{"date":"2026-07-07T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1002/ece3.73377","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["237890 bytes"],"formats":[],"version":"6","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Understanding how populations respond to environmental variability is\n crucial for interpreting long-term monitoring data, especially for\n migratory species with complex life cycles that are susceptible to\n changing weather conditions and may show variation in stopover behaviour.\n Environmental pressures can influence the timing and duration of migratory\n stopovers, which may affect local species composition and capture patterns\n at observatories. This study analyses a 50-year bird ringing dataset from\n Hilbre Bird Observatory, a long-term monitoring site on England’s western\n coast that offers valuable insights into migratory bird patterns. Findings\n show that Hilbre Island has become warmer and wetter over time, and that\n the interaction of temperature and rainfall influences observed migration\n timing. While warmer conditions at the observatory coincide with earlier\n arrivals, concurrent increases in rainfall can delay arrivals,\n illustrating how local weather conditions affect patterns of capture.\n Seasonal ecological differences in residency were observed: in spring,\n warmer and wetter conditions result in shorter stopovers, whereas in\n autumn, the same conditions lead to longer stays. Analyses of abundance\n revealed species-specific variation, but no significant relationships were\n detected between local weather variables and overall capture numbers.\n Overall, the study highlights the complex, and sometimes opposing, effects\n of temperature and precipitation on bird migration and residency at Hilbre\n and emphasizes the value of long-term ringing data for detecting these\n patterns. The findings underscore the importance of considering\n weather-related sampling limitations when interpreting observatory data\n and provide a foundation for further studies on how local conditions\n influence observed migratory behaviour."},{"descriptionType":"TechnicalInfo","description":"# Data from: Fifty years of bird ringing reveal opposing seasonal\n responses of migration timing to temperature and rainfall on Hilbre Island\n Dataset DOI:\n [10.5061/dryad.h44j0zq04](https://doi.org/10.5061/dryad.h44j0zq04) ##\n Description of the data and file structure Data was collected by the\n Hilbre Bird Observatory as per protocols set by the British Trust\n Ornithology. ### Files and variables * abundance_data.xlsx - dataset used\n for abundance modelling * autumnphen_data.xlsx - dataset used for autumn\n phenology modelling * climate_data.xlsx - dataset used for climate\n modelling * diversity_data.xlsx - dataset used for species modelling *\n springphen_data.xlsx - dataset used for spring phenology modelling\n **Description:**  ##### Variables * row_no: row number * ring_no: ring ID\n * record_type: ringing record type * scheme: affiliated ringing scheme  *\n species: bird species * age: bird age * sex: bird sex * wing_length: wing\n length (cm) * weight: bird weight (g) * processor: identity of person\n measuring bird * bill: bill length (cm) * fat: fat score (1-5) *\n visit_date: date of capture * visit_year: year of capture * visit_month:\n month of capture * visit_day: day of capture * visit_date_orig: original\n date of capture * YRAV_Snow_LOC_GRP: annual snowfall for local area (mm) *\n YRAV_Rain_LOC_GRP: annual rainfall for local area (mm) *\n YRAV_Temp_LOC_GRP: annual temperature for local area (°C) *\n MTH_Rain_LOC_GRP: monthly rainfall for local area (mm) * MTH_Temp_LOC_GRP:\n monthly temperature for local area (°C) * YRAV_Snow_UK: annual snowfall\n for UK (mm) * YRAV_Rain_UK: annual rainfall for UK (mm) * YRAV_Temp_UK:\n annual temperature for UK (°C) * MTH_Rain_UK: monthly rainfall for UK (mm)\n * MTH_Temp_UK: monthly temperature for UK (°C) ## Code/software All data\n was analysed using a Bayesian approach with all models fitted and\n estimated using Hamiltonian Monte Carlo methods and Stan software (Stan\n Development Team 2021) with the *brms* package (Bürkner 2018) within R (R\n Core Team 2022). ## Access information Data was provided by the Warden of\n Hilbre Bird Observatory."}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.h44j0zq04","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":3,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-07-07T17:37:08Z","registered":"2026-07-07T17:37:09Z","published":null,"updated":"2026-07-07T17:37:09Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.j6q573npr","type":"dois","attributes":{"doi":"10.5061/dryad.j6q573npr","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["University of California, Davis"],"name":"Rodriguez-Caton, Milagros","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-9608-0150"}]},{"nameType":"Personal","affiliation":["Universidad Nacional"],"name":"Rojas-Gonzales, Andres","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Universidad Nacional"],"name":"Campos-Valverde, Rebeca","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of California, Los Angeles"],"name":"Bigwood, Julia","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of California, Los Angeles"],"name":"Dierick, Diego","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of New Brunswick"],"name":"Wong, Christopher Y.S.","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of California, Los Angeles"],"name":"Seibt, Ulrike","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of California, Los Angeles"],"name":"Stutz, Jochen","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of California, Davis"],"name":"Magney, Troy","nameIdentifiers":[]}],"titles":[{"title":"CO2 response curves and leaf reflectance data for La Selva Biological Station, Costa Rica"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Earth and related environmental sciences","subjectScheme":"fos"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Tropical forests","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"gas exchange"},{"subject":"hyperspectral reflectance"}],"contributors":[],"dates":[{"date":"2024-10-15T16:22:52Z","dateType":"Created"},{"date":"2026-03-03T12:31:26Z","dateType":"Submitted"},{"date":"2026-07-07T00:00:00Z","dateType":"Issued"},{"date":"2026-07-07T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["1569531 bytes"],"formats":[],"version":"8","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"This dataset includes data collected in a broadleaf wet tropical forest\n located La Selva Biological Station in the Costa Rican Caribbean region\n (10.432S, -84.0106W). The site is an old-growth forest equipped with an\n Eddy Covariance Tower. The dataset contains leaf-level gas exchange and\n hyperspectral reflectance data collected between May 2023 and February\n 2024 at La Selva."},{"descriptionType":"Methods","description":"Photosynthetic carbon dioxide (CO2) response curves and\n hyperspectral reflectance were measured in sun-exposed leaves from six\n tree species, including five overstory species (Pentaclethra macroloba,\n Virola koschnyi, Sacoglottis trichogyna, Goethalsia meiantha, Virola\n sebifera) and one understory species (Warszewiczia coccinea). Most leaves\n were light adapted but the dataset includes some dark adapted leaves.\n 20-50cm branches were detached from the main tree and recut underwater to\n avoid xylem cavitation and maintain the water column intact. We selected a\n healthy and mature leaf from each branch to run the CO2 response curve.\n CO2 response curves were performed for each leaf using the LI-6800\n portable photosynthesis system (LICOR, Lincoln, NE, USA). For each CO2\n response curve, CO2 concentration varied from 50 to 1400 μmol/mol with a\n reference CO2 of 420 μmol/mol. Leaves were inducted with 1000 or 1500\n μmol/mol irradiances, 30° C temperature, and 65 % relative humidity.\n Observations were initiated when carbon assimilation rate (A) was stable,\n with values no less than 5 μmol/mol and stomatal conductance values\n greater than or equal to 0.06 mol H2O m-2 s-1, preferably. After\n performing the CO2 response curves for each leaf, we measured leaf\n reflectance using a hyperspectral spectroradiometer (SVC, HR-1024i,\n Spectra Vista Corporation, Poughkeepsie, NY, USA) equipped with a leaf\n clip LC-RP-PRO. The specifications on this spectroradiometer were a 25°\n FOV optic fiber with an autointegration time of over 3-5 seconds to\n maximize signal. We run R packages spectrolab and pavo to splice sensor\n overlap regions and obtain 1 nanometer (nm) resolution reflectance between\n 350 and 2500 nm, respectively. A white reference panel attached to the\n leaf clip was used before each reflectance measurement. We conducted three\n spectral measurements per leaf and the values reported here correspond to\n leaf averages. Spectral measurements were performed on intact tissue on\n the adaxial part of the leaves."},{"descriptionType":"TechnicalInfo","description":"# CO2 response curves and leaf reflectance data for La Selva Biological\n Station, Costa Rica Dataset DOI:\n [10.5061/dryad.j6q573npr](https://doi.org/10.5061/dryad.j6q573npr) ##\n Description of the data and file structure # CO2 response curves and leaf\n reflectance at La Selva Biological Station, Costa Rica\n [https://doi.org/10.5061/dryad.j6q573npr](https://doi.org/10.5061/dryad.j6q573npr) ## Description of the data and file structure This folder contains THREE files: ### FILE #1- \"RodriguezCaton_2024_SampleDetails.csv\" This file has 7 columns: **SampleID**: The SampleID corresponds to the metadata associated with each measurement: date, replicate, species code, leaf number, light/dark adapted. For example the code \"20230513.1.VISE.1.D\" is composed by: '20230513': Date. Year, month and day in the format YYYYMMDD '4': Number of replicate of the day or 'hour' (note this is not the actual hour of the day). In this example '4' corresponds to the fourth replicate of the day for this species 'VISE': Species code '1': Leaf number. For each replicate 3 leaves were sampled. **Date**: Year, month, and day in the format YYYYMMDD **Site**: corresponds to the site code, in this study, LSVA refers to the site La Selva, Costa Rica **Species_Name**: Scientific Name of the species **Species_Code**: The code used in this study to identify each species **Light_or_Dark_adapted**: Specifies whether the leaf was adapted to Light = 'L' or Dark = 'D' ### FILE #2- \"RodriguezCaton_2024_Aci_data.csv\" This file contains the A/Ci response curves and has 11 columns: **SampleID:** Same as FILE#1 **obs:** Observation number **A:** μmol m-2 s- Assimilation rate **Ci:** μmol mol-1 Intercellular CO2 **CO2_s:** μmol mol-1 Sample cell CO2 concentration **CO2_r:** μmol mol-1 Reference cell CO2 concentration **gsw:** mol m-2 s-1 Stomatal conductance to water vapor **Pa:** kPa Atmospheric pressure **Qin:** μmol m-2 s-1 PPFD incident on the leaf **RHcham:** % Relative humidity in the chamber **Tleaf:** °C Leaf temperature ### FILE #3- \"RodriguezCaton_2024_Reflectance.csv\" This file contains the leaf reflectance data and has 2152 columns: Column 1 \"SampleID\" Same as FILE#1 Column 2 to 2152 reflectance measurements from 350 nm to 2500 nm (1 nm resolution)."}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"National Aeronautics and Space Administration","funderIdentifier":"https://ror.org/027ka1x80","awardNumber":"80NSSC21K1713"},{"funderIdentifierType":"ROR","funderName":"Organization For Tropical Studies","funderIdentifier":"https://ror.org/02kk9ec90","awardNumber":"521/571"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.j6q573npr","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":2,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-07-07T17:29:59Z","registered":"2026-07-07T17:29:59Z","published":null,"updated":"2026-07-07T17:29:59Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.95x69p91r","type":"dois","attributes":{"doi":"10.5061/dryad.95x69p91r","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["University of Oslo"],"name":"Thaureau, Marion","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0000-8943-6502"}]},{"nameType":"Personal","affiliation":["University of Oslo"],"name":"Voje, Kjetil Lysne","nameIdentifiers":[]}],"titles":[{"title":"Modeling within-lineage evolution as a consequence of changes in the adaptive landscape across short and long timescales: Open source data"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"subject":"timeseries"},{"subject":"Ornstein-Uhlenbeck process"},{"subject":"adaptation-inertia framework"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Macroevolution","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Microevolution","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Biological sciences","subjectScheme":"fos"}],"contributors":[],"dates":[{"date":"2026-06-29T10:02:16Z","dateType":"Created"},{"date":"2026-06-29T10:02:17Z","dateType":"Submitted"},{"date":"2026-07-07T00:00:00Z","dateType":"Issued"},{"date":"2026-07-07T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["5266361 bytes"],"formats":[],"version":"5","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"The adaptive landscape has been suggested as a potential conceptual bridge\n between phenotypic evolution on generational and macroevolutionary\n timescales. However, this potential remains largely untapped due to a\n limited understanding of how it changes through time. Here, we assessed\n the dynamics of the adaptive landscape across different time intervals by\n analyzing phenotypic time series using multivariate models of adaptation.\n First, we examined whether a decrease in river water flow affected the\n optimal body mass of a salmon population over a few decades. Second, we\n explored whether temperature changes affected the optimal size of\n coccoliths in a species of coccolithophore across about a hundred thousand\n years in the Cretaceous. Finally, we analyzed the extent to which\n temperature changes affected the optimal shell size in a lineage of\n planktic foraminifera over a few million years during the Miocene. We find\n evidence that evolution induced by changes in the adaptive landscape,\n driven by environmental change, occurred in the salmon and foraminifer\n datasets, but we do not detect a causal link between the investigated\n paleo‑proxies and size changes in the coccolithophore lineage. We argue\n that applying the same modeling framework to datasets covering different\n time spans not only offers a promising way to better connect evolutionary\n processes across timescales but also enables tests of adaptive hypotheses\n about within-lineage evolution."},{"descriptionType":"TechnicalInfo","description":"# Modeling within-lineage evolution as a consequence of changes in the\n adaptive landscape across short and long timescales: Open source data\n **Purpose of the framework:** This framework analyses adaptation in three\n phenotypic time series spanning different ranges on the timescales\n continuum. We test whether, and to what extent, different hypothesized\n environmental factors drive within-lineage phenotypic changes using\n multivariate OU models. The repository contains the phenotypic and\n environmental time series (*Data*); the R scripts with the models testing\n different interactions between environmental and phenotypic time series\n (*Script*); and the results of the analyses (*Results*). **Time series\n analyzed:** First, we examined whether a decrease in river water flow\n affected the optimal body mass of a salmon population over a few decades.\n Second, we explored whether temperature changes affected the optimal size\n of coccoliths in a species of coccolithophore across about a hundred\n thousand years in the Cretaceous. Finally, we analyzed the extent to which\n temperature changes affected the optimal shell size in a lineage of\n planktic foraminifera over a few million years during the Miocene.\n **Results and their significance:** We find evidence that evolution\n induced by changes in the adaptive landscape, driven by environmental\n change, occurred in the salmon and foraminifer datasets, but we do not\n detect a causal link between the investigated paleo‑proxies and size\n changes in the coccolithophore lineage. We argue that applying the same\n modeling framework to datasets covering different time spans not only\n offers a promising way to better connect evolutionary processes across\n timescales but also enables tests of adaptive hypotheses about\n within-lineage evolution. ## Description of the files: The dataset is\n contained within the zipped file \"Thaureau-Voje-2026-data.zip\".\n Folders (*Data*, *Script*, *Results*) contain a subfolder for each\n analyzed dataset: the salmon dataset, the coccolithophore dataset, and the\n foraminifera dataset. ### 1. *Data* This folder contains the phenotypic\n and environmental time series and the evoTs objects. * *Salmon_data* *\n **Atlantic_salmon_size.txt** contains all salmon catch recorded in river\n Eira from 1925 to 2016, the year and exact date of the catch, and the body\n mass of the salmon in grams along with an ID number for each catch. Note\n that some ID numbers and exact dates of catch are not available,\n especially for the oldest data. * **Discharge.txt** is the recorded water\n flow volume in the Eira river from 1931 to 2016. It contains both a yearly\n average and an average from June to September in m³s⁻¹. *\n **salmon_evoTS_obj.RData** contains the multivariate evoTs object made of\n the salmon body mass and river water flow data, their variance, sample\n size, and time for each datapoint. * *Coccolith_data* *\n **Biscutum_TotalcoccolithLength.txt** contains the coccolith length of the\n Cretaceous coccolithophore lineage *B. constans* measured in micrometers\n across 160,000 years. For every 50 datapoints in time, the file also\n contains the variance, the sample size (60), and the age of the sample. *\n **isotopes.txt** contains the geochemistry data measured for the same\n samples from which the coccolith length was measured. The file contains\n oxygen and carbon isotopic values in addition to CaCO3 and TOC values. *\n **coccolith_evoTS_obj.RData** contains the multivariate evoTs object made\n of the coccolith length and isotopic data, their variance, sample size,\n and time for each datapoint. * *Foraminifera_data* *\n **Hodell_and_Vayavananda_1993_Fohsella_length.txt** contains the test\n length of the Miocene lineage *Globorotalia (Fohsella)* measured in\n micrometers across 3 million years. For every 114 datapoints in time, the\n file also contains the variance, the sample size (60), and the age of the\n sample. * **table_d18O.csv** and **table_d13C.csv** contain the isotopic\n values recorded in the same samples where the foraminifera test sizes were\n measured along with the variance, sample size (1; or 2 when averaged over\n two species), and age of the sample. * **foraminifera_evoTS_obj.RData**\n contains the multivariate evoTs object made of the foraminifera test\n length and isotopic data, their variance, sample size, and time for each\n datapoint. ### 2. *Script* This folder contains the R scripts used to run\n the different multivariate OU models. They all test either for independent\n evolution (both the trait and the environment are changing randomly);\n independent adaptation (the trait is evolving toward a fixed optimum); or\n dependent adaptation (the trait is evolving toward a moving optimum, and\n changes in that optimum are driven by the environmental variable. The\n input variables for each model are the evoTs object containing the time\n series, and the matrices **A** and **Σ** (also called matrix **R** in the\n scripts), filled with either 0 or 1, which respectively define the\n absence/presence of causality and/or correlation. Each model is run 100\n times with different starting values to explore the loglikelihood\n landscape. The 100 iterations have sometimes been split into different\n scripts to improve efficiency while running the models. For example, in\n the scripts for model 2a, **salmon_script_model_2a_it1.R** contains\n iterations 1 to 50; and **salmon_script_model_2a_it50.R** contains\n iterations 50 to 100. * *Salmon_scripts* * **salmon_script_model_*.R** are\n the script files for each model run on the salmon dataset. We tested nine\n different models. One set of models allows both variables to follow an\n unbiased random walk (i.e., no deterministic part in the OU process). We\n ran versions without (model 1a) and with correlated random changes (model\n 1b) between the variables (i.e., non-zero off-diagonal elements in the Σ\n matrix (also called the R matrix in the scripts)). A second set of models\n allows the salmon body size to evolve as an OU process, while the water\n flow changes according to a random walk. We ran both versions with absence\n (model 2a) and presence of correlated random changes (model 2b), and a\n version where the water flow did affect the optimum of the salmon body\n size (dependent adaptation-model 4a). Finally, a third set of models\n allows both salmon body size and water flow to vary as OU processes.\n Independent adaptation versions of this last set were run with the absence\n (model 3a) and presence of correlated changes (model 3b), as well as\n dependent adaptation versions where water flow affects the optimum of the\n salmon body mass without (model 5a) and with correlated random changes\n (model 5b). * **salmon_script_modelsp_*.R** and\n **salmon_script_modelsp2_*.R** are versions run a second time, for which\n we set up additional input variables. We define some specific starting\n values obtained from the best model to increase the efficiency of the\n searching algorithm: \\_modelsp\\_ is used when we parametrized the full\n **A** matrix and the diagonal elements of **Σ** matrix; \\_modelsp2\\_ is\n used when we parametrized only the diagonal elements of both **A** matrix\n and **Σ** matrix. * **salmon_script_results_table.R** and\n **salmon_script_results_table_SE.R** contain the scripts used to produce\n the result tables for parameter estimates of the tested model, and their\n standard errors. * *Coccolith_scripts* and *Foraminifera_scripts* *\n **coccolith_script_model_*.R** and **foraminifera_script_model_*.R** are\n the script files for each model, respectively, run on the coccolithophore\n and foraminifera datasets. We tested the same twelve models for both\n datasets. In the first set of models, all three variables (trait, oxygen\n isotope, and carbon isotope) change according to an unbiased random walk\n without (model 1a) and with correlated random stochastic changes (model\n 1b). In the second set of models, the size trait evolves as an OU process\n towards a fixed optimum (independent adaptation), while the environmental\n proxies change according to an unbiased random walk. In this set, we\n investigate both the absence (model 2a) and the presence of correlated\n random changes among all three variables (model 2b). The trait and the two\n environmental variables follow OU processes in the third set of models\n (Figure 4C). Two of the models in the third set describe independent\n adaptation in the trait without (model 3a) and with correlated random\n changes among the three variables (model 3b). Six of the models in the\n third set describe dependent adaptation without and with correlated random\n changes among all variables, where the oxygen isotopes (models 4a and 4b),\n carbon isotopes (models 5a and 5b), or both variables influence the trait\n optimum (models 6a and 6b) * **coccolith_script_model_*_sp2.R** and\n **foraminifera_script_model_*_sp2.R** are versions run a second time, for\n which we set up starting values obtained from the best model, to increase\n efficiency of the searching algorithm. In these versions, we parametrized\n only the diagonal elements of both **A** matrix and **Σ** matrix. *\n **coccolith_script_results_table.R**,\n **coccolith_script_results_table_SE.R**,\n **foraminifera_script_results_table.R**, and\n **foraminifera_script_results_table_SE.R** contain the scripts used to\n produce the result tables for parameter estimates of the tested model, and\n their standard errors. ### 3. *Results* This folder contains the results\n for each model and tables summarizing the best parameter estimates with\n their standard errors. The output variables include the loglikelihood and\n AICc, which allow us to compare the goodness of the model fit. We also\n obtain different parameter estimates characterizing each time series\n included in the model: z is the ancestral value, and θ is the primary\n optimum (for two time series such as the salmon body mass and the river\n waterflow, we get two z and two θ). Finally, we get output for the\n matrices **A** and **Σ** (dimensions corresponding to the number of time\n series in the model), which characterize the strength of causality and the\n strength of correlation between each pair of time series included in the\n modeling framework. A **A** matrix filled with 0 indicates an absence of\n causality and no dependent adaptation. A **Σ** filled with 0 indicates an\n absence of correlation. * *Salmon_results* * **salmon_run_model_*.txt**\n are the output text files, printed while the models were running. The time\n of convergence for each iteration and output for the best iteration are\n printed. This file is available for each model. *\n **salmon_result_table.csv** and **salmon_result_table_SE.csv** are tables\n respectively giving the best parameter estimates and their standard errors\n for each model. * *S_Rdatafiles* \u0026gt; **salmon_allruns_model_*.RData**\n contains the result output for each iteration. This file is available for\n each model. * *S_Rdatafiles* \u0026gt; **salmon_bestrun_model_*.RData**\n contains the result output for the best iteration. This file is available\n for each model. * *S_Rdatafiles* \u0026gt; **salmon_bestmodel.RData** contains\n a duplicate of the best iteration for the best model output. *\n *Coccolith_results* and *Foraminifera_results* Results for the\n coccolithophore and foraminifera datasets are presented in the same way as\n results for the salmon dataset. The RData files are respectively called\n *C_Rdatafiles* and *F_Rdatafiles*. ### 4. *Supplementary_Material* *\n *Simulation_temporal_trend* * **Simulation_temporal_trend.R** is the\n script simulating two independent time series sharing a temporal trend and\n then fitting different models (independent evolution, independent\n adaptation, dependent adaptation) to the simulated data (100 iterations).\n * **Results_Simulation_temporal_trend.RData** is the result of the\n simulation saved in an R object. *\n **Results_Simulation_temporal_trend.out** is the printed output of the\n results. This folder includes a simulation demonstrating that the\n multivariate OU framework does not mistake a shared temporal trend for\n evidence of a causal relationship between variables. *\n *Sensitivity_analyses* * *Salmon_SA* * *Data* \u0026gt;\n **salmon_evoTS_obj.RData** is the evoTs object built with the salmon body\n mass and Eira river water flow time series (value, variance, sample size,\n year). * *Data* \u0026gt; **salmon_bestmodel.RData** is the best iteration for\n the best model output for the salmon dataset. * *Scripts* \u0026gt;\n **Simulation_sensitivity_salmon_it\\*.R** is the script simulating time\n series with the output of the best model for the salmon dataset. It then\n fits models of independent evolution, independent adaptation, and\n dependent adaptation to the simulated data. * *Results* \u0026gt;\n *salmon_sensitivity_analysis_it\\*.RData* is the result for each iteration,\n saved as an R object. The 100 iterations are split into 4 different\n scripts. * *Results* \u0026gt; *result_salmon_sensitivity_it\\*.out* is the\n printed output for each run. * *Foraminifera_SA* * Results for the\n sensitivity analyses for the timeseries foraminifera test length - oxygen\n isotopic ratio are presented in the same way as results for the\n sensitivity analysis on the salmon dataset. This folder contains the\n results for model recovery analyses we ran for the two cases where\n dependent adaptation was supported (salmon body mass – river water flow;\n foraminifera body length – oxygen isotope). ## Sharing/Access information\n The data, scripts, and results are also available in a GitHub repository:\n *\n \\[[https://github.com/Marion-Thaureau/Thaureau-Voje-2026](https://github.com/Marion-Thaureau/Thaureau-Voje-2026)] For more information on how evoTS works: * \\[[https://github.com/Marion-Thaureau/Thaureau-Voje-2026](https://github.com/Marion-Thaureau/Thaureau-Voje-2026)] Time series data were obtained from published studies: * Salmon dataset: Jensen et al. (2022), [[https://doi.org/10.1073/pnas.2207634119](https://doi.org/10.1073/pnas.2207634119)] * Coccolith dataset: Bornemann and Mutterlose (2006), [[https://doi.org/10.1016/j.geobios.2005.05.005](https://doi.org/10.1016/j.geobios.2005.05.005)] * Isotopic data for the coccolith dataset: Bornemann et al. (2005), [[https://doi.org/10.1144/0016764903-171](https://doi.org/10.1144/0016764903-171)] * Foraminifera dataset: Hodell and Vayavananda (1993), [[https://doi.org/10.1016/03778398(93)90019-T](https://doi.org/10.1016/03778398\\(93\\)90019-T)] The time series can also be downloaded directly as an evoTs object from the Phenotypic Evolution Time Series database: * \\[[https://pets.nhm.uio.no/PETS/](https://pets.nhm.uio.no/PETS/)] ## Code/Software All scripts were run using R version 4.2.1 and the package evoTS version 1.0.3. Models were mostly run in parallel loops to optimize the efficiency of the algorithm. The computations were performed on resources provided by Sigma2 - the National Infrastructure for High-Performance Computing and Data Storage in Norway."}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"European Research Council","funderIdentifier":"https://ror.org/0472cxd90","awardTitle":"ERC-STG Starting Grant","awardNumber":"948465"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.95x69p91r","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-07-07T17:13:02Z","registered":"2026-07-07T17:13:03Z","published":null,"updated":"2026-07-07T17:13:03Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.3ffbg79zn","type":"dois","attributes":{"doi":"10.5061/dryad.3ffbg79zn","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["The Ohio State University"],"name":"Zadrozny, Joseph","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-1309-6545"}]},{"nameType":"Personal","affiliation":["Colorado State University"],"name":"Crans, Debbie","nameIdentifiers":[]}],"titles":[{"title":"Data from: Ligand-to-metal charge transfer control of \u003csup\u003e51\u003c/sup\u003eV NMR thermal sensitivity"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Chemical sciences","subjectScheme":"fos"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Vanadium","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Nuclear magnetic resonance","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Inorganic chemistry","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[],"dates":[{"date":"2026-07-02T01:47:02Z","dateType":"Created"},{"date":"2026-07-02T01:47:06Z","dateType":"Submitted"},{"date":"2026-07-07T00:00:00Z","dateType":"Issued"},{"date":"2026-07-07T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1039/d6cc02350a","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["152685447 bytes"],"formats":[],"version":"3","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Understanding thermal sensitivity of magnetic nuclei is an important step\n toward noninvasive thermometers in magnetic resonance imaging\n applications. This data set is associated with the exploration of a\n specific nucleus, 51V, and its associated temperature dependent\n spectroscopic properties in a series of four V(V)\n complexes: [VO(3-OEtHshed)(tbad)] (1, tbad =\n 5-(adamantan-1-yl)-3-(tert-butyl)benzene-1,2-diol, 3-OEtHshed =\n (E)-2-ethoxy-6-(((2-((2-hydroxyethyl)amino)ethyl)imino)methyl)phenol),\n [VO(3-OEtHshed)(cat)] (2, cat = catechol), [VO(3-OEtHshed)(2OHP)] (3, 2OHP\n = 2-(hydroxymethyl)phenol) and [VO2(3-OEtHshed)] (4). These data\n demonstrate an increasing thermal sensitivity for the 51V nuclear magnetic\n resonance signal when the supporting ligand enables a low-energy\n ligand-to-metal-charge-transfer transition. This is the first\n demonstration of any sort of design strategy to increase the thermal\n sensitivity of the 51V nucleus."},{"descriptionType":"Methods","description":"Methods for analysis are described in detail in the manuscript\n and accompanying supplementary information available from the Royal\n Society of Chemistry at: http://dx.doi.org/10.1039/D6CC02350A Citation for manuscript: A. C. Bates, J. V. Grundy, J. R. Stapf, Ö. Üngör, D. C. Crans, J. M. Zadrozny; Ligand-to-Metal Charge Transfer Control of 51V NMR Thermal Sensitivity \u003cem\u003eChemical Communications\u003c/em\u003e, 2026, DOI: 10.1039/D6CC02350A"},{"descriptionType":"TechnicalInfo","description":"# Data from: Ligand-to-metal charge transfer control of ^51^V NMR thermal\n sensitivity Dataset DOI:\n [10.5061/dryad.3ffbg79zn](https://doi.org/10.5061/dryad.3ffbg79zn) ##\n Description of the data and file structure All contained data are either\n the raw data directly from the instruments used to acquire the data, or\n aggregated collections of the data in a format sufficient for plotting and\n analysis in external programs. The data that are attached are compressed,\n zip folders of the raw data and other data used to make figures for the\n manuscript. Unzip these folders to access the raw data. All contained data\n are either the raw data directly from the instruments used to acquire the\n data, or aggregated collections of the data in a format sufficient for\n plotting and analysis in external programs. Most of the data was processed\n using Excel and MestreNova, with OriginPro allowing for final production\n of manuscript figures. ### Files and variables ### File:\n UV-Vis_Spectra.zip This compressed folder has five different files in it.\n The UV-Vis summary Excel file \"UV-Vis summary.xlsx\" contains the\n final tabulated electronic absorption spectroscopy data for compounds 1-4\n from the main manuscript. The data is saved as an Excel file. Data are\n organized in columns: wavelength (nm), absorbance (A), and molar\n absorptivity (epsilon) for each of the complexes, respectively. An\n additional column that converts the wavelength to wavenumbers is provided\n for convenience. The main manuscript contains the plot of epsilon versus\n wavenumber as Figure 3. See main manuscript for measurement conditions,\n used instrument, and other relevant experimental points. The other four\n files, labeled as \"compound name 0.1mM.csv\" are the raw\n instrumental outputs saved in comma-delimited files that can be opened via\n any text editor and Excel. These data were all collected at 0.1 mM\n concentration. These .csv files have all experimental data. Briefly, the\n block of text under \"summary\" contains instrumental summary\n information (spectrometer, model, software version, sample name);\n \"parameter\" has explicit instrumental collection conditions,\n e.g., the wavelength window, the slit width, the scanning speed; and below\n that are the actual data in two columns, one of wavelength, and one of\n absorbance. ### **File: Low-field_NMR.zip** This compressed file contains\n two folders that constitute the worked up (\"processed\") and raw\n (\"unprocessed\") low-field NMR spectra. In the unprocessed data\n folder, there are four subfolders labeled with the compound #s from the\n manuscript. Dragging one of these folders into MestreNova (see NMR program\n details below) will immediately open the results of the experiment. There\n are subfolders with instrumental parameters and a .jdx file, which is a\n standardized spectral formula (see NMR notes below). The processed data\n folder contains the worked-up data for each compound. They are saved as\n tab-delimited .csv files, each with two columns, ppm on the left and\n spectral intensity on the right. These can be opened in any graphing\n program. The folder also contains all of them collected into a single\n MNova file as well, which can be opened with MestreNova.  ### **File:\n RT_51V_NMR_spectra.zip** This compressed data set has the room-temperature\n ^51^V NMR spectra for complexes 1-4, which are depicted in Figure 3 in the\n main manuscript. This folder contains ten different files. There are four\n tab-delimited .csv files, one for each complex, which are the xy\n coordinates for the NMR spectra as depicted in Figure 3. These data are\n separated into two columns, left is chemical shift in ppm and the right\n one is the 51V NMR signal intensity. These data can be opened with any\n standard text edit/spreadsheet software. There are also five folders\n entitled, e.g., \"Compound 1 25 dC\". These folders contain the\n raw instrumental output from the NMR for each of the four compounds, all\n collected at 25 degrees Celsius. These folders can be dragged, in their\n entirety, into the program MestreNova (see notes below) and it will give\n an initial process of the corresponding NMR spectrum. The final file in\n this folder is \"All compounds in MeCN at 25dC.mnova\". This is\n the aggregate data set from the room temperature ^51^V NMR spectra, all\n collected in acetonitrile (MeCN) in a format that can be opened by\n MestreNova by simply dragging the file into the opened program window (see\n notes below about MestreNova and NMR processing).  ### File: 2D_NMRs.zip\n This compressed folder contains all of the 2-dimensional NMR spectra used\n to assign structure for complexes 1-4. In this folder are four compressed\n folders that contain relevant NMR spectra for the labeled compound. The\n folder for compound 1 contains the one-dimensional proton NMR spectra,\n labeled as 1H and 1H D2O spike for this complex. The files including COSY\n and NOESY in this folder correspond to the 2D spectra depicted in the\n manuscript SI file. The folder for compound 2 contains the one-dimensional\n 1H NMR spectrum (labeled as \"1H\") and the two-dimensional\n spectra are labeled with \"COSY\" and \"NOESY\". The\n folder for compound 3 contains the one-dimensional proton (\"1H\")\n and COSY and NOESY spectra. Compound 4 only contains the COSY and NOESY 1H\n spectra. For all folders, data is supplied as folders that can be opened\n with MestreNova (or other appropriate NMR software, see below) or the\n MestreNova files themselves. ### File: VT_NMR_data.zip The compressed\n folder with this data contains variable-temperature NMR spectra for all\n complexes and tabulated variable-temperature NMR linewidths discussed in\n the manuscript. All temperatures are in degrees C. There are four\n subfolders associated with this data set, which are labeled according to\n the complexes (1-4) which they correspond to. Within these subfolders,\n there are a set of subfolders labeled with the name of the complex, e.g.,\n \"VO(3OEtHSHED)(TBAD)\" for complex 1 and then a number, ranging\n from 25 to 50. These folders are the solution phase NMR spectra for the\n complexes, and the number corresponds to the temperature of the\n experiment. Like with other NMR spectra collected here, these folders can\n be dragged into the MNova (or other) software for analysis. We have also\n collected the worked up variable temperature data for the compound into a\n single MNova file in each of these folders. Finally, there is an Excel\n spreadsheet that collects the temperature dependence of peak positions and\n their linewidths. This spreadsheet can be opened in Excel or other\n spreadsheet software. The data are organized in this spreadsheet by\n compound (1 to 4, from left to right). The solvent of measurement is\n listed under the name of the compound. The top rows of variable\n temperature data contain the chemical shift information. Below that, in\n the data sets marked, e.g., \"Peak 4 (SW)\" we have the signal\n linewidths in Hz and then the same values in ppm even further down. There\n is a \"Dd/DT\" row which is the thermal sensitivity of the\n chemical shift for the variable temperature data sets and then a\n \"Resolution\" row even further down which contains values of\n thermal sensitivity divided by the linewidth. ## Code/software Data that\n is recorded as .csv, .txt, or in other common spreadsheet extensions can\n be opened in Excel or other standard plotting software. The majority of\n our NMR data were analysed and processed using\n [MNova](https://mestrelab.com/main-product/mnova). MestreNova (or MNova)\n is a common NMR-processing software. Many universities have access to it\n by purchasing a license, and analysis of the data can be performed simply\n by dragging the FID files into the program. There are alternatives for\n analysis, e.g.\n [Topspin](https://www.bruker.com/en/products-and-solutions/mr/nmr-software/topspin.html). There are also free software packages available for analysis. MNova has [NMR Lite](https://mestrelab.com/download-nmr-lite), which is a cheaper version and can be used free for an extended period. The program [NUTS](https://www.aiinmr.com/NUTS-Program-Download) is also a free software package that is available from Anasazi Instruments. In some cases, NMR spectra are saved in the .jdx file type, which can be opened with many different spectral software, including the free [jspecview](https://jspecview.sourceforge.net/). See [here](https://opg.optica.org/as/abstract.cfm?uri=as-47-8-1093) for more details. ## Access information Other publicly accessible locations of the data: * Figures and some tabulated data are available in the SI of the manuscript at DOI: 10.1039/D6CC02350A Data was derived from the following sources: * N/A"}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"Division of Chemistry","funderIdentifier":"https://ror.org/01ar8dr59","awardTitle":"CAREER: Robust Coherence and High Sensitivity in Metal-Ion Nuclear-Spin Qubits","awardNumber":"2047325"},{"funderIdentifierType":"ROR","funderName":"Directorate for Mathematical \u0026 Physical Sciences","funderIdentifier":"https://ror.org/029b7h395","awardTitle":"CAREER: Robust Coherence and High Sensitivity in Metal-Ion Nuclear-Spin Qubits","awardNumber":"2419717"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.3ffbg79zn","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-07-07T16:51:35Z","registered":"2026-07-07T16:51:36Z","published":null,"updated":"2026-07-07T16:51:36Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.fttdz096q","type":"dois","attributes":{"doi":"10.5061/dryad.fttdz096q","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["North Carolina State University"],"name":"Hunter, Sierra","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0000-5173-2947"}]},{"nameType":"Personal","affiliation":["North Carolina State University"],"name":"Wang, Mary","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-6290-481X"}]},{"nameType":"Personal","affiliation":["Duke University"],"name":"Thomas, Brittany","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Duke University"],"name":"Filiano, Anthony","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["North Carolina State University"],"name":"Muddiman, David","nameIdentifiers":[]}],"titles":[{"title":"Data from: Spatially resolved lipids in a mouse brain model of globoid cell leukodystrophy via IR-MALDESI MSI and parallel reaction monitoring MSI"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"subject":"globoid cell leukodystrophy"},{"subject":"mass spectrometry imaging"},{"subject":"psychosine"},{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Physical sciences","subjectScheme":"fos"}],"contributors":[],"dates":[{"date":"2026-01-07T20:11:54Z","dateType":"Created"},{"date":"2026-02-04T19:36:27Z","dateType":"Submitted"},{"date":"2026-07-07T00:00:00Z","dateType":"Issued"},{"date":"2026-07-07T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1007/s00216-026-06326-3","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementedBy","relatedIdentifier":"https://metaspace2020.org/project/hunter-2025","relatedIdentifierType":"URL"}],"relatedItems":[],"sizes":["1638690062 bytes"],"formats":[],"version":"5","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Globoid cell leukodystrophy (GLD) is a genetic neurodegenerative disease\n caused by mutations in galactosylceramide β-galactosidase (GALC) that\n results in the accumulation of the cytotoxic sphingolipid, psychosine. As\n psychosine is a biomarker specific to GLD, identifying the most afflicted\n regions of the nervous system can assist in better understanding the\n disease mechanism. Infrared matrix-assisted laser desorption electrospray\n ionization (IR-MALDESI) mass spectrometry imaging (MSI) and parallel\n reaction monitoring were utilized to elucidate the spatial distribution of\n the psychosine analyte and confirm the identity of the ion in a sagittal\n section of a GALC-deficient mouse brain. The presence of the psychosine\n was increased in specific anatomical regions of the brain responsible for\n the bodily functions that are impaired by GLD (cerebellum and brain stem).\n Several electrospray solvent additives (dopants) have enhanced the\n detection of various analyte types but with little success in enhancing\n the detection of sphingolipids. This study investigates the usefulness of\n ammonium fluoride electrospray doping in the positive ion mode for\n lipidomic IR-MALDESI MSI analysis."},{"descriptionType":"TechnicalInfo","description":"# Data from: Spatially resolved lipids in a mouse brain model of globoid\n cell leukodystrophy via IR-MALDESI MSI and parallel reaction monitoring\n MSI Dataset DOI:\n [10.5061/dryad.fttdz096q](https://doi.org/10.5061/dryad.fttdz096q) ##\n Description of the data and file structure Data collected for this\n manuscript. All files were converted to .mzML and then to .imzML format\n for analysis in MSiReader software. Otherwise, .raw data files may be\n opened in Freestyle. These data can also be opened within OpenChrom,\n however, this software was not used in the corresponding study and has not\n been tested by the authors with these data. ### Disclosure David C.\n Muddiman is a part-owner of MSI Software Solutions, LLC, which produces\n MSiReader software used in the analyses of these data. ### Files and\n variables #### File: NH4F_Doped_with_Ice_Matrix.raw **Description:** Image\n of Twitcher mouse brain at 25 μm collected with an applied ice matrix via\n infrared matrix-assisted laser desorption electrospray ionization\n (IR-MALDESI). Electrospray solvent comprised of 50:50 water/acetonitrile\n (v/v) modified with 0.2 % formic acid and 70 μM ammonium fluoride. This\n data was used for evaluating the efficacy of ammonium fluoride doping in\n the positive ion mode of IR-MALDESI analysis. #### File:\n NH4F_Doped_No_Ice_Matrix.raw **Description:** Image of Twitcher mouse\n brain at 25 μm collected via infrared matrix-assisted laser desorption\n electrospray ionization. Electrospray solvent comprised of 50:50\n water/acetonitrile (v/v) modified with 0.2 % formic acid and 70 μM\n ammonium fluoride. This data was used for evaluating the efficacy of\n ammonium fluoride doping in the positive ion mode of IR-MALDESI analysis.\n #### File: Standard_No_Ice_Matrix.raw **Description:** Image of Twitcher\n mouse brain at 25 μm collected via infrared matrix-assisted laser\n desorption electrospray ionization. Electrospray solvent comprised of\n 50:50 water/acetonitrile (v/v) modified with 0.2 % formic acid. This data\n was used for evaluating the efficacy of ammonium fluoride doping in the\n positive ion mode of IR-MALDESI analysis. #### File:\n Twitcher_Replicate_2.raw **Description:** Replicate 2 of imaging Twitcher\n mouse brain at 25 μm to determine relative quantification and spatial\n distribution of the target analyte, psychosine, in a healthy (wild-type)\n and in a diseased (Twitcher) mouse brain model. Spatial resolution 140 x\n 140 μm. Ice matrix applied to sample and electrospray solvent comprised of\n 50:50 water/acetonitrile (v/v) modified with 0.2 % formic acid.  ####\n File: Twitcher_Replicate_1.raw **Description:** Replicate 1 of imaging\n Twitcher mouse brain at 25 μm to determine relative quantification and\n spatial distribution of the target analyte, psychosine, in a healthy\n (wild-type) and in a diseased (Twitcher) mouse brain model. Spatial\n resolution 100 x 100 μm. The same conditions from Twitcher_Replicate_2\n apply to this replicate. #### File: Twitcher_Replicate_3.raw\n **Description:** Replicate 3 of imaging Twitcher mouse brain at 25 μm to\n determine relative quantification and spatial distribution of the target\n analyte, psychosine, in a healthy (wild-type) and in a diseased (Twitcher)\n mouse brain model. Spatial resolution 150 x 150 μm. The same conditions\n from Twitcher_Replicate_2 apply to this replicate. #### File:\n Twitcher_Replicate_4.raw **Description:** Replicate 4 of imaging Twitcher\n mouse brain at 25 μm to determine relative quantification and spatial\n distribution of the target analyte, psychosine, in a healthy (wild-type)\n and in a diseased (Twitcher) mouse brain model. Spatial resolution 150 x\n 150 μm. The same conditions from Twitcher_Replicate_2 apply to this\n replicate. This file also applies as the Standard_Ice_Matrix for\n evaluating the efficacy of ammonium fluoride doping in the positive ion\n mode of infrared matrix-assisted laser desorption electrospray ionization\n mass spectrometry imaging. Collected with an applied ice matrix via\n infrared matrix-assisted laser desorption electrospray ionization.\n Electrospray solvent comprised of 50:50 water/acetonitrile (v/v) modified\n with 0.2 % formic acid.  #### File: Wild-Type_Replicate_2.raw\n **Description:** Replicate 2 of imaging wild-type mouse brain at 25 μm to\n determine relative quantification and spatial distribution of the target\n analyte, psychosine, in a healthy (wild-type) and in a diseased (Twitcher)\n mouse brain model. Spatial resolution 140 x 140 μm. The same conditions\n from Twitcher_Replicate_2 apply to this replicate. #### File:\n Wild-Type_Replicate_3.raw **Description:** Replicate 3 of imaging\n wild-type mouse brain at 25 μm to determine relative quantification and\n spatial distribution of the target analyte, psychosine, in a healthy\n (wild-type) and in a diseased (Twitcher) mouse brain model. Spatial\n resolution 150 x 150 μm. The same conditions from Twitcher_Replicate_2\n apply to this replicate. #### File: Wild-Type_Replicate_1.raw\n **Description:** Replicate 1 of imaging wild-type mouse brain at 25 μm to\n determine relative quantification and spatial distribution of the target\n analyte, psychosine, in a healthy (wild-type) and in a diseased (Twitcher)\n mouse brain model. Spatial resolution 100 x 100 μm. The same conditions\n from Twitcher_Replicate_2 apply to this replicate. ## Code/software\n Freestyle 1.8 SP 2 MSiReader v3.14 ## Access information Other publicly\n accessible locations of the data: *\n METASPACE: [https://metaspace2020.org/project/hunter-2025](https://metaspace2020.org/project/hunter-2025)"}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"National Institute of General Medical Sciences","funderIdentifier":"https://ror.org/04q48ey07","awardTitle":"\n        Development and Application of New Ionization Methods for Biological\n        Mass Spectrometry\n      ","awardNumber":"5R01GM087964-13","awardUri":"https://reporter.nih.gov/project-details/11044192"},{"funderIdentifierType":"ROR","funderName":"Hartwell Foundation","funderIdentifier":"https://ror.org/038cgyc59"},{"funderName":"Larry H. \u0026 Gail Miller Family Foundation"},{"funderName":"Rosenau Family Research Foundation"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.fttdz096q","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":1,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-07-07T16:23:45Z","registered":"2026-07-07T16:23:46Z","published":null,"updated":"2026-07-07T16:23:46Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.5mkkwh7k6","type":"dois","attributes":{"doi":"10.5061/dryad.5mkkwh7k6","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Okayama University"],"name":"Takasaki, Ryuji","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-5093-7681"}]},{"nameType":"Personal","affiliation":["The Hokkaido University Museum"],"name":"Kobayashi, Yoshitsugu","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["New Mexico Museum of Natural History and Science"],"name":"Fiorillo, Anthony","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["North Carolina Museum of Natural Sciences"],"name":"Chinzorig, Tsogtbaatar","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Stony Brook University"],"name":"Funston, Gregory","nameIdentifiers":[]}],"titles":[{"title":"Data from: Gastrolith shape as an indicator of digestive function and its implications for dinosaurian digestive strategies"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Earth and related environmental sciences","subjectScheme":"fos"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Dinosaurs","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Digestion","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Animal evolution","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[],"dates":[{"date":"2026-01-20T03:25:31Z","dateType":"Created"},{"date":"2026-07-01T22:01:12Z","dateType":"Submitted"},{"date":"2026-07-07T00:00:00Z","dateType":"Issued"},{"date":"2026-07-07T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsSourceOf","relatedIdentifier":"10.5281/zenodo.21217213","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["1092961 bytes"],"formats":[],"version":"4","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"The evolution of the muscular stomach was a key innovation in the avian\n body plan, enabling high metabolic rates by functionally replacing oral\n processing. However, its deep–time origins within Archosauria have\n remained poorly understood, primarily due to the extreme rarity of\n fossilized stomachs. Here, we demonstrate that gastrolith shape provides a\n robust proxy for digestive function, and by extension, stomach muscularity\n and diet. By assembling a comprehensive dataset from 104 individuals\n across 46 extant archosaur species, we establish a strong correlation\n between gastrolith shape, stomach muscularity, and diet. Applying this\n validated method to the fossil record reveals divergent digestive\n strategies among major dinosaur clades. Herbivorous ornithischians\n retained angular gastroliths, consistent with limited gastric abrasion and\n compensation via oral processing. Sauropods likewise retained angular\n gastroliths, consistent with limited gastric abrasion and digestion\n relying more on long retention times and fermentation. Conversely, the\n muscular stomach, indicated by rounded gastroliths, evolved early within\n Theropoda, appearing at least by the base of Maniraptoriformes. This\n innovation was likely a crucial prerequisite for repeated transitions to\n herbivory in maniraptoriform theropods with reduced dentition, by shifting\n mechanical processing from the jaws to the stomach and reducing reliance\n on heavy jaw adductor musculature."},{"descriptionType":"TechnicalInfo","description":"# Data from: Gastrolith shape as an indicator of digestive function and\n its implications for dinosaurian digestive strategies Dataset DOI:\n [10.5061/dryad.5mkkwh7k6](https://doi.org/10.5061/dryad.5mkkwh7k6) ##\n Description of the data and file structure This dataset supports a study\n that uses the shape of gastroliths (stomach stones) to infer digestive\n function (stomach muscularity) and diet in extant archosaurs (birds and\n crocodylians) and then applies the calibrated model to extinct archosaurs\n (non-avian dinosaurs and Mesozoic birds). Two complementary shape proxies\n are used throughout: * a **quantitative** proxy — three dimensionless\n ImageJ shape descriptors (Circularity, Roundness, Solidity) measured on\n each individual gastrolith, and * a **qualitative** proxy — assignment of\n each gastrolith to one of five visual angularity classes (Angular,\n Sub-angular, Sub-rounded, Rounded, Well-rounded). The files below provide\n the raw per-gastrolith measurements, the specimen-level training data, the\n fossil test data, the phylogenetic framework, the analysis scripts, and\n every results table and supplementary figure referenced in the manuscript.\n ### How the files link together Every specimen has a unique alphanumeric\n **ID** (e.g., `croc_07`, `HoUM_30011`, `IVPP V14287`); the alphabetical\n prefix encodes the holding institution and the number is its\n catalogue/individual code. This ID is the key that links the files: * the\n ID is the **file name** of the corresponding per-gastrolith file in `Data\n S1` (e.g., `croc_07.csv`), * the same ID is the first column of `Data\n S2.csv` (extant specimens) and of `Table S6` (which is `Data S2` plus two\n provenance columns), * fossil IDs in `Data S3.csv` are carried through to\n `Table S5`. Specimen-level values in `Data S2.csv` / `Table S6` are\n summaries of the per-gastrolith rows in the matching `Data S1` file:\n `Area`, `Minor`, `Circularity`, `Roundness` (`Round`), and `Solidity` are\n the **specimen means** of the individual gastroliths, and `Largest` is the\n **maximum** per-gastrolith minor-axis length in that specimen. ###\n Missing-data code Empty cells and `NA` denote data that were not\n available, not applicable, or not measured (e.g., body mass unknown for a\n museum skeleton, dentition unknown for a fragmentary fossil). ### Shared\n definitions and interpretation keys These keys are referenced by the\n per-file variable lists below and are not repeated for each file. **Diet\n categories** (`DietCat`, `Diet`) — Elton Traits scheme of Wilman et al.\n (2014); each extant species is assigned to the category with the highest\n summed diet score: | Label | Meaning | | -------------- |\n ------------------------------------------------------------------------------ | | `VertFishScav` | Vertebrate feeders, including fish-eaters and scavengers | | `PlantSeed` | Plant and seed feeders | | `Invertebrate` | Invertebrate feeders | | `Omnivore` | Omnivores | | `unknown` | Diet not assigned a priori (used for all fossil taxa, whose diet is predicted) | **Angularity classes** (columns `Angular`, `Subangular`/`Sub-angular`, `Subrounded`/`Sub-rounded`, `Rounded`, `Well_rounded`/`Well-rounded`) — visual roundness classes of Best and Gionfriddo (1991). Each cell is the **number of gastroliths** in that specimen scored into the class; the five class counts sum to the specimen's evaluated gastrolith count. Class definitions: | Class | Definition | | ------------ | ------------------------------------------- | | Angular | Sharp and irregular corners | | Sub-angular | Corners slightly rounded, inlets sharp | | Sub-rounded | Corners rounded, inlets more or less smooth | | Rounded | Corners well-rounded, only a few inlets | | Well-rounded | Smoothly rounded, no corners or inlets | **Quantitative shape descriptors** (`Circularity`, `Roundness`/`Round`, `Solidity`; also columns `Circ`, `Round`, `Solid` in `Data S1`) — dimensionless two-dimensional descriptors measured in ImageJ v1.8.0 (Schneider et al. 2012) on gastroliths larger than 0.5 mm in minor axis. All range from 0 to 1: | Descriptor | Formula (ImageJ) | Interpretation | | ----------- | ---------------------------- | --------------------------------------------------------------- | | Circularity | 4π · Area / Perimeter² | 1 = perfect circle; lower = more elongate/irregular outline | | Roundness | 4 · Area / (π · major-axis²) | 1 = circular; lower = more elongate (low aspect ratio) | | Solidity | Area / convex-hull area | 1 = fully convex (no concavities); lower = more concave/angular | **Body size / digestive-function measures** | Variable | Units | Meaning | | ------------- | --------- | ---------------------------------------------------- | | `BM` | grams (g) | Body mass of the individual | | `Ventriculus` | grams (g) | Mass of the ventriculus (fundic stomach / \"gizzard\") | Stomach muscularity, used in the analyses, is the derived quantity `log10(Ventriculus / BM)`; it is computed inside `Data S5.R` and is not stored as a column. ## Files and variables ### Data files #### File: Data_S1.zip **Description:** Raw gastrolith shape data. Unzips to a folder `Data S1/` containing **one CSV file per specimen** (104 files) — a one-to-one match to the 104 specimens (rows) of `Data S2.csv`, with no specimen missing or extra. Each file is named with the specimen ID (e.g., `croc_07.csv`, `HoUM_30011.csv`, `Bird_0002.csv`), so the file name is the key that links it to `Data S2.csv` and `Table S6`. All files share the same six columns (below). Each **row is one individual gastrolith** of that specimen; only gastroliths larger than 0.5 mm in minor axis were measured. The specimen-level values in `Data S2.csv` / `Table S6` are computed from these rows (means of `Area`, `Minor`, `Circ`, `Round`, and `Solid`; and the maximum `Minor` for `Largest`). ##### Variables (columns in each per-specimen CSV) * (first, unnamed column): sequential gastrolith index within the specimen (1, 2, 3, …) * `Area`: two-dimensional projected area of the gastrolith, as measured in ImageJ. The linear scale was set per image, so `Area` (and `Minor`) are **not standardized across specimen files**; only the three dimensionless descriptors below are directly comparable across specimens, which is why the analyses use only Circularity, Roundness, and Solidity. * `Minor`: minor-axis (breadth) length of the fitted ellipse, ImageJ units (see note under `Area`) * `Circ`: Circularity (dimensionless, 0–1; see shared key) * `Round`: Roundness (dimensionless, 0–1; see shared key) * `Solid`: Solidity (dimensionless, 0–1; see shared key) #### File: Data_S2.csv **Description:** Gastrolith shape, size, and diet type of the extant archosaurs (one row per individual specimen). This is the training dataset. `Area`, `Minor`, `Circularity`, `Round`, and `Solidity` are specimen means of the matching `Data S1` file; `Largest` is the maximum minor-axis length in that specimen. ##### Variables * `ID`: specimen/individual identifier (matches the `Data S1` file name) * `Taxa`: species (genus + species; underscores separate words) * `Order`: taxonomic order (e.g., Anseriformes, Charadriiformes, Crocodylia, Palaeognathae) * `Group`: broad group — `Extant bird` or `Crocodylia` * `BM`: body mass, grams (g); empty if not recorded * `Ventriculus`: ventriculus (fundic stomach) mass, grams (g); empty if not recorded * `Gastrolith_count`: number of gastroliths scored into the angularity classes for this specimen (equals the sum of `Angular`…`Well_rounded`). For specimens hosting very large gastrolith masses, ~1,000 randomly selected stones were scored rather than all of them. * `Area`: specimen-mean gastrolith projected area, mm² * `Minor`: specimen-mean gastrolith minor-axis length, mm * `Largest`: minor-axis length of the largest gastrolith in the specimen, mm * `Circularity`: specimen-mean Circularity (0–1; see shared key) * `Round`: specimen-mean Roundness (0–1; see shared key) * `Solidity`: specimen-mean Solidity (0–1; see shared key) * `Angular`: count of gastroliths classed Angular * `Subangular`: count of gastroliths classed Sub-angular * `Subrounded`: count of gastroliths classed Sub-rounded * `Rounded`: count of gastroliths classed Rounded * `Well_rounded`: count of gastroliths classed Well-rounded * `DietCat`: diet category (see shared key) #### File: Data_S3.csv **Description:** Gastrolith shape distribution of the extinct archosaurs (one row per fossil specimen). This is the test dataset whose diets are predicted by the model trained on `Data S2.csv`. ##### Variables * `ID`: fossil specimen identifier (institutional catalogue number) * `Taxa`: genus + species (underscores separate words) * `Group_1`: major clade used for plotting — `Ornithischia`, `Sauropoda`, `Theropoda`, or `Avialae` * `Group_2`: broad grouping — `Dinosauria` or `Fossil bird` * `Gastrolith_count`: number of gastroliths scored into the angularity classes (equals the sum of `Angular`…`Well_rounded`) * `Angular`: count of gastroliths classed Angular * `Subangular`: count of gastroliths classed Sub-angular * `Subrounded`: count of gastroliths classed Sub-rounded * `Rounded`: count of gastroliths classed Rounded * `Well_rounded`: count of gastroliths classed Well-rounded * `Diet`: diet label; `unknown` for all fossils (the quantity the model predicts) * `Teeth`: dentition state of the taxon — `Present`, `Absent`, `Partial`, or `NA` (unknown) * `FAD`: First Appearance Datum (oldest age bound), millions of years ago (Ma); used to time-calibrate the tree * `LAD`: Last Appearance Datum (youngest age bound), millions of years ago (Ma) * `Data acquired from`: how the shape data were obtained — `Direct observation` (specimen examined firsthand) or `Photographs` #### File: Data_S4.nex **Description:** Dinosaur phylogenetic framework used for the ancestral-state reconstruction. A NEXUS-format phylogenetic tree of the fossil taxa in `Data S3.csv`, read and time-scaled (using the `FAD`/`LAD` ages) by `Data S5.R`. #### File: Data_S5.R **Description:** R script that runs the main analyses. It loads `Data S2.csv`, `Data S3.csv`, the per-specimen files in `Data S1/`, and the tree in `Data S4.nex`; performs the linear discriminant analyses (qualitative and quantitative), the subsampling accuracy assessment, the principal component analyses, the standardized major-axis regressions, and the ancestral-state reconstruction; and writes out the results underlying Tables S1–S5 and the figures. See the **Code/software** section for package versions. #### File: Data_S6.R **Description:** R script for the sensitivity (equilibrium) test. For a single specimen file from `Data S1/` (default `Bird_0002.csv`), it repeatedly subsamples increasing numbers of gastroliths and reports the subsample size at which the 95% interval of the specimen mean for Roundness, Solidity, and Circularity settles within a tolerance band of ±tol × (within-specimen SD), for tol = 0.1, 0.2, and 0.5. Output is the table `eq_tbl_metric`. ### Results tables Cells in the `.xlsx` tables carry only cosmetic formatting; see the **Formatting note** below. In every table the first, unlabeled column contains the specimen ID. #### File: TableS1.xlsx **Description:** Result of the linear discriminant analysis on the pruned **qualitative** dataset — per-specimen posterior probabilities of each diet class and the resulting classification (training specimens with ≥35 gastroliths). ##### Variables * (first column): specimen ID * `Taxa`: species * `Invertebrate`: posterior probability of the Invertebrate class (0–1) * `Omnivore`: posterior probability of the Omnivore class (0–1) * `PlantSeed`: posterior probability of the Plant + Seed class (0–1) * `VertFishScav`: posterior probability of the Vertebrate/Fish/Scavenger class (0–1) * `Diet`: observed (known) diet category of the specimen (see shared key) * `Predicted`: diet category predicted by the discriminant model #### File: TableS2.xlsx **Description:** As Table S1, but for the linear discriminant analysis on the pruned **quantitative** dataset (Circularity, Roundness, Solidity). ##### Variables * (first column): specimen ID * `Taxa`: species * `Invertebrate`: posterior probability of the Invertebrate class (0–1) * `Omnivore`: posterior probability of the Omnivore class (0–1) * `PlantSeed`: posterior probability of the Plant + Seed class (0–1) * `VertFishScav`: posterior probability of the Vertebrate/Fish/Scavenger class (0–1) * `Diet`: observed (known) diet category (see shared key) * `Predicted`: diet category predicted by the discriminant model #### File: TableS3.xlsx **Description:** Loadings (coefficients) of each gastrolith shape proxy on the linear discriminant (LD) and principal component (PC) axes. The sheet is split into a **Qualitative** block (rows = the five angularity classes) and a **Quantitative** block (rows = Circularity, Roundness, Solidity). `LD1`–`LD3` are the discriminant coefficients; `PC1`–`PC5` are the PCA rotation loadings (`PC4`/`PC5` are blank for the quantitative block, which has only three inputs). ##### Variables * (first column): shape proxy (angularity class, or Circularity/Roundness/Solidity) * `LD1`, `LD2`, `LD3`: loading on linear discriminant axes 1–3 * `PC1`, `PC2`, `PC3`, `PC4`, `PC5`: loading on principal component axes 1–5 #### File: TableS4.xlsx **Description:** Linear discriminant scores of the extant training specimens from the qualitative and quantitative analyses. The header spans two grouped blocks, `Qualitative` and `Quantitative`, each with LD1–LD3. ##### Variables * (first column): specimen ID * `Taxa`: species * `LD1`, `LD2`, `LD3` (Qualitative): discriminant scores from the qualitative analysis * `LD1`, `LD2`, `LD3` (Quantitative): discriminant scores from the quantitative analysis #### File: TableS5.xlsx **Description:** Predicted linear discriminant and principal component scores of the extinct archosaurs (fossil specimens in `Data S3.csv`), projected onto the axes trained on the extant data. ##### Variables * (first column): fossil specimen ID * `Taxa`: species * `LD1`, `LD2`, `LD3`: predicted linear discriminant scores * `PC1`, `PC2`, `PC3`, `PC4`, `PC5`: predicted principal component scores * `data source`: how the shape data were obtained — `Direct observation` or `Photographs` #### File: TableS6.xlsx **Description:** Summary of the extant archosaur gastrolith sampling — one row per individual specimen. This is `Data S2.csv` with two added provenance columns (`Region`, `Status`) requested during review. See the **Formatting note** below regarding the bold rows. ##### Variables * `ID`: specimen/individual identifier * `Taxa`: species * `Order`: taxonomic order * `Group`: `Extant bird` or `Crocodylia` * `BM`: body mass, grams (g); empty if not recorded * `Ventriculus`: ventriculus (fundic stomach) mass, grams (g); empty if not recorded * `#Gastroliths evaluated`: number of gastroliths scored into the angularity classes (equals the sum of `Angular`…`Well-rounded`; equivalent to `Gastrolith_count` in `Data S2.csv`) * `Area`: specimen-mean gastrolith projected area, mm² * `Minor`: specimen-mean gastrolith minor-axis length, mm * `Largest`: minor-axis length of the largest gastrolith in the specimen, mm * `Circularity`: specimen-mean Circularity (0–1; see shared key) * `Roundness`: specimen-mean Roundness (0–1; see shared key) * `Solidity`: specimen-mean Solidity (0–1; see shared key) * `Angular`: count of gastroliths classed Angular * `Sub-angular`: count of gastroliths classed Sub-angular * `Sub-rounded`: count of gastroliths classed Sub-rounded * `Rounded`: count of gastroliths classed Rounded * `Well-rounded`: count of gastroliths classed Well-rounded * `DietCat`: diet category (see shared key) * `Region`: geographic origin of the sampled animal (e.g., `Hokkaido`, `Tokyo`, `Shizuoka`, `NT, Australia`; `Unknown` if not recorded) * `Status`: living conditions of the sampled animal — `Wild`, `Captive` (from a zoo, aquarium, or crocodile farm), or `Unknown` **Formatting note (re: Excel formatting).** The only formatting in the `.xlsx` tables is **bold text**. In `Table S6`, bold marks the specimens that meet the **≥35-gastrolith threshold** and were therefore used to train the model (these correspond to the rows in Tables S1, S2, and S4). This formatting is **cosmetic and not required for re-analysis**: the identical subset is reproduced by filtering `#Gastroliths evaluated ≥ 35`. No cell value depends on formatting. ### Supplementary figures Figure S1–S3 are image files (`.tif`) illustrating the specimens; they are the authors' original creative works (see **Access information**). #### File: Figure_S1.tif **Description:** Ornithischian gastroliths. A, *Psittacosaurus mongoliensis* (AMNH FARB 6253); *Haya griva* (B, IGM 100/2015; C, IGM 100/3182); and D, *Edmontosaurus* (AMNH FARB 5863). Note that *Psittacosaurus* (A) is publicly exhibited, so the white arrow label on the specimen cannot be removed. #### File: Figure_S2.tif **Description:** Theropod gastroliths. A, large *Sinornithomimus dongi* (uncatalogued) in right lateral view; B, gradient map of gastrolith sizes for the *Sinornithomimus* in A (warm colors = small, cool colors = large); C, sagittal CT image of the gastrolith mass of the small *Sinornithomimus dongi* (uncatalogued); D, small *Sinornithomimus dongi* (uncatalogued) in right lateral view; E, *Deinocheirus mirificus* (MPC-D 100/127); F, *Limusaurus inextricabilis* (IVPP V15297) in left lateral view; G, *Limusaurus inextricabilis* (IVPP V15293) in right lateral view; H, *Tarbosaurus bataar* (MPC-D 552-1) in ventrolateral view; I, schematic drawing of the *Tarbosaurus* gastralia and gastrolith positions; J, *Caudipteryx dongi* (IVPP V12344) in left lateral view; K, *Caudipteryx* sp. (IVPP V12430) in left lateral view. #### File: Figure_S3.tif **Description:** Mesozoic bird gastroliths. *Archaeorhynchus spathula* (A, IVPP V14287; B, IVPP V17075; C, IVPP V17091; D, IVPP V20312); E, *Bellulornis rectusunguis* (IVPP V17970); F, *Changzuiornis ahgmi* (AGB 5840); *Gansus yumenensis* (G, GSGM 05-CM-014; H, GSGM 06-CM-011; I, GSGM 07-CM-001); *Iteravis huchzermeyeri* (J, IVPP V18958; K, IVPP V20133; L, IVPP V20134). ## Code/software All analyses were performed in R 4.4.1 (R Core Team 2020), using the R packages ape 5.8 (Paradis and Schliep 2019), MASS 7.3-60.2 (Venables and Ripley 2013), geiger 2.0.11 (Pennell et al. 2014), and smatr 3.4-8 (Warton et al. 2012); the scripts also load `car`, `phytools`, `paleotree`, `strap`, `dplyr`, `purrr`, `tidyr`, `viridis`, and `modelr`. Shape descriptors were measured in ImageJ v1.8.0 (Schneider et al. 2012). Scripts are provided as `Data S5.R` (main analyses) and `Data S6.R` (sensitivity test). To reproduce the main analyses, unzip `Data S1.zip` and place the resulting `Data S1/` folder together with `Data S2.csv`, `Data S3.csv`, and `Data S4.nex` in the working directory, then run `Data S5.R` (set the working directory at the top of the script). `Data S6.R` reads a single per-specimen file from `Data S1/` (default `Bird_0002.csv`). ## Access information * Tabular data files are released into the public domain under the Creative Commons CC0 1.0 waiver, as required by Dryad. * The supplementary figures (`Figure_S1.tif`, `Figure_S2.tif`, `Figure_S3.tif`) are original creative works of the authors. Consistent with the journal's licensing, they are shared under a CC-BY license (attribution required) rather than the CC0 waiver, as a coordinated Supplemental Information deposit linked to this dataset under \"Related works.\" * Specimen photographs and CT data derive from museum specimens held at the listed institutions (e.g., AMNH, IVPP, IGM, MPC-D, GSGM, AGB for fossils; Hokkaido University collections for the modern birds). Diet categories follow the Elton Traits database (Wilman et al. 2014)."}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"Japan Society for the Promotion of Science","funderIdentifier":"https://ror.org/00hhkn466","awardNumber":"23K13207"},{"funderIdentifierType":"ROR","funderName":"Japan Society for the Promotion of Science","funderIdentifier":"https://ror.org/00hhkn466","awardNumber":"17J06410"},{"funderIdentifierType":"ROR","funderName":"Japan Society for the Promotion of Science","funderIdentifier":"https://ror.org/00hhkn466","awardNumber":"20J01696"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.5mkkwh7k6","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-07-07T11:51:36Z","registered":"2026-07-07T11:51:37Z","published":null,"updated":"2026-07-07T11:51:37Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.brv15dvr6","type":"dois","attributes":{"doi":"10.5061/dryad.brv15dvr6","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["South China Agricultural University"],"name":"Awais, Mian Muhammad","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0009-7957-5547"}]},{"nameType":"Personal","affiliation":["South China Agricultural University"],"name":"Mehmood, Nasir","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["South China Agricultural University"],"name":"Yu, Wensheng","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["South China Agricultural University"],"name":"Sun, Jingchen","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["South China Agricultural University"],"name":"Bian, Haixu","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["South China Agricultural University"],"name":"Yan, Huichao","nameIdentifiers":[]}],"titles":[{"title":"Data from: \u003cem\u003eBmlebocin3\u003c/em\u003e could inhibit \u003cem\u003eBombyx Mori\u003c/em\u003e cytoplasmic polyhedrosis virus in silkworms via the IMD pathway and through the 20E modulations"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Agricultural sciences","subjectScheme":"fos"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Silkworms","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Host-pathogen interactions","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Immunity","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[],"dates":[{"date":"2026-06-21T07:10:36Z","dateType":"Created"},{"date":"2026-06-30T12:35:14Z","dateType":"Submitted"},{"date":"2026-07-07T00:00:00Z","dateType":"Issued"},{"date":"2026-07-07T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["4622682 bytes"],"formats":[],"version":"6","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"This dataset contains the data supporting the manuscript entitled\n \"BmLebocin3 Could Inhibit Bombyx mori Cytoplasmic Polyhedrosis Virus\n in Silkworms via the IMD Pathway and 20E-Mediated Regulation\". The\n dataset includes RT-qPCR data, western blot images, and molecular docking\n results used to generate the main and supplementary figures. These data\n support the investigation of the antiviral role of BmLebocin3 against\n Bombyx mori cytoplasmic polyhedrosis virus (BmCPV), as well as its\n regulation through the IMD signaling pathway and 20-hydroxyecdysone\n (20E)-mediated responses."},{"descriptionType":"TechnicalInfo","description":"# Data from: *Bmlebocin3* could inhibit *Bombyx mori* cytoplasmic\n polyhedrosis virus in silkworms via the IMD pathway and through the 20E\n modulations Dataset DOI:\n ([https://doi.org/10.5061/dryad.brv15dvr6](https://doi.org/10.5061/dryad.brv15dvr6)) ## Description of the data and file structure This dataset contains the original raw data supporting the manuscript entitled: \"Bmlebocin3 Could Inhibit Bombyx mori Cytoplasmic Polyhedrosis Virus in Silkworms via the IMD Pathway and Through the 20E Modulations.\" The study investigates the antiviral function of Bombyx mori Lebocin-3 (BmLEB3) against Bombyx mori cytoplasmic polyhedrosis virus (BmCPV) and explores its regulation through the Immune Deficiency (IMD) signaling pathway and 20-hydroxyecdysone (20E). The dataset contains the complete experimental data supporting the figures presented in the manuscript, including original RT-qPCR Ct values, molecular docking results, and uncropped Western blot images. ### Dataset Organisation The dataset is organised into three folders. #### Folder 1 = RT-qPCR Raw Data **Description** This folder contains ten Microsoft Excel workbooks comprising the original RT-qPCR data generated during this study. ##### Workbooks 1–5: These workbooks contain the original Ct values used to generate the main figures presented in the manuscript. **Workbook1** = Figure 1 complete This workbook contains the original RT-qPCR Ct values used to generate Figure 1 of the manuscript. Worksheet: **Figure 1A** Original Ct values used to generate Figure 1A: Treatment: different body tissues (e.g., midgut, hemolymph, Malpighian tubules, etc.) Control group: fat body tissue Worksheet: **Figure 1B** Original Ct values used to generate Figure 1B. Treatment group: BmCPV-infected midgut tissue Control group: Uninfected midgut tissue (PBS-fed control) Worksheet: **Figure 1C** Original Ct values used to generate Figure 1C. Treatment group: BmCPV-infected BmN cells Control group: PBS-treated BmN cells **Workbook2** = Figure 2 complete This workbook contains the original RT-qPCR Ct values used to generate Figure 2 of the manuscript. Worksheet: **Figure 2A** Ct values used to generate Figure 2A. Treatment groups: BmN cells treated with dsRNA targeting BmLEB3 at different concentrations: 1 µg dsRNA-BmLEB3 3 µg dsRNA-BmLEB3 5 µg dsRNA-BmLEB3 Control group: BmN cells treated with dsRED Worksheet2: **Figure 2B** This worksheet contains the CT values used to generate Figure 2B Treatment group: BmN cells transfected with 5 µg dsRNA targeting BmLEB3 Control group: BmN cells transfected with control dsRED Experimental variable: Different post-transfection time points Worksheet: **Figure 2C** This worksheet contains the CT values used to generate Figure 2C Treatment group: BmN cells transfected with dsRNA targeting BmLEB3 (dsBmLEB3) followed by BmCPV infection Control group: BmN cells transfected with dsRNA-dsRED followed by BmCPV infection Experimental variable: Different post-treatment time points after virus infection **Workbook3** = Figure 3 Complete The dataset includes Ct values used to generate the complete Figure 3 worksheet: **Figure 3B** This worksheet contains the CT values used to generate Figure 3B Treatment group: Cells transfected with pIEX-BmLEB3-V5 overexpression plasmid, followed by BmCPV infection Control group: Cells transfected with empty vector (pIEX), followed by BmCPV infection Objective: To evaluate the effect of BmLEB3 overexpression on viral infection and replication **Workbook**: Figure 4 Complete The dataset includes Ct values used to generate the complete Figure 4 Worksheet: **Figure 4A** The worksheet contains CT values used to generate Figure 4A Treatment groups: BmN cells treated with different concentrations of 20-hydroxyecdysone (20E) Control group: 0.025% DMSO-treated BmN cells Time point: Cells were collected at 24 hours post-treatment Objective: To evaluate the effect of 20E treatment on BmLEB3 in BmN cells. Worksheet: **Figure 4B** The worksheet contains the CT values used to generate Figure 4B Treatment group: BmN cells treated with 4 µM 20E Control group: BmN cells treated with 0.025% DMSO Experimental variable: Cells were collected at different time points after treatment Objective: To evaluate the time-dependent effect of 20E on BmLEB3 in BmN cells Worksheet: **4C** The worksheet contains the CT values used to generate Figure 4C Treatment group: BmN cells treated with 4 µM 20E and infected with BmCPV Control group: BmN cells treated with 0.025% DMSO and infected with BmCPV Experimental variable: Cells were collected at different post-treatment time points Objective: To evaluate the effect of 20E on BmCPV replication through increased BmLEB3 expression. Worksheet: **Figure 4D** The worksheet contains the CT values used to generate Figure 4D Treatment group: Midgut tissues treated with 20-hydroxyecdysone (20E) Dose: 4 µL of 2.4 µg/µL 20E solution Control group: DMSO injected midgut tissues Experimental variable: Samples collected at different time points after treatment Objective: To evaluate the effect of 20E on BmLEB3 expression over time in silkworm midgut tissue Worksheet: **Figure 4E** The worksheet contains the CT values used to generate Figure 4E Treatment group: Midgut tissues treated with 20E (4 µL of 2.4 µg/µL solution) and infected with BmCPV Control group: Midgut tissues treated with DMSO and infected with BmCPV Experimental objective: To evaluate the effect of 20E-induced upregulation of BmLEB3 on BmCPV replication **Workbook** = Figure 5 Complete The dataset includes Ct values used to generate the complete Figure 5 Worksheet: **Figure 5A** The worksheet contains the CT values used to generate Figure 5A Treatment group: BmCPV-infected BmN cells Control group: Cells treated with PBS Experimental variable: Samples collected at different time points post-treatment/infection Objective: To evaluate the effect of BmCPV infection on BmPGRP-S1 expression Worksheet: **Figure 5B** The worksheet contains the CT values used to generate Figure 5B Treatment group: Bmn cells infected with BmCPV Control group: BmN cells treated with PBS Experimental variable: Samples collected at different time points post-infection Objective: To evaluate the effect of BmCPV infection on the expression of the BmRelish. Worksheet: **Figure 5C** The worksheet contains the CT values used to generate Figure 5C Treatment group: BmN cells treated with dsRNA targeting BmPGRP-S1 Control group: BmN cells treated with dsRED Experimental variable: Samples collected at different time points after RNAi treatment Objective: To evaluate the effect of BmPGRP-S1 silencing on BmPGRPS1 Worksheet: **Figure 5D** The worksheet contains the CT values used to generate Figure 5D Treatment group: BmN cells treated with dsRNA targeting BmPGRP-S1 Control group: BmN cells treated with dsRED Experimental variable: Samples collected at different time points after RNAi treatment Objective: To evaluate the effect of BmPGRP-S1 silencing on BmRelish Worksheet: **Figure 5E** The worksheet contains the CT values used to generate Figure 5E Treatment group: BmN cells treated with dsRNA targeting BmPGRP-S1 Control group: BmN cells treated with dsRED Experimental variable: Samples collected at different time points after RNAi treatment Objective: To evaluate the effect of BmPGRP-S1 silencing on BmLEB3 Worksheet: **Figure 5F** The worksheet contains the CT values used to generate Figure 5F Treatment group: BmN cells treated with dsRNA targeting BmRelish Control group: BmN cells treated with dsRED Experimental variable: Samples collected at different time points after RNAi treatment Objective: To evaluate the effect of BmRelish silencing on BmRelish Worksheet: **Figure 5G** The worksheet contains the CT values used to generate Figure 5G Treatment group: BmN cells treated with dsRNA targeting BmRelish Control group: BmN cells treated with dsRED Experimental variable: Samples collected at different time points after RNAi treatment Objective: To evaluate the effect of BmRelish silencing on BmLEB3 ##### Workbooks 6–10: These workbooks contain the original CT and fluorescence intensity values used to generate the supplementary figures presented in the manuscript. **Workbook**: Supplementary figure 3 This workbook contains the original values used to generate the supplementary Figure 3. Worksheet = **Supplementary Figure 3** This worksheet contains the original CCK-8 assay values used to analyze the effect of BmLEB3 knockdown on cell viability in BmN cells. Treatment group: BmN cells treated with 5 µg dsRNA targeting BmLEB3 Control group: BmN cells treated with dsRED Experimental variable: Cells were collected at different time points after RNAi treatment Objective: To evaluate the effect of BmLEB3 silencing on cell viability and proliferation **Workbook**: Supplementary Figure 4 Complete The workbook contains CCk-8 values and CT values used to generate the supplementary Figure 4 Worksheet: **Supplementary Figure 4B** This worksheet contains the original CCK-8 assay values used to analyse the effect of different concentrations of 20E on cell viability in BmN cells. Treatment group: BmN cells treated with different concentrations of 20E Control group: BmN cells treated with 0.025% Experimental variable: Cells were collected 24 hours after treatment with 20E Objective: To evaluate the effect of 20 treatments on cell viability and proliferation Worksheet: **Supplementary Figure 4C** The worksheet contains the CT values used to generate the supplementary Figure 4C Treatment group: BmN cells treated with different concentrations of 20E Control group: BmN cells treated with 0.025% DMSO Experimental variable: Cells were collected 24 hours after treatment with 20E Objective: To evaluate the effect of different concentrations of 20E on BmLEB3 expression in BmN cells **Workbook** = Supplementary Figure 5 complete The workbook contains CT values used to generate the supplementary Figure 5 Worksheet: **Supplementary Figure 5A, B, and C** The worksheet contains the CT values used to generate Figures 5A, 5B, and 5C Treatment group: BmN cells treated with 4µM 20E Control group: BmN cells treated with 0.025% DMSO Experimental variable: Cells were collected 24 hours after treatment with 20E Objective: To evaluate the effect of 20 treatments on the expression of different genes (BmBRcZ4, BmECR, and BmETS). **Workbook** = Supplementary Figure 6 The workbook contains CT values used to generate the supplementary Figure 6 Worksheet: **Supplementary Figure 6A,B, and C** The sheet contains CT values used to generate Supplementary Figures 6A, 6B, and 6C. Treatment group: midgut of the silkworms injected with 20E (4 µL of 2.4 µg/µL solution). Control group: midgut of the silkworms injected with DMSO. Experimental variable: Objective: To evaluate the effect of 20 treatments on the expression of different genes (BmBRcZ4, BmECR, and BmETS). **Workbook** = Supplementary Figure7 The workbook contains CT values used to generate the supplementary Figure 7 Worksheet: **Supplementary Figure 7A** The worksheet contains Ct values used to generate Supplementary Figure 7A. Treatment group: BmN cells treated with 4µM 20E Control group: BmN cells treated with 0.025% DMSO Experimental variable: Samples collected at different time points after20E treatment Objective: To evaluate the effect of 20 treatments on the expression of BmPGRPS1. Worksheet: **Supplementary Figure 7B** The worksheet contains Ct values used to generate Supplementary Figure 7B. Treatment group: BmN cells treated with 4µM 20E Control group: BmN cells treated with 0.025% DMSO Experimental variable: Samples collected at different time points after 20E treatment Objective: To evaluate the effect of 20 treatments on the expression of BmRelish. #### Folder 2:Raw Western Blot Images Description The folder includes raw, unprocessed Western blot images (TIFF format) used to prepare figures for the manuscript. These images represent the original experimental data before any cropping or layout adjustments. These worksheets follow the same format as those used for the main figures. Panel descriptions **Panel A** Original uncropped immunoblot used to detect V5-tagged BmLEB3 in BmN cells transfected with pIEX-BmLEB3-V5. Protein expression was examined at different post-transfection time points using an anti-V5 antibody (1:2000 dilution). **Panel B** Original uncropped immunoblot of the empty pIEX vector control collected at the corresponding time points. This panel serves as the negative control for the BmLEB3 overexpression experiment. **Panel C** Original uncropped immunoblot of α-tubulin, used as the loading control for the corresponding samples Purpose These uncropped Western blot images are provided as the original experimental data. File format TIFF (.tif) #### Folder 3: Molecular Docking Data Description The Folder contains raw molecular docking data analysing the interaction between 20-hydroxyecdysone (20E) and BmPGRP-S1 using MOE software, including docking output files and visualisation data. **Figure 6 docking**: File description Docking Pose 1 Three-dimensional visualization of the docked BmPGRP-S1–20E complex viewed from the first orientation. Docking Pose 2 Three-dimensional visualization of the same BmPGRP-S1–20E docking complex viewed from a different angle. Docking Pose 3 Three-dimensional visualization of the same docking complex from an alternative perspective, highlighting the spatial arrangement of the ligand within the predicted binding pocket. Docking Pose 4 Three-dimensional visualization of the same docking complex viewed from another angle, providing a comprehensive representation of the protein–ligand interaction. BmPGRPS1+20E Interaction. moe Original MOE project file containing the complete molecular docking analysis, including the docked complex, docking scores, binding poses, and interaction information between BmPGRP-S1 and 20-hydroxyecdysone (20E). BmPGRPS1+20E Interaction Pose 1.mdb MOE database file containing the docking coordinates and interaction data for the highest-ranked binding pose (Pose 1). Ligand Interaction.jpeg Two-dimensional interaction diagram showing the predicted interactions between BmPGRP-S1 and 20-hydroxyecdysone (20E), including hydrogen bonds, hydrophobic interactions, and interacting amino acid residues. Ligand Interaction.txt Microsoft Word document containing the annotated ligand–protein interaction analysis and the corresponding two-dimensional interaction diagram. ## Software/Codes Software required includes Microsoft Excel for RT-qPCR data, MOE for molecular docking files, and standard image viewers for TIFF Western blot images"}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"National Natural Science Foundation of China","funderIdentifier":"https://ror.org/01h0zpd94","awardNumber":"32372951"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.brv15dvr6","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-07-07T11:32:05Z","registered":"2026-07-07T11:32:06Z","published":null,"updated":"2026-07-07T11:32:06Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.kkwh70sgr","type":"dois","attributes":{"doi":"10.5061/dryad.kkwh70sgr","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Universidad Autónoma de Madrid","Royal Botanic Gardens, Kew"],"name":"Ramos-Gutiérrez, Ignacio","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-8675-0114"}]},{"nameType":"Personal","affiliation":["Royal Botanic Gardens, Kew","Imperial College London"],"name":"Pipins, Sebastian","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Estación Biológica de Doñana"],"name":"Molina-Venegas, Rafael","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-5801-0736"}]},{"nameType":"Personal","affiliation":["Real Jardín Botánico","Universidad Autónoma de Madrid"],"name":"Fernández-Mazuecos, Mario","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Universidad Pablo de Olavide"],"name":"Jiménez-Mejías, Pedro","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2815-4477"}]},{"nameType":"Personal","affiliation":["Universidad Autónoma de Madrid"],"name":"Moreno-Saiz, Juan Carlos","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Royal Botanic Gardens, Kew"],"name":"Forest, Félix","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-2004-433X"}]}],"titles":[{"title":"Data and code from: Priority areas for preserving angiosperm evolutionary history in the Iberian Peninsula"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Earth and related environmental sciences","subjectScheme":"fos"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Biogeography","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Biodiversity","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Conservation biology","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[],"dates":[{"date":"2025-07-09T15:11:11Z","dateType":"Created"},{"date":"2026-06-15T10:47:59Z","dateType":"Submitted"},{"date":"2026-07-07T00:00:00Z","dateType":"Issued"},{"date":"2026-07-07T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1111/ddi.70224","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["4776444 bytes"],"formats":[],"version":"6","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Aim To identify areas with a high concentration of threatened evolutionary\n history and to evaluate how effectively the current network of protected\n areas encompasses them, focusing on the global conservation relevance of\n angiosperms native to a regional biodiversity hotspot. Location The area\n of study comprises the Iberian Peninsula and the Balearic Islands, one of\n the most angiosperm-rich areas within the Mediterranean hotspot. We have\n studied all native angiosperms (5411 species) using a high-resolution\n (10 × 10 km grid) occurrence dataset. Methods We have used the\n evolutionarily distinct and globally endangered (EDGE) metric to combine\n phylogenetic singularity with extinction risk and identify taxa that\n represent unique evolutionary history under threat. Individual EDGE scores\n were used to identify regions harbouring higher values of threatened\n evolutionary history, as well as to delineate areas that optimally\n preserve angiosperm evolutionary history. Results Our findings reveal that\n threatened angiosperm evolutionary history is primarily concentrated in\n mountainous and coastal regions. We identified a set of 22 complementary\n EDGE zones—areas containing unique and endangered evolutionary\n lineages—whose protection would secure the preservation of more than 90%\n of threatened evolutionary history. While several EDGE zones overlap with\n existing protected areas, particularly in mountains, others harbouring few\n but evolutionarily unique and highly threatened taxa remain largely\n unprotected. This study highlights the value of applying global\n conservation metrics such as EDGE at regional scales. Our results provide\n a foundation for integrating evolutionary history into conservation\n prioritisation in the Iberian Peninsula and offer a replicable framework\n for implementing the EDGE approach in other biodiversity-rich regions."},{"descriptionType":"TechnicalInfo","description":"# Data and code from: Priority areas for preserving angiosperm\n evolutionary history in the Iberian Peninsula ## Description of the\n dataset This repository includes the data and code to reproduce the\n results obtained in the paper 'Priority Areas for Preserving\n Angiosperm Evolutionary History in the Iberian Peninsula'\n (Ramos-Gutiérrez et al., 2026; https://doi.org/10.1111/ddi.70224). ###\n Files and data description 'files.zip' includes the data used\n for analyses and creation of figures for this paper, as well as\n Supplementary Data. It should be unzipped and stored in the same directory\n as the script to ensure it Works correctly. In case it is replicated, a\n results subdirectory may be created if the script is not edited. -\n '/grids' folder. This directory includes geographic information\n (MGRS 10x10 grid cells, as well as regional provinces). Grid cells\n ('UTM10x10.shp')were used to detect high EDGE areas and\n delineate EDGE zones(as it is the geographic information stored in the\n AFLIBER dataset, where angiosperm distributions was obtained from).\n Province borders ('PeninsulaIberica.shp') was used for aesthetic\n purposes when rendering figures. - '/lists' folder. This\n directory includes tabular data including angiosperm distributions and\n individual EDGE scores. 'afliber_matrix_10x10.csv' represents a\n wide-format array of angiosperm distributions after subsetting form\n AFLIBER dataset Ramos-Gutiérrez et al., 2021;\n https://doi.org/10.1111/geb.13363), where columns represent species, and\n rows MGRS 10x10 grid cells (name match those included in the shapefile).\n File 'Iberian_EDGE.csv' includes information for all 5,411\n native angiosperm species in the Iberian peninsula; specificattly taxon\n name ('taxon' column); its IUCN Red List category\n ('RLcat'); its classification as an EDGE species or not\n (is.EDGE, \"Y/N\"), and the median EDGE score across 200\n replicates /EDGE.median). - 'SupplementaryData' folder includes\n supplementary data to the manuscript to evaluate the degree of uncertainty\n derived from phylogenetic and extinction risk information. However, it is\n not needed to replicate analyses. File\n 'SuppDataS1a_Taxa_number_Family.csv' depict how many species per\n family were randomly inserted into the backbone phylogeny (column\n 'no_phylo'), and how many were already sampled\n ('yes_phylo). Also, it depicts how many species per family presented\n an IUCN Red List assessment ('yes_assessed') and how many were\n classified as threatened or not-threatened('no_assessed'). File\n 'SuppDataS1b_Taxa_number_Genus.csv' represents the same\n information but for the genus level rather than family. ### Analysis\n script 'IberianEDGE_DRYAD.R' includes the code used (in R\n software) for the analyses and creation of figures for this paper. To\n ensure reproducibility, make sure to unzip file 'files.zip' in\n the same directory as the script ('IberianEDGE_DRYAD.R'). Plot\n rendering lines are commented in the script to disable the creation of\n undesired plots."}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"Agencia Estatal de Investigación","funderIdentifier":"https://ror.org/003x0zc53","awardNumber":"TED2021-131234A-I00"},{"funderIdentifierType":"ROR","funderName":"Ministerio de Asuntos Económicos y Transformación Digital","funderIdentifier":"https://ror.org/03sv46s19","awardNumber":"TED2021‐131234A‐I00"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.kkwh70sgr","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-07-07T11:30:39Z","registered":"2026-07-07T11:30:40Z","published":null,"updated":"2026-07-07T11:30:40Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.xpnvx0krp","type":"dois","attributes":{"doi":"10.5061/dryad.xpnvx0krp","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Technical University of Munich","Florida Museum of Natural History"],"name":"Ortiz, Edgardo M.","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8052-1671"}]},{"nameType":"Personal","affiliation":["Technical University of Munich"],"name":"Höwener, Alina","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Technical University of Munich","University of Tsukuba"],"name":"Shigita, Gentaro","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Technical University of Munich"],"name":"Raza, Mustafa","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Meise Botanic Garden","Royal Botanic Gardens, Kew"],"name":"Maurin, Olivier","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Royal Botanic Gardens, Kew"],"name":"Zuntini, Alexandre","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Royal Botanic Gardens, Kew"],"name":"Forest, Félix","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Royal Botanic Gardens, Kew","Aarhus University"],"name":"Baker, William J.","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Technical University of Munich"],"name":"Schaefer, Hanno","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-7231-3987"}]}],"titles":[{"title":"Data from: A novel phylogenomics pipeline reveals extensive topological conflict in the evolution of the angiosperm order Cucurbitales"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"subject":"Angiosperms353"},{"subject":"Cucurbitaceae"},{"subject":"Cucurbitales"},{"subject":"gene tree discordance"},{"subject":"paralog filtering"},{"subject":"genome skimming"},{"subject":"phylotranscriptomics"},{"subject":"nitrogen-fixing clade"},{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Biological sciences","subjectScheme":"fos"}],"contributors":[],"dates":[{"date":"2024-12-25T12:24:56Z","dateType":"Created"},{"date":"2026-05-18T08:12:52Z","dateType":"Submitted"},{"date":"2026-07-07T00:00:00Z","dateType":"Issued"},{"date":"2026-07-07T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1101/2023.10.27.564367","relatedIdentifierType":"DOI"},{"relationType":"IsDerivedFrom","relatedIdentifier":"https://github.com/edgardomortiz/Captus","relatedIdentifierType":"URL"}],"relatedItems":[],"sizes":["37553989 bytes"],"formats":[],"version":"7","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"High-throughput sequencing data, such as target capture, RNA-Seq, genome\n skimming, and high-depth whole-genome sequencing, are used for\n phylogenomic analyses. Integrating these mixed data types into a single\n phylogenomic dataset requires several bioinformatic tools and significant\n computational resources. Here, we present Captus, a novel pipeline to\n analyze mixed data efficiently. Captus assembles these data types,\n searches for loci of interest, and produces paralog-filtered alignments.\n If reference target loci are not available for the studied taxon, Captus\n can also be used to discover new putative homologs via sequence\n clustering. Compared to other software, Captus allows the recovery of a\n greater number of more complete loci across more species. We apply Captus\n to assemble a comprehensive dataset, comprising the four types of\n sequencing data for the angiosperm order Cucurbitales, a clade of about\n 3,100 species in eight mainly tropical plant families, including begonias\n (Begoniaceae) and gourds (Cucurbitaceae). Our phylogenomic results support\n the currently accepted circumscription of Cucurbitales except for the\n position of the holoparasitic Apodanthaceae, which group with\n Rafflesiaceae in Malpighiales. A subset of mitochondrial gene regions\n supports the earlier divergence of Apodanthaceae in Cucurbitales. However,\n the nuclear regions and the majority of mitochondrial regions place\n Apodanthaceae in Malpighiales. Within Cucurbitaceae, we confirm the\n monophyly of all currently accepted tribes but also reveal hybridization\n and incomplete lineage sorting both in Cucurbitales and within\n Cucurbitaceae. We show that contradicting results among earlier\n phylogenetic studies in Cucurbitales can be reconciled when accounting for\n gene tree conflict and demonstrate the efficiency of Captus for complex\n datasets."},{"descriptionType":"TechnicalInfo","description":"# Data from: A novel phylogenomics pipeline reveals extensive topological\n conflict in the evolution of the angiosperm order Cucurbitales\n [https://doi.org/10.5061/dryad.xpnvx0krp](https://doi.org/10.5061/dryad.xpnvx0krp) ## Description of the data and file structure #### AppendixS1.zip * **Appendix S1.** Plastome segments used as reference targets provided as a file in FASTA format Plastome38.fasta ####  AppendixS2.zip * **Appendix S2.** Newly found nuclear putative homologs used as reference targets provided as a file in FASTA format RNA5435.fasta ####  AppendixS3.zip * **Appendix S3.** Estimated species trees in NEWICK format using coalescence (ASTRAL-Pro) or concatenation (IQ-TREE). Naming of the coalescent trees indicates the program used to estimate the gene trees (FastTree or IQ-TREE), the target file (Angiosperms353, Mega353, or RNA5435), and the Captus paralog filter (informed, naive, or unfiltered). Naming of concatenation species trees indicates the target file used (Angiosperms353, Mega353, or Plastome38), and the Captus paralog filter (informed or naive). ####  AppendixS4.zip * **Appendix S4.** Alignments and trees of the mitochondrial regions in NEXUS format. ####  AppendixS5.zip * **Appendix S5.** Benchmarking data: Trimmed gene alignments in FASTA format and their estimated gene and species trees in NEWICK format. Naming indicates the pipeline used (Captus, HybPiper-BLASTx, HybPiper-DIAMOND), the Captus paralog filter (informed, naive, or unfiltered), and whether paralogs were included for HybPiper or not (with_paralogs or without_paralogs). ####  AppendixS6.zip * **Appendix S6.** Reference targets sets used in the decontamination of *Lemurosicyos variegata* (Supplementary Method). Nuclear proteins (Cucumber_V3_chr_201810.pep.fa), plastome proteins (Cucumis_sativus_PTD.faa), mitochondrial proteins (Cucumis_sativus_MIT.faa), and non-coding sequences (Cucurbitales_NC.fna). ## Code/software **Captus:** [https://github.com/edgardomortiz/Captus](https://github.com/edgardomortiz/Captus)\\ **Captus' documentation:** [https://edgardomortiz.github.io/captus.docs/](https://edgardomortiz.github.io/captus.docs/)"}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"Deutsche Forschungsgemeinschaft","funderIdentifier":"https://ror.org/018mejw64","awardNumber":"SCHA 1875/4-2"},{"funderIdentifierType":"ROR","funderName":"Calleva Foundation","funderIdentifier":"https://ror.org/00jwxmn37"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.xpnvx0krp","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":1,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-07-07T11:09:38Z","registered":"2026-07-07T11:09:39Z","published":null,"updated":"2026-07-07T11:09:39Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.kprr4xhkf","type":"dois","attributes":{"doi":"10.5061/dryad.kprr4xhkf","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Nanjing Normal University"],"name":"Zhang, Yuyang","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0008-1115-495X"}]},{"nameType":"Personal","affiliation":["Nanjing Normal University"],"name":"Wang, Jiang","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Nanjing Normal University","Chinese Academy of Sciences"],"name":"Li, Yanxia","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Nanjing Normal University"],"name":"Chen, Chuanwu","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-3974-853X"}]},{"nameType":"Personal","affiliation":["Nanjing Normal University"],"name":"Wang, Yanping","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-3743-3937"}]}],"titles":[{"title":"Data from: Global patterns and drivers of extinction risk in island endemic terrestrial vertebrates"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Biological sciences","subjectScheme":"fos"},{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Earth and related environmental sciences","subjectScheme":"fos"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Extinction risk","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Island biogeography","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Conservation biology","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Macroecology","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[{"name":"Nanjing Normal University","contributorType":"Sponsor","affiliation":[],"nameIdentifiers":[]}],"dates":[{"date":"2026-01-30T12:51:09Z","dateType":"Created"},{"date":"2026-04-23T06:14:02Z","dateType":"Submitted"},{"date":"2026-07-07T00:00:00Z","dateType":"Issued"},{"date":"2026-07-07T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["13312638 bytes"],"formats":[],"version":"7","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"This Dryad repository contains the supplementary datasets supporting The\n Global Patterns and Drivers of Extinction Risk in Island Endemic\n Terrestrial Vertebrates. This repository integrates taxonomic information,\n distribution pattern data, compiled ecological and life-history traits,\n environmental and anthropogenic variables, as well as phylogenetic\n comparative analyses and statistical model outputs generated during the\n study. The datasets include more than 31,000 non-Data Deficient\n terrestrial vertebrate species, including birds, mammals, amphibians, and\n reptiles, based on data compiled from the IUCN Red List (version 2024-1)\n and subsequently standardized and refined. The repository is organized\n into 11 supplementary datasets. These datasets include: (1) taxonomic\n filtering procedures and dataset construction workflows; (2) species-level\n taxonomic information, endemicity categories, conservation status, and\n population trends; (3) raw ecological and life-history variables prior to\n imputation and standardization; (4) archipelago-level summaries of species\n richness and high-risk endemic richness; (5) family-level analyses of\n phylogenetic clustering in extinction risk; (6) contingency table analyses\n linking IUCN threat categories with extinction risk across taxonomic and\n endemicity groups; (7) correlation matrices for predictor variables; and\n (8–11) outputs from phylogenetic linear models and phylogenetic logistic\n regression analyses, including both univariate and model-averaged results.\n The repository contains categorical, binary, ordinal, and continuous\n variables compiled and harmonized from multiple global biodiversity\n databases. Missing values in raw datasets are consistently coded as NA.\n Statistical outputs include parameter estimates, confidence intervals,\n odds ratios, significance tests, model importance values and other related\n analytical outputs. All datasets are provided in Excel file format, with\n detailed descriptions of variables and worksheet structures documented in\n the Supplementary Materials. These datasets have broad reuse potential for\n comparative macroecology, conservation biogeography, extinction risk\n modeling, island biodiversity research, phylogenetic comparative analyses,\n and studies examining taxon-specific responses to environmental and\n anthropogenic pressures. The repository also provides standardized data\n structures and reusable analytical outputs that may facilitate future\n cross-taxon conservation studies and meta-analyses. All data were compiled\n from publicly available databases, and the original data sources are\n indicated within the corresponding Excel files. The repository contains no\n human subject data or sensitive personal information. Species nomenclature\n was taxonomically reconciled during dataset construction. All derived\n datasets are intended for scientific research and conservation purposes,\n with appropriate citation of the original data sources and associated\n publication."},{"descriptionType":"TechnicalInfo","description":"# Data from: Global patterns and drivers of extinction risk in island\n endemic terrestrial vertebrates Dataset DOI: 10.5061/dryad.kprr4xhkf ##\n Description of the data and file structure This README describes the\n structure, content, and purpose of all Supplementary Data files associated\n with the study on global patterns and drivers of extinction risk in\n island-endemic terrestrial vertebrates. All datasets are provided as\n separate files and are referenced as Data S1–Data S11 in the main text and\n Supplementary Information. NA indicates data that were unavailable or not\n collected from the sources; Not_applicable indicates variables or analyses\n that did not apply to a particular taxonomic group or model structure;\n and Not_included indicates variables that were excluded from model\n averaging or multivariable analyses following variable screening, model\n selection, or significance filtering procedures. ## Files and variables\n #### File:\n Data_S1._Taxonomic_filtering_and_dataset_construction_for_32_214_non-Data_Deficient_terrestrial_vertebrate_species..xlsx Description: Data S1 documents the full taxonomic reconciliation, habitat filtering, and data-availability screening pipeline applied to terrestrial vertebrates from the IUCN Red List (version 2024-1). It provides transparency and reproducibility for all inclusion and exclusion steps. #### Key variables： * iucn_assessment_id, iucn_taxon_id: Unique IUCN Red List identifiers * scientific_name: Accepted binomial name after taxonomic reconciliation * iucn_category: IUCN Red List category (EX and EW retained and treated as extinct) * excluded_dd: Indicator for exclusion of Data Deficient species * synonym_or_subspecies, synonym_type: Resolved synonyms or excluded subspecies * marine_system, freshwater_system, terrestrial_system: Habitat system flags * excluded_taxonomic_issue, excluded_non_terrestrial: Filtering indicators * retained_in_final_dataset: Species retained after taxonomic and habitat filtering (n = 31,892) * missing_core_traits: Species lacking required ecological or life-history traits * included_in_analysis: Species entering phylogenetic imputation and modelling (n = 30,711) #### File: Data_S2._Summary_of_non-DD_Terrestrial_vertebrate_species.xlsx Description: Data S2 provides core taxonomic, biogeographic, and conservation assessment information for all non-Data Deficient terrestrial vertebrates included in the study. #### Key variables： Scientific name and IUCN assessment identifier Taxonomic classification (class, order, family) Endemicity category: 1 = island endemic 2 = continental endemic 3 = dual-range (islands and continents) IUCN Red List category and assessment criteria Criteria version (IUCN v3.1) Population trend (IUCN 2024-1) #### File: Data_S3._Raw_data_for_the_30_711_analyzed_tetrapod_species_before_any_imputation.xlsx Description: Data S3 contains the original, unimputed species-level data compiled from multiple sources. All variables are provided exactly as assembled prior to taxonomic imputation, log-transformation, or standardization. Missing values are indicated as NA (not available). #### Key variables： | Variable | Description | | ------------------------------- | -------------------------------------------------------------------------------------------------------------------------------- | | ScientificName | Scientific name of the species. | | GenusName | Genus of the species. | | BodyMass | Adult body mass (g). | | BodyLength | Maximum adult body length (mm; amphibians only). | | Verticality | Vertical stratum preference (0 = fossorial, 1 = semifossorial, 2 = terrestrial/aquatic, 3 = semi-arboreal, 4 = arboreal/aerial). | | TrophicLevel | Trophic level (0 = omnivore, 1 = herbivore, 2 = carnivore). | | Nocturnality | Activity pattern (0 = nocturnal, 1 = diurnal, 2 = cathemeral/crepuscular). | | TypeGrow | Larval development mode for amphibians (1 = indirect, 2 = direct, 3 = viviparous). | | MajorHabitatSum | Number of IUCN major habitat types occupied (1–13). | | Latitude | Range centroid latitude (decimal degrees). | | AnnuMeanTemp | Annual mean temperature (°C). | | AnnuPrecip | Annual mean precipitation (mm). | | TempSeasonality | Temperature seasonality (SD × 100). | | PrecipSeasonality | Precipitation seasonality (coefficient of variation). | | Elevation | Mean elevation (m). | | RangeSize | Geographic range size (number of occupied 1 km × 1 km grid cells). | | HumanFootPrint | Human Footprint Index (0–50). | | InvasiveSpeciesResidual | Residual invasive species richness after controlling for range size. | | DescriptionTime | Years since species description (2024 − year of description). | | DirectSourceBodyMass/BodyLength | Original data source for body mass or body length. | | DirectSourceTrophicLevel | Original data source for trophic level. | | DirectSourceNocturnality | Original data source for activity pattern. | | DirectSourceVerticality | Original data source for verticality. | | DirectSourceHabitat | Original data source for habitat information. | | DirectSourceGeo | Original data source for geographic and environmental variables. | | SourceAnthropogenicPressures | Original data source for anthropogenic pressure variables. | #### File: Data_S4._Species_richness_of_island_endemic_terrestrial_vertebrates_by_archipelago_and_taxonomic_class.xlsx Description: Data S4 summarizes patterns of island endemic species richness and high-risk richness across global archipelagos and taxonomic classes. This dataset contains 11 sheets: (1) Archipelago label and name correspondence table; (2) Overall endemic richness (all tetrapods); (3) high-risk endemic richness (all tetrapods); (4–7) endemic richness for birds/mammals/reptiles/amphibians; (8–11) high-risk endemic richness for each class. #### Key variables： | Variable | Description | | ----------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------- | | Label | Numeric identifier assigned to each archipelago and used in the figures. | | Archipelago | Name of the archipelago. | | Endemic\\_Richness | Number of island endemic species recorded for their primary area of occurring archipelago . | | High\\_Risk\\_Endemic\\_Richness | Number of island endemic species classified as Vulnerable (VU), Endangered (EN), Critically Endangered (CR), or Extinct (EX) for each archipelago. | #### File: Data_S5._Taxonomic_clustering_of_extinction_risk_among_island_endemic_terrestrial_vertebrate_families.xlsx Description: Data S5 reports family-level analyses of extinction-risk clustering among island endemic terrestrial vertebrates, conducted separately for each vertebrate class. This dataset contains four worksheets, one for each vertebrate class. #### Key variables： | Variable | Description | | ----------------- | --------------------------------------------------------------------------------------------- | | Family | Taxonomic family name. | | N\\_Species | Number of island endemic species in the family. | | N\\_High\\_Risk | Number of high-risk island endemic species (VU, EN, CR, and EX) in the family. | | High\\_Risk\\_Prop | Proportion of high-risk species in the family. | | P\\_Value | Two-sided exact binomial test *P* value. | | Direction | Direction of deviation from the expected proportion (\"Higher\" or \"Lower\"). | | Significant | Indicates whether the result remained statistically significant after Dunn–Šidák correction. | | Observed\\_Prop | Observed proportion of high-risk species in the family. | | Expected\\_Prop | Expected proportion of high-risk species based on the corresponding vertebrate class. | | Ci\\_Lower | Lower bound of the Clopper–Pearson confidence interval. | | Ci\\_Upper | Upper bound of the Clopper–Pearson confidence interval. | | Alpha\\_Original | Original significance level (α = 0.05). | | Alpha\\_Adjusted | Dunn–Šidák-adjusted significance level. | | Confidence\\_Level | Adjusted confidence level corresponding to the Dunn–Šidák correction. | | N\\_Comparisons | Number of family-level comparisons used for multiple-testing correction. | | Method | Statistical method applied to the family. | | Risk\\_Category | Classification of the family's extinction risk pattern. | | Test\\_Performed | Indicates whether a formal statistical test was performed. | | Test\\_Note | Additional notes describing the statistical analysis or explaining why no test was performed. | #### File: Data_S6._Contingency_table_analyses_testing_the_association_between_major_IUCN_threat_types_and_Red_List_categories_in_threatened_non-Data_Deficient_terrestrial_vertebrates.xlsx Description: Data S6 contains contingency table analyses testing associations between major IUCN threat types and Red List categories in threatened non-DD terrestrial vertebrates. This Excel workbook contains 14 sheets.  Sheet 1: Summary of Pearson’s χ² tests of independence for each of the eight taxonomic class × endemism combinations (island endemic, continental endemic, dual-range). Columns include: taxonomic class, endemism status, total χ² statistic, degrees of freedom, raw p-value, and significance level (***p \u0026lt; 0.001, **p \u0026lt; 0.01, *p \u0026lt; 0.05, ns = not significant). Sheets 2–13: Detailed cell-by-cell results for each class × endemism combination (e.g., \"Aves_Island_Endemic\", \"Aves_Continental_Endemic\", \"Aves_Dual-Range\"). Columns report, for every combination of the 11 major IUCN threat types (threat1 to threat11) and the four Red List categories (VU, EN, CR, EX): observed count, expected count under the null hypothesis of independence, standardized Pearson residual ((observed - expected)/√expected), χ² contribution ((observed - expected)²/expected), raw cell p-value, FDR-adjusted p-value (Benjamini–Hochberg correction within each group), and significance of the cell-level χ² contribution based on adjusted p-value (***p_adj \u0026lt; 0.001, **p_adj \u0026lt; 0.01, *p_adj \u0026lt; 0.05, blank otherwise). Sheet 14: IUCN threats classification scheme (version 3.1; Threat 12 (Other threat) and Unknown were excluded. #### Key variables： | Variable | Description | | ------------------ | --------------------------------------------------------------------------------------------------------------------- | | Threat\\_Type | IUCN major threat category (Threat 1–Threat 11, following the IUCN Threats Classification Scheme v3.1). | | RedlistGroup | IUCN Red List category (VU, EN, CR, or EX). | | Count | Observed number of species in the corresponding threat type and Red List category. | | Expected | Expected count under the null hypothesis of independence. | | Residual | Standardized Pearson residual, calculated as (Observed − Expected)/√Expected. | | Chi\\_Squared | Cell contribution to the Pearson χ² statistic, calculated as (Observed − Expected)²/Expected. | | P\\_Value\\_Cell | Raw *P* value for the individual cell. | | P\\_Adj | False discovery rate (FDR)-adjusted *P* value using the Benjamini–Hochberg procedure. | | Significance\\_Cell | Significance level based on the adjusted *P* value (\\*\\*\\* \u0026lt; 0.001, \\*\\* \u0026lt; 0.01, \\* \u0026lt; 0.05; blank = not significant). | **NOTE: Variables included in DATA S7-11 are defined in the Common variable definitions for Data S7–S11 section below.** #### File: Data_S7._Species-level_correlation_matrices.xlsx Description: Data S7 contains 32 sheets of correlation matrices showing pairwise Spearman correlation coefficients among all univariate predictor variables used in the extinction risk analyses. Correlations were calculated for four vertebrate classes (birds, mammals, amphibians, reptiles) and multiple species subgroups defined by endemicity and IUCN criteria, respectively. Notably, “criteria2” sheets correspond to subsets of species within each class and endemicity type for which species classified as threatened under IUCN Criteria B or D2 were excluded. For each dataset, Spearman correlation coefficients were first Fisher z-transformed, averaged across 100 paired datasets, and then inverse-transformed to obtain mean correlation values. Each sheet corresponds to one class-subgroup combination, with variable names mapped to standardized analysis identifiers. Values range from –1 to 1, with positive values indicating positive associations between variables. #### File: Data_S8._Univariate_phylogenetic_linear_model_results_implemented_in_phylolm.xlsx Description: Data S8 reports the results of univariate phylogenetic linear models (PLMs) implemented using the phylolm framework, evaluating the association between individual predictor variables and extinction risk (ordinal IUCN categories, LC = 0 to EX = 5) across global terrestrial vertebrates. For each predictor, effect estimates, confidence intervals, and statistical significance are reported separately for each vertebrate group and endemicity category. These univariate analyses were used to characterize the marginal effects of species traits and external variables prior to multivariable model averaging. #### File: Data_S9._Model-averaged_phylogenetic_linear_model_results_implemented_in_phylolm.xlsx Description: Data S9 summarizes model-averaged parameter estimates derived from phylogenetic linear models (PLMs) implemented using the phylolm framework fitted to 100 paired datasets. Estimates incorporate both within-dataset and between-dataset variance, and are reported for each vertebrate group, endemicity category, and extinction risk criteria subset. #### File: Data_S10_Univariate_phylogenetic_logistic_regression_model_results_implemented_in_phyloglm.xlsx Description: Data S10 contains the results of univariate phylogenetic logistic regression models implemented using the phyloglm framework, evaluating the association between individual predictor variables and extinction risk across global terrestrial vertebrates. Extinction risk is treated as a binary response based on IUCN Red List category. For each predictor variable, model outputs include effect estimates, standard errors, z-values, p-values, and 95% confidence intervals. In addition, phylogenetically corrected model parameters, including the phylogenetic signal parameter where applicable, are reported. Results are provided separately for each vertebrate class, endemicity category, and extinction risk criterion subset. #### File: Data_S11_Model-averaged_phylogenetic_logistic_regression_results_implemented_in_phyloglm.xlsx Description: Data S11 summarizes model-averaged parameter estimates derived from phylogenetic logistic regression models implemented using the phyloglm framework fitted across multiple candidate model sets. Estimates incorporate both within-dataset and between-dataset variance, and are reported for each vertebrate class, endemicity category, and extinction risk criteria subset. For each predictor variable, the dataset provides model-averaged effect estimates, standard errors, z-values, p-values, and model importance values, together with odds ratios and their corresponding 95% confidence intervals. #### Common variable definitions for Data S7–S11 ##### Dataset identifiers | Variable | Description | | ------------ | -------------------------------------------------------------------------------------------------- | | ClassName | Vertebrate class. | | EndemicType | Endemism category analyzed. | | CriteriaB/D2 | Indicates whether species listed solely under IUCN Criteria B or D2 were included in the analysis. | ##### Predictor variables The predictor variables (BodyMass, BodyLength, GrowthType, Verticality, Nocturnality, TrophicLevel, MajorHabitatSum, RangeSize, Latitude, AnnuMeanTemp, AnnuPrecip, TempSeasonality, PrecipSeasonality, Elevation, HumanFootPrint, InvasiveSpeciesResidual, and DescriptionTime) correspond to the species traits and environmental variables described for Data S3. ##### Model output variables | Variable | Description | | ------------------ | ---------------------------------------------------------------------------------------------------------------------------- | | Estimate | Rubin’s rule pooled regression coefficient obtained from repeated phylogenetic model fits across 100 paired datasets. | | SE / StdErr | Rubin’s rule pooled standard error, combining within-dataset variance and between-dataset variance across repeated analyses. | | t\\_stat | *t* statistic from univariate phylogenetic linear models (PLMs) fitted using phylolm. | | z.value / z\\_value | Wald *z* statistic. | | p.value / p\\_value | Two-sided P value derived from the Wald statistic under the standard normal distribution assumption. | | Lower\\_CI | Lower bound of the 95% confidence interval derived from Rubin-pooled uncertainty. | | Upper\\_CI | Upper bound of the 95% confidence interval derived from Rubin-pooled uncertainty. | | Mean\\_Lambda | Mean Pagel’s λ estimated from phylogenetic models (phylolm) across 100 paired datasets. | | Mean\\_Alpha | Mean α parameter from univariate phylogenetic logistic regression models (phyloglm) across 100 paired datasets. | | Degrees\\_Freedom | Residual degrees of freedom from individual phylogenetic model fits (varies across iterations; not Rubin pooled). | | Mean\\_R2 | Mean coefficient of determination (R²) across repeated phylogenetic linear model fits. | | Mean\\_adjR2 | Mean adjusted R² across repeated phylogenetic linear model fits. | | Mean\\_Pseudo\\_R2 | Mean pseudo-R² across phylogenetic logistic regression models fitted in repeated analyses. | | Mean\\_AIC | Mean Akaike Information Criterion (AIC) across all candidate models prior to model averaging (phyloglm). | | Importance | Variable importance derived from model averaging weights. | | Odds\\_Ratio | Exponentiated pooled coefficient (exp(Estimate)), interpreted as multiplicative effect size in phylogenetic logistic models. | | OR\\_Lower | Lower bound of the 95% confidence interval for the odds ratio. | | OR\\_Upper | Upper bound of the 95% confidence interval for the odds ratio. | #### Code/software Microsoft Excel is recommended to view our data."}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"National Natural Science Foundation of China","funderIdentifier":"https://ror.org/01h0zpd94","awardNumber":"32271734"},{"funderIdentifierType":"ROR","funderName":"National Natural Science Foundation of China","funderIdentifier":"https://ror.org/01h0zpd94","awardNumber":"32571931"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.kprr4xhkf","contentUrl":null,"metadataVersion":3,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":1,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-07-07T07:12:27Z","registered":"2026-07-07T07:12:28Z","published":null,"updated":"2026-07-07T10:53:21Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.pvmcvdnzp","type":"dois","attributes":{"doi":"10.5061/dryad.pvmcvdnzp","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["University of Wisconsin–Madison"],"name":"Kenney, Shannon","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-9655-6526"}]},{"nameType":"Personal","affiliation":["University of Wisconsin–Madison"],"name":"Fogarty, Stuart","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Wisconsin–Madison"],"name":"Singh, Deo","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Wisconsin–Madison"],"name":"Nelson, Scott","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Wisconsin–Madison"],"name":"Calandranis, Maria","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Wisconsin–Madison"],"name":"Zhang, Yitao","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Wisconsin–Madison"],"name":"Pawelski, Abigail","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Wisconsin–Madison"],"name":"Kansra, Alisha","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Wisconsin–Madison"],"name":"White, Sophie","nameIdentifiers":[]}],"titles":[{"title":"IRF6 controls Epstein-Barr virus (EBV) lytic reactivation and differentiation in EBV-infected epithelial cells"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Biological sciences","subjectScheme":"fos"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Epstein-Barr virus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"IRF6"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Nasopharyngeal carcinoma","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Gastric cancer","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"epithelial cell differentiation"}],"contributors":[],"dates":[{"date":"2025-08-25T18:57:19Z","dateType":"Created"},{"date":"2025-08-25T18:58:19Z","dateType":"Submitted"},{"date":"2026-07-07T00:00:00Z","dateType":"Issued"},{"date":"2026-07-07T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1371/journal.ppat.1013236","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["23669553 bytes"],"formats":[],"version":"3","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Latent Epstein-Barr virus (EBV) infection promotes undifferentiated\n nasopharyngeal carcinoma (NPC) and gastric carcinoma (GC), while EBV\n infection of normal differentiated oropharyngeal epithelial cells is lytic\n and kills the cell. Establishment of viral latency within epithelial cells\n is likely essential for the development of EBV-induced NPCs and GCs, but\n the mechanism(s) by which EBV latency is maintained in epithelial cells\n are not fully understood. Here we demonstrate that the cellular tumor\n suppressor protein IRF6, a master regulator of squamous cell epithelial\n cell differentiation, plays a critical role in promoting TPA-induced lytic\n EBV reactivation in vitro in both EBV-infected NPC cells and EBV-infected\n GC cells. Using a telomerase-immortalized normal oral keratinocyte cell\n line (NOKs) model which retains the ability to differentiate in response\n to TPA treatment, we show that TPA-induced lytic EBV reactivation requires\n the PKCδ-RIPK4-IRF6 signaling pathway. RIPK4 is a PKCδ (PRKCD)-activated\n cellular S/T kinase that phosphorylates and activates the IRF6\n transcription factor. We demonstrate that inhibition of PKCδ, RIPK4 or\n IRF6 expression is sufficient to suppress TPA-induced epithelial cell\n differentiation, as well as lytic EBV reactivation, in NOKs. Furthermore,\n we find that latent EBV infection in NOKs inhibits the expression of IRF6.\n Importantly, we show that inducible expression of a constitutively active\n (phospho-mimetic) IRF6 mutant is sufficient to activate the lytic form of\n EBV infection in both EBV-infected NOKs and EBV-infected SNU719 GC cells.\n Finally, we demonstrate that the ability of constitutively active IRF6 to\n promote lytic EBV infection in NOKs is at least partially mediated by\n IRF6-induced expression of the BLIMP1 transcription factor, which we\n previously showed synergistically activates expression of the two EBV\n immediate-early proteins, BZLF1 and BRLF1, in conjunction with KLF4. Thus,\n suppression of IRF6 expression may promote NPC and GC tumors by blocking\n lytic EBV reactivation and differentiation."},{"descriptionType":"TechnicalInfo","description":"# IRF6 controls Epstein-Barr virus (EBV) lytic reactivation and\n differentiation in EBV-infected epithelial cells Dataset DOI:\n [10.5061/dryad.pvmcvdnzp](10.5061/dryad.pvmcvdnzp) ## Description of the\n data and file structure This dataset contains uncropped Western blots,\n microscopy images, and metadata relevant to: Fogarty SA, Singh DR, Nelson\n SE, Calandranis ME, Zhang Y, Pawelski AS, et al. IRF6 controls\n Epstein-Barr virus (EBV) lytic reactivation and differentiation in\n EBV-infected epithelial cells. PLoS Pathog. 2025 Jun;21(6):e1013236.\n doi:10.1371/journal.ppat.1013236 PubMed PMID: 40569988; PubMed Central\n PMCID: PMC12200665. ### Files Uncropped_blots_from_IRF6_paper.pdf: This\n pdf file contains images of uncropped Western blots and microscopy images.\n Blot_Key.ods: This spreadsheet contains a key for samples and\n abbreviations used in Uncropped_blots_from_IRF6_paper.pdf."}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"National Institute of Allergy and Infectious Diseases","funderIdentifier":"https://ror.org/043z4tv69","awardNumber":"R01AI147060-05"},{"funderIdentifierType":"ROR","funderName":"National Cancer Institute","funderIdentifier":"https://ror.org/040gcmg81","awardNumber":"R01CA229673-05"},{"funderIdentifierType":"ROR","funderName":"National Cancer Institute","funderIdentifier":"https://ror.org/040gcmg81","awardNumber":"P30CA014520-50"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.pvmcvdnzp","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":1,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-07-07T00:17:56Z","registered":"2026-07-07T00:17:57Z","published":null,"updated":"2026-07-07T00:17:57Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.gxd2547nc","type":"dois","attributes":{"doi":"10.5061/dryad.gxd2547nc","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Universidade do Estado de Mato Grosso"],"name":"Silva, Naiane","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Ben-Gurion University of the Negev"],"name":"Caetano, Gabriel","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-4472-5663"}]},{"nameType":"Personal","affiliation":["Universidade de Brasília"],"name":"Campelo, Pedro","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Instituto Federal do Piauí"],"name":"Cavalcante, Vitor","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-7617-3907"}]},{"nameType":"Personal","affiliation":["Universidade do Estado de Mato Grosso"],"name":"Godinho, Leandro","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Ohio University"],"name":"Miles, Donald","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-5768-179X"}]},{"nameType":"Personal","affiliation":["Universidade de Brasília"],"name":"Paulino, Henrique","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Universidade do Estado de Mato Grosso"],"name":"da Silva, Júlio","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Universidade do Estado de Mato Grosso"],"name":"de Souza, Bruno","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Universidade do Estado de Mato Grosso"],"name":"da Silva, Hosmano","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Universidade de Brasília"],"name":"Colli, Guarino","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-2628-5652"}]}],"titles":[{"title":"Data from: Effects of caudal autotomy on the locomotor performance of \u003cem\u003emicrablepharus atticolus\u003c/em\u003e (squamata, gymnophthalmidae)"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Biological sciences","subjectScheme":"fos"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Predation","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Biological defense mechanisms","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Lizards","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[],"dates":[{"date":"2025-08-08T21:55:57Z","dateType":"Created"},{"date":"2022-07-19T15:07:05Z","dateType":"Submitted"},{"date":"2026-07-07T00:00:00Z","dateType":"Issued"},{"date":"2026-07-07T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.3390/d13110562","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["63903 bytes"],"formats":[],"version":"7","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Caudal autotomy is a striking adaptation used by many lizard species to\n evade predators. Most studies to date indicate that caudal autotomy\n impairs lizard locomotor performance. Surprisingly, some species bearing\n the longest tails show negligible impacts of caudal autotomy on sprint\n speed. Part of this variation has been attributed to lineage effects. For\n the first time, we model the effects of caudal autotomy on the locomotor\n performance of a gymnophthalmid lizard, Micrablepharus atticolus, which\n has a long and bright blue tail. To improve model accuracy, we\n incorporated the effects of several covariates. We found that body\n temperature, pregnancy, mass, collection site, and the length of the\n regenerated portion of the tail were the most important predictors of\n locomotor performance. However, sprint speed was unaffected by tail loss.\n Apparently, the long tail of M. atticolus is more useful when using\n undulation amidst the leaf litter and not when using quadrupedal\n locomotion on a flat surface. Our findings highlight the intricate\n relationships among physiological, morphological, and behavioral traits.\n We suggest that future studies about the impacts of caudal autotomy among\n long-tailed lizards should consider the role of different\n microhabitats/substrates on locomotor performance, using laboratory\n conditions that closely mimic their natural environments."},{"descriptionType":"TechnicalInfo","description":"# Data from: Effects of caudal autotomy on the locomotor performance of\n micrablepharus atticolus (squamata, gymnophthalmidae) **Locomotor\n performance of the Cerrado lizard** ***Micrablepharus atticolus***:\n effects of body temperature and caudal autotomy ## Associated publication\n Silva NA, Caetano GHO, Campelo PH, Cavalcante VHGL, Godinho LB, Miles DB,\n Paulino HM, Silva JMA, Souza BA, Silva HBF, Colli GR. 2021. *Effects of\n caudal autotomy on the locomotor performance of Micrablepharus atticolus*\n (Squamata, Gymnophthalmidae). *Diversity* 13:562.\n [https://doi.org/10.3390/d13110562](https://doi.org/10.3390/d13110562) ##\n Description of the dataset This dataset contains individual-level\n locomotor performance data for the gymnophthalmid lizard *Micrablepharus\n atticolus*. The data were collected to evaluate how sprint performance\n varies with body temperature, body size, body mass, sex, reproductive\n condition, locality, caudal autotomy, and regenerated tail length. Lizards\n were collected from two Cerrado localities in Brazil: Reserva Ecológica do\n IBGE, Brasília, Distrito Federal, and Parque do Bacaba, Nova Xavantina,\n Mato Grosso. Individuals were measured morphologically and then tested\n repeatedly under laboratory conditions across a range of body\n temperatures. Each row in the data file represents a single sprint trial\n performed by an individual at a specific body temperature. The data file\n deposited here is a harmonized version of the original locality-specific\n data files. The two original files were merged into a single tab-delimited\n file, the column order was standardized, and categorical codes were\n converted to explicit values to improve reusability. ## Files included ###\n 1. `Micrablepharus_atticolus_locomotor_performance_data.tsv` Merged,\n harmonized tab-delimited data file containing all locomotor performance\n observations. * Number of rows: 481 sprint trials * Number of columns: 13\n * Number of individuals: 103 lizards * Number of localities: 2 Rows by\n locality: * `Distrito_Federal`: 170 sprint trials from 39 individuals *\n `Nova_Xavantina`: 311 sprint trials from 64 individuals ### 2.\n `Micrablepharus_atticolus_locomotor_performance_analysis.R` R script used\n to validate the merged dataset, recreate the derived variables, fit the\n generalized additive mixed models (GAMMs), conduct model selection and\n model averaging, and export optional summary tables and figures. ### 3.\n `README.md` This README file. ## Data structure Each row represents one\n locomotor performance trial. Each individual appears in multiple rows\n because each lizard was tested repeatedly at different body temperatures.\n The variable `Identity` identifies repeated observations belonging to the\n same individual and should be used as the grouping variable in\n repeated-measures or mixed-effects analyses. Most individuals have five\n observations. Some individuals have three or four observations because not\n all planned sprint trials yielded usable records. ## Variable dictionary |\n Variable | Description | Units / values | | ------------- |\n ----------------------------------------------------------------------- |\n ------------------------------------ | | `Identity` | Unique identifier\n for each individual lizard | Character string | | `Sex` | Biological sex |\n `Female`, `Male` | | `Gravid` | Reproductive condition | `No`, `Yes` | |\n `SVL` | Snout-vent length, measured from snout tip to anterior margin of\n cloaca | millimetres (mm) | | `CC` | Total tail length | millimetres (mm)\n | | `Mass` | Body mass | grams (g) | | `Temperature` | Body temperature\n immediately before the sprint trial | degrees Celsius (°C) | | `Speed1` |\n Maximum sprint speed recorded during the trial | metres per second (m\n s\\^-1) | | `RC` | Length of regenerated portion of the tail | millimetres\n (mm) | | `AU` | Caudal autotomy status | `Intact`, `Autotomized` | | `CCr`\n | Relative tail length, calculated as `CC / SVL` | dimensionless ratio | |\n `RCr` | Relative regenerated tail length, calculated as `RC / SVL` |\n dimensionless ratio | | `Local` | Collection locality |\n `Distrito_Federal`, `Nova_Xavantina` | ## Detailed variable notes ###\n `Identity` Unique identifier for each individual. The same identity can\n occur in multiple rows because each lizard was tested repeatedly at\n different body temperatures. ### `Sex` Sex of the individual, coded as\n `Female` or `Male`. ### `Gravid` Reproductive condition, coded as `No` or\n `Yes`. In the original locality-specific files, reproductive condition was\n coded inconsistently: the Distrito Federal file used `N`, and the Nova\n Xavantina file used `n` for non-gravid and `s` for gravid. In the merged\n data file, these values were standardized to `No` and `Yes`. Males are\n coded as `No`. ### `SVL` Snout-vent length in millimeters. This is the\n standard measure of lizard body size from the tip of the snout to the\n anterior margin of the cloaca. ### `CC` Total tail length in millimeters.\n For individuals with regenerated tails, this variable includes the\n remaining original portion and the regenerated portion. ### `Mass` Body\n mass in grams. ### `Temperature` Body temperature in degrees Celsius\n immediately before the sprint trial. Individuals were tested across a\n range of body temperatures to estimate thermal performance curves. ###\n `Speed1` Maximum sprint speed in meters per second for the trial. Values\n equal to zero correspond to trials in which the individual did not run at\n body temperatures near critical thermal limits. These observations were\n retained because they define the lower and upper ends of thermal\n performance curves. ### `RC` Length of the regenerated portion of the tail\n in millimeters. Individuals with intact tails have `RC = 0`. Some\n autotomized individuals also have `RC = 0`, indicating that the tail was\n autotomized but the regenerated portion was absent or too small to\n measure. ### `AU` Caudal autotomy status. * `Intact`: no evidence of\n caudal autotomy. * `Autotomized`: naturally autotomized tail, with or\n without measurable regeneration. All autotomy recorded in this dataset was\n naturally occurring. Tails were not experimentally removed. ### `CCr`\n Relative tail length, calculated as: `CC / SVL` This variable is\n dimensionless. ### `RCr` Relative regenerated tail length, calculated as:\n `RC / SVL` This variable is dimensionless. ### `Local` Collection\n locality. * `Distrito_Federal`: Reserva Ecológica do IBGE, Brasília,\n Distrito Federal, Brazil. * `Nova_Xavantina`: Parque do Bacaba, Nova\n Xavantina, Mato Grosso, Brazil. ## File format The data file is a\n UTF-8-encoded tab-delimited text file with a header row. Recommended\n import commands: In R: ```r dat \u0026lt;-\n read.table(\"Micrablepharus_atticolus_locomotor_performance_data.tsv\", header = TRUE, sep = \"\\t\") ``` In Python: ```python import pandas as pd dat = pd.read_csv(\"Micrablepharus_atticolus_locomotor_performance_data.tsv\", sep=\"\\t\") ``` ## Missing values The merged data file contains no missing values. If missing values are introduced in future versions, they should be coded as `NA`. ## Quality-control checks applied to the merged file The following checks were performed before deposition: 1. The two locality-specific source files were read using their column names, avoiding errors due to their different column order. 2. The merged data file was written with a single standardized column order. 3. The `Gravid` variable was standardized to `No` and `Yes`. 4. The `AU` variable was standardized to `Intact` and `Autotomized`. 5. The `Local` variable was standardized to `Distrito_Federal` and `Nova_Xavantina`. 6. The merged file was checked for missing values; none were present. 7. The derived ratios `CCr = CC / SVL` and `RCr = RC / SVL` were checked against the archived values, allowing only negligible rounding differences. 8. The final file was checked for the expected 481 rows, 13 columns, and 103 unique individuals. ## Software and code The accompanying R script requires R and the following packages: * `ggplot2` * `mgcv` * `MuMIn` * `nlme` * `psych` * `visreg` The script does not install packages automatically. Users should install any missing packages before running the script. The script performs the following steps: 1. Reads `Micrablepharus_atticolus_locomotor_performance_data.tsv`. 2. Validates column names, categorical values, missing data, and ratio calculations. 3. Converts categorical variables to factors. 4. Creates a derived variable called `Tail_condition` for descriptive summaries. 5. Produces summary statistics. 6. Fits a null GAMM and a full GAMM. 7. Computes approximate deviance-based R-squared and adjusted R-squared. 8. Performs a likelihood-ratio test comparing the full and null models. 9. Performs AICc-based model selection and model averaging. 10. Optionally exports figures and results tables. ## Derived variables in the R script The R script creates one derived variable that is not included in the deposited data file: `Tail_condition` This variable has three categories: * `Intact`: `AU = Intact` * `Autotomized_not_regenerated`: `AU = Autotomized` and `RC = 0` * `Autotomized_regenerated`: `AU = Autotomized` and `RC \u0026gt; 0` This variable is used only for descriptive summaries. The primary statistical models use `AU` and `RCr`, following the analytical structure of the associated publication. ## Relationship to the associated publication These data were used to evaluate whether caudal autotomy affects sprint performance in *Micrablepharus atticolus* while accounting for body temperature, reproductive condition, body mass, body size, locality, and regenerated tail length. The associated publication reports that locomotor performance was strongly related to body temperature, pregnancy, body mass, locality, and the relative length of the regenerated tail, whereas caudal autotomy status itself did not substantially reduce sprint speed. ## Sharing and access information These data are intended for public reuse through Dryad. The data were not derived from another public dataset."}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"Coordenação de Aperfeicoamento de Pessoal de Nível Superior","funderIdentifier":"https://ror.org/00x0ma614"},{"funderIdentifierType":"ROR","funderName":"National Council for Scientific and Technological Development","funderIdentifier":"https://ror.org/03swz6y49"},{"funderIdentifierType":"ROR","funderName":"Foundation for Research Support of the Federal District","funderIdentifier":"https://ror.org/04djvx395"},{"funderIdentifierType":"ROR","funderName":"United States Agency for International Development","funderIdentifier":"https://ror.org/01n6e6j62"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.gxd2547nc","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":1,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-07-07T00:12:58Z","registered":"2026-07-07T00:12:59Z","published":null,"updated":"2026-07-07T00:12:59Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.59zw3r2c2","type":"dois","attributes":{"doi":"10.5061/dryad.59zw3r2c2","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Universidade de Brasília"],"name":"Nappo, Humberto","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-7810-1635"}]},{"nameType":"Personal","affiliation":["Universidade de Brasília"],"name":"Campelo, Pedro","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Universidade de Brasília"],"name":"Machado, Laís","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-6782-3189"}]},{"nameType":"Personal","affiliation":["Instituto Federal do Piauí"],"name":"Cavalcante, Vitor Hugo","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-7617-3907"}]},{"nameType":"Personal","affiliation":["Universidade de Brasília"],"name":"Colli, Guarino","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-2628-5652"}]}],"titles":[{"title":"Habitat suitability and centrality—not peripherality—predict demographic performance in a Neotropical lizard"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"subject":"abundant-center model"},{"subject":"Center-Periphery Hypothesis"},{"subject":"Cerrado"},{"subject":"demographic performance"},{"subject":"developmental instability"},{"subject":"environmental suitability"},{"subject":"lizard"},{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Biological sciences","subjectScheme":"fos"},{"subject":"fluctuating asymmetry"}],"contributors":[],"dates":[{"date":"2023-06-28T14:45:08Z","dateType":"Created"},{"date":"2023-06-28T14:47:01Z","dateType":"Submitted"},{"date":"2026-07-07T00:00:00Z","dateType":"Issued"},{"date":"2026-07-07T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1111/jbi.14688","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["321726 bytes"],"formats":[],"version":"8","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Aim The centre–periphery hypothesis (CPH) sustains that peripheral\n populations are subjected to harsher environmental conditions, which\n reduces demographic performance manifested, among others, by lower\n abundance, higher inbreeding and higher developmental instability.\n However, range centrality/peripherality is not necessarily a good proxy\n for environmental suitability since geographical distributions are often\n irregular in shape, barriers may limit dispersal towards suitable areas,\n and ancestral range shifts may have occurred unevenly in each direction.\n We evaluated predictions of the CPH for populations of the Neotropical\n lizard Notomabuya frenata by modelling the effects of environmental\n suitability and geographical centrality/peripherality upon demographic\n performance. Location Brazilian Cerrado.TaxonNotomabuya frenata (Squamata,\n Scincidae). Methods We used populational values of body size, body mass,\n body condition, tail autotomy and fluctuating asymmetry (FA; a proxy of\n developmental instability) to estimate demographic performance. Then, we\n built correlative models to estimate environmental suitability for each\n sampled population. Finally, we used hierarchical Bayesian models based on\n a combination of stochastic partial differential equations and integrated\n nested Laplace approximations to model the relationships between\n demographic performance and predictor variables (geographical\n centrality/peripherality and environmental suitability) and make\n predictions across the geographical distribution of N. frenata. Results\n Generalized boosting produced the best environmental suitability models.\n The total suitability area spanned more than 4 million km2 in cis‐Andean\n South America. All scalation counts presented some degree of asymmetry.\n Only three response variables—all related to FA—responded significantly to\n predictor changes. Contrary to the predictions of the CPH, one scalation\n count presented higher asymmetry closer to the range center and another in\n more suitable environments. Peripherality did not significantly influence\n any of the response variables. Main Conclusions Populations may respond\n differently to geographical centrality/peripherality and environmental\n suitability, as these characteristics do not necessarily coincide. Studies\n testing the CPH should ideally incorporate these different predictors of\n demographic performance."},{"descriptionType":"Methods","description":"We recorded 17 meristic scalation counts using a\n stereomicroscope: nuchals (NU), parietals (PA), supraoculars (SO),\n prefrontals (PREF), supranasals (SN), nasals (N), postnasals (POSN),\n loreals (LO), lower preoculars (LP), supraciliaries (SC), temporals (T),\n supralabials (SPL), infralabials (IL), chin shields (CHS), sublabials\n (SBL), fourth finger subdigital lamellae (FFSL) and fourth toe subdigital\n lamellae (FTSL)."},{"descriptionType":"TechnicalInfo","description":"# Habitat suitability and centrality—not peripherality—predict demographic\n performance in a Neotropical lizard Associated manuscript: Nappo HC,\n Campelo PH, Machado LPC, Cavalcante VHGL, Colli GR. 2023. *Habitat\n suitability and centrality—not peripherality—predict demographic\n performance in a Neotropical lizard*. *Journal of Biogeography*\n 50:1778–1788.\n [https://doi.org/10.1111/jbi.14688](https://doi.org/10.1111/jbi.14688).\n The study evaluates whether demographic performance of *Notomabuya\n frenata* populations is better predicted by environmental suitability or\n by geographical centrality/peripherality across the species'\n distribution. --- ## Description of the Dataset This dataset contains the\n raw morphological and meristic data used to quantify demographic\n performance and fluctuating asymmetry in populations of the Neotropical\n skink *Notomabuya frenata* sampled throughout its geographic distribution.\n The files include: * morphometric measurements obtained from museum\n specimens and field-collected individuals; * tail autotomy records; *\n bilateral scale counts used to estimate fluctuating asymmetry. These data\n were used to calculate body size, body mass, body condition, tail autotomy\n frequency, and fluctuating asymmetry metrics, which were analyzed in the\n accompanying publication. --- ## Files included in this dataset ### 1.\n Morphometry_data.txt Tab-delimited text file containing morphometric\n measurements and tail autotomy information for individual lizards.\n **Number of records:** 3,305 individuals. #### Variables | Variable |\n Description | Units / Values | | -------------- |\n ------------------------------------------------------- |\n ------------------------------------------------------- | | species |\n Scientific name | Character string | | collection\\_id | Voucher or field\n identification number of each specimen | Character string | | municipality\n | Municipality where the specimen was collected | Character string | | lat\n | Latitude of collection locality | Decimal degrees (WGS84); south is\n negative | | lon | Longitude of collection locality | Decimal degrees\n (WGS84); west is negative | | svl | Snout–vent length | millimetres (mm) |\n | mass | Body mass | grams (g) | | autotomy | Tail condition | y =\n autotomized tail; n = intact tail; NA = unavailable | #### Notes * Each\n row represents one individual. * Museum voucher numbers correspond to the\n original institutional catalog numbers or field identifiers. * Geographic\n coordinates identify collection localities. * Some specimens lack body\n measurements or tail-condition information because these data were\n unavailable from the original source. --- ### 2. Scalation_data.txt\n Tab-delimited text file containing bilateral meristic characters used to\n quantify fluctuating asymmetry. **Number of records:** 531 individuals.\n Each row corresponds to one specimen. #### Variables ##### Identification\n variables | Variable | Description | | -------------- |\n -------------------------------------- | | species | Scientific name | |\n collection\\_id | Voucher or field identification number | | municipality |\n Municipality of collection | | lat | Latitude (decimal degrees, WGS84) | |\n lon | Longitude (decimal degrees, WGS84) | ##### Bilateral scale counts\n For paired characters, suffixes indicate body side: * **_l** = left side *\n **_r** = right side | Variable | Description | |\n ------------------------------------------------ |\n ---------------------------------------------------------------------------------------------------------- | | nuchals\\_l, nuchals\\_d | Number of nuchal scales (left and right sides; original variable names retained from the authors' dataset) | | parietals\\_l, parietals\\_r | Number of parietal scales | | supraoculars\\_l, supraoculars\\_r | Number of supraocular scales | | prefrontals\\_l, prefrontals\\_r | Number of prefrontal scales | | supranasals\\_l, supranasals\\_r | Number of supranasal scales | | nasals\\_l, nasals\\_r | Number of nasal scales | | postnasals\\_l, postnasals\\_r | Number of postnasal scales | | loreals\\_l, loreals\\_r | Number of loreal scales | | lower\\_preoculars\\_l, lower\\_preoculars\\_r | Number of lower preocular scales | | supraciliaries\\_l, supraciliaries\\_r | Number of supraciliary scales | | temporals\\_l, temporals\\_r | Number of temporal scales | | supralabials\\_l, supralabials\\_r | Number of supralabial scales | | infralabials\\_l, infralabials\\_r | Number of infralabial scales | | chin\\_shields\\_l, chin\\_shields\\_r | Number of chin shields | | sublabials\\_l, sublabials\\_r | Number of sublabial scales | | 4finger\\_subd\\_lamel\\_l, 4finger\\_subd\\_lamel\\_r | Number of subdigital lamellae beneath the fourth finger | | 4toe\\_subd\\_lamel\\_l, 4toe\\_subd\\_lamel\\_r | Number of subdigital lamellae beneath the fourth toe | #### Notes * Bilateral scale counts were used to estimate fluctuating asymmetry. * Missing values (NA) indicate that the character could not be scored because of specimen damage, preservation artifacts, or incomplete specimens. --- ## File format Both data files are plain text (.txt) with tab-separated columns. They can be imported into most statistical software, including: * R * Python (pandas) * Microsoft Excel * LibreOffice Calc * SPSS * SAS * Stata Files are encoded as UTF-8 text. --- ## Missing data Missing values are coded as: **NA** Reasons for missing values include: * unavailable morphometric measurements; * damaged or incomplete museum specimens; * characters that could not be confidently scored. --- ## Geographic coordinate system Latitude and longitude are reported in decimal degrees using the WGS84 geographic coordinate system. Negative latitude values indicate the Southern Hemisphere. Negative longitude values indicate the Western Hemisphere. --- ## Taxonomic information Species included: *Notomabuya frenata* (Cope, 1862) Family: Scincidae --- ## Relationship between files The two tables are complementary. The shared fields * species * collection_id * municipality * lat * lon allow records from the morphometric and scalation datasets to be linked when the same individual is represented in both files. Not every specimen has both morphometric and scalation data because different subsets of museum specimens were available for different analyses. --- ## Data collection Morphometric data were obtained from field studies and museum specimens sampled throughout the geographic distribution of *Notomabuya frenata*. Scalation data were collected from preserved specimens through direct counts of paired head scales and subdigital lamellae. Detailed sampling procedures, study localities, and analytical methods are described in the associated publication. --- ## Use of the dataset These data were used to investigate relationships among: * environmental suitability, * geographic centrality/peripherality, * body size, * body mass, * body condition, * tail autotomy frequency, * fluctuating asymmetry, in populations of *Notomabuya frenata* across South America. --- ## Sharing/access Information Links to other publicly accessible locations of the data: Was data derived from another source? No. If yes, list source(s):"}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"National Council for Scientific and Technological Development","funderIdentifier":"https://ror.org/03swz6y49","awardNumber":"311054/2019-6"},{"funderIdentifierType":"ROR","funderName":"Coordenação de Aperfeicoamento de Pessoal de Nível Superior","funderIdentifier":"https://ror.org/00x0ma614","awardNumber":"88881.068430/2014-01"},{"funderIdentifierType":"ROR","funderName":"Foundation for Research Support of the Federal District","funderIdentifier":"https://ror.org/04djvx395","awardNumber":"00193-00000139/2019-15"},{"funderIdentifierType":"ROR","funderName":"United States Agency for International Development","funderIdentifier":"https://ror.org/01n6e6j62"},{"funderIdentifierType":"ROR","funderName":"National Council for Scientific and Technological Development","funderIdentifier":"https://ror.org/03swz6y49"},{"funderName":"Fundação de Apoio à Pesquisa do Distrito Federal"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.59zw3r2c2","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":1,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-07-07T00:00:25Z","registered":"2026-07-07T00:00:26Z","published":null,"updated":"2026-07-07T00:00:26Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.jwstqjqrg","type":"dois","attributes":{"doi":"10.5061/dryad.jwstqjqrg","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Umeå University"],"name":"Cunow, Johannes","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0006-4830-1162"}]},{"nameType":"Personal","affiliation":["Umeå University"],"name":"Pijcke, Femke","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Umeå University"],"name":"Olofsson, Johan","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Oulu"],"name":"Väisänen, Maria","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Umeå University"],"name":"Blume-Werry, Gesche","nameIdentifiers":[]}],"titles":[{"title":"Reindeer grazing induces spatial and functional shifts in root systems of boreal pine forests"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Earth and related environmental sciences","subjectScheme":"fos"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Plant-herbivore interactions","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Soil Microclimate"},{"subject":"Reindeer grazing"},{"subject":"Functional Root Traits"},{"subject":"Spatial Root Distribution"},{"subject":"Lichen Removal"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Lateral roots","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Soil carbon","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[],"dates":[{"date":"2026-06-30T09:12:26Z","dateType":"Created"},{"date":"2026-06-30T09:12:30Z","dateType":"Submitted"},{"date":"2026-07-06T00:00:00Z","dateType":"Issued"},{"date":"2026-07-06T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1002/oik.12211","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["83529603 bytes"],"formats":[],"version":"2","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"The spatial distribution of roots, laterally and vertically, is critical\n for nutrient and water uptake and for driving root-associated carbon\n inputs and turnover. Large herbivores, such as reindeer, influence\n ecosystems belowground by altering aboveground vegetation composition and\n soil microclimate. However, how these herbivore-driven changes translate\n into shifts in root distribution and functional traits remains largely\n unexplored. We compared root systems in northern boreal Scots pine forests\n with and without \u0026gt;50 years of reindeer exclusion. Specifically, we\n assessed how grazing affects the spatial distribution of root biomass and\n traits (root length density, specific root length, root tissue density,\n diameter, and branching frequency) across soil depths and distances from\n trees, in relation to grazing-induced changes in vegetation and soil\n microclimate. Fine root biomass declined with soil depth but not\n distance from trees; grazing reduced biomass predominantly in the top 5\n cm. Grazing caused a functional shift toward thicker, shorter,\n and less-branched roots, especially near the soil surface and farther from\n trees. These shifts coincided with lower aboveground ericoid\n shrub biomass and amplified soil temperature extremes. Reindeer grazing\n decreased fine root biomass and modified the functional composition of\n root systems and soil microclimatic conditions across the forest floor."},{"descriptionType":"TechnicalInfo","description":"# Reindeer grazing induces spatial and functional shifts in root systems\n of boreal pine forests Dataset DOI:\n [10.5061/dryad.jwstqjqrg](https://doi.org/10.5061/dryad.jwstqjqrg) ##\n Description of the data and file structure The data contains 3 files that\n were derived from the approach below. Units are noted in the file, except\n for the soil microclimate data. *Study Design and Methods:* We selected\n three boreal oligotrophic forest sites in northern Finland: Angeli\n (68°57'14.0\"N 25°42'07.8\"E, WGS84), Kätkäsuvanto\n (68°07'49.9\"N 23°21'04.0\"E), and Raja-Jooseppi\n (68°28'18.4\"N 28°28'27.4\"E) with a known history of\n reindeer exclusion., These sites are located on flat terrain featuring\n Scots pine (*Pinus sylvestris* L.) stands on dry, podzolized sandy soils\n with shallow organic layers, and topsoil leaching. Average tree ages are\n approximately 210 years (Raja-Jooseppi) and 50 years (Angeli and\n Kätkäsuvanto). Tree density varies between sites from ca. 400 trees ha^-1^\n (\u0026gt; 7.5 cm diameter at 1.3m height) at Raja-Jooseppi to 1140 at Angeli,\n and 1860 at Kätkäsuvanto. Average tree heights were 7.9 ± 0.8 m, 8.9 ± 1.4\n m, and 10.4 ± 2.0 m at Angeli, Kätkäsuvanto, and Raja-Jooseppi,\n respectively, with an average diameter of 12.5 ± 1.8 cm, 16.7 ± 0.9 cm,\n and 28.3 ± 5.0 cm at 1.3 m height. The climate is sub-continental, with\n mean annual temperatures of 0.22 °C (Angeli), 0.12 °C (Kätkäsuvanto), and\n 0.68 °C (Raja-Jooseppi), and precipitation of 438 mm (Angeli), 504 mm\n (Kätkäsuvanto), and 484 mm (Raja-Jooseppi) at the closest weather stations\n from 2014 to 2023 (Inari Angeli Lintupuoliselkä, Muonio Oustajärv, Inari\n Raja-Jooseppi). Snow cover typically persists from November to May. The\n understory vegetation consists of ericaceous shrubs (*Empetrum nigrum* L.,\n *Vaccinium vitis-idaea* L., and at Angeli also *V. uliginosum* L.).\n Graminoids and mosses are almost absent. At each of the three sites,\n fences were built during the past century to control reindeer movement as\n a part of reindeer herding practices. These fences were built irrespective\n of forest management, soil conditions, and with similar topography on both\n sides of the fence. Consequently, one side of the fence has been grazed\n constantly while the other side has not been grazed since. More precisely,\n reindeer did not have access to the other side of the fence since the\n 1920s (Raja-Jooseppi), 1960s (Angeli), and 1970s (Kätkäsuvanto). Reindeer\n have access to grazed areas year-round.\n *SoilMicroClimateData_RJANKA2025.txt* We used a transect design for soil\n microclimate monitoring. We selected individual pine trees as “starting\n points” for a transect along which we created sampling points at 1, and 3\n m distances, and made sure that no other tree was closer to the sampled\n points. The 3 m distance corresponded to the typical maximal gap radius in\n these forests. Trees were defined as having a diameter ≥7.5 cm at 1.3 m\n height. The transects were roughly equally spaced along the fence within\n an area spanning 130 m alongside the fence at Raja-Jooseppi and 50 m in\n Angeli and Kätkäsuvanto. For the soil microclimate, we set up 12 transects\n in total, four at each site, with two on each site of the fence. We\n recorded soil temperature at 6 cm depth in the mineral layer and soil\n moisture in the upper 10 cm (Tomst TMS-4) at 1 m and 3 m from the nearest\n tree (a transect with two measurement points), with two replicates per\n grazing regime at each site. One logger broke (n = 23 loggers). The\n vegetation around the logger resembled typical lichen cover and vegetation\n at the study site. Loggers recorded data every 15 minutes\n *Data_RJANKA_2025_RootTraits.csv* We used a transect design for root\n sampling. We selected individual pine trees as “starting points” for a\n transect along which we created sampling points at 1, 2, and 3 m\n distances, and made sure that no other tree was closer to the sampled\n points. The 3 m distance corresponded to the typical maximal gap radius in\n these forests. Trees were defined as having a diameter ≥7.5 cm at 1.3 m\n height. The transects were roughly equally spaced along the fence within\n an area spanning 130 m alongside the fence at Raja-Jooseppi and 50 m in\n Angeli and Kätkäsuvanto. For roots, 27 transects were placed in total\n across sites, with four per treatment in Angeli and Kätkäsuvanto and five\n (grazed) and six (ungrazed) in Raja-Jooseppi. The transect placement was\n irrespective with regards to vegetation and orientation and positioned at\n least 2.3 m from the fence and ≥5 m from trees on the opposite side of the\n fence. At each site, soil cores were collected from each of the three\n distances (1, 2, and 3 m), which resulted in n=81 soil cores to analyze\n the lateral root trait distribution. We used an 11.1 cm diameter auger\n down to 25 cm depth, after clipping aboveground vegetation and removing\n litter. Sampling was conducted between August and September 2022. The dead\n lichen layer, which was moist and compressed, was left intact because\n roots grew within it. Cores were segmented into 5 cm slices (n = 395) to\n analyze vertical root distribution, and roots of all sizes were collected\n (largest diameter 25 mm). Lichen height, organic layer depth, and leaching\n depth were recorded for each soil core, and lichen height was averaged\n from five random measurements per core. Samples were stored at 4 °C until\n processing. Soil samples were soaked and rinsed to extract living roots\n from the community-level root samples, which were sorted into fine (\u0026lt; 2\n mm diameter) and coarse roots (≥ 2 mm diameter ≤ 25 mm) and thoroughly\n cleaned. A subsample was scanned (Epson Expression 12000XL, 1800 dpi) to\n obtain fine root traits (RhizoVision Explorer, Seethepalli et al., 2021).\n The scanned subsamples were extrapolated to whole samples based on total\n dry weights. Root length density was calculated as root length per soil\n volume, specific root length as root length per dry weight, and root\n tissue density as dry weight per root volume. We also determined fine root\n biomass, average fine root diameter and fine root branching frequency,\n whereas we only analyzed biomass for coarse roots.\n *Data_RJANKA_2025_FieldLayerBiomass.csv* Ericoid shrub and lichen biomass\n were sampled from ten (Angeli, Kätkäsuvanto) and twelve (Raja-Jooseppi) 25\n × 25 cm plots on each site of the fence arranged in a grid parallel to the\n fence. Plots were located at two distances from the fence (2.5 m and 7.5\n m) and spaced at 10 m apart along the fence line. These plots were on\n average 2.2 m away from the closest tree, ranging between 0.3 and 6.3 m\n (n=64). Vegetation was sorted into dwarf shrub species (*E. nigrum, V.\n vitis-idaea, V. uliginosum*)** and lichen biomass, dried, and weighed. \n \n ### Files and variables #### File: SoilMicroClimateData_RJANKA2025.txt\n **Description:**  ##### Variables * Site: 3 Locations in northern\n Finland, Angeli (68°57'14.0\"N 25°42'07.8\"E, WGS84),\n Kätkäsuvanto (68°07'49.9\"N 23°21'04.0\"E), and\n Raja-Jooseppi (68°28'18.4\"N 28°28'27.4\"E). Each site\n includes grazed and non grazed areas at 1 m and 3 m nearest-tree distance\n * Grazing: Grazed or not grazed by reindeer * Distance: 1 or 3 m distance\n from the nearest tree * Replic: per site x treatment transect replicate *\n Datetime: Year.Month.Day Hour:Minute * Timezone: \"UTC\" * Tsoil:\n °C * Tsurface: °C * Tair: °C * SoilMoisture: sensor counts (dimensionless)\n * SoilMoisture.vol: cm³ cm^-^³ * time: H:M * year: year * month: month *\n day: day * hour: hour * minute: minute * doy: day of the year * season:\n autumn, winter, summer * Logger_ID: numeric unit identifier #### File:\n Data_RJANKA_2025_FieldLayerBiomass.csv **Description:**  ##### Variables *\n ID:  * Site: 3 Locations in northern Finland, Angeli\n (68°57'14.0\"N 25°42'07.8\"E, WGS84), Kätkäsuvanto\n (68°07'49.9\"N 23°21'04.0\"E), and Raja-Jooseppi\n (68°28'18.4\"N 28°28'27.4\"E). Each site includes grazed\n and non grazed areas at varying distance to the nearest tree * Treatment:\n Grazed or not grazed by reindeer * Plot: 1-12 \u0026amp; 1-24, site specific ID\n * Lichen: g m^-2^, dry biomass * Litter: g m^-2^, dry biomass * Moss: g\n m^-2^, dry biomass * Pinus seedling: g m^-2^, dry biomass * Festuca\n ovina: g m^-2^, dry biomass * Festuca rubra: g m^-2^, dry biomass *\n V.vitis-idaea: g m^-2^, dry biomass * V.uliginosum: g m^-2^, dry biomass *\n E.nigrum: g m^-2^, dry biomass * FenceDistance: m, distance to the fence\n separating grazed from not grazed * Shrubs: g m^-2^, dry biomass *\n Vascular: g m^-2^, dry biomass * TreeDistance:  m, distance from the\n nearest tree * lingon_prop: % of ericoid biomass per plot * crow_prop: % %\n of ericoid biomass per plot * odon_prop: % % of ericoid biomass per plot\n #### File: Data_RJANKA_2025_RootTraits.csv **Description:**  #####\n Variables * Treatment: Grazed or not grazed by reindeer * Distance: 1, 2,\n or 3 m distance from the nearest tree * Depth: 5cm thick slices from 0-25\n cm soil depth * FocusTree: transect identifier * Site: 3 Locations in\n northern Finland, Angeli (68°57'14.0\"N 25°42'07.8\"E,\n WGS84), Kätkäsuvanto (68°07'49.9\"N 23°21'04.0\"E), and\n Raja-Jooseppi (68°28'18.4\"N 28°28'27.4\"E). Each site\n includes grazed and non grazed areas at 1 m, 2m, and 3 m nearest-tree\n distance * FineRootBiomass: g m^-2^, dry biomass *\n CoarseRootBiomass_NoLargeStructuralRoots: g m^-2^, dry biomass, excluding\n thick structural pine roots * CoarseRootBiomass: g m^-2^, dry biomas *\n RTD: g cm^-3^, root tissue density * RLD: m m^-3^, root length density *\n SRL: cm g^-1^, specific root length * Avg.D: mm, average diameter * Br.Fr:\n points mm^-1^, Branching frequency * Transect: transect identifier *\n org_lay_height: cm, organic layer depth * lichen_height: cm, lichen height\n * tree_height:, m, height of the nearest tree ## Code/software ``` R\n version 4. 4. 1 ``` ## Access information Other publicly accessible\n locations of the data: * \n \n Data was derived from the following sources: *"}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"\n        Swedish Research Council for Environment Agricultural Sciences and\n        Spatial Planning\n      ","funderIdentifier":"https://ror.org/03pjs1y45","awardNumber":"2022-01196"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.jwstqjqrg","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-07-06T23:15:49Z","registered":"2026-07-06T23:15:50Z","published":null,"updated":"2026-07-06T23:15:50Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.hqbzkh1zg","type":"dois","attributes":{"doi":"10.5061/dryad.hqbzkh1zg","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Fu-Jen Catholic University Hospital"],"name":"Chang, Che-Cheng","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-9395-4804"}]},{"nameType":"Personal","affiliation":["Fu Jen Catholic University"],"name":"Lu, Chi-Jie","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Fu Jen Catholic University"],"name":"Liu, Tzu-Chi","nameIdentifiers":[]}],"titles":[{"title":"Amplicon sequence variant (ASV) count table for paired baseline (T0) and 6-month follow-up (T1) fecal samples from 15 patients with Myasthenia gravis (MG)"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Computer and information sciences","subjectScheme":"fos"},{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Medical and health sciences","subjectScheme":"fos"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Myasthenia gravis","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Microbiota"},{"subject":"ASV"}],"contributors":[],"dates":[{"date":"2026-06-15T02:44:24Z","dateType":"Created"},{"date":"2026-06-27T14:51:10Z","dateType":"Submitted"},{"date":"2026-07-06T00:00:00Z","dateType":"Issued"},{"date":"2026-07-06T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["935913 bytes"],"formats":[],"version":"6","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"This dataset comprises bacterial 16S rRNA gene amplicon sequencing data\n generated to characterize the gut microbial community composition of human\n participants. Total genomic DNA was extracted from homogenized fecal\n material, and the V3–V4 hypervariable region was amplified using the\n 341F/806R primer pair and sequenced on an Illumina MiSeq platform (2 × 300\n bp, paired-end). The deposit contains samples derived from\n 15 participants. Primary data are provided as demultiplexed raw\n paired-end reads in FASTQ format. Accompanying processed files—generated\n in QIIME 2 (v2021.4) with DADA2—include the amplicon sequence variant\n (ASV) feature table, representative ASV sequences, and taxonomic\n assignments made against the SILVA reference database. A sample metadata\n file links each sequence file to its corresponding sample. These data\n support standard microbiome workflows, including α-diversity (observed\n ASVs, Shannon, Simpson) and β-diversity estimation, taxonomic profiling,\n and differential-abundance testing (e.g., LEfSe). Because the deposit\n provides exact, single-nucleotide-resolution ASVs rather than clustered\n OTUs, it is well suited to reuse in cross-study comparisons,\n meta-analyses, reference-classifier benchmarking, and methodological\n development. All fecal samples were obtained from human\n participants with written informed consent and approval by the Research\n Ethics Committee of Fu-Jen Catholic University Hospital (FJUH109054) and\n conducted in accordance with the Declaration of Helsinki. Sequence data\n and metadata have been de-identified, and no personally identifiable\n information is included."},{"descriptionType":"Methods","description":"\u003cstrong\u003eFecal sample collection and DNA\n extraction\u003c/strong\u003e Fecal samples were collected\n from each participant in sterile containers, snap-frozen within 2 h of\n voiding, and stored at –80 °C until processing. Total genomic DNA was\n isolated from approximately 200 mg of homogenized fecal material using the\n EasyPrep Stool Genomic DNA Kit (Biotools, New Taipei City, Taiwan),\n following the manufacturer's instructions. All extracts were\n normalized to 5 ng/μL before amplification.\n \u003cstrong\u003e16S rRNA gene library construction and\n sequencing\u003c/strong\u003e The V3–V4 hypervariable\n region of the bacterial 16S rRNA gene was amplified using the universal\n primer pair 341F and 806R, following the Illumina 16S Metagenomic\n Sequencing Library Preparation protocol. Each 25-μL reaction contained\n 12.5 ng of template DNA combined with KAPA HiFi HotStart ReadyMix (Roche,\n Basel, Switzerland). Amplicons (approximately 500 bp) were resolved on\n 1.5% agarose gels and purified using AMPure XP magnetic beads (Beckman\n Coulter, Brea, CA, USA). Dual-indexed sequencing adapters were appended in\n the second PCR step using the Nextera XT Index Kit (Illumina, San Diego,\n CA, USA). Library quality was monitored using a Qubit 4.0 fluorometer and\n Qsep100 capillary electrophoresis system (BiOptic, Taipei, Taiwan). The\n library was sequenced (2 × 300 bp paired-end) using an Illumina MiSeq\n platform. \u003cstrong\u003e16S rRNA sequencing\n analysis\u003c/strong\u003e Raw reads were demultiplexed\n using sample-specific barcodes, and primer and adapter sequences were\n trimmed using the QIIME 2 cutadapt plugin (v2021.4). Quality filtering,\n dereplication, parametric error-model learning, paired-end joining, and\n chimera removal were performed using DADA2 as implemented in QIIME 2, with\n a maximum expected error threshold of two per read (maxEE = 2) . DADA2\n resolves single-nucleotide differences in merged amplicons, returning\n exact ASVs rather than clustered operational taxonomic units. Taxonomic\n assignments were performed using the q2-feature-classifier plugin trained\n against the SILVA database. \u003cstrong\u003eCommunity\n diversity and differential abundance analyses\u003c/strong\u003e\n ASV tables were rarefied to the minimum library depth across\n samples before diversity computation. Within-sample α-diversity was\n measured using the observed ASV count, Shannon's and Simpsons index.\n paired Wilcoxon signed-rank test. Differentially abundant taxa were\n identified using linear discriminant analysis effect size (LEfSe) .\n Subsequently, a linear discriminant analysis (LDA) is applied to the\n bacterial taxa identified as significantly different to determine the\n effect size of each differentially abundant taxon. In the present study,\n taxa with an LDA score \u0026gt; 2 were considered significant."},{"descriptionType":"TechnicalInfo","description":"# Amplicon sequence variant (ASV) count table for paired baseline (T0) and\n 6-month follow-up (T1) fecal samples from 15 patients with Myasthenia\n gravis (MG) Dataset DOI:\n [10.5061/dryad.hqbzkh1zg](https://doi.org/10.5061/dryad.hqbzkh1zg) ##\n Description of the data and file structure ### Files and variables ####\n **File: asv_table.even.txt, ASV_table_MG_T0_T1.xlsx** Description: Same\n rarefied ASV data as the xlsx in long format (ASV × sample) with taxonomy,\n both time points combined. Tab-delimited UTF-8 text; 1,267 data rows\n (ASVs) × 32 columns (ASV ID + 30 samples + taxonomy). No missing values; 0\n denotes a true zero count. Variables: * **#OTU_num:** ASV identifier\n (matches `ASV_*` in the xlsx and the headers in `asv_reps.fasta`). *\n **MG.02:** baseline (T0) rarefied read count, patient 02; integer ≥ 0,\n unitless. * **MG.05:** baseline (T0) rarefied read count, patient 05;\n integer ≥ 0, unitless. * **MG.06:** baseline (T0) rarefied read count,\n patient 06; integer ≥ 0, unitless. * **MG.07:** baseline (T0) rarefied\n read count, patient 07; integer ≥ 0, unitless. * **MG.09:** baseline (T0)\n rarefied read count, patient 09; integer ≥ 0, unitless. * **MG.10:**\n baseline (T0) rarefied read count, patient 10; integer ≥ 0, unitless. *\n **MG.11:** baseline (T0) rarefied read count, patient 11; integer ≥ 0,\n unitless. * **MG.12:** baseline (T0) rarefied read count, patient 12;\n integer ≥ 0, unitless. * **MG.13:** baseline (T0) rarefied read count,\n patient 13; integer ≥ 0, unitless. * **MG.14:** baseline (T0) rarefied\n read count, patient 14; integer ≥ 0, unitless. * **MG.15:** baseline (T0)\n rarefied read count, patient 15; integer ≥ 0, unitless. * **MG.16:**\n baseline (T0) rarefied read count, patient 16; integer ≥ 0, unitless. *\n **MG.17:** baseline (T0) rarefied read count, patient 17; integer ≥ 0,\n unitless. * **MG.22:** baseline (T0) rarefied read count, patient 22;\n integer ≥ 0, unitless. * **MG.23:** baseline (T0) rarefied read count,\n patient 23; integer ≥ 0, unitless. * **MGP.02:** 6-month follow-up (T1)\n rarefied read count, patient 02; integer ≥ 0, unitless. * **MGP.05:**\n 6-month follow-up (T1) rarefied read count, patient 05; integer ≥ 0,\n unitless. * **MGP.06:** 6-month follow-up (T1) rarefied read count,\n patient 06; integer ≥ 0, unitless. * **MGP.07:** 6-month follow-up (T1)\n rarefied read count, patient 07; integer ≥ 0, unitless. * **MGP.09:**\n 6-month follow-up (T1) rarefied read count, patient 09; integer ≥ 0,\n unitless. * **MGP.10:** 6-month follow-up (T1) rarefied read count,\n patient 10; integer ≥ 0, unitless. * **MGP.11:** 6-month follow-up (T1)\n rarefied read count, patient 11; integer ≥ 0, unitless. * **MGP.12:**\n 6-month follow-up (T1) rarefied read count, patient 12; integer ≥ 0,\n unitless. * **MGP.13:** 6-month follow-up (T1) rarefied read count,\n patient 13; integer ≥ 0, unitless. * **MGP.14:** 6-month follow-up (T1)\n rarefied read count, patient 14; integer ≥ 0, unitless. * **MGP.15:**\n 6-month follow-up (T1) rarefied read count, patient 15; integer ≥ 0,\n unitless. * **MGP.16:** 6-month follow-up (T1) rarefied read count,\n patient 16; integer ≥ 0, unitless. * **MGP.17:** 6-month follow-up (T1)\n rarefied read count, patient 17; integer ≥ 0, unitless. * **MGP.22:**\n 6-month follow-up (T1) rarefied read count, patient 22; integer ≥ 0,\n unitless. * **MGP.23:** 6-month follow-up (T1) rarefied read count,\n patient 23; integer ≥ 0, unitless. * **taxonomy:** taxonomic lineage\n assigned against the SILVA database (QIIME 2 q2-feature-classifier);\n greengenes-style, semicolon-separated seven ranks `k__; p__; c__; o__;\n f__; g__; s__` (kingdom, phylum, class, order, family, genus, species);\n unresolved ranks left empty after the prefix. **File: asv_reps.fasta**\n Description: FASTA file of representative nucleotide sequences for the\n ASVs (DADA2 in QIIME 2). Contains 1,287 records. Each record header is the\n ASV identifier (`\u0026gt;ASV_n`, matching `#OTU_num` / the `ASV_*` columns),\n followed by the representative 16S rRNA V3–V4 sequence. No variables\n (sequence file). ## Code/software **Viewing the data files.** No\n proprietary software is required. The tabular data can be opened in any\n spreadsheet application or read programmatically. The `.xlsx` workbook\n opens in any spreadsheet program; `asv_table_even.txt` is a UTF-8,\n tab-delimited text file readable in any text editor or imported into R\n (`read.delim`) or Python (`pandas.read_csv(sep=\"\\t\")`);\n `asv_reps.fasta` is a plain-text FASTA file viewable in any text editor or\n sequence viewer. **Software used to generate the data.** Raw 16S rRNA\n V3–V4 reads were processed in QIIME 2 (v2021.4): primer/adapter trimming\n with the cutadapt plugin; denoising, dereplication, paired-end merging,\n and chimera removal with DADA2 (maxEE = 2), which produced the amplicon\n sequence variants (ASVs) and their representative sequences\n (`asv_reps.fasta`); and taxonomic classification with the\n q2-feature-classifier plugin trained against the SILVA reference database\n (`taxonomy` column). ASV count tables were rarefied to an even depth of\n 29,004 reads per sample to yield the files provided here. **Downstream\n analysis.** Statistical analysis and modelling were performed in Python\n (v3.8.8) in Jupyter Notebook (v6.3.0) ## Access information N/A ## Human\n subjects data The study protocol was approved by the Research Ethics\n Committee of Fu-Jen Catholic University Hospital (FJUH109054) and\n conducted in accordance with the Declaration of Helsinki. All participants\n provided written informed consent."}],"geoLocations":[],"fundingReferences":[{"funderName":"Fu-Jen Catholic University Hospital","awardNumber":"PL-202308007-V"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.hqbzkh1zg","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-07-06T23:15:32Z","registered":"2026-07-06T23:15:32Z","published":null,"updated":"2026-07-06T23:15:32Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.r7sqv9snh","type":"dois","attributes":{"doi":"10.5061/dryad.r7sqv9snh","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Lakehead University"],"name":"Ross, Alexander","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-0340-7407"}]},{"nameType":"Personal","affiliation":["Lakehead University"],"name":"Bucci, Kennedy","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Lakehead University"],"name":"Dey, Cody","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Lakehead University"],"name":"Dombroskie, Wade","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Lakehead University"],"name":"MacLeod, Haley","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Lakehead University"],"name":"Hayhurst, Lauren","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Lakehead University"],"name":"Veneruzzo, Cody","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Lakehead University"],"name":"Rennie, Michael","nameIdentifiers":[]}],"titles":[{"title":"Province-wide changes in water clarity and dissolved organic carbon in Ontario, Canada lakes over six decades"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"subject":"brownification"},{"subject":"boreal shield"},{"subject":"lentic"},{"subject":"freshwater"},{"subject":"dissolved organic matter"},{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Biological sciences","subjectScheme":"fos"}],"contributors":[],"dates":[{"date":"2024-11-22T20:20:56Z","dateType":"Created"},{"date":"2026-06-26T21:20:12Z","dateType":"Submitted"},{"date":"2026-07-06T00:00:00Z","dateType":"Issued"},{"date":"2026-07-06T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["2019334 bytes"],"formats":[],"version":"5","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Declining water clarity driven primarily by increases in dissolved organic\n carbon (DOC; i.e., ‘brownification’) has been broadly reported across the\n Northern Hemisphere. In Ontario, Canada, increasing lake DOC\n concentrations have been reported regionally; however, no broad spatial\n and temporal water clarity trends for Ontario lakes currently exist. Here,\n we use data from two provincial monitoring programs to analyze historical\n (1962–1984), contemporary (2008–2023), and long-term (1962–2023) changes\n in water clarity across 684 Ontario lakes using DOC concentrations and\n Secchi depth measurements. Our findings suggest that water clarity in\n Ontario lakes has declined since 1962, with the greatest declines in water\n clarity occurring in the contemporary time period. Water clarity declines\n were greatest in oligotrophic lakes, where DOC concentrations were also\n shown to have increased over time. Our findings highlight the importance\n of regional, temporal, and trophic contexts when considering the magnitude\n and direction of water clarity changes in Ontario lakes."},{"descriptionType":"Methods","description":"Data were part of two Ontario Ministry of Natural Resourcess\n datasets -- the Aquatic Habitat Index (1962-1986) and the Broadscale\n Monitoring Program (2003 to present)"},{"descriptionType":"TechnicalInfo","description":"# Province-wide changes in water clarity and dissolved organic carbon in\n Ontario, Canada lakes over six decades\n [https://doi.org/10.5061/dryad.r7sqv9snh](https://doi.org/10.5061/dryad.r7sqv9snh) ## Description of the data and file structure The following files can be used to recreate the analyses within the manuscript. ### Files and variables #### File: dataForDryadRepo_landscapeDOC_AR_20241122.csv **Description:** Main dataset for analyses ##### Variables * commonID: Matching lakeID for AHI and BsM dataset * lat: Latitude (decimal degrees) * long: Longitude (decimal degrees) * samplingProgram: Provincial Sampling Program (AHI or BsM) * secchiDepth: Secchi depth (m) * maxDepth: Lake maximum depth (m) * yearSample: Sample year * DOC: dissolved organic carbon (mg/L) * TDP: total dissolved phosphorus (ug/L) * scaled_yearSample: centered year sample * scaled_secchi: centered year sample * scaled_maxDepth: centered year sample #### File: commonDataIDs_aruToBsm_20240307.csv **Description:** This file was used to create a commonID between the AHI and BsM datasets ##### Variables * BsM_lakeName: Lake name as written in BsM dataset * latBsM: Latitude (decimal degrees) * longBsM: Longitude (decimal degrees) * bsmWaterbodyID: Unique lake ID for waterbody within BsM dataset * ARU_lakeName: Lake name as written in AHI dataset * latARU_conv: Latitude (decimal degrees) * longARU_conv: Longitude (decimal degrees) * ARU_LID: Unique lake ID for waterbody within AHI dataset * Notes: comments (text) * commonID: Matched ID for indicating paired data between BsM and AHI dataasets #### File: statDatWide_updatedCyc3_20240520.csv **Description:** This is a datasheet that is required for a certain chunk of code ##### Variables * BsM_Cycle: Unique Ontario broadscale managment program cycle (1,2,3) * FMZ: Ontario MNR fishery management zone * lakeName: Lake name as reported in BsM database * waterbodyID: Unique lake ID for waterbody within BsM dataset * secchiDepth: Secchi depth (m) * surfaceArea: Lake surface area (ha) * lakeVolume: Lake volume (m3) * maxDepth: Maximum lake depth (m) * meanDepth: Mean lake depth (m) * lat: Latitude (decimal degrees) * long: Longitude (decimal degrees) * yearSample: Sample year for observation (yr) * COLTR (TCU): True Colour (TCU) * pH: Lake pH (unitless) * cond: Specific conductivity (us/cm) * alk: Alkalinity (mg/L) * Ca: Calcium (mg/L) * MGUT (mg/L): Magnesium (mg/L) * Na: Sodium (mg/L) * KKUT (mg/L): Potassium (mg/L) * CLIDUR (mg/L): Chloride (mg/L) * SSO4UR (mg/L): Dissolved sulphate (mg/L) * SIO3UR (mg/L): Silicates (mg/L) * FEUT: Total unfiltered iron (mg/L) * DIC: Dissolved inorganic carbon (mg/L) * DOC: Dissolved organic carbon (mg/L) * NNHTUR (mg/L): Total Unfiltered Ammonium + Ammonia (mg/L) * NNOTUR (ug/L): Total unfiltered nitrate and nitrite (ug/L) * TKN: Total Kjeldahl Nitrogen (mg/L) * TDP: Total dissolved phosphorus (mg/L) * dateSample: Sample date for water chemistry * NtoPratio: Total N to total P ratio #### File: summaryOfLakeVisits_20260308.xlsx **Description:** This file describes how many times a lake (i.e., commonID) was visited for between 1962 to 2023 ##### Variables * commonID: lake name * Count of commoinID: The amount of times a given lake was sampled between 1962 to 2023 (i.e., inclusive of all AHI and BsM samples #### File: Province.zip **Description:** Zip folder that includes simple shapefile for outline of Ontario ##### Variables * Province.shp: Shapefile file, including all necessary auxiliary files #### File: BsM_ARU_elevation_20240320.csv **Description:** File including elevation above sea level for all study lakes ##### Variables * BsM_lakeName: Unique lake name as described in BsM dataset * latBsM: BsM lake latitude from BsM (decimal degrees) * longBsM: BsM lake longitude from BsM (decimal degrees) * bsmWaterbodyID: unique lakeID from BsM dataset * ARU_lakeName: Unique lake name as described in AHI dataset * latARU_conv: lake latitude from AHI (decimal degrees, converted from UTMs) * longARU_conv: lake longitude from AHI (decimal degrees, converted from UTMs) * ARU_LID: Unique lakeID as described in AHI dataset * Notes: Comments * elevation: Elevation (meters above sea level) #### File: ohnWaterbodies_csvWithCoords.csv **Description:** Geographic description of lakes associated with BsM data ##### Variables * OGF_ID: Unique lakeID for Ontario-wide OHN database * WATERBODY_TYPE: Waterbody type * OFFICIAL_NAME_LABEL: Given name for waterbody * Longitude: Longitude (decimal degrees) * Latitude: Latitude (decimal degrees) ## Code/software #### Files: manuscriptSubmissionScript_20260217.R, theme_DOC.R **Description:** Analysis and visualization script associated with manuscript. All analyses were conducted using R version 4.4. All packages used are described within the manuscript. To maintain working directory dependency and code functionality, create an R project and within that project create a \"data\" subfolder in which all necessary data files are saved. Create another subfolder for \"scripts\", in which \"theme_DOC.R\" can be saved, which maintains plot formating. All necessary packages are freely available through CRAN"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.r7sqv9snh","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-07-06T23:06:24Z","registered":"2026-07-06T23:06:25Z","published":null,"updated":"2026-07-06T23:06:25Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.stqjq2cb7","type":"dois","attributes":{"doi":"10.5061/dryad.stqjq2cb7","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Texas A\u0026M University System"],"name":"Wolf, Lilianna","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Zara Environmental LLC"],"name":"Sprouse, Peter","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Parque Ecológico Chipinque"],"name":"Gomez-Ruiz, Emma","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Texas A\u0026M University System"],"name":"Lacher, Jr., Thomas","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-2398-3439"}]},{"nameType":"Personal","affiliation":["Austin Peay State University"],"name":"Haase, Catherine","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-7682-0625"}]}],"titles":[{"title":"Data and code from: White-nose syndrome suitability of Mexican Sierra Madre Oriental karst to sustain \u003cem\u003ePseudogymnoascus destructans\u003c/em\u003e fungus"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Biological sciences","subjectScheme":"fos"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Bats","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Caves","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Chiroptera","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Conservation science","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Pathogens","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[],"dates":[{"date":"2024-05-30T16:37:25Z","dateType":"Created"},{"date":"2026-06-01T21:51:16Z","dateType":"Submitted"},{"date":"2026-06-10T00:00:00Z","dateType":"Issued"},{"date":"2026-06-10T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsDerivedFrom","relatedIdentifier":"10.5281/zenodo.11396912","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["838478 bytes"],"formats":[],"version":"7","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"White-nose syndrome (WNS), caused by the emerging fungal pathogen\n Pseudogymnoascus desctructans, has led to a steep decline in North\n American bat populations since the disease was first described in 2006.\n Although previously thought to only threaten bat populations in northern\n latitudes with prolonged hibernation in cool climates, recent reports of\n bat mortalities in Texas [USA] and Oaxaca [southern Mexico] indicate that\n populations in warmer regions are also vulnerable to WNS, posing risks to\n ecologically and agriculturally important bats in Mexico. Pseudogymnoascus\n destructans can persist on cave substrates under certain microclimate\n conditions, even without a host. As a result, caves that are not\n consistently used as hibernacula may still serve as reservoirs for\n pathogen transmission. Our objective was to assess microclimate\n suitability within selected caves in northern Mexican karst systems to\n characterize the region’s ability to sustain sufficient growth of P.\n destructans and the ability of Mexican caves to act as foci for the spread\n of this pathogen. We measured internal temperature from 10 caves in the\n Sierra Madre Oriental region of Mexico and correlated monthly mean\n temperatures to external surface variables (elevation, latitude, monthly\n mean surface temperature) to predict internal cave temperatures of known\n caves in surrounding karst systems within the Sierra Madre Oriental\n region. We then assessed the potential suitability for P. descructans in\n these cave systems given known relationships between the fungus and cave\n temperatures. Our results indicate that caves within the Sierra Madre\n Oriental region are likely able to sustain internal microclimates that\n support the growth of P. destructans, with the western ridge of the range\n containing a high density of caves that are particularly suitable for\n fungal growth. Our results suggest that the Sierra Madre Oriental region\n is an important region to monitor for the spread of P. destructans and WNS\n in Mexico."},{"descriptionType":"Methods","description":"We collected internal temperature data from 10 caves across\n Mexico from January to December 2019 in six states and two mountain\n ranges: The Sierra Madre Oriental, which runs longitudinally through six\n states in the central-eastern part of the country, and the Eje Volcánico\n Transversal, a seismically active volcanic belt that runs latitudinal\n through twelve states in south-central Mexico. Most caves were selected\n from existing records provided by collaborating biologists. Two caves\n sampled in Nuevo Leon were new to science and first documented during an\n expedition survey in the summer of 2019; for these caves, data loggers\n were deployed at the time of the initial survey. The geographical position\n of the caves reflected a wide gradient of external surface temperature,\n annual precipitation, lithology, and elevation. All caves were visited to\n inspect for qualifying criteria for the study, which included: (i)\n potential to sustain a wintering bat population, indicated by evidence of\n bat presence during the winter season (e.g., presence of an individual,\n guano); (ii) low levels of human disturbance, avoiding potential\n interferences with the deployed equipment; (iii) representativeness of\n gradients of external factors such as elevation, climate, lithology, and\n spatial distribution.  EasyLog EL-USB-2 data loggers\n were deployed in each cave deemed to be suitable for data collection.\n These data loggers recorded temperature (° C) and relative humidity (%) at\n 1 h intervals throughout the course of their deployment. Data loggers were\n deployed in the coolest room of each cave, which typically was the room\n furthest from the entrance of the cave. Each data logger was attached to\n the wall of a cave, far enough off the floor of the cave to be near an\n area where an overwintering bat might roost, and in an area where the\n equipment would be safe from disturbance throughout the time of its\n deployment. Each data logger was left to record data for at least one\n winter season. The recorded data was downloaded using EasyLog system\n software. We calculated monthly mean internal temperature and relative\n humidity for analyses on the monthly sampling scale.\n External microclimate and landscape data were obtained from\n various open-source databases. Monthly mean surface temperatures (°C) were\n obtained from rasters provided by NASA Earth Observations. A digital\n elevation model (DEM) raster (resolution 30m) was provided by the Amazon\n Web Services Terrain Titles and the Open Topography global datasets from R\n (package “elevatr”)."},{"descriptionType":"TechnicalInfo","description":"# Data from: White-nose syndrome stepping-stones: suitability of the\n Sierra Madre Oriental Mexican karst regions to sustain Pseudogymnoascus\n destructans fungus https://doi.org/10.5061/dryad.stqjq2cb7 ## Description\n of the data and file structure There are 2 data sheets in this dataset:\n `MeasuredCaveData` are measured internal temperature data from 10 caves\n across varying karst landscapes in Mexico; `KnownMexicanCaveData` are\n extracted surface data from known caves in the karst system. We analyzed\n the internal temperature data (MeasuredCaveData) from 10 caves to reveal\n internal thermal patterns—especially those correlated with suitable\n internal microclimates for fungal growth—that are significantly correlated\n with external variables (latitude and elevation). From this, we generate a\n predictive model to assess the potential suitability for *Pseudogymnoascus\n destructans* in Mexican karst systems based on the external features\n (KnownMexicanDaveData). The dataset includes measured microclimate data\n (MeasuredCaveData) from 10 sampled caves, including the monthly mean\n internal temperature (in Celsius; measured by EasyLog EL-USB-2 data\n loggers), latitude (shorten to 2 decimal places for sensitivity of cave\n locations), and elevation (m) gathered from a digital elevation model\n (DEM) raster (resolution 30m) by the Amazon Web Services Terrain Titles\n and the Open Topography global datasets from R (package “elevatr”); the\n dataset also includes extracted mean monthly surface temperature (in\n Celsius; provided by NASA Earth Observations) and elevation at 2,032 caves\n throughout Mexico (KnownMexicanDaveData). The attached code provides the\n predictive model relating external variables to internal cave temperature,\n the fungal growth model, and the scaling parameters for fungal growth.  ##\n Sharing/Access information Exact latitude and longitude values are not\n shareable as they are sensitive locations. Full location data are\n available from P. Sprouse. Data was derived from the following sources: *\n Internal temperature: EasyLog EL-USB-2 data loggers * External surface\n temperature: NASA Earth Observations * Elevation: Amazon Web Services\n Terrain Titles and the Open Topography global datasets from R (package\n “elevatr”) All data available in Wolf_et_al_dataset.xlsx.  ##\n Code/Software Available code is annotated and available for Rv4.3.1. Code\n stored on Zenodo via Software Related Work. R Core Team (2023). *R: A\n Language and Environment for Statistical Computing*. R Foundation for\n Statistical Computing, Vienna, Austria.\n [https://www.R-project.org/](https://www.R-project.org/)."}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"Texas A\u0026M University System","funderIdentifier":"https://ror.org/0034eay46"},{"funderIdentifierType":"ROR","funderName":"Rufford Foundation","funderIdentifier":"https://ror.org/02bxrrf91"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.stqjq2cb7","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-06-10T21:08:18Z","registered":"2026-06-10T21:08:19Z","published":null,"updated":"2026-07-06T23:02:20Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.tmpg4f5dj","type":"dois","attributes":{"doi":"10.5061/dryad.tmpg4f5dj","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Institut für Rechtspolitik an der Universität Trier"],"name":"Gronefeld, Sarah Christin","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0008-6955-709X"}]},{"nameType":"Personal","affiliation":["Instituto de Productos Naturales y Agrobiología"],"name":"López, Heriberto","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Zurich"],"name":"Hawlitschek, Oliver","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Universität Trier"],"name":"Schulte-Middelmann, Tobias","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau"],"name":"Hofmann, Felicia","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Universität Trier"],"name":"Tandukar, Prabina","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Musée National d'Histoire Naturelle"],"name":"Hochkirch, Axel","nameIdentifiers":[]}],"titles":[{"title":"Data from: Comparison of assessment parameters for identifying Key Biodiversity Areas"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Biological sciences","subjectScheme":"fos"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Zoology","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Canary Islands","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Insects","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"SNP"}],"contributors":[],"dates":[{"date":"2026-02-24T18:40:05Z","dateType":"Created"},{"date":"2026-06-26T12:05:06Z","dateType":"Submitted"},{"date":"2026-07-06T00:00:00Z","dateType":"Issued"},{"date":"2026-07-06T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1016/j.biocon.2026.111863","relatedIdentifierType":"DOI"},{"relationType":"IsDerivedFrom","relatedIdentifier":"https://github.com/TheC0der856/Compare_KBA_Criteria","relatedIdentifierType":"URL"}],"relatedItems":[],"sizes":["113501443947 bytes"],"formats":[],"version":"7","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"The dataset comprises raw reads of ddRADseq libraries prepared for\n paired-end sequencing (2 x 150bp) and the final SNP dataset (5237 loci,\n 345 individuals) of Ariagona margaritae collected between 2009 and 2024 on\n Tenerife, La Gomera, and El Hierro. The final SNP was published as a .stru\n file. All raw reads from SNP sequencing were also published as fastq.gz\n files. Barcodes can be found in SuppTab. 7 of the associated article. A\n picture of a female adult of A. margaritae was published as a jpg.\n Furthermore, we included the coordinates as a .shp file in the same\n resolution as they were already published in SuppTab. 2 of the associated\n article. We also included all important polygons or points used to\n identify Key Biodiversity Areas (KBAs) based on the metrics: (ii) Area of\n occupancy (AOO), (iii) Extent of suitable habitat (ESH), (iv) Range, and\n (v) Locality. AOO, localities and the range were deposited as .shp files.\n The habitat model was deposited instead of the extent of suitable habitat,\n as ESH is easy to create by subtracting the range from the suitable\n habitat. It was also deposited as a .shp file. The structure cluster\n proportion in each potential KBA was deposited as a .shp file, so the\n Example Code in the Supplements of the associated article can be executed.\n The file contains polygons for each potential KBA and means of STRUCTURE\n cluster proportions across individuals sampled at each potential KBA. As\n already stated in the associated article we have organised the necessary\n permissions to collect data on the Canary islands. There were no ethics\n considerations necessary."},{"descriptionType":"TechnicalInfo","description":"# Data from: Comparison of assessment parameters for identifying Key\n Biodiversity Areas Dataset DOI:\n [10.5061/dryad.tmpg4f5dj](https://doi.org/10.5061/dryad.tmpg4f5dj) ##\n Description of the data and file structure raw reads: DNA was extracted\n using the Qiagen DNeasy® Blood \u0026amp; Tissue kit (Hilden, Germany).\n ddRADseq libraries were prepared for paired-end sequencing on an Illumina\n NovaSeq platform (2 x 150bp) (Suchan et al. 2024). In contrast to the\n library preparation protocol, 24 different barcodes were used for 96\n samples, which were assigned to four different Illumina indexes (SuppTab.\n 6 in associated publication). The raw reads were evaluated together with\n some raw reads of *A. margaritae* that have been already published\n (Gronefeld et al., 2026). Gronefeld, S. C., López, H., Schmidt, R., and\n Hochkirch, A. 2026. Identifying Key Biodiversity Areas Based on Distinct\n Genetic Diversity. *Molecular Ecology Resources* 26(2):e70094.\n [https://doi.org/10.1111/1755-0998.70094](https://doi.org/10.1111/1755-0998.70094). Suchan, T., Dufresnes, C., Schmidt, R., Ambu, J., Gippner, S., and Vences, M. 2024. ddRADseq for animal population genomics/phylogenomics. protocols.io. DOI: dx.doi.org/10.17504/protocols.io.kxygx3nzwg8j/v1. To create Ariagona_SNPdataset: Stacks 2.6.6 was used to demultiplex, filter, trim raw reads to 65bp, and create an assembly and a catalogue of loci to finally identify SNPs (-M 3, -n 4, -p 22, -r 0.8, -write-single-snp, --min-maf 0.05) The 29 populations correspond to collection sites, which are at least 1 km apart and were calculated for the number of localities. Individuals with more than 40% missing data were removed. Afterwards, populations were recalculated. sf objects were created using this code: ([https://github.com/TheC0der856/Compare_KBA_Criteria](https://github.com/TheC0der856/Compare_KBA_Criteria)). ### Files and variables #### File: Ariagona.jpg **Description:** It is a picture of a female, mature *Ariagona margaritae* Kraus, 1892 (Tettigoniidae, Orthoptera) individual, collected in Icod in 2024 (SG213 oder SG214). The picture was taken within month of collection. #### File: range.prj **Description:** needed for range.shp, which is representing the updated range of *Ariagona margaritae*. #### File: range.dbf **Description:** needed for range.shp, which is representing the updated range of *Ariagona margaritae*. #### File: range.shx **Description:** needed for range.shp, which is representing the updated range of *Ariagona margaritae*. #### File: suitable_habitat.dbf **Description:** needed for suitable_habitat.shp, which is representing the suitable habitat for *Ariagona margarita* resulting from our habitat model. #### File: suitable_habitat.prj **Description:** needed for suitable_habitat.shp, which is representing the suitable habitat for *Ariagona margarita* resulting from our habitat model. #### File: suitable_habitat.shp **Description:** Representing the suitable habitat for *Ariagona margarita* resulting from our habitat model. #### File: range.shp **Description:** Representing the updated range of *Ariagona margaritae*. #### File: suitable_habitat.shx **Description:** needed for suitable_habitat.shp, which is representing the suitable habitat for *Ariagona margarita* resulting from our habitat model. #### File: 1_R1_001.fastq.gz **Description:** Raw reads of *Ariagona margaritae*. #### File: 1_R2_001.fastq.gz **Description:** Raw reads of *Ariagona margaritae*. #### File: 2_R1_001.fastq.gz **Description:** Raw reads of *Ariagona margaritae*. #### File: 2_R2_001.fastq.gz **Description:** Raw reads of *Ariagona margaritae*. #### File: 3_R1_001.fastq.gz **Description:** Raw reads of *Ariagona margaritae*. #### File: 3_R2_001.fastq.gz **Description:** Raw reads of *Ariagona margaritae*. #### File: 4_R1_001.fastq.gz **Description:** Raw reads of *Ariagona margaritae*. #### File: 4_R2_001.fastq.gz **Description:** Raw reads of *Ariagona margaritae*. #### File: 5_R1_001.fastq.gz **Description:** Raw reads of *Ariagona margaritae*. #### File: 5_R2_001.fastq.gz **Description:** Raw reads of *Ariagona margaritae*. #### File: 6_R1_001.fastq.gz **Description:** Raw reads of *Ariagona margaritae*. #### File: 6_R2_001.fastq.gz **Description:** Raw reads of *Ariagona margaritae*. #### File: 7_R1_001.fastq.gz **Description:** Raw reads of *Ariagona margaritae*. #### File: 7_R2_001.fastq.gz **Description:** Raw reads of *Ariagona margaritae*. #### File: 8_R1_001.fastq.gz **Description:** Raw reads of *Ariagona margaritae*. #### File: 8_R2_001.fastq.gz **Description:** Raw reads of *Ariagona margaritae*. #### File: 10_R1_001.fastq.gz **Description:** Raw reads of *Ariagona margaritae*. #### File: 10_R2_001.fastq.gz **Description:** Raw reads of *Ariagona margaritae*. #### File: 11_R1_001.fastq.gz **Description:** Raw reads of *Ariagona margaritae*. #### File: 11_R2_001.fastq.gz **Description:** Raw reads of *Ariagona margaritae*. #### File: 13_R1_001.fastq.gz **Description:** Raw reads of *Ariagona margaritae*. #### File: 13_R2_001.fastq.gz **Description:** Raw reads of *Ariagona margaritae*. #### File: 15_R1_001.fastq.gz **Description:** Raw reads of *Ariagona margaritae*. #### File: 15_R2_001.fastq.gz **Description:** Raw reads of *Ariagona margaritae*. #### File: Ariagona_SNPdataset.stru **Description:** SNP dataset of *Ariagona margaritae*. 345 individuals, 5237 SNPs, diploid, one extra row with information on populations, 0 is representing missing data. #### File: potential_KBAs.dbf **Description:** needed for potential_KBAs.shp, which shows how we delinated potential KBAs we tested for identification with KBA criteria. #### File: potential_KBAs.prj **Description:** needed for potential_KBAs.shp, which shows how we delinated potential KBAs we tested for identification with KBA criteria. #### File: potential_KBAs.shp **Description:** The potential KBAs we tested for identification with KBA criteria. #### File: potential_KBAs.shx **Description:** needed for potential_KBAs.shp, which shows how we delinated potential KBAs we tested for identification with KBA criteria. #### File: AOO.dbf **Description:** needed for opening AOO.shp, which is showing the 2 x 2 km grids with occurrence points of *Ariagona margaritae*. #### File: AOO.prj **Description:** needed for opening AOO.shp, which is showing the 2 x 2 km grids with occurrence points of *Ariagona margaritae*. #### File: AOO.shp **Description:** showing the 2 x 2 km grids with occurrence points of *Ariagona margaritae*. #### File: AOO.shx **Description:** needed for opening AOO.shp, which is showing the 2 x 2 km grids with occurrence points of *Ariagona margaritae*. #### File: localities.dbf **Description:** needed to open localities.shp, which is showing all localities for *Ariagona margaritae*. #### File: localities.prj **Description:** needed to open localities.shp, which is showing all localities for *Ariagona margaritae*. #### File: localities.shp **Description:** showing all localities for *Ariagona margaritae*. #### File: localities.shx **Description:** needed to open localities.shp, which is showing all localities for *Ariagona margaritae*. #### File: coordinates.dbf **Description:** needed for opening coordinates.shp, which is showing all coordinates where *Ariagona margaritae* was found. #### File: coordinates.prj **Description:** needed for opening coordinates.shp, which is showing all coordinates where *Ariagona margaritae* was found. #### File: coordinates.shp **Description:** showing all coordinates where *Ariagona margaritae* was found. #### File: coordinates.shx **Description:** needed for opening coordinates.shp, which is showing all coordinates where *Ariagona margaritae* was found. #### **File: structure_cluster_proportion_in_each_potential_KBA.dbf** **Description**: This file is needed to open structure_cluster_proportion_in_each_potential_KBA.shp #### **File: structure_cluster_proportion_in_each_potential_KBA.prj** **Description**: This file is needed to open structure_cluster_proportion_in_each_potential_KBA.shp #### **File: structure_cluster_proportion_in_each_potential_KBA.shp** **Description**: This file shows the mean cluster proportion between all individuals occuring in the potential KBA. This file can be used for the example code in the Supplements of the Paper \"Comparison of metrics for identifying Key Biodiversity Areas\" #### **File: structure_cluster_proportion_in_each_potential_KBA.shx** **Description**: This file is needed to open structure_cluster_proportion_in_each_potential_KBA.shp ## Code/software [https://github.com/TheC0der856/Compare_KBA_Criteria](https://github.com/TheC0der856/Compare_KBA_Criteria) See repository landing page for a description of these files."}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"BiodivERsA","funderIdentifier":"https://ror.org/05cvqmv08","awardNumber":"16LW0320"},{"funderIdentifierType":"ROR","funderName":"General Secretariat for Research and Technology","funderIdentifier":"https://ror.org/04yeh8h63"},{"funderIdentifierType":"ROR","funderName":"Ministero dell'università e della ricerca","funderIdentifier":"https://ror.org/0341vw408"},{"funderIdentifierType":"ROR","funderName":"Deutsche Forschungsgemeinschaft","funderIdentifier":"https://ror.org/018mejw64"},{"funderIdentifierType":"ROR","funderName":"VDI/VDE Innovation + Technik","funderIdentifier":"https://ror.org/05dna8580"},{"funderIdentifierType":"ROR","funderName":"Agencia Estatal de Investigación","funderIdentifier":"https://ror.org/003x0zc53"},{"funderIdentifierType":"ROR","funderName":"Innovation Fund Denmark","funderIdentifier":"https://ror.org/00daj4111"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.tmpg4f5dj","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-07-06T22:57:19Z","registered":"2026-07-06T22:57:20Z","published":null,"updated":"2026-07-06T22:57:20Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.mpg4f4rgm","type":"dois","attributes":{"doi":"10.5061/dryad.mpg4f4rgm","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["University of Bologna"],"name":"Gribaudo, Giorgia","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-5034-7446"}]}],"titles":[{"title":"Decision tree model to assess consequences and costs associated with therapy administration pathways for patients with HER2+ breast cancer in Italian oncological centers"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Medical and health sciences","subjectScheme":"fos"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Breast cancer","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Drug administration","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Health economics","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"HTA"},{"subject":"trastuzumab"},{"subject":"pertuzumab"},{"subject":"treatment administration pathway"},{"subject":"subcutaneous"}],"contributors":[],"dates":[{"date":"2026-06-09T12:39:01Z","dateType":"Created"},{"date":"2026-06-26T12:43:22Z","dateType":"Submitted"},{"date":"2026-07-06T00:00:00Z","dateType":"Issued"},{"date":"2026-07-06T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1371/journal.pone.0351548","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["2734581 bytes"],"formats":[],"version":"6","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Human Epidermal Growth Factor Receptor 2-positive (HER2+) breast cancer\n poses significant therapeutic challenges, particularly concerning\n treatment administration pathways and their associated costs. This study\n evaluates the managerial and economic impacts of different therapeutic\n administration scenarios for HER2-positive breast cancer patients,\n focusing on optimizing hospital workflows, resource utilization, and\n patient outcomes in Italian oncological centers. A decision tree model was\n developed to simulate and compare five treatment administration pathways:\n Standard, Drug-Change, Drug Day, Dedicated Ambulatory, and Optimal Pathway\n scenarios. The model integrates patient and healthcare professional (HCP)\n activity and waiting times, infusion chair occupation, and direct and\n indirect costs. Sensitivity analyses assessed variability in model\n outcomes. Switching from endovenous (EV) to subcutaneous (SC)\n administration substantially reduced patient throughput times and HCP\n workloads. The Optimal Pathway scenario yielded the highest resource\n optimisation, reducing HCP activity time by up to 48 hours, infusional\n chair occupational time by up to 150 hours, and patients’ total time by up\n to 753 hours per 100 patients monthly. Cost analyses indicated significant\n savings in both direct and indirect cost for all the proposed scenarios in\n comparison to the Standard one. The adoption of SC formulations and\n innovative pathway optimizations enhances treatment organizational\n efficiency and reduces both direct and indirect costs. These findings\n underscore the value of tailored approaches to administration based on the\n structural and organizational characteristics of individual oncology\n centers, aligning with current Italian healthcare reforms."},{"descriptionType":"TechnicalInfo","description":"# Decision tree model to assess consequences and costs associated with\n therapy administration pathways for patients with HER2+ breast cancer in\n Italian oncological centers Dataset DOI:\n [10.5061/dryad.mpg4f4rgm](https://doi.org/10.5061/dryad.mpg4f4rgm) ##\n Description of the data and file structure This dataset contains a\n Microsoft Excel macro-enabled decision tree model developed to assess the\n organizational consequences, time outcomes, and costs associated with\n different therapy administration pathways for patients with HER2-positive\n breast cancer treated with pertuzumab and trastuzumab. The model compares\n five alternative administration schemes: 1. Standard scheme 2. Drug-change\n scheme 3. Drug-day scheme 4. Dedicated ambulatory scheme 5. Optimal\n pathway scheme The model includes clinical, organizational, time, and cost\n inputs, and estimates patient time, healthcare professional active time,\n infusion chair occupation time, caregiver time, catheter use, adverse\n events, direct healthcare costs, indirect costs, and total costs. The\n workbook includes deterministic analyses, probabilistic sensitivity\n analyses, and one-way sensitivity analyses. Input parameters can be\n modified in the dedicated input sheets, while the results sheets\n automatically report the consequences and costs associated with each\n scenario. The dataset does not contain individual patient-level data or\n identifiable personal information. It consists of aggregated model\n parameters, assumptions, calculations, and model outputs. ### Files and\n variables #### File: Model_submission.xlsm **Description:**  The dataset\n includes one Excel macro-enabled workbook: **Model_submission.xlsm** The\n workbook contains the following sheets: - **Cover**: title page and\n authorship information. - **Background**: description of the workbook\n structure and purpose of each sheet. - **Decision tree**: graphical\n representation of the simulation model. - **Input Parameters**: editable\n clinical, organizational, and time parameters used in the model, including\n number of patients, number of treatment accesses, proportion of patients\n treated with intravenous therapy, proportion requiring blood sample exams,\n patient weight, catheter use, adverse event risks, working status,\n caregiver presence, travel time, and phase-specific activity/waiting\n times. - **Input Parameters - cost**: editable cost parameters, including\n healthcare professional hourly costs, infusion chair costs, drug\n acquisition costs, adverse event costs, and productivity loss inputs. -\n **Results_Deterministic A**: deterministic results for model outcomes and\n costs. - **Results_Probabilistic A**: probabilistic analysis results,\n reporting uncertainty around model outcomes and costs. - **Results_OWSA**:\n results of the one-way sensitivity analysis. - **Parameters_Sensitivity**:\n parameters used for sensitivity analyses. - **Simulation_1Cycle**:\n deterministic simulation for one therapeutic cycle. -\n **Simulation_NCycle**: deterministic simulation for multiple therapeutic\n cycles. - **OWSA**: one-way sensitivity analysis calculations. - **PSA\n Simulation - 1Cycle**: probabilistic sensitivity analysis simulation for\n one therapeutic cycle. - **PSA Simulation - NCycle**: probabilistic\n sensitivity analysis simulation for multiple therapeutic cycles. -\n **Foglio1**: support sheet containing lookup values and model settings.\n Main input variables include: - Number of patients per day - Number of\n treatment accesses - Percentage of patients receiving intravenous therapy\n - Percentage of patients requiring blood sample exams - Mean patient body\n weight - Catheter use by administration type - Risk of catheter-related\n bloodstream infection or pocket infection - Risk of thrombotic events -\n Percentage of working patients - Percentage of patients with a caregiver -\n Percentage of working caregivers - Travel time - Patient active time -\n Patient waiting time - Healthcare professional active time - Infusion\n chair occupation time - Healthcare professional hourly costs - Infusion\n chair cost per minute - Drug acquisition costs - Adverse event costs -\n Working cost per hour Main output variables include: - Patient active time\n - Patient waiting time - Patient inpatient time - Patient total time -\n Caregiver time - Healthcare professional active time - Infusion chair\n occupation time - Number of patients with catheter - Number of adverse\n events - Healthcare professional costs - Infusion chair costs - Treatment\n costs - Adverse event costs - Direct healthcare costs - Indirect\n productivity costs - Total costs ## Code/software The model was developed\n in Microsoft Excel as a macro-enabled workbook (.xlsm). All calculations\n are embedded within the workbook through Excel formulas and macros. To\n reproduce the analyses: 1. Open the file **Model_submission.xlsm** in\n Microsoft Excel. 2. Enable macros when prompted. 3. Review or modify the\n editable input cells in the sheets **Input Parameters** and **Input\n Parameters - cost**. 4. The deterministic results are reported in\n **Results_Deterministic A**. 5. The probabilistic sensitivity analysis\n results are reported in **Results_Probabilistic A** and calculated through\n the PSA simulation sheets. 6. The one-way sensitivity analysis results are\n reported in **Results_OWSA** and calculated through the OWSA-related\n sheets. The workbook was designed for use in Microsoft Excel.\n Compatibility with other spreadsheet software, such as LibreOffice or\n Google Sheets, is not guaranteed because the file contains macros and\n Excel-specific formulas. ## Access information Other publicly accessible\n locations of the data: * None. The dataset is made publicly available\n through this Dryad repository. Data was derived from the following\n sources: * The model structure, assumptions, and input parameters were\n derived from published literature, publicly available sources, and expert\n elicitation, as described in the associated manuscript. * No individual\n patient-level data or identifiable personal health information were used.\n * The dataset does not include third-party datasets requiring separate\n access permissions. * Where applicable, source references for model inputs\n are reported in the associated manuscript. * The submitted Excel workbook\n contains aggregated parameters, assumptions, calculations, and model\n outputs generated for the purposes of the analysis. ### Files included in\n this dataset #### Model_submission.xlsm Formatted Excel version of the\n model. The file has been retained because the original spreadsheet\n functions as a structured model/tool and the formatting supports\n interpretation and navigation of the model. Figures/images have been\n removed from the Excel file. #### Background.csv Unformatted CSV export of\n the “Background” worksheet from the Excel model. #### Cover.csv\n Unformatted CSV export of the “Cover” worksheet from the Excel model. ####\n Foglio1.csv Unformatted CSV export of the “Foglio1” worksheet from the\n Excel model. #### Input_Parameters.csv Unformatted CSV export of the\n “Input Parameters” worksheet from the Excel model, containing model input\n parameters. #### Input_Parameters-_cost.csv Unformatted CSV export of the\n cost-related input parameters worksheet from the Excel model. ####\n OWSA.csv Unformatted CSV export of the one-way sensitivity analysis\n worksheet. #### Parameters_Sensitivity.csv Unformatted CSV export of the\n sensitivity analysis parameters worksheet. ####\n Results_Deterministisc_A.csv Unformatted CSV export of the deterministic\n analysis results worksheet. The filename is kept as uploaded. ####\n Results_OWSA.csv Unformatted CSV export of the one-way sensitivity\n analysis results worksheet. #### Results_Probabilistic_A.csv Unformatted\n CSV export of the probabilistic analysis results worksheet. ####\n Simulation_1Cycle.csv Unformatted CSV export of the one-cycle simulation\n worksheet. #### Simulation_NCycle.csv Unformatted CSV export of the\n N-cycle simulation worksheet. #### PSA_Simulation_-_1Cycle.csv Unformatted\n CSV export of the one-cycle probabilistic sensitivity analysis simulation\n worksheet. #### PSA_Simulation_-_NCycle.csv Unformatted CSV export of the\n N-cycle probabilistic sensitivity analysis simulation worksheet. ####\n worksheet_export_summary.csv Technical summary file documenting the\n worksheet-to-CSV export process and the cleaning steps applied to the CSV\n files. #### README_Dryad_cleaning_notes.txt Technical note describing the\n cleaning performed on the original Excel file and CSV exports, including\n removal of images/figures, unnecessary formatting, blank rows, and header\n issues."}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"Roche (Italy)","funderIdentifier":"https://ror.org/01x544h30"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.mpg4f4rgm","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-07-06T22:41:11Z","registered":"2026-07-06T22:41:12Z","published":null,"updated":"2026-07-06T22:41:12Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.3j9kd51zx","type":"dois","attributes":{"doi":"10.5061/dryad.3j9kd51zx","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Northwest Indian College"],"name":"Lapsansky, Anthony","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-7530-7830"}]},{"nameType":"Personal","affiliation":["University of Alberta"],"name":"Wylie, Douglas","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of British Columbia"],"name":"Altshuler, Douglas","nameIdentifiers":[]}],"titles":[{"title":"Data from: Pigeons make slow divergent eye movements during flight, and large convergent eye movements when landing"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Biological sciences","subjectScheme":"fos"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Neuroscience","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Organismal biology"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Eye movements","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[],"dates":[{"date":"2026-01-26T20:53:50Z","dateType":"Created"},{"date":"2026-06-26T18:19:32Z","dateType":"Submitted"},{"date":"2026-07-06T00:00:00Z","dateType":"Issued"},{"date":"2026-07-06T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1016/j.cub.2026.06.015","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["84142437403 bytes"],"formats":[],"version":"6","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Birds are highly visual, but it remains unknown if they move their eyes\n during flight, and if so, whether these eye movements are used to control\n flight. We developed an onboard camera system to separately track eye\n movements and record optic flow from homing pigeons released several\n kilometers from their home loft. During flight, the eyes of pigeons are\n oriented laterally and exhibit slow, temporal eye movements consistent\n with a divergent optokinetic response. The speed and direction of these\n eye movements matched optic flow along the horizon generated by forward\n flight. We confirmed that head-restrained pigeons exhibited binocular and\n divergent eye movements in response to computer-generated visual stimuli\n simulating self-translation. In contrast, during landing, as measured in\n both the open field and laboratory, pigeons exhibited large convergent\n binocular eye movements directed toward the perch. These results indicate\n that both optokinetic and goal-directed eye movements are important for\n avian flight control."},{"descriptionType":"TechnicalInfo","description":"# Data from: Pigeons make slow divergent eye movements during flight, and\n large convergent eye movements when landing Dataset DOI:\n [10.5061/dryad.3j9kd51zx](https://doi.org/10.5061/dryad.3j9kd51zx) ##\n Description of the data and file structure This Dryad folder contains the\n data used for the article titled \"Pigeons make slow divergent eye\n movements during flight, and large convergent eye movements when\n landing.\" Data include raw videos of eye movements collected using\n Raspberry Pi computers mounted on free moving pigeons, videos of eye\n movements from (gently) restrained pigeons, scripts used to collect and\n analyze data, and data (eye position/movement, visual motion statistics)\n derived from these data. This work is currently accepted at Current\n Biology. ### Files and variables #### File: pigeonHoodBlock.stl\n **Description:** A 3D model of a pigeon head used as a hood block for\n making the masked used to carry camera equipment. #### File:\n analysisScripts.7z **Description:** A folder of 10 scripts for analysis of\n data presented in the manuscript. See comments in individual files for\n additional details. All files point to folders local to my PC, so these\n will need to be changed to fit your file organization. *\n Lapsanskyetal_pigeons_frameTimer_batch.py -- A Python script that reads in\n videos recorded using a Raspberry Pi (see piScripts/camera_split.py) and\n outputs text files with the timecode, as determined using optical text\n recognition (pytesseract). INPUT: Videos in /labeledVideos.7z. OUTPUT:\n files in /frameTimer.7z. * Lapsanskyetal_pigeons_eyeTracker.mlx -- A\n MATLAB script that reads in the output of the frame timer (above, stored\n in /frameTimer.7z), kinematic data (generated by DeepLabCut and stored in\n /digitizedEyeData.7z), and IMU (inertial measurement unit data, stored in\n /imuData.7z) from outdoor eye tracking videos. The output (eye position,\n velocity in degrees + behavioral categories, etc.) is exported as .CSV\n files and moved to /summaryData.7z. *\n Lapsanskyetal_pigeons_indoorEyeTracker.mlx -- A MATLAB script similar to\n ...eyeTracker.mlx except for indoor perch-to-perch flights (Fig. 4 in\n manuscript). A simplified version of the full ...eyeTracker.mlx with\n unneeded bits removed. Reads in the output of the frame timer (above,\n stored in /frameTimer.7z), kinematic data (generated by DeepLabCut and\n stored in /digitizedEyeData.7z), and IMU (inertial measurement unit data,\n stored in /imuData.7z) from indoor eye tracking videos. *\n Lapsansky_headFixPigeon_eyeTracker.py -- A Python script for tracking the\n position of the pupil of head-fixed pigeons using thresholding and contour\n detection. Input videos are those of head fixed pigeons as shown in Figure\n 3 of the manuscript. Outputs are .CSV files of pupil angles also stored in\n /digitizedHeadFixed.7z. * Lapsanskyetal_pigeons_headFixedTracker.mlx -- A\n MATLAB script for generating summary data from raw pupil orientations\n generated by Lapsansky_headFixPigeon_eyeTracker.py. Inputs are .CSV files\n in the /digitizedHeadFixed.7z folder. Outputs are .CSV files stored in\n /summaryData.7z. * Lapsansky_horizon_detector.py -- A Python script for\n determining the roll and tilt of the birds-eye-view camera relative to the\n perspective of the camera. Given a folder of rectilinear images, the\n script selects a pseudo-random subset and finds the horizon. It then\n reports the tilt and roll of the horizon in the camera view. These values,\n plus the pre-set yaw angle, can be used to reproject the rectilinear\n frames into an equirectangular projection using\n Lapsansky_persp2equirect_parallel.py. *\n Lapsansky_persp2equirect_parallel.py -- A Python script which will convert\n a stack of rectilinear PNG images into an equirectangular projection given\n a roll, tilt, and yaw angle. Inputs are folders of images in\n /flowData.7z/TRIAL/rect. Outputs are folders of images for the same trial\n in /flowData.7z/TRIAL/equi. * Lapsansky_DISopticalFlow_batch.py -- A\n Python script which takes in a stack of equirectangular PNGs and computes\n optical flow between frames using Dense Inverse Search (DIS), correcting\n for distortion inherent in this image representation (proportional to the\n cosine of the latitude). Flow is cropped to the region of interest and\n summarized to reduce file size, saved as .h5. Flow data are then passed to\n Lapsansky_DISopticalFlow_batch.py.mlx for analysis. Inputs are files in\n /flowData.7z/TRIAL/equi. Output is a .h5 file in /flowData.7z/TRIAL/. *\n Lapsanskyetal_pigeons_flowTracker.mlx -- Much like ...eyeTracker.mlx, this\n script takes in frameTimer files, IMU data, and flow data to produce\n optical flow data summarized by time, region in the visual field, and\n behavior. Specifically, this reads in the output of the frame timer\n (above, stored in /frameTimer.7z), flow data (/digitizedFlowData.7z), and\n IMU (inertial measurement unit data, stored in /imuData.7z). The output is\n exported as .CSV files and moved to /summaryData.7z. *\n Lapsanskyetal_pigeons_Figures\u0026amp;Analysis.Rmd -- An R Markdown file for\n taking the data in /summaryData.7z and producing figures and statistical\n comparisons described in the manuscript. #### File: frameTimer.7z\n **Description:** CSV files containing the timestamps extracted from\n Raspberry Pi videos. These are used for synchronization in the .mlx files.\n Column headers: | frame | timecode | timecodeRaw | | :------------- |\n :-------------------------------------------------------------------- |\n :--------------------------------------------------------------------- | |\n Frame of video | Timecode output by frameTimer.py and filtered to remove\n common typoes | Raw timecode extracted using optical text recognition in\n frameTimer.py | #### File: digitizedEyeData.7z **Description:** Cleaned\n versions of kinematic data for the eye and surrounding facial features\n generated by DeepLabCut for free moving pigeons. Column headers: File row\n names and header names correspond to standard naming conventions from\n DeepLabCut. The number of columns varies based on the number of digitized\n points for a given file. The topmost row, \"scorer\", describes\n the details of the DeepLabCut model used for tracking. The second row,\n \"bodyparts\", contains the name of the digitized point in the\n DeepLabCut. Bodyparts starting with \"t\" are from the dorsal\n region of the eye ring. Those starting with \"b\" are from the\n ventral margin of the eye ring. Those labeled \"front\" and\n \"back\" are along the nasal and caudal margins of the eye ring\n and used to determine the origination of the eye in \\_eyeTracker.mlx. The\n point labeled \"hood\" is an arbitrary but trackable piece of the\n leather mask used to hold the camera. Points starting with\n \"pupil\" lie around the margin of the pupil. Further details are\n contained in the \\_eyeTracker.mlx file. The third row, \"coords\",\n indicates whether the data below that are for the x position, y position,\n or likelihood, as determined by DeepLabCut. #### File: imuData.7z\n **Description:** Accelerometer, gyroscope, and magnetometer data for each\n flight (eye tracking and optic flow). These are named to make the name of\n each trial. For each trial, there is also an IMU file ending in\n \\_full.log. These contain the same data plus data from when that IMU was\n stationary on the desk, useful for removing gyroscope offsets. Column\n headers: IMU data have no column headers. The first column is the\n timecode. Columns 2-5 contain accelerometer data. Columns 6-9 contain\n gyroscope data. Columns 10-13 contain magnetometer data. Additional\n details on these data can be found in the corresponding \\_eyeTracker.mlx\n or \\_flowTracker.mlx file. #### File: eyeCalibration.7z\n **Description:** Data for converting pupil position to eye-in-head angle\n including the position of pupil margins generated by DeepLabCut\n (/calibrationPoints), morphometric data from animals for plotting in R\n (eyeCal_plotting.csv), and a MATLAB script for obtaining the radius of\n rotation of the eye using calibrationPoints files\n (Lapsansky_centreOfEyeRotation.m).\n DistanceBetweenPupilAndEyeCenterOfRotation.csv contains intermediate data\n for determining the distance between the pupil and the eye center of\n rotation. A desciption of these data is included within the csv file.\n EyeRingLengthInMM.csv contains measurements of the length of the eye ring,\n used for converting from pixel units to eye-in-head degrees. A description\n of these data is included within the csv file. #### File: utilScripts.7z\n **Description:** Scripts I found useful in analyzing data. Only\n fit_ellipse.m is required (for eyeTracker.mlx) and is from the MATLAB file\n exchange\n ([https://www.mathworks.com/matlabcentral/fileexchange/3215-fit_ellipse](https://www.mathworks.com/matlabcentral/fileexchange/3215-fit_ellipse)). Author is [Ohad Gal.](https://www.mathworks.com/matlabcentral/profile/authors/869576) The original author retains all rights. For this reason, this file has been excluded from the Dryad submission, as all data deposited at Dryad must comply with a CC0 license waiver. #### File: piScripts.7z **Description:** Scripts used to record data from the Raspberry Pi Zero. Camera_split.py records videos in chunks. IMU_st.py records IMU data. #### File: summaryData.7z **Description:** The processed data output by the MATLAB files listed above and read into the R script called Lapsanskyetal_pigeons_Figures\u0026amp;Analysis.Rmd. ##### File Naming Scheme (ALLCAPS portions are placeholders) | File Naming Scheme | Description | Data output by... | | :------------------------------------------------ | :-------------------------------------------------------------------------------------------------------------------------------------------------- | :------------------------------------------- | | eyeCal\\_plotting.csv | Summary data for calculated distance between the pupil and the eye center of rotation, used for scaling from pixels to degrees of eye-in-head angle | Lapsansky\\_eyeCenterOfRotation.m | | headFixed\\_PIGEONBANDNUMBER\\_TIMEOFRECORDING.csv | Summary data from eye tracking head fixed pigeons | Lapsanskyetal\\_pigeons\\_headFixedTracker.mlx | | indoor\\_PIGEONNAME\\_Data.csv | Summary data from eye tracking during indoor trials | Lapsanskyetal\\_pigeons\\_indoorEyeTracker.mlx | | PIGEONBANDNUMBER\\_HOODIDENTIFIER\\_EyeTRIALNUMBER | Summary data from eye tracking during outdoor trials | Lapsanskyetal\\_pigeons\\_eyeTracker.mlx | | PIGEONBANDNUMBER\\_HOODIDENTIFIER\\_FlowTRIALNUMBER | Summary data from outward facing optic flow cameras | Lapsanskyetal\\_pigeons\\_flowTracker.mlx | Column headers: Column headers for /summaryData are described in the corresponding code file that produced a given file in /summaryData. Many of these files have nearly 150 columns. To keep this README file from becoming overly long, please see the description of the column headers in the appropriate code file. For 'eyeCal_plotting.csv', see '/eyeCalibration/Lapsansky_eyeCenterOfRotation.m'. For files starting with 'headFixed_', see '/analysisScripts/Lapsanskyetal_pigeons_headFixedTracker.mlx'. For files starting with 'indoor_', see '/analysisScripts/Lapsanskyetal_pigeons_indoorEyeTracker.mlx'. For files containing \"Eye\", see '/analysisScripts/Lapsanskyetal_pigeons_eyeTracker.mlx'. For files containing 'Flow', see see '/analysisScripts/Lapsanskyetal_pigeons_flowTracker.mlx'. #### File: labeledVideos.7z **Description:** Eye tracking videos with markers labeled using DeepLabCut. Names correspond to naming scheme in /summaryData for indoor and outdoor trials. See table above. #### File: digitizedFlowData.7z **Description:** Output optical flow data from Lapsansky_DISopticalFlow_batch.py for each video from outward facing camera. Input are image files. #### File: digitizedHeadFixed.7z **Description:** Pupil orientation data for head fixed pigeons, described in Figure 2 of the manuscript. The blue camera (csv files appended \"b\") was on the right side of the bird. The red camera (csv files appended \"r\") was on the left side of the bird. Files starting with \"OKR_stimulus_\" contain information on the randomly ordered stimulus used to ellicit optokinetic responses in head fixed pigeons. These and the 'data.csv' files are input to \"Lapsanskyetal_pigeons_headFixedTracker.mlx\". Descriptions of the column headers are included within that code file. #### File: eyeDLCModels.7z **Description:** DeepLabCut models for each eye tracking trial, included in the folder /eyeTrackedVideos.7z. Inputs are these videos for each like-named DeepLabCut Model. These models must be directed toward the appropriate video location (i.e., in an unzipped version of /eyeTrackedVideos.7z) to accomplish tracking. #### File: eyeTrackedVideos.7z **Description:** Untracked eye videos used in this manuscript. Both indoor and outdoor are included within this folder. ## Code/software Python version 3.9.18 MATLAB version R2024A R 4.4.1 All packages required for running scripts are listed within individual code files. ## Access information Other publicly accessible locations of the data: * NA Data was derived from the following sources: * All data are original."}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"U.S. National Science Foundation","funderIdentifier":"https://ror.org/021nxhr62","awardTitle":"\n        NSF Postdoctoral Fellowship in Biology FY 2021: Linking biomechanics to\n        neurobiology: how coordinated bird flight emerges from visual motion\n        processing\n      ","awardNumber":"2109873"},{"funderIdentifierType":"ROR","funderName":"Natural Sciences and Engineering Research Council of Canada","funderIdentifier":"https://ror.org/01h531d29","awardNumber":"RGPIN−2021–02977"},{"funderIdentifierType":"ROR","funderName":"Michael Smith Health Research BC","funderIdentifier":"https://ror.org/020x39229","awardNumber":"RT−2023–3226"},{"funderName":"Parkinson Society British Columbia","awardNumber":"RT−2023–3226"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.3j9kd51zx","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-07-06T22:36:12Z","registered":"2026-07-06T22:36:13Z","published":null,"updated":"2026-07-06T22:36:13Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.2rbnzs84m","type":"dois","attributes":{"doi":"10.5061/dryad.2rbnzs84m","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Fu Wai Hospital"],"name":"Pan, Xiangbin","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-7090-4414"}]}],"titles":[{"title":"Antithrombotic therapy for migraine in patients with patent foramen ovale: A randomized clinical trial"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Clinical medicine","subjectScheme":"fos"},{"subject":"Patent foramen ovale"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Migraine","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Randomized controlled trials","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[],"dates":[{"date":"2026-05-04T06:50:30Z","dateType":"Created"},{"date":"2026-06-21T06:37:53Z","dateType":"Submitted"},{"date":"2026-06-25T00:00:00Z","dateType":"Issued"},{"date":"2026-06-25T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1016/j.ahj.2023.12.011","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["560647 bytes"],"formats":[],"version":"6","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"This dataset contains anonymized individual participant data from the\n COMPETE trial, an investigator-initiated, multicenter, randomized,\n active-controlled clinical study evaluating the efficacy and safety of\n antithrombotic therapy for migraine prevention in patients with patent\n foramen ovale (PFO). Participants underwent a 12-week screening period\n followed by a 12-week treatment phase, during which they were randomly\n assigned to receive aspirin, clopidogrel, rivaroxaban, or metoprolol. The\n dataset includes demographic and baseline clinical characteristics,\n treatment allocation, headache diary–derived outcomes (including migraine\n days and attacks), response rate (≥50% reduction), adverse events, and\n quality-of-life measures. Data were prospectively collected using\n standardized headache diaries and structured follow-up assessments across\n 39 study sites. These data support the primary and secondary analyses\n reported in the associated manuscript, as well as additional subgroup, and\n sensitivity analyses."},{"descriptionType":"Methods","description":"Data were derived from the COMPETE trial, an\n investigator-initiated, multicenter, randomized, active-controlled\n clinical study conducted across 39 sites in China. Eligible participants\n with migraine and patent foramen ovale (PFO) underwent a 12-week screening\n period followed by a 12-week randomized treatment phase.\n Clinical and demographic data were collected at baseline using\n standardized case report forms. Headache outcomes were prospectively\n recorded using structured daily headache diaries, in which participants\n documented headache characteristics, associated symptoms, functional\n impact, and use of acute medications in real time. Follow-up assessments\n were conducted at 4, 8, and 12 weeks after randomization via office\n visits, telephone, or mobile applications. Safety data, including adverse\n events, were collected throughout the study period.\n Data were entered into a centralized electronic data capture\n system and underwent routine data validation procedures, including range\n checks, consistency checks, and query resolution. Headache diary data were\n centrally reviewed by trained neurologists to ensure consistency with\n diagnostic criteria. For the shared dataset, all data\n were de-identified prior to release. Variables were harmonized and coded\n according to predefined definitions, and derived variables (e.g., changes\n in monthly migraine days, changes in monthly migraine attacks, and\n responder status) were calculated based on prespecified analysis rules.\n Missing data were retained as recorded, with imputation procedures applied\n only in sensitivity analysis of primary endpoint as described in the\n associated manuscript."},{"descriptionType":"TechnicalInfo","description":"# Antithrombotic therapy for migraine in patients with patent foramen\n ovale: A randomized clinical trial Dataset DOI:\n [10.5061/dryad.2rbnzs84m](https://doi.org/10.5061/dryad.2rbnzs84m) ##\n Description of the data and file structure This dataset was generated from\n a multicenter, randomized clinical trial evaluating the efficacy and\n safety of antithrombotic therapies for migraine in patients with patent\n foramen ovale (PFO). The dataset includes two subject-level datasets\n (i.e., ADSL and ADACRM), along with detailed variable definitions and key\n statistical programs. Analysis-ready data are provided in Microsoft Excel\n format, and the statistical programs used to generate tables, listings,\n and figures (TLFs) are provided as SAS scripts. The datasets capture\n participant demographics, baseline clinical characteristics, treatment\n allocation, headache diary–derived outcomes (including migraine days and\n migraine attacks), and safety outcomes. A comprehensive variable\n dictionary is included to facilitate interpretation of all variables.\n These materials are provided to support transparency and reproducibility\n of the results reported in the study. ### Files and variables ### File\n structure The dataset is organized into three main components: 1.\n **code.zip**\\ Contains all statistical analysis programs written in SAS. *\n **Macros/** * `allfmt.sas`: Defines variable formats used across datasets\n and analyses. * SAS macro code: * `M_CT_BW_NO.sas`: Analysis for baseline\n continuous variables and secondary continuous variables; *\n `M_CG_BW_R_ALL_.sas`, `M_CG_BW_R_ALL.sas`: Analysis for baseline\n categorical variables and safety outcomes; *\n `T_CT_BW_CI.sas`,`T_CG_BW_R2_diff.sas`:Analysis for secondary endpoints\n (between-group differences); *\n `M_CG_BW_R_ALL_NT.sas`,`T_CG_CI_CMH_CRD.sas`: Analysis for primary\n endpoint (descriptive analysis,  between-group differences and\n comparisons) and secondary categorical variables; *\n `T_CG_CI_CMH_CRD_WP.sas`: subgroup analysis; * `blank.sas`: A nested\n macro. * `TLF.sas`: Key code used to generate Tables, Listings, and\n Figures (TLFs). 2. **data.zip**\\ Contains two datasets in Excel format for\n analysis: * `adsl.xls`: dataset for analysis population determination; *\n `adacrm.xls`: dataset for analysis of baseline characteristics, primary\n endpoint, secondary endpoints and safety outcomes. 3. **Variable\n dictionary.xls**\\ Provides detailed definitions for all variables included\n in the datasets, including variable names, labels, variable coding, and\n derivation rules. --- ### Dataset description `adsl.xls` is a\n participant-level dataset containing treatment group assignment and\n protocol deviation information. Each row represents one participant.\n `adacrm.xls` is the key analysis dataset, including analysis population\n flag variable, baseline variables, migraine diary–derived variables, MSQ\n scores, safety outcomes, derived endpoints, and subgroup variables. Each\n row represents one participant. --- ## Variables `subjid` indicates the\n random number. `group` indicates treatment group, coded as 1=Aspirin,\n 2=Clopidogrel, 3=Rivaroxaban, and 4=Metoprolol. `pdtype` indicates the\n type of protocol deviation. `fasfl` indicates inclusion in the full\n analysis set (FAS), coded as 1=Yes and 2=No. `pprotfl` indicates inclusion\n in the per-protocol set (PPS), coded as 1=Yes and 2=No. `age round`\n indicates rounded age in years. `sex` indicates gender, coded as 1=Male\n and 2=Female. `bmi cat` indicates body mass index category. `comy`\n indicates duration of migraine in years. `migzz_v1` indicates migraine\n with aura, coded as 1=Yes and 2=No. `ech_v1` indicates transthoracic\n echocardiography (TTE), coded as 1=Yes and 2=No. `xztee_v1` indicates\n transesophageal echocardiography (TEE), coded as 1=Yes and 2=No.\n `xztee1a_v1` indicates contrast transthoracic echocardiography (cTTE),\n coded as 1=Yes and 2=No. `xztee1b_v1` indicates contrast transesophageal\n echocardiography (cTEE), coded as 1=Yes and 2=No. `xztee1c_v1` indicates\n contrast transcranial Doppler (cTCD), coded as 1=Yes and 2=No. `ech29_v1`\n indicates right-to-left shunt (RLS) grade, coded as 0=Grade 0, 1=Grade 1,\n 2=Grade 2, and 3=Grade 3. `migpl1_v1` indicates the number of migraine\n days per month at baseline. `migpl3_v1` indicates the number of migraine\n attacks per month at baseline. `msqr_v1` indicates the role restrictive\n (RR) domain of the Migraine-Specific Quality of Life Questionnaire (MSQ)\n at baseline. `msqp_v1` indicates the role preventive (RP) domain of MSQ at\n baseline. `msqe_v1` indicates the emotional function (EF) domain of MSQ at\n baseline. `mi` indicates history of myocardial infarction, coded as 1=Yes\n and 2=No. `ang` indicates angina, coded as 1=Yes and 2=No. `smoke`\n indicates smoking status, coded as 1=Yes and 2=No. `hyp` indicates\n hypertension, coded as 1=Yes and 2=No. `dia` indicates diabetes mellitus,\n coded as 1=Yes and 2=No. `hype` indicates hyperlipidemia, coded as 1=Yes\n and 2=No. `snore` indicates snoring, coded as 1=Yes and 2=No. `palp`\n indicates palpitations, coded as 1=Yes and 2=No. `jsxt` indicates\n psychiatric disorders, coded as 1=Yes and 2=No. `tws` indicates history of\n head trauma, coded as 1=Yes and 2=No. `pill` indicates contraceptive drug\n use, coded as 1=Yes and 2=No. `cein` indicates cerebral infarction, coded\n as 1=Yes and 2=No. `tia` indicates transient ischemic attack, coded as\n 1=Yes and 2=No. `wzjb` indicates peripheral vascular disease, coded as\n 1=Yes and 2=No. `rein` indicates respiratory insufficiency, coded as 1=Yes\n and 2=No. `nyha` indicates NYHA class I heart function, coded as 1=Yes and\n 2=No. `atfi` indicates atrial fibrillation, coded as 1=Yes and 2=No.\n `arrhy` indicates other arrhythmias, coded as 1=Yes and 2=No. `jwpci`\n indicates previous percutaneous coronary intervention, coded as 1=Yes and\n 2=No. `jwcabg` indicates previous coronary artery bypass grafting, coded\n as 1=Yes and 2=No. `jwtt` indicates previous migraine preventive\n treatment, coded as 1=Yes and 2=No. `jxyy` indicates acute medication use,\n coded as 1=Yes and 2=No. `migpl3_v1a` indicates monthly migraine attacks\n at baseline and is derived from `migpl3_v1`. `migpl1_v1a` indicates\n monthly migraine days at baseline and is derived from `migpl1_v1`.\n `pl3_v3`, `pl3_v4`, and `pl3_v5` indicate monthly migraine attacks at\n weeks 4, 8, and 12. `pl1_v3`, `pl1_v4`, and `pl1_v5` indicate monthly\n migraine days at weeks 4, 8, and 12. `msqr_v1a`, `msqp_v1a`, and\n `msqe_v1a` indicate baseline MSQ domain scores derived from original\n variables. `pl7_v3–pl7_v5`, `pl8_v3–pl8_v5`, and `pl9_v3–pl9_v5` indicate\n MSQ domain scores at follow-up visits. `msqt1v`, `msqt3v`, `msqt4v`, and\n `msqt5v` indicate total MSQ scores at baseline and follow-up visits. `ae`\n indicates any adverse event, coded as 1=Yes and 2=No. `impae` indicates\n adverse events related to investigational medicinal products (IMPs), coded\n as 1=Yes and 2=No. `sae` indicates any serious adverse event, coded as\n 1=Yes and 2=No. `impsae` indicates serious adverse events related to IMPs,\n coded as 1=Yes and 2=No. `bleed` indicates any bleeding event, coded as\n 1=Yes and 2=No. `hemor` indicates major bleeding, coded as 1=Yes and 2=No.\n `rrydl` indicates the primary endpoint-respondse rate, coded as 1=Yes and\n 2=No, defined as a ≥50% reduction in monthly migraine days or attacks at\n 12 weeks compared with baseline. `rrydllo` indicates response rate using\n LOCF imputation. `rdlt` indicates response rate based on migraine days\n only, coded as 1=Yes and 2=No. `rdltlo` indicates response rate based on\n migraine days only using LOCF imputation. `avdm` and `avcm` indicate\n absolute reductions in migraine days and attacks. `avdmr` and `avcmr`\n indicate percentage reductions in migraine days and attacks. `coce`\n indicates complete cessation of migraine, coded as 1=Yes and 2=No. `chrr`,\n `chrp`, and `chef` indicate changes in MSQ domain scores. `chmsq`\n indicates change in total MSQ score. `pttyx` indicates migraine frequency\n subgroup, coded as 1=\u0026lt;15 days/month and 2=≥15 days/month. `ageyz`\n indicates age subgroup, coded as 1=≥38.5 years and 2=\u0026lt;38.5 years. ---\n ## Missing data Missing values are represented by blank cells. In\n addition, indicator variables (e.g., `migpl31v`, `pl33v`, `msqr1v`)\n identify missingness at specific time points, coded as 1=Yes and 2=No. ---\n ## Code/software The datasets are provided in Microsoft Excel (.xls)\n format and can be opened using Microsoft Excel, LibreOffice Calc,\n OpenOffice Calc, or other compatible software. Statistical analysis code\n is provided as SAS programs (.sas). Analyses were originally conducted\n using SAS software. Recommended software: * SAS version 9.4 or later for\n reproducing analyses * Microsoft Excel or equivalent for viewing datasets\n * Any text editor for reviewing SAS code ### Workflow 1. Open datasets in\n `data.zip`. 2. Refer to `Variable dictionary.xls` for variable\n definitions. 3. Load macro and format files from `code.zip/Macros/`. 4.\n Run `TLF.sas` to generate tables, listings, and figures. 5. Individual\n scripts may also be run separately for specific analyses. No proprietary\n software is required to view the datasets; however, SAS is required to\n reproduce the analyses. --- ## Access information Other publicly\n accessible locations of the data: None. The data are exclusively available\n through the Dryad Digital Repository associated with this study. Data\n source: The data were generated prospectively as part of a multicenter\n randomized clinical trial conducted by the study investigators. ## Human\n subjects data All participants provided written informed consent prior to\n enrollment, which included consent for the use of their data for research\n purposes and for sharing of de-identified data in the public domain. Prior\n to data sharing, all datasets were fully de-identified to protect\n participant privacy. Direct identifiers (including names, contact\n information, identification numbers, and exact addresses) were removed.\n Indirect identifiers were minimized or generalized where necessary,\n including dates and site-level information, to reduce the risk of\n re-identification. Each participant was assigned a unique study\n identification number with no linkage to personal identifiers. The shared\n dataset contains no information that could reasonably be used to identify\n individual participants."}],"geoLocations":[],"fundingReferences":[{"funderName":"National High Level Hospital Clinical Research Funding","awardNumber":"2022-GSP-GG-18"},{"funderIdentifierType":"ROR","funderName":"Ministry of Science and Technology of the People's Republic of China","funderIdentifier":"https://ror.org/027s68j25","awardNumber":"2023YFC2412705"},{"funderName":"CAMS Innovation Fund for Medical Sciences"},{"funderName":"CAMS Clinical and Translational Medicine Research Fund","awardNumber":"2024-I2M-C\u0026T-C-006"},{"funderName":"\n        Yunnan Pan Xiangbin Expert Workstation under the Yunnan Provincial\n        Project for Scientific and Technological Talents and Platforms\n      ","awardNumber":"202305AF150069"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.2rbnzs84m","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":4,"downloadCount":5,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-06-25T22:23:22Z","registered":"2026-06-25T22:23:22Z","published":null,"updated":"2026-07-06T22:35:31Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.p5hqbzm3m","type":"dois","attributes":{"doi":"10.5061/dryad.p5hqbzm3m","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Università degli Studi della Tuscia"],"name":"Damiani, Gianluca","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-6225-6309"}]},{"nameType":"Personal","affiliation":["Bielefeld University"],"name":"Porporato, Gaia","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Bielefeld University"],"name":"Chacarov, Nayden","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Leibniz Institute for Zoo and Wildlife Research"],"name":"Czirjak, Gabor Arpad","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Ornis Italica"],"name":"Dell'Omo, Giacomo","nameIdentifiers":[]}],"titles":[{"title":"Data from: Birds of prey and the city: Variation in immunity and parasites exposure along an urbanization gradient"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Natural sciences","subjectScheme":"fos"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Ecology","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Immunology","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Urbanization","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[],"dates":[{"date":"2026-06-24T13:23:07Z","dateType":"Created"},{"date":"2026-06-24T13:23:08Z","dateType":"Submitted"},{"date":"2026-07-06T00:00:00Z","dateType":"Issued"},{"date":"2026-07-06T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["53920 bytes"],"formats":[],"version":"5","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Pathogens are major ecological and evolutionary forces, and the immune\n system is central to coping with these challenges. Environmental\n variation, including urbanisation, can alter both pathogen exposure and\n immune function, making it important to understand how responses vary\n across habitats. We addressed the impact of urbanization on both immunity\n and parasite exposure using common kestrel (Falco tinnunculus) nestlings\n as our study species. This generalist raptor occurs in both urban and\n non-urban areas, offering a model to investigate how urban environments\n affect early-life immunity. Previous studies on kestrels reported baseline\n immune differences but did not test induced responses, limiting\n understanding of immune function. We challenged nestlings with\n phytohemagglutinin (PHA), collecting blood before and 24 hours after\n injection, and measured six immunological markers: haptoglobin, bacterial\n killing activity, lysozyme, hemagglutination, hemolysis, and IgY. We also\n quantified exposure to two types of ecto- and hemo-parasites. PHA induced\n an inflammatory response, but this response was similar across habitats,\n indicating that the habitat did not influence the responsiveness to this\n particular antigen. Baseline innate immunity generally increased along the\n urbanisation gradient, whereas baseline adaptive immunity (IgY) rose\n within a season but in a similar way across habitats. Exposure to\n ectoparasites (Carnus hemapterus) was significantly higher in natural\n habitat compared to urban habitat, but did not explain individual\n variation in the response to the PHA challenge. Exposure to hemo-parasites\n was negligible, as we found only two out of 108 nestlings positive. Our\n results suggest that urbanisation does not influence the immune response\n to an experimental immune challenge. Further studies are needed to\n determine whether these findings can be generalized to other antigens or\n live parasites, as well as to populations with differing histories of\n parasite exposure or habitat conditions."},{"descriptionType":"TechnicalInfo","description":"# Data from: Birds of prey and the city: Variation in immunity and\n parasites exposure along an urbanization gradient Dataset DOI:\n [10.5061/dryad.p5hqbzm3m](https://doi.org/10.5061/dryad.p5hqbzm3m) ##\n Description of the data and file structure **File:\n kestrels_parassiti.csv** This dataset contains immune, biometric, and\n environmental data collected from common kestrel (*Falco tinnunculus*)\n nestlings sampled during the breeding season. The data were used in the\n study *Birds of prey and the city: variation in immunity and parasite\n exposure along an urbanization gradient*. Each row represents one sampled\n nestling. ### Variables * **Nest_ID** – Unique identifier of the nest. *\n **Individual_ID** – Unique identifier of each nestling. * **Treatment** –\n Experimental treatment (PHA-challenged or control). * **Sampling_day** –\n Sampling occasion (Day 1 = before treatment; Day 2 = approximately 20\n hours after treatment). * **Sex** – Sex of the nestling. * **Age** –\n Estimated age of the nestling (days). * **Body_mass** – Body mass (g). *\n **Wing_length** – Wing length (mm). * **Brood_size** – Number of nestlings\n in the brood. * **Habitat** – Habitat category (Urban, Rural, or Natural).\n * **Urbanization_index** – Quantitative measure of urbanization\n surrounding the nest. * **IgY** – Plasma immunoglobulin Y concentration. *\n **Hemolysis** – Hemolysis score. * **Hemagglutination** – Hemagglutination\n score. * **Haptoglobin** – Plasma haptoglobin concentration. * **BKA** –\n Bacterial Killing Ability of plasma. * **Lysozyme** – Plasma lysozyme\n activity. Additional variables included in the dataset correspond to\n environmental and biometric covariates used in the statistical analyses\n described in the manuscript. ### Missing values Missing values are\n indicated as **NA**. ### Units Biometric measurements are reported in\n grams (g) or millimetres (mm), as indicated by the corresponding variable.\n Immune variables are reported in the units generated by the respective\n laboratory assays and are described in detail in the associated\n manuscript."}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.p5hqbzm3m","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-07-06T22:16:55Z","registered":"2026-07-06T22:16:55Z","published":null,"updated":"2026-07-06T22:16:55Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.6q573n6df","type":"dois","attributes":{"doi":"10.5061/dryad.6q573n6df","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["University of Virginia"],"name":"Ramirez, Abbey L. Ramirez","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0000-4698-6432"}]},{"nameType":"Personal","affiliation":["University of Virginia"],"name":"Gibson, Amanda","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-0867-4953"}]}],"titles":[{"title":"Temperature alters specificity in a host-parasite interaction"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"subject":"Host-parasite interaction"},{"subject":"Red Queen"},{"subject":"GxGxE"},{"subject":"antagonistic coevolution"},{"subject":"negative frequency-dependent selection"},{"subject":"Pasteuria"},{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Biological sciences","subjectScheme":"fos"}],"contributors":[{"name":"University of Virginia","contributorType":"Sponsor","affiliation":[],"nameIdentifiers":[]}],"dates":[{"date":"2026-05-09T05:13:45Z","dateType":"Created"},{"date":"2026-06-22T14:40:16Z","dateType":"Submitted"},{"date":"2026-07-06T00:00:00Z","dateType":"Issued"},{"date":"2026-07-06T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.64898/2026.05.11.724370","relatedIdentifierType":"DOI"},{"relationType":"IsDerivedFrom","relatedIdentifier":"\n      https://github.com/abalynn/Temperature-alters-specificity-in-a-host-parasite-interaction\n    ","relatedIdentifierType":"URL"}],"relatedItems":[],"sizes":["111849 bytes"],"formats":[],"version":"4","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"The Red Queen Hypothesis proposes that genetic variation is maintained in\n populations through antagonistic coevolution of hosts and parasites. A\n major assumption of the Red Queen Hypothesis is tight genetic specificity\n for infection. However, it has been argued that this genetic interaction\n of host and parasite (GHxGP) is sensitive to environmental context\n (GHxGPxE). Environmental change could accordingly disrupt coevolutionary\n oscillations on relevant time scales, calling into question antagonistic\n coevolution as a general and robust explanation for the maintenance of\n genetic diversity. To evaluate this critique, we used the plant-parasitic\n nematode Meloidogyne arenaria and its natural bacterial parasite Pasteuria\n penetrans to determine if specificity is altered by temperature. We\n exposed six isofemale host lines to five parasite sources at three\n ecologically relevant temperatures. We found that, at two of three\n temperatures, susceptibility to infection depended on the specific\n combination of host line and parasite source (GHxGP). This specificity\n varied across temperatures, consistent with a GHxGPxE effect. This\n three-way interaction was driven both by quantitative changes in the\n strength of specificity across temperatures and in the susceptibility\n rankings of host-parasite combinations. Our study contributes a rare\n experimental test of a proposed challenge to the Red Queen Hypothesis and\n suggests the potential for environmental context to change host-parasite\n specificity."},{"descriptionType":"TechnicalInfo","description":"# Temperature alters specificity in a host-parasite interaction Dataset\n DOI: [10.5061/dryad.6q573n6df](https://doi.org/10.5061/dryad.6q573n6df) ##\n Description of the data and file structure In this study, we used\n *Meloidogyne* *arenaria* and *Pasteuria* *penetrans* to test whether three\n temperatures (25, 30, and 35 °C) altered specificity. We used six distinct\n iso-female host lines and five parasite sources for a total of 30 unique\n combinations. Hosts were exposed to parasites for 48 hours and then we\n performed an attachment assay, where the first 50 hosts were examined in\n each sample, and attached spores were counted on each host. From these\n data, we then calculated both attachment rate (the number of hosts with\n spores attached/total hosts) and attachment load (the mean number of\n spores per host, zeros excluded). ## **Data Dictionary** * Host_ID:\n Original identifier for the host genotype/isofemale line of *Meloidogyne\n arenaria* used in the attachment assay. * Refactored Host ID: Recoded or\n simplified host identifier used for analysis/plotting.  * Parasite_ID:\n Identifier for the *Pasteuria penetrans* parasite source/population used\n in the assay. * Rep: Replicate number for a given\n host-parasite-temperature combination. * Sample_ID: Unique sample\n identifier for the experimental unit/sample. * Block: Experimental block,\n representing the assay batch in which samples were processed. *\n Temperature: Temperature treatment used during the assay. Units: degrees\n Celsius. * Treatment: Temperature treatment category defined as\n low/medium/high. * FlaskID: Unique identifier for each assay\n flask/replicate. * ComboID: Unique identifier for each host-parasite\n combination, regardless of temperature. * ComboTempID: Unique identifier\n for each host-parasite-temperature combination. * A-AX: Individual\n nematode counted in the particular replicate. NA's in the dataset\n represent no nematode available. ## Code/software Data files\n (Meloidogyne_Pasteuria_GxGxE_Dryad.csv) are provided as .csv files and can\n be viewed using any spreadsheet software or text editor. Analyses\n (Temperature_alters_specificity_For_Submission.R) were conducted in R\n version 4.4.0 (2024-04-24), “Puppy Cup.” The included R script reads the\n raw .csv data, formats data, calculates attachment rate and attachment\n load, fits generalized linear mixed models, performs model comparisons and\n diagnostics, and generates summary figures. The following R packages were\n used: lme4 v1.1-35.3, DHARMa v0.4.6, glmmTMB v1.1.11, car v3.1-2, bbmle\n v1.0.25.1, emmeans v1.10.2, tidyverse v2.0.0, performance v0.13.0, viridis\n v0.6.5, and see v0.10.0. ## Access information Other publicly accessible\n locations of the data: *\n [https://github.com/abalynn/Temperature-alters-specificity-in-a-host-parasite-interaction.git](https://github.com/abalynn/Temperature-alters-specificity-in-a-host-parasite-interaction.git) Data was derived from the following sources: * Original experimental data"}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"Division of Graduate Education","funderIdentifier":"https://ror.org/00whkrf32","awardTitle":"\n        NRT-ROL: Interdisciplinary Studies of the Phenotype: EXPANDing Training\n        in Research and Careers\n      ","awardNumber":"2021791"},{"funderIdentifierType":"ROR","funderName":"National Institute of General Medical Sciences","funderIdentifier":"https://ror.org/04q48ey07","awardTitle":"\n        A general test of the genetic basis of parasite resistance across\n        genetic and environmental contexts\n      ","awardNumber":"3R35GM137975-05S1","awardUri":"https://reporter.nih.gov/project-details/11082780"},{"funderIdentifierType":"ROR","funderName":"University of Virginia","funderIdentifier":"https://ror.org/0153tk833"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.6q573n6df","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":1,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-07-06T22:12:46Z","registered":"2026-07-06T22:12:46Z","published":null,"updated":"2026-07-06T22:12:46Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.rr4xgxdk2","type":"dois","attributes":{"doi":"10.5061/dryad.rr4xgxdk2","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Bigelow Laboratory for Ocean Sciences","University of Maine"],"name":"Farrell, Shane","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0004-6906-507X"}]},{"nameType":"Personal","affiliation":["Bigelow Laboratory for Ocean Sciences","University of Maine"],"name":"Yiu, Dara","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Bigelow Laboratory for Ocean Sciences"],"name":"Ryan, Stuart","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Bigelow Laboratory for Ocean Sciences","University of Maine"],"name":"Francolini, Rene","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Oxford"],"name":"Stuart, Courtney","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Bigelow Laboratory for Ocean Sciences"],"name":"Shah Esmaeili, Yasmina","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Maryland Center for Environmental Science"],"name":"Lefcheck, Jonathan","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Bigelow Laboratory for Ocean Sciences"],"name":"Rasher, Douglas","nameIdentifiers":[]}],"titles":[{"title":"Data from: From kelp forests to turf reefs: patterns, drivers, and impacts on functional diversity"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Earth and related environmental sciences","subjectScheme":"fos"},{"subject":"Turf Reefs"},{"subject":"turf algae"},{"subject":"state shift"},{"subject":"kelp forest"},{"subject":"phase shift"},{"subject":"functional diversity"}],"contributors":[{"name":"Bigelow Laboratory for Ocean Sciences","contributorType":"Sponsor","affiliation":[],"nameIdentifiers":[]}],"dates":[{"date":"2025-03-25T13:54:24Z","dateType":"Created"},{"date":"2026-02-19T15:22:42Z","dateType":"Submitted"},{"date":"2026-03-12T00:00:00Z","dateType":"Issued"},{"date":"2026-03-12T00:00:00Z","dateType":"Available"},{"date":"2026-07-06T00:00:00Z","dateType":"Updated"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1002/ecy.70408","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["16329574 bytes"],"formats":[],"version":"7","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Kelp forests are declining in many regions due to ocean warming, predator\n loss, and other anthropogenic stressors. In areas of rapid ocean warming,\n including the southern Gulf of Maine, these ecosystems have transitioned\n to a novel state dominated by low-lying mats of turf algae. However, the\n pace, drivers, and ecological consequences of this transition remain\n unclear. Here, we used field surveys from 32 sites over five years\n (2018–2023) to reveal a continuation of kelp forest collapse and northward\n expansion of turf algae across Maine’s coast. Next, we united data on\n benthic cover with key environmental variables in a structural equation\n model to show that turf algae were directly enhanced by higher ocean\n temperatures and decreased wave disturbance and indirectly enhanced by a\n warming-induced loss of kelp cover. Lastly, the shift from kelp to turf\n yielded a seaweed assemblage dominated by traits associated with rapid\n growth, high surface-area-to-volume ratios, and markedly reduced canopy\n height, indicating declines in habitat provisioning and carbon storage\n with kelp forest loss. Our findings highlight the accelerating impacts of\n climate change on temperate reef ecosystems and the vital services they\n provide. Further, we provide insights into a new state shift that is now\n occurring globally and underscore the need for urgent actions to mitigate\n further loss of foundational kelp forests."},{"descriptionType":"TechnicalInfo","description":"# Data from: From kelp forests to turf reefs: patterns, drivers, and\n impacts on functional diversity Dataset DOI:\n [10.5061/dryad.rr4xgxdk2](10.5061/dryad.rr4xgxdk2) ## Description of the\n data and file structure ### **Coordinates.csv** **Description**:\n Coordinates of sites and associated metadata. **Variables** * Site: name\n of each site. * Subregion: name of each subregion (Casco Bay, Midcoast,\n Penobscot Bay, Mount Desert Island, Downeast). * Lat: Latitude (decimal\n degrees). * Long: Longitude  (decimal degrees). ###\n **Biomass_21_22_23.csv** **Description**: This dataset contains\n measurements of seaweed biomass collected from 1m² quadrats across study\n sites over three years. (See “*Macroalgal percent cover and biomass*” in\n the methods section for detailed methods). Taxonomic designation for the\n species, as inferred from an unpublished version of the NEAS key (draft\n 2021viii20) by Gary Saunders and the associated website\n \"[http://seaweedcanada.wordpress.com](http://seaweedcanada.wordpress.com)\". \\* To note for this paper \"Turf\" communities (i.e., those comprised of red, green, and/or brown filamentous and uniseriate algae, ~1 cm to ~15 cm in canopy height, that form upright filaments, mats, or tufts) can include any of the following species: *Acrosiphonia_spp, Antithamnion_spp, Antithamnionella_spp, Audouinella_spp, Bonnemaisonia_hamifera, Carradoriella_elongata, Ceramium_spp, Cladophora_spp, Dasysiphonia_japonica, Ectocarpus_spp, Kaprunia_schneideri, Leptosiphonia_spp, Melanothamnus_harveyi, Other, Polysiphonia_stricta, Pterothamnion_plumula, Red_tufts, Rhodomela_spp, Scagelia_pylaisaei, Spermothamnion_repens, Sphacelaria_spp, Stylonema_alsidii, Vertebrata_fucoides, Vertebrata_nigra, Vertebrata_spp* \\* Kelps include *Agarum_clathratum, Alaria_esculenta, Laminaria_digitata, Saccharina_latissima,* and Laminarian_juvenile. All other species are non-kelp canopy-forming species (*Desmarestia_viridis* and *Desmarestia_aculeata*) and understory species (all others). **Variables**: * Year: The year in which the survey took place (2021, 2022, or 2023). * Month: The month (1–12) when the site was surveyed. * Day: The day of the month the site was surveyed. * Site: The site at which the survey took place. * Meter_Mark – The specific meter mark along the transect where the survey took place (5, 10, 15, 20, 25, 30, 35, and 40). Surveys happened at 4-6 meter marks per site. * name: The name of the organism species or complex identified (e.g., red_tufts, red_tubes). Other = indiscernible mixture of taxa that could not be teased apart. * wet_weight_g: The weight of the seaweed in grams after being spun to remove excess water. 0 = true absence, 0.001 = present but below scale detection limit (.01g). * phylum: The phylum the seaweed or complex belongs to. Other = indiscernible mixture of taxa that could not be teased apart. * class: The class the seaweed or complex belongs to. Other = indiscernible mixture of taxa that could not be teased apart. * order: The order the seaweed or complex belongs to. Other = indiscernible mixture of taxa that could not be teased apart. * genus: The genus the seaweed or complex belongs to if identifiable. Other = indiscernible mixture of taxa that could not be teased apart. * species: The species the seaweed or complex belongs to, if identifiable. * survey_area_m2: The area the seaweed was surveyed in (1m² for large brown seaweed, 0.25m² for all other seaweeds). * m2_weight_g: Standardized biomass per 1m². Since some seaweeds were surveyed in 0.25m², those values were multiplied by 4 to be comparable to 1m². ### **Percent_Cover_21_22_23.csv** Description: This dataset contains measurements of seaweed percent cover collected from quadrats across study sites over three years. (See “*Macroalgal percent cover and biomass*” in the methods section for detailed methods). Taxonomic designation for the species was inferred from an unpublished version of the NEAS key (draft 2021viii20) by Gary Saunders and the associated website \"[http://seaweedcanada.wordpress.com](http://seaweedcanada.wordpress.com)\". * Year: The year in which the survey took place (2021, 2022, or 2023). * Month: The month (1–12) when the site was surveyed. * Day: The day of the month the site was surveyed. * Site: The site at which the survey took place. * Meter_Mark: The specific meter mark along a 50 meter transect tape where the quadrat was placed. N=8 per sampling event. * name: The name of the algae, invertebrate, substrate, or complex identified (e.g., red_tufts, red_tubes). * PCT_Cov: The percent cover of the specific algae, invertebrate, substrate, or complex. Note percent cover can be over 100% due to the multilayered nature of the seaweed community. * Subregion: name of each subregion (Casco Bay, Midcoast, Penobscot Bay, Mount Desert Island, Downeast). ### **Heatwave_Timeseries_Temp.zip** **Description**: This dataset contains satellite-derived sea surface temperature data (NASA's MUR SST) from all of our study sites from 2002-2024 (the full extent of the datasets records. (See “*In situ and satellite environmental data*” in the methods section for detailed methods). Each .csv file within the zip corresponds to a site and has the same layout.  **Variables** * Time (UTC format): Year-Month-Day  hours:Minutes:Seconds * Latitude (Degrees_north): Latitude of the point * Longitude (Degrees_east): Longitude of the point * Analysed_SST (Degree_C): Sea Surface Temperature in Celcius.  **cover_env_reg.csv** **Description**: This dataset contains biotic (kelp and turf percent cover; recorded at the quadrat-level) and environmental data (temperature and significant wave height; recorded at the site level). This dataset is used to model the direct and indirect drivers of turf abundance through space and time using a Structural Equation Model. Included in this dataset are group-mean centered metrics and sub-regional means for these predictors. (See “*Statistical analysis: Identifying drivers of turf spread using SEM*” in the methods section for detailed methods). **Variables** * year: Calendar year for each measurement. * date: Measurement date at each site (MM/DD/YYYY). * subRegion: Subregional grouping of sites used in the analysis. * site – Individual sampling site within each subregion. * meter_mark: The specific meter mark along a 50 meter transect tape where the quadrat was placed. N=8 per sampling event. * kelp_cover: Percent cover of kelp at each site (%). * turf_cover: Percent cover of turf algae at each site (%). * mean_sst_1yr: Mean site sea surface temperature anomaly over the previous year (°C). * dd_gt10: Degree-days above 10°C (°C·days) at a site over the previous year leading up to the survey. * dd_gt15: Degree-days above 15°C (°C·days) at a site over the previous year leading up to the survey. * dd_lt5: Degree-days below 5°C (°C·days) at a site over the previous year leading up to the survey. * mean_wave_1yr: Site-level mean significant wave height anomaly over the past year (meters). * mean_wave_30d: Site-level mean significant wave height anomaly over the past 30 days (meters). * subregion_mean_sst: Subregional mean sea surface temperature (°C) the previous year leading up to the survey date. * subregion_mean_wave: Subregional mean wave height (meters) the previous year leading up to the survey date. * subregion_mean_kelp:  Subregional mean kelp percent cover (%). * subregion_mean_turf: Subregional mean turf percent cover (%). * mean_sst_centered_reg: Site-level SST centered by the Subregional mean (°C). * wave_1yr_centered_reg: Site-level annual wave height centered by Subregional mean (meters). * wave_30d_centered_reg: Site-level 30-day wave height centered by Subregional mean (meters). * kelp_centered_reg:  Site-level kelp cover centered by the Subregional mean (%). * turf_centered_reg: Site-level turf cover centered by the Subregional mean (%). ### **fd.csv** **Description**: This dataset contains species or complex-specific trait measurements for 7 different traits. (See “*Seaweed functional trait measurements*” in the methods section for detailed methods). **Variables** * Species: The name of the individual species/genus or complex * TDMC: Avergae thallus Dry Matter Content (no units): obtained by dividing dry mass (g) by fresh mass (g) * Thickness: Average thickness (mm) taken haphazardly along the blades of a sample and avoiding, when applicable, the midrib. Thickness measured via calipers when the alga was big enough or cross-section was done on small filamentour forms. * Branching_order: Average number of divisions of the main axes of a macroalga from its holdfast to the tip of the blades taken haphazardly within the sample; no units. * SA:V: Average surface Area to Volume ratio (mm^2^/ mL ^-1^): obtained by dividing the area (mm^2^) of a sample by its volume (mL). * STA: Average specific Thallus Area (mm^2^/g^-1^): obtained by dividing the area (mm^2^) of a sample by its dry mass (g). * C:N: Average Carbon to Nitrogen ratio (no units): obtained by dividing Carbon content (%) by Nitrogen content (%). * Maximum_length: Average maximum length (cm) of a sample, from the base of the holdfast (or any other anchoring system) to the tip of the longest blade. ## Code/Software ### **Script1_Turf_Spread_MS_Temp_v2.ipynb** Description: Code (Jupyter notebook file) which uses the long-term (2002-2024) temperature data. Using these data, we calculate the rise in average temperature and marine heatwave metrics over space and time.  ### **Script2_Turf_Spread_MS_Percent_Cover_v5.ipynb** Description: Code (Jupyter notebook file) which uses the Percent cover and biomass data. In this script we analyze turf/kelp cover trends over space and time (GLMMs) and use the biomass data to analyze turf taxonomic richness (GLMM) over space and time.  ### **Script3_Turf_Spread_MS_Diversity_v4.ipynb** Description: Code (Jupyter notebook file) which uses the biomass data and functional trait measurements. In this script, we calculate our Simpson's diversity index, functional diversity metrics, and community weighted means.  ### **Script4_Turf_Spread_MS_SEM_v2.ipynb** Description: Code (Jupyter notebook file) which uses the Percent cover and environmental (waves and temperature) data to construct a Piecewise SEM to identify the direct and indirect drivers of turf spread. "}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"U.S. National Science Foundation","funderIdentifier":"https://ror.org/021nxhr62","awardNumber":"OIA-1489227"},{"funderName":"The Louise H. \u0026 David S. 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