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    Author Correction: Genomic analysis of sewage from 101 countries reveals global landscape of antimicrobial resistance

    Research Group for Genomic Epidemiology, Technical University of Denmark, Kgs, Lyngby, DenmarkPatrick Munk, Christian Brinch, Frederik Duus Møller, Thomas N. Petersen, Rene S. Hendriksen, Anne Mette Seyfarth, Jette S. Kjeldgaard, Christina Aaby Svendsen & Frank M. AarestrupCentre for Immunity, Infection and Evolution, University of Edinburgh, Edinburgh, UKBram van Bunnik & Mark WoolhouseCentre for Antibiotic Resistance Research (CARe), University of Gothenburg, Gothenburg, SwedenFanny Berglund & D. G. Joakim LarssonDepartment of Viroscience, Erasmus MC, Rotterdam, The NetherlandsMarion KoopmansInstitute of Public Health, Tirana, AlbaniaArtan BegoUniversidad de Buenos Aires, Buenos Aires, ArgentinaPablo PowerMelbourne Water Corporation, Melbourne, AustraliaCatherine Rees & Kris CoventryCharles Darwin University, Darwin, AustraliaDionisia LambrinidisUniversity of Copenhagen, Frederiksberg C, DenmarkElizabeth Heather Jakobsen Neilson & Yaovi Mahuton Gildas HounmanouCharles Darwin University, Darwin Northern Territory, AustraliaKaren GibbCanberra Hospital, Canberra, AustraliaPeter CollignonALS Water, Scoresby, AustraliaSusan CassarAustrian Agency for Health and Food Safety (AGES), Vienna, AustriaFranz AllerbergerUniversity of Dhaka, Dhaka, BangladeshAnowara Begum & Zenat Zebin HossainEnvironmental Protection Department, Bridgetown, St. Michael, BarbadosCarlon WorrellLaboratoire Hospitalier Universitaire de Bruxelles (LHUB-ULB), Brussels, BelgiumOlivier VandenbergAQUAFIN NV, Aartselaar, BelgiumIlse PietersPolytechnic School of Abomey-Calavi, Abomey-Calavi, BeninDougnon Tamègnon VictorienUniversidad Catσlica Boliviana San Pablo, La Paz, BoliviaAngela Daniela Salazar Gutierrez & Freddy SoriaPublic Health Institute of the Republic of Srpska, Faculty of Medicine University of Banja Luka, Banja Luka, Bosnia and HerzegovinaVesna Rudić GrujićPublic Health Institute of the Republic of Srpska, Banja Luka, Bosnia and HerzegovinaNataša MazalicaBotswana International University of Science and Technology, Palapye, BotswanaTeddie O. RahubeUniversidade Federal de Minas Gerais, Belo Horizonte, BrazilCarlos Alberto Tagliati & Larissa Camila Ribeiro de SouzaOswaldo Cruz Institute, Rio de Janeiro, BrazilDalia RodriguesVale Institute of Technology, Belιm, PA, BrazilGuilherme OliveiraNational Center of Infectious and Parasitic Diseases, Sofia, BulgariaIvan IvanovUniversity of Ouagadougou, Ouagadougou, Burkina FasoBonkoungou Isidore Juste & Traoré OumarInstitut Pasteur du Cambodge, Phnom Penh, CambodiaThet Sopheak & Yith VuthyCentre Pasteur du Cameroun, Yaoundι, CameroonAntoinette Ngandjio, Ariane Nzouankeu & Ziem A. Abah Jacques OlivierUniversity of Regina, Regina, CanadaChristopher K. YostEau Terre Environnement Research Centre (INRS-ETE), Quebec City G1K 9A9, Canada and Indian Institute of Technology, Jammu, IndiaPratik KumarEau Terre Environnement Research Centre (INRS-ETE), Quebec City G1K 9A9, Canada and Lassonde School of Enginerring, York University, Toronto, CanadaSatinder Kaur BrarUniversity of N’Djamena, N’Djamena, ChadDjim-Adjim TaboEscuela de Medicina Veterinaria, Facultad de Ciencias de la Vida, Universidad Andres Bello, Santiago, ChileAiko D. AdellInstitute of Public Health, Santiago, ChileEsteban Paredes-Osses & Maria Cristina MartinezUniversidad Catolica del Maule, Centro de Biotecnología de los Recursos Naturales, Facultad de Ciencias Agrarias y Forestales, Talca, ChileSara Cuadros-OrellanaGuangdong Provincial Center for Disease Control and Prevention, Guangzhou, ChinaChangwen Ke, Huanying Zheng & Li BaishengThe Hong Kong Polytechnic University, Hong Kong, ChinaLok Ting Lau & Teresa ChungShantou University Medical College, Shantou, ChinaXiaoyang JiaoNanjing University of Information Science and Technology, Nanjing, ChinaYongjie YuCenter for Disease Control and Prevention of Henan province, Zhengzhou, ChinaZhao JiaYongColombian Integrated Program for Antimicrobial Resistance Surveillance – Coipars, CI Tibaitatα, Corporaciσn Colombiana de Investigaciσn Agropecuaria (AGROSAVIA), Tibaitatα – Mosquera, Cundinamarca, ColombiaJohan F. Bernal Morales, Maria Fernanda Valencia & Pilar Donado-GodoyInstitut Pasteur de Côte d’Ivoire, Abidjan, Côte d’IvoireKalpy Julien CoulibalyUniversity of Zagreb, Zagreb, CroatiaJasna HrenovicAndrija Stampar Teaching Institute of Public Health, Zagreb, CroatiaMatijana JergovićVeterinary Research Institute, Brno, Czech RepublicRenáta KarpíškováCentre de Recherche en Sciences Naturelles de Lwiro (CRSN-LWIRO), Bukavu, Democratic Republic of CongoZozo Nyarukweba DeogratiasBIOFOS A/S, Copenhagen K, DenmarkBodil ElsborgTechnical University of Denmark, Kgs., Lyngby, DenmarkLisbeth Truelstrup Hansen & Pernille Erland JensenSuez Canal University, Ismailia, EgyptMohamed AbouelnagaUniversity of Sadat City, Sadat City, EgyptMohamed Fathy SalemMinistry of Health, Environmental Microbiology, Tallinn, EstoniaMarliin KoolmeisterAddis Ababa University, Addis Ababa, EthiopiaMengistu Legesse & Tadesse EgualeUniversity of Helsinki, Helsinki, FinlandAnnamari HeikinheimoFrench Institute Search Pour L’exploitation De La Mer (Ifremer), Nantes, FranceSoizick Le Guyader & Julien SchaefferInstituto Nacional de Investigaciσn en Salud Pϊblica-INSPI (CRNRAM), Galαpagos, Quito, EcuadorJose Eduardo VillacisNational Public Health Laboratories, Ministry of Health and Social Welfare, Kotu, GambiaBakary SannehNational Center for Disease Control and Public Health, Tbilisi, GeorgiaLile MalaniaRobert Koch Institute, Berlin, GermanyAndreas Nitsche & Annika BrinkmannTechnische Universitδt Dresden, Institute of Hydrobiology, Dresden, GermanySara Schubert, Sina Hesse & Thomas U. BerendonkUniversity for Development Studies, Tamale, GhanaCourage Kosi Setsoafia SabaUniversity of Ghana, Accra, GhanaJibril MohammedKwame Nkrumah University of Science and Technology, Kumasi, PMB, GhanaPatrick Kwame FegloCouncil for Scientific and Industrial Research Water Research Institute, Accra, GhanaRegina Ama BanuVeterinary Research Institute of Thessaloniki, Hellenic Agricultural Organisation-DEMETER, Thermi, GreeceCharalampos KotzamanidisAthens Water Supply and Sewerage Company (EYDAP S.A.), Athens, GreeceEfthymios LytrasUniversidad de San Carlos de Guatemala, Guatemala City, GuatemalaSergio A. LickesSemmelweis University, Institute of Medical Microbiology, Budapest, HungaryBela KocsisUniversity of Veterinary Medicine, Budapest, HungaryNorbert SolymosiUniversity of Iceland, Reykjavνk, IcelandThorunn R. ThorsteinsdottirCochin University of Science and Technology, Cochin, IndiaAbdulla Mohamed HathaKasturba Medical College, Manipal, IndiaMamatha BallalApollo Diagnostics, Mangalore, IndiaSohan Rodney BangeraShiraz University of Medical Sciences, Shiraz, IranFereshteh FaniShahid Beheshti University of Medical Sciences, Tehran, IranMasoud AlebouyehNational University of Ireland Galway, Galway, IrelandDearbhaile Morris, Louise O’Connor & Martin CormicanBen Gurion University of the Negev and Ministry of Health, Beer-Sheva, IsraelJacob Moran-GiladIstituto Zooprofilattico Sperimentale del Lazio e della Toscana, Rome, ItalyAntonio Battisti, Elena Lavinia Diaconu & Patricia AlbaCNR – Water Research Institute, Verbania, ItalyGianluca Corno & Andrea Di CesareNational Institute of Infectious Diseases, Tokyo, JapanJunzo Hisatsune, Liansheng Yu, Makoto Kuroda, Motoyuki Sugai & Shizuo KayamaNational Center of Expertise, Taldykorgan, KazakhstanZeinegul ShakenovaMount Kenya University, Thika, KenyaCiira KiiyukiaKenya Medical Research Institute, Nairobi, KenyaEric Ng’enoUniversity of Prishtina “Hasan Prishtina” & National Institute of Public Health of Kosovo, Pristina, KosovoLul RakaKuwait Institute for Scientific Research, Kuwait City, KuwaitKazi Jamil, Saja Adel Fakhraldeen & Tareq AlaatiInstitute of Food Safety, Riga, LatviaAivars Bērziņš, Jeļena Avsejenko, Kristina Kokina, Madara Streikisa & Vadims BartkevicsAmerican University of Beirut, Beirut, LebanonGhassan M. MatarCentral Michigan University & Michigan Health Clinics, Saginaw, MI, USAZiad DaoudNational Food and Veterinary Risk Assessment Institute, Vilnius, LithuaniaAsta Pereckienė & Ceslova Butrimaite-AmbrozevicieneLuxembourg Institute of Science and Technology, Belvaux, LuxembourgChristian PennyInstitut Pasteur de Madagascar, Antananarivo, MadagascarAlexandra Bastaraud & Jean-Marc CollardUniversity of Antananarivo, Centre d’Infectiologie Charles Mιrieux, Antananarivo, MadagascarTiavina Rasolofoarison, Luc Hervé Samison & Mala Rakoto AndrianariveloUniversity of Malawi, Blantyre, MalawiDaniel Lawadi BandaMalaysian Genomics Resource Centre Berhad, Kuala Lumpur, MalaysiaArshana AminAIMST University, COMBio, Kedah, MalaysiaHeraa Rajandas & Sivachandran ParimannanWater Services Corporation, Luqa, MaltaDavid SpiteriEnvironmental Health Directorate, St. Venera, MaltaMalcolm Vella HaberUniversity of Mauritius, Reduit, MauritiusSunita J. SantchurnInstitute for Public Health Montenegro, Podgorica, MontenegroAleksandar Vujacic & Dijana DjurovicInstitut Pasteur du Maroc, Casablanca, MoroccoBrahim Bouchrif & Bouchra KarraouanCentro de Investigaηγo em Saϊde de Manhiηa (CISM), Maputo, MozambiqueDelfino Carlos VubilAgriculture and Forestry University, Kathmandu, NepalPushkar PalNational Institute for Public, Health and the Environment (RIVM), Bilthoven, The NetherlandsHeike Schmitt & Mark van PasselUniversity of Otago, Dunedin, New ZealandGert-Jan Jeunen & Neil GemmellUniversity of Otago, Christchurch, New ZealandStephen T. ChambersUniversity of Central America, Managua, NicaraguaFania Perez Mendoza & Jorge Huete-PιrezUniversidad Nacional Autσnoma de Nicaragua-Leσn, Leσn, NicaraguaSamuel VilchezUniversity of Ilorin, Ilorin, NigeriaAkeem Olayiwola Ahmed, Ibrahim Raufu Adisa & Ismail Ayoade OdetokunUniversity of Ibadan, Ibadan, NigeriaKayode FashaeNorwegian Institute of Public Health, Oslo, NorwayAnne-Marie Sørgaard & Astrid Louise WesterVEAS, Slemmestad, NorwayPia Ryrfors & Rune HolmstadUniversity of Agriculture, Faisalabad, PakistanMashkoor MohsinAga Khan University, Karachi, PakistanRumina Hasan & Sadia ShakoorLaboratorio Central de Salud Publica, Asuncion, ParaguayNatalie Weiler Gustafson & Claudia Huber SchillInstituto Nacional de Salud, Lima, PeruMaria Luz Zamudio RojasUniversidad de Piura, Piura, PeruJorge Echevarria Velasquez & Felipe Campos YauceWHO Environmental and Occupational Health, Manila, PhilippinesBonifacio B. MagtibayMaynilad Water Services, Inc., Quezon City, PhilippinesKris Catangcatang & Ruby SibuloNational Veterinary Research Institute, Pulawy, PolandDariusz WasylUniversidade Catσlica Portuguesa, CBQF – Centro de Biotecnologia e Quνmica Fina – Laboratσrio Associado, Escola Superior de Biotecnologia, Porto, PortugalCelia Manaia & Jaqueline RochaAguas do Tejo Atlantico, Lisboa, PortugalJose Martins & Pedro ÁlvaroGwangju Institute of Science and Technology, Gwangju, Republic of KoreaDoris Di Yoong Wen, Hanseob Shin & Hor-Gil HurKorea Advanced Institute of Science and Technology, Daejeon, Republic of KoreaSukhwan YoonInstitute of Public Health of the Republic of North Macedonia, Skopje, Republic of North MacedoniaGolubinka Bosevska & Mihail KochubovskiState Medical and Pharmaceutical University, Chișinău, Republic of MoldovaRadu CojocaruNational Agency for Public Health, Chișinău, Republic of MoldovaOlga BurduniucKing Abdullah University of Science and Technology, Thuwal, Saudi ArabiaPei-Ying HongUniversity of Edinburgh, Edinburgh, Scotland, UKMeghan Rose PerryInstitut Pasteur de Dakar, Dakar, SenegalAmy GassamaInstitute of Veterinary Medicine of Serbia, Belgrade, SerbiaVladimir RadosavljevicNanyang Technological University, Singapore, SingaporeMoon Y. F. Tay, Rogelio Zuniga-Montanez & Stefan WuertzPublic Health Authority of the Slovak Republic, Bratislava, SlovakiaDagmar Gavačová, Katarína Pastuchová & Peter TruskaNational Laboratory of Health, Environment and Food, Ljubljana, SloveniaMarija TrkovIndependent consultant, Johannesburg, South AfricaKaren KeddyDaspoort Waste Water Treatment Works, Pretoria, South AfricaKerneels EsterhuyseKorea Advanced Institute of Science and Technology, Daejeon, South KoreaMin Joon SongSchool of Veterinary Sciences, Lugo, SpainMarcos Quintela-BalujaLabaqua, Santiago de Compostela, SpainMariano Gomez LopezIRTA, Centre de Recerca en Sanitat Animal (CReSA, IRTA-UAB), Campus de la Universitat Autonoma de Barcelona, Bellaterra, SpainMarta Cerdà-CuéllarUniversity of Kelaniya, Ragama, Sri LankaR. R. D. P. Perera, N. K. B. K. R. G. W. Bandara & H. I. PremasiriMedical Research Institute, Colombo, Sri LankaSujatha PathirageCaribbean Public Health Agency, Catries, Saint LuciaKareem CharlemagneThe Sahlgrenska Academy at the University of Gothenburg, Gothenburg, SwedenCarolin RutgerssonSwedish University of Agricultural Sciences, Uppsala, SwedenLeif Norrgren & Stefan ÖrnFederal Food Safety and Veterinary Office (FSVO), Bern, SwitzerlandRenate BossAra Region Bern AG, Herrenschwanden, SwitzerlandTanja Van der HeijdenCenters for Disease Control, Taipei, TaiwanYu-Ping HongKilimanjaro Clinical Research Institute, Moshi, TanzaniaHappiness Houka KumburuSokoine University of Agriculture, Morogoro, TanzaniaRobinson Hammerthon MdegelaFaculty of Science and Technology, Suratthani Rajabhat University, Surat Thani, ThailandKaknokrat ChonsinFaculty of Public Health, Mahidol University, Bangkok, ThailandOrasa SuthienkulFaculty of Medicine Siriraj Hospital, Bangkok, ThailandVisanu ThamlikitkulNational Institute for Public Health and the Environment (RIVM), Bilthoven, NetherlandsAna Maria de Roda HusmanNational Institute of Hygiene, Lomι, TogoBawimodom BidjadaAgence de Mιdecine Prιventive, Dapaong, TogoBerthe-Marie Njanpop-LafourcadeDivision of Integrated Surveillance of Health Emergencies and Response, Lomι, TogoSomtinda Christelle Nikiema-PessinabaPublic Health Institution of Turkey, Ankara, TurkeyBelkis LeventHatay Mustafa Kemal University, Hatay, TurkeyCemil KurekciMakerere University, Kampala, UgandaFrancis Ejobi & John Bosco KaluleAbu Dhabi Public Health Center, Abu Dhai, United Arab EmiratesJens ThomsenDubai municipality, WWTP Al Aweer, Dubai, UAEOuidiane ObaidiRashid Hospital, Dubai, UAELaila Mohamed JassimNorthumbrian Water, Northumbria House, Abbey Road, Pity Me, Durham, UKAndrew MooreUniversity of Exeter Medical School, Cornwall, UKAnne Leonard, Lihong Zhang & William H. GazeNewcastle University, Newcastle upon Tyne, UKDavid W. Graham & Joshua T. BunceBrightwater Treatment Plant, Woodinville, WA, USABrett LeforDepartment of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USADrew Capone & Joe BrownUniversity of North Carolina, Chapel Hill, USAEmanuele Sozzi & Mark D. SobseyUniversity of Washington, Seattle, WA, USAJohn Scott Meschke, Nicola Koren Beck, Pardi Sukapanpatharam & Phuong TruongBaylor University, Waco, USAMichael DavisColumbia Boulevard WWTP, Portland, USARonald LilienthalEastern Illinois University, Charleston, USASanghoon KangThe Ohio State University, Columbus Ohio, USAThomas E. WittumLaboratorio Tecnolσgico del Uruguay, Montevideo, UruguayNatalia Rigamonti & Patricia BaklayanInstitute of Public Health in Ho Chi Minh City, Ho Chi Minh, VietnamChinh Dang Van, Doan Minh Nguyen Tran & Nguyen Do PhucUniversity of Zambia, Lusaka, ZambiaGeoffrey Kwenda More

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    Anaerobic methanotroph ‘Candidatus Methanoperedens nitroreducens’ has a pleomorphic life cycle

    Reeburgh, W. S. Oceanic methane biogeochemistry. Chem. Rev. 107, 486–513 (2007).Article 
    CAS 

    Google Scholar 
    Chadwick, G. L. et al. Comparative genomics reveals electron transfer and syntrophic mechanisms differentiating methanotrophic and methanogenic archaea. PLoS Biol. 20, e3001508 (2022).Article 

    Google Scholar 
    Haroon, M. F. et al. Anaerobic oxidation of methane coupled to nitrate reduction in a novel archaeal lineage. Nature 500, 567–570 (2013).Article 
    CAS 

    Google Scholar 
    Hallam, S. J. et al. Reverse methanogenesis: testing the hypothesis with environmental genomics. Science 305, 1457–1462 (2004).Article 
    CAS 

    Google Scholar 
    McGlynn, S. E. Energy metabolism during anaerobic methane oxidation in ANME Archaea. Microbes Environ. 32, 5–13 (2017).Article 

    Google Scholar 
    Beal, E. J., House, C. H. & Orphan, V. J. Manganese- and iron-dependent marine methane oxidation. Science 325, 184–187 (2009).Article 
    CAS 

    Google Scholar 
    McGlynn, S. E., Chadwick, G. L., Kempes, C. P. & Orphan, V. J. Single cell activity reveals direct electron transfer in methanotrophic consortia. Nature 526, 531–535 (2015).Article 
    CAS 

    Google Scholar 
    Wegener, G., Krukenberg, V., Riedel, D., Tegetmeyer, H. E. & Boetius, A. Intercellular wiring enables electron transfer between methanotrophic archaea and bacteria. Nature 526, 587–590 (2015).Article 
    CAS 

    Google Scholar 
    Cai, C. et al. A methanotrophic archaeon couples anaerobic oxidation of methane to Fe(III) reduction. ISME J. 12, 1929–1939 (2018).Article 
    CAS 

    Google Scholar 
    Ettwig, K. F. et al. Archaea catalyze iron-dependent anaerobic oxidation of methane. Proc. Natl Acad. Sci. USA 113, 12792–12796 (2016).Article 
    CAS 

    Google Scholar 
    Leu, A. O. et al. Anaerobic methane oxidation coupled to manganese reduction by members of the Methanoperedenaceae. ISME J. 14, 1030–1041 (2020).Article 
    CAS 

    Google Scholar 
    Leu, A. O. et al. Lateral gene transfer drives metabolic flexibility in the anaerobic methane-oxidizing archaeal family Methanoperedenaceae. mBio 11, e01325-20 (2020).Cai, C. et al. Response of the anaerobic methanotrophic archaeon Candidatus ‘Methanoperedens nitroreducens’ to the long-term ferrihydrite amendment. Front. Microbiol. 13, 799859 (2022).Arshad, A. et al. A metagenomics-based metabolic model of nitrate-dependent anaerobic oxidation of methane by Methanoperedens-like Archaea. Front. Microbiol. 6, 1423 (2015).Article 

    Google Scholar 
    Raghoebarsing, A. A. et al. A microbial consortium couples anaerobic methane oxidation to denitrification. Nature 440, 918–921 (2006).Article 
    CAS 

    Google Scholar 
    Walker, D. J. F. et al. The archaellum of Methanospirillum hungatei is electrically conductive. mBio 10, e00579-19 (2019).Article 
    CAS 

    Google Scholar 
    Krukenberg, V. et al. Gene expression and ultrastructure of meso- and thermophilic methanotrophic consortia. Environ. Microbiol. 20, 1651–1666 (2018).Article 
    CAS 

    Google Scholar 
    Schubert, C. J. et al. Evidence for anaerobic oxidation of methane in sediments of a freshwater system (Lago di Cadagno). FEMS Microbiol. Ecol. 76, 26–38 (2011).Article 
    CAS 

    Google Scholar 
    Stahl, D. A. & Amann, R. in Nucleic Acid Techniques in Bacterial Systematics (eds Stackebrandt, E. & Goodfellow, M.) 205–248 (Wiley, 1991).Wallner, G., Amann, R. & Beisker, W. Optimizing fluorescent in situ hybridization with rRNA-targeted oligonucleotide probes for flow cytometric identification of microorganisms. Cytometry 14, 136–143 (1993).Article 
    CAS 

    Google Scholar 
    Bowers, R. M. et al. Minimum information about a single amplified genome (MISAG) and a metagenome- assembled genome (MIMAG) of Bacteria and Archaea. Nat. Biotechnol. 36, 660 (2018).Article 
    CAS 

    Google Scholar 
    Vo, C. H., Goyal, N., Karimi, I. A. & Kraft, M. First observation of an acetate switch in a methanogenic autotroph (Methanococcus maripaludis S2). Microbiol. Insights 13, 1178636120945300 (2020).Article 

    Google Scholar 
    Cai, C. et al. Acetate production from anaerobic oxidation of methane via intracellular storage compounds. Environ. Sci. Technol. 53, 7371–7379 (2019).Article 
    CAS 

    Google Scholar 
    Ratcliff, W. C. & Denison, R. F. Bacterial persistence and bet hedging in Sinorhizobium meliloti. Commun. Integr. Biol. 4, 98–100 (2011).Article 
    CAS 

    Google Scholar 
    Ma, K., Schicho, R. N., Kelly, R. M. & Adams, M. W. Hydrogenase of the hyperthermophile Pyrococcus furiosus is an elemental sulfur reductase or sulfhydrogenase: evidence for a sulfur-reducing hydrogenase ancestor. Proc. Natl Acad. Sci. USA 90, 5341–5344 (1993).Article 
    CAS 

    Google Scholar 
    Simon, G.-C. et al. Response of the anaerobic methanotroph “Candidatus Methanoperedens nitroreducens” to oxygen stress. Appl. Environ. Microbiol. 84, e01832-18 (2018).
    Google Scholar 
    van der Star, W. R. L. et al. The membrane bioreactor: a novel tool to grow anammox bacteria as free cells. Biotechnol. Bioeng. 101, 286–294 (2008).Article 

    Google Scholar 
    Duggin, I. G. et al. CetZ tubulin-like proteins control archaeal cell shape. Nature 519, 362–365 (2015).Article 
    CAS 

    Google Scholar 
    Schwarzer, S., Rodriguez-Franco, M., Oksanen, H. M. & Quax, T. E. F. Growth phase dependent cell shape of Haloarcula. Microorganisms 9, 231 (2021).Article 
    CAS 

    Google Scholar 
    Dang, H. Y. & Lovell, C. R. Microbial surface colonization and biofilm development in marine environments. Microbiol. Mol. Biol. Rev. 80, 91–138 (2016).Article 
    CAS 

    Google Scholar 
    Howard-Varona, C., Hargreaves, K. R., Abedon, S. T. & Sullivan, M. B. Lysogeny in nature: mechanisms, impact and ecology of temperate phages. ISME J. 11, 1511–1520 (2017).Article 

    Google Scholar 
    Pires, D. P., Melo, L. D. R. & Azeredo, J. Understanding the complex phage–host interactions in biofilm communities. Annu. Rev. Virol. 8, 73–94 (2021).Canchaya, C., Proux, C., Fournous, G., Bruttin, A. & Brüssow, H. Prophage genomics. Microbiol. Mol. Biol. Rev. 67, 238–276 (2003).Article 
    CAS 

    Google Scholar 
    Zhang, X. et al. Polyhydroxyalkanoate-driven current generation via acetate by an anaerobic methanotrophic consortium. Water Res. 221, 118743 (2022).Article 
    CAS 

    Google Scholar 
    Knittel, K., Lösekann, T., Boetius, A., Kort, R. & Amann, R. Diversity and distribution of methanotrophic Archaea at cold seeps. Appl. Environ. Microbiol. 71, 467–479 (2005).Article 
    CAS 

    Google Scholar 
    Orphan, V. J., House, C. H., Hinrichs, K.-U., McKeegan, K. D. & DeLong, E. F. Multiple archaeal groups mediate methane oxidation in anoxic cold seep sediments. Proc. Natl Acad. Sci. USA 99, 7663–7668 (2002).Article 
    CAS 

    Google Scholar 
    Orphan, V. J. et al. Geological, geochemical, and microbiological heterogeneity of the seafloor around methane vents in the Eel River Basin, offshore California. Chem. Geol. 205, 265–289 (2004).Article 
    CAS 

    Google Scholar 
    Ackermann, M. A functional perspective on phenotypic heterogeneity in microorganisms. Nat. Rev. Microbiol. 13, 497–508 (2015).Article 
    CAS 

    Google Scholar 
    Robinson, R. W. Life cycles in the methanogenic archaebacterium Methanosarcina mazei. Appl. Environ. Microbiol. 52, 17–27 (1986).Article 
    CAS 

    Google Scholar 
    Daims, H., Stoecker, K. & Wagner, M. in Molecular Microbial Ecology (eds Osborn, A. M. & Smith, C. J.) 213–239 (Taylor & Francis, 2005).Ludwig, W. et al. ARB: a software environment for sequence data. Nucleic Acids Res. 32, 1363–1371 (2004).Article 
    CAS 

    Google Scholar 
    Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).Article 
    CAS 

    Google Scholar 
    Yilmaz, L. S., Parnerkar, S. & Noguera, D. R. mathFISH, a web tool that uses thermodynamics-based mathematical models for in silico evaluation of oligonucleotide probes for fluorescence in situ hybridization. Appl. Environ. Microbiol. 77, 1118–1122 (2011).Article 
    CAS 

    Google Scholar 
    Stoecker, K., Dorninger, C., Daims, H. & Wagner, M. Double labeling of oligonucleotide probes for fluorescence in situ hybridization (DOPE-FISH) improves signal intensity and increases rRNA accessibility. Appl. Environ. Microbiol. 76, 922–926 (2010).Article 
    CAS 

    Google Scholar 
    Fuchs, B. M., Glockner, F. O., Wulf, J. & Amann, R. Unlabeled helper oligonucleotides increase the in situ accessibility to 16S rRNA of fluorescently labeled oligonucleotide probes. Appl. Environ. Microbiol. 66, 3603–3607 (2000).Article 
    CAS 

    Google Scholar 
    Manz, W., Amann, R., Ludwig, W., Wagner, M. & Schleifer, K.-H. Phylogenetic oligodeoxynucleotide probes for the major subclasses of Proteobacteria: problems and solutions. Syst. Appl. Microbiol. 15, 593–600 (1992).Article 

    Google Scholar 
    Ostle, A. G. & Holt, J. G. Nile blue A as a fluorescent stain for poly-beta-hydroxybutyrate. Appl. Environ. Microbiol. 44, 238–241 (1982).Article 
    CAS 

    Google Scholar 
    Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, e1 (2013).Article 
    CAS 

    Google Scholar 
    Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).Article 
    CAS 

    Google Scholar 
    Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 1091 (2019).Article 
    CAS 

    Google Scholar 
    Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).Article 
    CAS 

    Google Scholar 
    Eren, A. M., Vineis, J. H., Morrison, H. G. & Sogin, M. L. A filtering method to generate high quality short reads using Illumina paired-end technology. PLoS ONE 8, e66643 (2013).Article 

    Google Scholar 
    Minoche, A. E., Dohm, J. C. & Himmelbauer, H. Evaluation of genomic high-throughput sequencing data generated on Illumina HiSeq and genome analyzer systems. Genome Biol. 12, R112 (2011).Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).Article 
    CAS 

    Google Scholar 
    Warren, R. L. et al. LINKS: scalable, alignment-free scaffolding of draft genomes with long reads. GigaScience 4, 35 (2015).Article 

    Google Scholar 
    Walker, B. J. et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS ONE 9, e112963 (2014).Article 

    Google Scholar 
    Wick, R. R., Schultz, M. B., Zobel, J. & Holt, K. E. Bandage: interactive visualization of de novo genome assemblies. Bioinformatics 31, 3350–3352 (2015).Article 
    CAS 

    Google Scholar 
    Wick, R. R. et al. Trycycler: consensus long-read assemblies for bacterial genomes. Genome Biol. 22, 266 (2021).Article 
    CAS 

    Google Scholar 
    Kolmogorov, M., Yuan, J., Lin, Y. & Pevzner, P. A. Assembly of long, error-prone reads using repeat graphs. Nat. Biotechnol. 37, 540–546 (2019).Article 
    CAS 

    Google Scholar 
    Wick, R. R. & Holt, K. E. Benchmarking of long-read assemblers for prokaryote whole genome sequencing. F1000Res. 8, 2138 (2021).Vaser, R. & Šikić, M. Time- and memory-efficient genome assembly with Raven. Nat. Comput. Sci. 1, 332–336 (2021).Article 

    Google Scholar 
    Wick, R. R. & Holt, K. E. Polypolish: short-read polishing of long-read bacterial genome assemblies. PLoS Comput. Biol. 18, e1009802 (2022).Article 
    CAS 

    Google Scholar 
    Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2019).
    Google Scholar 
    Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).Article 
    CAS 

    Google Scholar 
    Poplin, R. et al. Scaling accurate genetic variant discovery to tens of thousands of samples. Preprint at bioRxiv https://doi.org/10.1101/201178 (2017).Article 

    Google Scholar 
    Heller, D. & Vingron, M. SVIM: structural variant identification using mapped long reads. Bioinformatics 35, 2907–2915 (2019).Article 
    CAS 

    Google Scholar 
    Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).Article 
    CAS 

    Google Scholar 
    Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).Article 
    CAS 

    Google Scholar 
    Suzek, B. E., Huang, H., McGarvey, P., Mazumder, R. & Wu, C. H. UniRef: comprehensive and non-redundant UniProt reference clusters. Bioinformatics 23, 1282–1288 (2007).Article 
    CAS 

    Google Scholar 
    Tatusov, R. L. et al. The COG database: an updated version includes eukaryotes. BMC Bioinformatics 4, 41 (2003).Article 

    Google Scholar 
    Finn, R. D. et al. The Pfam protein families database: towards a more sustainable future. Nucleic Acids Res. 44, D279–D285 (2016).Article 
    CAS 

    Google Scholar 
    Haft, D. H. et al. TIGRFAMs and genome properties in 2013. Nucleic Acids Res. 41, D387–D395 (2013).Article 
    CAS 

    Google Scholar 
    Zhou, Z. et al. METABOLIC: high-throughput profiling of microbial genomes for functional traits, metabolism, biogeochemistry, and community-scale functional networks. Microbiome 10, 33 (2022).Article 
    CAS 

    Google Scholar 
    Eddy, S. R. Accelerated profile HMM searches. PLoS Comput. Biol. 7, e1002195 (2011).Article 
    CAS 

    Google Scholar 
    Bateman, A. et al. The Pfam protein families database. Nucleic Acids Res. 32, D138–D141 (2004).Article 
    CAS 

    Google Scholar 
    Amann, R. I. et al. Combination of 16S rRNA-targeted oligonucleotide probes with flow cytometry for analyzing mixed microbial populations. Appl. Environ. Microbiol. 56, 1919–1925 (1990).Article 
    CAS 

    Google Scholar 
    Daims, H., Brühl, A., Amann, R., Schleifer, K. H. & Wagner, M. The domain-specific probe EUB338 is insufficient for the detection of all Bacteria: development and evaluation of a more comprehensive probe set. Syst. Appl. Microbiol. 22, 434–444 (1999).Article 
    CAS 

    Google Scholar 
    Schmid, M. C. et al. Biomarkers for in situ detection of anaerobic ammonium-oxidizing (anammox) bacteria. Appl. Environ. Microbiol. 71, 1677–1684 (2005).Article 
    CAS 

    Google Scholar 
    Yu, N. Y. et al. PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics 26, 1608–1615 (2010).Article 
    CAS 

    Google Scholar 
    Bendtsen, J. D., Nielsen, H., von Heijne, G. & Brunak, S. Improved prediction of signal peptides: SignalP 3.0. J. Mol. Biol. 340, 783–795 (2004).Article 

    Google Scholar  More

  • in

    Unaltered fungal community after fire prevention treatments over widespread Mediterranean rockroses (Halimium lasianthum)

    Cairney, J. W. G. & Bastias, B. A. Influences of fire on forest soil fungal communities. Can. J. For. Res. 37, 207–215 (2007).Article 

    Google Scholar 
    Fernández, C., Vega, J. A. & Fonturbel, T. The effects of fuel reduction treatments on runoff, infiltration and erosion in two shrubland areas in the north of Spain. J. Environ. Manage. 105, 96–102 (2012).Article 

    Google Scholar 
    Reazin, C., Morris, S., Smith, J. E., Cowan, A. D. & Jumpponen, A. Fires of differing intensities rapidly select distinct soil fungal communities in a Northwest US ponderosa pine forest ecosystem. For. Ecol. Manage. 377, 118–127 (2016).Article 

    Google Scholar 
    Durán-Manual, F. et al. Prescribed burning in Pinus cubensis-dominated tropical natural forests: A myco-friendly fire-prevention tool. For. Syst. 31, e012 (2022).
    Google Scholar 
    Busse, M. D., Hubbert, K. R., Fiddler, G. O., Shestak, C. J. & Powers, R. F. Lethal soil temperatures during burning of masticated forest residues. Int. J. Wildl. Fire 14, 267–276 (2005).Article 

    Google Scholar 
    Frazão, D. F. et al. Cistus ladanifer (Cistaceae): A natural resource in Mediterranean-type ecosystems. Planta 247, 289–300 (2018).Article 

    Google Scholar 
    Keeley, J. E., Bond, W. J., Bradstock, R. A., Pausas, J. G. & Rundel, P. W. Fire in mediterranean ecosystems. Fire Medit. Ecosyst. https://doi.org/10.1017/cbo9781139033091 (2011).Article 

    Google Scholar 
    Louro, R., Peixe, A. & Santos-silva, C. New insights on Cistus salviifolius L. micropropagation. J. Bot. Sci. 6, 10–14 (2017).CAS 

    Google Scholar 
    Valbuena, L., Tarrega, R. & Luis, E. Influence of heat on seed germination of Cistus laurifolius and Cistus ladanifer. J. Wildl. Fire 2, 15–20 (1992).Article 

    Google Scholar 
    Martín-Pinto, P., Vaquerizo, H., Peñalver, F., Olaizola, J. & Oria-De-Rueda, J. A. Early effects of a wildfire on the diversity and production of fungal communities in Mediterranean vegetation types dominated by Cistus ladanifer and Pinus pinaster in Spain. For. Ecol. Manage. 225, 296–305 (2006).Article 

    Google Scholar 
    Comandini, O., Contu, M. & Rinaldi, A. C. An overview of Cistus ectomycorrhizal fungi. Mycorrhiza 16, 381–395 (2006).Article 
    CAS 

    Google Scholar 
    Zuzunegui, M. et al. Growth response of Halimium halimifolium at four sites with different soil water availability regimes in two contrasted hydrological cycles. Plant Soil 247, 271–281 (2002).Article 

    Google Scholar 
    Civeyrel, L. et al. Molecular systematics, character evolution, and pollen morphology of Cistus and Halimium (Cistaceae). Plant Syst. Evol. 295, 23–54 (2011).Article 

    Google Scholar 
    Leonardi, M., Furtado, A. N. M., Comandini, O., Geml, J. & Rinaldi, A. C. Halimium as an ectomycorrhizal symbiont: New records and an appreciation of known fungal diversity. Mycol. Prog. 19, 1495–1509 (2020).Article 

    Google Scholar 
    Oria-De-Rueda, J. A., Martín-Pinto, P. & Olaizola, J. Bolete productivity of cistaceous scrublands in northwestern Spain. Econ. Bot. 62, 323–330 (2008).Article 

    Google Scholar 
    Fernández, C., Vega, J. A. & Fonturbel, T. Does shrub recovery differ after prescribed burning, clearing and mastication in a Spanish heathland?. Plant Ecol. 216, 429–437 (2015).Article 

    Google Scholar 
    Ponte, E. D., Costafreda-Aumedes, S. & Vega-Garcia, C. Lessons learned from arson wildfire incidence in reforestations and natural stands in Spain. Forests 10, 1–18 (2019).Article 

    Google Scholar 
    Franco-Manchón, I., Salo, K., Oria-de-Rueda, J. A., Bonet, J. A. & Martín-Pinto, P. Are wildfires a threat to fungi in European Pinus forests? A case study of boreal and Mediterranean forests. Forests 10, 309 (2019).Article 

    Google Scholar 
    Mediavilla, O., Oria-de-Rueda, J. A. & Martin-Pinto, P. Changes in sporocarp production and vegetation following wildfire in a Mediterranean Forest Ecosystem dominated by Pinus nigra in Northern Spain. For. Ecol. Manage. 331, 85–92 (2014).Article 

    Google Scholar 
    Tomao, A., Antonio Bonet, J., Castaño, C. & De-Miguel, S. How does forest management affect fungal diversity and community composition? Current knowledge and future perspectives for the conservation of forest fungi. For. Ecol. Manage. 457, 117678 (2020).
    Article 

    Google Scholar 
    Espinosa, J., Rodríguez de Rivera, O., Madrigal, J., Guijarro, M. & Hernando, C. Predicting potential cambium damage and fire resistance in Pinus nigra Arn. ssp. salzmannii. For. Ecol. Manage. 474, 118372 (2020).Article 

    Google Scholar 
    Potts, J. B. & Stephens, S. L. Invasive and native plant responses to shrubland fuel reduction: Comparing prescribed fire, mastication and treatment season. Biol. Conserv. 142, 1657–1664 (2009).Article 

    Google Scholar 
    Agee, J. K. & Skinner, C. N. Basic principles of forest fuel reduction treatments. For. Ecol. Manage. 211, 83–96 (2005).Article 

    Google Scholar 
    Fernández, C., Vega, J. A. & Fonturbel, T. Fuel reduction at a Spanish heathland by prescribed fire and mechanical shredding: Effects on seedling emergence. J. Environ. Manage. 129, 621–627 (2013).Article 

    Google Scholar 
    Huggett, R. J., Abt, K. L. & Shepperd, W. Efficacy of mechanical fuel treatments for reducing wildfire hazard. For. Policy Econ. 10, 408–414 (2008).Article 

    Google Scholar 
    Fernández, C. & Vega, J. A. Shrub recovery after fuel reduction treatments and a subsequent fire in a Spanish heathland. Plant Ecol. 215, 1233–1243 (2014).Article 

    Google Scholar 
    Fernández, C., Vega, J. A. & Fonturbel, T. Does fire severity influence shrub resprouting after spring prescribed burning?. Acta Oecologica 48, 30–36 (2013).Article 
    ADS 

    Google Scholar 
    Ellsworth, J. W., Harrington, R. A. & Fownes, J. H. Seedling emergence, growth, and allocation of Oriental bittersweet: Effects of seed input, seed bank, and forest floor litter. For. Ecol. Manage. 190, 255–264 (2004).Article 

    Google Scholar 
    Castaño, C. et al. Resistance of the soil fungal communities to medium-intensity fire prevention treatments in a Mediterranean scrubland. For. Ecol. Manage. 472, 118217 (2020).Article 

    Google Scholar 
    Anderson, I. C., Bastias, B. A., Genney, D. R., Parkin, P. I. & Cairney, J. W. G. Basidiomycete fungal communities in Australian sclerophyll forest soil are altered by repeated prescribed burning. Mycol. Res. 111, 482–486 (2007).Article 
    CAS 

    Google Scholar 
    Hernández-Rodríguez, M. et al. Soil fungal community composition in a Mediterranean shrubland is primarily shaped by history of major disturbance, less so by current fire fuel reduction treatments. Unpublished (2015).Oria de Rueda, J. A., Martín-Pinto, P. & Olaizola, J. Boletus edulis PRODUCTION IN XEROPHILIC AND PIROPHITIC SCHRUBS OF Cistus ladanifer AND Halimium lasianthum IN WESTERN SPAIN. in IV International Workshop on Edible Mycorrhizal Mushrooms (2005).Hart, B. T. N., Smith, J. E., Luoma, D. L. & Hatten, J. A. Recovery of ectomycorrhizal fungus communities fifteen years after fuels reduction treatments in ponderosa pine forests of the Blue Mountains. Oregon. For. Ecol. Manage. 422, 11–22 (2018).Article 

    Google Scholar 
    Hernández-Rodríguez, M., Oria-de-Rueda, J. A., Pando, V. & Martín-Pinto, P. Impact of fuel reduction treatments on fungal sporocarp production and diversity associated with Cistus ladanifer L. ecosystems. For. Ecol. Manage. 353, 10–20 (2015).Article 

    Google Scholar 
    Fernandes, P. M. Scientific support to prescribed underburning in southern Europe: What do we know?. Sci. Total Environ. 630, 340–348 (2018).Article 
    ADS 
    CAS 

    Google Scholar 
    Day, N. J. et al. Wildfire severity reduces richness and alters composition of soil fungal communities in boreal forests of western Canada. Glob. Chang. Biol. 25, 2310–2324 (2019).Article 
    ADS 

    Google Scholar 
    Salo, K., Domisch, T. & Kouki, J. Forest wildfire and 12 years of post-disturbance succession of saprotrophic macrofungi (Basidiomycota, Ascomycota). For. Ecol. Manage. 451, 117454 (2019).Article 

    Google Scholar 
    Zakaria, A. J. & Boddy, L. Mycelial foraging by Resinicium bicolor: Interactive effects of resource quantity, quality and soil composition. FEMS Microbiol. Ecol. 40, 135–142 (2002).Article 
    CAS 

    Google Scholar 
    Hul, S. et al. Fungal community shifts in structure and function across a boreal forest fire chronosequence. Appl. Environ. Microbiol. 81, 7869–7880 (2015).Article 
    ADS 

    Google Scholar 
    Vázquez-Veloso, A. et al. Prescribed burning in spring or autumn did not affect the soil fungal community in Mediterranean Pinus nigra natural forests. For. Ecol. Manage. 512, 120161 (2022).Article 

    Google Scholar 
    Lindahl, B. D. et al. Spatial separation of litter decomposition and mycorrhizal nitrogen uptake in a boreal forest. New Phytol. 173, 611–620 (2007).Article 
    CAS 

    Google Scholar 
    Salomón, R., Rodríguez-Calcerrada, J., González-Doncel, I., Gil, L. & Valbuena-Carabaña, M. On the general failure of coppice conversion into high forest in Quercus pyrenaica stands: A genetic and physiological approach. Folia Geobot. 52, 101–112 (2017).Article 

    Google Scholar 
    Williams, R. J., Hallgren, S. W. & Wilson, G. W. T. Frequency of prescribed burning in an upland oak forest determines soil and litter properties and alters the soil microbial community. For. Ecol. Manage. 265, 241–247 (2012).Article 

    Google Scholar 
    Semenova-Nelsen, T. A., Platt, W. J., Patterson, T. R., Huffman, J. & Sikes, B. A. Frequent fire reorganizes fungal communities and slows decomposition across a heterogeneous pine savanna landscape. New Phytol. 224, 916–927 (2019).Article 

    Google Scholar 
    Oliver, A. K., Callaham, M. A. & Jumpponen, A. Soil fungal communities respond compositionally to recurring frequent prescribed burning in a managed southeastern US forest ecosystem. For. Ecol. Manage. 345, 1–9 (2015).Article 

    Google Scholar 
    Sanz-Benito, I., Mediavilla, O., Casas, A., Oria-de-Rueda, J. A. & Martín-Pinto, P. Effects of fuel reduction treatments on the sporocarp production and richness of a Quercus/Cistus mixed system. For. Ecol. Manage. 503, 119798 (2022).Article 

    Google Scholar 
    Santos-Silva, C., Gonçalves, A. & Louro, R. Canopy cover influence on macrofungal richness and sporocarp production in montado ecosystems. Agrofor. Syst. 82, 149–159 (2011).Article 

    Google Scholar 
    Lin, W. R. et al. The impacts of thinning on the fruiting of saprophytic fungi in Cryptomeria japonica plantations in central Taiwan. For. Ecol. Manage. 336, 183–193 (2015).Article 

    Google Scholar 
    Aragón, G., López, R. & Martínez, I. Effects of Mediterranean dehesa management on epiphytic lichens. Sci. Total Environ. 409, 116–122 (2010).Article 
    ADS 

    Google Scholar 
    Hämäläinen, A., Kouki, J. & Lohmus, P. The value of retained Scots pines and their dead wood legacies for lichen diversity in clear-cut forests: The effects of retention level and prescribed burning. For. Ecol. Manage. 324, 89–100 (2014).Article 

    Google Scholar 
    Schimmel, J. & Granstrom, A. Fire severity and vegetation response in the boreal Swedish. Ecol. Soc. Am. 77, 1436–1450 (1996).
    Google Scholar 
    Hinojosa, M. B., Albert-Belda, E., Gómez-Muñoz, B. & Moreno, J. M. High fire frequency reduces soil fertility underneath woody plant canopies of Mediterranean ecosystems. Sci. Total Environ. 752, 141877 (2021).Article 
    ADS 
    CAS 

    Google Scholar 
    Clemmensen, K. E. et al. Carbon sequestration is related to mycorrhizal fungal community shifts during long-term succession in boreal forests. New Phytol. 205, 1525–1536 (2015).Article 
    CAS 

    Google Scholar 
    Tedersoo, L. et al. Disentangling global soil fungal diversity. Science 346, 1052–1053 (2014).Article 

    Google Scholar 
    Adamo, I. et al. Sampling forest soils to describe fungal diversity and composition. Which is the optimal sampling size in Mediterranean pure and mixed pine oak forests?. Fungal Biol. https://doi.org/10.1016/j.funbio.2021.01.005 (2021).Article 

    Google Scholar 
    Tedersoo, L. et al. Regional-scale in-depth analysis of soil fungal diversity reveals strong pH and plant species effects in northern Europe. Front. Microbiol. 11, 1953 (2020).Article 

    Google Scholar 
    Peay, K., Garbelotto, M. & Bruns, T. Evidence of dispersal limitation in soil microorganisms: Isolation reduces species richness on mycorrhizal tree islands. Ecology 91, 3631–3640 (2010).Article 

    Google Scholar 
    Koivula, M. & Vanha-Majamaa, I. Experimental evidence on biodiversity impacts of variable retention forestry, prescribed burning, and deadwood manipulation in Fennoscandia. Ecol. Process. 9, 1–22 (2020).Article 

    Google Scholar 
    Fox, S. et al. Fire as a driver of fungal diversity—A synthesis of current knowledge. Mycologia 00, 1–27 (2022).
    Google Scholar 
    Raudabaugh, D. B. et al. Where are they hiding? Testing the body snatchers hypothesis in pyrophilous fungi. Fungal Ecol. 43, 100870 (2020).Article 

    Google Scholar 
    Izzo, A., Canright, M. & Bruns, T. D. The effects of heat treatments on ectomycorrhizal resistant propagules and their ability to colonize bioassay seedlings. Mycol. Res. 110, 196–202 (2006).Article 

    Google Scholar 
    Kipfer, T., Moser, B., Egli, S., Wohlgemuth, T. & Ghazoul, J. Ectomycorrhiza succession patterns in Pinus sylvestris forests after stand-replacing fire in the Central Alps. Oecologia 167, 219–228 (2011).Article 
    ADS 

    Google Scholar 
    Glassman, S. I., Levine, C. R., Dirocco, A. M., Battles, J. J. & Bruns, T. D. Ectomycorrhizal fungal spore bank recovery after a severe forest fire: Some like it hot. ISME J. 10, 1228–1239 (2016).Article 

    Google Scholar 
    Buscardo, E. et al. Impact of wildfire return interval on the ectomycorrhizal resistant propagules communities of a Mediterranean open forest. Fungal Biol. 114, 628–636 (2010).Article 

    Google Scholar 
    Pringle, A., Vellinga, E. & Peay, K. The shape of fungal ecology: Does spore morphology give clues to a species’ niche?. Fungal Ecol. 17, 213–216 (2015).Article 

    Google Scholar 
    Zhang, K., Cheng, X., Shu, X., Liu, Y. & Zhang, Q. Linking soil bacterial and fungal communities to vegetation succession following agricultural abandonment. Plant Soil 431, 19–36 (2018).Article 
    ADS 
    CAS 

    Google Scholar 
    Xiang, X. et al. Arbuscular mycorrhizal fungal communities show low resistance and high resilience to wildfire disturbance. Plant Soil 397, 347–356 (2015).Article 
    CAS 

    Google Scholar 
    Dove, N. C., Klingeman, D. M., Carrell, A. A., Cregger, M. A. & Schadt, C. W. Fire alters plant microbiome assembly patterns: Integrating the plant and soil microbial response to disturbance. New Phytol. 230, 2433–2446 (2021).Article 
    CAS 

    Google Scholar 
    Fernandes, P. M. Fire-smart management of forest landscapes in the Mediterranean basin under global change. Landsc. Urban Plan. 110, 175–182 (2013).Article 

    Google Scholar 
    Fontúrbel, M. T., Fernández, C. & Vega, J. A. Prescribed burning versus mechanical treatments as shrubland management options in NW Spain: Mid-term soil microbial response. Appl. Soil Ecol. 107, 334–346 (2016).Article 

    Google Scholar 
    Geml, J. et al. Large-scale fungal diversity assessment in the Andean Yungas forests reveals strong community turnover among forest types along an altitudinal gradient. Mol. Ecol. 23, 2452–2472 (2014).Article 
    CAS 

    Google Scholar 
    Chu, H. et al. Effects of slope aspects on soil bacterial and arbuscular fungal communities in a boreal forest in China. Pedosphere 26, 226–234 (2016).Article 

    Google Scholar 
    Geml, J. Soil fungal communities reflect aspect-driven environmental structuring and vegetation types in a Pannonian forest landscape. Fungal Ecol. 39, 63–79 (2019).Article 

    Google Scholar 
    Castaño, C. et al. Soil microclimate changes affect soil fungal communities in a Mediterranean pine forest. New Phytol. 220, 1211–1221 (2018).Article 

    Google Scholar 
    Collado, E. et al. Mushroom productivity trends in relation to tree growth and climate across different European forest biomes. Sci. Total Environ. 689, 602–615 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    Ihrmark, K., Bödeker, I. & Cruz-Martinez, K. New primers to amplify the fungal ITS2 region—Evaluation by 454-sequencing of artificial and natural communities. FEMS Microbiol. Ecol. 82, 666–677 (2012).Article 
    CAS 

    Google Scholar 
    White, T., Bruns, S., Lee, S. & Taylor, J. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. In PCR Protocols: A Guide to Methods and Applications (eds Innis, M. A. et al.) 315–322 (Academic Press, 1990).
    Google Scholar 
    Kent, M. Vegetation Description and Data Analysis: A Practical Approach (Wiley, 2011).

    Google Scholar 
    Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).Article 
    CAS 

    Google Scholar 
    Kõljalg, U. et al. Towards a unified paradigm for sequence-based identification of fungi. Mol. Ecol. 22, 5271–5277 (2013).Article 

    Google Scholar 
    Abarenkov, K. et al. Plutof-a web based workbench for ecological and taxonomic research, with an online implementation for fungal its sequences. Evol. Bioinforma. 2010, 189–196 (2010).
    Google Scholar 
    Põlme, S. et al. FungalTraits: A user-friendly traits database of fungi and fungus-like stramenopiles. Fungal Divers. 105, 1–16 (2020).Article 

    Google Scholar 
    Agerer, R. Fungal relationships and structural identity of their ectomycorrhizae. Mycol. Prog. 5, 67–107 (2006).Article 

    Google Scholar 
    Tedersoo, L. & Smith, M. E. Lineages of ectomycorrhizal fungi revisited: Foraging strategies and novel lineages revealed by sequences from belowground. Fungal Biol. Rev. 27, 83–99 (2013).Article 

    Google Scholar 
    Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & Team, R. C. Nlme: Linear and Nonlinear Mixed Effects Models. R Package Version 3.1–128. http://CRAN.R-project.org/package=nlme (2016).Chao, A. & Chiu, C. Species richness: Estimation and comparison. Wiley StatsRef https://doi.org/10.1002/9781118445112.stat03432.pub2 (2016).Article 

    Google Scholar 
    Chiu, C. H., Wang, Y. T., Walther, B. A. & Chao, A. An improved nonparametric lower bound of species richness via a modified good-turing frequency formula. Biometrics 70, 671–682 (2014).Article 
    MathSciNet 
    MATH 

    Google Scholar 
    Oksanen, J. et al. Vegan: Community Ecology Package. R package version 2.4–2. https://CRAN.R-project.org/package=vegan. (2017).Oksanen, J., Blanchet, F., Kindt, R. & Al, E. vegan: Community Ecology Package. R package version 2.3–0. (2015). More

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    Warmth shifts symbionts

    Abigail Meyer from the University of Minnesota, USA, and colleagues from the USA, investigated the physiological and morphological responses to experimental warming and CO2 additions in the widespread forest lichen Evernia mesomorpha. While impacts of CO2 were largely negligible, warming and associated drying was linked to decreases in biomass, carbon assimilation and respiration rates. As well as bleaching of the lichen, indicative of death of the photobiont, the authors found evidence of shifts in internal algal communities, including increased proportions of certain algal clades under warming. While the study reveals the sensitivity of lichen algae to warming, further work is needed to reveal whether photobiont turnover may assist in lichen acclimation and recovery. More

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    Primates facing climate crisis in a tropical forest hotspot will lose climatic suitable geographical range

    Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).Article 
    ADS 
    CAS 

    Google Scholar 
    Sandel, B. et al. The influence of late Quaternary climate-change velocity on species endemism. Science 334(6056), 660–664 (2011).Article 
    ADS 
    CAS 

    Google Scholar 
    Scheffers, B. R. et al. The broad footprint of climate change from genes to biomes to people. Science https://doi.org/10.1126/science.aaf7671 (2016).Article 

    Google Scholar 
    Davis, M. B. & Shaw, R. G. Range shifts and adaptive responses to quaternary climate change. Science 292(5517), 673–679 (2001).Article 
    ADS 
    CAS 

    Google Scholar 
    Lane, J. E., Kruuk, L. E. B., Charmantier, A., Murie, J. O. & Dobson, F. S. Delayed phenology and reduced fitness associated with climate change in a wild hibernator. Nature 489, 554–557 (2012).Article 
    ADS 
    CAS 

    Google Scholar 
    Pecl, G. T. et al. Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science https://doi.org/10.1126/science.aai9214 (2017).Article 

    Google Scholar 
    Dawson, T. P., Jackson, S. T., House, J. I., Prentice, I. C. & Mace, G. M. Beyond predictions: Biodiversity conservation in a changing climate. Science https://doi.org/10.1126/science.1200303 (2011).Article 

    Google Scholar 
    Pacifici, M. et al. Species’ traits influenced their response to recent climate change. Nat. Clim. Change 7, 205–208 (2017).Article 
    ADS 

    Google Scholar 
    Schloss, C. A., Nuñez, T. A. & Lawler, J. J. Dispersal will limit ability of mammals to track climate change in the Western Hemisphere. PNAS 109(22), 8606–8611 (2012).Article 
    ADS 
    CAS 

    Google Scholar 
    Perry, A. L., Low, P. J., Ellis, J. R. & Reynolds, J. D. Climate change and distribution shifts in marine fishes. Science 308(5730), 1912–1915 (2005).Article 
    ADS 
    CAS 

    Google Scholar 
    Bradshaw, W. E., Zani, P. A. & Holzapfel, C. M. Adaptation to temperate climates. Evolution 58(8), 1748–1762 (2004).
    Google Scholar 
    Thomas, C. D. et al. Ecological and evolutionary processes at expanding range margins. Nature 411(6837), 577–581 (2001).Article 
    ADS 
    CAS 

    Google Scholar 
    Urban, M. C. Accelerating extinction risk from climate change. Science 348(6234), 571–573 (2015).Article 
    ADS 
    CAS 

    Google Scholar 
    Waller, N. L., Gynther, I. C., Freeman, A. B., Lavery, T. H. & Leung, L. K. P. The bramble cay melomys Melomys rubicola (Rodentia:Muridae): A first mammalian extinction caused by human-induced climate change?. Wildl. Res. 44(1), 9–21 (2017).Article 

    Google Scholar 
    Murray, K. A., Rosauer, D., McCallum, H. & Skerratt, L. F. Integrating species traits with extrinsic threats: Closing the gap between predicting and preventing species declines. Proc. R. Soc. B: Biol. Sci. 278(1711), 1515–1523 (2011).Article 

    Google Scholar 
    Stevens, G. C. The latitudinal gradient in geographical range: How so many species coexist in the Tropics. Am. Nat. 133(2), 240–256 (1989).Article 

    Google Scholar 
    Hickling, R., Roy, D. B., Hill, J. K., Fox, R. & Thomas, C. D. The distributions of a wide range of taxonomic groups are expanding polewards. Glob. Change Biol. 12(3), 450–455 (2006).Article 
    ADS 

    Google Scholar 
    Virkkala, R., Heikkinen, R. K., Leikola, N. & Luoto, M. Projected large-scale range reductions of northern-boreal land bird species due to climate change. Biol. Conserv. 141(5), 1343–1353 (2008).Article 

    Google Scholar 
    Sales, L. P. et al. Niche conservatism and the invasive potential of the wild boar. J. Anim. Ecol. 86(5), 1214–1223 (2017).Article 

    Google Scholar 
    Gouveia, S. F. et al. Climate and land use changes will degrade the configuration of the landscape for titi monkeys in eastern Brazil. Glob. Change Biol. 22(6), 2003–2012 (2016).Article 
    ADS 

    Google Scholar 
    Pearson, R. G. & Dawson, T. P. Predicting the impacts of climate change on the distribution of species: Are bioclimate envelope models useful?. Glob. Ecol. Biogeogr. 12(5), 361–371 (2003).Article 

    Google Scholar 
    Engler, R. et al. Predicting future distributions of mountain plants under climate change: does dispersal capacity matter?. Ecography 32(1), 34–45 (2009).Article 

    Google Scholar 
    Ozinga, W. A. et al. Predictability of plant species composition from environmental conditions is constrained by dispersal limitation. Oikos 108(3), 555–561 (2005).Article 

    Google Scholar 
    Takahashi, K. & Kamitani, T. Effect of dispersal capacity on forest plant migration at a landscape scale. J. Ecol. 92(5), 778–785 (2004).Article 

    Google Scholar 
    Koo, K. A. & Park, S. U. The effect of interplays among climate change, land-use change, and dispersal capacity on plant redistribution. Ecol. Indic. 142, 109192 (2022).Article 

    Google Scholar 
    Chen, I. C., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333(6045), 1024–1026 (2011).Article 
    ADS 
    CAS 

    Google Scholar 
    Poloczanska, E. S. et al. Global imprint of climate change on marine life. Nat. Clim. Change 3(10), 919–925 (2013).Article 
    ADS 

    Google Scholar 
    Vanderwal, J. et al. Focus on poleward shifts in species’ distribution underestimates the fingerprint of climate change. Nat. Clim. Change 3, 239–243 (2013).Article 
    ADS 

    Google Scholar 
    Lira, A. F. de A., Badillo-Montaño, R., Lira-Noriega, A. & de Albuquerque, C. M. R. Potential distribution patterns of scorpions in north-eastern Brazil under scenarios of future climate change. Austral Ecol. 45(2), 215–228 (2020).Castro, M. B. et al. Will the emblematic southern conifer Araucaria angustifolia survive to climate change in Brazil?. Biodivers. Conserv. 29(2), 591–607 (2020).Article 

    Google Scholar 
    Wilson, O. J., Walters, R. J., Mayle, F. E., Lingner, D. V. & Vibrans, A. C. Cold spot microrefugia hold the key to survival for Brazil’s Critically Endangered Araucaria tree. Glob. Change Biol. 25(12), 4339–4351 (2019).Article 
    ADS 

    Google Scholar 
    Esser, L. F. et al. Future uncertainties for the distribution and conservation of Paubrasilia echinata under climate change. Acta Bot. Bras. 33(4), 770–776 (2019).Article 

    Google Scholar 
    Cabanne, G. S. et al. Effects of Pleistocene climate changes on species ranges and evolutionary processes in the Neotropical Atlantic Forest. Biol. J. Linn. Soc. 119(4), 856–872 (2016).Article 

    Google Scholar 
    Iturralde-Pólit, P., Dangles, O., Burneo, S. F. & Meynard, C. N. The effects of climate change on a mega-diverse country: predicted shifts in mammalian species richness and turnover in continental Ecuador. Biotropica 49(6), 821–831 (2017).Article 

    Google Scholar 
    Vu, T. T. et al. An assessment of the impact of climate change on the distribution of the grey-shanked douc Pygathrix cinerea using an ecological niche model. Primates 61(2), 267–275 (2020).Article 

    Google Scholar 
    Sales, L. P., Ribeiro, B. R., Pires, M. M., Chapman, C. A. & Loyola, R. Recalculating route: dispersal constraints will drive the redistribution of Amazon primates in the Anthropocene. Ecography 42(10), 1789–1801 (2019).Article 

    Google Scholar 
    Hill, S. E. & Winder, I. C. Predicting the impacts of climate change on Papio baboon biogeography: Are widespread, generalist primates ‘safe’?. J. Biogeogr. 46(7), 1380–1405 (2019).
    Google Scholar 
    Gillings, S., Balmer, D. E. & Fuller, R. J. Directionality of recent bird distribution shifts and climate change in Great Britain. Glob. Change Biol. 21(6), 2155–2168 (2015).Article 
    ADS 

    Google Scholar 
    Fernández, D. et al. The current status of the world’s primates: Mapping threats to understand priorities for primate conservation. Int. J. Primatol. 43, 15–39 (2022).Article 

    Google Scholar 
    Stewart, B. M., Turner, S. E. & Matthews, H. D. Climate change impacts on potential future ranges of non-human primate species. Clim. Change 162, 2301–2318 (2020).Article 
    ADS 
    CAS 

    Google Scholar 
    Estrada, A. et al. Primates in peril: The significance of Brazil, Madagascar, Indonesia and the Democratic Republic of the Congo for global primate conservation. PeerJ 6, e4869; https://doi.org/10.7717/peerj.4869 (2018).Estrada, A. et al. Impending extinction crisis of the world’s primates: Why primates matter. Sci. Adv. https://doi.org/10.1126/sciadv.1600946 (2017).Article 

    Google Scholar 
    Graham, T. L., Matthews, H. D. & Turner, S. E. A global-scale evaluation of primate exposure and vulnerability to climate change. Int. J. Primatol. 37(2), 158–174 (2016).Article 

    Google Scholar 
    Meyer, A. L. S., Pie, M. R. & Passos, F. C. Assessing the exposure of lion tamarins (Leontopithecus spp.) to future climate change. Am. J. Primatol. 76(6), 551–562 (2014).Article 

    Google Scholar 
    Braz, A. G., Lorini, M. L. & Vale, M. M. Climate change is likely to affect the distribution but not parapatry of the Brazilian marmoset monkeys (Callithrix spp.). Divers. Distrib. 25(4), 536–550 (2019).Article 

    Google Scholar 
    Lima, A. A. de, Ribeiro, M. C., Grelle, C. E. de V. & Pinto, M. P. Impacts of climate changes on spatio-temporal diversity patterns of Atlantic Forest primates. Perspect. Ecol. Conserv. 17(2), 50–56 (2019).Colombo, A. F. & Joly, C. A. Brazilian Atlantic Forest lato sensu: the most ancient Brazilian forest, and a biodiversity hotspot, is highly threatened by climate change. Braz. J. Biol. 70(3), 697–708 (2010).Article 
    CAS 

    Google Scholar 
    Zwiener, V. P., Lira-Noriega, A., Grady, C. J., Padial, A. A. & Vitule, J. R. Climate change as a driver of biotic homogenization of woody plants in the Atlantic Forest. Glob. Ecol. Biogeogr. 27(3), 298–309 (2018).Article 

    Google Scholar 
    Lemes, P., Melo, A. S. & Loyola, R. D. Climate change threatens protected areas of the Atlantic Forest. Biodivers. Conserv. 23(2), 357–368 (2014).Article 

    Google Scholar 
    Rezende, G. C., Sobral-Souza, T. & Culot, L. Integrating climate and landscape models to prioritize areas and conservation strategies for an endangered arboreal primate. Am. J. Primatol. 82(12), e23202. https://doi.org/10.1002/ajp.23202 (2020).Article 

    Google Scholar 
    Silva, L. B. et al. How future climate change and deforestation can drastically affect the species of monkeys endemic to the eastern Amazon, and priorities for conservation. Biodivers. Conserv. 31, 971–988 (2022).Article 

    Google Scholar 
    Sales, L., Ribeiro, B. R., Chapman, C. A. & Loyola, R. Multiple dimensions of climate change on the distribution of Amazon primates. Perspect. Ecol. Conserv. 18(2), 83–90 (2020).
    Google Scholar 
    Moraes, B., Razgour, O., Souza-Alves, J., Boubli, J. & Bezerra, B. Habitat suitability for primate conservation in north-east Brazil. Oryx 54(6), 803–813 (2020).Article 

    Google Scholar 
    Hanson, J. O. et al. Global conservation of species’ niches. Nature 580, 232–234 (2020).Article 
    ADS 
    CAS 

    Google Scholar 
    Hanson, J. O., Rhodes, J. R., Riginos, C. & Fuller, R. A. Environmental and geographic variables are effective surrogates for genetic variation in conservation planning. PNAS 114(48), 12755–12760 (2017).Article 
    ADS 
    CAS 

    Google Scholar 
    Scheele, B. C., Foster, C. N., Banks, S. C. & Lindenmayer, D. B. Niche contractions in declining species: Mechanisms and consequences. Trends Ecol. Evol. 32(5), 346–355 (2017).Article 

    Google Scholar 
    Travis, J. M. J. et al. Dispersal and species’ responses to climate change. Oikos 122, 1532–1540 (2013).Article 

    Google Scholar 
    Lenoir, J. & Svenning, J.-C. Climate-related range shifts – a global multidimensional synthesis and new research directions. Ecography 38, 15–28 (2015).Article 

    Google Scholar 
    Raghunathan, N., François, L., Huynen, M. C., Oliveira, L. C. & Hambuckers, A. Modelling the distribution of key tree species used by lion tamarins in the Brazilian Atlantic forest under a scenario of future climate change. Reg. Environ. Change 15, 683–693 (2015).Article 

    Google Scholar 
    Lawler, J. J., Ruesch, A. S., Olden, J. D. & McRae, B. H. Projected climate-driven faunal movement routes. Ecol. Lett. 16(8), 1014–1022 (2013).Article 
    CAS 

    Google Scholar 
    Årevall, J., Early, R., Estrada, A., Wennergren, U. & Eklöf, A. C. Conditions for successful range shifts under climate change: The role of species dispersal and landscape configuration. Divers. Distrib. 24, 1598–1611 (2018).Article 

    Google Scholar 
    Carroll, C., Lawler, J. J., Roberts, D. R. & Hamann, A. Biotic and climatic velocity identify contrasting areas of vulnerability to climate change. PLoS ONE 10(10), e0142024. https://doi.org/10.1371/journal.pone.0140486 (2015).Article 
    CAS 

    Google Scholar 
    Davies, T. J., Purvis, A. & Gittleman, J. L. Quaternary climate change and the geographic ranges of mammals. Am. Nat. 174(3), 297–307 (2009).Article 

    Google Scholar 
    Gaston, K.J. The structure and dynamics of geographic ranges (Oxford University Press, 2003).Meyer, A. L. S. & Pie, M. R. Climate change estimates surpass rates of climatic niche evolution in primates. Int. J. Primatol. 43, 40–56 (2021).Article 

    Google Scholar 
    Zeigler, S. L., Fagan, W. F., DeFries, R. & Raboy, B. E. Identifying important forest patches for the long-term persistence of the endangered golden-headed lion tamarin (Leontopithecus chrysomelas). Trop. Conserv. Sci. 3(1), 63–77 (2010).Article 

    Google Scholar 
    Dosen, J., Fortin, M. J. & Raboy, B. E. Restoration strategies to improve connectivity for golden-headed lion tamarins (Leontopithecus chrysomelas) in the Bahian Atlantic Forest. Brazil. Int. J. Primatol. 38(5), 962–983 (2017).Article 

    Google Scholar 
    Piffer, P. R., Rosa, M. R., Tambosi, L. R., Metzger, J. P. & Uriarte, M. Turnover rates of regenerated forests challenge restoration efforts in the Brazilian Atlantic Forest. Environ. Res. Lett. 17(4), 045009. https://doi.org/10.1088/1748-9326/ac5ae1 (2022).Article 
    ADS 

    Google Scholar 
    Estrada, A., Raboy, B. E. & Oliveira, L. C. Agroecosystems and primate conservation in the tropics: A review. Am. J. Primatol. 74, 696–711 (2012).Article 

    Google Scholar 
    Galea, B., Humle, T. Identifying and mitigating the impacts on primates of transportation and service corridors. Conserv. Biol. 36, e13836; https://doi.org/10.1111/cobi.13836 (2022).Gouveia, S. F. et al. Functional planning units for the management of an endangered Brazilian titi monkey. Am. J. Primatol. 79(5), e22637; https://doi.org/10.1002/ajp.22637 (2017).Rezende, G. et al. Leontopithecus chrysopygus. The IUCN Red List of Threatened Species, e.T11505A17935400; https://doi.org/10.2305/IUCN.UK.2020-2.RLTS.T11505A17935400.en (2020).Culot, L. et al. ATLANTIC-PRIMATES: A dataset of communities and occurrences of primates in the Atlantic Forests of South America. Ecology 100(1), e02525; https://doi.org/10.1002/ecy.2525 (2018).Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37(12), 4302–4315 (2017).Article 

    Google Scholar 
    Quinn, G. P. & Keough, M. J. Experimental design and data analysis for biologists (Cambridge University Press, 2002).Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1(1), 3–14 (2010).Article 

    Google Scholar 
    Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17(1), 43–57 (2011).Article 

    Google Scholar 
    Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Modell. 190(3–4), 231–259 (2006).Article 

    Google Scholar 
    Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29(2), 129–151 (2006).Article 

    Google Scholar 
    Muscarella, R. et al. ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for ecological niche models. Methods Ecol. Evol. 5(11), 1198–1205 (2014).Article 

    Google Scholar 
    Pearson, R. G., Raxworthy, C. J., Nakamura, M. & Townsend Peterson, A. Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar. J. Biogeogr. 34(1), 102–117 (2007).Article 

    Google Scholar 
    Shcheglovitova, M. & Anderson, R. P. Estimating optimal complexity for ecological niche models: A jackknife approach for species with small sample sizes. Ecol. Modell. 269, 9–17 (2013).Article 

    Google Scholar 
    Wenger, S. J. & Olden, J. D. Assessing transferability of ecological models: An underappreciated aspect of statistical validation. Methods Ecol. Evol. 3(2), 260–267 (2012).Article 

    Google Scholar 
    Hidasi-Neto, J. et al. Climate change will drive mammal species loss and biotic homogenization in the Cerrado Biodiversity Hotspot. Perspect. Ecol. Conserv. 17(2), 57–63 (2019).
    Google Scholar 
    Bowman, J., Jaeger, J. A. G. & Fahrig, L. Dispersal distance of mammals is proportional to home range size. Ecology 83(7), 2049–2055 (2002).Article 

    Google Scholar 
    Galán-Acedo, C., Arroyo-Rodríguez, V., Andresen, E. & Arasa-Gisbert, R. Ecological traits of the world’s primates. Sci. Data 6, 55. https://doi.org/10.1038/s41597-019-0059-9 (2019).Article 

    Google Scholar 
    Pacifici, M. et al. Generation length for mammals. Nat. Conserv. 5, 89–94 (2013).Article 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing Vienna Austria (2017).QGIS Development Team. QGIS Geographic Information System (2016). More

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    Altered gut microbiota in individuals with episodic and chronic migraine

    ParticipantsIn total, 80, 63, and 56 participants in the EM, CM, and control groups, respectively, initially agreed to participate in this study. Nevertheless, 28, 12, and 13 individuals in the EM, CM, and control groups, respectively, withdrew their participation and did not bring any fecal samples to the study site. After providing fecal samples, 10 and 6 individuals with EM and CM, respectively, reported intake of probiotics and were excluded from the analysis. No participant in the control group consumed probiotics during the study period. Eventually, 42, 45, and 43 participants in the EM, CM, and control groups, respectively, were enrolled (Fig. 1). The demographic and clinical characteristics of participants are summarized in Table 1. All participants with EM and CM used acute treatments for migraine. Moreover, 25 (59.5%) and 27 (60.0%) participants with EM and CM, respectively, received prophylactic treatment for migraine. Of the 42 participants with EM, 20 used anti-epileptic medications, 11 used beta blockers, 2 used an anti-depressant, and 1 used a calcium-channel blocker for prophylactic treatment. Of the 45 participants with CM, 23 used anti-epileptic medications, 8 used beta blockers, 1 used an anti-depressant, and no participant used calcium-channel blockers for prophylactic treatment. No participant in the EM, CM, and control groups was infected with SARS-CoV-2 before or during participation in the study.Figure 1Flow of participants in a study on the composition of gut microbiota in participants with episodic or chronic migraine.Full size imageTable 1 Demographic and clinical characteristics of participants with episodic and chronic migraine and the control.Full size tableCollection of 16 s RNA sequencing dataWe obtained 7,802,425 read sequences, accounting for 99.8% of the valid sequences from the fecal samples of 130 participants. According to barcode and primer sequence filtering, an average of 59,305 (range, 3716–90,832) observed sequences per sample was recovered for downstream analysis. Thus, 2,242,325 sequences were obtained from the controls for phylogenetic analysis, whereas 2,747,952 and 2,812,148 sequences were obtained from the EM and CM groups, respectively.Microbial diversityAlpha diversity was defined as microbial community richness and evenness. Alpha diversities in the genus richness, as evaluated by Chao1 (Fig. 2A), Shannon (Fig. 2B), and Simpson (Fig. 2C) indices, did not differ significantly among the EM, CM, and control groups. Beta diversity represented the community composition dissimilarity between samples. PCoA with the weighted UniFrac distance (Fig. 3A and Supplementary Fig. S1A, p = 0.176, permutational multivariate analysis of variance [PERMANOVA]), the unweighted UniFrac distance (Fig. 3B and Supplementary Fig. S1B, p = 0.132, PERMANOVA), and the Bray–Curtis dissimilarity index (Fig. 3C and Supplementary Fig. S1C, p = 0.220, PERMANOVA) for beta diversity at the genus level among the EM, CM, and control groups revealed that these three groups could not be separated.Figure 2Alpha diversity at the genus level using Chao1 (A), Shannon (B), and Simpson (C) indices*,†. *Controls (green) and participants with episodic migraine (blue) and chronic migraine (yellow). †In the box plots, the lower boundary of the box indicates the 25th percentile; a blue line within the box marks the median, and the upper boundary of the box indicates the 75th percentile. Whiskers above (red) and below the box (green) indicate the highest and the lowest values, respectively.Full size imageFigure 3Beta diversity of microbiota in principal coordinate analysis plot with the weighted UniFrac distance (A), the unweighted UniFrac distance (B) and the Bray–Curtis dissimilarity index (C)*. *Controls (green) and participants with episodic migraine (blue) and chronic migraine (yellow).Full size imageRelative abundance of fecal microbes between participants with EM and the controlRelative abundance of fecal microbes at the phylum level did not differ significantly among participants in the control, EM, and CM groups (Supplementary Fig. S2). Moreover, Tissierellales (p = 0.001) and Tissierellia (p = 0.001) were more abundant in the EM group than that in the control group at the order and class levels, respectively (Fig. 4A). At the family level, Peptoniphilaceae (p = 0.001) and Eubacteriaceae (p = 0.045) occurred at a significantly higher proportion in the EM group than that in the control group. Furthermore, at the genus level, the abundance of 11 genera differed significantly between the two groups, including one more abundant and 10 less abundant genera in the EM group. Catenibacterium (p = 0.031) and Olsenella (p = 0.038) had the highest relative abundance in the control and EM groups, respectively.Figure 4Taxonomic differences in fecal microbiota among participants. The fold change (log2) denotes the difference in relative abundance between participants with episodic migraine and the control (A), between those with chronic migraine and the control (B), and between those with episodic and chronic migraine (C). CM chronic migraine; EM episodic migraine.Full size imageRelative abundance of fecal microbes between participants with CM and the controlThe analysis results at the class, order, family, genus, and species levels between CM and control groups are illustrated in Fig. 4B. Tissierellia (p = 0.001), Tissierellales (p = 0.001), and Peptoniphilaceae (p = 0.001) were more abundant in the CM group than that in the control group at the class, order, and family levels, respectively; however, at the genus level, the abundances of 18 genera differed significantly, including four more abundant and 14 less abundant genera in the CM group than in the control group.Relative abundance of fecal microbes between participants with EM and CMThe analysis results at the class, order, family, and genus levels between CM and EM groups are summarized in Fig. 4C. At the class level, Bacilli (p = 0.033) were less abundant in the CM group than that in the EM group; however, at the order level, Selenomonadales (p = 0.016) and Lactobacillales (p = 0.034) were less abundant in the CM group than that in the EM group. Moreover, at the class level, Selenomonadaceae (p = 0.016) and Prevotellaceae (p = 0.012) were less abundant in the CM group than that in the EM group. Furthermore, at the genus level, PAC001212_g (p = 0.019) revealed relative positive predominancy in the CM groups, whereas Prevotella (p = 0.019), Holdemanella (p = 0.009), Olsenella (p = 0.033), Adlercreutzia (p = 0.018), and Coprococcus (p = 0.040) revealed relative positive predominancy in the EM group.Association among fecal microbiota and clinical characteristics and comorbidities of migraineAmong the five genera (Roseburia, Eubacterium_g4, Agathobacter, PAC000195_g, and Catenibacterium) depicting predominance or less-predominance both in EM and CM groups, we conducted additional analyses for clinical characteristics and migraine comorbidities.Combining the results of the 42 and 45 participants with EM and CM, respectively, the Poisson regression analysis for relative abundance of microbiota revealed that a higher composition of PAC000195_g (p = 0.040) was significantly associated with lower headache frequency (Table 2). Furthermore, Agathobacter (p = 0.009) had a negative association with severe headache intensity (Table 3). Anxiety was associated with Catenibacterium (p = 0.027); however, depression did not reveal any association with the five genera (Table 3).Table 2 The association between headache frequency and the relative abundance of microbiota.*Full size tableTable 3 The association of severe headache intensity and comorbidities with the relative abundance of microbiota*.Full size tableRelative abundance of fecal microbes in participants with EM based on prophylactic treatmentAlpha and beta diversities in participants with EM did not differ significantly based on their prophylactic treatment (Supplementary Figs S3A–C, S4A–C, and S5A–C). At the genus level, Klebsiella (p = 0.009), Enterobacteriaceae_g (p = 0.006), and Faecalibacterium (p = 0.046) were more abundant in the prophylactic group than the non-prophylactic group (Supplementary Fig. S6A).Relative abundance of fecal microbes in participants with CM based on prophylactic treatmentAlpha and beta diversities in participants with CM did not differ significantly based on prophylactic treatment (Supplementary Figs S7A–C, S8A–C, and S9A–C). Emergencia (p = 0.043), Ruthenibacterium (p = 0.005), Eggerthella (p = 0.003), PAC000743_g (p = 0.034), and Anaerostipes (p = 0.039) were more abundant in the prophylactic group, whereas PAC000196_g (p = 0.049), Fusicatenibacter (p = 0.028), and Faecalibacterium (p = 0.021) were more abundant in the non-prophylactic group at the genus level (Supplementary Fig. S6B). More

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    Accuracy of tropical peat and non-peat fire forecasts enhanced by simulating hydrology

    Edwards, R. B., Naylor, R. L., Higgins, M. M. & Falcon, W. P. Causes of Indonesia’s forest fires. World Dev. 127, 104717 (2020).Article 

    Google Scholar 
    Page, S. E., Rieley, J. O. & Banks, C. J. Global and regional importance of the tropical peatland carbon pool. Glob. Chang. Biol. 17, 798–818 (2011).Article 
    ADS 

    Google Scholar 
    Page, S., et al. Tropical Fire Ecology Ch. 9 (Springer, 2009).Page, S. E. & Hooijer, A. In the line of fire: the peatlands of Southeast Asia. Philos. Trans. R. Soc. Lond., B, Biol. Sci. 371, 20150176 (2016).Huijnen, V. et al. Fire carbon emissions over maritime southeast Asia in 2015 largest since 1997. Sci. Rep. 6, 1–8 (2016).Article 

    Google Scholar 
    Kusumaningtyas, S. D. A. & Aldrian, E. Impact of the June 2013 Riau province Sumatera smoke haze event on regional air pollution. Environ. Res. Lett. 11, 075007 (2016).Article 
    ADS 

    Google Scholar 
    Gaveau, D. L. et al. Major atmospheric emissions from peat fires in Southeast Asia during non-drought years: Evidence from the 2013 Sumatran fires. Sci. Rep. 4, 1–7 (2014).Article 

    Google Scholar 
    Tacconi, L. Preventing fires and haze in Southeast Asia. Nat. Clim. Chang. 6, 640–643 (2016).Article 
    ADS 

    Google Scholar 
    Posa, M. R. C., Wijedasa, L. S. & Corlett, R. T. Biodiversity and conservation of tropical peat swamp forests. Bioscience 61, 49–57 (2011).Article 

    Google Scholar 
    Harrison, M. E. & Rieley, J. O. Tropical peatland biodiversity and conservation in Southeast Asia. Mires Peat 22, 1–7 (2018).
    Google Scholar 
    Purnomo, H. et al. Fire economy and actor network of forest and land fires in Indonesia. For. Policy Econ. 78, 21–31 (2017).Article 

    Google Scholar 
    Wösten, J. H. M., Clymans, E., Page, S. E., Rieley, J. O. & Limin, S. H. Peat–water interrelationships in a tropical peatland ecosystem in Southeast Asia. CATENA 73, 212–224 (2008).Article 

    Google Scholar 
    Taufik, M., Setiawan, B. I. & Van Lanen, H. A. Increased fire hazard in human-modified wetlands in Southeast Asia. Ambio 48, 363–373 (2019).Article 

    Google Scholar 
    Taufik, M. et al. Amplification of wildfire area burnt by hydrological drought in the humid tropics. Nat. Clim. Chang. 7, 428–431 (2017).Article 
    ADS 

    Google Scholar 
    Fanin, T. & Werf, G. R. Precipitation–fire linkages in Indonesia (1997–2015). Biogeosciences 14, 3995–4008 (2017).Article 
    ADS 

    Google Scholar 
    Field, R. D. et al. Indonesian fire activity and smoke pollution in 2015 show persistent nonlinear sensitivity to El Niño-induced drought. Proc. Natl. Acad. Sci. U.S.A. 113, 9204–9209 (2016).Article 
    ADS 
    CAS 

    Google Scholar 
    Hirano, T. et al. Effects of disturbances on the carbon balance of tropical peat swamp forests. Glob. Chang. Biol. 18, 3410–3422 (2012).Article 
    ADS 

    Google Scholar 
    Ohkubo, S., Hirano, T. & Kusin, K. Influence of fire and drainage on evapotranspiration in a degraded peat swamp forest in Central Kalimantan Indonesia. J. Hydrol. 603, 126906 (2021).Article 

    Google Scholar 
    Nikonovas, T., Spessa, A., Doerr, S. H., Clay, G. D. & Mezbahuddin, S. Near-complete loss of fire-resistant primary tropical forest cover in Sumatra and Kalimantan. Commun. Earth Environ. 1, 1–8 (2020).Article 

    Google Scholar 
    Lin, Y., Wijedasa, L. S. & Chisholm, R. A. Singapore’s willingness to pay for mitigation of transboundary forest-fire haze from Indonesia. Environ. Res. Lett. 12, 024017 (2017).Article 
    ADS 

    Google Scholar 
    Nikonovas, T., Spessa, A., Doerr, S. H., Clay, G. & Mezbahuddin, S. ProbFire: A probabilistic fire early warning system for Indonesia. Nat. Hazards Earth Syst. Sci. 22, 303–322 (2022).Article 
    ADS 

    Google Scholar 
    Taufik, M., Veldhuizen, A. A., Wösten, J. H. M. & van Lanen, H. A. J. Exploration of the importance of physical properties of Indonesian peatlands to assess critical groundwater table depths, associated drought and fire hazard. Geoderma 347, 160–169 (2019).Article 
    ADS 

    Google Scholar 
    Sloan, S., Tacconi, L. & Cattau, M. E. Fire prevention in managed landscapes: Recent success and challenges in Indonesia. Mitig. Adapt. Strateg. Glob. Chang. 26, 1–30 (2021).Article 

    Google Scholar 
    Lestari, I., Murdiyarso, D. & Taufik, M. Rewetting tropical peatlands reduced net greenhouse gas emissions in Riau Province Indonesia. Forests 13, 505 (2022).Article 

    Google Scholar 
    Spessa, A. C. et al. Seasonal forecasting of fire over Kalimantan Indonesia. Nat. Hazards Earth Syst. Sci. 15, 429–442 (2015).Article 
    ADS 

    Google Scholar 
    Mezbahuddin, M., Grant, R. F. & Hirano, T. How hydrology determines seasonal and interannual variations in water table depth, surface energy exchange, and water stress in a tropical peatland: Modeling versus measurements. J. Geophys. Res. Biogeosci. 120, 2132–2157 (2015).Article 

    Google Scholar 
    Mezbahuddin, M., Grant, R. F. & Hirano, T. Modelling effects of seasonal variation in water table depth on net ecosystem CO2 exchange of a tropical peatland. Biogeosciences 11, 577–599 (2014).Article 
    ADS 

    Google Scholar 
    Cobb, A. R. & Harvey, C. F. Scalar simulation and parameterization of water table dynamics in tropical peatlands. Water Resour. Res. 55, 9351–9377 (2019).Article 
    ADS 

    Google Scholar 
    Dadap, N. C., Cobb, A. R., Hoyt, A. M., Harvey, C. F. & Konings, A. G. Satellite soil moisture observations predict burned area in Southeast Asian peatlands. Environ. Res. Lett. 14, 094014 (2019).Article 
    ADS 

    Google Scholar 
    Evans, C. D. et al. Rates and spatial variability of peat subsidence in Acacia plantation and forest landscapes in Sumatra Indonesia. Geoderma 338, 410–421 (2019).Article 
    ADS 

    Google Scholar 
    Hooijer, A. et al. Subsidence and carbon loss in drained tropical peatlands. Biogeosciences 9, 1053–1071 (2012).Article 
    ADS 
    CAS 

    Google Scholar 
    Couwenberg, J. & Hooijer, A. Towards robust subsidence-based soil carbon emission factors for peat soils in south-east Asia, with special reference to oil palm plantations. Mires Peat 12, 1–13 (2013).
    Google Scholar 
    Khasanah, N. M. & van Noordwijk, M. Subsidence and carbon dioxide emissions in a smallholder peatland mosaic in Sumatra Indonesia. Mitig. Adapt. Strateg. Glob. Chang. 24, 147 (2019).Article 

    Google Scholar 
    Marwanto, S., Watanabe, T., Iskandar, W., Sabiham, S. & Funakawa, S. Effects of seasonal rainfall and water table movement on the soil solution composition of tropical peatland. Soil Sci. Plant Nutr. 64, 386–395 (2018).Article 
    CAS 

    Google Scholar 
    Lubis, M. E. S. et al. Changes in water table depth in an oil palm plantation and its surrounding regions in Sumatra Indonesia. J. Agron. 13, 140–146 (2014).Article 

    Google Scholar 
    Page, S. E., Rieley, J. O. & Wüst, R. Developments in Earth Surface Processes (Volume 9) Ch. 3 (Elsevier, 2006).Haffiez, N. et al. Exploration of machine learning algorithms for predicting the changes in abundance of antibiotic resistance genes in anaerobic digestion. Sci. Total Environ. 839, 156211 (2022).Article 
    ADS 
    CAS 

    Google Scholar 
    Grant, R. F., Desai, A. R. & Sulman, B. N. Modelling contrasting responses of wetland productivity to changes in water table depth. Biogeosciences 9, 4215–4231 (2012).Article 
    ADS 
    CAS 

    Google Scholar 
    Mezbahuddin, M., Grant, R. F. & Flanagan, L. B. Modeling hydrological controls on variations in peat water content, water table depth, and surface energy exchange of a boreal western Canadian fen peatland. J. Geophys. Res. Biogeosci. 121, 2216–2242 (2016).Article 

    Google Scholar 
    Dimitrov, D. D., Grant, R. F., Lafleur, P. M. & Humphreys, E. R. Modeling the effects of hydrology on gross primary productivity and net ecosystem productivity at Mer Bleue bog. J. Geophys. Res. Biogeosci. 116, G04010 (2011).Article 
    ADS 

    Google Scholar 
    Dimitrov, D. D., Bhatti, J. S. & Grant, R. F. The transition zones (ecotone) between boreal forests and peatlands: Modelling water table along a transition zone between upland black spruce forest and poor forested fen in central Saskatchewan. Ecol. Modell. 274, 57–70 (2014).Article 

    Google Scholar 
    Hengl, T. et al. SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).Article 

    Google Scholar 
    Hodnett, M. G. & Tomasella, J. Marked differences between van Genuchten soil water-retention parameters for temperate and tropical soils: A new water-retention pedo-transfer functions developed for tropical soils. Geoderma 108, 155–180 (2002).Article 
    ADS 
    CAS 

    Google Scholar 
    Funk, C. et al. The climate hazards infrared precipitation with stations-a new environmental record for monitoring extremes. Sci. Data 2, 1–21 (2015).Article 

    Google Scholar 
    Osaki, M., Hirose, K., Segah, H. & Helmy, F. Tropical Peatland Ecosystems Ch. 9 (Springer, 2016).Razavi, S. Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling. Environ. Modell. Softw. 144, 105159 (2021).Article 

    Google Scholar  More

  • in

    Nature-positive goals for an organization’s food consumption

    Mace, G. M. et al. Aiming higher to bend the curve of biodiversity loss. Nat. Sustain. 1, 448–451 (2018).Article 

    Google Scholar 
    Díaz, S., et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).Díaz, S. et al. Set ambitious goals for biodiversity and sustainability. Science 370, 411 (2020).Article 

    Google Scholar 
    Locke, H., et al. A Nature-Positive World: The Global Goal for Nature (Wildlife Conservation Society, 2020); https://library.wcs.org/doi/ctl/view/mid/33065/pubid/DMX3974900000.aspxOpen-ended Working Group on the Post-2020 Global Biodiversity Framework. First Draft of the Post-2020 Global Biodiversity Framework CBD/WG2020/3/3 (Convention on Biological Diversity, 2021).Open-Ended Working Group on the Post-2020 Global Biodiversity Framework. Draft Recommendation Submitted by the Co-Chairs CBD/WG2020/4/L.2-ANNEX (Convention on Biological Diversity, 2022).Environment Act 2021 (UK) (HM Government, 2021); https://www.legislation.gov.uk/ukpga/2021/30/contents/enactedBull, J. W. & Strange, N. The global extent of biodiversity offset implementation under no net loss policies. Nat. Sustain. 1, 790–798 (2018).Article 

    Google Scholar 
    Prendeville, S., Cherim, E. & Bocken, N. Circular cities: mapping six cities in transition. Environ. Innov. Soc. Transit. 26, 171–194 (2018).de Silva, G. C., Regan, E. C., Pollard, E. H. B. & Addison, P. F. E. The evolution of corporate no net loss and net positive impact biodiversity commitments: understanding appetite and addressing challenges. Bus. Strategy Environ. 28, 1481–1495 (2019).Article 

    Google Scholar 
    zu Ermgassen, S. O. S. E. et al. Exploring the ecological outcomes of mandatory biodiversity net gain using evidence from early‐adopter jurisdictions in England. Conserv. Lett. 14, e12820 (2021).Article 

    Google Scholar 
    McGlyn, J., et al. Science-Based Targets for Nature: Initial Guidance for Business (Science Based Targets Network, 2020); https://sciencebasedtargetsnetwork.org/resource-repository/zu Ermgassen, S. O. S. E. et al. Are corporate biodiversity commitments consistent with delivering ‘nature-positive’ outcomes? A review of ‘nature-positive’ definitions, company progress and challenges. J. Clean. Prod. 379, 134798 (2022).Article 

    Google Scholar 
    Addison, P. F. E., Bull, J. W. & Milner‐Gulland, E. J. Using conservation science to advance corporate biodiversity accountability. Conserv. Biol. 33, 307–318 (2019).Article 

    Google Scholar 
    Smith, T. et al. Biodiversity means business: reframing global biodiversity goals for the private sector. Conserv. Lett. 13, e12690 (2020).Article 

    Google Scholar 
    Maron, M. et al. Setting robust biodiversity goals. Conserv. Lett. https://doi.org/10.1111/conl.12816 (2021).Newing, H. & Perram, A. What do you know about conservation and human rights? Oryx 53, 595–596 (2019).Article 

    Google Scholar 
    Standard on Biodiversity Offsets (The Business and Biodiversity Offsets Programme, 2012).Arlidge, W. N. S., et al. A mitigation hierarchy approach for managing sea turtle captures in small-scale fisheries. Front. Mar. Sci. 7, 49 (2020).Squires, D. & Garcia, S. The least-cost biodiversity impact mitigation hierarchy with a focus on marine fisheries and bycatch issues. Conserv. Biol. 32, 989–997 (2018).Article 

    Google Scholar 
    Booth, H., Squires, D. & Milner-Gulland, E. J. The mitigation hierarchy for sharks: a risk-based framework for reconciling trade-offs between shark conservation and fisheries objectives. Fish Fish. 21, 269–289 (2020).Article 

    Google Scholar 
    Gupta, T. et al. Mitigation of elasmobranch bycatch in trawlers: a case study in Indian fisheries. Front. Mari. Sci. 7, 571 (2020).Budiharta, S. et al. Restoration to offset the impacts of developments at a landscape scale reveals opportunities, challenges and tough choices. Global Environ. Change 52, 152–161 (2018).Article 

    Google Scholar 
    Bull, J. W. et al. Net positive outcomes for nature. Nat. Ecol. Evol. 4, 4–7 (2020).Article 

    Google Scholar 
    Arlidge, W. N. S. et al. A global mitigation hierarchy for nature conservation. BioScience 68, 336–347 (2018).Article 

    Google Scholar 
    Milner-Gulland, E. J. et al. Four steps for the Earth: mainstreaming the post-2020 global biodiversity framework. One Earth 4, 75–87 (2021).Article 
    ADS 

    Google Scholar 
    Wolff, A., Gondran, N. & Brodhag, C. Detecting unsustainable pressures exerted on biodiversity by a company. Application to the food portfolio of a retailer. J. Clean. Prod. 166, 784–797 (2017).Article 

    Google Scholar 
    FAOSTAT Analytical Brief 15 Land Use and Land Cover Statistics: Global, Regional and Country Trends, 1990–2018 (FAO, 2020).Williams, D. R. et al. Proactive conservation to prevent habitat losses to agricultural expansion. Nat. Sustain. 4, 314–322 (2021).Article 

    Google Scholar 
    Leclère, D. et al. Bending the curve of terrestrial biodiversity needs an integrated strategy. Nature 585, 551–556 (2020).Article 
    ADS 

    Google Scholar 
    Springmann, M. et al. Health and nutritional aspects of sustainable diet strategies and their association with environmental impacts: a global modelling analysis with country-level detail. Lancet Planet. Health 2, e451–e461 (2018).Article 

    Google Scholar 
    Clark, M. A., Springmann, M., Hill, J. & Tilman, D. Multiple health and environmental impacts of foods. Proc. Natl Acad. Sci. USA 116, 23357 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    Willett, W. et al. Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems. Lancet 393, 447–492 (2019).Article 

    Google Scholar 
    Poore, J. & Nemecek, T. Reducing food’s environmental impacts through producers and consumers. Science 360, 987 (2018).Article 
    ADS 
    CAS 

    Google Scholar 
    Wiedmann, T., Lenzen, M., Keyßer, L. T. & Steinberger, J. K. Scientists’ warning on affluence. Nat. Commun. 11, 3107 (2020).Article 
    ADS 
    CAS 

    Google Scholar 
    Benton, T. G. et al. A ‘net zero’ equivalent target is needed to transform food systems. Nat. Food 2, 905–906 (2021). 2021.Article 

    Google Scholar 
    Crenna, E., Sinkko, T. & Sala, S. Biodiversity impacts due to food consumption in Europe. J. Clean. Prod. 227, 378–391 (2019).Article 
    CAS 

    Google Scholar 
    Bull, J. W., et al. Analysis: the biodiversity footprint of the University of Oxford. Nature 604, 420–424 (2022).Harrington, R. A., Adhikari, V., Rayner, M. & Scarborough, P. Nutrient composition databases in the age of big data: foodDB, a comprehensive, real-time database infrastructure. BMJ Open 9, e026652 (2019).Article 

    Google Scholar 
    Chaudhary, A., Verones, F., De Baan, L. & Hellweg, S. Quantifying land use impacts on biodiversity: combining species–area models and vulnerability indicators. Environ. Sci. Technol. 49, 9987–9995 (2015).Article 
    ADS 
    CAS 

    Google Scholar 
    Winter, L., Lehmann, A., Finogenova, N. & Finkbeiner, M. Including biodiversity in life cycle assessment—state of the art, gaps and research needs. Environ. Impact Assess. Rev. 67, 88–100 (2017).Article 

    Google Scholar 
    Chaudhary, A. & Kastner, T. Land use biodiversity impacts embodied in international food trade. Global Environ. Change 38, 195–204 (2016).Article 

    Google Scholar 
    Lenzen, M. et al. International trade drives biodiversity threats in developing nations. Nature 486, 109–112 (2012).Article 
    ADS 
    CAS 

    Google Scholar 
    Bates, B., et al. National Diet and Nutrition Survey Years 1 to 9 of the Rolling Programme (2008/2009–2016/2017): Time Trend and Income Analyses (Public Health England & Food Standards Agency, 2019).Stewart, C., Piernas, C., Cook, B. & Jebb, S. A. Trends in UK meat consumption: analysis of data from years 1–11 (2008–09 to 2018–19) of the National Diet and Nutrition Survey rolling programme. Lancet Planet. Health 5, e699–e708 (2021).Article 

    Google Scholar 
    Nielsen, K. S. et al. Improving climate change mitigation analysis: a framework for examining feasibility. One Earth 3, 325–336 (2020).Article 
    ADS 

    Google Scholar 
    Selinske, M. J. et al. We have a steak in it: eliciting interventions to reduce beef consumption and its impact on biodiversity. Conserv. Lett. 13, e12721 (2020).Article 

    Google Scholar 
    Hollands, G. J. et al. The TIPPME intervention typology for changing environments to change behaviour. Nat. Hum. Behav. 1, 1–9 (2017).Article 

    Google Scholar 
    Marteau, T. M., Hollands, G. J. & Fletcher, P. C. Changing human behavior to prevent disease: the importance of targeting automatic processes. Science 337, 1492–1495 (2012).Article 
    ADS 
    CAS 

    Google Scholar 
    Michie, S., van Stralen, M. M. & West, R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement. Sci. 6, 42 (2011).Article 

    Google Scholar 
    Moran, D., Giljum, S., Kanemoto, K. & Godar, J. From satellite to supply chain: new approaches connect earth observation to economic decisions. One Earth 3, 5–8 (2020).Article 
    ADS 

    Google Scholar 
    Godar, J., Suavet, C., Gardner, T. A., Dawkins, E. & Meyfroidt, P. Balancing detail and scale in assessing transparency to improve the governance of agricultural commodity supply chains. Environ. Res. Lett. 11, 035015 (2016).Article 
    ADS 

    Google Scholar 
    DeFries, R. S., Fanzo, J., Mondal, P., Remans, R. & Wood, S. A. Is voluntary certification of tropical agricultural commodities achieving sustainability goals for small-scale producers? A review of the evidence. Environ. Res. Lett. 12, 033001 (2017).Article 
    ADS 

    Google Scholar 
    Bull, J. W., Suttle, K. B., Gordon, A., Singh, N. J. & Milner-Gulland, E. J. Biodiversity offsets in theory and practice. Oryx 47, 369–380 (2013).Article 

    Google Scholar 
    zu Ermgassen, S. O. S. E. et al. The ecological outcomes of biodiversity offsets under “no net loss” policies: a global review. Conserv. Lett. 12, e12664 (2019).Article 

    Google Scholar 
    Waddock, S. Achieving sustainability requires systemic business transformation. Glob. Sustain. 3, e12 (2020).Travers, H., Walsh, J., Vogt, S., Clements, T. & Milner-Gulland, E. J. Delivering behavioural change at scale: what conservation can learn from other fields. Biol. Conserv. 257, 109092 (2021).Article 

    Google Scholar 
    Gaupp, F. et al. Food system development pathways for healthy, nature-positive and inclusive food systems. Nat. Food 2, 928–934 (2021).Article 

    Google Scholar 
    Astill, J. et al. Transparency in food supply chains: a review of enabling technology solutions. Trends Food Sci. Technol. 91, 240–247 (2019).Article 
    CAS 

    Google Scholar 
    Poore, J & Nemecek, T. Full Excel model: life-cycle environmental impacts of food drink products. Oxford University Research Archive https://ora.ox.ac.uk/objects/uuid:a63fb28c-98f8-4313-add6-e9eca99320a5 (2018).Clark, M., et al. Estimating the environmental impacts of 57,000 food products. Proc. Natl Acad. Sci. USA 119, e2120584119 (2022).Clark, M., et al. Supplemental Data for ‘Estimating the environmental impacts of 57,000 food products’. Oxford University Research Archive https://ora.ox.ac.uk/objects/uuid:4ad0b594-3e81-4e61-aefc-5d869c799a87 (2022).Bianchi, F., Dorsel, C., Garnett, E., Aveyard, P. & Jebb, S. A. Interventions targeting conscious determinants of human behaviour to reduce the demand for meat: a systematic review with qualitative comparative analysis. IJBNPA 15, 102 (2018).
    Google Scholar 
    Bianchi, F., Garnett, E., Dorsel, C., Aveyard, P. & Jebb, S. A. Restructuring physical micro-environments to reduce the demand for meat: a systematic review and qualitative comparative analysis. Lancet Planet. Health 2, e384–e397 (2018).Article 

    Google Scholar 
    Hillier-Brown, F. C. et al. The impact of interventions to promote healthier ready-to-eat meals (to eat in, to take away or to be delivered) sold by specific food outlets open to the general public: a systematic review. Obes. Rev. 18, 227–246 (2017).Article 
    CAS 

    Google Scholar 
    von Philipsborn, P. et al. Environmental interventions to reduce the consumption of sugar-sweetened beverages and their effects on health. Cochrane Database Syst. Rev. 6, Cd012292 (2019).
    Google Scholar 
    Attwood, S., Voorheis, P., Mercer, C., Davies, K. & Vennard, D. Playbook for Guiding Diners toward Plant-Rich Dishes in Food Service (World Resources Institute, 2020); https://www.wri.org/research/playbook-guiding-diners-toward-plant-rich-dishes-food-serviceGarnett, E. E., Balmford, A., Sandbrook, C., Pilling, M. A. & Marteau, T. M. Impact of increasing vegetarian availability on meal selection and sales in cafeterias. Proc. Natl Acad. Sci. USA 116, 20923 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    Reinders, M. J., Huitink, M., Dijkstra, S. C., Maaskant, A. J. & Heijnen, J. Menu-engineering in restaurants—adapting portion sizes on plates to enhance vegetable consumption: a real-life experiment. IJBNPA 14, 41 (2017).
    Google Scholar 
    Brunner, F., Kurz, V., Bryngelsson, D. & Hedenus, F. Carbon label at a university restaurant—label implementation and evaluation. Ecol. Econ. 146, 658–667 (2018).Article 

    Google Scholar 
    McClain, A. D., Hekler, E. B. & Gardner, C. D. Incorporating prototyping and iteration into intervention development: a case study of a dining hall-based intervention. J. Am. Coll. Health 61, 122–131 (2013).Article 

    Google Scholar 
    de Vaan, J. Eating Less Meat: How to Stimulate the Choice for a Vegetarian Option without Inducing Reactance. MSc thesis, Radboud Univ. (2018). More