Philippa Kaur

Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USARobert C. Reiner Jr., Catherine A. Welgan, Christopher E. Troeger, Mathew M. Baumann, Aniruddha Deshpande, Brigette F. Blacker, Molly K. Miller-Petrie, Lucas Earl, Daniel C. Casey, Aubrey J. Cook, Farah Daoud, Nicole Davis Weaver, Samath Dhamminda Dharmaratne, Laura Dwyer-Lindgren, Valery L. Feigin, Joseph Jon Frostad, Kimberly B. Johnson, Alice Lazzar-Atwood, Kate E. LeGrand, Stephen S. Lim, Paulina A. Lindstedt, Laurie B. Marczak, Benjamin K. Mayala, Ali H. Mokdad, Jonathan F. Mosser, Chrisopher J. L. Murray, QuynhAnh P. Nguyen, David M. Pigott, Puja C. Rao, David L. Smith, Emma Elizabeth Spurlock & Simon I. HayDepartment of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USARobert C. Reiner Jr., Samath Dhamminda Dharmaratne, Laura Dwyer-Lindgren, Stephen S. Lim, Ali H. Mokdad, Chrisopher J. L. Murray, David M. Pigott, Benn Sartorius, David L. Smith & Simon I. HayMalaria Atlas Project, University of Oxford, Oxford, UKDaniel J. Weiss & Susan Fred RumishaImperial College London, London, UKSamir BhattDepartment of Laboratory Medicine, Karolinska University Hospital, Huddinge, SwedenHassan AbolhassaniResearch Center for Immunodeficiencies, Tehran University of Medical Sciences, Tehran, IranHassan Abolhassani & Nima RezaeiDepartment of Public Health, Debre Berhan University, Debre Berhan, EthiopiaAkine Eshete AbosetugnDepartment of Clinical Sciences, University of Sharjah, Sharjah, United Arab EmiratesEman Abu-GharbiehPopulation Health Sciences, King’s College London, London, EnglandVictor AdekanmbiCentre of Excellence for Epidemiological Modelling and Analysis, Stellenbosch University, Stellenbosch, South AfricaOlatunji O. AdetokunbohDepartment of Global Health, Stellenbosch University, Cape Town, South AfricaOlatunji O. AdetokunbohDepartment of Epidemiology and Biostatistics, Qom University of Medical Sciences, Qom, IranMohammad AghaaliFaculty of Medicine and Public Health, Jenderal Soedirman University, Purwokerto, IndonesiaBudi AjiMayo Evidence-based Practice Center, Mayo Clinic Foundation for Medical Education and Research, Rochester, MN, USAFares AlahdabJohn T. Milliken Department of Internal Medicine, Washington University in St. Louis, St. Louis, MO, USAZiyad Al-AlyClinical Epidemiology Center, Department of Veterans Affairs, St Louis, MO, USAZiyad Al-AlyInstitute of Health Research, University of Health and Allied Sciences, Ho, GhanaRobert Kaba AlhassanDepartment of Information Systems, College of Economics and Political Science, Sultan Qaboos University, Muscat, OmanSaqib AliInfectious and Tropical Disease Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, IranHesam AlizadeDepartment of Health Policy and Management, Kuwait University, Safat, KuwaitSyed Mohamed AljunidInternational Centre for Casemix and Clinical Coding, National University of Malaysia, Bandar Tun Razak, MalaysiaSyed Mohamed AljunidDepartment of Epidemiology, Arak University of Medical Sciences, Arak, IranAmir Almasi-Hashiani, Rahmatollah Moradzadeh & Maryam ZamanianMedical Research Center, Jazan University, Jazan, Saudi ArabiaHesham M. Al-MekhlafiDepartment of Parasitology, Sana’a University, Sana’a, YemenHesham M. Al-MekhlafiPediatric Intensive Care Unit, King Saud University, Riyadh, Saudi ArabiaKhalid A. Altirkawi & Mohamad-Hani TemsahResearch Group in Health Economics, University of Cartagena, Cartagena, ColombiaNelson Alvis-GuzmanResearch Group in Hospital Management and Health Policies, ALZAK Foundation, Cartagena, ColombiaNelson Alvis-GuzmanSchool of Medicine, University of Adelaide, Adelaide, SA, AustraliaAzmeraw T. AmareCollege of Medicine and Health Science, Bahir Dar University, Bahir Dar, EthiopiaAzmeraw T. AmareHealth Services Management Department, Arak University of Medical Sciences, Arak, IranSaeed AminiMaternal and Child Wellbeing, African Population and Health Research Center, Nairobi, KenyaDickson A. AmugsiPharmacy Department, Carol Davila University of Medicine and Pharmacy, Bucharest, RomaniaRobert AncuceanuCardiology Department, Carol Davila University of Medicine and Pharmacy, Bucharest, RomaniaCatalina Liliana AndreiResearch Center for Evidence Based Medicine, Tabriz University of Medical Sciences, Tabriz, IranFereshteh AnsariRazi Vaccine and Serum Research Institute, Agricultural Research, Education, and Extension Organization (AREEO), Tehran, IranFereshteh AnsariDepartment of Parasitology, Mazandaran University of Medical Sciences, Sari, IranDavood AnvariDepartment of Parasitology, Iranshahr University of Medical Sciences, Iranshahr, IranDavood AnvariDepartment of Sociology and Social Work, Kwame Nkrumah University of Science and Technology, Kumasi, GhanaSeth Christopher Yaw AppiahCenter for International Health, Ludwig Maximilians University, Munich, GermanySeth Christopher Yaw AppiahHealth Management and Economics Research Center, Iran University of Medical Sciences, Tehran, IranJalal Arabloo & Ahmad GhashghaeeDepartment of Public Health, Birmingham City University, Birmingham, UKOlatunde AremuFaculty of Nursing, Philadelphia University, Amman, JordanMaha Moh’d Wahbi AtoutSchool of Business, University of Leicester, Leicester, UKMarcel AusloosDepartment of Statistics and Econometrics, Bucharest University of Economic Studies, Bucharest, RomaniaMarcel Ausloos, Claudiu Herteliu & Adrian PanaGastro-enterology Department, University of Liège, Liège, BelgiumFloriane AusloosDepartment of Health Policy Planning and Management, University of Health and Allied Sciences, Ho, GhanaMartin Amogre AyanoreDepartment of Nursing, Debre Berhan University, Debre Berhan, EthiopiaYared Asmare AynalemDepartment of Reproductive Health, University of Gondar, Gondar, EthiopiaZelalem Nigussie AzenePublic Health Risk Sciences Division, Public Health Agency of Canada, Toronto, ON, CanadaAlaa BadawiDepartment of Nutritional Sciences, University of Toronto, Toronto, ON, CanadaAlaa BadawiUnit of Biochemistry, Sultan Zainal Abidin University (Universiti Sultan Zainal Abidin), Kuala Terengganu, MalaysiaAtif Amin BaigDepartment of Hypertension, Medical University of Lodz, Lodz, PolandMaciej BanachPolish Mothers’ Memorial Hospital Research Institute, Lodz, PolandMaciej BanachDepartment of Community Medicine, Gandhi Medical College Bhopal, Bhopal, IndiaNeeraj BediJazan University, Jazan, Saudi ArabiaNeeraj BediDepartment of Social and Clinical Pharmacy, Charles University, Hradec Kralova, Czech RepublicAkshaya Srikanth BhagavathulaInstitute of Public Health, United Arab Emirates University, Al Ain, United Arab EmiratesAkshaya Srikanth BhagavathulaSchool of Public Health, University of Adelaide, Adelaide, SA, AustraliaDinesh BhandariPublic Health Research Laboratory, Tribhuvan University, Kathmandu, NepalDinesh BhandariDepartment of Anatomy, Government Medical College Pali, Pali, IndiaNikha BhardwajDepartment of Community Medicine and Family Medicine, All India Institute of Medical Sciences, Jodhpur, IndiaPankaj BhardwajSchool of Public Health, All India Institute of Medical Sciences, Jodhpur, IndiaPankaj BhardwajDepartment of Statistical and Computational Genomics, National Institute of Biomedical Genomics, Kalyani, IndiaKrittika BhattacharyyaDepartment of Statistics, University of Calcutta, Kolkata, IndiaKrittika BhattacharyyaCentre for Global Child Health, University of Toronto, Toronto, ON, CanadaZulfiqar A. BhuttaCentre of Excellence in Women & Child Health, Aga Khan University, Karachi, PakistanZulfiqar A. BhuttaSocial Determinants of Health Research Center, Babol University of Medical Sciences, Babol, IranAli BijaniPlanning, Monitoring and Evaluation Directorate, Amhara Public Health Institute, Bahir Dar, EthiopiaTesega Tesega Mengistu BirhanuNutrition Department, St. Paul’s Hospital Millennium Medical College, Addis Ababa, EthiopiaZebenay Workneh BitewSt. Paul’s Hospital Millennium Medical College, Addis Ababa, EthiopiaZebenay Workneh BitewDepartment of Internal Medicine, Manipal Academy of Higher Education, Mangalore, IndiaArchith BoloorDepartment of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UKOliver J. BradySchool of Public Health and Health Systems, University of Waterloo, Waterloo, ON, CanadaZahid A. ButtAl Shifa School of Public Health, Al Shifa Trust Eye Hospital, Rawalpindi, PakistanZahid A. ButtCentre for Population Health Sciences, Nanyang Technological University, Singapore, SingaporeJosip CarDepartment of Primary Care and Public Health, Imperial College London, London, UKJosip Car & Salman RawafResearch Unit on Applied Molecular Biosciences (UCIBIO), University of Porto, Porto, PortugalFelix CarvalhoDepartment of Medicine, University of Toronto, Toronto, ON, CanadaVijay Kumar ChattuGlobal Institute of Public Health (GIPH), Thiruvananthapuram, IndiaVijay Kumar ChattuMaternal and Child Health Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, BangladeshMohiuddin Ahsanul Kabir ChowdhuryDepartment of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USAMohiuddin Ahsanul Kabir ChowdhuryFaculty of Biology, Hanoi National University of Education, Hanoi, VietnamDinh-Toi ChuLaboratory of Malaria Immunology and Vaccinology, National Institutes of Health, Bethesda, MD, USACamila H. CoelhoClinical Dermatology, IRCCS Istituto Ortopedico Galeazzi, University of Milan, Milan, ItalyGiovanni DamianiDepartment of Dermatology, Case Western Reserve University, Cleveland, OH, USAGiovanni DamianiDepartment of Public Health, Ambo University, Ambo, EthiopiaJiregna Darega GelaDepartment of Pediatrics, Tanta University, Tanta, EgyptAmira Hamed DarwishToxoplasmosis Research Center, Mazandaran University of Medical Sciences, Sari, IranAhmad DaryaniDivision of Women and Child Health, Aga Khan University, Karachi, PakistanJai K. DasWellcome Trust Brighton and Sussex Centre for Global Health Research, Brighton and Sussex Medical School, Brighton, UKKebede DeribeSchool of Public Health, Addis Ababa University, Addis Ababa, EthiopiaKebede DeribeSchool of Nursing and Midwifery, Haramaya University, Harar, EthiopiaAssefa DesalewDepartment of Community Medicine, University of Peradeniya, Peradeniya, Sri LankaSamath Dhamminda DharmaratneDepartment of Epidemiology and Biostatistics, Shahroud University of Medical Sciences, Shahroud, IranMostafa DianatinasabDepartment of Epidemiology, Shiraz University of Medical Sciences, Shiraz, IranMostafa DianatinasabCenter of Complexity Sciences, National Autonomous University of Mexico, Mexico City, MexicoDaniel DiazFaculty of Veterinary Medicine and Zootechnics, Autonomous University of Sinaloa, Culiacán Rosales, MexicoDaniel DiazDevelopment of Research and Technology Center, Ministry of Health and Medical Education, Tehran, IranShirin DjalaliniaDepartment of Medical Laboratory Sciences, Iran University of Medical Sciences, Tehran, IranFariba DorostkarInstitute of Microbiology and Immunology, University of Belgrade, Belgrade, SerbiaEleonora DubljaninSchool of Public Health, Hawassa University, Hawassa, EthiopiaBereket DukoSchool of Public Health, Curtin University, Perth, WA, AustraliaBereket Duko & Ted R. MillerCentre Clinical Epidemiology and Biostatistics, University of Newcastle, Newcastle, NSW, AustraliaAndem EffiongReference Laboratory of Egyptian Universities Hospitals, Ministry of Higher Education and Research, Cairo, EgyptMaysaa El Sayed ZakiPediatric Dentistry and Dental Public Health Department, Alexandria University, Alexandria, EgyptMaha El TantawiDepartment of Microbiology and Immunology, Suez Canal University, Ismailia, EgyptShymaa EnanyResearch Center for Environmental Determinants of Health, Kermanshah University of Medical Sciences, Kermanshah, IranNazir Fattahi & Masoud MoradiNational Institute for Stroke and Applied Neurosciences, Auckland University of Technology, Auckland, New ZealandValery L. FeiginResearch Center of Neurology, Moscow, RussiaValery L. FeiginAssociated Laboratory for Green Chemistry (LAQV), University of Porto, Porto, PortugalEduarda FernandesResearch Center on Public Health, University of Milan Bicocca, Monza, ItalyPietro FerraraInstitute of Gerontological Health Services and Nursing Research, Ravensburg-Weingarten University of Applied Sciences, Weingarten, GermanyFlorian FischerInstitute of Gerontology, National Academy of Medical Sciences of Ukraine, Kyiv, UkraineNataliya A. FoigtDepartment of Child Dental Health, Obafemi Awolowo University, Ile-Ife, NigeriaMorenike Oluwatoyin FolayanDepartment of Medical Parasitology, Abadan Faculty of Medical Sciences, Abadan, IranMasoud ForoutanDepartment of Dermatology, Kobe University, Kobe, JapanTakeshi FukumotoDepartment of Community Medicine, Datta Meghe Institute of Medical Sciences, Wardha, IndiaAbhay Motiramji Gaidhane, Zahiruddin Quazi Syed & Deepak SaxenaDepartment of Pediatric Nursing, Aksum University, Aksum, EthiopiaHailemikael Gebrekidan G. K. GebrekrstosSchool of Pharmacy, Aksum University, Aksum, EthiopiaLeake GebremeskelDepartment of Pharmacy, Mekelle University, Mekelle, EthiopiaLeake GebremeskelDepartment of Reproductive Health, Mekelle University, Mekelle, EthiopiaAssefa Ayalew GebreslassieTelethon Kids Institute, Perth Children’s Hospital, Nedlands, WA, AustraliaPeter W. GethingCurtin University, Bentley, WA, AustraliaPeter W. GethingDepartment of Biostatistics, Mekelle University, Mekelle, EthiopiaKebede Embaye GezaeInfectious Disease Research Center, Kermanshah University of Medical Sciences, Kermanshah, IranKeyghobad GhadiriPediatric Department, Kermanshah University of Medical Sciences, Kermanshah, IranKeyghobad GhadiriStudent Research Committee, Iran University of Medical Sciences, Tehran, IranAhmad GhashghaeeHealth Systems and Policy Research, Indian Institute of Public Health Gandhinagar, Gandhinagar, IndiaMahaveer GolechhaDepartment of Family and Community Medicine, University Of Sulaimani, Sulaimani, IraqMohammed Ibrahim Mohialdeen GubariDepartment of Pediatrics and Child Health, Mekelle University, Mekelle, EthiopiaFikaden Berhe HadguSchool of Health and Environmental Studies, Hamdan Bin Mohammed Smart University, Dubai, United Arab EmiratesSamer HamidiDepartment of Public Health, Wachemo University, Hossana, EthiopiaDemelash Woldeyohannes HandisoDepartment of Public Health, Jigjiga University, Jijiga, EthiopiaAbdiwahab Hashi & Muktar Omer OmerCenter for International Health (CIH), University of Bergen, Bergen, NorwayShoaib HassanBergen Center for Ethics and Priority Setting (BCEPS), University of Bergen, Bergen, NorwayShoaib HassanInstitute of Pharmaceutical Sciences, University of Veterinary and Animal Sciences, Lahore, PakistanKhezar HayatDepartment of Pharmacy Administration and Clinical Pharmacy, Xian Jiaotong University, Xian, ChinaKhezar HayatSchool of Business, London South Bank University, London, UKClaudiu HerteliuDepartment of Urban Planning and Design, University of Hong Kong, Hong Kong, ChinaHung Chak HoKasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal, IndiaRamesh Holla & Priya RathiInstitute of Research and Development, Duy Tan University, Da Nang, VietnamMehdi Hosseinzadeh & Yasser VasseghianDepartment of Computer Science, University of Human Development, Sulaymaniyah, IraqMehdi HosseinzadehCollege of Science and Engineering, Hamad Bin Khalifa University, Doha, QatarMowafa HousehSchool of Pharmaceutical Sciences, University of Science Malaysia, Penang, MalaysiaRabia HussainDepartment of Occupational Safety and Health, China Medical University, Taichung, TaiwanBing-Fang HwangDepartment of Health Promotion and Education, University of Ibadan, Ibadan, NigeriaSegun Emmanuel IbitoyeDepartment of Community Medicine, University of Ibadan, Ibadan, NigeriaOlayinka Stephen IlesanmiDepartment of Community Medicine, University College Hospital, Ibadan, Ibadan, NigeriaOlayinka Stephen IlesanmiFaculty of Medicine, University of Belgrade, Belgrade, SerbiaIrena M. IlicDepartment of Epidemiology, University of Kragujevac, Kragujevac, SerbiaMilena D. IlicResearch Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, IranSeyed Sina Naghibi IrvaniDepartment of Environmental Health Engineering, Guilan University of Medical Sciences, Rasht, IranJalil JaafariHealth Informatic Lab, Boston University, Boston, MA, USATahereh JavaheriDepartment of Community Medicine, Dr. Baba Saheb Ambedkar Medical College & Hospital, Delhi, IndiaRavi Prakash JhaDepartment of Community Medicine, Banaras Hindu University, Varanasi, IndiaRavi Prakash JhaDepartment of Ophthalmology, Heidelberg University, Heidelberg, GermanyJost B. JonasBeijing Institute of Ophthalmology, Beijing Tongren Hospital, Beijing, ChinaJost B. JonasDepartment of Family Medicine and Public Health, University of Opole, Opole, PolandJacek Jerzy JozwiakMinimally Invasive Surgery Research Center, Iran University of Medical Sciences, Tehran, IranAli KabirInstitute for Prevention of Non-communicable Diseases, Qazvin University of Medical Sciences, Qazvin, IranRohollah KalhorHealth Services Management Department, Qazvin University of Medical Sciences, Qazvin, IranRohollah KalhorDepartment of Forensic Medicine and Toxicology, All India Institute of Medical Sciences, Jodhpur, IndiaTanuj KanchanInstitute for Epidemiology and Social Medicine, University of Münster, Münster, GermanyAndré KarchInternational Research Center of Excellence, Institute of Human Virology Nigeria, Abuja, NigeriaGbenga A. KayodeJulius Centre for Health Sciences and Primary Care, Utrecht University, Utrecht, NetherlandsGbenga A. KayodeOpen, Distance and eLearning Campus, University of Nairobi, Nairobi, KenyaPeter Njenga KeiyoroDepartment of Public Health, Jordan University of Science and Technology, Irbid, JordanYousef Saleh KhaderDepartment of Global Health, University of Washington, Seattle, WA, USAIbrahim A. Khalil & Sonali KochharDepartment of Population Science, Jatiya Kabi Kazi Nazrul Islam University, Mymensingh, BangladeshMd Nuruzzaman KhanEpidemiology Department, Jazan University, Jazan, Saudi ArabiaMaseer KhanDepartment of Medical Microbiology & Immunology, United Arab Emirates University, Al Ain, United Arab EmiratesGulfaraz KhanFaculty of Health and Wellbeing, Sheffield Hallam University, Sheffield, UKKhaled KhatabCollege of Arts and Sciences, Ohio University, Zanesville, OH, USAKhaled KhatabDepartment of Medical Parasitology, Cairo University, Cairo, EgyptMona M. KhaterGlobal Evidence Synthesis Initiative, Datta Meghe Institute of Medical Sciences, Wardha, IndiaMahalaqua Nazli KhatibDepartment of Public Health, Kermanshah University of Medical Sciences, Kermanshah, IranNeda KianipourSchool of Traditional Chinese Medicine, Xiamen University Malaysia, Sepang, MalaysiaYun Jin KimDepartment of Nutrition, Simmons University, Boston, MA, USARuth W. KimokotiDepartment of Nursing and Health Promotion, Oslo Metropolitan University, Oslo, NorwaySezer KisaSchool of Health Sciences, Kristiania University College, Oslo, NorwayAdnan KisaGlobal Community Health and Behavioral Sciences, Tulane University, New Orleans, LA, USAAdnan KisaDepartment of Pediatrics, University of British Columbia, Vancouver, BC, CanadaNiranjan KissoonGlobal Healthcare Consulting, New Delhi, IndiaSonali KochharDepartment of Environmental Health Engineering, Arak University of Medical Sciences, Arak, IranAli KoolivandSchool of Population and Public Health, University of British Columbia, Vancouver, BC, CanadaJacek A. KopecArthritis Research Canada, Richmond, BC, CanadaJacek A. KopecCIBERSAM, San Juan de Dios Sanitary Park, Sant Boi de Llobregat, SpainAi KoyanagiCatalan Institution for Research and Advanced Studies (ICREA), Barcelona, SpainAi KoyanagiDepartment of Anthropology, Panjab University, Chandigarh, IndiaKewal KrishanInternational Institute for Population Sciences, Mumbai, IndiaPushpendra KumarFaculty of Health and Life Sciences, Coventry University, Coventry, UKOm P. KurmiDepartment of Medicine, McMaster University, Hamilton, ON, CanadaOm P. KurmiImperial College Business School, Imperial College London, London, UKDian KusumaFaculty of Public Health, University of Indonesia, Depok, IndonesiaDian KusumaPublic Health Foundation of India, Gurugram, IndiaDharmesh Kumar LalDepartment of Community and Family Medicine, University of Baghdad, Baghdad, IraqFaris Hasan LamiUnit of Genetics and Public Health, Institute of Medical Sciences, Las Tablas, PanamaIván LandiresMinistry of Health, Herrera, PanamaIván LandiresMedical Director, HelpMeSee, New York, NY, USAVan Charles LansinghGeneral Director, Mexican Institute of Ophthalmology, Queretaro, MexicoVan Charles LansinghDepartment of Otorhinolaryngology, Father Muller Medical College, Mangalore, IndiaSavita LasradoDepartment of Clinical Sciences and Community Health, University of Milan, Milan, ItalyCarlo La VecchiaSchool of Nursing, Hong Kong Polytechnic University, Hong Kong, ChinaPaul H. LeeCentre for Tropical Medicine and Global Health, University of Oxford, Oxford, UKSonia LewyckaOxford University Clinical Research Unit, Wellcome Trust Asia Programme, Hanoi, VietnamSonia LewyckaDepartment of Sociology, Shenzhen University, Shenzhen, ChinaBingyu LiDepartment of Systems, Populations, and Leadership, University of Michigan, Ann Arbor, MI, USAXuefeng LiuDepartment of Vector Biology, Liverpool School of Tropical Medicine, Liverpool, UKJoshua LongbottomIndependent Consultant, Melbourne, VIC, AustraliaAlan D. LopezRadiology Department, Egypt Ministry of Health and Population, Mansoura, EgyptHassan Magdy Abd El RazekGrants, Innovation and Product Development Unit, South African Medical Research Council, Cape Town, South AfricaPhetole Walter MahashaEnvironmental Health, Tehran University of Medical Sciences, Tehran, IranAfshin MalekiEnvironmental Health Research Center, Kurdistan University of Medical Sciences, Sanandaj, IranAfshin Maleki & Shadieh MohammadiInstitute for Social Science Research, The University of Queensland, Indooroopilly, QLD, AustraliaAbdullah A. MamunDepartment of Epidemiology and Biostatistics, Tehran University of Medical Sciences, Tehran, IranMohammad Ali MansourniaCampus Caucaia, Federal Institute of Education, Science and Technology of Ceará, Caucaia, BrazilFrancisco Rogerlândio Martins-MeloICF International, DHS Program, Rockville, MD, USABenjamin K. MayalaDepartment of Pharmacy, Wollo University, Dessie, EthiopiaBirhanu Geta MeharieDepartment of Medical Laboratory Sciences, Bahir Dar University, Bahir Dar, EthiopiaAddisu MelesePeru Country Office, United Nations Population Fund (UNFPA), Lima, PeruWalter MendozaForensic Medicine Division, Imam Abdulrahman Bin Faisal University, Dammam, Saudi ArabiaRitesh G. MenezesDepartment of Reproductive Health and Population Studies, Bahir Dar University, Bahir Dar, EthiopiaEndalkachew Worku MengeshaCenter for Translation Research and Implementation Science, National Institutes of Health, Bethesda, MD, USAGeorge A. MensahDepartment of Medicine, University of Cape Town, Cape Town, South AfricaGeorge A. MensahBreast Surgery Unit, Helsinki University Hospital, Helsinki, FinlandTuomo J. MeretojaUniversity of Helsinki, Helsinki, FinlandTuomo J. MeretojaClinical Microbiology and Parasitology Unit, Dr. Zora Profozic Polyclinic, Zagreb, CroatiaTomislav MestrovicUniversity Centre Varazdin, University North, Varazdin, CroatiaTomislav MestrovicPacific Institute for Research & Evaluation, Calverton, MD, USATed R. MillerInternal Medicine Programme, Kyrgyz State Medical Academy, Bishkek, KyrgyzstanErkin M. MirrakhimovDepartment of Atherosclerosis and Coronary Heart Disease, National Center of Cardiology and Internal Disease, Bishkek, KyrgyzstanErkin M. MirrakhimovHeidelberg Institute of Global Health (HIGH), Heidelberg University, Heidelberg, GermanyBabak Moazen & Shafiu MohammedInstitute of Addiction Research (ISFF), Frankfurt University of Applied Sciences, Frankfurt, GermanyBabak MoazenDepartment of Biostatistics, Hamadan University of Medical Sciences, Hamadan, IranNaser Mohammad Gholi MezerjiResearch Institute for Health Development, Kurdistan University of Medical Sciences, Sanandaj City, IranShadieh MohammadiHealth Systems and Policy Research Unit, Ahmadu Bello University, Zaria, NigeriaShafiu MohammedComputer, Electrical, and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi ArabiaPaula MoragaClinical Research Development Center, Kermanshah University of Medical Sciences, Kermanshah, IranMehdi NaderiResearch and Analytics Department, Initiative for Financing Health and Human Development, Chennai, IndiaAhamarshan Jayaraman NagarajanDepartment of Research and Analytics, Bioinsilico Technologies, Chennai, IndiaAhamarshan Jayaraman NagarajanDepartment of Pediatrics, Arak University of Medical Sciences, Arak, IranJavad NazariDisease Control and Environmental Health, Makerere University, Kampala, UgandaRawlance NdejjoDepartment of General Surgery, Carol Davila University of Medicine and Pharmacy, Bucharest, RomaniaIonut NegoiDepartment of General Surgery, Emergency Hospital of Bucharest, Bucharest, RomaniaIonut NegoiDepartment of Biological Sciences, University of Embu, Embu, KenyaJosephine W. NgunjiriInstitute for Global Health Innovations, Duy Tan University, Hanoi, VietnamHuong Lan Thi Nguyen & Hai Quang PhamSouth African Medical Research Council, Cape Town, South AfricaChukwudi A. Nnaji & Charles Shey WiysongeSchool of Public Health and Family Medicine, University of Cape Town, Cape Town, South AfricaChukwudi A. Nnaji & Charles Shey WiysongeCentre for Heart Rhythm Disorders, University of Adelaide, Adelaide, SA, AustraliaJean Jacques NoubiapUnit of Microbiology and Public Health, Institute of Medical Sciences, Las Tablas, PanamaVirginia Nuñez-SamudioDepartment of Public Health, Ministry of Health, Herrera, PanamaVirginia Nuñez-SamudioDepartment of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, CanadaAndrew T. OlagunjuDepartment of Psychiatry, University of Lagos, Lagos, NigeriaAndrew T. OlagunjuCentre for Healthy Start Initiative, Lagos, NigeriaJacob Olusegun Olusanya & Bolajoko Olubukunola OlusanyaDepartment of Pharmacology and Therapeutics, University of Nigeria Nsukka, Enugu, NigeriaObinna E. OnwujekweLaboratory of Public Health Indicators Analysis and Health Digitalization, Moscow Institute of Physics and Technology, Dolgoprudny, RussiaNikita Otstavnov & Stanislav S. OtstavnovDepartment of Project Management, National Research University Higher School of Economics, Moscow, RussiaStanislav S. OtstavnovDepartment of Medicine, University of Ibadan, Ibadan, NigeriaMayowa O. OwolabiDepartment of Medicine, University College Hospital, Ibadan, Ibadan, NigeriaMayowa O. OwolabiDepartment of Respiratory Medicine, Jagadguru Sri Shivarathreeswara Academy of Health Education and Research, Mysore, IndiaMahesh P ADepartment of Forensic Medicine, Manipal Academy of Higher Education, Mangalore, IndiaJagadish Rao PadubidriDepartment of Health Metrics, Center for Health Outcomes & Evaluation, Bucharest, RomaniaAdrian PanaSchool of Global Public Health, New York University, New York, NY, USAEmmanuel K. PeprahDepartment of Parasitology and Entomology, Tarbiat Modares University, Tehran, IranMajid PirestaniUniversity Medical Center Groningen, University of Groningen, Groningen, NetherlandsMaarten J. PostmaSchool of Economics and Business, University of Groningen, Groningen, NetherlandsMaarten J. PostmaDepartment of Pharmacology, Imam Abdulrahman Bin Faisal University, Dammam, Saudi ArabiaFaheem Hyder PottooDepartment of Nutrition and Food Sciences, Maragheh University of Medical Sciences, Maragheh, IranHadi PourjafarDietary Supplements and Probiotic Research Center, Alborz University of Medical Sciences, Karaj, IranHadi PourjafarThalassemia and Hemoglobinopathy Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, IranFakher RahimMetabolomics and Genomics Research Center, Tehran University of Medical Sciences, Tehran, IranFakher RahimSina Trauma and Surgery Research Center, Tehran University of Medical Sciences, Tehran, IranVafa Rahimi-MovagharDepartment of Community Medicine, Maharishi Markandeshwar Medical College & Hospital, Solan, IndiaMohammad Hifz Ur RahmanDepartment of Oral Pathology, Srinivas Institute of Dental Sciences, Mangalore, IndiaSowmya J. RaoAcademic Public Health England, Public Health England, London, UKSalman RawafWHO Collaborating Centre for Public Health Education and Training, Imperial College London, London, UKDavid Laith RawafUniversity College London Hospitals, London, UKDavid Laith RawafSchool of Health, Medical and Applied Sciences, CQ University, Sydney, NSW, AustraliaLal RawalDepartment of Computer Science, Boston University, Boston, MA, USAReza RawassizadehSchool of Public Health, Haramaya University, Harar, EthiopiaLemma Demissie RegassaSchool of Social Sciences and Psychology, Western Sydney University, Penrith, NSW, AustraliaAndre M. N. RenzahoTranslational Health Research Institute, Western Sydney University, Penrith, NSW, AustraliaAndre M. N. RenzahoNetwork of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, IranNima RezaeiPediatric Infectious Diseases Research Center, Mazandaran University of Medical Sciences, Sari, IranMohammad Sadegh RezaiEpidemiology Research Unit Institute of Public Health (EPIUnit-ISPUP), University of Porto, Porto, PortugalAna Isabel RibeiroDepartment of Surgery, University of Minnesota, Minneapolis, MN, USAJennifer RickardDepartment of Surgery, University Teaching Hospital of Kigali, Kigali, RwandaJennifer RickardFaculty of Medical Sciences, Research Department, National University of Caaguazu, Cnel. Oviedo, ParaguayCarlos Miguel Rios-GonzálezDepartment of Research and Publications, National Institute of Health, Asunción, ParaguayCarlos Miguel Rios-GonzálezDepartment of Health Statistics, National Institute for Medical Research, Dar es Salaam, TanzaniaSusan Fred RumishaDepartment of Epidemiology, Shahid Beheshti University of Medical Sciences, Tehran, IranSiamak SabourDepartment of Phytochemistry, Soran University, Soran, IraqS. Mohammad SajadiDepartment of Nutrition, Cihan University-Erbil, Kurdistan Region, IraqS. Mohammad SajadiCenter for Health Policy & Center for Primary Care and Outcomes Research, Stanford University, Stanford, CA, USAJoshua A. SalomonDrug Applied Research Center, Tabriz University of Medical Sciences, Tabriz, IranHossein Samadi KafilDepartment of Entomology, Ain Shams University, Cairo, EgyptAbdallah M. SamyDepartment of Surgery, Marshall University, Huntington, WV, USAJuan SanabriaDepartment of Nutrition and Preventive Medicine, Case Western Reserve University, Cleveland, OH, USAJuan SanabriaFaculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UKBenn SartoriusDepartment of Epidemiology, Indian Institute of Public Health, Gandhinagar, IndiaDeepak SaxenaGlobal Programs, Medical Teams International, Seattle, WA, USALauren E. SchaefferDepartment of Pediatric Newborn Medicine, Brigham and Women’s Hospital, Boston, MA, USALauren E. SchaefferEmergency Department, Manian Medical Centre, Erode, IndiaSubramanian SenthilkumaranCenter for Biomedical Information Technology, Shenzhen Institutes of Advanced Technology, Shenzhen, ChinaFeng ShaPublic Health Division, An-Najah National University, Nablus, PalestineAmira A. ShaheenIndependent Consultant, Karachi, PakistanMasood Ali ShaikhUniversity School of Management and Entrepreneurship, Delhi Technological University, Delhi, IndiaRajesh SharmaCentre for Medical Informatics, University of Edinburgh, Edinburgh, UKAziz SheikhDivision of General Internal Medicine, Harvard University, Boston, MA, USAAziz SheikhInstitute for Population Health, King’s College London, London, UKKenji ShibuyaNational Institute of Infectious Diseases, Tokyo, JapanMika ShigematsuCollege of Medicine, Yonsei University, Seoul, South KoreaJae Il ShinDepartment of Law, Economics, Management and Quantitative Methods, University of Sannio, Benevento, ItalyBiagio SimonettiWSB University in Gdańsk, Gdansk, PolandBiagio SimonettiSchool of Medicine, University of Alabama at Birmingham, Birmingham, AL, USAJasvinder A. SinghMedicine Service, US Department of Veterans Affairs (VA), Birmingham, AL, USAJasvinder A. SinghNursing Care Research Center, Semnan University of Medical Sciences, Semnan, IranAmin SoheiliDepartment of Infectious Diseases, Kharkiv National Medical University, Kharkiv, UkraineAnton SokhanDivision of Community Medicine, International Medical University, Kuala Lumpur, MalaysiaChandrashekhar T. SreeramareddyDepartment of Community Medicine, Ahmadu Bello University, Zaria, NigeriaMu’awiyyah Babale SufiyanSchool of Medicine, University of California San Francisco, San Francisco, CA, USAScott J. SwartzJoint Medical Program, University of California Berkeley, Berkeley, CA, USAScott J. SwartzDepartment of Nursing, Aksum University, Aksum, EthiopiaDegena Bahrey TadesseDepartment of Midwifery, University of Gondar, Gondar, EthiopiaAnimut Tagele TamiruDepartment of Clinical Pharmacy, University of Gondar, Gondar, EthiopiaYonas Getaye TeferaDepartment of Epidemiology and Biostatistics, University of Gondar, Gondar, EthiopiaZemenu Tadesse TessemaK.A. Timiryazev Institute of Plant Physiology, Russian Academy of Sciences, Moscow, RussiaMariya Vladimirovna TitovaLaboratory of Public Health Indicators Analysis and Health Digitalization, Moscow Institute of Physics and Technology, Moscow, RussiaMariya Vladimirovna TitovaDepartment of Health Economics, Hanoi Medical University, Hanoi, VietnamBach Xuan TranFaculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, NetherlandsPhuong N. TruongKasturba Medical College, Manipal Academy of Higher Education, Mangalore, IndiaBhaskaran UnnikrishnanAmity Institute of Biotechnology, Amity University Rajasthan, Jaipur, IndiaEra UpadhyayUKK Institute, Tampere, FinlandTommi Juhani VasankariDepartment of Medical and Surgical Sciences, University of Bologna, Bologna, ItalyFrancesco S. ViolanteOccupational Health Unit, Sant’Orsola Malpighi Hospital, Bologna, ItalyFrancesco S. ViolanteCenter of Excellence in Behavioral Medicine, Nguyen Tat Thanh University, Ho Chi Minh City, VietnamGiang Thu VuFoundation University Medical College, Foundation University Islamabad, Islamabad, PakistanYasir WaheedCultures, Societies and Global Studies, & Integrated Initiative for Global Health, Northeastern University, Boston, MA, USARichard G. WamaiSchool of Public Health, University of Nairobi, Nairobi, KenyaRichard G. WamaiDepartment of Human Nutrition and Food Sciences, Debre Markos University, Debre Markos, EthiopiaEmebet Gashaw WassieDepartment of Midwifery, Adigrat University, Adigrat, EthiopiaFissaha Tekulu WelayDepartment of Community Medicine, Rajarata University of Sri Lanka, Anuradhapura, Sri LankaNuwan Darshana WickramasingheDepartment of Epidemiology, Johns Hopkins University, Baltimore, MD, USAKirsten E. WiensDepartment of Neurology, University of Melbourne, Melbourne, VIC, AustraliaTissa WijeratneDepartment of Medicine, University of Rajarata, Saliyapura Anuradhapuraya, Sri LankaTissa WijeratneDepartment of Public Health, Samara University, Samara, EthiopiaTemesgen Gebeyehu WondmenehDepartment of Diabetes and Metabolic Diseases, University of Tokyo, Tokyo, JapanTomohide YamadaSchool of International Development and Global Studies, University of Ottawa, Ottawa, ON, CanadaSanni YayaThe George Institute for Global Health, University of Oxford, Oxford, UKSanni YayaDepartment of Nursing, Arba Minch University, Arba Minch, EthiopiaYordanos Gizachew YeshitilaCentre for Suicide Research and Prevention, University of Hong Kong, Hong Kong, ChinaPaul YipDepartment of Social Work and Social Administration, University of Hong Kong, Hong Kong, ChinaPaul YipDepartment of Neuropsychopharmacology, National Center of Neurology and Psychiatry, Kodaira, JapanNaohiro YonemotoDepartment of Public Health, Juntendo University, Tokyo, JapanNaohiro YonemotoDepartment of Epidemiology and Biostatistics, Wuhan University, Wuhan, ChinaChuanhua YuCancer Institute, Hacettepe University, Ankara, TurkeyDeniz YuceDepartment of Health Care Management and Economics, Urmia University of Medical Science, Urmia, IranHasan YusefzadehDepartment of Medicine, University Ferhat Abbas of Setif, Sétif, AlgeriaZoubida ZaidiSocial Development and Health Promotion Research Center, Kermanshah University of Medical Sciences, Kermanshah, IranAlireza ZangenehSchool of Medicine, Wuhan University, Wuhan, ChinaZhi-Jiang ZhangSchool of Public Health, Wuhan University of Science and Technology, Wuhan, ChinaYunquan ZhangHubei Province Key Laboratory of Occupational Hazard Identification and Control, Wuhan University of Science and Technology, Wuhan, ChinaYunquan ZhangDepartment of Health Education and Health Promotion, Kermanshah University of Medical Sciences, Kermanshah, IranArash ZiapourManaging the estimation or publication process. L.B.D. T.B.C. Writing the first draft of the manuscript. R.C.R.J. Primary responsibility for this manuscript focused on: applying analytical methods to produce estimates. L.B.D. T.B.C. Primary responsibility for this manuscript focused on: seeking, cataloguing, extracting, or cleaning data; production or coding of figures and tables. L.B.D. T.B.C. Providing data or critical feedback on data sources. L.B.D. T.B.C. and S.I.H. Development of methods or computational machinery. R.C.R.J. and L.B.D. T.B.C. Providing critical feedback on methods or results. L.B.D. T.B.C. and S.I.H. Drafting the manuscript or revising it critically for important intellectual content. R.C.R.J., L.B.D. T.B.C., and S.I.H. Management of the overall research enterprise (for example, through membership in the Scientific Council). L.B.D. T.B.C. and S.I.H. Consortia author contributions Managing the estimation or publication process. B.F.B., M.K.M.P. Writing the first draft of the manuscript. R.C.R.J. Primary responsibility for this manuscript focused on: applying analytical methods to produce estimates. C.A.W. Primary responsibility for this manuscript focused on: seeking, cataloguing, extracting, or cleaning data; production or coding of figures and tables. M.M.B. Providing data or critical feedback on data sources D.J.W., A.D., C.E.T., H.A., A.E.A., V.A., O.O.A., M.A., B.A., F.A., S.A., H.A., S.M.A., A.A.-H., N.A.-G., A.T.A., S.A., C.L.A., F.A., D.A., S.C.Y.A., J.A., O.A., M.A., F.A., Y.A.A., A.B., M.B., N.B., A.S.B., A.B., V.K.C., D.-T.C., G.D., J.D.G., A.D., S.D.D., M.D., A.E., M.El.S.Z., S.E., T.F., A.M.G., L.G., P.W.G., K.G., A.G., M.G., A.H., S.H., K.H., C.H., H.C.H., M.H., M.H., S.S.N.I., T.J., J.B.J., J.J.J., A.K., G.A.K., Y.S.K., I.A.K., M.N.K., M.K., K.K., M.N.K., Y.J.K., S.K., A.K., N.K., K.K., P.K., D.K., D.K.L., F.H.L., V.C.L., S.L., A.L.-A., K.E.L., S.S.L., P.A.L., X.L., H.M.A.E.R., M.A.M., B.K.M., W.M., R.G.M., E.M.M., B.M., N.M.G.M., S.M., S.M., A.H.M., M.M., A.J.N., J.N., I.N., J.W.N., Q.P.N., H.L.T.N., C.A.N., J.J.N., A.T.O., J.O.O., B.O.O., O.E.O., N.O., S.S.O., M.O.O., M.P.A., J.R.P., A.P., E.K.P., H.Q.P., M.P., M.J.P., H.P., Z.Q.S., F.R., V.R.-M., S.J.R., P.R., S.R., D.L.R., L.R., R.R., A.M.N.R., N.R., J.R., C.M.R.-G., S.S., S.M.S., A.M.S., B.S., D.S., A.A.S., M.A.S., J.I.S., J.A.S., A.S., E.S., C.T.S., S.J.S., D.B.T., A.T.T., B.X.T., P.N.T., B.U., E.U., T.J.V., Y.V., G.T.V., Y.W., R.G.W., T.W., C.S.W., T.G.W., S.Y., Y.G.Y., N.Y., C.Y., H.Y., Z.Z., A.Z., and S.I.H. Development of methods or computational machinery R.C.R.J., C.A.W., M.M.B., A.D., L.E., S.B., C.E.T., H.A., D.A., Y.A.A., A.S.B., D.C.C., V.K.C., F.D. A.D., M.D., M.E.S.Z., N.F., J.J.F., P.W.G., M.H., K.B.J., S.K., A., A.D.L., S.M., A.H.M., J.W.N., Q.P.N., S.F.R., A.M.S., E.E.S., S.J.S., E.U., Y.V., K.E.W., Y.G.Y., and N.Y. Providing critical feedback on methods or results C.A.W., A.D., C.E.T., H.A., A.E.A., E.A.-G., V.A., O.O.A., M.A., B.A., F.A., Z.A.-A., R.K.A., S.A., H.A., A.A.-H., H.M.A.M., K.A.A., N.A.-Gu., A.T.A., S.A., D.A.A., C.L.A., F.A., D.A., S.C.Y.A., J.A., O.A., M.M.W.A., M.A., F.A., Y.A.A., Z.N.A., A.B., M.B., A.S.B., D.B., N.B., P.B., K.B., O.J.B., Z.A.B., A.B., Z.W.B., A.B., Z.A.B., V.C., M.A.K.C., D.-T.C., C.H.C., G.D., J.D.G., A.H.D., A.D., J.K.D., K.D., A.D., S.D.D., M.D., D.D., S.D., F.D., B.D., L.D.-L., A.E., V.L.F., F.F., N.A.F., M.O.F., M.F., T.F., A.M.G., H.G.G.K.G., L.G., A.A.G., K.E.G., A.G., M.G., F.B.H., S.H., A.H., S.H., C.H., H.C.H., R.H., M.H., M.H., R.H., B.-F.H., S.E.I., O.S.I., I.M.I., M.D.I., S.S.N.I., T.J., R.P.J., J.B.J., J.J.J., A.K., R.K., T.K., A.K., G.A.K., P.N.K., Y.S.K., I.A.K., M.N.K., M.K., K.K., M.M.K., M.N.K., Y.J.K., R.W.K., S.K., A.K., N.K., S.K., A.K., J.A.K., A.K., K.K., P.K., O.P.K., D.K., D.K.L., S.L., K.E.L., S.L., B.L., X.L., A.D.L., H.M.A.E.R., P.W.M., A.A.M., M.A.M., L.B.M., F.R.M.-M., B.K.M., W.M., R.G.M., E.W.M., T.J.M., T.R.M., E.M.M., B.M., N.M.G.M., S.M., S.M., A.H.M., R.M., J.F.M., M.N., A.J.N., J.N., R.N., I.N., J.W.N., H.L.T.N., C.A.N., J.J.N., A.T.O., J.O.O., B.O.O., M.O.O., O.E.O., N.O., S.S.O., M.O.O., M.P.A., J.R.P., A.P., E.K.P., H.Q.P., M.J.P., F.H.P., H.P., Z.Q.S., F.R., V.R.-M., S.J.R., P.R., S.R., D.L.R., L.R., R.R., L.D.R., A.M.N.R., N.R., M.S.R., A.I.R., J.R., C.M.R.-G., S.S., S.M.S., J.A.S., H.S.K., A.M.S., J.S., B.S., D.S., L.E.S., S.S., F.S., A.A.S., M.A.S., A.S., K.S., M.S., J.I.S., B.S., J.A.S., D.L.S., A.S., E.E.S., C.T.S., M.B.S., D.B.T., A.T.T., Y.G.T., M.-H.T., Z.T.T., M.V.T., B.X.T., P.N.T., B.U., E.U., Y.V., F.S.V., G.T.V., Y.W., R.G.W., E.G.W., F.T.W., N.D.W., K.E.W., T.W., C.S.W., T.G.W., T.Y., S.Y., Y.G.Y., P.Y., N.Y., C.Y., D.Y., Z.Z., M.Z., Z.-J.Z., Y.Z., and S.I.H. Drafting the manuscript or revising it critically for important intellectual content R.C.R.J., C.A.W., M.K.M.-P., L.E., H.A., E.A.-G., V.A., O.O.A., M.A., B.A, F.A., R.K.A., H.A., A.A.-H., N.A.-G., A.T.A., S.A., D.A.A., R.A., C.L.A., J.A., O.A., M.M.W.A., M.A., F.A., M.A.A., Z.N.A., A.B., A.A.B., M.B., N.B. A.S.B., D.B., K.B., T.T.M.B., O.J.B., J.C., F.C., V.K.C., G.D., A.D., N.D.W., K.D., S.D.D., D.D., E.D., A.E., M.E.S.Z., M.E.T., S.E., V.L.F., E.F., P.F., F.F., N.A.F., M.O.F., M.F., T.F., A.M.G., L.G., A.G., M.I.M.G., D.W.H., A.H., S.H., C.H., H.C.H., R.H., M.H., S.E.I., O.S.I., I.M.I., M.D.I., S.S.N.I., J.J., R.P.J., J.B.J., J.J.J., A.K., A.K., G.A.K., M.N.K., M.K., G.K., K.K., M.M.K., M.N.K., A.K., N.K., A.K., A.K., K.K., P.K., O.P.K., D.K., I.L., S.L., C.L.V., P.H.L., K.E.L., J.L., A.D.L., H.M.A.E.R., P.W.M., A.M., A.A.M., M.A.M., L.B.M., F.R.M.-M., B.G.M., W.M., R.G.M., E.W.M., G.A.M., T.J.M., T.M., T.R.M., B.M., S.M., S.M., A.H.M., R.M., P.M., J.F.M., A.J.N., J.N., I.N., J.W.N., H.L.T.N., V.N.-S., A.T.O., J.O.O., B.O.O., M.O.O., O.E.O., N.O., S.S.O., M.O.O., M.P.A., J.R.P., A.P., H.Q.P., M.J.P., Z.Q.S., F.R., V.R.-M., M.H.U.R., S.J.R., S.R., D.L.R., L.R., N.R., A.I.R., J.R., C.M.R.-G., S.F.R., S.S., J.A.S., H.S.K., A.M.S., J.S., D.S., R.S., M.S., J.A.S., A.S., C.T.S., M.B.S., D.B.T., A.T.T., M.V.T., B.X.T., B.U., E.U., T.J.V., Y.V., F.S.V., G.T.V., R.G.W., N.D.W., K.E.W., T.W., .C.S.W., S.Y., Y.G.Y., Z.Z., M.Z., Z.-J.Z., and S.I.H. Management of the overall research enterprise (for example, through membership in the Scientific Council) B.F.B., A.J.C., P.W.G., J.A.K., A.H.M., C.J.L.M., P.C.R., J.A.S., B.S., and S.I.H. More

SamplesWe examined 1,022 avian species (~ 10% recorded species) in this study, representing 418 genera, from 91 families (37% recorded families) and 29 orders (73% of all orders). Specimens were from the skin collection of the World Museum Liverpool, Tring Natural History Museum, Manchester Museum and Wollaton Hall Museum, all situated in the United Kingdom. All work was carried out in accordance with ethical regulations at Manchester Metropolitan University and with the permission of all aforementioned museums. Only the best-preserved adult specimens (no signs of cut off feathers or holes in the skin near the beak) were chosen for this study to ensure accurate measurements of bristle length, shape and presence, which should not be affected by the process of skin removal and specimen conservation. Species were randomly chosen, without targeting our sampling towards species known a priori to have bristles. Where possible, two specimens per species were measured (occurring in 82% of all species examined). Specimens of each sex were measured when present; however, this was not always possible since labelling was often inaccurate or missing. In total, the sample included 508 males, 412 females and 374 individuals of unknown sex. Both sexes were examined in 274 species and there was no difference whatsoever between the presence of bristles on male or female species (n = 97 with bristles present and n = 180 with bristles absent for both males and females). Length (Mann–Whitney U test, W = 37,962, N = 552, P = 0.94) and shape (Chi-square test, χ2 = 0, N = 552, df = 3, P = 1) of rictal bristles also did not significantly differ between males and females. Therefore, rictal bristles are likely to be sexually monomorphic and data for males and females was pooled for further analyses. Overall, rictal bristles were absent in 64% of species examined (n = 656) and just over a third of species (n = 366) had bristles present.Bristle descriptionsFacial bristles were initially identified by sight and touch in each specimen. Bristles were recorded as either present or absent from the upper rictal, lorial, lower rictal, narial and interramal regions (Fig. 1a). We use the term ‘rictal bristle’ here for bristles on both the upper rictal and/or the lorial region, since there was no clear differentiation and morphological differences between the bristles found in these regions forming a continuum of bristles above the edge of the beak. When present, rictal bristle shape was recorded as: (i) unbranched rictal bristles, (ii) rictal bristles with barbs only at the base (“Base”) and (iii) branched rictal bristles (“Branched”), i.e. barbs and barbules present along the bristle rachis (Fig. 1b). The three longest rictal bristles were measured on both sides of the head of each specimen using digital callipers, and these lengths were averaged to provide a mean length of rictal bristles per species. In species lacking rictal bristles, a length of “0” and a shape category of “Absent” was recorded.Ancestral reconstruction of facial bristle presenceFollowing Felice et al.19, a single consensus phylogenetic tree was generated from the Hackett posterior distribution of trees from Birdtree.org20 with a sample size of 10,000 post burn-in, using the TreeAnnotator utility in BEAST software21 with a burn-in of 0. Maximum Clade Credibility (MCC) with the option “-heights ca” was selected as the method of reconstruction. The common ancestor trees option (-heights ca) builds a consensus tree by summarising clade ages across all posterior trees. Both the consensus tree and posterior distribution of 10,000 trees were imported into RStudio v. 1.2.5 for R22,23 and pruned so that only species present in the dataset of this study remained in the phylogeny. Taxon names were modified where necessary to match those from the Birdtree.org (http://birdtree.org) species record. Negative terminal branches in our consensus tree were slightly lengthened to be positive using ‘edge.length[tree$edge.length  More

Identification, phylogeny, and distribution of five phylaTo advance our understanding of marine sediment microbial diversity, we obtained over 30 billion paired DNA sequences from 42 marine sediment samples (coastal and deep sea) (Supplementary Data 1). From this, we reconstructed over 8000 ( >50% complete, 95%) to genes from coastal waters (Venezuela), a hypersaline pond in Carpinteria (US), sediments in Garolim Bay (Korea), and others (Supplementary Data 6 and 7). The worldwide distribution of these five phyla suggests that they have potentially overlooked ecological roles across many environments.Detection of novel protein familiesTo explore novel metabolic capabilities of these bacteria, we employed a recently described approach to identify and characterize unknown genes exclusive to uncultivated taxa17. Using this computational method, we identified 1,934 novel protein families (NPFs) and 6,893 novel singletons (NSs) in the 55 MAGs. The former can be define as families that do not show any homology in broadly used databases (including eggNOG, pfamA, pfamB, and RefSeq, see “Methods”) while the latter (NSs) are NPFs that are detected only once in each given genome or group of genomes. To determine if this novelty was specific to the five phyla or distributed across other uncultivated prokaryotic taxa, we mapped these NPFs and NSs against a comprehensive dataset of 169,642 bacterial and archaeal genomes covered in Rodriguez del Río et al.17. Using an in-house pipeline (Supplementary Fig. 4), we found that 44.6% of these NPFs and NSs are present in other uncultured taxa, highlighting the novel and undescribed metabolic repertoire that these five phyla share with other uncultured prokaryotic lineages17. Specifically, we found that these proteins are also present in Marinisomatota, Bacteroidota, and WOR-3 from publicly available genomes obtained from both marine and terrestrial environments17. When comparing the total number of NPFs per genome in the novel bacterial phyla against the genomic dataset (approximately 170,000 genomes), we found that the novel taxa described in this study have a higher than average percentage of novel proteins per genome (5.68 ± 4.89%) (p  0.7) and widespread (coverage > 0.7) within each phylum are shown in dark purple bars. The number of novel protein families with conserved neighboring genes are shown in light gray bars. c, d, Selected examples of phylogenetic trees and novel protein family genomic context marked in gray with a black outline) in Blakebacterota and Arandabacterota. The protein families are similar between these two phyla and have conserved neighboring genes, including translation initiation factor IF-3 gene (infC), large subunit ribosomal protein L20 gene (rplT), phenylalanyl-tRNA synthetase genes (pheST), cell division protein gene (zapA), phosphodiesterase gene (ymdB), methenyltetrahydrofolate cyclohydrolase gene (folD), and exodeoxyribonuclease genes (xseAB). e Phylogenetic tree and genomic context of a novel protein family uniquely distributed in Joyebacterota. The novel protein family has conserved genomic neighbors related to energy conservation (Rnf complex genes, rnfABCDEG). The phylogeny was generated using FastTree2 and numbers on the top and bottom of the branch represent the bootstrap and branch length, respectively. Source data are provided as a Source Data file.Full size imageMetabolic pathways are often encoded by ‘genome neighborhoods’ (gene clusters and/or operons)18. Therefore, we calculated the genomic context conservation of the NPFs containing three or more sequences (3773 NPFs in total) and examined the annotation of genes found in genomic proximity of the NPFs to determine their potential function. Of the inspected families, 513 (14%) had a conservation score ≥ 0.9 (see “Methods”) indicating a high degree of conserved neighboring proteins. Manual annotation of these neighboring proteins indicated they are potentially involved in sulfur reduction, energy conservation, as well as the degradation of organics such as starch, fatty acids, and amino acids (highlighted in red in Supplementary Fig. 5). For example, a NPF predominantly found in Blakebacterota is neighbored by putative menaquinone reductases (QrcABCD), a conserved complex related to energy conservation in sulfate reducing bacteria19,20,21,22. However, metabolic annotations of Blakebacterota genomes that encode QrcABCD indicate that they largely lack the key enzymes for sulfate reduction, dissimilatory sulfite reductases (DsrABC), suggesting this QrcABCD complex may be involved in other bioenergetic contexts such as linking periplasmic hydrogen and formate oxidation to the menaquinone pool22.In some instances, we found NPFs coded near genes predicted to produce key proteins in nitrogen cycling. Two of the Joyebacterota MAGs code NPF neighboring proteins with homology to hydroxylamine dehydrogenases (HAO). HAO is a key enzyme in marine nitrogen cycling that has traditionally been thought to catalyze the oxidation of hydroxylamine (NH2OH) to nitrite (NO2−) in ammonia oxidizing bacteria. Recently, it has been suggested that HAO may also convert hydroxylamine to nitric oxide (NO) as an intermediate, which is then further oxidized to nitrite by an unknown mechanism. Hydroxylamine is also known to be an intermediate in the nitrogen cycle. It is a potential precursor of nitrous oxide (N2O), a potent greenhouse gas that is a byproduct of denitrification, nitrification23,24, and anaerobic ammonium oxidation25. The presence of HAO within the genomic context of these NPFs suggests they may be involved in mediating hydroxylamine metabolism, and thus may play an important role in nitrogen cycling.A number of NPFs are colocalized with genes predicted to be involved in the utilization of organic carbon. For example, one NPF found in Blakebacterota genomes is adjacent to a peptidase (PepQ; K01271) for dipeptide degradation. Another NPF, only detected in Blakebacterota, is neighbored by long-chain acyl-CoA synthetase (FadD; K01897), a key enzyme in fatty acid degradation (Supplementary Fig. 6). In Joyebacterota, as well as in publicly available Bacteroidetes and Latescibacteria we identified an NPF that is colocalized with amylo-alpha-1,6-glucosidase (Glycoside Hydrolase Family 57), suggesting a potential role in starch degradation.We also identified NPFs that are specific and very conserved in AABM5, Blakebacterota, Orphanbacterota, Arandabacterota, and Joyebacterota (2, 39, 3, 16, and 26 respectively). These NPFs were found in at least 70% of the MAGs belonging to each phylum, and rarely present in other genomes across the tree of life. Due to their unique nature, the 86 unique NPFs could be used as marker genes for future characterizations of the novel bacteria described in this study. When examining the genomic context of the phyla-specific NPFs, we found that more than half of the NPFs (49 of 86) shared the same gene order and are next to genes predicted to be involved in various catabolic and anabolic processes. For example, an NPF in Joyebacterota MAGs is adjacent to an Rnf complex26, which is important for energy conservation in numerous organisms21 (Fig. 2e). Also, two different NPFs in Blakebacterota and Arandabacterota MAGs were located next to tRNA synthesis genes (Fig. 2c, d). Additional phyla-specific NPFs were colocalized with genes predicted to be involved in other important processes, including peptidoglycan biosynthesis (Supplementary Fig. 6a), F-type ATPase (Supplementary Fig. 6b), acyl-CoA dehydrogenase, elements for transportation, sulfur assimilation (Supplementary Fig. 6c), and others (Supplementary Fig. 6d).Metabolic potential of the novel bacterial phylaIn addition to NPF-based analyses, we compared the predicted proteins in the novel lineages to a variety of databases and gene phylogenies to understand their metabolism (see “Methods”). The distribution of key metabolic proteins based on presence/absence of protein families (using MEBS: see methods) in the 61 MAGs is largely consistent with their phylogeny (Fig. 1a). Below, we detail the predicted metabolism of each novel bacterial phyla based on these analyses (Supplementary Fig. 5 and Supplementary Data 8 and 9, see details in Supplementary Information).JoyebacterotaJoyebacterota is composed of 20 MAGs predominantly reconstructed from hydrothermal vent sediments (blue, lower right side in the phylogeny shown in Fig. 1a). Metabolic inference suggests that these bacteria are obligate anaerobes encoding extracellular carbohydrate-active enzymes (CAZymes) with the potential to degrade pectate or pectin, photosynthetically fixed carbon in marine diatoms, macrophytes27, and terrestrial plants28. Furthermore, Joyebacterota seems to be involved in the sulfur cycle. Seven Joyebacterota MAGs encode sulfide:quinone oxidoreductases (SQR). Phylogenetic analysis indicate these SQR belong to the membrane-bound type I and III29. Interestingly, these SQR type I sequences are closely related to those sequences mostly found in terrestrial environments, e.g., freshwater, soil, and hot spring, while SQR-III  have been previously suggested to play a key role maintaining the sulfide homeostasis or bioenergetics in deep-sea sediments30. The presence of these pathways highlight the potential adaptation of Joyebacterota to several environments, contributing to recycling of carbon and sulfur.BlakebacterotaThe Blakebacterota phylum is composed of 11 MAGs predominantly reconstructed from the surface layer of GB sediments (0–6 cm). In this environment, temperatures range from 25 to 29 °C, CH4 measures 0.4–0.8 mM, CO2 reaches up to 10 mM, and SO42− concentrations are high (up to 28 mM)30. Metabolic inference using MEBS31 suggests Blakebacterota play an important role in N and S cycles. These findings were supported by the presence of key enzymes in these cycles. For example, we identified a nitrous oxide reductase in Blakebacterota, the only known enzyme to catalyze the reduction of nitrous oxide to nitrogen gas. This reaction acts as a sink for nitrous oxide, and thus is an important removal mechanism for this potent greenhouse gas. In addition to nitrogen cycling, we identified key genes involved in sulfur cycling in Blakebacterota. Six of the MAGs possess genes that code for SQR with sulfate or nitrous oxide as the final electron accepter. In addition, seven of the MAGs contain genes for thiosulfate dehydrogenase (doxD), which may convert thiosulfate to tetrathionate. Finally, one MAG is predicted to produce dimethyl sulfide (DMS) under oxic conditions via methanethiol S-methyltransferase (MddA) from methylate L-methionine or methanethiol (MeSH). Thus, these bacteria may play important roles in a variety of intermediate steps in nitrogen and sulfur cycling.ArandabacterotaLike Joyebacterota, Arandabacterota were largely recovered from shallow (2–14 cm) GB and deep (26–38 cm) BS sediments. This phylum contains 11 MAGs that are predicted to be anaerobic polysulfide and elemental sulfur reducers. They may mediate sulfur reduction via sulfhydrogenases (HydGB), which results in the production of sulfide32,33. Thus, Arandabacterota may contribute to sulfur cycling in marine sediments. Arandabacterota also code distinct hydrogenases, [NiFe] 3c and 4g types, (Fig. 3) for H2 oxidation. In addition, Arandabacterota may reduce nitrite via periplasmic dissimilatory nitrite reductases (NrfAH) present in Meg22_24_Bin_129, BHB10-38_Bin_9, and SY70-4-3_Bin_59. This mechanism for energy conservation is more efficient than polysulfide and elemental sulfur reduction. Therefore, they are likely to use sulfur species as electron donors in the absence of nitrite.Fig. 3: Maximum likelihood phylogenetic tree of NiFe hydrogenases from the novel phyla.The majority of NiFe hydrogenases identified from the five phyla in this study are highlighted in the gray background. Most hydrogenases are types 4g and 3c. Starred branches denote the minor NiFe hydrogenases identified in this study. Bootstrap values ≥ 80 are shown in circles. Source data are provided as a Source Data file.Full size imageOrphanbacterotaOrphanbacterota is composed of seven MAGs that were mostly obtained from the BS, and appear to be metabolically versatile, facultative aerobes. The BS has an average water depth of 18 m and is strongly influenced by anthropogenic activities in China, mainly the terrestrial input of nutrients and organic matter34. Orphanbacterota code a diversity of CAZymes for the degradation of complex carbohydrates. We identified genes coding for extracellular glycoside hydrolase family 16 (GH16), which may be involved in the degradation of laminarin, releasing glucose and oligosaccharides35. Six Orphanbacterota genomes also contain genes predicted to produce extracellular peptidases belonging to family M28 and S8, which are nonspecific peptidases (Supplementary Fig. 7 and Supplementary Data 10–14). The released amino acids could be taken up via ABC transporters coded by these bacteria.Consistent with their recovery from shallow sediment habitats (Supplementary Data 1), Orphanbacterota have a diverse repertoire of terminal cytochrome oxidase genes (Supplementary Data 9) suggesting they are capable of surviving in a range of oxygen concentrations. Based on the presence of isocitrate lyase and malate synthase, they may use the glyoxylate cycle for carbohydrate synthesis when sugar is not available, or use simple two-carbon compounds for energy conservation36,37. They also appear capable of reducing nitrate to nitrite via periplasmic nitrate reductases (NapAB)38. Moreover, they could reduce nitrate via the membrane-bound nitrate reductase for energy conservation and reducing nitrous oxide.One Orphanbacterota genome (M3-44_Bin_119) has genes predicted to mediate sulfate/sulfite reduction, including DsrABC, QmoABC, and membrane bound Rnf complexes (Supplementary Fig. 8a, b and Supplementary Data 8 and 9). Another Orphanbacterota (LQ108M_Bin_12) is predicted to contain diverse metabolic pathways, including MmdA for DMS production, SQR for sulfide oxidation, the Rnf complex for energy conservation21 or detoxification (Supplementary Fig. 8c), and sulfhydrogenases (HydABDG) for H2 oxidation. In addition to energy conservation and detoxification, sulfide oxidation is important for preventing the loss of sulfur through H2S volatilization. This is predicted to be an important process in sulfur-rich sediments, where large quantities of the self-produced H2S are produced during heterotrophic growth29.AABM5AABM5 (12 genomes, 7 obtained in this study) is an understudied bacterial group that has largely been recovered from shallow (4–12 cm) sediments in GB and deep (44–62 cm) sediments in BS. Despite the distinct environments where they have been found, genomes within this phylum have several shared metabolic abilities. In contrast to the strict anaerobic lifestyle that was previously reported in a subgroup within AABM5 (candidate division LCP–89)12, we predict they are facultative anaerobes. In support of this, we identified cytochrome c oxidase (CtaDCEF) and cytochrome bd ubiquinol oxidase (CydAB) for aerobic respiration39. In addition, we identified DsrABC in nine genomes (Supplementary Fig. 8 and Supplementary Data 15), indicating these organisms can potentially reduce sulfate/sulfite for energy conservation. Several AABM5 genomes are predicted to use H2 as an electron donor due to the presence of type 3c [NiFe] hydrogenase (MvhADG) (Fig. 3, Supplementary Fig. 9, and Supplementary Data 8 and 9). The metabolic versatility in this phylum better explains their global distribution.Ecological significance of the new phylaThese previously overlooked bacterial phyla appear to be involved in key biogeochemical processes in marine sediments, namely sulfur and nitrogen cycling, and the degradation of organic carbon. However, we did not find any evidence for complete autotrophic metabolisms (Wood-Ljungdahl pathway, Calvin–Benson–Bassham, reductive tricarboxylic acid, 3-hydroxypropionate bicycle, 3-hydroxypropionate-4-hydroxybutyrate, and dicarboxylate-4-hydroxybutyrate cycles) in any of these bacteria. Instead, they have a variety of pathways for the utilization of organic compounds as detailed above. These novel bacteria phyla (all except Blakebacterota) have the potential to degrade the algal glycan laminarin, one of the most important complex carbon compounds in the ocean40. These novel phyla encode extracellular laminarinases that specifically cleave the laminarin into more readily degradable sugars, e.g., glucose and oligosaccharide (Supplementary Fig. 7 and Supplementary Data 10–12). Laminarin glycan is produced in the surface ocean by microalgae that sequester CO2 as an important carbon sink in the oceans41. This is a key process of the global carbon cycle, and most studies have focused on understanding aerobic laminarin-degrading bacteria in the surface oceans41,42. Recently, it has been shown that laminarin plays a prominent role in oceanic carbon export and energy flow to higher trophic levels and the deep ocean40, yet the organisms responsible for laminarin degradation under anoxic conditions are unknown. The discovery of  these novel bacterial phyla opens new doors for future studies exploring laminarin degradation in the deep sea. In addition, most of them contain genes predicted to code for sulfatases. Blakebacterota, Orphanbacterota, Arandabacterota, and Joyebacterota code for arylsulfatase, mainly arylsulfatase A, for desulfation of galactosyl moiety of sulfatide. They also code choline sulfatase, iduronate 2-sulfatase and some uncharacterized sulfatases for different types of substrates43. This suggests they are capable of cleaving organic sulfate ester bonds as a source of sulfur and organic carbon on the ocean floor.Many metabolic processes identified here, including pathways for polysaccharide degradation, sulfur, and nitrogen metabolism are often incomplete (Fig. 4). This may be due to the incompleteness of these genomes, or it suggests that these processes occur via metabolic handoffs within the community. Some of the phyla are capable of mediating a variety of sulfur and nitrogen redox reactions (Fig. 4a, b). For example, four phyla code DsrABC, suggesting they play an overlooked role in inorganic matter degradation in marine sediments through sulfate reduction. The resultant sulfide may be reoxidized to sulfur intermediates and organic sulfur compounds by these newly described bacteria. Four phyla (Blakebacterota, Orphanbacterota, Arandabacterota, and Joyebacterota) code an SQR for producing elemental sulfur from sulfide. Methanethiol S-methyltransferase (MddA) is predicted to be produced by individual MAGs Blakebacterota (M3-38_Bin_215) and Orphanbacterota (LQ108M_Bin_12) for the production of DMS from methionine44. DMS is important in climate regulation and sulfur cycling in marine environments45,46, though little is known about the fate or production of DMS in anoxic environments like marine sediments. As detailed above, Blakebacterota contains genes for the conversion of thiosulfate to tetrathionate. Four phyla (AABM5, Orphanbacterota, Arandabacterota, and Joyebacterota) are predicted to disproportionate thiosulfate to sulfite via thiosulfate/3-mercaptopyruvate sulfurtransferase. Thus, we suspect these bacteria may be capable of mediating intermediate sulfur species in anoxic environments. These results provide a predictive framework for future physiological studiesto confirm our genomic-based predictions.Fig. 4: Genomic-based predictions of the potential metabolic role of the novel bacterial phyla.Key steps in the (a) sulfur and (b) nitrogen cycles predicted in the five bacterial phyla. Compounds (in gray triangle frames) were arranged according to the standard Gibbs free energy of formation of each sulfur or nitrogen compound (values next to the compound taken from Caspi et al.93). Star, square, triangle, pentagon, and diamond shapes correspond to AABM5, Blakebacterota, Orphanbacterota, Arandabacterota and Joyebacterota, respectively. Colored shapes represent the presence of genes in a given pathway. Fully colored shapes indicate the presence of genes in over 50% of the phyla. Half colored shapes signify that less than 50% of the phyla code for those genes. Uncolored shapes indicate presence of genes in only one MAG. Note that only pathways encoded in at least one MAG are shown. The red dotted line indicates the assimilatory process. The blue soild line indicates the confirmed pathway with phylogeny of key genes. c Phylogenetic tree and genomic context of a novel protein family (NPF) next to putative menaquinone reductase complex genes (qrcABCD) found in Blakebacterota and Orphanbacterota. d Phylogenetic tree and genomic context of a NPF next to hydroxylamine oxidoreductase genes (hao) in Joyebacterota.Full size imageIn addition to potential roles in sulfur cycling, the phyla described here may play key roles in nitrogen processes, for example several MAGs contain genes that code predicted hydroxylamine dehydrogenase proteins (HAO, confirmed by different databases)47,48. HAO is a precursor of nitrous oxide (N2O), a potent greenhouse gas and ozone destructing agent in the atmosphere. Marine N2O stems from nitrification and denitrification processes which depend on organic matter cycling and dissolved oxygen. Since hydroxylamine is a precursor of N2O, deciphering the organisms that can mediate the formation of N2O has important implications for Earth’s climate49. In addition, three phyla (AABM5, Blakebacterota, and Orphanbacterota) code for periplasmic and/or transmembrane nitrate reductase, and two phyla (AABM5 and Arandabacterota) are predicted to reduce nitrite via dissimilatory nitrite reductase.In recent years, there have been large advances in the exploration of novel microbial diversity. Genomic data has provided crucial insights into the ecological roles and biology of these new microbes. The recovery of bacterial genomes belonging to five overlooked, globally distributed phyla with considerably novel protein composition reminds us there is much to be learned about the microbial world. The identification of NPFs provides targets for future studies to elucidate the ecophysiology of these organisms. The presence of genes for organic carbon degradation and sulfur and nitrogen cycling in these new bacteria suggests they contribute to a variety of key processes in marine sediments. Thus, the addition of these bacterial genomes to ecosystem models will likely transform our understanding of how microbial communities drive carbon degradation and nutrient cycling in the oceans. More

Goulson, D., Nicholls, E., Botías, C. & Rotheray, E. L. Bee declines driven by combined stress from parasites, pesticides, and lack of flowers. Science 347, 1255957 (2015).Article 
PubMed 

Google Scholar 
– Potts, S. G., et al. Summary for Policymakers of the Assessment Report of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services on pollinators, pollination, and food production (eds. Potts, S. G. et al.). 36 pages. (Bonn, Germany, 2016).Dolezal, A. G. et al. Interacting stressors matter: Diet quality and virus infection in honeybee health. R. Soc. Open Sci. 6, 181803 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Annoscia, D. et al. Neonicotinoid Clothianidin reduces honeybee immune response and contributes to Varroa mite proliferation. Nat. Commun. 11, 1–7 (2020).Article 

Google Scholar 
Macías-Macías, J. O. et al. Nosema ceranae causes cellular immunosuppression and interacts with thiamethoxam to increase mortality in the stingless bee Melipona colimana. Sci. Rep. 10, 1–8 (2020).Article 

Google Scholar 
Michener, C. D. Pot-honey. In Pot-Honey: A Legacy of Stingless Bees (eds Vit, P. et al.) 3–17 (Springer, 2013).Chapter 

Google Scholar 
Rosa, C. A. et al. Yeast communities associated with stingless bees. FEMS Yeast Res. 4, 271–275 (2003).Article 
PubMed 

Google Scholar 
Menezes, C., Vollet-Neto, A. & Fonseca, V. L. I. An advance in the in vitro rearing of stingless bee queens. Apidologie 44, 491–500 (2013).Article 

Google Scholar 
Morais, P. B., Calaça, P. S. S. T. & Rosa, C. A. Microorganisms associated with stingless bees. In Pot-Honey Bees (eds Vit, P. et al.) 173–186 (Springer, 2013).Chapter 

Google Scholar 
Menegatti, C. et al. Paenibacillus polymyxa associated with the stingless bee Melipona scutellaris produces antimicrobial compounds against entomopathogens. J. Chem. Ecol. 44, 1158–1169 (2018).Article 
PubMed 

Google Scholar 
Paludo, C. R. et al. Stingless bee larvae require fungal steroid to pupate. Sci. Rep. 8, 1122321 (2018).Article 

Google Scholar 
Paludo, C. R. et al. Microbial community modulates growth of symbiotic fungus required for stingless bee metamorphosis. PLoS ONE 14, e0219696 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Hamzah, S. A., Zawawi, N. & Sabri, S. A review on the association of bacteria with stingless bees. Sains Malays. 49, 1853–1863 (2020).Article 

Google Scholar 
de Paula, G. T., Menezes, C., Pupo, M. T. & Rosa, C. A. Stingless bees and microbial interactions. Curr. Opin. Insect Sci. 44, 41–47 (2020).Article 
PubMed 

Google Scholar 
Menezes, C. et al. A Brazilian social bee must cultivate fungus to survive. Curr. Biol. 25, 2851–2855 (2015).Article 
PubMed 

Google Scholar 
– Flechtmann, C. H. W. & de Camargo, C. A. Acari associated with stingless bees (Meliponidae, Hymenoptera) from Brazil. in Proceedings of the 4th International Congress of Acarology, Saalfelden (Austria)/edited by Edward Piffl (Budapest, Akademiai Kiado,1979).Dorigo, A. S. et al. In vitro larval rearing protocol for the stingless bee species Melipona scutellaris for toxicological studies. PLoS ONE 14, e0213109 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Rosa-Fontana, A., Dorigo, A. S., Galaschi-Teixeira, J. S., Nocelli, R. C. F. & Malaspina, O. What is the most suitable native bee species from the neotropical region to be proposed as model-organism for toxicity tests during the larval phase?. Environ. Pollut. 265, 114849 (2020).Article 
PubMed 

Google Scholar 
Miotelo, L., Dos Reis, A. L. M., Malaquias, J. B., Malaspina, O. & Roat, T. C. Apis mellifera and Melipona scutellaris exhibit differential sensitivity to thiamethoxam. Environ. Pollut. 268, 115770 (2021).Article 
PubMed 

Google Scholar 
Rosa, A. E., André, H. & Flechtmann, C. H. W. Acari domun meliponirarum brasiliensium habitantes. Proctotydaeus alvearii 45(1–2), 79–83 (1985).
Google Scholar 
Da-Costa, T., dos Santos, C. F., Rodighero, L. F., Ferla, N. J. & Blochtein, B. Mite diversity is determined by the stingless bee host species. Apidologie 52(5), 950–959. https://doi.org/10.1007/s13592-021-00878-2 (2021).Article 

Google Scholar 
de Rosa, A. S. et al. Consumption of the neonicotinoid thiamethoxam during the larval stage affects the survival and development of the stingless bee Scaptotrigona aff. depilis. Apidologie 47, 729–738 (2016).Article 

Google Scholar 
Wu, J. Y., Anelli, C. M. & Sheppard, W. S. Sub-lethal effects of pesticide residues in brood comb on worker honeybee (Apis mellifera) development and longevity. PLoS One 6, e14720 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Tavares, D. A., Roat, T. C., Carvalho, S. M., Silva-Zacarin, E. C. M. & Malaspina, O. In vitro effects of thiamethoxam on larvae of Africanized honeybee Apis mellifera (Hymenoptera: Apidae). Chemosphere 135, 370–378 (2015).Article 
PubMed 

Google Scholar 
Biani, N. B., Mueller, U. G. & Wcislo, W. T. Cleaner mites: sanitary mutualism in the miniature ecosystem of neotropical bee nests. Am. Nat. 173, 841–847 (2009).Article 
PubMed 

Google Scholar 
Gilliam, M., Roubik, D. W. & Lorenz, B. J. Microorganisms associated with pollen, honey, and brood provisions in the nest of a stingless bee Melipona fasciata. Apidologie 21, 89–97 (1990).Article 

Google Scholar 
Rebelo, K. S., Ferreira, A. G. & Carvalho-Zilse, G. A. Physicochemical characteristics of pollen collected by Amazonian stingless bees. Ciência Rural 46, 927–932 (2016).Article 

Google Scholar 
Mohammad, S. M., Mahmud-Ab-Rashid, N.-K. & Zawawi, N. Stingless bee-collected pollen (bee bread): Chemical and microbiology properties and health benefits. Molecules 26, 957 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
da Cruz Landim, C. (2009). Abelhas. Unesp.Rosa, A. S. et al. Quantification of larval food and its pollen content in the diet of stingless bees: Subsidies for toxicity bioassays studies. Braz. J. Biol. 75(3), 771–772. https://doi.org/10.1590/1519-6984.22314 (2015).Article 
PubMed 

Google Scholar 
Vollet-Neto, A., Maia-Silva, C., Menezes, C. & Imperatriz-Fonseca, V. L. Newly emerged workers of the stingless bee Scaptotrigona aff. depilis prefer stored pollen to fresh pollen. Apidologie 48, 204–210 (2017).Article 

Google Scholar 
Hartfelder, K. & Engels, W. The composition of larval food in stingless bees: evaluating nutritional balance by chemosystematic methods. Insect. Soc. 36, 1–14 (1989).Article 

Google Scholar 
Costa, R. A. C. & da Cruz-Landim, C. Distribution of acid phosphatases in the hypopharyngeal glands from workers, queens, and males of a Brazilian stingless bee Scaptotrigona postica Latreille: An ultrastructural cytochemical study. Histochem. J. 33, 653–662 (2001).Article 
PubMed 

Google Scholar 
de Moraes, R. L. M. S., Brochetto-Braga, M. R. & Azevedo, A. Electrophoretical studies of proteins of the hypopharyngeal glands and of the larval food of Melipona quadrifasciata anthidioides Lep. (Hymenoptera, Meliponinae). Insect. Soc. 43, 183–188 (1996).Article 

Google Scholar 
Fernandes-da-Silva, P. G., Muccillo, G. & Zucoloto, F. S. Determination of minimum quantity of pollen and nutritive value of different carbohydrates for Scaptotrigona depilis Moure (Hymenoptera, Apidae). Apidologie 24, 73–79 (1993).Article 

Google Scholar 
Fernandes-da-Silva, P. G. & Serrão, J. E. Nutritive value and apparent digestibility of bee-collected and bee-stored pollen in the stingless bee, Scaptotrigona postica Latr. (Hymenoptera, Apidae, Meliponini). Apidologie 31, 39–45 (2000).Article 

Google Scholar 
Crailsheim, K. & Stolberg, E. Influence of diet, age and colony condition upon intestinal proteolytic activity and size of the hypopharyngeal glands in the honeybee (Apis mellifera L.). J. Insect Physiol. 35, 595–602 (1989).Article 

Google Scholar 
Oliveira, R. A., Roat, T. C., Carvalho, S. M. & Malaspina, O. Side-effects of thiamethoxam on the brain and midgut of the africanized honeybee Apis mellifera (Hymenopptera: Apidae). Environ. Toxicol. 29, 1122–1133 (2014).Article 
PubMed 

Google Scholar 
Christen, V., Schirrmann, M., Frey, J. E. & Fent, K. Global transcriptomic effects of environmentally relevant concentrations of the neonicotinoids clothianidin, imidacloprid, and thiamethoxam in the brain of honeybees (Apis mellifera). Environ. Sci. Technol. 52, 7534–7544 (2018).Article 
PubMed 

Google Scholar 
Moreira, D. R. et al. Toxicity and effects of the neonicotinoid thiamethoxam on Scaptotrigona bipunctata Lepeletier, 1836 (Hymenoptera: Apidae). Environ. Toxicol. 33, 463–475 (2018).Article 
PubMed 

Google Scholar 
Tavares, D. A., Roat, T. C., Silva-Zacarin, E. C. M., Nocelli, R. C. F. & Malaspina, O. Exposure to thiamethoxam during the larval phase affects synapsin levels in the brain of the honeybee. Ecotoxicol. Environ. Saf. 169, 523–528 (2019).Article 
PubMed 

Google Scholar 
Roat, T. C. et al. Using a toxicoproteomic approach to investigate the effects of thiamethoxam into the brain of Apis mellifera. Chemosphere 258, 127362 (2020).Article 
PubMed 

Google Scholar 
Caesar, L. et al. The virome of an endangered stingless bee suffering from annual mortality in southern Brazil. J. Gen. Virol. 100, 1153–1164 (2019).Article 
PubMed 

Google Scholar 
Guimarães-Cestaro, L. et al. Occurrence of virus, microsporidia, and pesticide residues in three species of stingless bees (Apidae: Meliponini) in the field. Sci. Nat. 107, 1–14 (2020).Article 

Google Scholar 
Teixeira, É. W. et al. European Foulbrood in stingless bees (Apidae: Meliponini) in Brazil: Old disease, renewed threat. J. Invertebr. Pathol. 172, 107357 (2020).Article 
PubMed 

Google Scholar 
Alberoni, D., Gaggìa, F., Baffoni, L. & Di Gioia, D. Beneficial microorganisms for honeybees: problems and progresses. Appl. Microbiol. Biotechnol. 100, 9469–9482 (2016).Article 
PubMed 

Google Scholar 
Manley, R., Boots, M. & Wilfert, L. Emerging viral disease risk to pollinating insects: ecological, evolutionary, and anthropogenic factors. J. Appl. Ecol. 52, 331–340 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Manley, R. et al. Knock- on community impacts of a novel vector: spillover of emerging DWV- B from Varroa- infested honeybees to wild bumblebees. Ecol. Lett. 22, 1306–1315 (2019).PubMed 
PubMed Central 

Google Scholar 
Graystock, P., Blane, E. J., McFrederick, Q. S., Goulson, D. & Hughes, W. O. H. Do managed bees drive parasite spread and emergence in wild bees?. Int. J. Parasitol. Parasit. Wildl. 5, 64–75 (2016).Article 

Google Scholar 
Requier, F. et al. The conservation of native honeybees is crucial. Trends Ecol. Evol. 34, 789–798 (2019).Article 
PubMed 

Google Scholar 
Test No. 237: Honey Bee (Apis Mellifera) Larval Toxicity Test, Single Exposure. (2013). OECD. https://doi.org/10.1787/9789264203723-enMoral, R. A., Hinde, J. & Demétrio, C. G. Half-normal plots and overdispersed models in R: the hnp package. J. Stat. Softw. 81(1), 1–23 (2017).
Google Scholar 
– Kassambara, A. Survminer. GitHub repository. https://github.com/kassambara/survminer (2020).- Therneau, T., Crowson, C., & Atkinson, E. Multi-state models and competing risks. CRAN-R https://cran.r-project.org/web/packages/survival/vignettes/compete (2020). More

It is well known that parrots are excellent vocal learners; here we quantified that ability across a wide variety of species, using human mimicry as a proxy for vocal learning of natural repertoires. Results confirm that parrot vocal mimicry varies substantially both within and among species22. Parrot age, social interactions, and sex do not appear to be universal drivers of vocal learning ability within the order Psittaciformes, but all of these factors may have effects within individual species.Vocal learning variation by speciesWithin species, mimicry sound repertoires are extremely variable bird to bird; for example, our data indicate that a grey parrot may mimic anywhere from 0 to 600 different human words. Many other species showed smaller repertoires but similar variability. It is not entirely clear whether this range of variation would be present in natural sounds within wild parrot populations, but research has demonstrated intraspecific repertoire size variation in multiple species of parrots30,31.The vast majority of parrots presented a pattern in which their repertoire size was largest for words, intermediate for phrases (composed of the reported words), and smallest for non-linguistic sounds (Fig. 2). In the wild, parrots mimic the most socially relevant vocalizations, and presumably do so in captivity as well15. Thus, the spoken word and phrase interactions with their human “flock” likely reflect the most socially relevant cues. The interesting exceptions to this pattern were Fischer’s lovebirds, cockatiels, and Senegal parrots who all used more sounds than phrases. Cockatiels are well-known in the pet world to be excellent whistlers, and thus it was satisfying to see that our data support that informal information. We suspect that deviations from the typical patterns may represent acoustic learning preferences, templates, or limitations32.Although individual variation was substantial, we nevertheless saw strong evidence that overall vocal learning abilities differed by species. Pacific parrotlets and sun parakeets showed very limited human mimicry, while grey parrots, Amazona parrots, cockatoos, and macaws were generally very accomplished mimics. The patterns that we documented appeas to reflect natural vocal repertoire variation across species. The documented calls of wild parrots generally range from 5 to 15 calls25,33,34,35,36. Several species, however, present additional complexity: yellow-naped parrots (Amazona auropalliata), palm cockatoos (Probosciger aterrimus), and grey parrots all have natural repertoires of more than 25 discrete elements, with additional elements given in duets13,27,37 Members of these three groups, grey parrots, Amazona parrots and cockatoos also had relatively large repertoires in our study. In several of these species (particularly grey parrots) our measure of mimicked “words” (60) was higher than estimates of natural call “elements” (39) in the literature27. This discrepancy suggests that parrots are capable of learning vocalizations with more than 25 elements and, simultaneously, might reflect a sampling bias wherein survey-takers are more likely to report on individuals with high mimicry ability.Parrot species varied in their tendency to improvise new combinations of elements, although most species did rearrange words to some degree. Research shows that parrot vocalization length and structure carry signal content, so there may be selective pressures favoring this ability24,33. If so, then our data suggest that those pressures are strongest in some cockatoos and weakest in sun parakeets and green-cheeked parakeets. In general, species with larger repertoires also showed more vocal flexibility (Fig. 2, Appendix 6). Additionally, wild birds typically use particular vocalizations in set contexts, so the ability to do so is likely to be adaptive24. Previous studies of captive parrots have demonstrated contextual use of mimicked words, both in tutored lab settings and in home-raised birds28,38. In our sample, contextual use of learned sounds was supported across 89% of individuals and most species. Survey-taker responses on this topic are necessarily subjective, so we emphasize that this rate of contextual use should be interpreted as a general estimate. Nevertheless, the data indicated that parrots frequently associated mimicked human sounds with appropriate human contexts. This finding is particularly revealing because the relevant human contexts are, by their nature, outside the range of typical wild parrot experiences. Contextual vocalization use must, therefore, rely on extremely flexible vocal learning mechanisms.Vocal learning variation by ageOn average, birds aged with high confidence were younger than those aged with low or medium confidence. This pattern might indicate that people tend to overestimate the age of captive birds of uncertain age. This pattern might also reflect the facts that older birds are more likely to be wild-caught and that younger birds are more likely to have good hatch-date documentation. In either case, there are few ramifications of inaccurate age estimates relating to vocal behavior because our data gave no evidence that adult vocal mimicry repertoires varied with age. Our analyses of grey parrots confirmed that repertoires expanded through the juvenile phase, but did not show reliable expansion among adults. Studies of wild birds indicate that parrots can learn vocalizations throughout life; such open-ended learning is limited to a subset of vocal learning species, and can generate different outcomes as animals age15. In some species, animals can add new vocal features over the course of a lifetime, leading to repertoire expansion39,40. In other species, animals may replace parts of their repertoire with newly-learned vocalizations, leading to stable vocal production repertoire sizes across age groups39,41. Our data suggest that parrots fit the second pattern; although they are open-ended vocal learners, their adult repertoires change more by element replacement, than by expansion. This does not necessarily imply that vocalizations are “forgotten” through time, but merely that some sounds are no longer used as conditions change42. Many parrot vocalizations function in social coordination with flock-mates22. The fission–fusion nature of parrot flocks creates changing social conditions for each individual over its lifetime43. A vocal replacement model for repertoire learning would allow individuals to adjust their vocal signatures to match new social situations and stop producing vocalizations that are no longer socially relevant11,44.Vocal learning variation by sexOur analyses of the full data set confirmed the generally held understanding that males and females in most species of parrots have similar vocal learning abilities15. We did, however see sex differences in some species that merit future study. First, we found a substantial overrepresentation of males in our sample. This could be interpreted several ways; (1) there are legitimately more males in the parrot pet trade, (2) pet owners are giving us accurate data but are more likely to give us data on males or (3) some bias exists in which pet owners assume their talking parrots are males, rather than females. Possibilities 1 and 2 seem unlikely because after we eliminated all parrots sexed with low confidence, we were left with a nearly 1:1 ratio of males:females in the subset of parrots that were sexed with high confidence. That trend suggests that the male bias in our data comes (at least in part) from a human tendency to label their pet parrots as male when the sex is not clear. Among songbirds, there is a strong tendency to assume that singing birds are male, and a similar bias may hold true for parrots45. It is unclear whether parrots in this study were mislabeled as male because they vocalize or, more simply, because that is the default human tendency for any animal.Although we conclude that some of the male bias in our data is human error, we also saw patterns that suggest real sex differences in vocal learning some species. For example, Pacific parrotlets are a dimorphic species, and all of our sampled birds were sexed by plumage46. Thus, we expect sexing in this species to be fairly accurate. Our data set included 10 males and no females, a bias unlikely to result purely from sampling error. We saw a similar trend in cockatiels for which there was a large overabundance of males in the data set, even among the 17 birds sexed with high confidence. Humans may be more likely to report on parrots that are good mimics. Therefore, the results likely reflect a real-world tendency for male cockatiels to mimic more human sounds than females. Figure 3 suggests that the same might be true for galahs, sulphur-crested cockatoos, rose-ringed parakeets, Senegal parrots, and budgerigars. Existing research supports the idea that sex differences in vocal behavior are important in several of these species. Among galahs, male and female calls evoke different responses47, and patterns of call adjustment vary by sex among budgerigars20. We also note that several of these species (Pacific parrotlets, rose-ringed parakeets, budgerigars, and cockatiels; Appendix 2b) show sex-based differences in both plumage and vocal learning, raising questions about whether those traits co-evolve.In addition to sex-based differences in the tendency to mimic humans, several well-sampled species showed evidence of sex-based differences in repertoire sizes. Particularly interesting are the blue-and-yellow macaws, in which repertoire size was significantly male-biased. We had more females (15) than males (9) in the data set, but males used on average 3–4 times as many mimicry sounds, phrases and words as females did. Galahs and budgerigars showed a similar male-bias in repertoire sizes, matching the trend of males being overrepresented in our data set for those two species. Prior research on galahs and budgerigars has found that males can be more vocal and more flexible with their vocalizations; perhaps these abilities translate to learning more call types20,47. A similar, but weaker, male mimicry increase occurred in rose-ringed parakeets. In only one species, yellow-headed parrots, did females show a significantly larger mimicry repertoire than males in any category (Appendix 5). Interestingly, the tendency to mimic humans (measured as sampling in the data set) and repertoire sizes did not always show the same patterns. Among sulphur-crested cockatoos, cockatiels, and Senegal parrots, males were more likely to show human mimicry, but their repertoires were not larger than the repertoires of females. This suggests that in some species, females may be less likely to mimic vocalizations, but when they do so they have just as large a vocabulary as males.The reported sex differences in parrot vocal mimicry repertoires are intriguing, but also are tentative conclusions. In many species, including our best sampled species, grey parrots, we saw no evidence of sex-differences in repertoire size. The sex-biases that we did document lose statistical significance after controlling for the many comparisons that we conducted. Nevertheless, we expect that some of our data represent true biological differences, especially because studies of wild birds have shown similar trends47,48. Thus, we offer our data as a starting point for additional research. Taken together, the analyses by sex provide interesting points of comparison to other vocal learning animals. Our combined analyses suggest that sex differences in vocal learning are vastly smaller and less common among parrots than they are among oscine passerines and hummingbirds45,49,50. Sex-based patterns of vocal learning in parrots appear more similar to those of vocal learning mammals than to those of other vocal learning birds51. Overall, parrots and songbirds present excellent comparative study systems for all aspects of sex differences in song learning, from the mechanistic to the functional17,51.Vocal learning variation by social contextMany parrot vocalizations function in social organization for individuals within flocks, and the ability to learn from conspecifics is essential to parrot familial and social integration12,15,52. Although our study specifically examined vocal learning of human sounds, we thought it possible that the presence of other parrots would increase mimicry rates if parrots learned human vocalizations from their parrot companions. Anecdotal stories of parrots teaching words to other parrots abound53, and studies of grey parrot cognition show that vocal modeling by multiple tutors can lead to better learning of human words54. Most existing results, however, are based on human tutoring, with controlled studies of parrot-parrot word transmission lacking. Here we tested whether social interactions with other parrots correlated with more vocal learning of human sounds. Our data gave no evidence that parrot-parrot social interactions drive human vocal mimicry. This was true across the full sample (controlling for species identity), and for our best sampled species, grey parrots. Although companion parrots are known to learn from conspecifics, that learning does not appear to shape repertoire sizes53. Open questions remain about whether signal complexity, repertoire size, or aspects of vocal learning covary with social complexity at a larger scale among parrots55. Follow up studies should address these questions using phylogenetically-controlled methods56. More

To estimate the total number of non-broadcasting vessels, including those that were not detected by SAR, we: (1) obtained SAR detections of vessels from RADARSAT-2 and the corresponding vessel lengths as estimated from the SAR image; (2) processed a global feed of AIS data to identify every broadcasting vessel that should have appeared in the SAR images at the moment the images were taken; (3) developed a novel technique to determine which vessels in AIS matched to detections in SAR, which AIS vessels were not detected by SAR, and which SAR detections represented non-broadcasting vessels; (4) after matching SAR to AIS, we could then (a) model the relationship between a vessel’s actual length and the length as estimated by the SAR image (Fig. 3b) and (b) model the relationship between the likelihood that a vessel is detected and its length (Fig. 3a); and (5) finally, we combined these relationships to develop an estimate of the number and lengths of non-broadcasting vessels in the region.SAR imagery and vessel detectionsWorking with the satellite company Kongsberg Satellite Services (KSAT), we tasked the Canadian Space Agency’s satellite RADARSAT-2 to acquire SAR images from its ship detection mode (DVWF mode, GRD product), with a pixel size of about 40 m and a swath width over 400 km (19). These images were processed following standard procedures for GRD products (e.g. applying radiometric calibration and geometric corrections)29,30. Vessel locations were extracted from the images with the widely used ship detection algorithms, which discriminates objects at sea based on the backscatter difference (pixel values) between the sea clutter and the targets31. Vessel lengths were estimated by measuring distances directly on the images with the aid of a graphical user interface tool31.Identifying Vessels using AISIn each region, AIS data, obtained from satellite providers ORBCOMM and Spire, were processed using Global Fishing Watch’s data pipeline1. The identities and lengths of all AIS devices that operated near the SAR scenes in both space and time were first obtained using Global Fishing Watch’s database1. To be sure vessels were identified correctly, two analysts reviewed the tracks of every AIS device in each region.In both regions, it is common practice for fishers to put AIS beacons on their longlines, likely to aid in retrieving them, meaning that many AIS devices were longline gear and not vessels. Because gear outnumbered vessels by several-fold, it was critical to differentiate gear and fishing vessels. In the Indian Ocean, 521 unique AIS devices associated with gear were detected that were likely within the SAR scenes, and 390 unique AIS devices associated with gear in the Pacific that were likely within the SAR scenes. Transponders were determined to be associated with gear by inspecting the name broadcast in the AIS messages (gear frequently broadcasts one of several standard names and/or a voltage reading) and classification using the Global Fishing Watch vessel classification algorithm1. Most gear also had an MMSI number (unique identifier number for AIS) that started with 1, 8, or 9 or broadcast names that signified gear. We eliminated all gear from the analysis because (1) these gear buoys have reflectors that are only ~ 1 m in size, and they should not be visible in ~ 40 m resolution SAR images, and (2) we found that gear matched to SAR detections only when traveling faster than 2 knots (and thus was on the deck of a boat); of 159 instances of gear in scenes where the gear was traveling slower than two knots, zero matched to a radar detection (Fig. S9).Generating probability rasters for matching AIS to SARMost AIS positions did not correspond to the exact time when the SAR images were taken. Hence, to determine the likelihood that a vessel broadcasting AIS corresponded to a specific SAR detection, we first developed probability rasters of where a vessel was likely to be minutes before or after a GPS position was recorded (Figs. S1,S2). We mined one year of global AIS data, including roughly 10 billion GPS positions, and computed these rasters for six different vessel classes (trawlers, purse seines, tug, cargo or tanker, drifting longlines, and others) and considered six different speeds (1, 3, 5, 7, 9, and 12.5 knots) and 36 time intervals (− 448, − 320, − 224, − 160, − 112, − 80, − 56, − 40, − 28, − 20, − 14, − 10, − 7, − 5, − 3.5, − 2.5, − 1.5, − 0.5, 0.5, 1.5, 2.5, 3.5, 5, 7, 10, 14, 20, 28, 40, 56, 80, 112, 160, 224, 320, and 448 min).For example, we queried a year of AIS data to find every example of where a tugboat had two positions that were 10 min apart from one another when the vessel had been traveling at 10 knots at the first position. We then recorded each of these locations relative to the location the vessel would have been if it traveled in a straight line, with x coordinates being in the direction of travel and the y coordinates being perpendicular to the direction of travel. When collected for hundreds of thousands of examples across the AIS dataset, the result is a heatmap of where tug boats are located 10 min after a position when it was traveling at 10 knots. The raster is centered on a point that is the extrapolated position of the vessel based on its speed. For instance, the purse seine raster that corresponds to a vessel traveling between 6 and 8 knots between 96 and 128 min after the most recent position is centered at a point that is 13.1 km (7 knots × 112 min) straight ahead of the direction the vessel was traveling. Figure S1 shows samples of these rasters for different vessels.We built rasters of 1000 by 1000 pixels for each vessel class and time interval, with the area covered by the raster dependent on the time interval (longer time intervals imply longer traveled distances, covering more area). The scale of each pixel was given by:$${text{pixel}};{text{width = max(1, }}Delta {text{m) / 1000}}$$
(1)
where Δm is the time interval in minutes, and pixel width is measured in km. Thus, if the Δm is under one minute, the entire raster is one kilometer wide with each pixel one meter by one meter. If the time is 10 min, then each pixel is 10 m wide, and the entire raster is 10 km by 10 km.Since the pixel width varies between rasters, the units of the rasters are probability per km2, thus summing the area of each pixel times its value equals one. Six vessel classes with 36 time intervals for each and six speeds led to 1296 different rasters. This probability raster approach could be seen as a utilization distribution32—for each vessel class, speed and time interval—where the space is relative to the position of the individual.Combining probability rasters to produce a matching scoreFor a few vessels (~ 4%) there was only one AIS position available before or after the scene. This resulted from a long gap in the AIS data due to poor reception, a weak AIS device, or cases where the vessels disabled their AIS. For these vessels, we used the raster values for a single position. For the vast majority of vessels, however, there was a GPS position right before and after the scene, and thus two probability rasters. We used two methods to combine these probability rasters to obtain information about the most likely location:Multiply and renormalize the rastersTo multiply the rasters, we interpolated the raster values, using bilinear interpolation, to a constant grid at the highest resolution between the before and after rasters. Then, we multiplied the values at each point and renormalized the resulting raster (Fig. S2):$$p_{i} = frac{{p_{ai} cdot p_{bi} }}{{mathop sum nolimits_{k = 0}^{N} p_{ak} cdot p_{bk} cdot da}}$$
(2)
where pi is the probability in vessel density per km2 at location i, pai is the value of the raster before the image, pbi is the value of the raster after the image. The denominator is the sum of all multiplied values across the raster, scaled by the area of each cell, da.Weight and average the rasters For this method, we weighted the raster by the squared value of the probabilities of that scene. This has the effect of giving the concentrated raster a higher weight, thus weighting higher the raster that is closer in time to the image:$${w}_{a}=sum_{k=0}^{N} {p}_{ak}^{2}cdot da$$
(3)
and the weighted average at location i is:$${p}_{i}=frac{{p}_{ai}cdot {w}_{a}+{p}_{bi}cdot {w}_{b}}{{w}_{a}+{w}_{b}}$$
(4)
where wa is the weight for raster a, wb the weight for raster b (calculation analogous to wa’s in Eq. 3), pi is the probability in vessel density per km2 at location i.To determine whether we should multiply (Eq. 2) or average (Eq. 4) the probabilities, we compared the performance of these two metrics against a direct inspection of the detections. We found that at short intervals, multiplying the rasters and renormalizing often made probability values extremely small ( {d}_{d}cdot {p}_{d} + {p}_{f}$$
(5)
where ({p}_{v}) is the probability density of the vessel presence at the location of the SAR detection (the score listed above), ({p}_{d}) is the probability that the vessel is detected by SAR, ({d}_{d}) is the density of non-broadcasting vessels in the region, and ({p}_{f}) is the density of false detections in the scene. The greater ({p}_{d}), the more dark vessels there are in a scene, and the more likely it is that any given detection is a dark vessel instead of a vessel broadcasting AIS. The right-hand side of the equation ({d}_{d}cdot {p}_{d} + {p}_{f}) should roughly equal the number of detections per unit area that do not match to AIS in the region. In other words, the probability of the vessel with AIS being at that specific location and detected by SAR (left side of the equation) should be greater than the probability of a dark vessel or a false detection at that location (right side of the equation).The total number of unmatched vessels in each studied region normalized by total area covered gives a density of non-broadcasting vessels of 2.6–2.8 × 10–5 vessels km-2 (Indian Ocean) and 6.8–7.2 × 10–6 vessels km−2 (Pacific Ocean), similar to the thresholds estimated by analysts. For the most likely number of matched vessels, we use a threshold that is halfway between the higher and lower bound of the analyst (5 × 10–5 to 1 × 10–4), 2.5 × 10–5 which is also roughly equal to the theoretical estimate of the Indian Ocean.This threshold approach performed significantly better than a metric based on the distance between the SAR detection and the most likely location of the vessel, where the likely location is based on extrapolating speed and course of the position closest in time to the image (Fig. S4).Determining whether a vessel with AIS was within a sceneVessel positions from AIS are usually available before and/or after the SAR images, and sometimes it is unclear if a vessel should have been within the scene footprint at the time of the image.To estimate the probability that a vessel (with AIS) was within a scene, we used the multiplied probability raster, summing the values inside the scene boundaries. This provides an estimate of the likelihood that the vessel was within the scene footprint at the time of the image. We applied this to every vessel that had at least one AIS position within 12 h and 200 nautical miles of the scene footprint. The vast majority of vessels were either very likely inside or outside the scene footprints, with 516 vessels having a probability of  > 95% and only 16 having a probability between 5 and 95%. We filtered out all vessels that were definitely outside of the image footprint before matching.Estimating the likelihood of detecting a vessel with SARThe AIS data show that not all vessels broadcasting AIS were captured by the RADARSAT-2 images (Fig. 3a). Using the known lengths of detected vessels with AIS, we estimated the likelihood of detecting a vessel with SAR as a function of vessel length (Fig. 3a). For vessels shorter than 60 m, we approximated the detection rate as a linear function. Treating each vessel as an individual detection, we fitted the 50th percentile using quantile regression to approximate the detection rate. For vessels above 60 m, we assumed a constant detection rate as very few vessels above this length did now show up in the SAR images. Of the 46 unique vessels larger than 62 m, 42 were detected, implying a detection rate of ~ 91%. Given that it is highly likely that large vessels will be captured by medium-resolution SAR imagery, we manually reviewed these cases to confirm that they were (almost surely) inside the scene footprints at the time the images were taken.We should note that the probability of detecting a vessel in SAR also depends on the sea state, incidence angle, polarization, material of the vessel, and orientation of the vessel. We are unable, however, to measure these effects directly so we cannot explicitly model these effects.With sufficient scenes, these effects should be randomly distributed across our scenes, so they likely account for some of the variability in detectability and the inaccuracy in our length estimates from SAR.Estimating the number and length of non-broadcasting vesselsBecause SAR does not detect all vessels, and because the length as estimated by SAR can be incorrect, there are many possible distributions of actual non-broadcasting vessels that could have produced the distribution of unmatched SAR detections that we found in the scenes. To estimate the most likely such distribution, we built a model to combine the two key relationships—between vessel length and likelihood of detection, and between vessel length and the length as estimated by SAR. This model allowed us to estimate, based on the number and distribution of SAR vessels, the likely number and distribution of actual vessels present (Fig. 3c,d).We binned the likelihood of vessel detection as a function of length into 1 m intervals, yielding a vector (alpha) of length 400. We also binned into 1 m intervals the population of lengths of all detected vessels ((ell_{D})) as reported by AIS (i.e. number of vessels at each length bin), the population of expected SAR lengths ((ell_{E})), and the population of lengths of all vessels ((ell_{A}), the quantity we wish to estimate). Thus, (ell_{D}) can be expressed as the product of (alpha) and (ell_{A}):$$ell_{D} = {upalpha } odot ell_{{text{A}}}$$
(6)
where (odot) is the element-wise product. We then estimated a matrix (L_{{}}) that relates (ell_{D}) to (ell_{E}).$$ell_{E} = Lell_{D}$$
(7)
where each element (L_{ij}) represents the probability that a vessel with length in bin j would be estimated by SAR to be of length in bin i. We calculated these probabilities as lognormal probability density functions, with one distribution per column. To estimate the scale and shape parameters of these distributions, we first fitted a quantile regression using the (non-binned) lengths from AIS of detected vessels as the predictor for the lengths reported by SAR. Assuming that the predicted 1/3 and 2/3 quantiles (as shown in Fig. 3a) represent the quantiles of a lognormal distribution, allow us to calculate the shape and scale parameters. We chose a lognormal distribution because: 1) the variable of interest, length, was always greater than zero, 2) the population of lengths was skewed towards larger values, and 3) there is an explicit and relatively simple relationship between the lognormal quantiles and the shape and scale parameters that simplified the calculations.Combining Eqs. (6) and (7) provides a relation between (ell_{A}) and (ell_{E}):$$ell_{E} = {text{L}}left( {alpha odot ell_{A} } right)$$
(8)
To estimate ({mathcal{l}}_{A}) we minimized an objective function (O({mathcal{l}}_{E},{mathcal{l}}_{o})) between the vector of expected counts binned by length (({mathcal{l}}_{E})) and the vector of counts observed in SAR binned by length (({mathcal{l}}_{o})). For this objective function, we chose the sum of the Kolmogorov –Smirnov distance between length distributions and the squared difference of the total numbers of detections. The first term controls the shape of the resulting distribution while the second one controls the magnitude. Specifically:$$Oleft( {ell_{E} ,ell_{o} } right) = max left( {left| {C_{E} – C_{O} } right|} right) + left( {T_{E} – T_{O} } right)^{2}$$
(9)
where:$$T_{x} = mathop sum limits_{ } ell_{x}$$$$D_{x} = ell_{x} /T_{x}$$$$C_{x} = cumsumleft( {D_{x} } right)$$Assessing the uncertainty in the estimationTo test how accurately our approach predicts the correct number of vessels, we performed a bootstrap simulation. We computed the vector (alpha) and the matrix L from a random subset of vessels with AIS that had a high confidence ( > 95%) of appearing within the scenes. We then used our method on the SAR detections that matched the remaining vessels to predict the number of vessels they corresponded to ((ell_{text{A}})). By running 10,000 experiments we found a mean absolute percent error of + − 9% (Figs. S5 and S6). This provides a rough estimate of the uncertainty in our prediction due to the estimation process itself. We used the distribution of these samples to estimate the 90% confidence interval that we report with our estimates. We note that this uncertainty refers to the parametrization of the model and there may be other sources of error, such as the possibility that vessels without AIS have different radar properties (e.g. made out of materials with different reflectiveness), that we did not account for in our model.Catch and effort data in the overlapping area between WCPFC and IATTCWe downloaded gridded effort and catch data from the WCPFC and IATTC websites, and compared the reported number of hooks and catch from September to December of 2019 for the area between − 140 to − 150 longitude and − 5 to − 15 latitude, a bounding box that contains our study region in the Pacific and which is entirely within both the WCPFC and IATTC convention zones. We found that the reported number of hooks for Korea is three times higher for the IATTC as it is for the WCPFC (Fig. S7), and the numbers of hooks also disagree by more than 10% for most other flag states. Catch is also 2.5 times higher for IATTC than for WCPFC for Korea as well, with catch also differing by more than 10% for most other flag states. This finding suggests that the different RFMOs may not be accounting for the same vessels in the overlap region between the two RFMOs. More

As part of my PhD thesis at Colorado State University in Fort Collins, I study the Texas brown tarantula (Aphonopelma hentzi) in the short-grass prairie. My colleagues and I work on the Southern Plains Land Trust, a piece of private conservation land about an hour south of Lamar, Colorado. These tarantulas’ habitats range from Louisiana to this southern part of Colorado. The prairie is a harsh environment — super dry, windy and sometimes very hot or cold. The tarantulas’ burrows become their lifeline; they stay in there for the long haul. Only the males, once mature, leave their burrows to wander aimlessly, looking for love.Tarantulas are ambush predators, meaning that they wait for food to walk by. We want to know if they build burrows in a consistent way, and how their burrows help them to survive the prairie’s harsh environment.We lure the tarantulas out of their burrow using a piece of grass, and then we collect them with a one-litre plastic cup. We pour quick-set plaster of Paris into the burrow. Once it’s dry, we dig out the cast. The first one, that I’m holding here, turned out to be 60 centimetres deep. This does destroy the burrow, but we dig the tarantula a new starter burrow nearby.The casts show us that some spiders are very clean and keep their burrows empty, whereas others are trashy, keeping previous moults or leftovers from eaten beetle. One of the burrows looked as if it had been borrowed from a much bigger animal. That is high-end lazy.About 90% of US prairies are gone because of agriculture and ranching. We strive to preserve the prairie and the creatures in it. Tarantulas serve as a force for keeping insect and even rodent populations under control in the prairie ecosystem. Tarantulas are big, but they won’t hurt you. Want fewer insects? Let spiders live in your house. They’re in your bathtub only because they are thirsty. More

Global statistics on declining biodiversity can give the impression that every population of every species is in a downward spiral. In fact, many populations are stable or growing, while a small number of species faces truly existential challenges. These photos capture some specific crises. They are images of threats unfolding, of desperate attempts at species defence and of the beautiful living world that is at stake.
The 15th United Nations Biodiversity Conference, COP15, opens in Montreal, Canada, on 7 December. At the meeting, delegates will attempt to agree on goals for stabilizing species’ declines by 2030 and reverse them by mid-century. The current draft framework agreement promises nothing less than a “transformation in society’s relationship with biodiversity”.
Help for the kelp. Tasmania’s forests of giant kelp (Macrocystis pyrifera) are dying as climate change shifts ocean currents, bringing warm water to the east coast of the temperate Australian island. The kelp forests host an entire ecosystem, including abalone and crayfish — both economically important species and part of local food culture. Now, researchers at the Institute for Marine and Antarctic Studies in Hobart are breeding kelp plants that can tolerate warmer conditions, and replanting them along the coast — a trial for what they hope will become a landscape-scale restoration. More