{"data":[{"id":"10.5068/d1709n","type":"dois","attributes":{"doi":"10.5068/d1709n","identifiers":[],"creators":[{"name":"Magee, Andrew","nameType":"Personal","givenName":"Andrew","familyName":"Magee","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-7403-5455","nameIdentifierScheme":"ORCID"}]},{"name":"Holbrook, Andrew","nameType":"Personal","givenName":"Andrew","familyName":"Holbrook","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Pekar, Jonathan","nameType":"Personal","givenName":"Jonathan","familyName":"Pekar","affiliation":["University of California San Diego"],"nameIdentifiers":[]},{"name":"Caviedes-Solis, Itzue","nameType":"Personal","givenName":"Itzue","familyName":"Caviedes-Solis","affiliation":["Swarthmore College"],"nameIdentifiers":[]},{"name":"Matsen IV, Frederick","nameType":"Personal","affiliation":["Fred Hutch Cancer Center"],"nameIdentifiers":[]},{"name":"Baele, Guy","nameType":"Personal","givenName":"Guy","familyName":"Baele","affiliation":["KU Leuven"],"nameIdentifiers":[]},{"name":"Wertheim, Joel","nameType":"Personal","givenName":"Joel","familyName":"Wertheim","affiliation":["University of California San Diego"],"nameIdentifiers":[]},{"name":"Ji, Xiang","nameType":"Personal","givenName":"Xiang","familyName":"Ji","affiliation":["Tulane University"],"nameIdentifiers":[]},{"name":"Lemey, Philippe","nameType":"Personal","givenName":"Philippe","familyName":"Lemey","affiliation":["KU Leuven"],"nameIdentifiers":[]},{"name":"Suchard, Marc","nameType":"Personal","givenName":"Marc","familyName":"Suchard","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]}],"titles":[{"title":"Data from: Random-effects substitution models for phylogenetics via scalable gradient approximations"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"subject":"Phylogeography","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Hamiltonian Monte Carlo"},{"subject":"Bayesian inference"},{"subject":"FOS: Mathematics","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Mathematics","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2023-09-26T00:04:38Z","dateType":"Created"},{"date":"2024-04-30T03:04:57Z","dateType":"Submitted"},{"date":"2026-05-20T00:00:00Z","dateType":"Issued"},{"date":"2026-05-20T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1093/sysbio/syae019","relatedIdentifierType":"DOI"},{"relationType":"IsDerivedFrom","relatedIdentifier":"https://github.com/suchard-group/approximate_substitution_gradient_supplement","relatedIdentifierType":"URL"}],"relatedItems":[],"sizes":["14889682 bytes"],"formats":[],"version":"7","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Phylogenetic and discrete-trait evolutionary inference depend heavily on\n an appropriate characterization of the underlying character substitution\n process. In this paper, we present random-effects substitution models that\n extend common continuous-time Markov chain models into a richer class of\n processes capable of capturing a wider variety of substitution dynamics.\n As these random-effects substitution models often require many more\n parameters than their usual counterparts, inference can be both\n statistically and computationally challenging. Thus, we also propose an\n efficient approach to compute an approximation to the gradient of the data\n likelihood with respect to all unknown substitution model parameters. We\n demonstrate that this approximate gradient enables scaling of\n sampling-based inference, namely Bayesian inference via Hamiltonian Monte\n Carlo, under random-effects substitution models across large trees and\n state-spaces. Applied to a dataset of 583 SARS-CoV-2 sequences, an HKY\n model with random-effects shows strong signals of nonreversibility in the\n substitution process, and posterior predictive model checks clearly show\n that it is a more adequate model than a reversible model. When analyzing\n the pattern of phylogeographic spread of 1441 influenza A virus (H3N2)\n sequences between 14 regions, a random-effects phylogeographic\n substitution model infers that air travel volume adequately predicts\n almost all dispersal rates. A random-effects state-dependent substitution\n model reveals no evidence for an effect of arboreality on the swimming\n mode in the tree frog subfamily Hylinae. Simulations reveal that\n random-effects substitution models can accommodate both negligible and\n radical departures from the underlying base substitution model. We show\n that our gradient-based inference approach is over an order of magnitude\n more time efficient than conventional approaches.","descriptionType":"Abstract"},{"description":"# Data from: Random-effects substitution models for phylogenetics via\n scalable gradient approximations\n [https://doi.org/10.5068/D1709N](https://doi.org/10.5068/D1709N) * The\n file `supp_mat.pdf` contains supplementary text for the manuscript\n \"Random-effects substitution models for phylogenetics via scalable\n gradient approximations'' by Magee *et al.* * The file\n `Archive.zip` contains datasets and analysis code, also available\n uncompressed at\n [https://github.com/suchard-group/approximate_substitution_gradient_supplement](https://github.com/suchard-group/approximate_substitution_gradient_supplement). * Usage instructions: 1. Create a folder named `approximate_substitution_gradient_supplement` 2. Download `Archive.zip` and place it in `approximate_substitution_gradient_supplement` 3. Uncompress `Archive.zip`, ensuring its contents are at the top level of `approximate_substitution_gradient_supplement` 4. Open and follow the instructions in `approximate_substitution_gradient_supplement/README.md` * Contents are described in detail in `README` files (main or otherwise) in the archive, but briefly it includes: * `README.md`: a top-level overview and instruction file, to be read first * `piBUSS/`: a directory containing source code to perform the posterior predictive simulations in the manuscript * `simulations/`: a directory containing code for creating the simulated datasets from the manuscript and for generating BEAST XMLs to analyze the simulated datasets * `acknowledgements_table.xlsx`: GISAID accession IDs for the SARS-CoV-2 genome sequences used in the SARS-CoV-2 analysis. Refer to the study originating this dataset, [Pekar et al., 2021](https://www.science.org/doi/full/10.1126/science.abf8003), for further details.","descriptionType":"TechnicalInfo"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"DMS 2152774","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"DMS 2236854","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Institute of Allergy and Infectious Diseases","awardNumber":"R01 AI153044","funderIdentifier":"https://ror.org/043z4tv69","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Institute of Allergy and Infectious Diseases","awardNumber":"R01 AI162611","funderIdentifier":"https://ror.org/043z4tv69","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Institute of Allergy and Infectious Diseases","awardNumber":"K25 AI153816","funderIdentifier":"https://ror.org/043z4tv69","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Institute of Allergy and Infectious Diseases","awardNumber":"AI135992","funderIdentifier":"https://ror.org/043z4tv69","funderIdentifierType":"ROR"},{"awardUri":"https://reporter.nih.gov/project-details/11128520","schemeUri":"https://ror.org","awardTitle":"San Diego Biomedical Informatics Education \u0026 Research (SABER)","funderName":"United States National Library of Medicine","awardNumber":"5T15LM011271-14","funderIdentifier":"https://ror.org/0060t0j89","funderIdentifierType":"ROR"},{"funderName":"DURABLE EU4Health project*"},{"schemeUri":"https://ror.org","funderName":"European Research Council","awardNumber":"725422-ReservoirDOCS","funderIdentifier":"https://ror.org/0472cxd90","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"European Research Council","awardNumber":"874850","funderIdentifier":"https://ror.org/0472cxd90","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"Research Foundation - Flanders","awardNumber":"G098321N","funderIdentifier":"https://ror.org/03qtxy027","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"Research Foundation - Flanders","awardNumber":"G0E1420N","funderIdentifier":"https://ror.org/03qtxy027","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.5068/D1709N","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":8,"downloadCount":2,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-10-04T22:19:59Z","registered":"2023-10-04T22:19:59Z","published":null,"updated":"2026-05-20T00:19:06Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5068/d1wc7k","type":"dois","attributes":{"doi":"10.5068/d1wc7k","identifiers":[],"creators":[{"name":"Tangherlini, Timothy","nameType":"Personal","givenName":"Timothy","familyName":"Tangherlini","affiliation":[],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-1775-2052","nameIdentifierScheme":"ORCID"}]},{"name":"Crist, Sean","nameType":"Personal","givenName":"Sean","familyName":"Crist","affiliation":[],"nameIdentifiers":[]},{"name":"Broadwell, Peter M.","nameType":"Personal","givenName":"Peter M.","familyName":"Broadwell","affiliation":[],"nameIdentifiers":[]},{"name":"Gabriel, David","nameType":"Personal","givenName":"David","familyName":"Gabriel","affiliation":[],"nameIdentifiers":[]},{"name":"Urban, Kryztof","nameType":"Personal","givenName":"Kryztof","familyName":"Urban","affiliation":[],"nameIdentifiers":[]},{"name":"Vijunas, Aurelijus","nameType":"Personal","givenName":"Aurelijus","familyName":"Vijunas","affiliation":[],"nameIdentifiers":[]},{"name":"Crawford, Jackson","nameType":"Personal","givenName":"Jackson","familyName":"Crawford","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"IceMorph morphological analysis data files"}],"publisher":"Dryad","container":{},"publicationYear":2014,"subjects":[{"subject":"Morphosyntactic tagging"},{"subject":"Old Icelandic"},{"subject":"Old Icelandic dictionaries"},{"subject":"Old Icelandic training data"},{"subject":"POS-tagging"}],"contributors":[],"dates":[{"date":"2014-06-09T13:16:39Z","dateType":"Issued"},{"date":"2014-06-09T13:16:39Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1353/scd.2014.0036","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["17428138 bytes"],"formats":[],"version":"1","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"This dataset consists of four main resources: a concatenated dictionary of\n Old Icelandic parsed for word class and inflectional detail; a corpus of\n Old Icelandic sagas in plain text and chunked by chapter; a tagged version\n of the same text, output of the IceMorph system; a training corpus labeled\n \"Expert\" for training and testing a machine learning module; and\n a training corpus labeled \"Gold\" for training and testing a\n machine learning module.","descriptionType":"Abstract"},{"description":"Datasets (1) dictionary (2a) saga texts were generated using OCR.\n Dataset (2b) is the output of the IceMorph tagging system. Datasets (3a)\n and (3b) were generated by hand-tagging.","descriptionType":"Methods"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.5068/D1WC7K","contentUrl":null,"metadataVersion":18,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":440,"downloadCount":15,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2014-06-09T20:16:40Z","registered":"2014-06-09T20:16:41Z","published":null,"updated":"2026-04-10T12:33:57Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5068/d1sx1w","type":"dois","attributes":{"doi":"10.5068/d1sx1w","identifiers":[],"creators":[{"name":"Lazarus, Michael","nameType":"Personal","givenName":"Michael","familyName":"Lazarus","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2654-9221","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Student comments on Advanced Clinical Skills Course"}],"publisher":"Dryad","container":{},"publicationYear":2022,"subjects":[{"subject":"FOS: Clinical medicine","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Clinical medicine","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"clinical skills"},{"subject":"visual intelligence"},{"subject":"pattern recognition"},{"subject":"clinical reasoning"},{"subject":"birding"},{"subject":"burnout mitigation"}],"contributors":[],"dates":[{"date":"2022-12-07T21:46:47Z","dateType":"Submitted"},{"date":"2022-12-13T00:00:00Z","dateType":"Issued"},{"date":"2022-12-13T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.12688/mep.19397.1","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["684221 bytes"],"formats":[],"version":"5","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Proficiency in clinical examination skills upon graduation from medical\n school is a core competency. Over the last few decades, the ability and\n confidence in this fundamental and crucial skill set have declined. The\n motivation and interest in meticulous physical examination by recently\n graduated residents have also eroded. In this case study, we describe a\n comprehensive, innovative, and immersive advanced clinical skills elective\n taken during the second half of the final year of medical school for\n students at the David Geffen School of Medicine. The course utilizes novel\n approaches to inspire, refresh and consolidate essential bedside\n observation skills and examination techniques. This approach gives senior\n students the confidence and fundamental understanding of how dedication to\n the patient exam can improve the doctor-patient relationship, core\n clinical reasoning and the practice of cost-effective and evidence-based\n care through their careers. We describe how the integration of fine art\n appreciation and introductory biding techniques are used to help students\n hone their visual diagnostic skills. We show how this is solidified\n through a longitudinal series of clinical image review sessions with\n diagnostic reasoning principles to formulate a clear differential.\n Point-of-care ultrasound, EKG analysis, advanced cardiac auscultation and\n diagnostic imaging skills are integrated in a comprehensive and memorable\n fashion. We present this case study to inspire clinical skills teachers\n everywhere to replicate our methods in resurrecting the importance of\n physical exams for their learners. Opening their trainees’ eyes to new\n methods of honing their visual intelligence and developing healthy habits\n for stress and burnout reduction will aid the rest of their professional\n careers.","descriptionType":"Abstract"},{"description":"\u003cstrong\u003eMethods\u003c/strong\u003e All\n students rotate through all of these sessions and field trips over the 15\n days of the course. \u003cstrong\u003eVisual\n Diagnosis\u003c/strong\u003e This key component of the\n course occurs each morning from 9:00am to 10:30am. In our medical school,\n we have a large classroom equipped with 24 benches, each of which has two\n flat-screen monitors. The 50 students are randomly assigned to small\n groups of 4–5 students, who share a bench. At the control screen, the\n three course chairs reveal up to ten high-resolution clinical images on\n each group monitor per session.  These clinical images were obtained and\n curated by the course chairs over their combined 70 years of inpatient and\n outpatient clinical practice in academic teaching hospital departments:\n general internal medicine, neurology, ophthalmology, emergency medicine,\n pulmonary and critical care settings. All images were obtained with\n patient permission or were de-identified. Most are of actual clinical\n signs but where appropriate, images are augmented with radiologic and\n micro and macro pathologic slides as a means to narrow the differential\n diagnosis or to clarify the clinical findings shown during the discussion.\n The small groups of students at each table are given 3–5 minutes to view\n and discuss the image, and on the second attached screen at each table,\n they generate a differential diagnosis of up to five conditions. After all\n groups post their differential, the course chairs choose the best\n differential to share with the groups on each of their screens. Each table\n then votes on which of the five is the most likely diagnosis. One of the\n course chairs then discusses the options starting with the least likely\n and provides confirmation of the answer. The other educators present add\n additional details such as clinical anecdotes, prevalence, positive and\n negative likelihood ratios or an interesting or amusing commentary\n (clinical tangent) which all help clarify and solidify the material for\n the learners. A running tally of which table contributed the most correct\n differential lists is kept and the teams/tables with the top three correct\n lists receive a small prize at the end of the course (usually a\n second-hand copy of a book from an essential reading list from the course,\n covering broad medical and non-medical topics). As the course progresses,\n the goal is to show more challenging images and common conditions with\n more unusual presentations as well as reducing the time allowed for\n discussion of the differential at each table to encourage efficiency and\n mastery of the subject matter. \u003cstrong\u003eHighly\n meticulous physical exam technique demonstration and prioritization of\n signs by likelihood ratios\u003c/strong\u003e From 11am to\n 12pm, a faculty master clinician demonstrates a physical exam to the\n entire group of students. These sessions are traditionally organ-system\n based covering cardiovascular, pulmonary, abdominal and neurologic exams.\n The goal of these sessions is to act as a refresher for the students from\n what they learnt earlier in medical school as well as to give an\n opportunity to provide tips on higher yield maneuvers that are supported\n by the literature, with high sensitivity and specificity and positive and\n negative likelihood ratios. The \u003cem\u003eJournal of the American Medical\n Association (JAMA) Rational Clinical Exam\u003c/em\u003e series is referenced\n as well as the book Evidenced Based Physical Exam, fifth edition by Steven\n McGee.  \u003cstrong\u003eReal patient encounters (clinic\n and hospital)\u003c/strong\u003e Each afternoon of the\n course begins with clinical encounters, which consist of 90-minute\n inpatient and outpatient sessions that run concurrently; groups of\n students participate in the inpatient session while other groups attend\n the outpatient session. These sessions offer a core clinical experience of\n encounters with real patients who have pertinent histories and\n educationally valuable physical findings. Session one runs from 1pm-2:30pm\n and the second session from 2:30pm-4:00pm. Outpatient sessions take place\n in the medical school. Tutorial rooms are equipped with a standard\n outpatient exam bed, a sink, a privacy curtain, internet access and a dry\n erase board. The course chairs have cultivated a group of patients with\n chronic stable medical conditions who were identified either after\n admission to the inpatient teaching service, being recommended to one of\n the course chairs by a colleague or who are existing outpatients of the\n course chairs. This group of clinical teaching partners (CTP) have classic\n clinical exam findings such as crescendo-decrescendo systolic murmurs of\n aortic stenosis, the classic cutaneous signs of systemic sclerosis, or\n chronic stable rheumatoid arthritis. These patients are also adept at\n presenting their clinical histories in a concise and logical order and are\n comfortable interacting with, and being examined by, medical students.\n They are compensated at an hourly rate for their time and in some cases\n for travel expenses. They are also provided lunch each day and parking\n validation. Some CTPs will bring hard copies of their old x-rays, images\n of acute exacerbations of their condition (Raynaud’s Phenomenon) or their\n medical devices like portable oxygen canisters with oximizer pendants.\n Each group of students will see at least three CTPs during their 90-minute\n afternoon session. Every student has the opportunity to examine the CTPs\n in a quiet space and receive instruction from a seasoned clinician\n educator every day. While these sessions are proceeding, the inpatient\n sessions are taking place across the street in the teaching hospital for\n the rest of the students. A clinician educator meets 4-5 students in the\n lobby and proceeds to the Emergency Department (ED), medicine ward or\n Intensive Care Unit (ICU). A daily list of patients with interesting\n clinical findings is supplied by on-service hospitalists or chief\n residents. Patients are similarly selected for their clinical signs and\n willingness to be seen by a small group of students. Some examples of\n patients would include those with pericardial friction rubs, neurologic\n signs like hemiparesis, cranial nerve lesions, clonus, muscle wasting or\n skin rashes, purpura and petechiae. Sometimes patients may not be in their\n rooms and faculty preceptors will then usually pivot to discussion of\n interesting radiologic findings such as the patient’s echocardiogram,\n electrocardiogram (EKG) or chest x-ray until the\n patient returns or move down their list to the next patient.\n \u003cstrong\u003eAdvanced EKG interpretation and cardiac murmur\n review\u003c/strong\u003e Three one-hour sessions\n from 11am-12pm are devoted to advanced electrocardiogram analysis and\n auscultation of common cardiac murmurs on a simulator provided by a\n senior, teaching award-winning cardiologist. \n \u003cstrong\u003eAdvanced clinical imaging\u003c/strong\u003e\n This thread of this part of the course consists of three review\n lectures on plain radiologic film reading, reviewing computational\n tomography scans (CT) and a session on analyzing Magnetic resonance\n imaging (MRI) films by a senior radiology faculty member. Each session is\n based on being able to detect common radiologic findings seen in clinical\n medicine and emergency medicine settings. To solidify these concepts and\n to correlate clinical exam findings with radiologic images, the course\n chairs have curated a set of over 100 slides that show a physical exam\n finding with a radiographic image or microbiologic/pathology slide.\n Examples of this include images of the hands of a patient with\n sclerodactyly, juxtaposed with the associated radiologic features of\n calcinosis, flexion contractures and acro-osteolysis. These sessions take\n place during the afternoon for students not assigned to the clinic or\n inpatient rounds and are given by one of the course chairs for 90 minutes.\n Each student attends 2–3 of these sessions per course.\n \u003cstrong\u003eClinical Reasoning Session\u003c/strong\u003e\n Clinical reasoning is a topic that fits naturally into our\n course. Given that we review and expand upon many of the fundamentals of\n clinical medicine, we felt that adding a few sessions on this topic was\n essential. In the final week of the course when the students have\n refreshed and honed the clinical skills of history taking, physical exam\n and differential diagnosis formulation, we provide two sessions on\n clinical reasoning. We teach them how to integrate all of the information\n obtained into a problem representation, revising the information they\n gleaned from the patient with the goal of an ordered differential. In\n these sessions, we make the students aware of all the biases that may\n affect their thinking and how to critically appraise their own reasoning\n processes. Dual process theory and metacognition principles are reviewed\n and expanded upon in these sessions.\n \u003cstrong\u003ePoint of Care Ultrasound (POCUS) workshop and hands\n on practice\u003c/strong\u003e This thread consists of\n small group Point of Care Ultrasound (POCUS) sessions where students scan\n ultrasound simulators and each other with faculty preceptors to build on\n their skills of image acquisition and interpretation. Students complete\n assigned videos prior to the session in order to maximize hands-on time.\n Two lectures, \u003cem\u003eTropical\u003c/em\u003e \u003cem\u003emedicine\u003c/em\u003e\n \u003cem\u003eultrasound\u003c/em\u003e and \u003cem\u003ePulmonary\u003c/em\u003e\n \u003cem\u003eultrasound\u003c/em\u003e, are given that focus on image\n interpretation and clinical integration. POCUS has been demonstrated to\n improve diagnostic accuracy, decrease time to diagnosis, improve patient\n satisfaction and reduce healthcare costs, among other benefits. Over the\n last three years, we have trained our students in POCUS to further build\n upon their diagnostic capabilities. This imaging technology is unique in\n that it brings the clinician back to the bedside and strengthens the\n patient-physician relationship, a value that is core to this course. POCUS\n not only augments the physical exam, it also refines a clinician’s exam,\n creating more astute clinicians even in the absence of ultrasound. An\n example of this is evaluating the jugular venous pressure (JVP), a skill\n that many find elusive. When students are asked to first estimate the JVP\n on a patient by exam and then they are able to visually confirm the JVP,\n see the course of the internal jugular vein and witness hepatojugular\n reflux by ultrasound, their comprehension of this exam maneuver\n grows.  \u003cstrong\u003eEnhancing observational\n skills\u003c/strong\u003e \u003cem\u003eFine art appreciation\n at a local museum\u003c/em\u003e Teaching visual literacy\n and taking students out of the clinical setting was the basis for using\n fine art principles to develop observational skills. One of the\n recommended texts for the course is Visual Intelligence by noted art\n historian, Amy Herman. She utilized her degree in fine art to create the\n successful, “Art of Perception” program and trains\n thousands of professionals from Secret Service agents to medical students.\n We were fortunate to recruit a senior clinician educator to the course who\n had fine art training as an undergraduate before participating in a\n landmark study: “Training the Eye: Improving the Art of Physical\n Diagnosis” while in medical school14. This study conducted at Harvard\n School of Medicine and the Boston Museum of Fine Art, consisted of eight\n paired sessions of art observation exercises with didactics that integrate\n fine arts concepts with physical diagnosis topics and an elective life\n drawing session for 24 pre-clinical students who were then compared to 34\n classmates at a similar stage who did not do this training. The frequency\n of accurate observations on a visual skills examination was used to\n evaluate pre- \u003cem\u003eversus\u003c/em\u003e post-course descriptions of\n patient photographs and art imagery. Those participants who were\n randomized to the art appreciation arm of the study performed\n significantly better than their peers in their ability to make clinical\n observations and in terms of their level of sophistication when describing\n both art and clinical images. During our course, the students receive a\n talk given by the lead author of the above study on fine art concepts and\n correlation with clinical observations. On two Friday mornings of the\n course, field trips are organized to the UCLA Hammer Museum of Art, just a\n short walk from the School of Medicine. This gallery with a permanent\n collection of historical works and special exhibits that include local and\n national artists of edgier contemporary art has collaborated with us for\n over seven years. The morning sessions begin in the permanent collection\n with pairs of students describing a work of art to a fellow student who\n creates a drawing based on their description. Subsequent activities during\n the two-hour outing, include sessions in the contemporary exhibits which\n explore themes such as structural racism, empathy and unconscious bias;\n all highly relevant themes to current clinical practice.\n \u003cstrong\u003eBird identification at the University Botanical\n Garden\u003c/strong\u003e Another novel addition to the\n course is birding. Five years ago, after a close family member of one of\n the course chairs discovered birding and pulled the entire family into\n this activity, it become apparent that the skill set of the bird\n enthusiast is very useful for training one’s eye and cultivating visual\n intelligence. In birding, one uses all one’s senses to spot birds in the\n field. Plumage or field marks, bird sounds, behavior and the location you\n encounter the bird in, are all essential for identifying it in a natural\n environment that can be complex and dynamic. Birding can also provide a\n focal point for noticing, reconsidering, and understanding everyday spaces\n that may go unobserved, despite a high frequency of visitation. For\n example, features of an office courtyard or parking lot may become visible\n – and take on new significance – when used as birding locations. The head\n of educational programming for Los Angeles Audubon Society was contacted\n and so began a lasting collaboration with our course. She now provides an\n hour-long introduction to birding to the class with an emphasis on\n drawing. Basic bird shapes and sizes are reviewed to get the students in\n the mood for the Friday activity. To maintain a more manageable teaching\n experience, one-third of the students then join her on three separate\n hour-long walks through the UCLA Mildred Mathias Botanical Garden starting\n at 8:30am. This activity usually takes place on the midpoint of the course\n and also serves as an opportunity to get out into nature and address how\n wellbeing and healthy habits can foster a long career in clinical\n practice. This experience was validated recently in an editorial in the\n \u003cem\u003eNew England Journal of Medicine\u003c/em\u003e where the author, who\n came to birding late in life, describes the cognitive requirements of\n birding and correlates them with the diagnostic skill required for\n clinical neurology. The use of birding skills to enhance clinical practice\n is not well studied in the medical literature but its recent increase in\n popularity as a pandemic pastime has led to more articles being written\n including a recent publication by Koontz et al. in the radiology\n literature. Our conviction is that birding enhances observational and\n clinical diagnostic skills in two important ways: 1)    Frequent observation of what\n is common enables quicker recognition leading to faster and more accurate\n clinical diagnosis. 2)    Frequent observation of what\n is common enables recognition of features that are out-of-the-ordinary,\n and this equates to advanced clinical diagnosis.\n Furthermore, the act of disengaging from work and getting out\n into nature is the perfect antidotes to the stresses of the hospital and\n clinic. Burnout mitigation, while still actively honing our visual\n intelligence, has been an incredible addition to the curriculum. We now\n receive just as many birding images as we do clinical images from our\n course alumni. \u003cstrong\u003eSample\n schedules\u003c/strong\u003e The scheduling of CTPs for\n outpatient encounters is based on the premise that the students never see\n the same patient during the course twice. CTPs are chosen to provide\n variety, but in some instances, a few recurring physical exam features\n appear, such as aortic stenosis murmurs, Heberden's nodes of\n osteoarthritis, corneal arcus, rosacea, and onychomycosis. Owing to the\n presence of a busy and active lung and liver transplant population at our\n institution, the signs of pulmonary fibrosis, spider angoimata,\n gynecomastia, and Dupuyren’s contractures are also more frequently\n encountered. The faculty preceptors for our course are seasoned and gifted\n teachers a large number of whom have won teaching awards in the medical\n school. While the outpatient teaching environment is relatively calm and\n predictable, the faculty member who does the inpatient rounds need to be\n efficient and pragmatic. Acute medical emergencies, code-blues, emergent\n testing and procedures will derail even the best planned inpatient\n sessions. Having the flexibility and experience to pivot on the fly is a\n key feature of our hospitalist faculty who often will utilize their entire\n inpatient list in a given afternoon. Having prior knowledge of the patient\n details reduces the stress or a brief teaching script is provided by\n course organizers. \u003cstrong\u003eModifications for\n distance learning during the pandemic of 2020\u003c/strong\u003e\n The course is usually offered in the last two weeks of February\n through the first week of March each year. In 2020, as the global Covid-19\n pandemic was beginning we were able to successfully complete the course in\n person. We then had the luxury of an entire year to plan the 2021 course\n virtually. The move to all virtual instruction was seamless. The physical\n exam technique demonstrations were filmed and edited to maximize\n visualization of highly meticulous and accurate technique and also serve\n as a course library to be used in perpetuity. Most of our seasoned\n clinical teaching partners were able to master zoom, either on their own\n or with the aid of family members. Eighteen encounters were filmed\n remotely during which key history was provided to a course chair\n interviewer. Where possible obvious clinical exam findings were\n demonstrated by zooming the camera onto the relevant area. For example,\n sclerodactyly, digital clubbing, facial asymmetry, and arachnodactyly and\n hyperextensible digits in a patient with Marfans syndrome. During the\n course, the filmed 20-minute encounter is viewed by the group and\n preceptor and the CTP from the video then answers students’ questions in\n real-time. Although not as educationally rewarding as the real thing, it\n is still a good teaching experience. Teaching visual\n diagnosis remotely using our clinical images bank was also highly\n effective and students could be placed into ‘virtual breakout rooms’ in\n small groups to view and discuss the images and develop a differential\n diagnosis list. Each group's list is visible to the course chairs via\n a Google docs format in the main ‘zoom room’. The best differential is\n chosen, and students vote virtually on the best single diagnosis and\n faculty members provide discussion and teaching points as they would in\n person. Despite no hands-on instruction, the course remained highly rated\n by students as a valuable learning exercise. The overall course score and\n positive comments were not significantly different to in-person courses of\n prior years. \u003cstrong\u003eAssessments, evaluations and\n learner feedback\u003c/strong\u003e In its early\n incarnations, the students were expected to identify abnormal physical\n exam findings on hospitalized patients and were graded by faculty on\n examination technique. This has given way over the past two decades to the\n far less stressful and time-consuming visual diagnosis quiz. On the first\n Thursday morning of the course, a 20-question test consisting of 20\n clinical images is administered. Students have two minutes to answer a\n specific question such as “what is the diagnosis?” or ‘what is this\n clinical finding? At the end of the quiz, the faculty review the answers\n with the class, and they selfgrade. They are requested to note their\n scores as on the final Thursday of the course a 30-question quiz, which is\n more challenging, is held and again self-graded. Students overwhelmingly\n do better on the second quiz. For students, it is tangible evidence that\n their visual intelligence has improved in three short weeks. \n Course evaluations over the last 15 years have been based on\n 50–60 students providing a rating of 1–5 with 1 being poor and 5 being\n extremely effective. The overall course evaluation during this time period\n is 4.92/5. The most frequent, recurring comments that students have made\n about this course over the years are (a) this is the best clinical\n teaching in the whole of medical school, and (b) it should be made\n available to all students throughout their years in medical\n school.","descriptionType":"Methods"},{"description":"PDF","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.5068/D1SX1W","contentUrl":null,"metadataVersion":9,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":156,"downloadCount":15,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2022-12-13T19:11:24Z","registered":"2022-12-13T19:11:24Z","published":null,"updated":"2026-03-23T18:52:19Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5068/d1409q","type":"dois","attributes":{"doi":"10.5068/d1409q","identifiers":[],"creators":[{"name":"Van Dyke, Mary","nameType":"Personal","givenName":"Mary","familyName":"Van Dyke","affiliation":["Colorado State University"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-4971-4728","nameIdentifierScheme":"ORCID"}]},{"name":"Kraft, Nathan","nameType":"Personal","givenName":"Nathan","familyName":"Kraft","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]}],"titles":[{"title":"Flowering phenology for seven California annual species under two rainfall treatments"}],"publisher":"Dryad","container":{},"publicationYear":2025,"subjects":[{"subject":"FOS: Biological sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"flowering phenology"},{"subject":"coexistence"},{"subject":"Grasslands","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Global change ecology","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"competition"},{"subject":"precipitation"}],"contributors":[],"dates":[{"date":"2023-06-29T18:35:42Z","dateType":"Created"},{"date":"2024-11-08T22:09:57Z","dateType":"Submitted"},{"date":"2025-01-16T00:00:00Z","dateType":"Issued"},{"date":"2025-01-16T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsDerivedFrom","relatedIdentifier":"10.5281/zenodo.8097115","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1002/ajb2.70000","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["42131 bytes"],"formats":[],"version":"7","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Premise: Shifts in the timing of life history events, or phenology, have\n been recorded across many taxa and biomes in response to global change.\n These phenological changes are often studied in a single species context,\n but considering the community context is essential for anticipating the\n cascading effects on biotic interactions that are likely to occur.\n Focusing on an annual grassland plant community, we examined how\n experimental changes in precipitation affect flowering phenology in a\n community context and explore the implications of these shifts for\n competitive interactions and species coexistence. Methods: We\n experimentally manipulated rainfall with rainout shelters and recorded\n detailed flowering phenology data for seven annual species including two\n grasses and five forbs. We assessed how their first and peak flowering\n days were affected by changes in rainfall and explored how flowering\n overlap between competing species changed. Results: Changes in rainfall\n shifted flowering phenology of some species, but sensitivity differed\n among neighboring species. Four of the seven species studied started\n and/or peaked flowering earlier in response to reduced water availability.\n The idiosyncratic shifts in flowering phenology have the potential to\n alter existing temporal dynamics that may be maintaining coexistence, such\n as temporal separation of resource-use among neighbors. Conclusions: Our\n results show how species-specific phenological consequences of global\n change can impact community dynamics and competition between neighboring\n plants and warrant future research.","descriptionType":"Abstract"},{"description":"The experiment was conducted at the University of California,\n Santa Barbara’s Sedgwick Reserve in Santa Barbara County, USA. Twenty\n 0.75m*0.75m plots were established and seeded in October 2019 in a fenced\n area designed to exclude deer and gophers, the two primary mammalian\n herbivores in the system. One hundred seeds of seventeen cooccuring annual\n grasses and forbs were mixed and hand sown in each plot. The plots were\n paired into ten blocks. Rain reduction shelters designed to divert 50% of\n incoming rain were placed over five of the blocks and therefore half of\n the plots on February 8, 2020. This timing allowed all plants to germinate\n and establish in late December and January with the same ambient rainfall\n conditions, resulting in the rainfall exclusion treatment impacting just\n the growth and reproduction phase of the plants’ life cycle. The seven\n most abundent species that germinated were tracked over the course of\n their lifetime. We used daily photographs from cameras mounted at each\n plot to identify the flowering window for each species (i.e. first and\n last days of flowering). We then counted the number of flowers in the\n photos over that time period, avoiding images that were obscured by\n condensation, rain, or wind-driven camera and plant movement. This\n resulted in counts of open flowers every 1 to 5 days for each species\n during their flowering period. The plots were also visited in person every\n two weeks and every flowering individual was identified and\n counted. ","descriptionType":"Methods"},{"description":"# Flowering phenology for seven California annual species under two\n rainfall treatments This is the data and analysis code for the manuscript\n entitled, \"Changes in flowering phenology with altered rainfall and\n the potential community impacts in an annual grassland.\" It includes\n the number of flowers visible for 7 annual plant species (5 forbs, 2\n grasses), grown in mixed competition plots under two precipitation periods\n during the 2020 growing season. Contact Mary Van Dyke\n ([mary.vandyke@colostate.edu](mailto:mary.vandyke@colostate.edu)) with any\n questions. This manuscript has been accepted for publication in the\n American Journal of Botany (1/15/2025): Van Dyke, M.N., N.J.B. Kraft.\n Changes in flowering phenology with altered rainfall and the potential\n community impacts in an annual grassland. American Journal of Botany\n (accepted). There are three data files and three R scripts Data files\n include: ## all\\_species\\_flower\\_data.csv This file contains the\n flowering phenology data for 7 annual plant species under two\n precipitation treatments. All data was collected by the authors. |\n variables | units | description | | | :-------- | :---------: |\n :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :- | | species | categorical | species codes for 7 species: ACWR = Acmispon wrangelianus, FEMI = Festuca microstachys, HOMU = Hordeum murinum, LACA = Lasthenia californica, NAAT = Navarretia atractyloides, PLER = Plantago erecta, URLI = Uropappus lindleyi californica | | | plot | number | plots are numbered 1 through 20 | | | block | number | there were ten blocks which each included two plots of the same precipitation treatment | | | treatment | categorical | precipitation treatment, c = control, s = shelter | | | doy | number | day of year with January 1, 2020 as 1 | | | flower | number | number of flowers in bloom within the plot | | ## sedg\\_rain\\_2020.csv This file contains rainfall data collected at the experimental site from October, 2019 to August, 2020. | variables | units | description | | :--------------- | :----: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------- | | year | number | year data was collected - always 2020 | | month | number | month data was collected | | day | number | day of the month data was collected | | rain\\_cumulative | number | cumulative rainfall on ambient plots in mm since October 1, 2019 | | d\\_o\\_y | number | day of year with January 1, 2020 as 1 | | selter\\_rain | number | cumulative rainfall on reduced rainfall plots since October 1, 2019, assuming a 50 percent reduction after February 8, 2020 when rainout shelters were erected. | ## boots\\_pairs\\_w\\_sup.csv This file contains competition parameter estimates from previous experiment performed in the 2019 growing season. | variables | units | description | | | :----------- | :---------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :- | | focal | categorical | species codes for 7 species, ACWR = Acmispon wrangelianus, FEMI = Festuca microstachys, HOMU = Hordeum murinum, LACA = Lasthenia californica, NAAT = Navarretia atractyloides, PLER = Plantago erecta, URLI = Uropappus lindleyi californica | | | competitor | categorical | species codes for 7 species, same codes as focal variable | | | treatment | categorical | precipitation treatment, c = control (ambient), s = shelter (reduced rain) | | | snd | number | estimated stabilizing niche difference between the pair of species | | | fd\\_superior | number | estimated fitness difference between the pair | | ## All phenology data was collected in person and from photographs in the spring of 2020 from Sedgwick reserve in Santa Barbara county, CA Code files include: ## boots\\_final.R all code needed for analysis, need to run this before running either of the figure scripts ## final\\_figures.R code needed to create figures in the manuscript ## supplemental\\_figures.R code needed for all figures in supplementary material appendix 1","descriptionType":"TechnicalInfo"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"164461","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"2022810","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.5068/D1409Q","contentUrl":null,"metadataVersion":5,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":40,"downloadCount":11,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-10-04T22:45:28Z","registered":"2023-10-04T22:45:29Z","published":null,"updated":"2026-03-16T22:18:07Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5068/d1zh5t","type":"dois","attributes":{"doi":"10.5068/d1zh5t","identifiers":[],"creators":[{"name":"Claudepierre, Seth","nameType":"Personal","givenName":"Seth","familyName":"Claudepierre","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-5513-5947","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Pitch-angle resolved electron flux measurements in the Earth's radiation belt"}],"publisher":"Dryad","container":{},"publicationYear":2023,"subjects":[{"subject":"FOS: Physical sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Physical sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"radiation belt lifetime"},{"subject":"particle detection"},{"subject":"space physics"}],"contributors":[],"dates":[{"date":"2023-03-27T17:29:34Z","dateType":"Submitted"},{"date":"2023-03-30T00:00:00Z","dateType":"Issued"},{"date":"2023-03-30T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1029/2019gl086053","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementedBy","relatedIdentifier":"10.5068/d1rq2w","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1029/2023ja031679","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["303569700 bytes"],"formats":[],"version":"2","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"These data are electron flux measurements in the Earth's radiation\n belt obtained with the MagEIS instrument on the NASA Van Allen Probe B\n satellite. They are an extension of a previously published data set\n (https://doi.org/10.5068/D1RQ2W). In the original dataset, the fluxes were\n averaged between 80 and 100 degrees local pitch angle (i.e., equatorial\n pitch angles between ~70 and 110 degrees) and used to study electron decay\n time scales or \"lifetimes\" (see\n https://doi.org/10.1029/2019GL086053 and\n https://doi.org/10.1029/2019GL086056). In this new data set, the data are\n obtained and processed in the same way, but here multiple equatorial pitch\n angle bins are provided. The contents of the data files are described in\n greater detail in a \"readme\" file.","descriptionType":"Abstract"},{"description":"These data are daily (UTC) flux averages. The L parameter is\n McIlwain L and was obtained from the Olson-Pfitzer quiet 1977 magenetic\n field model. The data are provided in L bins of 0.1L width from L = 1.0 to\n 7.6 and in equatorial pitch angle bins from 0 to 180 degrees with 12\n degree bin width. Three flux variables are\n provided: FEDU: Uncorrected differential\n electron flux (cm\u003csup\u003e2\u003c/sup\u003e s sr\n keV)^\u003csup\u003e-1\u003c/sup\u003e FEDU_CORR: Background\n corrected (standard algorithm) differential electron flux\n (cm\u003csup\u003e2\u003c/sup\u003e s sr keV)^\u003csup\u003e-1\u003c/sup\u003e\n FEDU_CORR_ALT: Background corrected (alternative\n algorithm)  differential electron flux (cm\u003csup\u003e2\u003c/sup\u003e s sr\n keV)^\u003csup\u003e-1\u003c/sup\u003e. All flux\n arrays are 4D and have dimensions ~ [n\u003csub\u003etime\u003c/sub\u003e x\n n\u003csub\u003eenergy\u003c/sub\u003e x n\u003csub\u003eL\u003c/sub\u003e x\n n\u003csub\u003ealpha\u003c/sub\u003e].","descriptionType":"Methods"},{"description":"Notes on FEDU_CORR_ALT: For\n energies \u0026lt; 700 keV, it is identical to FEDU_CORR  (i.e., the\n \"standard\" background correction algorithm was used,\n doi:10.1002/2015JA021171). For L \u0026gt; 3.0, it is\n identical to FEDU_CORR (standard algorithm). For L\n \u0026lt; 3.0 and E \u0026gt; 700 keV, the \"alternative\"\n background correction algorithm was used\n (doi:10.1029/2018JA026349) Data from the\n \"MED\" unit, pixel 8 is not included, as it has considerable\n Poisson noise and there's a \"HIGH\" unit channel with nearly\n identical energy (~1 MeV, HIGH-P0). Notes\n (general) The lowest L bin (L=1.0-1.1) is\n set to fill in all of the flux variables.  Data from\n LOW/MED pixel 0 (P0) and pixel 1 (P1) are not included, as they are noisy\n (see https://doi.org/10.1007/s11214-021-00855-2). Days with HIGH unit operational testing at end of May 2014 (all pixels) and July 2013 (pixel 3 only) are set to fill (see https://doi.org/10.1007/s11214-021-00855-2). Data on 2017/12/07 is set to fill due to missing magnetic ephemeris ","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Aeronautics and Space Administration","awardNumber":"NAS501072","funderIdentifier":"https://ror.org/027ka1x80","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.5068/D1ZH5T","contentUrl":null,"metadataVersion":8,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":109,"downloadCount":4,"referenceCount":1,"citationCount":2,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-03-30T18:53:46Z","registered":"2023-03-30T18:53:47Z","published":null,"updated":"2026-03-14T21:02:19Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5068/d1xt3v","type":"dois","attributes":{"doi":"10.5068/d1xt3v","identifiers":[],"creators":[{"name":"Yuan, Luyao","nameType":"Personal","givenName":"Luyao","familyName":"Yuan","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Gao, Xiaofeng","nameType":"Personal","givenName":"Xiaofeng","familyName":"Gao","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-3331-9846","nameIdentifierScheme":"ORCID"}]},{"name":"Zheng, Zilong","nameType":"Personal","givenName":"Zilong","familyName":"Zheng","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Edmonds, Mark","nameType":"Personal","givenName":"Mark","familyName":"Edmonds","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Wu, Ying Nian","nameType":"Personal","givenName":"Ying Nian","familyName":"Wu","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Rossano, Federico","nameType":"Personal","givenName":"Federico","familyName":"Rossano","affiliation":["University of California San Diego"],"nameIdentifiers":[]},{"name":"Lu, Hongjing","nameType":"Personal","givenName":"Hongjing","familyName":"Lu","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-0660-1176","nameIdentifierScheme":"ORCID"}]},{"name":"Zhu, Yixin","nameType":"Personal","givenName":"Yixin","familyName":"Zhu","affiliation":["Peking University"],"nameIdentifiers":[]},{"name":"Zhu, Song-Chun","nameType":"Personal","givenName":"Song-Chun","familyName":"Zhu","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]}],"titles":[{"title":"Dataset from: In-situ bidirectional human-robot value alignment"}],"publisher":"Dryad","container":{},"publicationYear":2022,"subjects":[{"subject":"FOS: Engineering and technology","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Engineering and technology","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2022-07-13T18:09:04Z","dateType":"Submitted"},{"date":"2022-08-18T00:00:00Z","dateType":"Issued"},{"date":"2022-08-18T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1126/scirobotics.abm4183","relatedIdentifierType":"DOI"},{"relationType":"IsDerivedFrom","relatedIdentifier":"10.5281/zenodo.6809045","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["79978533 bytes"],"formats":[],"version":"6","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"A prerequisite for social coordination is bidirectional communication\n between teammates, each playing two roles simultaneously: as receptive\n listeners and expressive speakers. For robots working with humans in\n complex situations with multiple goals that differ in importance, failure\n to fulfill the expectation of either role could undermine group\n performance due to misalignment of values between humans and robots.\n Specifically, a robot needs to serve as an effective listener to infer\n human users' intents from instructions and feedback, and as an\n expressive speaker to explain its decision processes to users. In this\n paper, we investigate how to foster effective bidirectional human-robot\n communications in the context of value alignment---collaborative robots\n and users form an aligned understanding of the importance of possible task\n goals. We propose an explainable artificial intelligence (XAI) system in\n which a group of robots predicts users' values by taking in-situ\n feedback into consideration, while communicating their decision processes\n to users through explanations. To learn from human feedback, our XAI\n system integrates a cooperative communication model for inferring human\n values associated with multiple desirable goals. To be interpretable to\n humans, the system simulates human mental dynamics and predicts optimal\n explanations using graphical models. We conducted psychological\n experiments to examine the core components of the proposed computational\n framework. Our results show that real-time human-robot mutual\n understanding in complex cooperative tasks is achievable with a learning\n model based on bidirectional communication. We believe this interaction\n framework can shed light on bidirectional value alignment in communicative\n XAI systems, and more broadly, in future human-machine teaming systems.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"Defense Advanced Research Projects Agency","awardNumber":"N66001-17-2-4029","funderIdentifier":"https://ror.org/02caytj08","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.5068/D1XT3V","contentUrl":null,"metadataVersion":7,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":710,"downloadCount":89,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2022-08-18T15:45:51Z","registered":"2022-08-18T15:45:52Z","published":null,"updated":"2026-03-13T23:51:02Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5068/d1pd72","type":"dois","attributes":{"doi":"10.5068/d1pd72","identifiers":[],"creators":[{"name":"Dunham, Christopher","nameType":"Personal","givenName":"Christopher","familyName":"Dunham","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-9547-0053","nameIdentifierScheme":"ORCID"}]},{"name":"Mackenzie, Madelynn","nameType":"Personal","givenName":"Madelynn","familyName":"Mackenzie","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Nakano, Haruko","nameType":"Personal","givenName":"Haruko","familyName":"Nakano","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-5807-9127","nameIdentifierScheme":"ORCID"}]},{"name":"Kim, Alexis","nameType":"Personal","givenName":"Alexis","familyName":"Kim","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Juda, Michal","nameType":"Personal","givenName":"Michal","familyName":"Juda","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-0499-3700","nameIdentifierScheme":"ORCID"}]},{"name":"Nakano, Atsushi","nameType":"Personal","givenName":"Atsushi","familyName":"Nakano","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Stieg, Adam","nameType":"Personal","givenName":"Adam","familyName":"Stieg","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Gimzewski, James","nameType":"Personal","givenName":"James","familyName":"Gimzewski","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-4333-6957","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Data from: Pacemaker translocations and power laws in 2D stem cell-derived cardiomyocyte cultures"}],"publisher":"Dryad","container":{},"publicationYear":2022,"subjects":[{"subject":"FOS: Biological sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2022-02-04T03:02:04Z","dateType":"Submitted"},{"date":"2022-02-16T00:00:00Z","dateType":"Issued"},{"date":"2022-02-16T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1371/journal.pone.0263976","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["146416 bytes"],"formats":[],"version":"5","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"This repository contains the minimal information necessary for the\n analysis of pacemaker translocation quiescent periods. There is enough\n data provided to conduct a thorough analysis for power law behavior in\n pacemaker translocation activity using the powerlaw Python library,\n available via pip and at PyPi (https://pypi.org/project/powerlaw/).\n Additionally, this repository contains batch file information for the\n original files used in analysis and provides access to a Box link\n containing the original *.mcd format files of each microelectrode array\n (MEA) recording used to produce the data. The translocation algorithm is\n described in the associated manuscript. Article abstract: Power laws are\n of interest to several scientific disciplines because they can provide\n important information about the underlying dynamics (e.g. scale invariance\n and self-similarity) of a given system. Because power laws are of\n increasing interest to the cardiac sciences as potential indicators of\n cardiac dysfunction, it is essential that rigorous, standardized\n analytical methods are employed in the evaluation of power laws. This\n study compares the methods currently used in the fields of condensed\n matter physics, geoscience, neuroscience, and cardiology in order to\n provide a robust analytical framework for evaluating power laws in stem\n cell-derived cardiomyocyte cultures. One potential power law-obeying\n phenomenon observed in these cultures is pacemaker translocations, or the\n spatial and temporal instability of the pacemaker region, in a 2D cell\n culture. Power law analysis of translocation data was performed using\n increasingly rigorous methods in order to illustrate how differences in\n analytical robustness can result in misleading power law interpretations.\n Non-robust methods concluded that pacemaker translocations adhere to a\n power law while robust methods convincingly demonstrated that they obey a\n doubly truncated power law. The results of this study highlight the\n importance of employing comprehensive methods during power law analysis of\n cardiomyocyte cultures.","descriptionType":"Abstract"},{"description":"Dataset was collected in the manner described in the manuscript.\n No post-processing (e.g. signal filtering or smoothing) was applied to the\n recorded field potentials. Data processing is largely limited to\n calculation of pacemaker (time lag) data combined with application of the\n algorithm for detecting pacemaker translocations. The algorithm employs a\n distance threshold-based method to identify when the pacemaker moves\n (translocates).  Refer to the original manuscript for more\n information.","descriptionType":"Methods"},{"description":"The values given here are pacemaker translocation quiescent\n periods, as measured in beats.  Full recapitulation of the analysis would\n likely require access to MEA recordings. For 30 recordings, the file sizes\n equal approx. 7.5gb of data (in .mcd form; near 60gb in text form). If you\n want access to this data, please do not hesitate to contact the\n corresponding authors.  We will be happy to share the data with you in the\n form of a freely-accessible Box link.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.5068/D1PD72","contentUrl":null,"metadataVersion":12,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":174,"downloadCount":8,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2022-02-16T15:33:36Z","registered":"2022-02-16T15:33:38Z","published":null,"updated":"2026-03-13T21:33:05Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5068/d1h67f","type":"dois","attributes":{"doi":"10.5068/d1h67f","identifiers":[],"creators":[{"name":"Manson, Joseph","nameType":"Personal","givenName":"Joseph","familyName":"Manson","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-3049-2742","nameIdentifierScheme":"ORCID"}]},{"name":"Chua, Kristine Joy","nameType":"Personal","givenName":"Kristine Joy","familyName":"Chua","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Lukaszewski, Aaron","nameType":"Personal","givenName":"Aaron","familyName":"Lukaszewski","affiliation":["California State University, Fullerton"],"nameIdentifiers":[]}],"titles":[{"title":"Do early life experiences predict variation in the general factor of personality (GFP)?"}],"publisher":"Dryad","container":{},"publicationYear":2021,"subjects":[],"contributors":[],"dates":[{"date":"2021-09-28T20:56:04Z","dateType":"Submitted"},{"date":"2021-10-11T00:00:00Z","dateType":"Issued"},{"date":"2021-10-11T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1007/s40750-021-00177-1","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["177662 bytes"],"formats":[],"version":"4","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Evolutionary approaches to examine human personality variation have used\n the Big Five as given, tested higher order latent structures like the Big\n Two or the General Factor of Personality (GFP), or applied domain-specific\n psychological adaptations. Yet, debates regarding the adaptive\n significance of personality variation are ongoing. We focus on latent\n factor models and test adaptationist hypotheses linking facultative\n responses of the GFP, its subparts (i.e., metatrait alpha), and\n extraversion to early life experiences in 366 U.S. undergraduates. To\n address the problem of shared method variance, we assessed Big Five\n personality traits using both self-report and stranger-rating from brief\n videotaped interviews. Structural equation modeling, from the\n self-reported Big Five dimensions only, revealed a well-fitting GFP, which\n was related to father-closeness. A GFP comprised of the other-rated Big\n Five dimensions could not be extracted. Results from metatrait alpha were\n also unsupported. Lastly, we found some support for an alternative\n hypothesis that extraversion (men only) is ontogenetically calibrated to\n physical phenotypic traits that affect individuals’ bargaining power. Our\n findings cast doubt on the value of the GFP as a valid construct. We\n discuss how the field of personality science may benefit from adopting a\n bottom-up as opposed to a top-down approach.","descriptionType":"Abstract"},{"description":"See GFPdataReadMe.txt file.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.5068/D1H67F","contentUrl":null,"metadataVersion":11,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":185,"downloadCount":20,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2021-10-11T23:37:46Z","registered":"2021-10-11T23:37:47Z","published":null,"updated":"2026-03-13T20:00:27Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5068/d1kt10","type":"dois","attributes":{"doi":"10.5068/d1kt10","identifiers":[],"creators":[{"name":"Youngflesh, Casey","nameType":"Personal","givenName":"Casey","familyName":"Youngflesh","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-6343-3311","nameIdentifierScheme":"ORCID"}]},{"name":"Li, Yun","nameType":"Personal","givenName":"Yun","familyName":"Li","affiliation":["University of Delaware"],"nameIdentifiers":[]},{"name":"Lynch, Heather","nameType":"Personal","givenName":"Heather","familyName":"Lynch","affiliation":["Stony Brook University"],"nameIdentifiers":[]},{"name":"Delord, Karine","nameType":"Personal","givenName":"Karine","familyName":"Delord","affiliation":["La Rochelle Université"],"nameIdentifiers":[]},{"name":"Barbraud, Christophe","nameType":"Personal","givenName":"Christophe","familyName":"Barbraud","affiliation":["La Rochelle Université"],"nameIdentifiers":[]},{"name":"Ji, Rubao","nameType":"Personal","givenName":"Rubao","familyName":"Ji","affiliation":["Woods Hole Oceanographic Institution"],"nameIdentifiers":[]},{"name":"Jenouvrier, Stephanie","nameType":"Personal","givenName":"Stephanie","familyName":"Jenouvrier","affiliation":["Woods Hole Oceanographic Institution"],"nameIdentifiers":[]}],"titles":[{"title":"Lack of synchronized breeding success in a seabird community: extreme events, niche separation, and environmental variability"}],"publisher":"Dryad","container":{},"publicationYear":2021,"subjects":[{"subject":"Antarctica","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"environmental indicators"},{"subject":"extreme events"},{"subject":"niche separation"},{"subject":"Ecology","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2021-08-13T17:41:03Z","dateType":"Submitted"},{"date":"2021-08-19T00:00:00Z","dateType":"Issued"},{"date":"2021-08-19T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1111/oik.08426","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["105849 bytes"],"formats":[],"version":"3","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Synchrony in ecological systems, the degree to which elements respond\n similarly over time or space, can inform our understanding of how\n ecosystems function and how they are responding to global change. While\n studies of ecological synchrony are often focused on within-species\n dynamics, synchrony among species may provide important insights into how\n dynamics of one species are indicative of conditions relevant to the\n larger community, with both basic and applied implications. Ecological\n theory suggests there may be conditions under which communities might\n exhibit increased synchrony, however the degree to which these patterns\n are borne out in natural systems is currently unknown. We used long-term\n breeding success data from a community of Antarctic seabirds to assess the\n degree of interspecific, community synchrony and the role that extreme\n events play in driving these dynamics. We assessed theoretical links\n between community synchrony, niche separation, and environmental\n variability using data from this and three other seabird communities as\n well as a simulation study. Results show that reproductive success for\n individual species in the Antarctic seabird community fluctuated\n relatively independently from one another, resulting in little synchrony\n across this community, outside of extreme years. While an exceptionally\n poor year for a given species was not necessarily associated with an\n exceptionally poor year for any other species, one community-wide extreme\n year existed. When compared to other seabird communities, this group of\n Antarctic seabirds exhibited lower overall synchrony and higher estimated\n niche separation, supporting theoretical predictions. Empirical and\n simulation-derived results suggest that communities where temporal\n variation is small for conditions in which species respond substantially\n differently, and large for conditions in which species respond similarly,\n may exhibit more synchronous dynamics. Identifying where and why\n synchronous dynamics might be more apparent has the potential to inform\n how ecological communities might respond to future global change.","descriptionType":"Abstract"},{"description":"We collected data on five sympatrically breeding seabird species\n (Adélie penguin \u003ci\u003ePygoscelis adeliae\u003c/i\u003e, southern fulmar\n \u003ci\u003eFulmarus glacialoides\u003c/i\u003e, cape petrel \u003ci\u003eDaption\n capense\u003c/i\u003e, snow petrel \u003ci\u003ePagodroma nivea\u003c/i\u003e, and\n south polar skua \u003ci\u003eStercorarius maccormicki\u003c/i\u003e) at breeding\n sites at Pointe Géologie, Antarctica (66.67°S, 140.00°E) during the\n Antarctic summer (December – March). These five seabird species are highly\n site faithful and feed on prey items found in the marine environment\n (e.g., krill, fish, and squid), with the exception of south polar skua,\n which preys primarily upon Adélie penguin eggs and young during the\n breeding season at Pointe Géologie (Ridoux and Offredo 1989). The number\n of breeding pairs and number of chicks fledged were recorded from\n 1980-2016, although data were not available for every species in all years\n (Appendix A). Breeding success data were collected from the entire colony\n for southern fulmar, cape petrel, south polar skua, and Adélie penguin,\n while a subset of nests were monitored for snow petrel (approximately 180\n – 300 nests; [Chastel et al. 1993, Barbraud et al. 2015]). Given the\n well-defined nature of nests and survey methods, a high level of data\n accuracy was presumed. See Barbraud et al. (2015) for detailed data\n collection protocols.   \n Barbraud, C., K. Delord, and H.\n Weimerskirch. 2015. Extreme ecological response of a seabird community to\n unprecedented sea ice cover. Royal Society Open Science\n 2:140456–140456. Chastel, O., H. Weimerskirch, and P.\n Jouventin. 1993. High annual variability in reproductive success and\n survival of an Antarctic seabird, the snow petrel \u003ci\u003ePagodroma\n nivea\u003c/i\u003e. Oecologia 94:278–285. Ridoux, V., and C. Offredo. 1989. The diets\n of five summer breeding seabirds in Adélie Land, Antarctica. Polar Biology\n 9:137–145.","descriptionType":"Methods"},{"description":"We provide here the raw data from counts of breeding pairs and\n number of chicks for 5 species seabirds at Pointe Géologie. Before using\n the data please contact Christophe Barbraud (barbraud@cebc.cnrs.fr) or\n Karine Delord (delord@cebc.cnrs.fr). Data\n fields: YEAR - year\u003cbr\u003e SPECIES -\n species\u003cbr\u003e ABUN - number of breeding pairs\u003cbr\u003e CHICKS -\n number of chicks\u003cbr\u003e BS - chicks per pair\n   Other data used in these analyses:\n - Isle of May seabird data were from Lahoz-Monfort et al.\n 2013\u003cbr\u003e - Southeast Farallon Island seabird data were collected by\n PRBO Conservation Science in collaboration with the United States Fish and\n Wildlife Service (available here:\n https://data.prbo.org/cadc2/index.php?page=colony-data)\u003cbr\u003e - Tern\n Island seabird data were from Dearborn et al. 2001\u003cbr\u003e - Community\n evenness data (other than those derived from the seabird colonies) were\n from Sugihara et al. 2003\u003cbr\u003e - Chl-a data were derived from the\n Globcolour product (http://www.globcolour.info/)\n   \n Dearborn, D. C., A. D. Anders, and\n E. N. Flint. 2001. Trends in reproductive success of Hawaiian seabirds: is\n guild membership a good criterion for choosing indicator species?\n Biological Conservation 101:97–103. Lahoz-Monfort, J. J., B. J. Morgan, M. P.\n Harris, F. Daunt, S. Wanless, and S.  N. Freeman. 2013. Breeding together:\n modeling synchrony in productivity in a seabird community. Ecology\n 94:3–10. Sugihara, G.,\n L.-F. Bersier, T. R. E. Southwood, S. L. Pimm, and R. M. May. 2003.\n Predicted correspondence between species abundances and dendrograms of\n niche similarities. Proceedings of the National Academy of Sciences\n 100:5246–5251.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"funderName":"Expéditions Polaires Françaises*"},{"schemeUri":"https://ror.org","funderName":"Institut Polaire Français Paul Émile Victor","funderIdentifier":"https://ror.org/011ed2d57","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"Terres Australes et Antarctiques Françaises","funderIdentifier":"https://ror.org/05trnbe45","funderIdentifierType":"ROR"},{"funderName":"Zone Atelier Antarctique*"}],"url":"https://datadryad.org/dataset/doi:10.5068/D1KT10","contentUrl":null,"metadataVersion":11,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":173,"downloadCount":14,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2021-08-20T01:33:25Z","registered":"2021-08-20T01:33:27Z","published":null,"updated":"2026-03-13T17:12:36Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5068/d14h5c","type":"dois","attributes":{"doi":"10.5068/d14h5c","identifiers":[],"creators":[{"name":"Dunham, Christopher","nameType":"Personal","givenName":"Christopher","familyName":"Dunham","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-9547-0053","nameIdentifierScheme":"ORCID"}]},{"name":"Mackenzie, Madelynn","nameType":"Personal","givenName":"Madelynn","familyName":"Mackenzie","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Nakano, Haruko","nameType":"Personal","givenName":"Haruko","familyName":"Nakano","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-5807-9127","nameIdentifierScheme":"ORCID"}]},{"name":"Kim, Alexis","nameType":"Personal","givenName":"Alexis","familyName":"Kim","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Nakano, Atsushi","nameType":"Personal","givenName":"Atsushi","familyName":"Nakano","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Stieg, Adam","nameType":"Personal","givenName":"Adam","familyName":"Stieg","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Gimzewski, James","nameType":"Personal","givenName":"James","familyName":"Gimzewski","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-4333-6957","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Cardio PyMEA: A user-friendly, open-source Python application for cardiomyocyte microelectrode array analysis"}],"publisher":"Dryad","container":{},"publicationYear":2022,"subjects":[{"subject":"MEA analytical software"},{"subject":"Cardiomyocyte analysis software"},{"subject":"Cardiomyocyte MEA data"},{"subject":"Cardiomyocyte powerlaw analysis"},{"subject":"Python microelectrode array analysis software"},{"subject":"FOS: Biological sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2022-04-15T06:53:05Z","dateType":"Submitted"},{"date":"2022-05-06T00:00:00Z","dateType":"Issued"},{"date":"2022-05-06T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1101/2022.03.25.485780","relatedIdentifierType":"DOI"},{"relationType":"IsDerivedFrom","relatedIdentifier":"10.5281/zenodo.6462799","relatedIdentifierType":"DOI"},{"relationType":"IsSourceOf","relatedIdentifier":"10.5281/zenodo.6522426","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1371/journal.pone.0266647","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["1893816270 bytes"],"formats":[],"version":"3","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Open source analytical software for the analysis of electrophysiological\n cardiomyocyte data offers a variety of new functionalities to complement\n closed-source, proprietary tools. Here, we present the Cardio PyMEA\n application, a free, modifiable, and open source program for the analysis\n of microelectrode array (MEA) data obtained from cardiomyocyte cultures.\n Major software capabilities include: beat detection; pacemaker origin\n estimation; beat amplitude and interval; local activation time, upstroke\n velocity, and conduction velocity; analysis of cardiomyocyte\n property-distance relationships; and robust power law analysis of\n pacemaker spatiotemporal instability. Cardio PyMEA was written entirely in\n Python 3 to provide an accessible, integrated workflow that possesses a\n user-friendly graphical user interface (GUI) written in PyQt5 to allow for\n performant, cross-platform utilization. This application makes use of\n object-oriented programming (OOP) principles to facilitate the relatively\n straightforward incorporation of custom functionalities, e.g. power law\n analysis, that suit the needs of the user. Cardio PyMEA is available as an\n open source application under the terms of the GNU General Public License\n (GPL). The source code for Cardio PyMEA can be downloaded from Github at\n the following repository: https://github.com/csdunhamUC/cardio_pymea.","descriptionType":"Abstract"},{"description":"Dataset was collected in the manner described in the\n manuscript.","descriptionType":"Methods"},{"description":"Full MEA recordings and program executables for Windows 10 and\n Linux 5.16.16 can be found at this Box repository: https://ucla.box.com/s/vf13vjt8exxbqrsi96cd2wqkrk6otkn3. Use of the executables is not recommended; it is best to run cardio_pymea.py from the terminal and to use the Github file versions. Cardio PyMEA runs on MacOS when executed via terminal using the source files provided on Github.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Heart Lung and Blood Institute","awardNumber":"R21HL124503","funderIdentifier":"https://ror.org/012pb6c26","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.5068/D14H5C","contentUrl":null,"metadataVersion":10,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":151,"downloadCount":12,"referenceCount":0,"citationCount":2,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2022-05-06T14:30:11Z","registered":"2022-05-06T14:30:12Z","published":null,"updated":"2026-03-12T17:17:09Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5068/d1tm2g","type":"dois","attributes":{"doi":"10.5068/d1tm2g","identifiers":[],"creators":[{"name":"Reis, Fernando","nameType":"Personal","givenName":"Fernando","familyName":"Reis","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Lee, Johannes","nameType":"Personal","givenName":"Johannes","familyName":"Lee","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2420-4916","nameIdentifierScheme":"ORCID"}]},{"name":"Maesta-Pereira, Sandra","nameType":"Personal","givenName":"Sandra","familyName":"Maesta-Pereira","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Schuette, Peter","nameType":"Personal","givenName":"Peter","familyName":"Schuette","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-6308-6441","nameIdentifierScheme":"ORCID"}]},{"name":"Chakerian, Meghmik","nameType":"Personal","givenName":"Meghmik","familyName":"Chakerian","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Liu, Jinhan","nameType":"Personal","givenName":"Jinhan","familyName":"Liu","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"La-Vu, Mimi","nameType":"Personal","givenName":"Mimi","familyName":"La-Vu","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Tobias, Brooke","nameType":"Personal","givenName":"Brooke","familyName":"Tobias","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Canteras, Newton","nameType":"Personal","givenName":"Newton","familyName":"Canteras","affiliation":["Universidade de São Paulo"],"nameIdentifiers":[]},{"name":"Kao, Jonathan","nameType":"Personal","givenName":"Jonathan","familyName":"Kao","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Adhikari, Avishek","nameType":"Personal","givenName":"Avishek","familyName":"Adhikari","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-9187-9211","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Dorsal Periaqueductal gray ensembles represent approach and avoidance states"}],"publisher":"Dryad","container":{},"publicationYear":2021,"subjects":[{"subject":"FOS: Biological sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2020-11-17T18:02:03Z","dateType":"Submitted"},{"date":"2021-05-18T00:00:00Z","dateType":"Issued"},{"date":"2021-05-18T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1101/2020.11.19.389486","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.7554/elife.64934","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["513782264 bytes"],"formats":[],"version":"3","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Animals must balance needs to approach threats for risk-assessment and to\n avoid danger. The dorsal periaqueductal gray (dPAG) controls defensive\n behaviors, but it is unknown how it represents states associated with\n threat approach and avoidance. We identified a dPAG threat-avoidance\n ensemble in mice (Mus musculus) that showed higher activity far from\n threats such as the open arms of the elevated plus maze and a live\n predator. These cells were also more active during threat-avoidance\n behaviors such as escape and freezing, even though these behaviors have\n antagonistic motor output. Conversely, the threat-approach ensemble was\n more active during risk-assessment behaviors and near threats.\n Furthermore, unsupervised methods showed approach/avoidance states were\n encoded with shared activity patterns across threats. Lastly, the relative\n number of cells in each ensemble predicted threat-avoidance across mice.\n Thus, dPAG ensembles dynamically encode threat approach and avoidance\n states, providing a flexible mechanism to balance risk-assessment and\n danger avoidance.","descriptionType":"Abstract"},{"description":"Mice. Mice (Mus musculus) of the C57BL/6J strain (Jackson\n Laboratory stock No. 000664) were used for all experiments. Male mice\n between 2 and 5 months of age were used in all experiments. Mice were\n maintained on a 12-hour reverse light-dark cycle with food and water ad\n libitum. Sample sizes were chosen based on previous behavioral studies\n with miniaturized microscope recordings on defensive behaviors, which\n typically use 6-10 mice per group. All mice were handled for a minimum of\n 5 days prior to any behavioral task. In this work, analyses of the EPM\n environment used 8 mice, while any analyses involving rat exposure used 7\n mice. All procedures conformed to guidelines established by the National\n Institutes of Health and have been approved by the University of\n California, Los Angeles Institutional Animal Care and Use\n Committee. Rats. Male Long-Evans rats (250-400 grams)\n were obtained from Charles River and were individually housed on a\n standard 12-hour light-dark cycle and given food and water ad libitum.\n Rats were only used as a predatory stimulus. Rats were handled for several\n weeks prior to being used and were screened for low aggression to avoid\n attacks on mice. No attacks on mice were observed in this\n experiment. Surgeries. Eight-week-old mice were\n anaesthetized with 1.5-3.0% isoflurane and placed in a stereotaxic\n apparatus (Kopf Instruments). AAV9.Syn.GCaMP6s.WPRE.SV40 were packaged and\n supplied by UPenn Vector Core at titers 7.5 x 103 viral particles per ml\n and viral aliquots were diluted prior to use with artificial cortex buffer\n to a final titer of 5 x 1012 viral particles per ml. After performing a\n craniotomy, 100nl of virus was injected into the dPAG (coordinates in mm,\n from skull surface): -4.20 anteromedial, -0.85 lateral, -2.3 depth,\n 15-degree angle. Five days after virus injection, the animals underwent a\n second surgery in which two skull screws were inserted and a\n microendoscope was implanted above the injection site. A 0.5 mm diameter,\n ~4 mm long gradient refractive index (GRIN) lens (Inscopix, Palo Alto, CA)\n was implanted above the dPAG (-2.0 mm ventral to the skull surface)\n (Resendez et al., 2016). The lens was fixed to the skull with\n cyanoacrylate glue and adhesive cement (Metabond; Parkell). The exposed\n end of the GRIN lens was protected with transparent Kwik-seal glue and\n animals were returned to a clean cage. Two weeks later, a small aluminum\n base plate was cemented onto the animal’s head on top of the previously\n formed dental cement. Animals were provided with analgesic and\n anti-inflammatory (carprofen).  Behavioral timeline.\n Behavioral tests were combined in the following manner across days: EPM\n test, habituation 1, habituation 2, rat exposure. Three days after, the\n fear conditioning test was conducted in the following manner: habituation\n 1, habituation 2, fear conditioning and retrieval.\n Elevated Plus Maze (EPM) test. Mice were placed in the center of\n the EPM facing one of the closed arms and were allowed to freely explore\n the environment for 20 minutes. The length of each arm was 30 cm, the\n width was 7 cm and the height of the closed arm walls was 20 cm. The maze\n was 65 cm elevated from the floor by a camera stand. A total of 8 mice\n were analyzed. Rat Exposure Assay. Mice were habituated\n to a white rectangular box (70 cm length, 26 cm width, 44 cm height) for\n two consecutive days during 20-minute sessions. Mice were then exposed to\n an adult rat in this environment on the following day. The rat was secured\n by a harness tied to one of the walls and could freely ambulate only\n within a short perimeter. The mouse was placed near the wall opposite to\n the rat and freely explored the context for 20 minutes. No separating\n barrier was placed between the mouse and the rat, allowing for close\n naturalistic encounters that can induce a variety of robust defensive\n behaviors. A total of 7 mice were analyzed. Behavior\n and miniscope video capture. All videos were recorded at 30 frames/sec\n using a Logitech HD C310 webcam and custom-built head-mounted UCLA\n miniscope (Aharoni and Hoogland, 2019). Open-source UCLA Miniscope\n software and hardware (http://miniscope.org/) were used to capture and\n synchronize neural and behavioral video (Cai et al., 2016, Schuette et.\n al, 2020). Perfusion and histological verification.\n Mice were anesthetized with Fatal-Plus and transcardially perfused with\n phosphate buffered saline followed by a solution of 4% paraformaldehyde.\n Extracted brains were stored for 12 hours at 4°C in 4% paraformaldehyde.\n Brains were then placed in sucrose solution for a minimum of 24 hours.\n Brains were sectioned in the coronal plane in a cryostat, washed in\n phosphate buffered saline and mounted on glass slides using PVA-DABCO.\n Images were acquired using a Keyence BZ-X fluorescence microscope with a\n 10 or 20X air objective. Data Analysis was performed\n using custom-written code in MATLAB and Python.\n Miniscope postprocessing and co-registration. Miniscope videos\n were motion-corrected using the open-source UCLA miniscope analysis\n package (https://github.com/daharoni/Miniscope_Analysis) (Aharoni and\n Hoogland, 2019). They were spatially downsampled by a factor of two and\n temporally downsampled by a factor of four, and the cell footprints and\n activity were extracted using the open-source package Constrained\n Nonnegative Matrix Factorization for microEndoscopic data (CNMF-E;\n https://github.com/zhoupc/CNMF_E) (Zhou et al., 2018). Neurons were\n co-registered across sessions using the open-source probabilistic modeling\n package CellReg (https://github.com/zivlab/CellReg) (Sheintuch et al.,\n 2017). Artifact suppression. For suppression of long\n timescale artifacts, e.g. long-time scale fluctuations in calcium\n fluorescence shared across many neurons due to bleaching or other factors,\n we used PCA to identify large variance PCs (≥ 5% total variance)\n reflecting these artifacts. Cell activity was then reconstructed using\n these PCs excluded from reconstruction (O'Shea and Shenoy, 2018).\n This method was applied only to data for mouse 1 in the rat exposure\n assay. Variance thresholding. A minority of recorded\n cells had very small variance over the course of an experimental session.\n To exclude these cells from analysis, we identified a representative cell\n for each trial. Cells with less than 10% of the representative cell’s\n variance were discarded. The remaining cells were used for further\n analysis. Behavior detection. To extract the pose of\n freely-behaving mice in the described assays, we implemented DeepLabCut\n (Mathis et al., 2018), an open-source convolutional neural network-based\n toolbox, to identify mouse nose, ear and tail base xy-coordinates in each\n recorded video frame. These coordinates were then used to calculate\n velocity and position at each time point, as well as classify defensive\n behaviors in an automated manner using custom Matlab scripts. Freezing was\n defined as epochs of cessation of all movement except for breathing.\n Approach and escape were defined as epochs when the mouse moved,\n respectively towards or away from the rat at a velocity exceeding a\n minimum threshold.","descriptionType":"Methods"},{"description":"Each numbered folder corresponds to a mouse.  Within each\n folder: 'neural_data.mat' is a struct\n containing 'C_raw', the raw CNMF-E output neural data.\n ______________________________________________________________________________________________ 'BehaviorMS.mat' contains a series of vectors or matrices.  Pertinent to this study are: Rat sessions-\u003cbr\u003e -approachFrameMS and approachIndicesMS\u003cbr\u003e -stretchFrameMS and stretchIndicesMS\u003cbr\u003e -escapeFrameMS and escapeIndicesMS\u003cbr\u003e -freezeFrameMS and freezeIndicesMS EPM sessions-\u003cbr\u003e -openArmFrameMS and openArmIndicesMS\u003cbr\u003e -closedArmFrameMS and closedArmIndicesMS\u003cbr\u003e Additionally, the separate file 'headDip.mat' gives the headDipFrameMS and headDipIndicesMS for all \u003cbr\u003e head dips over the edge of the open arms of the EPM. All '*FrameMS' files give in/out points, aligned to the neural data, for each behavior. 'In' is\u003cbr\u003e column 1 and 'Out' is column 2. All '*IndicesMS' files give logical '0' or '1' values for whether a \u003cbr\u003e behavior is happening during a frame of neural data. _______________________________________________________________________________________________ 'Tracking.mat' is a struct containing the pertinent fields:\u003cbr\u003e FOR EPM, RAT, and TOY RAT, for each frame of neural data:\u003cbr\u003e 'mouse_positionMS', which provides the xy coordinates of the point between the mouse ears.\u003cbr\u003e 'mouseAngleMS' provides the angle in radians of mouse head direction.\u003cbr\u003e 'mouseVelMS' provides the frame-by-frame velocity of the mouse, in pixels per frame. FOR RAT and TOY RAT only, for each frame of neural data:\u003cbr\u003e 'rat_positionMS' provides the xy coordinates of the point between the rat ears.\u003cbr\u003e 'angleDiffMouseHeadDirRatMS' provides the difference in radians between the head direction of the mouse\u003cbr\u003e and the rat position.\u003cbr\u003e 'ratVelMS' provides the frame-by-frame velocity of the rat, in pixels per frame.\u003cbr\u003e 'distanceMouseRatMS' or 'distanceMouseToyRatMS' provides the distance in pixels between the mouse and rat.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Institute of Mental Health","awardNumber":"R00 MH106649","funderIdentifier":"https://ror.org/04xeg9z08","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Institute of Mental Health","awardNumber":"R01 MH119089","funderIdentifier":"https://ror.org/04xeg9z08","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"Brain \u0026 Behavior Research Foundation","awardNumber":"22663","funderIdentifier":"https://ror.org/03a63f080","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"Brain \u0026 Behavior Research Foundation","awardNumber":"27654","funderIdentifier":"https://ror.org/03a63f080","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"NSF-GRFP DGE-1650604","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"University of California, Los Angeles","awardNumber":"Affiliates fellowship","funderIdentifier":"https://ror.org/046rm7j60","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"Hellman Foundation","funderIdentifier":"https://ror.org/02g98ya79","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"São Paulo Research Foundation","awardNumber":"#2014/05432-9","funderIdentifier":"https://ror.org/02ddkpn78","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"São Paulo Research Foundation","awardNumber":"#2015/23092-3","funderIdentifier":"https://ror.org/02ddkpn78","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"São Paulo Research Foundation","awardNumber":"#2017/08668-1","funderIdentifier":"https://ror.org/02ddkpn78","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.5068/D1TM2G","contentUrl":null,"metadataVersion":13,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":181,"downloadCount":20,"referenceCount":0,"citationCount":2,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2021-05-18T20:44:21Z","registered":"2021-05-18T20:44:22Z","published":null,"updated":"2026-03-12T16:13:10Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5068/d1zd5s","type":"dois","attributes":{"doi":"10.5068/d1zd5s","identifiers":[],"creators":[{"name":"Yang, Xian-Jie","nameType":"Personal","givenName":"Xian-Jie","familyName":"Yang","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2866-7440","nameIdentifierScheme":"ORCID"}]},{"name":"Zhang, Xiangmei","nameType":"Personal","givenName":"Xiangmei","familyName":"Zhang","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-0419-9267","nameIdentifierScheme":"ORCID"}]},{"name":"Mandric, Igor","nameType":"Personal","givenName":"Igor","familyName":"Mandric","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Nguyen, Kevin","nameType":"Personal","givenName":"Kevin","familyName":"Nguyen","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-8327-268X","nameIdentifierScheme":"ORCID"}]},{"name":"Nguyen, Thao","nameType":"Personal","givenName":"Thao","familyName":"Nguyen","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Pellegrini, Matteo","nameType":"Personal","givenName":"Matteo","familyName":"Pellegrini","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Grove, James","nameType":"Personal","givenName":"James","familyName":"Grove","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Barnes, Steven","nameType":"Personal","givenName":"Steven","familyName":"Barnes","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-0496-1645","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Single cell transcriptomic analyses reveal the impact of bHLH factors on human retinal organoid development"}],"publisher":"Dryad","container":{},"publicationYear":2021,"subjects":[{"subject":"FOS: Biological sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2021-04-08T14:58:02Z","dateType":"Submitted"},{"date":"2021-04-21T00:00:00Z","dateType":"Issued"},{"date":"2021-04-21T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1101/2020.10.27.358135","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.3389/fcell.2021.653305","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["44225447 bytes"],"formats":[],"version":"9","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"The developing retina expresses multiple bHLH transcription\n factors. Their precise functions and interactions in uncommitted\n retinal progenitors remain to be fully elucidated. Here, we\n investigate the roles of bHLH factors ATOH7 and Neurog2 in human ES\n cell-derived retinal organoids.  Single-cell transcriptome\n analyses identify three states of proliferating retinal progenitors:\n pre-neurogenic, neurogenic, and cell cycle-exiting\n progenitors. Each shows different expression profile of bHLH\n factors. The cell cycle-exiting progenitors feed into a\n postmitotic heterozygous neuroblast pool that gives rise to early born\n neuronal lineages. Elevating ATOH7 or Neurog2 expression\n accelerates the transition from the pre-neurogenic to the neurogenic\n state, and expands the exiting progenitor and neuroblast\n populations. In addition, ATOH7 and Neurog2 significantly, yet\n differentially, enhance retinal ganglion cell and cone photoreceptor\n production. Moreover, single-cell transcriptome analyses reveal\n that ATOH7 and Neurog2 assert positive autoregulation, suppress key bHLH\n factors associated with the neurogenic progenitors, and elevate bHLH\n factors expressed by exiting progenitors and differentiating\n neuroblasts. This study thus provides novel insight regarding how\n ATOH7 and Neurog2 impact human retinal progenitor behaviors and neuroblast\n fate choices.","descriptionType":"Abstract"},{"description":"The datasets were collected by performing single-cell\n RNA-sequencing of human ESC-derived retinal organoids transduced by\n lentiviral vectors expressing EGFP, ATOH7, or Neurog2. Viral transduced\n cells were FACS sorted and subjected to 10X Genomics single-cell barcoding\n and cDNA library construction. Illumina NovaSeq6000 S2 paired-end 2x50bp\n mode was used to sequence the libraries. Spliced Transcripts Alignment\n to a Reference (STAR) version 2.5.1b (cellranger\n count) was used to perform sequence alignments to the reference human\n genome (GRCh38), barcode counts, and UMI counts to yield summary reports\n and t-Stochastic Neighboring Embedding (t-SNE) dimensionality\n reduction. For downstream analyses, cells with a number of unique molecular identifiers\n (UMI) \u0026gt; 2500 per cell and \u0026lt; 0.1 % mitochondrial\n gene expression were used. For LV-GFP, LV-AEP, LV-NEP samples, the mean\n reads per cell ranged from 139,000-195,000, with mean gene per cell\n ranging from 2935-3079. The resulting total single-cell counts used for\n analysis were 3004 for LV-EGFP, 2063 for LV-AEP, and 3909 for LV-NEP\n infected samples.","descriptionType":"Methods"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Eye Institute","awardNumber":"R01EY026319","funderIdentifier":"https://ror.org/03wkg3b53","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"Research to Prevent Blindness","awardNumber":"N/A","funderIdentifier":"https://ror.org/04drjs621","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Eye Institute","awardNumber":"P30EY000331","funderIdentifier":"https://ror.org/03wkg3b53","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.5068/D1ZD5S","contentUrl":null,"metadataVersion":16,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":210,"downloadCount":36,"referenceCount":0,"citationCount":2,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2021-04-21T15:27:51Z","registered":"2021-04-21T15:27:52Z","published":null,"updated":"2026-03-12T16:05:43Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5068/d1sq3k","type":"dois","attributes":{"doi":"10.5068/d1sq3k","identifiers":[],"creators":[{"name":"Gold, Zachary","nameType":"Personal","givenName":"Zachary","familyName":"Gold","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-0490-7630","nameIdentifierScheme":"ORCID"}]},{"name":"Sprague, Joshua","nameType":"Personal","givenName":"Joshua","familyName":"Sprague","affiliation":["National Park Service"],"nameIdentifiers":[]},{"name":"Kushner, David","nameType":"Personal","givenName":"David","familyName":"Kushner","affiliation":["National Park Service"],"nameIdentifiers":[]},{"name":"Zerecero, Erick","nameType":"Personal","givenName":"Erick","familyName":"Zerecero","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Barber, Paul","nameType":"Personal","givenName":"Paul","familyName":"Barber","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]}],"titles":[{"title":"eDNA metabarcoding as a biomonitoring tool for marine protected areas"}],"publisher":"Dryad","container":{},"publicationYear":2021,"subjects":[{"subject":"FOS: Biological sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2020-12-08T01:23:04Z","dateType":"Submitted"},{"date":"2021-01-12T00:00:00Z","dateType":"Issued"},{"date":"2021-01-12T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1101/2020.08.20.258889","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1371/journal.pone.0238557","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["6577501774 bytes"],"formats":[],"version":"5","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Monitoring of marine protected areas (MPAs) is critical for marine\n ecosystem management, yet current protocols rely on SCUBA-based visual\n surveys that are costly and time consuming, limiting their scope and\n effectiveness. Environmental DNA (eDNA) metabarcoding is a promising\n alternative for marine ecosystem monitoring, but more direct comparisons\n to visual surveys are needed to understand the strengths and limitations\n of each approach. This study compares fish communities inside and outside\n the Scorpion State Marine Reserve off Santa Cruz Island, CA using eDNA\n metabarcoding and underwater visual census surveys. Results from eDNA\n captured 76% (19/25) of fish species and 95% (19/20) of fish genera\n observed during pairwise underwater visual census. Species missed by eDNA\n were due to the inability of MiFish 12S barcodes to differentiate species\n of rockfishes (Sebastes, n=4) or low site occupancy rates of\n crevice-dwelling Lythrypnus gobies. However, eDNA detected an additional\n 23 fish species not recorded in paired visual surveys, but previously\n reported from prior visual surveys, highlighting the sensitivity of eDNA.\n Significant variation in eDNA signatures by location (50 m) and site\n (~1000 m) demonstrates the sensitivity of eDNA to address key questions\n such as community composition inside and outside MPAs. Results demonstrate\n the utility of eDNA metabarcoding for monitoring marine ecosystems,\n providing an important complementary tool to visual methods.","descriptionType":"Abstract"},{"description":"Materials and Methods\n Sample\n collection We conducted our study at\n Scorpion State Marine Reserve within the Channel Islands National Park and\n National Marine Sanctuary. To determine the degree to which eDNA could\n capture documented differences inside and outside this MPA, we sampled\n three sites: 1) inside the MPA (34.05223 N , 119.58253 W) 2) outside but\n adjacent (\u0026lt;0.5km) to the MPA (“edge site”; 34.04415 N, 119.54245\n W), and 3) 2.3 km outside the MPA boundary (“outside site”;\n 34.03837 N, 119.5253\n W; Fig 1). At each of these three sites, we sampled directly along a 100 m\n fixed transect used by the Kelp Forest Monitoring Program\n for visual monitoring,\n using a GPS to ensure transects overlapped [4]. We collected three replicate\n 1 L water samples from three locations on each transect, totaling nine\n spatially structured replicates per site. Due to fieldwork logistical\n challenges, each site was sampled on a different day with a maximum of 72\n hours between sampling events. \u003cb\u003eFig 1.\u003c/b\u003e Map of Scorpion State Marine Reserve off Santa Cruz Island, CA, USA. The map was generated using the free and open source software QGIS version 3.0. We collected seawater samples from 10 m below the surface and 1 m above the benthos using a 4 L Niskin bottle deployed from the UCLA RV Kodiak [25]. From each Niskin deployment, we transferred a single liter of seawater to an enteral feeding pouch and conducted gravity filtration through a sterile 0.22 µm Sterivex cartridge (MilliporeSigma, Burlington, MA, USA) in the field (Miya et al., 2016). Additionally, we processed three field blanks as a negative control that consisted of 1 L of distilled water following the method above. Finally, we dried Sterivex filters using a 3 mL syringe and then capped and stored the filters at -20˚C for DNA laboratory work back at UCLA (Miya et al., 2015). \u003cb\u003eDNA extraction and library preparation\u003c/b\u003e We extracted eDNA from the Sterivex cartridge using the DNAeasy Tissue and Blood Kit (Qiagen Inc., Germantown, MD) following modifications of Spens et al. (2017). We PCR amplified the extracted eDNA using the MiFish Universal Teleost \u003ci\u003e12S\u003c/i\u003e primer (Miya et al., 2015) with Nextera modifications following PCR and the library preparation methods of Curd et al. (2019) (See S1 Appendix for supplemental methods). All PCRs included a negative control where molecular grade water replaced the DNA extraction. For positive controls, we used DNA extractions of grass carp (\u003ci\u003eCtenopharyngodon idella, \u003c/i\u003eCyprinidae\u003ci\u003e)\u003c/i\u003e and Atlantic salmon (\u003ci\u003eSalmo salar, \u003c/i\u003eSalmonidae), both non-native to California. Libraries were sequenced on a MiSeq PE 2x300bp at the Technology Center for Genomics \u0026amp; Bioinformatics (University of California- Los Angeles, CA, USA), using Reagent Kit V3 with 20% PhiX added to all sequencing runs. \u003cb\u003eBioinformatics\u003c/b\u003e To determine community composition, we used the \u003ci\u003eAnacapa Toolkit\u003c/i\u003e (version: 1) to conduct quality control, amplicon sequence variant (ASV) parsing, and taxonomic assignment using user-generated custom reference databases [28]. The \u003ci\u003eAnacapa Toolkit \u003c/i\u003esequence QC and ASV parsing module relies on \u003ci\u003ecutadapt \u003c/i\u003e(version: 1.16)\u003ci\u003e \u003c/i\u003e[29], \u003ci\u003eFastX-toolkit \u003c/i\u003e(version: 0.0.13) [30], and \u003ci\u003eDADA2 \u003c/i\u003e(version 1.6) [31] as dependencies and the \u003ci\u003eAnacapa classifier\u003c/i\u003e modules relies on \u003ci\u003eBowtie2 \u003c/i\u003e(version 2.3.5)[32]\u003ci\u003e \u003c/i\u003eand a modified version of \u003ci\u003eBLCA\u003c/i\u003e [33] as dependencies. We processed sequences using the default parameters and assigned taxonomy using two \u003ci\u003eCRUX-\u003c/i\u003egenerated reference databases. We first assigned taxonomy using the FishCARD California fish specific reference database [34]. Second, we used the \u003ci\u003eCRUX\u003c/i\u003e-generated \u003ci\u003e12S\u003c/i\u003e reference database supplemented with FishCARD reference sequences to assign taxonomy using all available \u003ci\u003e12S \u003c/i\u003ereference barcodes to identify any non-fish taxa. We note that \u003ci\u003eCRUX \u003c/i\u003erelies on \u003ci\u003eecoPCR \u003c/i\u003e(version: 1.0.1)\u003ci\u003e \u003c/i\u003e[35]\u003ci\u003e, blastn \u003c/i\u003e(version: 2.6.0)\u003ci\u003e \u003c/i\u003e[36]\u003ci\u003e, \u003c/i\u003eand \u003ci\u003eEntrez-qiime \u003c/i\u003e(version: 2.0)\u003ci\u003e \u003c/i\u003e[37]\u003ci\u003e \u003c/i\u003eas\u003ci\u003e \u003c/i\u003edependencies. Raw ASV community table was decontaminated following Kelly et al. (2018) and McKnight et al. (2019) (See S1 Appendix). We chose a site occupancy cutoff score of 84% which corresponded with the minimum occupancy rate observed for three detections out of nine PCR replicates at a given location sampled. We then transformed all read counts into an eDNA index for beta-diversity statistics [16]. All non-fish species (mammals and birds) were removed prior to final analyses. \u003cb\u003eeDNA data analysis\u003c/b\u003e To test for alpha diversity differences, we compared total species richness for each site using an Analysis of Variance (ANOVA) and subsequent Levine’s test for equality of variance [39]. To determine whether our eDNA sampling design was sufficient to fully capture fish community diversity, we created species rarefaction curves using the \u003ci\u003eiNext\u003c/i\u003e package (version 2.0.2) [40]. We then compared species coverage estimates between each site, with and without site occupancy modeling, and using all three 1 L replicates taken at three locations along a 100 m transect (n=9) as well as only three 1 L biological replicates (n=3). We ran a piecewise regression analysis to identify breakpoints in the rate of species diversity found per sample collected using the \u003ci\u003eR \u003c/i\u003epackaged \u003ci\u003esegmented \u003c/i\u003e(version 1.3) [41]. To test for differences among fish communities, we calculated Bray-Curtis similarity distances on the eDNA index scores between all samples (See S2 Appendix for Supplemental Results) [23]. Specifically, we tested for the difference in community similarity variance between our three sites using an \u003ci\u003eadonis\u003c/i\u003e PEMANOVA (\u003ci\u003evegan \u003c/i\u003eversion: 2.4.2)[39], followed by a companion multivariate homogeneity of group dispersions test (BETADISPER) [39]. Both the PERMANOVA and BETADISPER were run using the following model: eDNA Index ~ Site + Location. We also visualized community beta diversity using non-metric multidimensional scaling (NMDS) [39]. To further investigate which species were driving eDNA community differences among sites, we conducted constrained analysis of principle components (CAP) [39]. \u003cb\u003eVisual underwater census methods\u003c/b\u003e To assess fish communities using underwater visual census techniques, SCUBA divers from the Kelp Forest Monitoring Program followed standard survey protocols following Kushner et al\u003ci\u003e. \u003c/i\u003e(2013). These protocols include survey types: visual fish transects, roving diver fish counts, and 1 m\u003csup\u003e2\u003c/sup\u003e quadrats. The visual fish transects targeted 13 indicator species of fish on visual fish transects recording the counts of adults and juveniles. This protocol consists of performing 2 m x 3 m x 50 m transects along the 100 m permanent transect. During roving diver fish count surveys all positively identified species are recorded. This protocol consists of 3-6 divers counting all fish species observed during a 30 minute time period, covering as much of the 2000 m\u003csup\u003e2\u003c/sup\u003e of bottom and entire water column as possible. The 1 m\u003csup\u003e2\u003c/sup\u003e quadrat records three small demersal species of fish\u003ci\u003e. \u003c/i\u003eAll visual surveys occurred along a permanent 100 m transect at each site and were conducted within two weeks of eDNA sampling (See S1 Appendix). \u003cb\u003eComparison of eDNA and visual underwater census methods\u003c/b\u003e We compared species detected by eDNA and underwater visual census approaches across corresponding transects at each site. We identified core taxa that were shared across all sites for eDNA and visual survey methods. In addition, we identified species that eDNA methods failed to detect but were observed in visual census surveys and vice versa. Given the few numbers of sites (n=3), we were unable to robustly compare abundance estimates between methods.","descriptionType":"Methods"},{"description":"See https://github.com/zjgold/Scorpion-SMR-eDNA-Metabarcoding  See https://www.authorea.com/users/330161/articles/457029-fishcard-fish-12s-california-current-specific-reference-database-for-enhanced-metabarcoding-efforts?commit=37fe31b2da0c2b30fb72b3088bb6eedc7460e913 for reference database","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.5068/D1SQ3K","contentUrl":null,"metadataVersion":12,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":277,"downloadCount":60,"referenceCount":0,"citationCount":2,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2021-01-12T21:19:01Z","registered":"2021-01-12T21:19:03Z","published":null,"updated":"2026-03-12T16:05:26Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5068/d1x95f","type":"dois","attributes":{"doi":"10.5068/d1x95f","identifiers":[],"creators":[{"name":"Cobo-Cuan, Ariadna","nameType":"Personal","givenName":"Ariadna","familyName":"Cobo-Cuan","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-4088-0367","nameIdentifierScheme":"ORCID"}]},{"name":"Grafe, T. Ulmar","nameType":"Personal","givenName":"T. Ulmar","familyName":"Grafe","affiliation":["Universiti Brunei Darussalam"],"nameIdentifiers":[]},{"name":"Narins, Peter M.","nameType":"Personal","givenName":"Peter M.","familyName":"Narins","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]}],"titles":[{"title":"Data from: Beyond the limits: identifying the high-frequency detectors in the anuran ear"}],"publisher":"Dryad","container":{},"publicationYear":2020,"subjects":[{"subject":"ultrasonic communication"},{"subject":"Hearing","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"amphibian"},{"subject":"Huia cavitympanum"},{"subject":"DPOAE"}],"contributors":[],"dates":[{"date":"2020-06-09T22:15:03Z","dateType":"Submitted"},{"date":"2020-07-15T00:00:00Z","dateType":"Issued"},{"date":"2020-07-15T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1098/rsbl.2020.0343","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["177358 bytes"],"formats":[],"version":"4","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Despite the predominance of low-frequency hearing in anuran amphibians, a\n few frog species have evolved high-frequency communication within certain\n environmental contexts. Huia cavitympanum is the most remarkable anuran\n with regard to upper frequency limits; it is the first frog species known\n to emit exclusively ultrasonic signals. Characteristics of the Distortion\n Product Otoacoustic Emissions from the amphibian papilla and the basilar\n papilla were analysed to gain insight into the structures responsible for\n high-frequency/ultrasound sensitivity. Our results confirm the matching of\n vocalization spectra and inner ear tuning in this species. Compared to\n most anurans, H. cavitympanum has a hyperextended hearing range spanning\n from audible to ultrasonic frequencies, far above the previously\n established “spectral limits” for the amphibian ear. The exceptional\n high-frequency sensitivity in the inner ear of H. cavitympanum illustrates\n the remarkable plasticity of the auditory system and the extent to which\n evolution can modify a sensory system to adapt it to its environment.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.5068/D1X95F","contentUrl":null,"metadataVersion":12,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":132,"downloadCount":8,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2020-07-16T06:29:52Z","registered":"2020-07-16T06:29:54Z","published":null,"updated":"2026-03-11T20:02:28Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5068/d1339f","type":"dois","attributes":{"doi":"10.5068/d1339f","identifiers":[],"creators":[{"name":"Beichman, Annabel","nameType":"Personal","givenName":"Annabel","familyName":"Beichman","affiliation":["University of Washington"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-6991-587X","nameIdentifierScheme":"ORCID"}]},{"name":"Robinson, Jacqueline","nameType":"Personal","givenName":"Jacqueline","familyName":"Robinson","affiliation":["University of California, San Francisco"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-5556-815X","nameIdentifierScheme":"ORCID"}]},{"name":"Lin, Meixi","nameType":"Personal","givenName":"Meixi","familyName":"Lin","affiliation":["Stanford University"],"nameIdentifiers":[]},{"name":"Moreno-Estrada, Andrés","nameType":"Personal","givenName":"Andrés","familyName":"Moreno-Estrada","affiliation":["Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional"],"nameIdentifiers":[]},{"name":"Nigenda-Morales, Sergio","nameType":"Personal","givenName":"Sergio","familyName":"Nigenda-Morales","affiliation":["California State University, San Marcos"],"nameIdentifiers":[]},{"name":"Harris, Kelley","nameType":"Personal","givenName":"Kelley","familyName":"Harris","affiliation":["University of Washington"],"nameIdentifiers":[]}],"titles":[{"title":"Data files associated with: Evolution of the mutation spectrum across a mammalian phylogeny"}],"publisher":"Dryad","container":{},"publicationYear":2023,"subjects":[{"subject":"Mutation","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"phylogeny"},{"subject":"phylogenetic signal"},{"subject":"Mus musculus"},{"subject":"Mus spretus"},{"subject":"Ursus arctos"},{"subject":"Phocoena sinus"},{"subject":"Balaenoptera physalus"},{"subject":"Ursus maritimus"},{"subject":"Pongo abelii"},{"subject":"Pongo pygmaeus"},{"subject":"Pan paniscus"},{"subject":"Pan troglodytes"},{"subject":"Canis lupus"},{"subject":"Gorilla gorilla"},{"subject":"mutation spectrum"},{"subject":"polymorphism"},{"subject":"FOS: Biological sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2023-08-16T13:39:03Z","dateType":"Created"},{"date":"2023-08-16T16:55:47Z","dateType":"Submitted"},{"date":"2023-09-11T00:00:00Z","dateType":"Issued"},{"date":"2023-09-11T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsDerivedFrom","relatedIdentifier":"https://github.com/harrispopgen/mammal_mutation_spectra/","relatedIdentifierType":"URL"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1101/2023.05.31.543114","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1093/molbev/msad213","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["184998676663 bytes"],"formats":[],"version":"2","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Although evolutionary biologists have long theorized that variation in DNA\n repair efficacy might explain some of the diversity of lifespan and cancer\n incidence across species, we have little data on the variability of normal\n germline mutagenesis outside of humans. Here, we shed light on the\n spectrum and etiology of mutagenesis across mammals by quantifying\n mutational sequence context biases using polymorphism data from thirteen\n species of mice, apes, bears, wolves, and cetaceans. After normalizing the\n mutation spectrum for reference genome accessibility and k-mer content, we\n use the Mantel test to deduce that mutation spectrum divergence is highly\n correlated with genetic divergence between species, whereas life history\n traits like reproductive age are weaker predictors of mutation spectrum\n divergence. Potential bioinformatic confounders are only weakly related to\n a small set of mutation spectrum features. We find that clocklike\n mutational signatures previously inferred from human cancers cannot\n explain the phylogenetic signal exhibited by the mammalian mutation\n spectrum, despite the ability of these clocklike signatures to fit each\n species’ 3-mer spectrum with high cosine similarity. In contrast, parental\n aging signatures inferred from human de novo mutation data appear to\n explain much of the mutation spectrum’s phylogenetic signal when fit to\n non-context-dependent mutation spectrum data in combination with a novel\n mutational signature. We posit that future models purporting to explain\n the etiology of mammalian mutagenesis need to capture the fact that more\n closely related species have more similar mutation spectra; a model that\n fits each marginal spectrum with high cosine similarity is not guaranteed\n to capture this hierarchy of mutation spectrum variation among species.","descriptionType":"Abstract"},{"description":"Mutation spectra were generated based on publicly-available whole\n genome sequencing polymorphism data (VCF format) from 13 mammal species\n (house mouse, Algerian mouse, humans, Bornean orangutan, Sumatran\n orangutan, chimpanzee, gorilla, bonobo, gray wolf, polar bear, brown bear,\n vaquita porpoise, and fin whale).  Spectra were\n generated for each species at the 1-mer, 3-mer, 5-mer and 7-mer level\n using the program mutyper and a pipeline that is described extensively in\n the paper's SI Methods section and on the project's GitHub\n repository (https://github.com/harrispopgen/mammal_mutation_spectra/). Data files are described in depth in the Dryad repository's README file.","descriptionType":"Methods"},{"description":"All code that was used to process the data, carry out analyses,\n and generate figures is on the projects GitHub repository (https://github.com/harrispopgen/mammal_mutation_spectra/).","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Institute of General Medical Sciences","awardNumber":"R35GM133428","funderIdentifier":"https://ror.org/04q48ey07","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Institute on Aging","awardNumber":"T32 AG066574","funderIdentifier":"https://ror.org/049v75w11","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"Burroughs Wellcome Fund","funderIdentifier":"https://ror.org/01d35cw23","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"Kinship Conservation Fellows","funderIdentifier":"https://ror.org/05pt1d114","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"Pew Charitable Trusts","funderIdentifier":"https://ror.org/02xhk2825","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"Alfred P. Sloan Foundation","funderIdentifier":"https://ror.org/052csg198","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.5068/D1339F","contentUrl":null,"metadataVersion":7,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":92,"downloadCount":27,"referenceCount":0,"citationCount":2,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-09-11T15:54:03Z","registered":"2023-09-11T15:54:04Z","published":null,"updated":"2026-03-05T22:57:42Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5068/d18h47","type":"dois","attributes":{"doi":"10.5068/d18h47","identifiers":[],"creators":[{"name":"Gold, Zachary","nameType":"Personal","givenName":"Zachary","familyName":"Gold","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-0490-7630","nameIdentifierScheme":"ORCID"}]},{"name":"Monuki, Keira","nameType":"Personal","givenName":"Keira","familyName":"Monuki","affiliation":["University of California, Davis"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-2130-4973","nameIdentifierScheme":"ORCID"}]},{"name":"Barber, Paul","nameType":"Personal","givenName":"Paul","familyName":"Barber","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]}],"titles":[{"title":"eDNA captures depth partitioning in a kelp forest ecosystem"}],"publisher":"Dryad","container":{},"publicationYear":2022,"subjects":[{"subject":"FOS: Biological sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2021-10-14T21:55:05Z","dateType":"Submitted"},{"date":"2022-09-27T00:00:00Z","dateType":"Issued"},{"date":"2022-09-27T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1371/journal.pone.0253104","relatedIdentifierType":"DOI"},{"relationType":"IsDerivedFrom","relatedIdentifier":"10.5281/zenodo.7110795","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1101/2021.06.01.446542","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["18740219885 bytes"],"formats":[],"version":"10","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Environmental DNA (eDNA) metabarcoding is an increasingly important tool\n for surveying biodiversity in marine ecosystems. However, the scale of\n temporal and spatial variability in eDNA signatures, and how this\n variation may impact eDNA-based marine biodiversity assessments, remains\n uncertain. To address this question, we systematically examined variation\n in vertebrate eDNA signatures across depth (0 m to 10 m) and horizontal\n space (nearshore kelp forest and surf zone) over three successive days in\n Southern California. Across a broad range of teleost fish and\n elasmobranchs, results showed significant variation in species richness\n and community assemblages between surface and depth, reflecting\n microhabitat depth preferences of common Southern California nearshore\n rocky reef taxa. Community assemblages between nearshore and surf zone\n sampling stations at the same depth also differed significantly,\n consistent with known habitat preferences. Additionally, assemblages also\n varied across three sampling days, but 69% of habitat preferences remained\n consistent. Results highlight the sensitivity of eDNA in capturing\n fine-scale vertical, horizontal, and temporal variation in marine\n vertebrate communities, demonstrating the ability of eDNA to capture a\n highly localized snapshot of marine biodiversity in dynamic coastal\n environments.","descriptionType":"Abstract"},{"description":"We conducted our study at Leo Carrillo State Beach, Malibu,\n California, USA (34.0446° N, 118.9407° W). We sampled on three successive\n days (September 24 to September 26 in 2018) to test for temporal stability\n of spatial variation in eDNA signatures. On each day, we sampled at the\n highest tide of the mixed semidiurnal tide to minimize the impact of tidal\n variation on sampling. To ensure that results reflected\n variation in spatial sampling, rather than time, we synchronized watches\n and worked in multiple teams to simultaneously sample from five depths on\n SCUBA along a vertical transect in a kelp forest ~140 m from shore: 0 m\n (at the ocean surface), 1 m, 5 m, 9 m, and 10 m (just above the sea\n floor). We also sampled a sixth station along the shore in the surf zone,\n where we collected samples approximately 1 m below the water surface where\n depth was approximately 2 meters. At each location, we collected\n triplicate seawater samples using one-liter enteral feeding bags\n (Kendall-Covidien – 702500) following the methods of Curd et al. After\n returning to shore, we immediately gravity filtered all samples through\n 0.22 μm Sterivex filters to isolate eDNA. We similarly filtered one liter\n of distilled water as a negative template control. Upon completion of\n filtration, we stored the dried filters at -20˚C until extraction 48 hours\n later. No permits were required for sampling seawater in state waters\n outside of designated protected areas. We extracted the\n DNA from the filters at UCLA using the Qiagen DNEasy Blood and Tissue kit\n (Qiagen, Valencia, CA, USA). To maximize eDNA recovery, we employed\n modifications made by Spens et al., adding proteinase K and ATL buffer\n directly to the filter cartridges before overnight incubation in a\n rotating incubator at 56˚C. We amplified the extracted eDNA using the\n \u003cem\u003e12S\u003c/em\u003e MiFish Universal Teleost (MiFish-U) and MiFish\n Elasmobranch (MiFish-E) primers with linker modifications for Nextera\n indices. Though the primers target teleost fish and elasmobranchs, they\n can also amplify other vertebrate species such as birds and mammals. PCR\n amplification and library preparation was conducted following the methods\n of Curd et al.. After library preparation, we sequenced the library on a\n NextSeq at the Technology Center for Genomics \u0026amp; Bioinformatics\n (University of California, Los Angeles, CA, USA) using Reagent Kit V3 with\n 30% PhiX added to the sequencing run. We processed the\n resulting sequencing data using the \u003cem\u003eAnacapa Toolkit\u003c/em\u003e\n (version 1.0) for quality control, amplicon sequence variant parsing, and\n taxonomic assignment using standard parameters. The \u003cem\u003eAnacapa\n Toolkit\u003c/em\u003e sequence quality control and amplicon sequence variant\n (ASV) parsing module relies on \u003cem\u003ecutadapt\u003c/em\u003e (version\n 1.16), \u003cem\u003eFastX-toolkit\u003c/em\u003e (version 0.0.13), and\n \u003cem\u003eDADA2\u003c/em\u003e (version 1.6) as dependencies and the\n \u003cem\u003eAnacapa classifier\u003c/em\u003e module relies on\n \u003cem\u003eBowtie2\u003c/em\u003e (version 2.3.5) and a modified version of\n \u003cem\u003eBLCA\u003c/em\u003e as dependencies. We processed sequences using\n the default parameters and assigned taxonomy using two\n \u003cem\u003eCRUX-\u003c/em\u003egenerated reference databases following the\n methods of Gold et al.. We first assigned taxonomy using the California\n Current Large Marine Ecosystem fish specific reference database. Second,\n we used the \u003cem\u003eCRUX\u003c/em\u003e-generated \u003cem\u003e12S\u003c/em\u003e\n reference database supplemented with California Current Large Marine\n Ecosystem fish specific references to assign taxonomy using all available\n \u003cem\u003e12S \u003c/em\u003ereference barcodes to identify any non-fish taxa\n following the methods of Gold et al. using a Bayesian cutoff score of 60.\n Although \u003cem\u003eCRUX \u003c/em\u003erelies on \u003cem\u003eecoPCR\n \u003c/em\u003e(version 1.0.1), \u003cem\u003eblastn\u003c/em\u003e (version 2.6.0),\n and \u003cem\u003eEntrez-qiime\u003c/em\u003e (version 2.0) as dependencies, we\n note that Bayesian cutoff scores are not directly analogous to percent\n identity from \u003cem\u003eblastn. \u003c/em\u003eThe \u003cem\u003eBLCA\n \u003c/em\u003eclassifier\u003cem\u003e \u003c/em\u003eincorporates alignment metrics,\n including percent identity and percent overlap, into the underlying\n Bayesian model which then returns the Bayesian cutoff score metric as a\n measure of confidence for each taxonomic rank for a given ASV.\n The resulting \u003cem\u003eAnacapa\u003c/em\u003e-generated taxonomic\n tables were transferred into R for further processing \n (https://datadryad.org/stash/share/aMH1xTddyGgAhaWYoV3kmmmWgqCzv6Lt9YtU9s4F6NA). We then decontaminated the taxonomic tables using methods developed by Kelly et al. and McKnight et al. as implemented in Gold, which removes sequences from index hopping and negative controls and conducts a site occupancy model to identify true rare sequences. We also manually removed sequences for species with taxonomic assignments for non-marine taxa (e.g. terrestrial mammals) in R. We then merged ASVs by summing reads by assigned taxonomy (e.g. summed all sequences reads from the 7 ASVs that assigned to Garibaldi, \u003cem\u003eHypsypops rubicundus\u003c/em\u003e). Following decontamination, we converted the taxonomic tables into \u003cem\u003ephyloseq\u003c/em\u003e objects (version 1.30.0) in \u003cem\u003eR.\u003c/em\u003e We analyzed the eDNA signatures across depth and across nearshore vs. surf zone habitats. We analyzed differences in eDNA across depth using only the nearshore signatures, excluding the samples collected in the adjacent surf zone. To examine species richness across depths, we conducted ANOVA and post-hoc Tukey tests using eDNA read counts. We then transformed eDNA read counts to eDNA index scores, which better correlates to abundance, following the methods of Kelly et al.. The eDNA index calculation standardizes eDNA abundance across samples and across taxa. To calculate the eDNA index values, we first calculated the relative abundance of each taxa in each sample. We then divided the relative abundance of each taxa by it’s maximum observed abundance across all samples to standardize the read counts per species per sample. This results in an index that ranges from 0 to 1 for each species where a value of 1 corresponds to the sample with the greatest relative abundance observed for that species. Although these eDNA index values allow for direct comparisons of relative abundance within individual species, allowing for direct comparisons in relative abundance by depth, location and/or time, it cannot be used for comparisons among different species. See Kelly et al. for more detail. To analyze the importance of sampling depth on eDNA vertebrate community composition, we conducted a PERMANOVA test using the \u003cem\u003evegan\u003c/em\u003e (version 2.5-6) package in \u003cem\u003eR\u003c/em\u003e. The PERMANOVA was run using Bray-Curtis dissimilarity and the model eDNA_Index ~ Depth + Day + Replicate. We also ran a multivariate homogeneity of group dispersions test using the betadisper function and Bray-Curtis dissimilarity using \u003cem\u003evegan\u003c/em\u003e. We then ran a Mantel Test and non-metric multi-dimensional scaling (NMDS) using \u003cem\u003evegan \u003c/em\u003eon Bray-Curtis dissimilarities to assess community composition differences across the depth gradient. We further analyzed vertical depth community composition by generating a gradient forest model using the \u003cem\u003egradientForest\u003c/em\u003e package (version 0.1-17) using 500 runs. The environmental variables in the vertical depth gradient forest model included sampling depth, sampling day, and replicate. We then extracted the taxa with the highest model performances and plotted their eDNA index values across depth. Using the broken stick method, we defined highest model performance as  ≥ 0.35 R\u003csup\u003e2\u003c/sup\u003e importance, as there was a steep drop-off in R\u003csup\u003e2\u003c/sup\u003e importance after 0.35. To analyze differences between the kelp forest and surf zone, we ran Welch t-tests, PERMANOVA and betadisper tests, only including samples taken at 1 m depths from nearshore and surf zone habitats. We used the Bray-Curtis dissimilarity for both tests and the model eDNA_Index ~ Habitat (nearshore vs. surf zone) + Depth + Day + Replicate for the PERMANOVA. We also ran an additional gradient forest model with the environmental variables sampling depth, nearshore vs. surf zone, sampling day, and replicate for eDNA index scores from all stations. We then extracted the top performing taxa and plotted their eDNA index distributions, defining highest model performance as ≥ 0.40 R\u003csup\u003e2\u003c/sup\u003e importance using the broken stick method, as there was a steep drop-off in R\u003csup\u003e2\u003c/sup\u003e importance after 0.40. Because of the additional nearshore vs. surf zone variable, the R\u003csup\u003e2 \u003c/sup\u003eimportance values were higher in this model, resulting in a different threshold R\u003csup\u003e2\u003c/sup\u003e value than the one in the depth gradient forest model. To test whether vertical and horizontal variation in eDNA signatures were consistent over time, we compared species richness across sampling days in an ANOVA framework, looking at both total community diversity as well as the eDNA index abundances. The linear models used for the eDNA index ANOVA tests were eDNA_Index ~ Station + Day + Station:Day.","descriptionType":"Methods"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.5068/D18H47","contentUrl":null,"metadataVersion":9,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":137,"downloadCount":13,"referenceCount":0,"citationCount":2,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2022-09-27T23:41:41Z","registered":"2022-09-27T23:41:42Z","published":null,"updated":"2026-03-05T19:11:06Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5068/d1b69n","type":"dois","attributes":{"doi":"10.5068/d1b69n","identifiers":[],"creators":[{"name":"Bossu, Christen","nameType":"Personal","givenName":"Christen","familyName":"Bossu","affiliation":["Colorado State University"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-0458-9305","nameIdentifierScheme":"ORCID"}]},{"name":"Heath, Julie","nameType":"Personal","givenName":"Julie","familyName":"Heath","affiliation":["Boise State University"],"nameIdentifiers":[]},{"name":"Kaltenecker, Gregory","nameType":"Personal","givenName":"Gregory","familyName":"Kaltenecker","affiliation":["Boise State University"],"nameIdentifiers":[]},{"name":"Helm, Barbara","nameType":"Personal","givenName":"Barbara","familyName":"Helm","affiliation":["University of Groningen"],"nameIdentifiers":[]},{"name":"Ruegg, Kristen","nameType":"Personal","givenName":"Kristen","familyName":"Ruegg","affiliation":["Colorado State University"],"nameIdentifiers":[]}],"titles":[{"title":"Clock-linked genes underlie seasonal migratory timing in a diurnal raptor"}],"publisher":"Dryad","container":{},"publicationYear":2022,"subjects":[{"subject":"seasonal migration"},{"subject":"circannual rhythms"},{"subject":"biological clock"},{"subject":"Genomics","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[],"dates":[{"date":"2022-04-07T06:20:18Z","dateType":"Submitted"},{"date":"2022-05-03T00:00:00Z","dateType":"Issued"},{"date":"2022-05-03T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1098/rspb.2021.2507","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["1242536 bytes"],"formats":[],"version":"4","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Seasonal migration is a dynamic natural phenomenon that allows organisms\n to exploit favorable habitats across the annual cycle. While the\n morphological, physiological, and behavioral changes associated with\n migratory behavior are well characterized, the genetic basis of migration\n and its link to endogenous biological timekeeping pathways is poorly\n understood. Historically, genome-wide research has focused on genes of\n large effect, whereas many genes of small effect may work together to\n regulate complex traits like migratory behavior. Here, we explicitly relax\n stringent outlier detection thresholds and, as a result, discover how\n multiple biological timekeeping genes are important to migratory timing in\n an iconic raptor species, the American Kestrel (Falco sparverius). To\n validate the role of candidate loci in migratory timing, we genotyped\n Kestrels captured across autumn migration and found significant\n associations between migratory timing and genetic variation in metabolic\n and light input pathway genes that modulate biological clocks (TOP1,\n PHLPP1, CPNE4, and PEAK1). Further, we demonstrate that migrating\n individuals originated from a single panmictic source population,\n suggesting the existence of distinct early and late migratory genotypes\n (i.e. chronotypes). Overall, our results provide empirical support for the\n existence of a within population-level polymorphism in genes underlying\n migratory timing in a diurnally migrating raptor.","descriptionType":"Abstract"},{"description":"We designed Fluidigm SNPtype assays and used them to screen\n additional breeding and migrating American Kestrels that were independent\n of the RAD-seq analyses above.  Specifically, we used the R package\n \u003ci\u003esnps2assays \u003c/i\u003e(Anderson 2015) to evaluate the efficacy of\n designing assays for candidate loci. We considered the assays designable\n if GC content was less than 0.65, there were no insertions or deletions\n (indels) within 30bp of the target variant, and there were no additional\n variants within 20bp of the targeted variable site. We filtered out assays\n with primers that mapped to multiple locations in the genome (\u003ci\u003ebwa\n mem\u003c/i\u003e:\u003ci\u003e \u003c/i\u003eLi and Durbin 2009), resulting in assays\n for nine loci in nine candidate genes. We used the resulting Fluidigm\n assays to genotype the nine candidate migration genes in 738 breeding\n American Kestrels from 83 sites and 165 migrating American Kestrels from a\n single migration station in Boise, Idaho collected in a three-month\n time-series spanning fall migration over two years. We\n then used a multi-gene and single gene framework to determine whether\n migratory timing was significantly associated with allele frequency shifts\n in the nine candidate migration genes. To determine how the nine candidate\n genes covary with each other, we conducted an ordinal principal component\n analysis (PCA) using the R software package \u003ci\u003egifi (\u003c/i\u003eMair\n and De Leeuw 2019) . We used a linear regression to evaluate whether\n migration timing (day of year when a fall migrant was captured) was\n associated with genetic variation as measured by PC1 and PC2, and included\n a covariate of sex to account for the potential influence of differential\n migration between sexes on migration timing. To investigate single gene\n effects, we fit linear regression models of each allele frequency of the\n top 4 candidate genes, \u003ci\u003ei.e.\u003c/i\u003e those that loaded strongly\n on PC1, \u003ci\u003eTOP1\u003c/i\u003e, \u003ci\u003ePEAK1\u003c/i\u003e,\n \u003ci\u003ePHLPP1\u003c/i\u003e and \u003ci\u003eCPNE4\u003c/i\u003e, to migration\n timing as defined by the midpoint day of each week during the autumn\n migration period and using the \u003ci\u003elm\u003c/i\u003e model in the R\n software package \u003ci\u003estats\u003c/i\u003e v 3.6.2 (R Core Team 2019). The\n nonlinear decline in allele frequency over time prompted the fitting of a\n curved regression model, and we tested whether this linear regression\n polynomial model provided a better fit using a likelihood ratio test in\n the R package \u003ci\u003elmtest\u003c/i\u003e v 0.9-37 (Zeileis\n and Hothorn 2002). To test whether seasonal allele\n frequency trends result from different populations migrating through the\n migration station at different times or distinct migratory chronotypes, we\n examined the association between PC1 and latitude as well as allele\n frequency in our 4 top ranked loci and latitude of kestrels breeding\n across the west. Further, we genotyped 151 of the 165 migrating birds from\n Boise, Idaho (all samples for which we had high quality DNA remaining)\n with population-specific SNP-type assays used in Ruegg \u003ci\u003eet\n al\u003c/i\u003e. (2021), and assigned these birds to the breeding population\n of origin using \u003ci\u003erubias\u003c/i\u003e (Anderson and Moran\n 2018).   Anderson, E. C.\n \u003ci\u003esnps2assays: Prepare SNP assay orders from ddRAD or RAD\n loci\u003c/i\u003e. (2015) Anderson, E. C. \u0026amp; Moran, B.\n \u003ci\u003erubias: Bayesian Inference from the Conditional Genetic Stock\n Identification Model\u003c/i\u003e. (2018) Li, H. \u0026amp;\n Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler\n transform. \u003ci\u003eBioinformatics\u003c/i\u003e \u003cb\u003e25\u003c/b\u003e,\n 1754–1760 (2009). Mair, P. \u0026amp; De Leeuw, J.\n \u003ci\u003eGifi: Multivariate analysis with optimal scaling\u003c/i\u003e.\n (2019). R Core Team.\n \u003ci\u003eR: A language and environment for statistical\n computing\u003c/i\u003e. (R Foundation for Statistical   Computing,\n 2019). Ruegg, K. C.\n \u003ci\u003eet al.\u003c/i\u003e The American Kestrel genoscape (\u003ci\u003eFalco\n sparverius\u003c/i\u003e): Implications for monitoring, management, and\n subspecies boundaries. \u003ci\u003eAuk\u003c/i\u003e (2021) Zeileis, A. \u0026amp; Hothorn, T.\n Diagnostic checking in regression relationships. R News 2(3), 7-10.\n \u003ci\u003eR News\u003c/i\u003e \u003cb\u003e2\u003c/b\u003e, 7–10 (2002).","descriptionType":"Methods"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"California Energy Commission","awardNumber":"EPC-15-043","funderIdentifier":"https://ror.org/05eaakg28","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Geographic Society","awardNumber":"WW-202R-17","funderIdentifier":"https://ror.org/04bqh5m06","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"NSF-1942313","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://www.crossref.org/services/funder-registry/","funderName":"Strategic Environmental Research and Development Program","awardNumber":"RC-2702","funderIdentifier":"https://doi.org/10.13039/100013316","funderIdentifierType":"Crossref Funder ID"}],"url":"https://datadryad.org/dataset/doi:10.5068/D1B69N","contentUrl":null,"metadataVersion":11,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":140,"downloadCount":16,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2022-05-03T18:01:14Z","registered":"2022-05-03T18:01:17Z","published":null,"updated":"2026-03-05T15:18:41Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5068/d19h5x","type":"dois","attributes":{"doi":"10.5068/d19h5x","identifiers":[],"creators":[{"name":"Schuette, Peter","nameType":"Personal","givenName":"Peter","familyName":"Schuette","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-6308-6441","nameIdentifierScheme":"ORCID"}]},{"name":"Wang, Weisheng","nameType":"Personal","givenName":"Weisheng","familyName":"Wang","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"La-Vu, Mimi","nameType":"Personal","givenName":"Mimi","familyName":"La-Vu","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Tobias, Brooke","nameType":"Personal","givenName":"Brooke","familyName":"Tobias","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Ceko, Marta","nameType":"Personal","givenName":"Marta","familyName":"Ceko","affiliation":["University of Colorado Boulder"],"nameIdentifiers":[]},{"name":"Kragel, Philip","nameType":"Personal","givenName":"Philip","familyName":"Kragel","affiliation":["University of Colorado Boulder"],"nameIdentifiers":[]},{"name":"Reis, Fernando","nameType":"Personal","givenName":"Fernando","familyName":"Reis","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Ji, Shiyu","nameType":"Personal","givenName":"Shiyu","familyName":"Ji","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Sehgal, Megha","nameType":"Personal","givenName":"Megha","familyName":"Sehgal","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Maesta-Pereira, Sandra","nameType":"Personal","givenName":"Sandra","familyName":"Maesta-Pereira","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Chakerian, Meghmik","nameType":"Personal","givenName":"Meghmik","familyName":"Chakerian","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Silva, Alcino","nameType":"Personal","givenName":"Alcino","familyName":"Silva","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Canteras, Newton","nameType":"Personal","givenName":"Newton","familyName":"Canteras","affiliation":["Universidade de São Paulo"],"nameIdentifiers":[]},{"name":"Wager, Tor","nameType":"Personal","givenName":"Tor","familyName":"Wager","affiliation":["University of Colorado Boulder"],"nameIdentifiers":[]},{"name":"Kao, Jonathan","nameType":"Personal","givenName":"Jonathan","familyName":"Kao","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Adhikari, Avishek","nameType":"Personal","givenName":"Avishek","familyName":"Adhikari","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]}],"titles":[{"title":"Data for: Dorsal premammillary projection to periaqueductal gray controls escape vigor from innate and conditioned threats"}],"publisher":"Dryad","container":{},"publicationYear":2021,"subjects":[],"contributors":[],"dates":[{"date":"2021-04-09T21:14:02Z","dateType":"Submitted"},{"date":"2021-04-23T00:00:00Z","dateType":"Issued"},{"date":"2021-04-23T00:00:00Z","dateType":"Available"},{"date":"2021-08-31T00:00:00Z","dateType":"Updated"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.7554/elife.69178","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["8459655391 bytes"],"formats":[],"version":"13","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Escape from threats has paramount importance for survival. However, it is\n unknown if a single circuit controls escape from innate and conditioned\n threats. The hypothalamic dorsal premammillary nucleus (PMd) may control\n escape, as it is strongly activated by escape-inducing threats and\n projects to the region most implicated in escape, the dorsolateral\n periaqueductal gray (dlPAG). We show that in mice cholecystokinin\n (cck)-expressing PMd cells are activated during escape, but not other\n defensive behaviors. PMd-cck ensemble activity can also predict future\n escape. Furthermore, PMd inhibition decreases escape speed from both\n innate and conditioned threats. Inhibition of the PMd-cck projection to\n the dlPAG also decreased escape speed. Lastly, human fMRI data show that a\n posterior hypothalamic-to-dlPAG pathway increases activity during exposure\n to aversive images, indicating that a similar pathway may possibly have a\n related role in humans. Our data identify the PMd as a central node of the\n escape network.","descriptionType":"Abstract"},{"description":"All procedures conformed to guidelines established by the\n National Institutes of Health and have been approved by the University of\n California, Los Angeles Institutional Animal Care and Use Committee or by\n the University of Sao Paulo Animal Bioethics committee. \u003cb\u003eMice.\u003c/b\u003e\n Cck-IRES-Cre mice (Jackson Laboratory stock No. 012706) and wild type\n C57BL/6J mice (Jackson Laboratory stock No. 000664) were used for all\n experiments. Male and female mice between 2 and 6 months of age were used\n in all experiments. Mice were maintained on a 12-hour reverse light-dark\n cycle with food and water ad libitum. Sample sizes were chosen based on\n previous behavioral optogenetics studies on defensive behaviors, which\n typically use 6-15 mice per group. All mice were handled for a minimum of\n 5 days prior to any behavioral task. \u003cb\u003eRats\u003c/b\u003e\u003ci\u003e.\u003c/i\u003e Male Long-Evans rats (250-400 grams) were obtained from Charles River Laboratories and were individually housed on a standard 12-hour light-dark cycle and given food and water \u003ci\u003ead libitum\u003c/i\u003e. Rats were only used as a predatory stimulus. Rats were handled for several weeks prior to being used and were screened for low aggression to avoid attacks on mice. No attacks on mice were observed in this experiment. \u003cb\u003eViral Vectors.\u003c/b\u003e All vectors were purchased from Addgene. \u003cb\u003eOptogenetics\u003c/b\u003e:   AAV9.EF1a.DIO.hChR2(H134R)-eYFP.WPRE.hGH, AAV9-EF1a-DIO-eYFP and AAV9-Ef1a-DIO-Arch-GFP. Chemogenetics: pAAV8-hSyn-DIO-hM4D(Gi)-mCherry and AAV8.Syn.DIO. mCherry Fiber Photometry AAV9.Syn.GCaMP6s.WPRE.SV40 and AAV9.Syn.FLEX.GCaMP6s.WPRE.SV40 \u003cb\u003eSurgeries.\u003c/b\u003e Surgeries were performed as described previously (Adhikari et al., 2015). Eight-week-old mice were anaesthetized with 1.5-3.0% isoflurane and placed in a stereotaxic apparatus (Kopf Instruments). A scalpel was used to open an incision along the midline to expose the skull. After performing a craniotomy, 40 nl of one of the viral vectors listed above at a titer of 2*1012 particles/ml was injected per site (PMd, amv dlPAG) using a 10 μl nanofil syringe (World Precision Instruments) at 0.08 μl/min. The syringe was coupled to a 33-gauge beveled needle, and the bevel was placed to face the anterior side of the animal. The syringe was slowly retracted 20 minutes after the start of the infusion. Mice received unilateral viral infusion and fiber optic cannula implantation. Infusion locations measured as anterior-posterior, medial-lateral and dorso-ventral coordinates from bregma were: dorsolateral periaqueductal gray (dlPAG) (-4.75, -0.45, -1.9), dorsal premammillary nucleus (PMd) (-2.46, -0.5, -5.35) and anteromedial ventral thalamus (amv) (-0.85, -0.5, -3.9). For optogenetic experiments, fiber optic cannula (0.22 NA, 200 μm diameter; Newdoon) were implanted bilaterally 0.15 mm above the viral infusion sites. Only mice with viral expression restricted to the intended targets were used for behavioral assays. For photometry experiments mice were injected with 0.16 uL at a titer of 3*10\u003csup\u003e12\u003c/sup\u003e of AAV9.Syn.Flex.GCaMP6s.WPRE.SV40 in the PMd of cck-cre mice. The same volume and titer of AAV9.Syn.GCaMP6s.WPRE.SV40 was injected into the dlPAG or amv.  Mice were implanted unilaterally with fiberoptic cannulae in the PMd, amv dlPAG. A 400 μm diameter, 0.48 NA optical fiber (Neurophotometrics) was used for photometry experiments. Adhesive cement (C\u0026amp;B metabond; Parkell, Edgewood, NY, USA) and dental cement (Stoelting, Wood Dale, IL, USA) were used to securely attach the fiber optic cannula to the skull. For miniaturized microscope experiments 40 nL of AAV9-DIO-GCaMP6s was injected in the PMd of cck-cre mice and a 7mm GRIN lens was implanted 200 uM above the infusion site. Three weeks following surgery animals were base-plated. \u003cb\u003eRat Exposure Assay. \u003c/b\u003eMice were accustomed to handling prior to any behavioral assay. On day 1, mice were habituated to a white rectangular box (70 cm length, 26 cm width, 44 cm height) for 20 minutes. Twenty-four hours later, mice were exposed to the same environment but in the presence of a toy rat for 20 minutes. Mice were then exposed to an adult rat or a toy rat in this environment on the two following days. The rat was secured by a harness tied to one of the walls and could freely ambulate only within a short radius of approximately 20 cm. The mouse was placed near the wall opposite to the rat and freely explored the context for 20 minutes. No separating barrier was placed between the mouse and the rat, allowing for close naturalistic encounters that can induce a variety of robust defensive behaviors. \u003cb\u003eContextual Fear Conditioning Test. \u003c/b\u003eTo better evaluate a broader species-specific defense repertoire in face of a conditioned stimulus, we used a modified version of the standard contextual fear conditioning method (Schuette et al., 2020). Pre-shock, fear conditioning and retrieval sessions were performed in a context (70 cm x 17 cm x 40 cm) with an evenly distributed light intensity of 40 lux and a Coulbourn shock grid (19.5 cm x 17 cm) set at the extreme end of the enclosure. Forty-eight hours after rat exposure, mice were habituated to this context and could freely explore the whole environment for 20 minutes. On the following day, the grid was activated, such that a single 0.7 mA foot shock was delivered for 2 seconds only on the first time the mouse fully entered the grid zone. Twenty-four hours later, retrieval sessions were performed in the same enclosure but without shock. Mice could freely explore the context for 20 minutes during pre-shock habituation, fear conditioning and retrieval sessions. \u003cb\u003eBehavioral quantification.\u003c/b\u003e To extract the pose of freely-behaving mice in the described assays, we implemented DeepLabCut (Nath et al., 2019), an open-source convolutional neural network-based toolbox, to identify mouse nose, ear and tailbase xy-coordinates in each recorded video frame. These coordinates were then used to calculate velocity and position at each timepoint, as well as classify behaviors such as escape runs and freezes in an automated manner using custom Matlab scripts. Specifically: 'Escapes' were defined as epochs for which (1) the mouse speed away from the rat or toy rat exceeded 2 cm/s. As there was little room for acceleration between the rat and opposite wall, the speed threshold was set to this relatively low value. 'Stretch-attend postures' were defined as epochs for which (1) the distance between mouse nose and tailbase exceeded a distance of approximately 1.2 mouse body lengths and (2) mouse tailbase speed fell below 1 cm/s. 'Freezes' were defined as periods for which mouse nose and tailbase speed fell below 0.25 cm/s for at least 0.33s (Schuette et al., 2020). All behaviors were manually checked by the experimenters for error. \u003cb\u003eFiber photometry. \u003c/b\u003ePhotometry was performed as described in detail previously (Kim et al., 2016). Briefly, we used a 405-nm LED and a 470-nm LED (Thorlabs, M405F1 and M470F1) for the Ca\u003csup\u003e2+\u003c/sup\u003e-dependent and Ca\u003csup\u003e2+\u003c/sup\u003eindependent isosbestic control measurements. The two LEDs were bandpass filtered (Thorlabs, FB410-10 and FB470-10) and then combined with a 425-nm longpass dichroic mirror (Thorlabs, DMLP425R) and coupled into the microscope using a 495-nm longpass dichroic mirror (Semrock, FF495-Di02-25 ×36). Mice were connected with a branched patch cord (400 μm, Doric Lenses, Quebec, Canada) using a zirconia sleeve to the optical system. The signal was captured at 20 Hz (alternating 405-nm LED and 470-nm LED). To correct for signal artifacts of a non biological origin (i.e. photobleaching and movement artifacts), custom Matlab scripts leveraged the reference signal (405-nm), unaffected by calcium saturation, to isolate and remove these effects from the calcium signal (470-nm). \u003cb\u003eFiber Photometry behavior-triggered averaging.\u003c/b\u003e To plot the behavior-triggered averages, only mice that displayed a minimum of three behavioral instances were included in the corresponding behavioral figure. \u003cb\u003eMiniscope video capture\u003c/b\u003e\u003ci\u003e.\u003c/i\u003e\u003cb\u003e \u003c/b\u003eAll videos were recorded at 30 frames/sec using a Logitech HD C310 webcam and custom-built head-mounted UCLA miniscope (Cai et al., 2016). Open-source UCLA Miniscope software and hardware (http://miniscope.org/) were used to capture and synchronize neural and behavioral video (Cai et al., 2016). \u003cb\u003eMiniscope postprocessing.\u003c/b\u003e The open-source UCLA miniscope analysis package (https://github.com/daharoni/Miniscope_Analysis) (Aharoni and Hoogland, 2019) was used to motion correct miniscope videos. They were then temporally downsampled by a factor of four and spatially downsampled by a factor of two. The cell activity and footprints were extracted using the open-source package Constrained Nonnegative Matrix Factorization for microEndoscopic data (CNMF-E; https://github.com/zhoupc/CNMF_E) (Schuette et al., 2020; Zhou et al., 2018). Only cells whose variance was greater than or equal to 25% of the maximum variance among non-outliers were used in the analysis. \u003cb\u003eBehavior decoding using PMd neural data.\u003c/b\u003e Discrete classification of escape behavior was performed using multinomial logistic regression. Timepoints following escape by 2 seconds were labelled 'escape,' and a matched number of non-escape timepoints were randomly selected for training and validation. Each time point was treated as an individual data point. Training and validation were performed using 5-fold cross-validation, with a minimum of 10 seconds between training and validation sets. As equal numbers of escape and non-escape samples were used to build the training and validation sets, chance accuracy was 50%. Sessions with less than 5 escapes were excluded from the analysis. The same analysis was performed for approach, stretch-attend postures, and freeze. To predict escape at negative time lags from behavior onset, the same analysis procedure was implemented, using 2-second epochs preceding escape by 2, 4, 6, 8 and 10 seconds. \u003cb\u003eBehavior cell classification.\u003c/b\u003e We used a generalized linear model (GLM) to identify cells that showed increased calcium activity during approach, stretch-attend, escape and freeze behaviors. We fit this model to each cell's activity, with behavior indices as the predictor variable and behavior coefficients as the measure of fit. Behavior onset times were then randomized 100 times and a bootstrap distribution built from the resulting GLM coefficients. A cell was considered a behavior-categorized cell if its coefficient exceeded 95% of the bootstrap coefficient values. \u003cb\u003ePosition and speed decoding. \u003c/b\u003eTo predict position and speed from neural data, these data had their dimensionality reduced by principal component analysis, such that the top principal components, representing at least 80% of the total variance, were used in the following decoding analysis. This output and the related position/speed data were then separated into alternating 60s training and testing blocks, with 10s of separation between blocks. Even blocks were used to train a generalized linear regression model (GLM; Matlab function ‘glmfit’) and withheld odd blocks were used to test the resulting model.  Accuracies of this withheld testing block were reported as mean squared error. \u003cb\u003eChemogenetics. \u003c/b\u003eMice used for chemogenetic experiments were exposed to each threat and control stimuli twice, once following treatment with saline and once following treatment with CNO (5 mg/kg, injected intraperitoneally) 40 minutes prior to the experiment. Only one control or threat-exposure assay was performed per day with each mouse. \u003cb\u003eBehavior video capture. \u003c/b\u003eAll behavior videos were captured at 30 frames/sec in standard definition (640x480) using a Logitech HD C310 webcam. To capture fiber-photometry synchronized videos, both the calcium signal and behavior were recorded by the same computer using custom Matlab scripts that also collected timestamp values for each calcium sample/behavioral frame. These timestamps were used to precisely align neural activity and behavior. \u003cb\u003eLight Delivery for optogenetics.\u003c/b\u003e For PMd-cck ChR2 mice, blue light was generated by a 473 nm laser (Dragon Lasers, Changchun Jilin, China) at 4.5 mW unless otherwise indicated. Green light was generated by a 532 nm laser (Dragon Lasers), and bilaterally delivered to mice at 10 mW. A Master-8 pulse generator (A.M.P.I., Jerusalem, Israel) was used to drive the blue laser at 20 Hz. This stimulation pattern was used for all ChR2 experiments. The laser output was delivered to the animal via an optical fiber (200 μm core, 0.22 numerical aperture, Doric Lenses, Canada) coupled to the fiberoptic implanted on the animals through a zirconia sleeve. \u003cb\u003eImmunostaining for cfos\u003c/b\u003e. Fixed brains were kept in 30% sucrose at 4\u003csup\u003eo\u003c/sup\u003eC overnight, and then sectioned on a cryostat (40 µm) slices. Sections were washed in PBS and incubated in a blocking solution (3% normal donkey serum and 0.3% triton-x in PBS) for 1 hour at room temperature. Sections were then incubated at 4oC for 12 hours with polyclonal anti-fos antibody made in rabbit (1/500 dilution) (c-Fos (9F6) Rabbit mAb CAT#2250, Cell Signalling Technology) in blocking solution. Following primary antibody incubation sections were washed in PBS 3 times for 10 minutes, and then incubated with anti-rabbit IgG (H+L) antibody (1/500 dilution) conjugated to Alexa Fluor 594 (red) (CAT# 8889S, cellsignal.com) for 1 hour at room temperature. Sections were washed in PBS 3 times for 10 minutes, incubated with DAPI (1/50000 dilution in PBS), washed again in PBS and mounted in glass slides using PVA-DABCO (Sigma). \u003cb\u003ePerfusion and histological verification.\u003c/b\u003e Mice were anesthetized with Fatal-Plus and transcardially perfused with phosphate buffered saline followed by a solution of 4% paraformaldehyde. Extracted brains were stored for 12 hs at 4°C in 4% paraformaldehyde. Brains were then placed in sucrose for a minimum of 24 hs. Brains were sectioned in the coronal plane in a cryostat, washed in phosphate buffered saline and mounted on glass slides using PVA-DABCO. Images were acquired using a Keyence BZ-X fluorescence microscope with a 10 or 20X air objective. \u003cb\u003eAcute brain slice preparation and electrophysiological recordings. \u003c/b\u003eCck-cre driver line mice were injected with AAV9-FLEX-ChR2-YFP in the PMd. Acute slices were prepared from these mie. For electrophysiological measurements, slices were transferred as needed to the recording chamber, where they were perfused with oxygenated aCSF at 32°C. The slices were held in place using a nylon net stretched within a U-shaped platinum wire. Visually-guided whole cell patch clamp recordings were made using infrared differential interference contrast optics. We also verified the identity of PMD neurons by only recording from YFP-positive neurons. All recordings were obtained using a MultiClamp 700B amplifier system (Molecular Devices, Union City, CA). Experiments were controlled by PClamp 10 software running on a PC, and the data were acquired using the Digidata 1440A acquisition system. All recording electrodes (3-8 MΩ) were pulled from thin-walled capillary glass (A-M Systems, Carlsborg, WA) using a Sutter Instruments P97 puller. The patch pipettes were filled with internal solution containing (in mM) 100 K- gluconate, 20 KCl, 4 ATP-Mg, 10 phospho-creatine, 0.3 GTP-Na, and 10 HEPES (in mM) with a pH of 7.3 and osmolarity of 300 mOsm. Only cells with a stable, uncorrected resting membrane potential (RMP) between -50 to -80 mV, overshooting action potentials, and an input resistance (RN) \u0026gt; 100 MW were used. To minimize the influence of voltage-dependent changes on membrane conductances, all cells were studied at a membrane potential near -60 mV (using constant current injection under current clamp mode). To study intrinsic firing properties of PMD neurons, WCRs were conducted under current clamp using the following protocol: (1) Voltage–current (V-I) relations were obtained using 400 ms current steps (range -50 pA to rheobase) and by plotting the plateau voltage deflection against current amplitude. Neuronal input resistance (RN) was determined from the slope of the linear fit of that portion of the V-I plot where the voltage sweeps did not exhibit sags or active conductance. (2) Intrinsic excitability measurements were obtained using 1s current steps (range 0 to 500 pA) and by plotting the number of action potentials fired against current amplitude. (3) Resting membrane potential (RMP) was calculated as the difference between mean membrane potential during the first minute immediately after obtaining whole cell configuration and after withdrawing the electrode from the neuron. For validating hM4Di in PMd-cck cells, acute brain slices preparation and electrophysiological recordings were performed using standard methods as previously described (Nagai et al., 2019). Briefly, Cck-Cre+ mice that had received AAV microinjections into PMd were deeply anesthetized with isoflurane and decapitated with sharp shears. The brains were placed and sliced in ice-cold modified artificial CSF (aCSF) containing the following (in mM): 194 sucrose, 30 NaCl, 4.5 KCl, 1 MgCl\u003csub\u003e2\u003c/sub\u003e, 26 NaHCO\u003csub\u003e3\u003c/sub\u003e, 1.2 NaH2PO\u003csub\u003e4\u003c/sub\u003e, and 10 D-glucose, saturated with 95% O\u003csub\u003e2\u003c/sub\u003e and 5% CO\u003csub\u003e2\u003c/sub\u003e. A vibratome (DSK-Zero1) was used to cut 300 μm brain sections. The slices were allowed to equilibrate for 30 minutes at 32-34°C in normal aCSF containing (in mM); 124 NaCl, 4.5 KCl, 2 CaCl\u003csub\u003e2\u003c/sub\u003e, 1 MgCl\u003csub\u003e2\u003c/sub\u003e, 26 NaHCO\u003csub\u003e3\u003c/sub\u003e, 1.2 NaH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e, and 10 D-glucose continuously bubbled with 95% O\u003csub\u003e2\u003c/sub\u003e and 5% CO\u003csub\u003e2\u003c/sub\u003e. Slices were then stored at 21–23°C in the same buffer until use. All slices were used within 2-6 hours of slicing. Slices were placed in the recording chamber and continuously perfused with 95% O\u003csub\u003e2\u003c/sub\u003e and 5% CO\u003csub\u003e2\u003c/sub\u003e bubbled normal aCSF. pCLAMP10.4 software and a Multi-Clamp 700B amplifier was used for electrophysiology (Molecular Devices). Whole-cell patch-clamp recordings were made from neurons in the PMd or dorsolateral PAG (dlPAG) using patch pipettes with a typical resistance of 4–5 MΩ. Neurons were selected based on reporter fluorescence,(mCherry for hM4Di-mCherry). The intracellular solution for recordings comprised the following (in mM) : 135 potassium gluconate, 5 KCl, 0.5 CaCl\u003csub\u003e2\u003c/sub\u003e, 5 HEPES, 5 EGTA, 2 Mg-ATP and 0.3 Na-GTP, pH 7.3 adjusted with KOH. The initial access resistance values were \u0026lt; 20 MΩ for all cells; if this changed by \u0026gt; 20% the cell was discarded. Light flashes (0.2 mW/mm2) from a blue LED light source (Sutter Instruments) were delivered via the microscope optics and a 40x water immersion objective lens and controlled remotely using TTL pulses from Clampex. Cell responses were recorded in whole-cell mode and recorded using an Axopatch 700B amplifier connected via a digitizer to a computer with pCLAMP10 software. To stimulate ChR2 expressed in PMd neurons or axons, 5 ms pulses were delivered at inter-pulse intervals of 200 ms, 50 ms or 25 ms for 5, 20 or 40 Hz optical stimulations, respectively. \u003cb\u003eFunctional Magnetic Resonance Imaging (fMRI) methods\u003c/b\u003e \u003cb\u003eParticipants. \u003c/b\u003eThis study included 48 adult participants (mean ± SD age: 25.1 ± 7.1; 27 male, 21 female; 7 left-handed; 40 white and 8 non-white (1 Hispanic, 5 Asian, 1 Black and 1 American Indian)). All participants were healthy, with normal or corrected to normal vision and normal hearing, and with no history of psychiatric, physiological or pain disorders and neurological conditions, no current pain symptoms and no MRI contraindications. Eligibility was assessed with a general health questionnaire, a pain safety screening form and an MRI safety screening form. Participants were recruited from the Boulder/Denver Metro Area. The institutional review board of the University of Colorado Boulder approved the study, and all participants provided written informed consent. \u003cb\u003eExperimental Paradigm. \u003c/b\u003eParticipants received five different types of aversive stimulation (mechanical pain, thermal pain, aversive auditory, aversive visual, and pleasant visual), each at four stimulus intensities. 24 stimuli of each type (6 per intensity) were presented over six fMRI runs in random order. Following stimulation on each trial, participants made behavioral ratings of their subjective experience. Participants were instructed to answer the question ‘How much do you want to avoid this experience in the future?’. Ratings were made with a non-linear visual analog rating scale, with anchors ‘Not at all’ and ‘Most’ displayed at the ends of the scale. \u003cb\u003eStimuli.\u003c/b\u003e Visual stimulation\u003ci\u003e \u003c/i\u003ewas administered on the MRI screen and included normed images from the International Affective Picture System (IAPS) database (Lang et al., 2008). To induce four ‘stimulus intensity levels’ we selected four groups of 7 images based on their   normed aversiveness ratings (averaged across male and female raters) available in the IAPS database and confirmed by \u003ci\u003eN\u003c/i\u003e = 10 lab members (5 male, 5 female) in response to ‘How aversive is this image? 1-100’. Selected images included photographs of animals (n=7), bodily illness and injury (n=12), industrial and human waste (n=9). Four stimulus levels were delivered to participants for 10 sec each. \u003cb\u003eMRI data acquisition and preprocessing.\u003c/b\u003e Whole-brain fMRI data were acquired on a 3T Siemens MAGNETOM Prisma Fit MRI scanner at the Intermountain Neuroimaging Consortium facility at the University of Colorado, Boulder. Structural images were acquired using high-resolution T1 spoiled gradient recall images (SPGR) for anatomical localization and warping to standard MNI space. Functional images were acquired with a multiband EPI sequence (TR = 460 ms, TE = 27.2 ms, field of view = 220 mm, multiband acceleration factor = 8, flip angle = 44°, 64 × 64 image matrix, 2.7 mm isotropic voxels, 56 interleaved slices, phase encoding posterior \u0026gt;\u0026gt; anterior). Six runs of 7.17 mins duration (934 total measurements) were acquired. Stimulus presentation and behavioral data acquisition were controlled using Psychtoolbox. FMRI data were preprocessed using an automated pipeline implemented by the Mind Research Network, Albuquerque, NM. Briefly, the preprocessing steps included: distortion correction using FSL’s top-up tool (https://fsl.fmrib.ox.ac.uk/fsl/), motion correction (affine alignment of first EPI volume (reference image) to T1, followed by affine alignment of all EPI volumes to the reference image and estimation of the motion parameter file (sepi_vr_motion.1D, AFNI, https://afni.nimh.nih.gov/), spatial normalization via subject’s T1 image (T1 normalization to MNI space (nonlinear transform), normalization of EPI image to MNI space (3dNWarpApply, AFNI,  https://afni.nimh.nih.gov/), interpolation to 2 mm isotropic voxels and smoothing with a 6 mm FWHM kernel (SPM 8, https://www.fil.ion.ucl.ac.uk/spm/software/spm8/). Prior to first level (within-subject) analysis, we removed the first four volumes to allow for image intensity stabilization. We also identified image-wise outliers by computing both the mean and the standard deviation (across voxels) of intensity values for each image for all slices to remove intermittent gradient and severe motion-related artifacts (spikes) that are present to some degree in all fMRI data. \u003cb\u003efMRI data analysis.\u003c/b\u003e Data were analyzed using SPM12 (http://www.fil.ion.ucl.ac.uk/spm) and custom MATLAB (The MathWorks, Inc., Natick, MA) code available from the authors’ website (http://github.com/canlab/CanlabCore). First-level general linear model (GLM) analyses were conducted in SPM12. The six runs were concatenated for each subject. Boxcar regressors, convolved with the canonical hemodynamic response function, were constructed to model periods for the 10-second stimulation and 4-7 second rating periods. The fixation cross epoch was used as an implicit baseline. A high-pass filter of 0.008 Hz was applied. Nuisance variables included (a) “dummy” regressors coding for each run (intercept for each run); (b) linear drift across time within each run; (c) the six estimated head movement parameters (x, y, z, roll, pitch, and yaw), their mean-centered squares, their derivatives, and squared derivative for each run (total 24 columns); and (d) motion outliers (spikes) identified in the previous step. A “single-trial model” was used to uniquely estimate the response to every stimulus in order to assess functional connectivity. \u003cb\u003eFunctional connectivity analysis. \u003c/b\u003eFunctional connectivity between the hypothalamus and PAG was estimated using Partial Least Squares (PLS) (Wold et al., 2001) regression, which identifies latent multivariate patterns that maximize the covariance between two blocks of data (i.e., BOLD activity in hypothalamus and PAG voxels). Here, data comprised single trial estimates of brain activation in response to aversive thermal, mechanical, auditory, and visual stimuli, in addition to a set of pleasant visual stimuli which were used as a control. For the PLS model, the predictor block of variables included all voxels in an anatomically defined mask of the hypothalamus (Pauli et al., 2018) (337 voxels) and the outcome block included all voxels in the PAG (Kragel et al., 2019) (42 voxels). Localization of the hypothalamus signal that covaries with the PAG responses was performed by bootstrapping the PLS regression and examining the distribution of PLS regression coefficients and their deviation from zero (using normal approximation for inference). Hyperalignment of fMRI data (Haxby et al., 2011) was conducted separately for each region as a preprocessing step, and leave-one-subject-out cross-validation was performed to estimate the strength of functional connections (i.e., the Pearson correlation between the first ‘X score’ and ‘Y score’ estimated by PLS, similar to the canonical correlation (Hardoon et al., 2004). A benefit of the pathway-identification model we employed is that it can, in principle, identify HTH and PAG patterns that distinctly participate in the HTH-PAG pathway. For example, the central nucleus of the amygdala (CeA) projects to both the hypothalamus and the PAG (Kim et al., 2013), and could indirectly explain variation in BOLD signals in the PAG. To test pathway specificity, we separately modeled a pathway between the CeA and the PAG using the approach described above. This allowed us to evaluate how much variation in PAG activity the HTH-PAG pathway explained above and beyond the CeA-PAG pathway. To evaluate this, we computed the partial correlation between latent sources in the hypothalamus and PAG, controlling for the latent source in the CeA. \u003cb\u003eStatistics. \u003c/b\u003eNonparametric Wilcoxon signed-rank or rank-sum tests were used, unless otherwise stated. Two-tailed tests were used throughout with α=0.05. Asterisks in the Figures indicate the \u003ci\u003ep\u003c/i\u003e values. Standard error of the mean was plotted in each Figure as an estimate of variation. Multiple comparisons were adjusted with the false discovery rate (FDR) method.","descriptionType":"Methods"},{"description":"Explanation of variables used:\n 'Tracking.mat' is a struct containing the DeepLabCut\n output and related behavioral metrics.  \n 'good_neurons.mat' is a logical vector indicating which\n neurons were used in the analysis, based on variance thresholding (see\n Methods \u0026gt; Miniscope postprocessing).\n \u003cstrong\u003eFiber\n photometry-specific\u003c/strong\u003e The\n workspace entitled\n \"Tracking_Behavior_Synced_\u003cem\u003ex\u003c/em\u003e.mat\" contains\n the synchronized fiber photometry signal and related tracking and\n behavioral output: \"sig_norm_sync\" is the\n neural data. All variables followed by\n \"...TS\" have been synced with this neural data, including\n tracking and categorized behaviors.\n \u003cstrong\u003eMiniscope-specific\u003c/strong\u003e 'BehaviorMS.mat' is a workspace that contains all the categorized behaviors for miniscope datasets: '\u003ci\u003ebehaviorName\u003c/i\u003eFrameMS' is a # behaviors x 2 matrix.  Column 1 is the in point and column 2 is the out point. '\u003ci\u003ebehaviorName\u003c/i\u003eIndicesMS' is a logical vector whose length matches the number of neural samples. '1' = behavior detected, '0' = behavior not detected. 'output_CNMF-E.mat' is a workspace containing the CNMF-E output. Most important is: 'neuron.C_raw' is the struct field containing the extracted putative neural traces used in the analysis. When a field from the Tracking.mat file ends in \"...MS\", it has been synchronized with the miniscope neural output. \u003cstrong\u003eBehavior-specific\u003c/strong\u003e 'Behavior.mat' workspaces contain all classified behaviors: \u003cem\u003e'behavior\u003c/em\u003eStart' is all the start points for a given behavior. Likewise, \u003cem\u003e'behavior\u003c/em\u003eEnd' is all the end points for a given behavior. \u003cem\u003e'behavior\u003c/em\u003eIndices' is a logical vector of whether or not a behavior was categorized for a given sample. \u003cstrong\u003efMRI-specific\u003c/strong\u003e The data file 'MPA_data_pHythal_PAG.mat' contains the following variables:\u003cbr\u003e CeM: a mask of the central amygdala from the SPM Anatomy toolbox - https://github.com/inm7/jubrain-anatomy-toolbox\u003cbr\u003e PAG: a mask of the PAG from Kragel et al. 2019 - https://doi.org/10.1523/JNEUROSCI.2043-18.2019\u003cbr\u003e hythal: a mask of the hypothalamus from the CIT168 Atlas - https://doi.org/10.1038/sdata.2018.63\u003cbr\u003e mask: a combined mask of these regions\u003cbr\u003e masked_dat: a CANLab data object for the single-trial estimates of fMRI activation within the above regions\u003cbr\u003e S: a vector denoting subject number for each trial\u003cbr\u003e VAL: a vector denoting the valence of the stimulus for each trial\u003cbr\u003e COND: a vector denoting the condition (stimulus modality) for each trial","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.5068/D19H5X","contentUrl":null,"metadataVersion":13,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":229,"downloadCount":31,"referenceCount":1,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2021-04-23T17:41:04Z","registered":"2021-04-23T17:41:05Z","published":null,"updated":"2026-03-05T00:40:43Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5068/d1w96h","type":"dois","attributes":{"doi":"10.5068/d1w96h","identifiers":[],"creators":[{"name":"Claudepierre, Seth","nameType":"Personal","givenName":"Seth","familyName":"Claudepierre","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-5513-5947","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Empirically estimated electron lifetimes in the Earth's radiation belts: 2. Comparison with theory"}],"publisher":"Dryad","container":{},"publicationYear":2019,"subjects":[],"contributors":[],"dates":[{"date":"2019-11-21T01:20:23Z","dateType":"Submitted"},{"date":"2019-12-03T00:00:00Z","dateType":"Issued"},{"date":"2019-12-03T00:00:00Z","dateType":"Available"},{"date":"2019-12-08T00:00:00Z","dateType":"Updated"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1029/2019gl086056","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1002/essoar.10501089.1","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["7866528 bytes"],"formats":[],"version":"5","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"These data are provided as supporting information for\n the manuscript \"Empirically estimated electron lifetimes in\n the Earth's radiation belts: 2. Comparison with theory\" by\n Claudepierre et al. These data include electron flux measurements in the\n Earth's radiation belt obtained with the MagEIS instrument\n on the NASA Van Allen Probe B satellite, along with the\n theoretical decay timescale estimates described in the manuscript. The\n contents of the data files are described in greater detail in the\n corresponding \"readme\" files.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.5068/D1W96H","contentUrl":null,"metadataVersion":14,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":159,"downloadCount":23,"referenceCount":0,"citationCount":2,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2019-12-03T17:50:35Z","registered":"2019-12-03T17:50:36Z","published":null,"updated":"2026-03-04T22:41:57Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5068/d1m100","type":"dois","attributes":{"doi":"10.5068/d1m100","identifiers":[],"creators":[{"name":"Perry, Susan","nameType":"Personal","givenName":"Susan","familyName":"Perry","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-5306-5383","nameIdentifierScheme":"ORCID"}]},{"name":"Carrera, Sofia","nameType":"Personal","givenName":"Sofia","familyName":"Carrera","affiliation":["University of Michigan"],"nameIdentifiers":[]},{"name":"Godoy, Irene","nameType":"Personal","givenName":"Irene","familyName":"Godoy","affiliation":["University of Exeter"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-4405-226X","nameIdentifierScheme":"ORCID"}]},{"name":"Gault, Colleen","nameType":"Personal","givenName":"Colleen","familyName":"Gault","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Mensing, Ashley","nameType":"Personal","givenName":"Ashley","familyName":"Mensing","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Damm, Juliane","nameType":"Personal","givenName":"Juliane","familyName":"Damm","affiliation":["Universidad Veracruzana"],"nameIdentifiers":[]},{"name":"Beehner, Jacinta","nameType":"Personal","givenName":"Jacinta","familyName":"Beehner","affiliation":["University of Michigan"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-6566-6872","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Data from: Stress responsiveness in a wild primate predicts survival across an extreme El Niño drought"}],"publisher":"Dryad","container":{},"publicationYear":2025,"subjects":[{"subject":"FOS: Biological sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Glucocorticoids"},{"subject":"Demography","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"FOS: Sociology","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"El Niño-Southern Oscillation","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Cebus capucinus imitator"},{"subject":"HPA axis"}],"contributors":[{"name":"University of California, Los Angeles","nameType":"Personal","givenName":"Los Angeles","familyName":"University of California","affiliation":[],"contributorType":"Sponsor","nameIdentifiers":[]}],"dates":[{"date":"2023-07-20T19:33:39Z","dateType":"Created"},{"date":"2024-12-17T14:38:35Z","dateType":"Submitted"},{"date":"2025-01-06T00:00:00Z","dateType":"Issued"},{"date":"2025-01-06T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsDerivedFrom","relatedIdentifier":"10.5281/zenodo.8247327","relatedIdentifierType":"DOI"},{"relationType":"IsSourceOf","relatedIdentifier":"10.5281/zenodo.14004141","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1126/sciadv.adq5020","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["2158639 bytes"],"formats":[],"version":"8","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"We know more about the costs of chronic stress than the benefits of the\n acute stress response – an adaptive response that buffers organisms from\n life-threatening challenges. Yet, no primate study has empirically\n identified how the stress response adaptively impacts evolutionary\n fitness. Here, we take advantage of a natural experiment – an El Niño\n drought – that produced unprecedented mortality for wild white-faced\n capuchins. Using a reaction norm approach, we provide evidence from\n primates that a more robust stress response to a stressor, measured using\n fecal glucocorticoids, predicts a greater likelihood of survival. We show\n that individuals with greater stress responsiveness to previous droughts\n later had higher survival across a severe El Niño drought. Evolutionary\n models need empirical data on how stress responsivity varies in adaptive\n ways. While we cannot buffer subjects from catastrophic events, we can use\n them to understand which aspects of the stress response help animals to\n “weather the storm”.","descriptionType":"Abstract"},{"description":"These are observational data: Demographic data and noninvasively\n obtained fecal samples were collected from a wild population of\n white-faced capuchin monkeys that are subjects of a long-term study at\n Lomas Barbudal Biological Reserve, Costa Rica. The rawest form of the\n demographic data and information about fecal samples are housed in a MySQL\n database at UCLA, which was queried to produce the data used in this\n study. Fecal samples were dried in the Lomas Barbudal Monkey Project field\n lab, and then extracted and assayed in a University of Michigan-Ann Arbor\n core facility lab.","descriptionType":"Methods"},{"description":"We used the following R packages:  brms\n (versions 2.20.4 and 2.21.0) sjPlot (version\n 2.8.14) standaRdized (version 1.0)\n tidyverse (version 2.0.0) ","descriptionType":"Other"},{"description":"# Data from: Stress responsiveness in a wild primate predicts survival\n across an extreme El Niño drought Included here are all data and code\n necessary to replicate findings from “Stress responsiveness in a wild\n primate predicts survival across an extreme El Niño drought”. Demographic\n and fecal sample information came from the Lomas Barbudal Monkey Project\n database. #### Description of the data and file structure THE FOLLOWING\n DATASETS ARE PROVIDED AS .CSV FILES: Date formats should be yyyy-mm-dd\n “data_droughtIndices.csv” - drought indices used in main analyses Indices:\n std_don_pverde, std_don_chelsa, SPI_Don_PVERDE, SPI_Don_CHELSA\n std_don_pverde: drought index based on Palo Verde estimates (unitless)\n std_don_chelsa: alternative drought index based on CHELSA estimates\n (unitless) SPI_Don_PVERDE: alternative drought index based on Palo Verde\n estimates, using SPI (unitless) SPI_Don_CHELSA: alternative drought index\n based on CHELSA estimates, using SPI (unitless)\n “data_glucocorticoids3.csv” - capuchin data used in main analyses Outcome\n variables: Survived (0: No, 1: Yes), LN_GCS (log glucocorticoids ng/g)\n Fixed effects: ctime (collection time, minutes into day), season\n (categorical: wet/dry), Pregnancy (categorical: other, Early pregnancy,\n Late pregnancy), group_size (continuous, # of capuchins in group), age\n (continuous, years), rank01 (proportion dominated) Random effects: ID\n (identity of subject), groupid (group of residence)\n \"data_rainfall.csv\" - expected rainfall columns day_index:\n numeric value for day of year Month: Month of year Day: Day of year MD:\n Month-Day (e.g., 01/Jan for January 1st) mu30: daily average precipitation\n estimate over 30-day window (mm) sd30: standard deviation of mu30 value\n data: categorical, dataset from which precipitation value comes from\n (PVERDE: Palo Verde; CHELSA) mm_rain_std: standardised value for daily mm\n average precipitation estimate over 30-day window (unitless)\n \"data_additional3.csv\" - age and rank at start of El Nino\n columns ID: categorical, identity of subject age_ElNino: age (years) of\n subject at start of El Niño drought rank_ELNino: rank (proportion of group\n dominated) of subject at start of El Niño File used to examine mortality\n due to El Niño: “data_adultFemaleMortality.csv” - data on female mortality\n Outcome variable: n died (number of females that died) Fixed effects:\n exposure (offset) (deathrate is ndied divided by exposure; exposure takes\n into account the fraction of the year that females were both adults and\n still alive) Files used to generate the drought indices found in\n “data_droughtIndices.csv”: “data_precip_CHELSA*1979-2016.csv” - CHELSA\n *daily precipitation measures.* columns Date: precipitation estimate date\n YEAR: precipitation estimate year MONTH: precipitation estiamte month DAY:\n precipitation estimate day MD: precipitation estimate day-month (e.g.,\n \"01/Jan\" for January 1st) YDAY: index for day of year\n rain_mean_mm: precipitation estimate for day rain_30daysSUm_CHELSA: sum\n for precipitation estimate for 30-day rolling window. The first 29 rows of\n data for the last column (which represents the sum of the past 30\n days' values) are \"NA\" due to the fact that 30 days of\n previous data would be required to calculate this sum, and such data do\n not exist. “data_precip_PaloVerde_19962020.csv” - Palo Verde daily\n precipitation measures Additional precipitation file for correlations\n between Lomas and Palo Verde date: rainfall estimate data final_rain:\n rainfall measure from Palo Verde OTS station NA for dates without rainfall\n measure year: rainfall estimate year month: rainfall estimate month day:\n rainfall estimate day mean_daily_30rain: mean daily rainfall for 30-days\n surrounding date, without filling missing data. This is the 15-days prior\n to date and the 15-days after date (does not include date). NA for dates\n without sufficient rainfall measures n_missing_daily: missing days of data\n in \"final_rain\" for calculating \"mean_daily_30rain\" NA\n for dates where \"mean_daily_30rain\" was not calculated (n=15,\n first 15 days in data set) dayrain: copy of \"final_rain\", but\n with missing values filled in, if possible, by values in\n \"main_daily_30rain\" NA for dates without updated rainfall\n measure pv_tot_30rain: 30-day rolling sum of rainfall, based on column\n \"dayrain\" NA for dates without rainfall measure n_missing_total:\n missing days of data estimate of \"pv_tot_30rain\" NA for dates\n where \"pv_tot_30rain\" was not calculated (n=29, first 29 days in\n data set) est_dayrain: copy of \"dayrain\", but with NA values for\n dates where there is excess missing data (i.e., more than 15 days of\n missing values) “data_precip_reallomas.csv” - Lomas Barbudal precipitation\n measures date: date of rainfall measure Year: year of rainfall measure\n Month: month of rainfall measure Day: day of rainfall measure (range:\n 1-31) rainfall: rainfall measure (mm) l_tot_rain: 30-day rolling sum of\n rainfall (mm) Additional precipitation file for the average monthly\n rainfall totals at Palo Verde \"total_monthly_rain_pv.csv\" - Palo\n Verde monthly rainfall totals (1996-2016) Files used for post-hoc foraging\n analyses: \"data_foraging1.csv\" - proportion of time foraging\n insects or fuit Y: calendar year M: calendar month D: calendar day n:\n number of group scans class: categorical, foraging insects (FI) or\n foraging fruit (FF) count: number of observations of foraging date: date\n of observation std_don_pverde: drought index based on Palo Verde estimates\n (unitless) day_index: numeric value for day of year MD: day-month (e.g.,\n \"01/Jan\" for January 1st) mm_rain_std: standardised value for\n mu30 (unitless) RESPONSE AND PREDICTOR VARIABLE DESCRIPTIONS FOR MAIN\n MODELS Columns used later as response variables: LN_GCS: log\n glucocorticoids ng/g Lived: whether a female survived (1) or not (0)\n during the El Niño drought Columns used later as predictor variables:\n std_don_pverde: STD drought risk index based on Palo Verde rainfall\n estimates (unitless) SPI_Don_PVERDE: SPI drought risk index based on Palo\n Verde rainfall estimates (unitless) std_don_chelsa: STD drought risk index\n based on CHELSA rainfall estimates (unitless) SPI_Don_CHELSA: SPI drought\n risk index based on CHELSA rainfall estimates (unitless) rain_mm_std:\n standardized measure of expected rainfall (rain seasonality) (unitless)\n Survived: whether a female survived (1) or not (0) the El Niño drought\n age: age of female (in years) at time of fecal sample collection\n age_ElNino: age of female (in years) at the start of the El Niño-related\n drought rank01: rank of female (proportion of group dominated) at the time\n of sample collection rank_ElNino: rank of female (proportion of group\n dominated) at the start of the El Niño-related drought group_size: number\n of capuchins in group Pregnancy: categorical female reproductive state at\n time of sample collection (’Other’, ‘Early pregnancy’, ‘Late pregnancy’)\n cdate: date of sample collection ctime: time of sample collection Columns\n used later for random effects: ID: identity of female groupid: group\n female was resident in at time of sample collection #### Sharing/Access\n information Precipitation estimates were derived from the following\n sources: * CHELSA estimates were accessed via:\n [[https://chelsa-climate.org](https://chelsa-climate.org)]. See the\n following reference for more information about the data set: Dirk N.\n Karger, Stefan Lange, Chantal Hari, Christopher P. O. Reyer, Niklaus E.\n Zimmermann (2021):* CHELSA-W5E5 v1.1: W5E5 v1.0 downscaled with CHELSA\n v2.0*. **ISIMIP\n Repository. **[https://doi.org/10.48364/ISIMIP.836809.1](https://doi.org/10.48364/ISIMIP.836809.1 \"https://doi.org/10.48364/ISIMIP.836809.1\") * Raw Palo Verde station data can be accessed via the Organization for Tropical Studies (OTS): [[https://tropicalstudies.org/portfolio/palo-verde-research-station/](https://tropicalstudies.org/portfolio/palo-verde-research-station/)] * Meteorological data can be assessed through: [[https://bixa.tropicalstudies.org/meteoro/default.php](https://bixa.tropicalstudies.org/meteoro/default.php)] #### Code/Software You will need to have R installed. Analyses were primarily conducted using R version 4.3.2. Download all files into one folder and set it as your working directory. You can create a subfolder ‘objects’ to save model objects in (all .rds files). We used the following R packages: brms (versions 2.20.4 and 2.21.0) sjPlot (version 2.8.14) standaRdized (version 1.0) tidyverse (version 2.0.0) Code used to run the main models are in the following .Rmd files. The code is written assuming at least 4 cores for running models in parallel. “1-drought-indices.Rmd” \\- requires the use of the following data files: \\- “data_precip_CHELSA_1979-2016.csv” \\- “data_precip_PaloVerde_19962020.csv” “2_Model_fGC.Rmd” \\- requires the use of the following data files: \\- “data_glucocorticoids3.csv” \\- “data_droughtIndices.csv” \\- \"data_rainfall.csv\" Files for visualization and model checks \"1a-drought-visualization.Rmd\" \\- can use file \"data_droughtIndices.csv\" \\- can use file \"analysis_std_spi_chelsa_PV.csv\" “2a_Model-check-and-visualization.Rmd” \\- can use file \"plotmodelsdata.csv\" Model outputs (objects) have also been saved as .rds files Preliminary models: prelim_pverde.rds prelim_chelsa.rds Reaction-norm-extraction model outputs (to generate reaction norm BLUPs): fGC_2x_spi_chelsa.rds fGC_2x_spi_pverde.rds fGC_2x_std_chelsa.rds fGC_2x_std_pverde.rds Glucocorticoid-predicting-survival model outputs: surv_all_spi_chelsa2.rds surv_all_spi_pverde2.rds surv_all_std_chelsa2.rds surv_all_std_pverde2.rds Survival-predicting-glucocorticoids model outputs (3-way interaction model): fGC_3x_spi_chelsa.rds fGC_3x_pverde.rds fGC_3x_std_chelsa.rds fGC_3x_std_pverde.rds for convenience, reaction norm BLUPs have also been saved as .csv files BLUP_std_pverde.csv BLUP_spi_pverde.csv BLUP_std_chelsa.csv BLUP_spi_chelsa.csv columns used in downstream analyses ID: categorical, identity of subject intDry_mu: BLUP value for dry season intercept intWet_mu: BLUP value for wet season intercept slpDry_mu: BLUP value for dry season slope slpWet_mu: BLUP value for wet season slope index: categorical, drought index used age_ElNino: age at start of el Niño drought rank_ElNino: rank (proportion of group dominated) at start of El Niño Survived: categorical, whether subject survived the drought (No: \"0\", Yes: \"1\") Lived: integer, whether subject survived the drought (No: \"0\", Yes: \"1\") SUPPLEMENTARY FILES Code for supplementary analyses are provided in the following .Rmd files. “S1 Mortality-analysis.Rmd” \\- requires the use of the following data file: \\- “data_adultFemaleMortality.csv” “S2 Compare-lomas-pv.Rmd” \\- requires the use of the following data files: \\- “data_precip_reallomas.csv” \\- “data_precip_PaloVerde_19962020.csv” \"SI_models_fGC_subset_rainy.Rmd\" \\- same analyses as in main text, but using only wet season data \\- requires the use of the following data files: \\- “data_glucocorticoids3.csv” \\- “data_droughtIndices.csv” \\- \"data_rainfall.csv\" \\- \"data_additional3.csv\" \"SI_models_foraging.Rmd\" \\- requires the use of the following data files: \\- “data_foraging1.csv” \\- “data_droughtIndices.csv” \\- \"data_rainfall.csv\"","descriptionType":"TechnicalInfo"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"848360","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"9633991","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"9870429","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","awardTitle":"Adolescence in Wild Cebus Capucinus: Personality, Demography and Life History","funderName":"Division of Behavioral and Cognitive Sciences","awardNumber":"0613226","funderIdentifier":"https://ror.org/05wgkzg12","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"1232371","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"1638428","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"1945701","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"Leakey Foundation","funderIdentifier":"https://ror.org/018kdpd27","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Geographic Society","funderIdentifier":"https://ror.org/04bqh5m06","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"Wenner-Gren Foundation","awardNumber":"80418","funderIdentifier":"https://ror.org/04qvvhf62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"Templeton World Charity Foundation","awardNumber":"0208","funderIdentifier":"https://ror.org/00x0z1472","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"University of Michigan–Ann Arbor","funderIdentifier":"https://ror.org/00jmfr291","funderIdentifierType":"ROR"},{"funderName":"Wild Capuchin Foundation*"},{"schemeUri":"https://ror.org","funderName":"University of California, Los Angeles","funderIdentifier":"https://ror.org/046rm7j60","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"Max Planck Institute for Evolutionary Anthropology","funderIdentifier":"https://ror.org/02a33b393","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Science Foundation","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.5068/D1M100","contentUrl":null,"metadataVersion":5,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":75,"downloadCount":19,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-10-04T22:45:49Z","registered":"2023-10-04T22:45:50Z","published":null,"updated":"2026-01-28T15:26:14Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5068/d1qt17","type":"dois","attributes":{"doi":"10.5068/d1qt17","identifiers":[],"creators":[{"name":"Perry, Susan","nameType":"Personal","givenName":"Susan","familyName":"Perry","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-5306-5383","nameIdentifierScheme":"ORCID"}]},{"name":"Jacobson, Odd","nameType":"Personal","givenName":"Odd","familyName":"Jacobson","affiliation":["Max Planck Institute for Animal Behavior"],"nameIdentifiers":[]},{"name":"Barrett, Brendan","nameType":"Personal","givenName":"Brendan","familyName":"Barrett","affiliation":["University of Konstanz"],"nameIdentifiers":[]}],"titles":[{"title":"A new approach to geostatistical synthesis of historical records reveals capuchin spatial responses to climate and demographic change"}],"publisher":"Dryad","container":{},"publicationYear":2024,"subjects":[{"subject":"FOS: Biological sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Cebus imitator"},{"subject":"home range"},{"subject":"Longitudinal studies","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Climate change","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"movement ecology"},{"subject":"natural history"},{"subject":"georeferencing"},{"subject":"Primatology","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"El Niño-Southern Oscillation","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[],"dates":[{"date":"2024-02-15T18:44:37Z","dateType":"Created"},{"date":"2024-05-04T12:56:18Z","dateType":"Submitted"},{"date":"2024-05-08T00:00:00Z","dateType":"Issued"},{"date":"2024-05-08T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsDerivedFrom","relatedIdentifier":"10.17617/3.hukms6","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1111/ele.14443","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["26548 bytes"],"formats":[],"version":"3","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"The recent proliferation of GPS technology has transformed animal movement\n research. Yet, time-series data from this recent technology rarely span\n beyond a decade, constraining longitudinal research. Long-term field sites\n hold valuable historic animal location records, including hand-drawn maps\n and semantic descriptions. Here, we introduce a generalized workflow for\n converting such records into reliable location data to estimate home\n ranges, using 30 years of sleep-site data from 11 white-faced capuchin\n (Cebus imitator) groups in Costa Rica. Our findings illustrate that\n historic sleep locations can reliably recover home range size and\n geometry. We showcase the opportunity our approach presents to resolve\n open questions that can only be addressed with very long-term data,\n examining how home ranges are affected by climate cycles and demographic\n change. We urge researchers to translate historical records into usable\n movement data before this knowledge is lost; it is essential to understand\n how animals are responding to our changing world.","descriptionType":"Abstract"},{"description":"# A new approach to geostatistical synthesis of historical records reveals\n capuchin spatial responses to climate and demographic change #### Raw\n sleep site data: Here we present two chunks of data that were created in\n the process of converting early historical data to something that could be\n linked to GPS locations so that the full process can be understood. We did\n not provide the more recent data for conservation reasons, as we want to\n protect the locations of habituated primates. Scientists with a legitimate\n reason to see the full data set can contact Susan Perry for more\n information. `Sleepsites_1997_oddshift.csv`\n `SleepsitesAA1990-95_bjbshift.csv` The file `Sleepsites_1997_oddshift.csv`\n is from the first season in which electronic devices (Psion Observers)\n were used to record data in the field (though these were still\n supplemented with field notes having hand-drawn maps to help us find sleep\n sites the following morning, as this was our first field season working\n with RR group, so its home range had not yet been mapped). The file\n `1993AAsubsetData.xlsx` is a chunk of the larger file\n `SleepsitesAA1990-95_bjbshift.csv` that is mentioned in the R code. This\n is from the first 3 years of the project when we focused exclusively on\n the AA group, recording data on microcassette recorders and writing\n additional notes about sleep site locations in field notebooks. Originally\n there were more columns, including (a) values for a 50 m grid system\n (which we abandoned when it became clear that we could rarely pinpoint the\n precise grid cell at that level of granularity), (b) columns indicating\n whether we saw the monkeys curl up in their sleep trees, or at least were\n with them during the last hour before sundown, (c) extensive comments\n about how certain we were about the specific grid cell assigned and\n listing other possible values (when we were considering listing all\n possible grid cell values), and (d) comments containing notes from Susan\n about which sleep sites needed to be double checked via older versions of\n our maps, and then checked in the field by finding those points and taking\n a GPS reading. When this became possible, we corrected the Grid100 value.\n The versions presented here use only the columns that are used in the\n present analysis, after we had refined our data processing workflow as\n described in the manuscript. * *date*: the date representing the evening\n on which we watched the monkeys settle into their sleep tree for the\n night. * *location*: a description of where the monkeys slept * *group*:\n the group of monkeys monitored on that date * *Grid100*: the number\n assigned to the grid cell in the 100 m2 grid. Note that the numbers in the\n entire grid published in the code repository have been systematically\n altered to protect these threatened monkeys, so that their sleep sites\n cannot be easily located by non-researchers. * *certainty?*: As described\n in the manuscript, a score of 0 indicates that we can confidently assign\n the sleep site to that grid cell. A score of 1 means that the sleep site\n lies within a radius of 1 away from the grid cell assigned, i.e. could be\n within that cell or one of the 8 neighboring grid cells (corresponding to\n 300 meters squared). A score of 2 means that the sleep site was within 2\n grid cells of the one chosen (corresponding to 500 meters squared), etc.\n #### Demographic data: `annual_group_sizes.csv` This file reports the\n group size (including members of all age-sex classes) for each social\n group in each year, derived from the Lomas Barbudal Monkey Project\n demographic database, using only census days in which trained observers\n were present in the group for at least 6 hours. #### Raw GPS data: For\n conservation reasons, i.e., to protect the animals from the pet trade and\n poachers, these data will not be made public. They are archived at\n Movebank, and can be accessed here, pending permission of the curator\n (Susan Perry):\n [https://www.movebank.org/cms/webapp?gwt_fragment=page=studies,path=study3389013696](https://www.movebank.org/cms/webapp?gwt_fragment=page=studies,path=study3389013696) ## Description of the relationship between the data and the code used to analyze it: To analyze these data, please refer to the code found at the following location:  [https://doi.org/10.17617/3.HUKMS6](https://doi.org/10.17617/3.HUKMS6) The descriptions of sleep sites were transformed to location data using the quadrant centroids in which they fell, using a map that had a 100x100m2 grid. The code for this and other procedures described below can be found in `01_georeferencing/scripts/00_hist_slp_data_processing.R.` All location data (georeferenced data, GPS-tracking data, and GPS sleep-site data) were used to estimate home ranges using the ctmm package in R. To do this, one can follow `02_validation/scripts` for annotated steps within the 01 and 02 scripts. Next, the home ranges using sleep site data were validated, using the scripts 03-06, by comparing them to home ranges estimated from tracking data. Then home ranges estimated from sleep site data, once validated, were used in the case study models to draw predictions about the effects of ENSO and group size on home range area. To generate the tables necessary for these models, run the 07 script within the `02_validation/scripts` folder. The statistical models and plots for the case study are found in the `03_case_study_models` folder. For more details please see the README.md file within the Edmond code repository linked above. # Code/Software The code used to process these raw data is housed in the following repository:  [https://doi.org/10.17617/3.HUKMS6](https://doi.org/10.17617/3.HUKMS6)","descriptionType":"TechnicalInfo"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"BCS-1638428","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"BCS-0613226","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"BCS-0848360","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"DDIG 1232371","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"9633991","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"SES-99870429","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Science Foundation","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Geographic Society","awardNumber":"7968-06","funderIdentifier":"https://ror.org/04bqh5m06","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Geographic Society","awardNumber":"8671-09","funderIdentifier":"https://ror.org/04bqh5m06","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Geographic Society","awardNumber":"20113909","funderIdentifier":"https://ror.org/04bqh5m06","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Geographic Society","awardNumber":"9795-15","funderIdentifier":"https://ror.org/04bqh5m06","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Geographic Society","awardNumber":"45176R-18","funderIdentifier":"https://ror.org/04bqh5m06","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"Leakey Foundation","awardNumber":"(9 grants)","funderIdentifier":"https://ror.org/018kdpd27","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"Templeton World Charity Foundation","awardNumber":"0208","funderIdentifier":"https://ror.org/00x0z1472","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"Wenner-Gren Foundation","awardNumber":"8409 plus one unnumbered grant","funderIdentifier":"https://ror.org/04qvvhf62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Geographic Society","awardNumber":"1 unnumbered grant (co-PI Barbara Smuts)","funderIdentifier":"https://ror.org/04bqh5m06","funderIdentifierType":"ROR"},{"funderName":"Wild Capuchin Foundation*"},{"schemeUri":"https://ror.org","funderName":"University of California, Los Angeles","funderIdentifier":"https://ror.org/046rm7j60","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"University of Michigan–Ann Arbor","funderIdentifier":"https://ror.org/00jmfr291","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"Max Planck Institute for Evolutionary Anthropology","funderIdentifier":"https://ror.org/02a33b393","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"American Society of Primatologists","funderIdentifier":"https://ror.org/04j0s0m53","funderIdentifierType":"ROR"},{"schemeUri":"https://www.crossref.org/services/funder-registry/","funderName":"ARCS Foundation","funderIdentifier":"https://doi.org/10.13039/100008227","funderIdentifierType":"Crossref Funder ID"},{"schemeUri":"https://ror.org","funderName":"Sigma Xi","funderIdentifier":"https://ror.org/04nmj5x57","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.5068/D1QT17","contentUrl":null,"metadataVersion":6,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":64,"downloadCount":9,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-10-04T22:45:51Z","registered":"2023-10-04T22:45:52Z","published":null,"updated":"2026-01-28T15:26:11Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5068/d1097t","type":"dois","attributes":{"doi":"10.5068/d1097t","identifiers":[],"creators":[{"name":"Chong, Su Kong","nameType":"Personal","givenName":"Su Kong","familyName":"Chong","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-2016-9802","nameIdentifierScheme":"ORCID"}]},{"name":"Lei, Chao","nameType":"Personal","givenName":"Chao","familyName":"Lei","affiliation":["The University of Texas at Austin"],"nameIdentifiers":[]},{"name":"Lee, Seng Huat","nameType":"Personal","givenName":"Seng Huat","familyName":"Lee","affiliation":["Pennsylvania State University"],"nameIdentifiers":[]},{"name":"Jaroszynski, Jan","nameType":"Personal","givenName":"Jan","familyName":"Jaroszynski","affiliation":["Florida State University"],"nameIdentifiers":[]},{"name":"Mao, Zhiqiang","nameType":"Personal","givenName":"Zhiqiang","familyName":"Mao","affiliation":["Pennsylvania State University"],"nameIdentifiers":[]},{"name":"MacDonald, Allan H.","nameType":"Personal","givenName":"Allan H.","familyName":"MacDonald","affiliation":["The University of Texas at Austin"],"nameIdentifiers":[]},{"name":"Wang, Kang L.","nameType":"Personal","givenName":"Kang L.","familyName":"Wang","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]}],"titles":[{"title":"Data from: Anomalous Landau quantization in intrinsic magnetic topological insulators"}],"publisher":"Dryad","container":{},"publicationYear":2023,"subjects":[{"subject":"FOS: Physical sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Physical sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Electrical resistance","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Electric conductivity","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Quantum state","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[],"dates":[{"date":"2023-07-01T12:28:34Z","dateType":"Created"},{"date":"2023-07-01T12:29:56Z","dateType":"Submitted"},{"date":"2023-07-17T00:00:00Z","dateType":"Issued"},{"date":"2023-07-17T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1038/s41467-023-40383-x","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["1739911 bytes"],"formats":[],"version":"2","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"The intrinsic magnetic topological insulator, Mn(Bi1-xSbx)2Te4, has been\n identified as a Weyl semimetal with a single pair of Weyl nodes in its\n spin-aligned strong-field configuration. A direct consequence of the Weyl\n state is the layer dependent Chern number, C. Previous reports in MnBi2Te4\n thin films have shown higher states either by increasing the film\n thickness or controlling the chemical potential. A clear picture of the\n higher Chern states is still lacking as data interpretation is further\n complicated by the emergence of surface-band Landau levels under magnetic\n fields. Here, we report a tunable layer-dependent C = 1 state\n with Sb substitution by performing a detailed analysis of the quantization\n states in Mn(Bi1-xSbx)2Te4 dual-gated devices—consistent with calculations\n of the bulk Weyl point separation in the doped thin films. The observed\n Hall quantization plateaus for our thicker Mn(Bi1-xSbx)2Te4 films under\n strong magnetic fields can be interpreted by a theory of surface and bulk\n spin-polarised Landau level spectra in thin film magnetic topological\n insulators.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"1936383","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"United States Army Research Office","awardNumber":"W911NF-20-2-0166","funderIdentifier":"https://ror.org/05epdh915","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"United States Army Research Office","awardNumber":"W911NF-16-1-0472","funderIdentifier":"https://ror.org/05epdh915","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"DMR-2039351","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"DMR-1644779","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.5068/D1097T","contentUrl":null,"metadataVersion":7,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":175,"downloadCount":7,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-07-18T05:05:58Z","registered":"2023-07-18T05:05:59Z","published":null,"updated":"2026-01-28T15:19:17Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5068/d1h98v","type":"dois","attributes":{"doi":"10.5068/d1h98v","identifiers":[],"creators":[{"name":"Ryznar, Emily","nameType":"Personal","givenName":"Emily","familyName":"Ryznar","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-4003-3662","nameIdentifierScheme":"ORCID"}]},{"name":"Smith, Lauren","nameType":"Personal","givenName":"Lauren","familyName":"Smith","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Hà, Benjamin","nameType":"Personal","givenName":"Benjamin","familyName":"Hà","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Grier, Shalanda","nameType":"Personal","givenName":"Shalanda","familyName":"Grier","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Fong, Peggy","nameType":"Personal","givenName":"Peggy","familyName":"Fong","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]}],"titles":[{"title":"Data for: Functional trait variability supports the use of mean trait values and identifies tradeoffs for marine macroalgae"}],"publisher":"Dryad","container":{},"publicationYear":2023,"subjects":[{"subject":"FOS: Biological sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Community ecology","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"trait-based ecology"},{"subject":"experiments"},{"subject":"functional traits"},{"subject":"mean trait value"},{"subject":"tradeoffs"},{"subject":"Algae","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[],"dates":[{"date":"2023-06-24T23:58:41Z","dateType":"Created"},{"date":"2023-06-24T23:59:50Z","dateType":"Submitted"},{"date":"2023-06-28T00:00:00Z","dateType":"Issued"},{"date":"2023-06-28T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1111/1365-2745.14161","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["199892 bytes"],"formats":[],"version":"2","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Trait-based ecology (TBE) has proven useful in the terrestrial realm and\n beyond for collapsing ecological complexity into traits that can be\n compared and generalized across species and scales. However, TBE for\n marine macroalgae is still in its infancy, motivating research to build\n the foundation of macroalgal TBE by leveraging lessons learned from other\n systems. Our objectives were to evaluate the utility of mean trait values\n (MTVs) across species, to explore the potential for intraspecific trait\n variability, and to identify macroalgal ecological strategies by\n clustering species with similar traits and testing for bivariate\n relationships between traits. To accomplish this, we measured thallus\n toughness, a trait associated with resistance to herbivory, and tensile\n strength, a trait associated with resistance to physical disturbance, in\n eight tropical macroalgal species across up to seven sites where they were\n found around Moorea, French Polynesia. We found interspecific trait\n variation generally exceeded intraspecific variation across species.\n Further, MTV within species varied across sites, suggesting future\n research should focus on whether these traits are influenced by\n site-specific differences in biotic and abiotic drivers. Species grouped\n into three clusters representing different ecological strategies: species\n that were defended against herbivores but not strong, species that were\n strong but not defended, and species that were neither. Intraspecific\n Standardized Major Axis regressions revealed five species exhibited\n significant or marginally significant positive relationships between these\n two traits, suggesting trait syndromes within species. Only one species\n exhibited a significant intraspecific tradeoff, as indicated by a negative\n regression slope. Synthesis: Our results point to three key takeaways that\n should provide a foundation to rapidly advance development of TBE for\n macroalgae in the future. First, our evidence supports the use of MTVs for\n macroalgae. Second, we identified significant spatial variability in\n macroalgal traits may indicate an ability to respond to shifting\n environmental drivers. Third, measuring even a few traits can be a\n powerful tool to identify different ecological strategies to resist\n disturbances such as herbivory and removal by wave action.  We\n hope these novel findings motivate future research into a wider suite of\n macroalgal traits, functions, and strategies to further develop\n trait-based approaches for marine macroalgae.","descriptionType":"Abstract"},{"description":"\u003cem\u003eApproaches\u003c/em\u003e Our overall\n approach was to measure MTV and the associated variance of two functional\n traits for eight common species of coral reef macroalgae across seven\n fringing reef sites in Moorea, French Polynesia. To maximize our\n probability of capturing a wide range of interspecific variation, we chose\n a diverse set of commonly occurring algal species while to maximize our\n probability of capturing a wide range of intraspecific variability, we\n collected these algae at a diverse set of fringing reefs. We aimed to\n determine if MTV differs between species as well as within species across\n sites. Next, we identified ecological strategies used by clusters of\n species that could be used to resistant either herbivory or physical\n disturbance. Finally, we assessed the nature of bivariate trait\n relationships for individuals within each species.\n \u003cem\u003eStudy system\u003c/em\u003e  We\n conducted our study on fringing reefs of Moorea, a high volcanic island in\n the Society Islands archipelago of French Polynesia in the South Pacific.\n Moorea is approximately 18 kilometers northwest of Tahiti. We targeted\n fringing reefs for our study because, while variable, macroalgae are\n generally abundant on these reef types around Moorea.\n We chose seven fringing reef sites with abundant and diverse\n macroalgae. From easternmost to westernmost, sites included Temae (TE),\n Maharepa (MA), Gump (GU), Green Marker (GM), Hilton (HI), Sailing School\n East (SSE), and Sailing School West (SSW). While all sites were fringing\n reefs, they varied in their continuity (continuous vs. patch reef) and\n exposure to wind and waves (within a bay with anthropogenic influences or\n along the more exposed open shore), which we reasoned maximized our\n likelihood of capturing each algal species’ potential for intraspecific\n trait variation. Temae, Maharepa, and Hilton likely experience the highest\n water movement as they are on the more exposed northern or eastern shore.\n In addition, a portion of Temae’s backreef was filled to create the\n airport, resulting in substantial wave driven current (pers. obs.). In\n contrast, Gump, Sailing School East, and Sailing School West are located\n within bays and are likely more enriched with nutrients due to greater\n proximity and exposure to local agricultural land (Clausing et al., 2016;\n De’ath \u0026amp; Fabricius 2010). Finally, herbivory pressure is generally\n higher at Maharepa and Hilton compared to sites within the bays (Bergman\n et al., 2016). Temae is an MPA and appears to have abundant herbivorous\n fish communities (pers. obs.); however, we could not find any formal\n surveys. \u003cem\u003eAlgal\n collection\u003c/em\u003e We collected eight macroalgal\n species from each of our seven sites where they were available and noted\n the life cycle type. If possible, we also noted the generation ID of the\n thallus collected as ploidy has been shown to influence algal responses to\n environmental drivers in some cases (see Krueger-Hadfield 2020; Table 1)\n and therefore may influence trait distributions four brown algae (class\n Phaeophyceae) Dictyota bartayresiana (Dictyotales), Padina boryana\n (Dictyotales; lightly calcified), Sargassum pacificum (Fucales), and\n Turbinaria ornata (Fucales), three red algae (division Rhodophyta)\n Acanthophora spicifera (Ceramiales), Amansia rhodantha (Ceramiales), and\n Galaxaura divaricata (Nemaliales; heavily calcified), and one green alga\n (division Chlorophyta) Halimeda opuntia (Bryopsidales; heavily calcified).\n D. bartayresiana, P. boryana, and A. spicifera tend to occur in more\n sheltered habitats (Guiry \u0026amp; Guiry 2022) and can be found in many\n fringing reef habitats in Moorea (pers. obs.). A. rhodantha, G.\n divaricata, H. opuntia, T. ornata, and S. pacificum can occur in sheltered\n areas to more high-energy, exposed areas of the reef, with T. ornata and\n S. pacificum the only species able to persist on the high-energy reef\n crest. H. opuntia, A. spicifera, T. ornata, S. pacificum are able to\n reproduce asexually via fragmentation (Kilar \u0026amp; McLachlan 1986;\n Walters et al., 2002), and some studies suggest species in the Dictyota\n genus are capable of reproduction via fragmentation as well (Herren et\n al., 2006). From each site, approximately 15 whole,\n macroscopic thalli that appeared healthy (no decay visible) for each\n species were haphazardly collected via snorkel at approximately 1- to 2-m\n depth. Although our target was n=10, extra thalli were collected in case\n trait measurements needed to be repeated (see below). Macroalgae were\n collected and tested the same day between April 21 and May 15, 2019,\n usually completing approximately one site per day. All collected algal\n thalli were immediately transported back to the UC Berkeley Gump South\n Pacific Research Station and placed in an outdoor flow-through water\n table. Using ambient seawater, thalli were cleaned of sediment and\n associated organisms and processed within approximately six hours of\n collection. \u003cem\u003eThallus\n toughness\u003c/em\u003e  To measure thallus toughness\n (“weight to penetrate”) for each species from each site, we attempted to\n collected similar-sized individuals for each species and sampled blades or\n tissue from the middle of each thallus (where applicable, see below), as\n all of the collected algal species exhibit apical growth. Using this\n location across all species maximized our ability to achieve a\n representative average for thallus toughness as it limited sampling tissue\n that may differ in toughness due to differences in age. For example, for\n more simple growth forms that were primarily blade-like (D. bartayresiana\n and P. boryana) or mainly composed of branches without distinct stipes (A.\n spicifera, G. divaricata, and H. opuntia), we sampled tissue from the\n blade or branch in the middle of each thallus. For more complex growth\n forms with distinct stipes and blades (S. pacificum, T. ornata) or a\n midrib (A. rhodantha), we sampled individual blades from the middle of\n each thallus and tested the middle of each blade (avoiding the midrib for\n A. rhodantha). Despite our best attempts to minimize variation in trait\n values due to algal age/size, it is possible that sites could differ in\n age of the algal communities (i.e., sites with new algal growth vs. no new\n growth), which would contribute to inter-site trait variation.\n To measure thallus toughness for each species, we secured each\n algal subsample below a penetrometer so the needle of the penetrometer\n gently rested on the surface of the thallus (adapted from Duffy \u0026amp;\n Hay 1991; Bittick et al., 2016; Ryznar et al., 2021). Then, we\n sequentially added weight to the penetrometer until the needle just\n pierced the thallus surface. This was repeated for 10 different replicate\n thalli for each species at each site. \u003cem\u003eThallus\n tensile strength\u003c/em\u003e  To measure thallus tensile\n strength (“weight to break”), we used 10 whole replicate thalli (apex\n through holdfast) of similar sizes for each species from each site. The\n basal end at the holdfast (where one was apparent) of each thallus was\n secured to a spring scale while the apical end was pulled until the\n thallus broke. The force (weight) required to break the thallus was used\n as a measure of tensile strength. Data were discarded if the thallus broke\n where it was secured at the base or where it was being pulled near the\n apical end.","descriptionType":"Methods"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Science Foundation","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"University of California, Los Angeles","funderIdentifier":"https://ror.org/046rm7j60","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"Phycological Society of America","funderIdentifier":"https://ror.org/00a4fk439","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.5068/D1H98V","contentUrl":null,"metadataVersion":7,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":150,"downloadCount":9,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-06-28T13:38:32Z","registered":"2023-06-28T13:38:33Z","published":null,"updated":"2026-01-28T15:12:56Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5068/d1n09c","type":"dois","attributes":{"doi":"10.5068/d1n09c","identifiers":[],"creators":[{"name":"Youngflesh, Casey","nameType":"Personal","givenName":"Casey","familyName":"Youngflesh","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-6343-3311","nameIdentifierScheme":"ORCID"}]},{"name":"Montgomery, Graham","nameType":"Personal","givenName":"Graham","familyName":"Montgomery","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]},{"name":"Saracco, James","nameType":"Personal","givenName":"James","familyName":"Saracco","affiliation":["The Institute for Bird Populations"],"nameIdentifiers":[]},{"name":"Miller, David","nameType":"Personal","givenName":"David","familyName":"Miller","affiliation":["Pennsylvania State University"],"nameIdentifiers":[]},{"name":"Guralnick, Robert","nameType":"Personal","givenName":"Robert","familyName":"Guralnick","affiliation":["University of Florida"],"nameIdentifiers":[]},{"name":"Hurlbert, Allen","nameType":"Personal","givenName":"Allen","familyName":"Hurlbert","affiliation":["University of North Carolina at Chapel Hill"],"nameIdentifiers":[]},{"name":"Siegel, Rodney","nameType":"Personal","givenName":"Rodney","familyName":"Siegel","affiliation":["The Institute for Bird Populations"],"nameIdentifiers":[]},{"name":"LaFrance, Raphael","nameType":"Personal","givenName":"Raphael","familyName":"LaFrance","affiliation":["University of Florida"],"nameIdentifiers":[]},{"name":"Tingley, Morgan","nameType":"Personal","givenName":"Morgan","familyName":"Tingley","affiliation":["University of California, Los Angeles"],"nameIdentifiers":[]}],"titles":[{"title":"Demographic consequences of phenological asynchrony for North American songbirds"}],"publisher":"Dryad","container":{},"publicationYear":2023,"subjects":[{"subject":"FOS: Biological sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Phenological mismatch"},{"subject":"Phenology"},{"subject":"Demography","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"FOS: Sociology","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"green-up"},{"subject":"Climate change","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Global Change"},{"subject":"Birds","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[],"dates":[{"date":"2023-06-14T15:09:55Z","dateType":"Submitted"},{"date":"2023-06-21T00:00:00Z","dateType":"Issued"},{"date":"2023-06-21T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1073/pnas.2221961120","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["281037 bytes"],"formats":[],"version":"1","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Changes in phenology in response to ongoing climate change have been\n observed in numerous taxa around the world. Differing rates of\n phenological shifts across trophic levels have led to concerns that\n ecological interactions may become increasingly decoupled in time, with\n potential negative consequences for populations. Despite widespread\n evidence of phenological change and a broad body of supporting theory,\n large-scale multi-taxa evidence for demographic consequences of\n phenological asynchrony remains elusive. Using data from a\n continental-scale bird banding program, we assess the impact of\n phenological dynamics on avian breeding productivity in 41 species of\n migratory and resident North American birds breeding in and around\n forested areas. We find strong evidence for a phenological optimum where\n breeding productivity decreases in years with both particularly early or\n late phenology and when breeding occurs early or late relative to local\n vegetation phenology. Moreover, we demonstrate that landbird breeding\n phenology did not keep pace with shifts in the timing of vegetation\n green-up over a recent 18-year period, even though avian breeding\n phenology has tracked green-up with greater sensitivity than arrival for\n migratory species. Species whose breeding phenology more closely tracked\n green-up tend to migrate shorter distances (or are resident over the\n entire year) and breed earlier in the season. These results showcase the\n broadest-scale evidence yet of the demographic impacts of phenological\n change. Future climate change-associated phenological shifts will likely\n result in a decrease in breeding productivity for most species, given that\n bird breeding phenology is failing to keep pace with climate change.","descriptionType":"Abstract"},{"description":"Bird capture data were collected as part of the Monitoring Avian\n Productivity and Survivorship (MAPS) program, a collaborative long-term\n bird-banding project operating across North America. Data were obtained\n from 179 banding stations. Each banding station consisted of 6–20 mist\n nets operated approximately every ten days beginning as early as May 1\n (start date varying slightly by location) through August 8 (ordinal dates\n 121–220 in a non-leap year), which span the breeding season for most birds\n in North America. Only species/locations/years with at least 15 total\n captures, at least 5 of those being juveniles, species/locations with at\n least 5 years of data, and species with at least 15 locations/years of\n data were considered. Bird breeding phenology was\n calculated using the capture dates of juvenile birds at MAPS stations.\n This measure of breeding phenology is indicative of the time of year at\n which young birds are fledging. For each species, at each location, in\n each year, our metric of breeding phenology was the mean date of first\n capture across all juveniles captured at that station that year. Following\n Saracco et al. 2019, we exclude subsequent captures of the same individual\n after its first capture.  For each station, effort\n hours was calculated as the proportion of net-hours (total area of mist\n nets multiplied by the number of hours that these nets were deployed)\n during the period where juveniles were captured, excluding the first 2.5%\n of juvenile captures to remove outliers, following the procedure used by\n Saracco et al. 2019.  Climate data was downloaded from\n climatena.ca. \u003cstrong\u003eWorks\n cited\u003c/strong\u003e J. F. Saracco, R. B. Siegel, L.\n Helton, S. L. Stock, D. F. DeSante, Phenology and productivity in a\n montane bird assemblage: Trends and responses to elevation and climate\n variation. Glob. Change Biol. 25, 985–996 (2019).","descriptionType":"Methods"},{"description":"Data from the Monitoring Avian Productivity and Survivorship\n (MAPS) program are curated and managed by The Institute for Bird\n Populations and were queried from the MAPS database on\n 2019-10-16.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"EF 1703048","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"EF 2033263","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"EF 1702708","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"University of California, Los Angeles","funderIdentifier":"https://ror.org/046rm7j60","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"Michigan State University","funderIdentifier":"https://ror.org/05hs6h993","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.5068/D1N09C","contentUrl":null,"metadataVersion":7,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":199,"downloadCount":19,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-06-21T16:00:16Z","registered":"2023-06-21T16:00:17Z","published":null,"updated":"2026-01-28T15:03:36Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5068/d1rt1j","type":"dois","attributes":{"doi":"10.5068/d1rt1j","identifiers":[],"creators":[{"name":"Serra Silva, Ana","nameType":"Personal","givenName":"Ana","familyName":"Serra Silva","affiliation":["University College London"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8020-3227","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Extended Lissamphibia: A tale of character non-independence, analytical parameters, and islands of trees"}],"publisher":"Dryad","container":{},"publicationYear":2024,"subjects":[{"subject":"FOS: Biological sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Lissamphibia","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Taxonomic instability"},{"subject":"Large island bias"},{"subject":"phylogenetic networks"},{"subject":"Character non-independence"},{"subject":"Inapplicable data"},{"subject":"Paleontology","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[],"dates":[{"date":"2023-12-08T17:41:07Z","dateType":"Created"},{"date":"2024-02-20T14:24:06Z","dateType":"Submitted"},{"date":"2024-03-05T00:00:00Z","dateType":"Issued"},{"date":"2024-03-05T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1093/sysbio/syab015","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementedBy","relatedIdentifier":"10.6084/m9.figshare.20085692.v1","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1073/pnas.2001424117","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1073/pnas.1706752114","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1080/14772019.2024.2321620","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["301805578 bytes"],"formats":[],"version":"6","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"The age, content, and interorder relationships of crown Lissamphibia\n remain a debated topic in vertebrate systematics. Recent phylogenetic\n analyses of fossil amphibians were used to propose an extended\n Lissamphibia, with Anura and Caudata nested in Dissorophoidea and with\n Gymnophiona nested in Stereospondyli, but this hypothesis was not\n supported by subsequent studies on updated matrices. In the parsimony\n context, the extended Lissamphibia hypothesis was shown to result from the\n effects of large island bias on the majority-rule consensus, which masked\n the presence of topologies supporting the restricted Lissamphibia\n hypothesis, with all extant orders nested in Dissorophoidea or\n Stereospondyli. Re-analysing this dataset taking into account the presence\n of inapplicable and polymorphic character states, and revising the scores\n for logically non-independent characters, shows that the phylogenies\n inferred from the morphological data matrix used to propose the extended\n Lissamphibia hypothesis are not robust to changes in analytical parameters\n and that great care should be taken when analysing fossil amphibian\n datasets. With the set of most parsimonious trees inferred from the\n unrevised matrix used to propose the extended Lissamphibia hypothesis, I\n also demonstrate that the phenomenon of large island bias extends to\n phylogenetic networks, but not to topology-based tests of taxonomic\n instability that do not rely on split-frequencies.","descriptionType":"Abstract"},{"description":"# Extended Lissamphibia: A tale of character non-independence, analytical\n parameters, and islands of trees --- Supplementary data for: Serra Silva,\n A. (2024). Extended Lissamphibia: A tale of character non-independence,\n analytical parameters, and islands of trees. *Journal of Systematic\n Palaeontology*, *22*(1).\n [https://doi.org/10.1080/14772019.2024.2321620](https://doi.org/10.1080/14772019.2024.2321620). ## Contents of each folder * instabilityTests.zip: contains tree files used for the leaf stability and RBIC analyses and their output * bayesianTrees folder * PardoCharacters.nex.trprobs is the Bayesian tree distribution from an analysis of Pardo et al.'s (2017) data matrix and was re-used from Serra Silva and Wilkinson (2021) * RnR-lsi_leafStabilityIndices_allTrees.txt is the output of the leaf stability analyses run in RogueNaRok * RogueNaRok_droppedRogues_*_allTrees.txt is the output of the RBIC analyses run in RogueNaRok * parsimonyTrees folder contains tree files used for and the output for the leaf stability and RBIC analyses for all most parsimonious trees inferred from the Pardo et al.'s (2017) data matrix and the tree islands identified in Serra Silva and Wilkinson (2021) * all subfolders have the following organisation * *.newick is the input file to RogueNaRok and contains the set of most parsimonious trees to be tested * LeafStabilityIndices_*.txt is the output of the leaf stability analyses run in RogueNaRok * RogueNaRok_droppedRogues_*.txt is the output of the RBIC analyses if the dropset was non-empty * phylogeneticNetworks.zip contains the eps and nex output files from the consensus and reticulation network analyses run in SplitsTree, the input for these analyses was the instabilityTests.zip/parsimonyTrees/AllTrees/mytrees.newick file. For details on the analyses see the main text. * phylogeneticReanylises.zip contains the input matrices, scripts and output for all phylogenetic analyses * phylogeneticReanylises.zip/Pardo folder * Morphy folder contains the input and output of the morphy analyses and the set of x-RF islands identified from the set of inferred trees * MrBayes_Partitioned folder contains the input, settings and output of an explicitlyt partitioned MrBayes analysis, details in the MS * PAUP folder contains a set of subfolders each for a different parsimony analysis, all subfolders contain the input, script (PAUP_morphoblock.nex) and output for each analysis (GapAsFifthState, GapAsFifthState+polymorphism, plolymorphism, bootstrap, postInferenceJackknife, matrix jackknifing (delete-half and Farris), and first-order taxon jackknifing on *Chinlestegophis*, *Eocaecilia* and *Rileymillerus*), details given in the MS and Paup_morphoblock.nex file in each folder * phylogeneticReanylises.zip/Schoch folder * PardoChars folder contains the input, script and output for a reanalysis of Schoch et al.'s 2021 matrix with only the characters shared with Pardo et al. 2019 * Schoch_fullmatrix contains a set of subfolders each for a different parsimony analysis, all subfolders contain input, script and output files * nonIndependentCharRecoding * recoding.r script to inspect and recode the non-independent characters in the Pardo and Schoch matrices * *Characters.nex are the original matrices * *Recoded.nex are the recoded matrices * subfolders contain the input, script and output files for various parsimony analyses ## Sharing/Access information Data also available in a Figshare repository (link in Related Works). If you use the original data matrices or island sets please cite: Pardo, J.D., Small, B.J. and Huttenlocker, A.K., 2017. Stem caecilian from the Triassic of Colorado sheds light on the origins of Lissamphibia. *Proceedings of the National Academy of Sciences*, 114(27), pp.E5389-E5395. Schoch, R.R., Werneburg, R. and Voigt, S., 2020. A Triassic stem-salamander from Kyrgyzstan and the origin of salamanders. *Proceedings of the National Academy of Sciences*, 117(21), pp.11584-11588. Silva, A.S. and Wilkinson, M., 2021. On defining and finding islands of trees and mitigating large island bias. *Systematic Biology*, 70(6), pp.1282-1294.","descriptionType":"TechnicalInfo"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"Natural Environment Research Council","awardNumber":"NE/L002434/1","funderIdentifier":"https://ror.org/02b5d8509","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.5068/D1RT1J","contentUrl":null,"metadataVersion":6,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":35,"downloadCount":9,"referenceCount":1,"citationCount":3,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-10-04T22:45:20Z","registered":"2023-10-04T22:45:21Z","published":null,"updated":"2026-01-28T15:03:13Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}}],"meta":{"total":224,"totalPages":9,"page":1},"links":{"self":"https://api.datacite.org/dois?prefix=10.5068","next":"https://api.datacite.org/dois?page%5Bnumber%5D=2\u0026page%5Bsize%5D=25\u0026prefix=10.5068"}}