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POS1552-HPR A SYNTHESIS OF GUIDANCE AVAILABLE FOR ASSESSING METHODOLOGICAL QUALITY AND GRADING OF EVIDENCE FROM QUALITATIVE RESEARCH TO INFORM CLINICAL RECOMMENDATIONS: A SYSTEMATIC REVIEW

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Annals of the Rheumatic Diseases 2022年 81卷 1120-1121页

作者： M. Sekhon A. De Thurah G.E. Fragoulis T. Stamm T.P.M. Vliet Vlieland B.A. Esbensen H. Lempp L. Bearne M. Kouloumas P. Pchelnikova T.W. Swinnen C. Blunt R.J.O. Ferreira L. Carmona E. Nikiphorou Kingston and St George's UNIVERSITY of London Centre for Applied Health and Social Care Research London United Kingdom King's College London School of Life Course & Population Sciences London United Kingdom Aarhus University Hospital Clinical Medicine Aarhus Denmark National and Kapodistrian University of Athens Joint Academic Rheumatology Program Athens Greece Medical University of Vienna Section for Outcomes Research Center for Medical Statistics Informatics and Intelligent Systems Vienna Austria Leiden University Medical Center (LUMC) Dept of Orthopaedics Rehabilitation and Physical Therapy Leiden Netherlands Copenhagen University Hospital Rigshospitalet Copenhagen Center for Arthritis Research Center for Rheumatology and Spine Diseases København Denmark Copenhagen University Department of Clinical Medicine København Denmark King's College London Guy's Campus School of Life Course & Population Sciences London United Kingdom Cyprus league Against Rheumatism Cyprus Federation of Patients' Assosiations Nicosia Cyprus PARE Research Partner EULAR Zurich Switzerland Katholieke Universiteit Leuven Research Group for Musculoskeletal Rehabilitation Leuven Belgium London School of Economics and Political Science Medicine Philosophy Interdisciplinary Studies London United Kingdom Centro Hospitalar e Universitário de Coimbra Rheumatology Department Coimbra Portugal Residence of Coimbra Nursing School Health Sciences Research Unit: Nursing Coimbra Portugal Instituto de Salud Musculoesquelética Inmusc Madrid Spain King's College London Centre for Rheumatic Diseases School of Immunology and Microbial Sciences London United Kingdom

Background Qualitative research is crucial to understand key stakeholders experiences and perspectives of care and health services. However, there is a lack of explicit frameworks and guidelines about how best to use qualitative evidence to formulate clinical recommendations. Part of the problem includes uncertainties about the contributions of qualitative research to the evidence, and the empirical and theoretical basis for appraising and synthesizing qualitative evidence in a standardized manner. In addition, most existing grading systems of qualitative research originates from quantitative research, and there is no clear guidance about how to incorporate qualitative research into the evidence hierarchy. Objectives To conduct a systematic literature review (SLR) to answer two research questions (RQ): RQ1) What guidance (e.g., tools, checklists, frameworks) exists to assess the methodological quality of qualitative research employed to inform clinical recommendations?; RQ2) What methods exist specifically to grade levels of evidence for qualitative research? Methods The protocol for this review was registered on *** (reviewregistry1240). Electronic databases (PubMed/Medline, EMBASE, Web of Science, COCHRANE, Emcare, PsycINFO, ERIC, Academic Search Premier, Sociological Abstracts, ProQuest Dissertations and Thesis Global) were searched for published and unpublished studies. Searches were completed from inception to 23 rd October 2020. No restrictions were applied to clinical population. Eligible studies for both questions included primary articles and guideline documents available in English, describing the: i) development; ii) application of validated tools (e.g., checklists); iii) guidance on how to assess methodological quality of qualitive research and iv) guidance on how to grade levels of qualitative evidence. Opinion pieces and conference abstracts were excluded. Manual searches of the reference lists of full text articles were conducted.

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Clinical validation of scoring systems of postoperative pancreatic fistula after pancreatoduodenectomy: applicability to Eastern cohorts?

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Hepatobiliary Surgery and Nutrition 2019年第3期8卷 211-218页

作者： Jae Seung Kang Taesung Park Youngmin Han Seungyeon Lee Jae Ri Kim Hongbeom Kim Wooil Kwon Sun-Whe Kim Jin Seok Heo Seong Ho Choi Dong Wook Choi Song Cheol Kim Tae Ho Hong Dong Sup Yoon Joon Seong Park Sang Jae Park Sung-Sik Han Sae-Byeol Choi Joo Seop Kim Chang-Sup Lim Jin-Young Jang Department of Surgery and Cancer Research Institute Seoul National University College of MedicineSeoulRepublic of Korea Department of Statistics and Interdisciplinary Program in Biostatistics Seoul National UniversitySeoulRepublic of Korea Department of Mathematics and Statistics Sejung UniversitySeoulRepublic of Korea Department of Surgery Samsung Medical CenterSungkyunkwan University School of MedicineSeoulRepublic of Korea Division of Hepatobiliary and Pancreatic Surgery Department of SurgeryUlsan University College of Medicine and Asan Medical CenterSeoulRepublic of Korea Department of Surgery Seoul St.Mary’s HospitalThe Catholic University of KoreaCollege of MedicineSeoulRepublic of Korea Pancreatobiliary Cancer Clinic Department of SurgeryGangnam Severance HospitalYonsei University College of MedicineSeoulRepublic of Korea Center for Liver Cancer National Cancer CenterGyeonggidoRepublic of Korea Department of Surgery Korea University Guro HospitalKorea University College of MedicineSeoulRepublic of Korea Department of Surgery Kangdong Sacred Heart HospitalHallym University College of MedicineSeoulRepublic of Korea Department of Surgery Seoul Metropolitan Government-Seoul National University Boramae Medical CenterSeoul National University College of MedicineSeoulRepublic of Korea

Background:Although several prediction models for the occurrence of postoperative pancreatic fistula(POPF)after pancreatoduodenectomy(PD)exist,all were established using Western ***-scale external validation studies in Eastern cohorts that consider demographic variables including lower body mass index(BMI)are *** purpose of this study was to externally validate POPF prediction models using nationwide large-scale Korean ***:Nine tertiary university hospitals in the Republic of Korea ***'preoperative characteristics,intraoperative factors,and pathologic findings were *** grades were determined according to the 2016 International Study Group on Pancreatic Surgery *** POPF risk models(Callery,Roberts,and Mungroop)were selected for external ***:A total of 1,898 PD patients were enrolled.A non-pancreatic disease diagnosis[hazard ratio(HR),1.856;95%confidence interval(CI),1.223–2.817;P=0.004),higher preoperative BMI(HR,1.069;95%CI,1.019–1.121;P=0.006),and soft pancreatic texture(HR,1.859;95%CI,1.264–2.735;P=0.002)were independent risk factors for clinically relevant POPF(CR-POPF).The area under the receiver operating characteristic curve(AUC)values were 0.61,0.64,and 0.63 on the Callery,Roberts,and Mungroop models,respectively;all were lower than those published in each external validation ***:Western POPF prediction models performed less well when applied to Korean ***,a large-scale Eastern-specific and externally validated POPF prediction model is needed.

关键词： Pancreatic fistula pancreatoduodenectomy(PD) predictive score

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Clinical validation of the 2017 international consensus guidelines on intraductal papillary mucinous neoplasm of the pancreas

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Pancreatology 2019年 19卷 S119-S119页

作者： Jae Seung Kang Taesung Park Youngmin Han Seungyeon Lee Heeju Lim Hyeong Seok Kim Se Hyung Kim Wooil Kwon Sun-Whe Kim Jin-Young Jang Seoul National University College of Medicine Surgery Seoul South Korea Seoul National University Statistics and Interdisciplinary Program in Biostatistics Seoul South Korea Sejong university Mathematics and Statistics Seoul South Korea Seoul National University College of Medicine Radiology Seoul South Korea

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Clinical validation of the 2017 international consensus guidelines on intraductal papillary mucinous neoplasm of the pancreas

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HPB 2019年 21卷 S228-S228页

作者： Jae Seung Kang Taesung Park Youngmin Han Seungyeon Lee Heeju Lim Hyeong Seok Kim Se Hyung Kim Wooil Kwon Sun-Whe Kim Jin-Young Jang Department of Surgery and Cancer Research Institute Seoul National University Hospital Republic of Korea Department of Statistics and Interdisciplinary Program in Biostatistics Seoul National University Republic of Korea Department of Mathematics and Statistics Sejong University Republic of Korea Department of Radiology Seoul National University Hospital Republic of Korea

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Multiwavelength Constraints on the Origin of a Nearby Repeating Fast Radio Burst Source in a Globular Cluster

arXiv

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arXiv 2023年

作者： Pearlman, Aaron B. Scholz, Paul Bethapudi, Suryarao Hessels, Jason W.T. Kaspi, Victoria M. Kirsten, Franz Nimmo, Kenzie Spitler, Laura G. Fonseca, Emmanuel Meyers, Bradley W. Stairs, Ingrid H. Tan, Chia Min Bhardwaj, Mohit Chatterjee, Shami Cook, Amanda M. Curtin, Alice P. Dong, Fengqiu Adam Eftekhari, Tarraneh Gaensler, B.M. Güver, Tolga Kaczmarek, Jane Leung, Calvin Masui, Kiyoshi W. Michilli, Daniele Prince, Thomas A. Sand, Ketan R. Shin, Kaitlyn Smith, Kendrick M. Tendulkar, Shriharsh P. Department of Physics McGill University MontréalQC Canada Trottier Space Institute McGill University MontréalQC Canada Division of Physics Mathematics and Astronomy California Institute of Technology PasadenaCA United States Dunlap Institute for Astronomy and Astrophysics University of Toronto TorontoON Canada Department of Physics and Astronomy York University TorontoON Canada Max-Planck-Institut für Radioastronomie Bonn Germany Anton Pannekoek Institute for Astronomy University of Amsterdam Amsterdam Netherlands ASTRON Netherlands Institute for Radio Astronomy Dwingeloo Netherlands Department of Space Earth and Environment Chalmers University of Technology Onsala Space Observatory Onsala Sweden MIT Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA United States Department of Physics and Astronomy West Virginia University MorgantownWV United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV United States International Centre for Radio Astronomy Research Curtin University BentleyWA Australia Department of Physics and Astronomy University of British Columbia VancouverBC Canada McWilliams Center for Cosmology Department of Physics Carnegie Mellon University PittsburghPA United States Cornell Center for Astrophysics and Planetary Science Cornell University IthacaNY United States David A. Dunlap Department of Astronomy and Astrophysics University of Toronto TorontoON Canada Center for Interdisciplinary Exploration and Research in Astrophysics Department of Physics and Astronomy Northwestern University EvanstonIL United States Department of Astronomy and Astrophysics University of California Santa Cruz Santa CruzCA United States Department of Astronomy and Space Sciences Science Faculty Istanbul University Istanbul Turkey Istanbul University Observatory Research and Application Center Istanbul University Istanbul T

The precise origins of fast radio bursts (FRBs) remain unknown. Multiwavelength observations of nearby FRB sources can provide important insights into the enigmatic FRB phenomenon. Here, we present results from a sensitive, broadband X-ray and radio observational campaign of FRB 20200120E, the closest known extragalactic repeating FRB source (located 3.63 Mpc away in an ∼10-Gyr-old globular cluster). We place deep limits on the persistent and prompt X-ray emission from FRB 20200120E, which we use to constrain possible origins for the source. We compare our results with various classes of X-ray sources, transients, and FRB models. We find that FRB 20200120E is unlikely to be associated with ultraluminous X-ray bursts, magnetar-like giant flares, or an SGR 1935+2154-like intermediate flare. Although other types of bright magnetar-like intermediate flares and short X-ray bursts would have been detectable from FRB 20200120E during our observations, we cannot entirely rule them out as a class. We show that FRB 20200120E is unlikely to be powered by an ultraluminous X-ray source or a young extragalactic pulsar embedded in a Crab-like nebula. We also provide new constraints on the compatibility of FRB 20200120E with accretion-based FRB models involving X-ray binaries. These results highlight the power of multiwavelength observations of nearby FRBs for discriminating between FRB models. © 2023, CC BY.

关键词： X-ray astronomy

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Elastic restricted Boltzmann machines for cancer data analysis

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Frontiers of Electrical and Electronic Engineering in China 2017年第2期5卷 159-172页

作者： Sai Zhang Muxuan Liang Zhongjun Zhou Chen Zhang Ning Chen Ting Chen Jianyang Zeng Institute for Interdisciplinary Information Sciences Tsinghua University Beijing 100084 China Department of Statistics University of Wisconsin-Madison Madison WI 53706-1685 USA Bioinformatics Division TNLIST Department of Computer Science and Technology Tsinghua University Beijing 100084 China Program in Computational Biology and Bioinformatics Univesity of Southern California Los Angeles CA 90089 USA

Background： Restricted Boltzmann machines （RBMs） are endowed with the universal power of modeling （binary） joint distributions. Meanwhile, as a result of their confining network structure, training RBMs confronts less difficulties when dealing with approximation and inference issues. But little work has been developed to fully exploit the capacity of these models to analyze cancer data, e.g., cancer genomic, transcriptomic, proteomic and epigenomic data. On the other hand, in the cancer data analysis task, the number of features/predictors is usually much larger than the sample size, which is known as the ＇~ 〉〉 N＂ problem and is also ubiquitous in other bioinformatics and computational biology fields. The ＂p 〉〉 N＂ problem puts the bias-variance trade-off in a more crucial place when designing statistical learning methods. However, to date, few RBM models have been particularly designed to address this issue. Methods： We propose a novel RBMs model, called elastic restricted Boltzmann machines （eRBMs）, which incorporates the elastic regularization term into the likelihood function, to balance the model complexity and sensitivity. Facilitated by the classic contrastive divergence （CD） algorithm, we develop the elastic contrastive divergence （eCD） algorithm which can train eRBMs efficiently. Results： We obtain several theoretical results on the rationality and properties of our model. We further evaluate the power of our model based on a challenging task -- predicting dichotomized survival time using the molecular profiling of tumors. The test results show that the prediction performance of eRBMs is much superior to that of the state-of-the-art methods. Conclusions： The proposed eRBMs are capable of dealing with the ＂p 〉〉 N＂ problems and have superior modeling performance over traditional methods. Our novel model is a promising method for future cancer data analysis.

关键词： RBMs regularization cancer data analysis survival time prediction

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GLOBAL OCEANS

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Bulletin of the American Meteorological Society 2024年第8期105卷 S156-S213页

作者： Johnson, G.C. Lumpkin, R. NOAA/OAR Pacific Marine Environmental Laboratory Seattle Washington NOAA/OAR Atlantic Oceanographic and Meteorological Laboratory Miami Florida NOAA/OAR Physical Sciences Laboratory Boulder Colorado KBR Inc. Greenbelt Maryland NASA Goddard Space Flight Center Greenbelt Maryland NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland Cooperative Institute for Climate Ocean and Ecosystem Studies University of Washington Seattle Washington Universities Space Research Association Columbia Maryland College of Marine Science University of South Florida St. Petersburg Florida School of Ocean and Earth Science University of Southampton Southampton United Kingdom International Center for Climate and Environment Sciences Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China Rosenstiel School of Marine Atmospheric and Earth Science University of Miami Miami Florida School of Earth and Atmospheric Sciences Georgia Institute of Technology Atlanta Georgia Science Systems and Applications Inc. Lanham Maryland ADNET Systems Inc. Hampton Virginia Department of Atmospheric and Oceanic Sciences University of Colorado Boulder Boulder Colorado Scripps Institution of Oceanography University of California San Diego La Jolla California Cooperative Institute for Marine and Atmospheric Studies University of Miami Miami Florida Cooperative Institute for Satellite Earth System Studies North Carolina State University Asheville North Carolina Center for Coastal Physical Oceanography Old Dominion University Norfolk Virginia Australian Antarctic Program Partnership Institute for Marine and Antarctic Studies Australian Research Council Centre of Excellence for Climate Extremes University of Tasmania Hobart Australia NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Department of Atmosphere Ocean an

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Topological data analysis can extract subgroups with high incidence rates of Type 2 diabetes

Topological data analysis can extract subgroups with high in...

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IEEE International Conference on Bioinformatics and Biomedicine

作者： Hyung Sun Kim Changwoo Yi Yongkang Kim Uhnmee Park Woong Kook Hyuk Kim Taesung Park Interdisciplinary Program in bioinformatics Seoul National University The Research Institute of Basic Sciences College of Natural Sciences Seoul National University Department of Statistics Seoul National University Department of Bioscience and biotechnology University of Suwon Department of Mathematical Sciences and Institute for Mathematical Data Analytics Research Center Seoul National University Department of Mathematical Sciences Seoul National University

ISBN: (纸本)9781538654897;9781538654880

Type 2 diabetes (T2D) is now a rapidly increasing, worldwide scourge, and the identification of genetic contributors is vital. However, current analyses of multiple, disease-contributing factors, and their combined interactions, remains quite difficult, using traditional approaches. Topological data analysis (TDA) shows what shape a data set can have, facilitating clustering analysis, by determining which components are close to each other. Thus, TDA can generate a network, using single-nucleotide polymorphism (SNP) data, revealing the genetic relatedness of specific individuals, and can derive multiple ordered subgroups, from one with a low patient concentration, to one with a high patient concentration. Since it is widely accepted that T2D pathogenesis is affected by multiple genetic factors, we performed TDA on T2D data from the Korea Association REsource (KARE) project, a population-based, genome-wide association study of the Korean adult population. Since KARE data contains follow-up information about the incidence of T2D, we compared the T2D status of each individual, at baseline, with that of 10 years later. For the TDA network-driven subgroups, ordered by prevalence, we compared the T2D incidence rate, after 10 years, for individuals initially without T2D. As a result, we found that the TDA network-driven, ordered subgroups had significantly increased incidence rates, linearly correlated with prevalence (p-value = 0.006914). Our results demonstrate the usefulness of TDA in both identifying genetic contributors (e.g., SNPs), and their interrelationships, in the pathology of complex diseases.

关键词： Data analysis Genetics Bioinformatics Diabetes Diseases Conferences Data mining

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Correction to: binomialRF: interpretable combinatoric efficiency of random forests to identify biomarker interactions

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BMC bioinformatics 2020年第1期21卷 495页

作者： Samir Rachid Zaim Colleen Kenost Joanne Berghout Wesley Chiu Liam Wilson Hao Helen Zhang Yves A Lussier Center for Biomedical Informatics and Biostatistics University of Arizona Health Sciences 1230 N. Cherry Ave Tucson AZ 85721 USA. The Graduate Interdisciplinary Program in Statistics The University of Arizona 617 N. Santa Rita Ave Tucson AZ 85721 USA. College of Medicine Tucson 1501 N. Campbell Ave Tucson AZ 85721 USA. Center for Biomedical Informatics and Biostatistics University of Arizona Health Sciences 1230 N. Cherry Ave Tucson AZ 85721 USA. hzhang@math.arizona.edu. The Graduate Interdisciplinary Program in Statistics The University of Arizona 617 N. Santa Rita Ave Tucson AZ 85721 USA. hzhang@math.arizona.edu. Department of Mathematics College of Sciences The University of Arizona 617 N. Santa Rita Ave Tucson AZ 85721 USA. hzhang@math.arizona.edu. Center for Biomedical Informatics and Biostatistics University of Arizona Health Sciences 1230 N. Cherry Ave Tucson AZ 85721 USA. Lussier.Y@***. The Graduate Interdisciplinary Program in Statistics The University of Arizona 617 N. Santa Rita Ave Tucson AZ 85721 USA. Lussier.Y@***. College of Medicine Tucson 1501 N. Campbell Ave Tucson AZ 85721 USA. Lussier.Y@***. The Center for Applied Genetic and Genomic Medicine 1295 N. Martin Tucson AZ 85721 USA. Lussier.Y@***. The University of Arizona Cancer Center 3838 N. Campbell Ave Tucson AZ 85721 USA. Lussier.Y@***. The University of Arizona BIO5 Institute 1657 E. Helen Street Tucson AZ 85721 USA. Lussier.Y@***.

An amendment to this paper has been published and can be accessed via the original article.

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Climate, food and humans predict communities of mammals in the United States

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Diversity and Distributions 2024年第9期30卷 e13900-e13900页

作者： Kays, Roland Snider, Matthew H. Hess, George Cove, Michael V. Jensen, Alex Shamon, Hila McShea, William J. Rooney, Brigit Allen, Maximilian L. Pekins, Charles E. Wilmers, Christopher C. Pendergast, Mary E. Green, Austin M. Suraci, Justin Leslie, Matthew S. Nasrallah, Sophie Farkas, Dan Jordan, Mark Grigione, Melissa LaScaleia, Michael C. Davis, Miranda L. Hansen, Chris Millspaugh, Josh Lewis, Jesse S. Havrda, Michael Long, Robert Remine, Kathryn R. Jaspers, Kodi J. Lafferty, Diana J. R. Hubbard, Tru Studds, Colin E. Barthelmess, Erika L. Andy, Katherine Romero, Andrea Hawkins, Melissa T. R. Lombardi, Jason V. Sergeyev, Maksim Fisher-Reid, M. Caitlin Rentz, Michael S. Nagy, Christopher Davenport, Jon M. Rega-Brodsky, Christine C. Appel, Cara L. Lesmeister, Damon B. Giery, Sean T. Whittier, Christopher A. Alston, Jesse M. Sutherland, Chris Rota, Christopher Murphy, Thomas Lee, Thomas E. Mortelliti, Alessio Bergman, Dylan L. Compton, Justin A. Gerber, Brian D. Burr, Jess Rezendes, Kylie DeGregorio, Brett A. Wehr, Nathaniel H. Benson, John F. Jachowski, David S. Gray, Morgan Beyer, Dean E. Belant, Jerrold L. Horan, Robert V. Lonsinger, Robert C. Kuhn, Kellie M. Hasstedt, Steven C. M. Zimova, Marketa Moore, Sophie M. Herrera, Daniel J. Fritts, Sarah Edelman, Andrew J. Flaherty, Elizabeth A. Petroelje, Tyler R. Neiswenter, Sean A. Risch, Derek R. Iannarilli, Fabiola van der Merwe, Marius Maher, Sean P. Farris, Zach J. Webb, Stephen L. Mason, David S. Lashley, Marcus A. Wilson, Andrew M. Vanek, John P. Wehr, Samuel R. Conner, L. Mike Beasley, James C. Bontrager, Helen L. Baruzzi, Carolina Ellis-Felege, Susan N. Proctor, Mike D. Schipper, Jan Weiss, Katherine C. B. Darracq, Andrea K. Barr, Evan G. Alexander, Peter D. Şekercioğlu, Çağan H. Bogan, Daniel A. Schalk, Christopher M. Fantle-Lepczyk, Jean E. Lepczyk, Christopher A. LaPoint, Scott Whipple, Laura S. Rowe, Helen Ivy Mullen, Kayleigh Bird, Tori Zorn, Adam Brandt, LaRoy Lathrop, Richard G. McCain, Craig Crupi, Anthony P. Clark, James Parsons, Arielle Department of Forestry and Environmental Resources North Carolina State University Raleigh NC United States North Carolina Museum of Natural Sciences Raleigh NC United States Department of Forestry and Environmental Conservation Clemson University Clemson SC United States Smithsonian's National Zoo and Conservation Biology Institute Front Royal VA United States Illinois Natural History Survey Prairie Research Institute University of Illinois Champaign IL United States Fort Hood Natural Resources Management Branch United States Army Garrison Fort Hood TX United States Environmental Studies Department Center for Integrated Spatial Research University of California Santa Cruz Santa Cruz CA United States Science Research Initiative University of Utah Salt Lake City UT United States Sageland Collaborative Salt Lake City UT United States Conservation Science Partners Inc. Truckee CA United States National Climate Adaptation Science Center United States Geological Survey Reston VA United States Biology Department Swarthmore College Swarthmore PA United States Entomology Department Cornell University Ithaca NY United States Information Technology Department Pace University Pleasantville NY United States Seattle University Department of Biology Seattle WA United States Department of Biology Pace University Pleasantville NY United States Department of Ecology and Evolutionary Biology Storrs CT United States Wildlife Biology Program Department of Ecosystem and Conservation Sciences University of Montana Missoula MT United States Department of Forest and Environmental Biology SUNY-ESF Syracuse NY United States Arizona State University College of Integrative Sciences and Arts Mesa AZ United States University of Washington School of Environmental and Forest Sciences Seattle WA United States Woodland Park Zoo Seattle WA United States Department of Biology Wildlife Ecology and Conservation Science Lab Northern Michigan University Marquette M

Aim: The assembly of species into communities and ecoregions is the result of interacting factors that affect plant and animal distribution and abundance at biogeographic scales. Here, we empirically derive ecoregions for mammals to test whether human disturbance has become more important than climate and habitat resources in structuring communities. Location: Conterminous United States. Time Period: 2010–2021. Major Taxa Studied: Twenty-five species of mammals. Methods: We analysed data from 25 mammal species recorded by camera traps at 6645 locations across the conterminous United States in a joint modelling framework to estimate relative abundance of each species. We then used a clustering analysis to describe 8 broad and 16 narrow mammal communities. Results: Climate was the most important predictor of mammal abundance overall, while human population density and agriculture were less important, with mixed effects across species. Seed production by forests also predicted mammal abundance, especially hard-mast tree species. The mammal community maps are similar to those of plants, with an east–west split driven by different dominant species of deer and squirrels. Communities vary along gradients of temperature in the east and precipitation in the west. Most fine-scale mammal community boundaries aligned with established plant ecoregions and were distinguished by the presence of regional specialists or shifts in relative abundance of widespread species. Maps of potential ecosystem services provided by these communities suggest high herbivory in the Rocky Mountains and eastern forests, high invertebrate predation in the subtropical south and greater predation pressure on large vertebrates in the west. Main Conclusions: Our results highlight the importance of climate to modern mammals and suggest that climate change will have strong impacts on these communities. Our new empirical approach to recognizing ecoregions has potential to be applied to expanded communities o

关键词： climate macroecology mammal communities masting species distribution models

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