Philippa Kaur

Cavanaugh CM, McKiness ZP, Newton ILG, Stewart FJ. Marine chemosynthetic symbioses. Prokaryotes. 2006;1:475–507.Article 

Google Scholar 
Beinart RA, Luo C, Konstantinidis KT, Stewart FJ, Girguis PR. The bacterial symbionts of closely related hydrothermal vent snails with distinct geochemical habitats show broad similarity in chemoautotrophic gene content. Front Microbiol. 2019;10:1818.Article 

Google Scholar 
Robidart JC, Bench SR, Feldman RA, Novoradovsky A, Podell SB, Gaasterland T, et al. Metabolic versatility of the Riftia pachyptila endosymbiont revealed through metagenomics. Environ Microbiol. 2008;10:727–37.Article 
CAS 

Google Scholar 
Ponnudurai R, Sayavedra L, Kleiner M, Heiden SE, Thürmer A, Felbeck H, et al. Genome sequence of the sulfur-oxidizing Bathymodiolus thermophilus gill endosymbiont. Stand Genom Sci. 2017;12:50.Article 

Google Scholar 
Duperron S, Bergin C, Zielinski F, Blazejak A, Pernthaler A, McKiness ZP, et al. A dual symbiosis shared by two mussel species, Bathymodiolus azoricus and Bathymodiolus puteoserpentis (Bivalvia: Mytilidae), from hydrothermal vents along the northern Mid-Atlantic Ridge. Environ Microbiol. 2006;8:1441–7.Article 
CAS 

Google Scholar 
Dubilier N, Bergin C, Lott C. Symbiotic diversity in marine animals: the art of harnessing chemosynthesis. Nat Rev Microbiol. 2008;6:725–40.Article 
CAS 

Google Scholar 
Sogin EM, Leisch N, Dubilier N. Chemosynthetic symbioses. Curr Biol. 2020;30:R1137–R1142.Article 
CAS 

Google Scholar 
Roeselers G, Newton ILG. On the evolutionary ecology of symbioses between chemosynthetic bacteria and bivalves. Appl Microbiol Biotechnol. 2012;94:1–10.Article 
CAS 

Google Scholar 
Moran NA. Symbiosis as an adaptive process and source of phenotypic complexity. Proc Natl Acad Sci USA. 2007;104 Suppl 1:8627–33.Article 
CAS 

Google Scholar 
McMullen JG, Peterson BF, Forst S, Blair HG, Patricia Stock S. Fitness costs of symbiont switching using entomopathogenic nematodes as a model. BMC Evol Biol. 2017;17. https://doi.org/10.1186/s12862-017-0939-6.Taylor JD, Glover E. Biology, evolution and generic review of the chemosymbiotic bivalve family Lucinidae. London, UK: Ray Society; 2021.Osvatic JT, Wilkins LGE, Leibrecht L, Leray M, Zauner S, Polzin J, et al. Global biogeography of chemosynthetic symbionts reveals both localized and globally distributed symbiont groups. Proc Natl Acad Sci USA. 2021;118. https://doi.org/10.1073/pnas.2104378118.Petersen JM, Kemper A, Gruber-Vodicka H, Cardini U, van der Geest M, Kleiner M, et al. Chemosynthetic symbionts of marine invertebrate animals are capable of nitrogen fixation. Nat Microbiol. 2016;2:16195.Article 
CAS 

Google Scholar 
Lim SJ, Davis B, Gill D, Swetenburg J, Anderson LC, Engel AS, et al. Gill microbiome structure and function in the chemosymbiotic coastal lucinid Stewartia floridana. FEMS Microbiol Ecol. 2021;97. https://doi.org/10.1093/femsec/fiab042.Lim SJ, Davis BG, Gill DE, Walton J, Nachman E, Engel AS, et al. Taxonomic and functional heterogeneity of the gill microbiome in a symbiotic coastal mangrove lucinid species. ISME J. 2019;13:902–20.Article 
CAS 

Google Scholar 
Gros O, Liberge M, Felbeck H. Interspecific infection of aposymbiotic juveniles of Codakia orbicularis by various tropical lucinid gill-endosymbionts. Mar Biol. 2003;142:57–66.Article 

Google Scholar 
Gros O, Elisabeth NH, Gustave SDD, Caro A, Dubilier N. Plasticity of symbiont acquisition throughout the life cycle of the shallow-water tropical lucinid Codakia orbiculata (Mollusca: Bivalvia). Environ Microbiol. 2012;14:1584–95.Article 
CAS 

Google Scholar 
Gros O, Frenkiel L, Mouëza M. Embryonic, larval, and post-larval development in the symbiotic clam Codakia orbicularis (Bivalvia: Lucinidae). Invertebr Biol. 1997;116:86–101.Article 

Google Scholar 
König S, Gros O, Heiden SE, Hinzke T, Thürmer A, Poehlein A, et al. Nitrogen fixation in a chemoautotrophic lucinid symbiosis. Nat Microbiol. 2016;2:16193.Article 

Google Scholar 
Fiore CL, Jarett JK, Olson ND, Lesser MP. Nitrogen fixation and nitrogen transformations in marine symbioses. Trends Microbiol. 2010;18:455–63.Article 
CAS 

Google Scholar 
Cardini U, Bednarz VN, Foster RA, Wild C. Benthic N2 fixation in coral reefs and the potential effects of human-induced environmental change. Ecol Evol. 2014;4:1706–27.Article 

Google Scholar 
Glover EA, Taylor JD. Lucinidae of the Philippines: highest known diversity and ubiquity of chemosymbiotic bivalves from intertidal to bathyal depths (Mollusca: Bivalvia). mém Mus Natl Hist Nat. 2016;208:65–234.
Google Scholar 
Taylor JD, Glover EA, Williams ST. Diversification of chemosymbiotic bivalves: origins and relationships of deeper water Lucinidae. Biol J Linn Soc Lond. 2014;111:401–20.Article 

Google Scholar 
von Cosel R. Taxonomy of tropical West African bivalves. VI. Remarks on Lucinidae (Mollusca, Bivalvia), with description of six new genera and eight new species. Zoosystema. 2006;28:805.
Google Scholar 
Glover EA, Taylor JD, Rowden AA. Bathyaustriella thionipta, a new lucinid bivalve from a hydrothermal vent on the Kermadec Ridge, New Zealand and its relationship to shallow-water taxa (Bivalvia: Lucinidae). J Mollusca Stud. 2004;70:283–95.Article 

Google Scholar 
Paulus E Shedding light on deep-sea biodiversity—a highly vulnerable habitat in the face of anthropogenic change. Front Mar Sci. 2021;8. https://doi.org/10.3389/fmars.2021.667048.Brown A, Thatje S. Explaining bathymetric diversity patterns in marine benthic invertebrates and demersal fishes: physiological contributions to adaptation of life at depth. Biol Rev Camb Philos Soc. 2014;89:406–26.Article 

Google Scholar 
Smith CR, De Leo FC, Bernardino AF, Sweetman AK, Arbizu PM. Abyssal food limitation, ecosystem structure and climate change. Trends Ecol Evol. 2008;23:518–28.Article 

Google Scholar 
Gage JD, Tyler PA. Deep-sea biology: a natural history of organisms at the deep-sea floor. Cambridge, UK: Cambridge University Press; 1991.Iken K, Brey T, Wand U, Voigt J, Junghans P. Food web structure of the benthic community at the Porcupine Abyssal Plain (NE Atlantic): a stable isotope analysis. Prog Oceanogr. 2001;50:383–405.Article 

Google Scholar 
von Cosel R, Bouchet P. Tropical deep-water lucinids (Mollusca: Bivalvia) from the Indo-Pacific: essentially unknown, but diverse and occasionally gigantic. mém Mus Natl Hist Nat. 2008;196:115–213.
Google Scholar 
Stearns REC Scientific results of explorations by the US Fish Commission steamer Albatross. No. XVII. Descriptions of new West American land, fresh-water, and marine shells, with notes and comments. Proceedings of the United States National Museum. 1890. https://repository.si.edu/bitstream/handle/10088/13174/1/USNMP-13_813_1890.pdf.Taylor JD, Glover EA. The lucinid bivalve genus Cardiolucina (Mollusca, Bivalvia, Lucinidae): systematics, anatomy and relationships. Bull Br Mus Nat Hist Zoo. 1997;63:93–122.
Google Scholar 
Coan EV, Valentich-Scott P, Sadeghian PS. Bivalve seashells of tropical West America: marine bivalve mollusks from Baja California to Northern Peru. Santa Barbara, USA: Museum of Natural History; 2012.von Cosel R, Gofas S. Marine bivalves of tropical West Africa: from Rio de Oro to southern Angola. Marseille, France: Muséum national d’Histoire naturelle, Paris; 2019. p 1104.Atkinson L, Sink K. Field guide to the offshore marine invertebrates of South Africa. 2018. https://doi.org/10.15493/SAEON.PUB.10000001.Montagu G. Testacea Britannica, or natural history of British shells. London, UK: JS Hollis; 1803.Taylor J, Glover E. New lucinid bivalves from shallow and deeper water of the Indian and West Pacific Oceans (Mollusca, Bivalvia, Lucinidae). ZooKeys. 2013;326:69–90.Article 

Google Scholar 
Apprill A, McNally S, Parsons R, Weber L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat Micro Ecol. 2015;75:129–37.Article 

Google Scholar 
Parada AE, Needham DM, Fuhrman JA. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol. 2016;18:1403–14.Article 
CAS 

Google Scholar 
Pjevac P, Hausmann B, Schwarz J, Kohl G, Herbold CW, Loy A, et al. An economical and flexible dual barcoding, two-step PCR approach for highly multiplexed amplicon sequencing. Front Microbiol. 2021;12:669776.Article 

Google Scholar 
McLaren MR, Callahan BJ. Silva 138.1 prokaryotic SSU taxonomic training data formatted for DADA2 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4587955.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.Article 
CAS 

Google Scholar 
Andersen KS, Kirkegaard RH, Karst SM, Albertsen M. ampvis2: an R package to analyse and visualise 16S rRNA amplicon data. 2018. https://www.biorxiv.org/content/10.1101/299537v1.Bushnell B. BBMap: a fast, accurate, splice-aware aligner. Berkeley, CA, USA: Lawrence Berkeley National Lab. (LBNL); 2014.Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27:824–34.Article 
CAS 

Google Scholar 
Nurk S, Bankevich A, Antipov D, Gurevich A, Korobeynikov A, Lapidus A, et al. Assembling genomes and mini-metagenomes from highly chimeric reads. In: Research in Computational Molecular Biology. Springer Berlin Heidelberg; 2013. p. 158–70.Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25:2078–9.Article 

Google Scholar 
Eren AM, Esen ÖC, Quince C, Vineis JH, Morrison HG, Sogin ML, et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ. 2015;3:e1319.Article 

Google Scholar 
Alneberg J, Bjarnason BS, de Bruijn I, Schirmer M, Quick J, Ijaz UZ, et al. Binning metagenomic contigs by coverage and composition. Nat Methods. 2014;11:1144–6.Article 
CAS 

Google Scholar 
Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 2019;7:e7359.Article 

Google Scholar 
Olm MR, Brown CT, Brooks B, Banfield JF. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017;11:2864–8.Article 
CAS 

Google Scholar 
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.Article 
CAS 

Google Scholar 
Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2019. https://doi.org/10.1093/bioinformatics/btz848.Parks DH, Chuvochina M, Chaumeil P-A, Rinke C, Mussig AJ, Hugenholtz P. A complete domain-to-species taxonomy for Bacteria and Archaea. Nat Biotechnol. 2020;38:1079–86.Article 
CAS 

Google Scholar 
Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil P-A, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36:996–1004.Article 
CAS 

Google Scholar 
Matsen FA, Kodner RB, Armbrust EV. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinform. 2010;11:538.Article 

Google Scholar 
Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:5114.Article 

Google Scholar 
Hyatt D, Chen G-L, Locascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 2010;11:119.Article 

Google Scholar 
Price MN, Dehal PS, Arkin AP. FastTree 2-approximately maximum-likelihood trees for large alignments. PLoS ONE. 2010;5:e9490.Article 

Google Scholar 
Eddy SR. Accelerated profile HMM searches. PLoS Comput Biol. 2011;7:e1002195.Article 
CAS 

Google Scholar 
Ondov BD, Treangen TJ, Melsted P, Mallonee AB, Bergman NH, Koren S, et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol. 2016;17:132.Article 

Google Scholar 
Trifinopoulos J, Nguyen L-T, von Haeseler A, Minh BQ. W-IQ-TREE: a fast online phylogenetic tool for maximum likelihood analysis. Nucleic Acids Res. 2016;44:W232–5.Article 
CAS 

Google Scholar 
Letunic I, Bork P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 2021;49:W293–W296.Article 
CAS 

Google Scholar 
Varghese NJ, Mukherjee S, Ivanova N, Konstantinidis KT, Mavrommatis K, Kyrpides NC, et al. Microbial species delineation using whole genome sequences. Nucleic Acids Res. 2015;43:6761–71.Article 
CAS 

Google Scholar 
Qin Q-L, Xie B-B, Zhang X-Y, Chen X-L, Zhou B-C, Zhou J, et al. A proposed genus boundary for the prokaryotes based on genomic insights. J Bacteriol. 2014;196:2210–5.Article 

Google Scholar 
Huerta-Cepas J, Szklarczyk D, Heller D, Hernández-Plaza A, Forslund SK, Cook H, et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 2019;47:D309–D314.Article 
CAS 

Google Scholar 
Huerta-Cepas J, Forslund K, Coelho LP, Szklarczyk D, Jensen LJ, von Mering C, et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-Mapper. Mol Biol Evol. 2017;34:2115–22.Article 
CAS 

Google Scholar 
Brettin T, Davis JJ, Disz T, Edwards RA, Gerdes S, Olsen GJ, et al. RASTtk: a modular and extensible implementation of the RAST algorithm for building custom annotation pipelines and annotating batches of genomes. Sci Rep. 2015;5:8365.Article 

Google Scholar 
Mahram A, Herbordt MC. NCBI BLASTP on high-performance reconfigurable computing systems. ACM Trans Reconfigurable Technol Syst. 2015;7:1–20.Article 

Google Scholar 
Yang Z. PAML: a program package for phylogenetic analysis by maximum likelihood. Comput Appl Biosci. 1997;13:555–6.CAS 

Google Scholar 
Osvatic J, Wilkins L. Strength of selection scripts. FigShare. 2022;8. https://doi.org/10.6084/m9.figshare.20626746.v1.Amann RI, Binder BJ, Olson RJ, Chisholm SW, Devereux R, Stahl DA. Combination of 16S rRNA-targeted oligonucleotide probes with flow cytometry for analyzing mixed microbial populations. Appl Environ Microbiol. 1990;56:1919–25.Article 
CAS 

Google Scholar 
Lan Y, Sun J, Chen C, Sun Y, Zhou Y, Yang Y, et al. Hologenome analysis reveals dual symbiosis in the deep-sea hydrothermal vent snail Gigantopelta aegis. Nat Commun. 2021;12:1165.Article 
CAS 

Google Scholar 
Leray M, Wilkins LGE, Apprill A, Bik HM, Clever F, Connolly SR, et al. Natural experiments and long-term monitoring are critical to understand and predict marine host-microbe ecology and evolution. PLoS Biol. 2021;19:e3001322.Article 
CAS 

Google Scholar 
Petersen Jillian M, Yuen B, Alexandre G. The symbiotic ‘all-rounders’: partnerships between marine animals and chemosynthetic nitrogen-fixing bacteria. Appl Environ Microbiol 2020;87:e02129–20.Johnson KS, Childress JJ, Hessler RR, Sakamoto-Arnold CM, Beehler CL. Chemical and biological interactions in the Rose Garden hydrothermal vent field, Galapagos spreading center. Deep Sea Res A. 1988;35:1723–44.Article 

Google Scholar 
Kennicutt ME II, Brooks JM, Burke RA Jr. Hydrocarbon seepage, gas hydrates, and authigenic carbonate in the northwestern Gulf of Mexico. Offshore Technology Conference; 1989. https://doi.org/10.4043/5952-ms.Lilley MD, Butterfield DA, Olson EJ, Lupton JE, Macko SA, McDuff RE. Anomalous CH4 and NH4+ concentrations at an unsedimented mid-ocean-ridge hydrothermal system. Nature. 1993;364:45–47.Article 
CAS 

Google Scholar 
Von Damm KL, Edmond JM, Measures CI, Grant B. Chemistry of submarine hydrothermal solutions at Guaymas Basin, Gulf of California. Geochim Cosmochim Acta. 1985;49:2221–37.Article 

Google Scholar 
Lee RW, Thuesen EV, Childress JJ. Ammonium and free amino acids as nitrogen sources for the chemoautotrophic symbiosis Solemya reidi Bernard (Bivalvia: Protobranchia). J Exp Mar Bio Ecol. 1992;158:75–91.Article 
CAS 

Google Scholar 
Sanders JG, Beinart RA, Stewart FJ, Delong EF, Girguis PR. Metatranscriptomics reveal differences in in situ energy and nitrogen metabolism among hydrothermal vent snail symbionts. ISME J. 2013;7:1556–67.Article 
CAS 

Google Scholar 
Touchette BW, Burkholder JM. Review of nitrogen and phosphorus metabolism in seagrasses. J Exp Mar Bio Ecol. 2000;250:133–67.Article 
CAS 

Google Scholar 
Fourqurean JW, Zieman JC, Powell GVN. Relationships between porewater nutrients and seagrasses in a subtropical carbonate environment. Mar Biol. 1992;114:57–65.Article 
CAS 

Google Scholar 
Williams SL. Experimental studies of Caribbean seagrass bed development. Ecol Monogr. 1990;60:449–69.Article 

Google Scholar 
Herbert RA. Nitrogen cycling in coastal marine ecosystems. FEMS Microbiol Rev. 1999;23:563–90.Article 
CAS 

Google Scholar 
Risgaard-Petersen N, Dalsgaard T, Rysgaard S, Christensen PB, Borum J, McGlathery K, et al. Nitrogen balance of a temperate eelgrass Zostera marina bed. Mar Ecol Prog Ser. 1998;174:281–91.Article 
CAS 

Google Scholar 
Bristow LA, Dalsgaard T, Tiano L, Mills DB, Bertagnolli AD, Wright JJ, et al. Ammonium and nitrite oxidation at nanomolar oxygen concentrations in oxygen minimum zone waters. Proc Natl Acad Sci USA. 2016;113:10601–6.Article 
CAS 

Google Scholar 
Karthäuser C, Ahmerkamp S, Marchant HK, Bristow LA, Hauss H, Iversen MH, et al. Small sinking particles control anammox rates in the Peruvian oxygen minimum zone. Nat Commun. 2021;12:3235.Article 

Google Scholar 
Kuypers MMM, Lavik G, Woebken D, Schmid M, Fuchs BM, Amann R, et al. Massive nitrogen loss from the Benguela upwelling system through anaerobic ammonium oxidation. Proc Natl Acad Sci USA. 2005;102:6478–83.Article 
CAS 

Google Scholar 
Johnson KS, Beehler CL, Sakamoto-Arnold CM, Childress JJ. In situ measurements of chemical distributions in a deep-sea hydrothermal vent field. Science. 1986;231:1139–41.Article 
CAS 

Google Scholar 
Childress JJ, Girguis PR. The metabolic demands of endosymbiotic chemoautotrophic metabolism on host physiological capacities. J Exp Biol. 2011;214:312–25.Article 
CAS 

Google Scholar 
Hentschel U, Hand S, Felbeck H. The contribution of nitrate respiration to the energy budget of the symbiont-containing clam Lucinoma aequizonata: a calorimetric study. J Exp Biol. 1996;199:427–33.Article 
CAS 

Google Scholar 
Breusing C, Mitchell J, Delaney J, Sylva SP, Seewald JS, Girguis PR, et al. Physiological dynamics of chemosynthetic symbionts in hydrothermal vent snails. ISME J. 2020;14:2568–79.Article 
CAS 

Google Scholar 
Amorim K, Loick-Wilde N, Yuen B, Osvatic JT, Wäge-Recchioni J, Hausmann B, et al. Chemoautotrophy, symbiosis and sedimented diatoms support high biomass of benthic molluscs in the Namibian shelf. Sci Rep. 2022;12:9731.Article 
CAS 

Google Scholar 
Breusing C, Johnson SB, Tunnicliffe V, Clague DA, Vrijenhoek RC, Beinart RA. Allopatric and sympatric drivers of speciation in Alviniconcha hydrothermal vent snails. Mol Biol Evol. 2020;37:3469–84.Article 
CAS 

Google Scholar 
Giovannoni SJ, Cameron Thrash J, Temperton B. Implications of streamlining theory for microbial ecology. ISME J. 2014;8:1553–65.Article 

Google Scholar 
Brissac T, Gros O, Merçot H. Lack of endosymbiont release by two Lucinidae (Bivalvia) of the genus Codakia: consequences for symbiotic relationships. FEMS Microbiol Ecol. 2009;67:261–7.Article 
CAS 

Google Scholar 
Werner GDA, Cornelissen JHC, Cornwell WK, Soudzilovskaia NA, Kattge J, West SA, et al. Symbiont switching and alternative resource acquisition strategies drive mutualism breakdown. Proc Natl Acad Sci USA. 2018;115:5229–34.Article 
CAS 

Google Scholar 
Sudakaran S, Kost C, Kaltenpoth M. Symbiont acquisition and replacement as a source of ecological innovation. Trends Microbiol. 2017;25:375–90.Article 
CAS 

Google Scholar 
Li Y, Liles MR, Halanych KM. Endosymbiont genomes yield clues of tubeworm success. ISME J. 2018;12:2785–95.Article 
CAS 

Google Scholar 
Moran NA, Yun Y. Experimental replacement of an obligate insect symbiont. Proc Natl Acad Sci USA. 2015;112:2093–6.Article 
CAS 

Google Scholar 
Sørensen MES, Wood AJ, Cameron DD, Brockhurst MA. Rapid compensatory evolution can rescue low fitness symbioses following partner switching. Curr Biol. 2021;31:3721–3728.e4.Article 

Google Scholar 
Taylor JD, Glover EA, Smith L, Ikebe C, Williams ST. New molecular phylogeny of Lucinidae: increased taxon base with focus on tropical Western Atlantic species (Mollusca: Bivalvia). Zootaxa. 2016;4196:zootaxa.4196.3.2.Article 

Google Scholar 
Osvatic J. Fig1 gtdb tree and alignment. figshare. 2021. https://doi.org/10.6084/m9.figshare.16837216.v1.Osvatic J. Figure 2: GTDB alignment and phylogeny. 2021. https://doi.org/10.6084/m9.figshare.16837237. More

Ai H, Fang X, Yang B, Huang Z, Chen H, Mao L et al. (2015) Adaptation and possible ancient interspecies introgression in pigs identified by whole-genome sequencing. Nat Genet 47:217–225Article 
CAS 

Google Scholar 
Alexander DH, Novembre J, Lange K (2009) Fast model-based estimation of ancestry in unrelated individuals. Genome Res 19:1655–1664Article 
CAS 

Google Scholar 
Alexandri P, Megens HJ, Crooijmans RPMA, Groenen MAM, Goedbloed DJ, Herrero-Medrano JM et al. (2017) Distinguishing migration events of different timing for wild boar in the Balkans. J Biogeogr 44:259–270Article 

Google Scholar 
Alexandri P, Triantafyllidis A, Papakostas S, Chatzinikos E, Platis P, Papageorgiou N et al. (2012) The Balkans and the colonization of Europe: the post-glacial range expansion of the wild boar, Sus scrofa. J Biogeogr 39:713–723Article 

Google Scholar 
Alves PC, Pinheiro I, Godinho R, Vicente JJ, Gortázar C, Scandura M et al. (2010) Genetic diversity of wild boar populations and domestic pig breeds (Sus scrofa) in South-western Europe. Biol J Linn Soc 101:797–822Article 

Google Scholar 
Apollonio M, Andersen R, Putman R (2010) European ungulates and their management in the 21st century (M Apollonio, R Andersen, and R Putman, Eds.) Cambridge University Press: Cambridge, UKAzzaroli A, De Giuli C, Ficcarelli G, Torre D (1988) Late pliocene to early mid-pleistocene mammals in Eurasia: Faunal succession and dispersal events. Palaeogeogr Palaeoclimatol Palaeoecol 66:77–100Article 

Google Scholar 
Bérénos C, Ellis PA, Pilkington JG, Pemberton JM (2016) Genomic analysis reveals depression due to both individual and maternal inbreeding in a free‐living mammal population. Mol Ecol 25:3152–3168Article 

Google Scholar 
Braga RT, Rodrigues JFM, Diniz-Filho JAF, Rangel TF (2019) Genetic population structure and allele surfing during range expansion in dynamic habitats. An da Academia Brasileira de Ciências 91:e20180179Article 

Google Scholar 
Bragina EV, Ives AR, Pidgeon AM, Kuemmerle T, Baskin LM, Gubar YP, Piquer-Rodríguez M, Keuler NS, Petrosyan VG, Radeloff VC (2015) Rapid Declines of Large Mammal Populations after the Collapse of the Soviet Union. Cons Biol 29:844–853Article 

Google Scholar 
Brewer S, Cheddadi R, de Beaulieu JL, Reille M, Allen J, Almqvist-Jacobson H et al. (2002) The spread of deciduous Quercus throughout Europe since the last glacial period. For Ecol Manag 156:27–48Article 

Google Scholar 
Cahill S, Llimona F, Cabañeros L, Calomardo F (2012) Characteristics of wild boar (Sus scrofa) habituation to urban areas in the Collserola Natural Park (Barcelona) and comparison with other locations. Anim Biodivers Conserv 35:221–233Article 

Google Scholar 
Canu A, Costa S, Iacolina L, Piatti P, Apollonio M, Scandura M (2014) Are captive wild boar more introgressed than free-ranging wild boar? Two case studies in Italy. Eur J Wildl Res 60:459–467Article 

Google Scholar 
Canu A, Vilaça STT, Iacolina L, Apollonio M, Bertorelle G, Scandura M (2016) Lack of polymorphism at the MC1R wild-type allele and evidence of domestic allele introgression across European wild boar populations. Mamm Biol 81:477–479Article 

Google Scholar 
Carranza J, Salinas M, de Andrés D, Pérez-González J (2016) Iberian red deer: paraphyletic nature at mtDNA but nuclear markers support its genetic identity. Ecol Evol 6:905–922Article 

Google Scholar 
Chang CC, Chow CC, Tellier LCAM, Vattikuti S, Purcell SM, Lee JJ (2015) Second-generation PLINK: Rising to the challenge of larger and richer datasets. Gigascience 4:1–16Article 

Google Scholar 
Cheddadi R, Bar-Hen A (2009) Spatial gradient of temperature and potential vegetation feedback across Europe during the late Quaternary. Clim Dyn 32:371–379Article 

Google Scholar 
Clark PU, Dyke AS, Shakun JD, Carlson AE, Clark J, Wohlfarth B et al. (2009) The Last Glacial Maximum. Science 325:710–714Article 
CAS 

Google Scholar 
DeGiorgio M, Rosenberg NA (2013) Geographic sampling scheme as a determinant of the major axis of genetic variation in principal components analysis. Mol Biol Evol 30:480–488Article 
CAS 

Google Scholar 
Deinet S, Ieronymidou C, McRae L, Burfield IJ, Foppen RP, Collen B, et al. (2013) Wildlife comeback in Europe. The recovery of selected mammal and bird species. London, UKEckert CG, Samis KE, Lougheed SC (2008) Genetic variation across species’ geographical ranges: the central–marginal hypothesis and beyond. Mol Ecol 17:1170–1188Article 
CAS 

Google Scholar 
Fang M, Berg F, Ducos A, Andersson L (2006) Mitochondrial haplotypes of European wild boars with 2n = 36 are closely related to those of European domestic pigs with 2n = 38. Anim Genet 37:459–464Article 
CAS 

Google Scholar 
Ferenčaković M, Sölkner J, Curik I (2013) Estimating autozygosity from high-throughput information: Effects of SNP density and genotyping errors. Genet Sel Evol 45:42Article 

Google Scholar 
Ferreira E, Souto L, Soares AMVM, Fonseca C (2009) Genetic structure of the wild boar population in Portugal: Evidence of a recent bottleneck. Mamm Biol 74:274–285Article 

Google Scholar 
Franois O, Currat M, Ray N, Han E, Excoffier L, Novembre J (2010) Principal component analysis under population genetic models of range expansion and admixture. Mol Biol Evol 27:1257–1268Article 

Google Scholar 
Frantz AC, Bertouille S, Eloy MC, Licoppe A, Chaumont F, Flamand MC (2012) Comparative landscape genetic analyses show a Belgian motorway to be a gene flow barrier for red deer (Cervus elaphus), but not wild boars (Sus scrofa). Mol Ecol 21:3445–3457Article 
CAS 

Google Scholar 
Fulgione D, Rippa D, Buglione M, Trapanese M, Petrelli S, Maselli V (2016) Unexpected but welcome. Artificially selected traits may increase fitness in wild boar. Evol Appl 9:769–776Article 
CAS 

Google Scholar 
Goedbloed DJ, Megens HJ, van Hooft P, Herrero-Medrano JM, Lutz W, Alexandri P et al. (2013a) Genome-wide single nucleotide polymorphism analysis reveals recent genetic introgression from domestic pigs into Northwest European wild boar populations. Mol Ecol 22:856–866Article 
CAS 

Google Scholar 
Goedbloed DJ, van Hooft P, Megens HJ, Langenbeck K, Lutz W, Crooijmans RPMA et al. (2013b) Reintroductions and genetic introgression from domestic pigs have shaped the genetic population structure of Northwest European wild boar. BMC Genet 14:2–10Article 

Google Scholar 
Groenen MAM, Archibald AL, Uenishi H, Tuggle CK, Takeuchi Y, Rothschild MF et al. (2012) Analyses of pig genomes provide insight into porcine demography and evolution. Nature 491:393–398Article 
CAS 

Google Scholar 
Herrero-Medrano JM, Megens H-J, Groenen MAM, Ramis G, Bosse M, Pérez-Enciso M et al. (2013) Conservation genomic analysis of domestic and wild pig populations from the Iberian Peninsula. BMC Genet 14:1–13Article 

Google Scholar 
Hewitt GM (1999) Post-glacial re-colonization of European biota. Biol J Linn Soc 68:87–112Article 

Google Scholar 
Hewitt GM (2004) Genetic consequences of climatic oscillations in the Quaternary. Philos Trans R Soc Lond Ser B Biol Sci 359:183–195Article 
CAS 

Google Scholar 
Hiemstra PH, Pebesma EJ, Twenhöfel CJW, Heuvelink GBM (2009) Real-time automatic interpolation of ambient gamma dose rates from the Dutch radioactivity monitoring network. Comput Geosci 35:1711–1721Article 
CAS 

Google Scholar 
Howrigan DP, Simonson MA, Keller MC (2011) Detecting autozygosity through runs of homozygosity: a comparison of three autozygosity detection algorithms. BMC Genomics 12:460Article 
CAS 

Google Scholar 
Huisman J, Kruuk LEB, Ellis PA, Clutton-Brock T, Pemberton JM (2016) Inbreeding depression across the lifespan in a wild mammal population. Proc Natl Acad Sci 113:3585–3590Article 
CAS 

Google Scholar 
Iacolina L, Corlatti L, Buzan E, Safner T, Šprem N (2019) Hybridisation in European ungulates: an overview of the current status, causes, and consequences. Mamm Rev 49:45–59Article 

Google Scholar 
Iacolina L, Pertoldi C, Amills M, Kusza S, Megens H-J, Bâlteanu VA et al. (2018) Hotspots of recent hybridization between pigs and wild boars in Europe. Sci Rep. 8:17372Article 
CAS 

Google Scholar 
Iacolina L, Scandura M, Goedbloed DJ, Alexandri P, Crooijmans RPMA, Larson G et al. (2016) Genomic diversity and differentiation of a managed island wild boar population. Heredity 116:60–67Article 
CAS 

Google Scholar 
Jombart T, Ahmed I (2011) adegenet 1.3-1: new tools for the analysis of genome-wide SNP data. Bioinformatics 27:1–2Article 

Google Scholar 
de Jong JF, Hooft van P, Megens HJ, Crooijmans RPMA, Groot de GA, Pemberton JM, Huisman J et al. (2020) Fragmentation and translocation distort the genetic landscape of ungulates: red deer in the Netherlands. Front Ecol Evol 8:535715Article 

Google Scholar 
Kamvar ZN, Tabima JF, Grünwald NJ (2014) Poppr: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2:e281Article 

Google Scholar 
Kardos M, Åkesson M, Fountain T, Flagstad Ø, Liberg O, Olason P et al. (2018) Genomic consequences of intensive inbreeding in an isolated wolf population. Nat Ecol Evol 2:124–131Article 

Google Scholar 
Kaplan JO, Krumhardt KM, Zimmermann N (2009) The prehistoric and preindustrial deforestation of Europe. Quat Sci Rev 28:3016–3034. https://doi.org/10.1016/j.quascirev.2009.09.028Koemle D, Zinngrebe Y, Yu X (2018) Highway construction and wildlife populations: Evidence from Austria. Land use policy 73:447–457Article 

Google Scholar 
Krže B (1982) Divji prašič: biologija, gojitev, ekologija. Lovska zveza Slovenije, Ljubljana
Google Scholar 
Kusza S, Podgórski T, Scandura M, Borowik T, Jávor A, Sidorovich VE et al. (2014) Contemporary genetic structure, phylogeography and past demographic processes of wild boar Sus scrofa population in central and eastern Europe. PLoS One 9:e91401Article 

Google Scholar 
Lorenzini R, Lovari S, Masseti M (2002) The rediscovery of the Italian roe deer: Genetic differentiation and management implications. Ital J Zool 69(4):367–379Article 

Google Scholar 
Lorenzini R, San José C, Braza F, Aragón S (2003) Genetic differentiation and phylogeography of roe deer in Spain, as suggested by mitochondrial DNA and microsatellite analysis. Ital J Zool 70(1):89–99Article 
CAS 

Google Scholar 
Magri D (2013) Early to Middle Pleistocene dynamics of plant and mammal communities in South West Europe. Quat Int 288:63–72Article 

Google Scholar 
Manunza A, Zidi A, Yeghoyan S, Balteanu VA, Carsai TC, Scherbakov O et al. (2013) A high throughput genotyping approach reveals distinctive autosomal genetic signatures for European and Near Eastern wild boar. PLoS One 8:e55891Article 
CAS 

Google Scholar 
Maselli V, Rippa D, De Luca A, Larson G, Wilkens B, Linderholm A et al. (2016) Southern Italian wild boar population, hotspot of genetic diversity. Hystrix 27:137–144
Google Scholar 
McVean G (2009) A genealogical interpretation of principal components analysis. PLoS Genet 5:e1000686Article 

Google Scholar 
Megens H-J, Crooijmans RP, Cristobal M, Hui X, Li N, Groenen MA (2008) Biodiversity of pig breeds from China and Europe estimated from pooled DNA samples: differences in microsatellite variation between two areas of domestication. Genet Sel Evol 40:103
Google Scholar 
Melis C, Szafrańska PA, Jȩdrzejewska B, Bartoń K (2006) Biogeographical variation in the population density of wild boar (Sus scrofa) in western Eurasia. J Biogeogr 33:803–811Article 

Google Scholar 
Mihalik B, Stéger V, Frank K, Szendrei L, Kusza S (2018) Barrier effect of the M3 highway in Hungary on the genetic diversity of wild boar (Sus scrofa) population. Res J Biotechnol 13:32–38
Google Scholar 
NCBI (2018) Genome Organism Overview: Sus scrofa (pig). https://www.ncbi.nlm.nih.gov/genome?term=sus%20scrofa%20%5BOrganism%5D&cmd=DetailsSearch&report=OverviewNikolov IS, Gum B, Markov G, Kuehn R (2009) Population genetic structure of wild boar Sus scrofa in Bulgaria as revealed by microsatellite analysis. Acta Theriol (Warsz) 54:193–205Article 

Google Scholar 
Nykänen M, Rogan E, Foote AD, Kaschner K, Dabin W, Louis M et al. (2019) Postglacial colonization of northern coastal habitat by bottlenose dolphins: a marine leading-edge expansion? J Hered 110:662–674Article 

Google Scholar 
Palombo M, Romana AV-G (2003) Remarks on the biochronology of mammalian faunal complexes from the Pliocene to the Middle Pleistocene in France. Geol Rom: 145–163Paradis E, Claude J, Strimmer K (2004) APE: analysis of phylogenetics and evolution in R language. Bioinformatics 20:289–290Article 
CAS 

Google Scholar 
Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D et al (2007) PLINK: A tool Set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575. www.cog-genomics.org/plink/1.9/Putman R, Apollonio M, Andersen R (2011) Ungulate management in Europe: problems and practices. Cambridge University Press, Cambridge, UKBook 

Google Scholar 
R Core Team (2018) R: A language and environment for statistical computing. Vienna, AustriaRejduch B, Sota E, Ró M, Ko M (2003) Chromosome number polymorphism in a litter of European wild boar (Sus scrofa scrofa L.). Anim Sci Pap Rep. 21:57–62
Google Scholar 
Scandura M, Iacolina L, Apollonio M (2011a) Genetic diversity in the European wild boar Sus scrofa: phylogeography, population structure and wild x domestic hybridization: Genetic variation in European wild boar. Mamm Rev 41:125–137Article 

Google Scholar 
Scandura M, Iacolina L, Cossu A, Apollonio M (2011b) Effects of human perturbation on the genetic make-up of an island population: The case of the Sardinian wild boar. Heredity 106:1012–1020Article 
CAS 

Google Scholar 
Scandura M, Iacolina L, Crestanello B, Pecchioli E, Di Benedetto MF, Russo V et al. (2008) Ancient vs. recent processes as factors shaping the genetic variation of the European wild boar: Are the effects of the last glaciation still detectable? Mol Ecol 17:1745–1762Article 
CAS 

Google Scholar 
Scandura M, Fabbri G, Caniglia R, Iacolina L, Mattucci F, Mengoni C, Pante G, Apollonio M, Mucci N (2022) Resilience to Historical Human Manipulations in the Genomic Variation of Italian Wild Boar Populations. Front Ecol Evol 10:833081Article 

Google Scholar 
Schmitt T, Varga Z (2012) Extra-Mediterranean refugia: the rule and not the exception. Front Zool 9:22Article 

Google Scholar 
Sommer RS, Fahlke JM, Schmölcke U, Benecke N, Zachos FE (2009) Quaternary history of the European roe deer Capreolus capreolus. Mamm Rev 39:1–16Article 

Google Scholar 
Sommer RS, Nadachowski A (2006) Glacial refugia of mammals in Europe: evidence from fossil records. Mamm Rev 36:251–265Article 

Google Scholar 
Sommer RS, Zachos FE (2009) Fossil evidence and phylogeography of temperate species: ‘glacial refugia’ and post-glacial recolonization. J Biogeogr 36:2013–2020Article 

Google Scholar 
Sommer RS, Zachos FE, Street M, Jöris O, Skog A, Benecke N (2008) Late Quaternary distribution dynamics and phylogeography of the red deer (Cervus elaphus) in Europe. Quat Sci Rev 27:714–733Article 

Google Scholar 
Stillfried M, Fickel J, Börner K, Wittstatt U, Heddergott M, Ortmann S et al. (2017) Do cities represent sources, sinks or isolated islands for urban wild boar population structure? J Appl Ecol 54:272–281Article 

Google Scholar 
Taberlet P, Fumagalli L, Wust-Saucy AG, Cossons JF (1998) Comparative phylogeography and post-glacial colonization routes in Europe. Mol Ecol 7:453–461.Article 
CAS 

Google Scholar 
Veličković N, Djan M, Ferreira E, Stergar M, Obreht D, Maletić V et al. (2015) From north to south and back: the role of the Balkans and other southern peninsulas in the recolonization of Europe by wild boar. J Biogeogr 42:716–728Article 

Google Scholar 
Veličković N, Ferreira E, Djan M, Ernst M, Obreht Vidaković D, Monaco A et al. (2016) Demographic history, current expansion and future management challenges of wild boar populations in the Balkans and Europe. Heredity 117:348–357Article 

Google Scholar 
Vernesi C, Crestanello B, Pecchioli E, Tartari D, Caramelli D, Hauffe H et al. (2003) The genetic impact of demographic decline and reintroduction in the wild boar (Sus scrofa): A microsatellite analysis. Mol Ecol 12:585–595Article 
CAS 

Google Scholar 
Vilaça ST, Biosa D, Zachos F, Iacolina L, Kirschning J, Alves PC et al. (2014) Mitochondrial phylogeography of the European wild boar: The effect of climate on genetic diversity and spatial lineage sorting across Europe. J Biogeogr 41:987–998Article 

Google Scholar 
Zachos FE, Frantz AC, Kuehn R, Bertouille S, Colyn M, Niedziałkowska M et al. (2016) Genetic structure and effective population sizes in European red deer (Cervus elaphus) at a continental scale: insights from microsatellite DNA. J Hered 107:318–326 More

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 
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, 1024–1026 (2011).Article 
ADS 
CAS 

Google Scholar 
Bowler, D. E. et al. Cross-realm assessment of climate change impacts on species’ abundance trends. Nat. Ecol. Evol. 1, 1–7 (2017).Article 

Google Scholar 
Barrett, J. E. et al. Persistent effects of a discrete warming event on a polar desert ecosystem. Glob. Change Biol. 14, 2249–2261 (2008).Article 
ADS 

Google Scholar 
Gooseff, M. N. et al. Decadal ecosystem response to an anomalous melt season in a polar desert in Antarctica. Nat. Ecol. Evol. 1, 1334–1338 (2017).Article 

Google Scholar 
Iknayan, K. J. & Beissinger, S. R. In transition: Avian biogeographic responses to a century of climate change across desert biomes. Glob. Change Biol. 26, 3268–3284 (2020).Article 
ADS 

Google Scholar 
Conradie, S. R., Woodborne, S. M., Cunningham, S. J. & McKechnie, A. E. Chronic, sublethal effects of high temperatures will cause severe declines in southern African arid-zone birds during the 21st century. Proc. Natl Acad. Sci. USA 116, 14065–14070 (2019).Article 
ADS 
CAS 

Google Scholar 
du Plessis, K. L., Martin, R. O., Hockey, P. A. R., Cunningham, S. J. & Ridley, A. R. The costs of keeping cool in a warming world: implications of high temperatures for foraging, thermoregulation and body condition of an arid-zone bird. Glob. Change Biol. 18, 3063–3070 (2012).Article 
ADS 

Google Scholar 
Ward, D. The Biology of Deserts (OUP Oxford, 2016).Reid, V. W. et al. Millennium Ecosystem Assessment, 2005. In Ecosystems and Human Well-being: Synthesis (Island Press, 2005).Zhou, L., Chen, H. & Dai, Y. Stronger warming amplification over drier ecoregions observed since 1979. Environ. Res. Lett. 10, 064012 (2015).Article 
ADS 

Google Scholar 
Hoegh-Guldberg, O. et al. 2018: Impacts of 1.5ºC Global Warming on Natural and Human Systems. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty (eds Masson-Delmotte, V. et al.) Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 175-312, https://doi.org/10.1017/9781009157940.005.Albright, T. P. et al. Mapping evaporative water loss in desert passerines reveals an expanding threat of lethal dehydration. Proc. Natl Acad. Sci. USA 114, 2283–2288 (2017).Article 
ADS 
CAS 

Google Scholar 
Friedrich, T., Timmermann, A., Tigchelaar, M., Timm, O. E. & Ganopolski, A. Nonlinear climate sensitivity and its implications for future greenhouse warming. Sci. Adv. 2, e1501923 (2016).Article 
ADS 

Google Scholar 
Kearney, M. R. & Porter, W. P. NicheMapR—an R package for biophysical modelling: the microclimate model. Ecography 40, 664–674 (2017).Article 

Google Scholar 
Huey, R. B. et al. Predicting organismal vulnerability to climate warming: roles of behaviour, physiology and adaptation. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 367, 1665–1679 (2012).Article 

Google Scholar 
Kearney, M. & Porter, W. Mechanistic niche modelling: combining physiological and spatial data to predict species’ ranges. Ecol. Lett. 12, 334–350 (2009).Article 

Google Scholar 
Bicudo, J. E. P., Buttemer, W. A., Chappell, M. A., Pearson, J. T. & Bech, C. Ecological and Environmental Physiology of Birds Vol. 2 (Oxford University Press, 2010).McKechnie, A. E. & Wolf, B. O. Climate change increases the likelihood of catastrophic avian mortality events during extreme heat waves. Biol. Lett. 6, 253–256 (2010).Article 

Google Scholar 
Riddell, E. A. et al. Exposure to climate change drives stability or collapse of desert mammal and bird communities. Science 371, 633–636 (2021).Article 
ADS 
CAS 

Google Scholar 
Williams, J. B. & Tieleman, B. I. Physiological adaptation in desert birds. BioScience 55, 416–425 (2005).Article 

Google Scholar 
Iknayan, K. J. & Beissinger, S. R. Collapse of a desert bird community over the past century driven by climate change. Proc. Natl Acad. Sci. USA 115, 8597–8602 (2018).Article 
ADS 
CAS 

Google Scholar 
Albright, T. P. et al. Combined effects of heat waves and droughts on avian communities across the conterminous United States. Ecosphere 1, art12 (2010).Article 

Google Scholar 
Cruz-McDonnell, K. K. & Wolf, B. O. Rapid warming and drought negatively impact population size and reproductive dynamics of an avian predator in the arid southwest. Glob. Change Biol. 22, 237–253 (2016).Article 
ADS 

Google Scholar 
Dawson, W. R. Temperature Regulation and Water Requirements of the Brown and Abert Towhees, Pipilo Fuscus and Pipilo Aberti.[With Plates.] (University of California Press, 1954).Riddell, E. A., Iknayan, K. J., Wolf, B. O., Sinervo, B. & Beissinger, S. R. Cooling requirements fueled the collapse of a desert bird community from climate change. Proc. Natl. Acad. Sci. USA https://doi.org/10.1073/pnas.1908791116 (2019).Wolf, B. Global warming and avian occupancy of hot deserts; a physiological and behavioral perspective. Rev. Chil. Hist. Nat. 73, 395–400 (2000).Article 

Google Scholar 
Kier, G. et al. A global assessment of endemism and species richness across island and mainland regions. Proc. Natl Acad. Sci. USA 106, 9322–9327 (2009).Article 
ADS 
CAS 

Google Scholar 
Ioffe, S. Improved consistent sampling, weighted Minhash and L1 sketching. In Proceedings of the 2010 IEEE International Conference on Data Mining 246–255 (IEEE Computer Society, 2010).Losos, E., Hayes, J., Phillips, A., Wilcove, D. & Alkire, C. Taxpayer-subsidized resource extraction harms species. BioScience 45, 446–455 (1995).Article 

Google Scholar 
Rodríguez-Estrella, R. Land use changes affect distributional patterns of desert birds in the Baja California peninsula, Mexico. Divers. Distrib. 13, 877–889 (2007).Article 

Google Scholar 
Stralberg, D. et al. Climate-change refugia in boreal North America: what, where, and for how long? Front. Ecol. Environ. 18, 261–270 (2020).Article 

Google Scholar 
Hinkel, J. et al. Sea-level rise scenarios and coastal risk management. Nat. Clim. Change 5, 188–190 (2015).Article 
ADS 

Google Scholar 
He, Q. & Silliman, B. R. Climate change, human impacts, and coastal ecosystems in the anthropocene. Curr. Biol. 29, R1021–R1035 (2019).Article 
CAS 

Google Scholar 
C. B. D. Zero Draft of the Post-2020 Global Biodiversity Framework CBD/WG2020/2/3. https://www.cbd.int/doc/c/efb0/1f84/a892b98d2982a829962b6371/wg2020-02-03-en.pdf Convention on Biology Diversity, Montreal, Canada (2020).Jung, M. et al. A global map of terrestrial habitat types. Sci. Data 7, 256 (2020).Article 

Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1 km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
Meigs, P. World distributions of arid and semi-arid homoclimates. In Review of Research on Arid Zone Hydrology (UNESCO, 1953).Holt, B. G. et al. An update of Wallace’s zoogeographic regions of the world. Science 339, 74–78 (2013).Article 
ADS 
CAS 

Google Scholar 
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015. Sci. Data 5, 170191 (2018).Article 

Google Scholar 
Qin, Y. et al. Agricultural risks from changing snowmelt. Nat. Clim. Change 10, 459–465 (2020).Article 
ADS 

Google Scholar 
Kearney, M. R. & Porter, W. P. NicheMapR – an R package for biophysical modelling: the microclimate model. Ecography 40, 664–674 (2017).Article 

Google Scholar 
Pattinson, N. B. et al. Heat dissipation behaviour of birds in seasonally hot arid-zones: are there global patterns? J. Avian Biol. 51, e02350 (2020).Smith, E. K., O’Neill, J., Gerson, A. R. & Wolf, B. O. Avian thermoregulation in the heat: resting metabolism, evaporative cooling and heat tolerance in Sonoran Desert doves and quail. J. Exp. Biol. 218, 3636–3646 (2015).Article 

Google Scholar 
Smith, E. K., O’Neill, J. J., Gerson, A. R., McKechnie, A. E. & Wolf, B. O. Avian thermoregulation in the heat: resting metabolism, evaporative cooling and heat tolerance in Sonoran Desert songbirds. J. Exp. Biol. 220, 3290–3300 (2017).
Google Scholar 
Kearney, M. NicheMapR: R implementation of Niche Mapper software for biophysical modelling. https://github.com/mrke/NicheMapR. (2020).Cunningham, S. J., Martin, R. O. & Hockey, P. A. Can behaviour buffer the impacts of climate change on an arid-zone bird? Ostrich 86, 119–126 (2015).Article 

Google Scholar 
Czenze, Z. J. et al. Regularly drinking desert birds have greater evaporative cooling capacity and higher heat tolerance limits than non-drinking species. Funct. Ecol. 34, 1589–1600 (2020).Article 

Google Scholar 
Smit, B. et al. Avian thermoregulation in the heat: phylogenetic variation among avian orders in evaporative cooling capacity and heat tolerance. J. Exp. Biol. 221, jeb174870 (2018).Worcester, S. E. The scaling of the size and stiffness of primary flight feathers. J. Zool. 239, 609–624 (1996).Article 

Google Scholar 
Wang, X., Nudds, R. L., Palmer, C. & Dyke, G. J. Size scaling and stiffness of avian primary feathers: implications for the flight of Mesozoic birds. J. Evol. Biol. 25, 547–555 (2012).Article 
CAS 

Google Scholar 
McKechnie, A. E., Gerson, A. R. & Wolf, B. O. Thermoregulation in desert birds: scaling and phylogenetic variation in heat tolerance and evaporative cooling. J. Exp. Biol. 224, jeb229211 (2021).Flint, L. E., Flint, A. L., Thorne, J. H. & Boynton, R. Fine-scale hydrologic modeling for regional landscape applications: the California Basin Characterization Model development and performance. Ecol. Process. 2, 25 (2013).Article 

Google Scholar 
Handbook of the Birds of the World and BirdLife International. Handbook of the Birds of the World and BirdLife International digital checklist of the birds of the world. Version 5. http://datazone.birdlife.org/userfiles/file/Species/Taxonomy/HBW-BirdLife_Checklist_v5_Dec20.zip (2020).Brooks, T. M. et al. Measuring terrestrial Area of Habitat (AOH) and its utility for the IUCN red list. Trends Ecol. Evol. 34, 977–986 (2019).Article 

Google Scholar 
Pastore, M. Overlapping: a R package for estimating overlapping in empirical distributions. J. Open Source Softw. 3, 1023 (2018).Article 
ADS 

Google Scholar 
UNEP-WCMC and IUCN, Protected Planet: The World Database on Protected Areas (WDPA) [Online], June 2021, Cambridge, UK: UNEP-WCMC and IUCN www.protectedplanet.net (2021).Butchart, S. H. M. et al. Shortfalls and solutions for meeting national and global conservation area targets. Conserv. Lett. 8, 329–337 (2015).Article 

Google Scholar 
Dudley, N. Guidelines for Applying Protected Area Management Categories (ICUN, 2008).Mangiafico, S. rcompanion: Functions to Support Extension Education Program Evaluation. https://CRAN.R-project.org/package=rcompanion. (2021).Crawford, C. L., Estes, L. D., Searchinger, T. D. & Wilcove, D. S. Consequences of underexplored variation in biodiversity indices used for land-use prioritization. Ecol. Appl. 31, e02396 (2021).Article 

Google Scholar 
Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).Article 
ADS 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020). More

Hsu-Kim H, Kucharzyk KH, Zhang T, Deshusses MA. Mechanisms regulating mercury bioavailability for methylating microorganisms in the aquatic environment: A critical review. Environ Sci Technol. 2013;47:2441–56.Article 
CAS 

Google Scholar 
Podar M, Gilmour CC, Brandt CC, Soren A, Brown SD, Crable BR, et al. Global prevalence and distribution of genes and microorganisms involved in mercury methylation. Sci Adv. 2015;1:e1500675.Article 

Google Scholar 
Liu YR, Johs A, Bi L, Lu X, Hu HW, Sun D, et al. Unraveling microbial communities associated with methylmercury production in paddy soils. Environ Sci Technol. 2018;52:13110–8.Article 
CAS 

Google Scholar 
Lee C-S, Fisher NS. Bioaccumulation of methylmercury in a marine copepod. Environ Toxicol Chem. 2017;36:1287–93.Article 
CAS 

Google Scholar 
Parks JM, Johs A, Podar M, Bridou R, Hurt RAJ, Smith SD, et al. The genetic basis for bacterial mercury methylation. Science 2013;339:1332–5.Article 
CAS 

Google Scholar 
McDaniel EA, Peterson BD, Stevens SLR, Tran PQ, Anantharaman K, McMahon KD. Expanded phylogenetic diversity and metabolic flexibility of mercury-methylating microorganisms. mSystems 2020;5:e00299–20.Article 
CAS 

Google Scholar 
Cooper CJ, Zheng K, Rush KW, Johs A, Sanders BC, Pavlopoulos GA, et al. Structure determination of the HgcAB complex using metagenome sequence data: Insights into microbial mercury methylation. Commun Biol. 2020;3:320.Article 
CAS 

Google Scholar 
Kerin EJ, Gilmour CC, Roden E, Suzuki MT, Coates JD, Mason RP. Mercury methylation by dissimilatory iron-reducing bacteria. Appl Environ Microbiol. 2006;72:7919–21.Article 
CAS 

Google Scholar 
Gilmour CC, Podar M, Bullock AL, Graham AM, Brown SD, Somenahally AC, et al. Mercury methylation by novel microorganisms from new environments. Environ Sci Technol. 2013;47:11810–20.Article 
CAS 

Google Scholar 
Capo E, Bravo AG, Soerensen AL, Bertilsson S, Pinhassi J, Feng C, et al. Deltaproteobacteria and Spirochaetes-like bacteria are abundant putative mercury methylators in oxygen-deficient water and marine particles in the Baltic Sea. Front Microbiol. 2020;11:574080.Article 

Google Scholar 
Gionfriddo CM, Tate MT, Wick RR, Schultz MB, Zemla A, Thelen MP, et al. Microbial mercury methylation in Antarctic sea ice. Nat Microbiol. 2016;1:16127.Article 
CAS 

Google Scholar 
Jones DS, Walker GM, Johnson NW, Mitchell CPJ, Coleman Wasik JK, Bailey JV. Molecular evidence for novel mercury methylating microorganisms in sulfate-impacted lakes. ISME J. 2019;13:1659–75.Article 
CAS 

Google Scholar 
Christensen GA, Gionfriddo CM, King AJ, Moberly JG, Miller CL, Somenahally AC, et al. Determining the reliability of measuring mercury cycling gene abundance with correlations with mercury and methylmercury concentrations. Environ Sci Technol. 2019;53:8649–63.Article 
CAS 

Google Scholar 
Villar E, Cabrol L, Heimburger-Boavida LE. Widespread microbial mercury methylation genes in the global ocean. Environ Microbiol Rep. 2020;12:277–87.Article 
CAS 

Google Scholar 
Lin H, Ascher DB, Myung Y, Lamborg CH, Hallam SJ, Gionfriddo CM, et al. Mercury methylation by metabolically versatile and cosmopolitan marine bacteria. ISME J. 2021;15:1810–25.Article 
CAS 

Google Scholar 
King JK, Kostka JE, Frischer ME, Saunders FM, Jahnke RA. A quantitative relationship that demonstrates mercury methylation rates in marine sediments are based on the community composition and activity of sulfate-reducing bacteria. Environ Sci Technol. 2001;35:2491–6.Article 
CAS 

Google Scholar 
Regnell O, Watras CJ. Microbial mercury methylation in aquatic environments: A critical review of published field and laboratory studies. Environ Sci Technol. 2019;53:4–19.Article 
CAS 

Google Scholar 
Xie R, Wang Y, Huang D, Hou J, Li L, Hu H, et al. Expanding Asgard members in the domain of Archaea sheds new light on the origin of eukaryotes. Sci China Life Sci. 2022;65:818–29.Article 
CAS 

Google Scholar 
Seitz KW, Dombrowski N, Eme L, Spang A, Lombard J, Sieber JR, et al. Asgard archaea capable of anaerobic hydrocarbon cycling. Nat Commun. 2019;10:1822.Article 

Google Scholar 
Zaremba-Niedzwiedzka K, Caceres EF, Saw JH, Backstrom D, Juzokaite L, Vancaester E, et al. Asgard archaea illuminate the origin of eukaryotic cellular complexity. Nature 2017;541:353–8.Article 
CAS 

Google Scholar 
Liu Y, Makarova KS, Huang W-C, Wolf YI, Nikolskaya AN, Zhang X, et al. Expanded diversity of Asgard archaea and their relationships with eukaryotes. Nature 2021;593:553–7.Article 
CAS 

Google Scholar 
Zhang JW, Dong HP, Hou LJ, Liu Y, Ou YF, Zheng YL, et al. Newly discovered Asgard archaea Hermodarchaeota potentially degrade alkanes and aromatics via alkyl/benzyl-succinate synthase and benzoyl-CoA pathway. ISME J. 2021;15:1826–43.Article 
CAS 

Google Scholar 
Cai M, Liu Y, Yin X, Zhou Z, Friedrich MW, Richter-Heitmann T, et al. Diverse Asgard archaea including the novel phylum Gerdarchaeota participate in organic matter degradation. Sci China Life Sci. 2020;63:886–97.Article 
CAS 

Google Scholar 
Baker BJ, De Anda V, Seitz KW, Dombrowski N, Santoro AE, Lloyd KG. Diversity, ecology and evolution of Archaea. Nat Microbiol. 2020;5:887–900.Article 
CAS 

Google Scholar 
Farag Ibrahim F, Zhao R, Biddle Jennifer F, Atomi H. “Sifarchaeota,” a novel Asgard phylum from Costa Rican sediment capable of polysaccharide degradation and anaerobic methylotrophy. Appl Environ Micro. 2021;87:e02584–20.
Google Scholar 
Adam PS, Borrel G, Brochier-Armanet C, Gribaldo S. The growing tree of Archaea: new perspectives on their diversity, evolution and ecology. ISME J. 2017;11:2407–25.Article 

Google Scholar 
Cai M, Richter-Heitmann T, Yin X, Huang W-C, Yang Y, Zhang C, et al. Ecological features and global distribution of Asgard archaea. Sci Total Environ. 2021;758:143581.Article 
CAS 

Google Scholar 
Zhang C-J, Chen Y-L, Sun Y-H, Pan J, Cai M-W, Li M. Diversity, metabolism and cultivation of archaea in mangrove ecosystems. Mar Life Sci Tech. 2020;3:252–62.Article 

Google Scholar 
Dai SS, Yang Z, Tong Y, Chen L, Liu SY, Pan R, et al. Global distribution and environmental drivers of methylmercury production in sediments. J Hazard Mater. 2021;407:124700.Article 
CAS 

Google Scholar 
Tang WL, Liu YR, Guan WY, Zhong H, Qu XM, Zhang T. Understanding mercury methylation in the changing environment: Recent advances in assessing microbial methylators and mercury bioavailability. Sci Total Environ. 2020;714:136827.Article 
CAS 

Google Scholar 
Tsui MTK, Finlay JC, Balogh SJ, Nollet YH. In situ production of methylmercury within a stream channel in northern California. Environ Sci Technol. 2010;44:6998–7004.Article 
CAS 

Google Scholar 
Liu Y, Zhou Z, Pan J, Baker BJ, Gu JD, Li M. Comparative genomic inference suggests mixotrophic lifestyle for Thorarchaeota. ISME J. 2018;12:1021–31.Article 
CAS 

Google Scholar 
Lei P, Zhong H, Duan D, Pan K. A review on mercury biogeochemistry in mangrove sediments: Hotspots of methylmercury production? Sci Total Environ. 2019;680:140–50.Article 
CAS 

Google Scholar 
Beckers F, Rinklebe J. Cycling of mercury in the environment: Sources, fate, and human health implications: A review. Crit Rev Env Sci Tec. 2017;47:693–794.Article 
CAS 

Google Scholar 
de Oliveira DC, Correia RR, Marinho CC, Guimaraes JR. Mercury methylation in sediments of a Brazilian mangrove under different vegetation covers and salinities. Chemosphere 2015;127:214–21.Article 

Google Scholar 
Li R, Xu H, Chai M, Qiu GY. Distribution and accumulation of mercury and copper in mangrove sediments in Shenzhen, the world’s most rapid urbanized city. Environ Moni Assess. 2016;188:87.Article 

Google Scholar 
O’Connor D, Hou D, Ok YS, Mulder J, Duan L, Wu Q, et al. Mercury speciation, transformation, and transportation in soils, atmospheric flux, and implications for risk management: A critical review. Environ Int. 2019;126:747–61.Article 

Google Scholar 
Obrist D, Kirk JL, Zhang L, Sunderland EM, Jiskra M, Selin NE. A review of global environmental mercury processes in response to human and natural perturbations: Changes of emissions, climate, and land use. Ambio 2018;47:116–40.Article 

Google Scholar 
Capo E, Peterson BD, Kim M, Jones DS, Acinas SG, Amyot M, et al. A consensus protocol for the recovery of mercury methylation genes from metagenomes. Mol Ecol Resour. 2022; https://doi.org/10.1111/1755-0998.13687.Gionfriddo CM, Wymore AM, Jones DS, Wilpiszeski RL, Lynes MM, Christensen GA, et al. An improved hgcAB primer set and direct high-throughput sequencing expand Hg-methylator diversity in nature. Front Microbiol. 2020;11:541554.Article 

Google Scholar 
Yu R-Q, Barkay T. Chapter two – microbial mercury transformations: Molecules, functions and organisms. Adv Appl Microbiol. 2022;118:31–90.Article 

Google Scholar 
Chételat J, Richardson MC, MacMillan GA, Amyot M, Poulain AJ. Ratio of methylmercury to dissolved organic carbon in water explains methylmercury bioaccumulation across a latitudinal gradient from north-temperate to arctic lakes. Environ Sci Technol. 2018;52:79–88.Article 

Google Scholar 
Liu Y-R, Dong J-X, Han L-L, Zheng Y-M, He J-Z. Influence of rice straw amendment on mercury methylation and nitrification in paddy soils. Environ Pollut. 2016;209:53–9.Article 
CAS 

Google Scholar 
Moreau JW, Gionfriddo CM, Krabbenhoft DP, Ogorek JM, DeWild JF, Aiken GR, et al. The effect of natural organic matter on mercury methylation by Desulfobulbus propionicus 1pr3. Front Microbiol. 2015;6:1389.Article 

Google Scholar 
Chen C-F, Ju Y-R, Chen C-W, Dong C-D. The distribution of methylmercury in estuary and harbor sediments. Sci Total Environ. 2019;691:55–63.Article 
CAS 

Google Scholar 
Bravo AG, Bouchet S, Guédron S, Amouroux D, Dominik J, Zopfi J. High methylmercury production under ferruginous conditions in sediments impacted by sewage treatment plant discharges. Water Res. 2015;80:245–55.Article 
CAS 

Google Scholar 
Wang H, Su J, Zheng T, Yang X. Insights into the role of plant on ammonia-oxidizing bacteria and archaea in the mangrove ecosystem. J Soil Sediment. 2015;15:1212–23.Article 
CAS 

Google Scholar 
Imachi H, Nobu MK, Nakahara N, Morono Y, Ogawara M, Takaki Y, et al. Isolation of an archaeon at the prokaryote–eukaryote interface. Nature 2020;577:519–25.Article 
CAS 

Google Scholar 
Zhou J, Riccardi D, Beste A, Smith JC, Parks JM. Mercury methylation by HgcA: Theory supports carbanion transfer to Hg(II). Inorg Chem. 2014;53:772–7.Article 
CAS 

Google Scholar 
Smith Steven D, Bridou R, Johs A, Parks Jerry M, Elias Dwayne A, Hurt Richard A, et al. Site-directed mutagenesis of HgcA and HgcB reveals amino acid residues important for mercury methylation. Appl Environ Micro. 2015;81:3205–17.Article 
CAS 

Google Scholar 
Sousa FL, Neukirchen S, Allen JF, Lane N, Martin WF. Lokiarchaeon is hydrogen dependent. Nat Microbiol. 2016;1:16034.Article 
CAS 

Google Scholar 
Schaefer JK, Rocks SS, Zheng W, Liang L, Gu B, Morel FMM. Active transport, substrate specificity, and methylation of Hg(II) in anaerobic bacteria. Proc Natl Acad Sci USA 2011;108:8714.Article 
CAS 

Google Scholar 
Sakai S, Imachi H, Hanada S, Ohashi A, Harada H, Kamagata Y. Methanocella paludicola gen. nov., sp. nov., a methane-producing archaeon, the first isolate of the lineage ‘Rice Cluster I’, and proposal of the new archaeal order Methanocellales ord. nov. Int J Syst Evol Microbiol. 2008;58:929–36.Article 

Google Scholar 
Dridi B, Fardeau ML, Ollivier B, Raoult D, Drancourt M. Methanomassiliicoccus luminyensis gen. nov., sp. nov., a methanogenic archaeon isolated from human faeces. Int J Syst Evol Microbiol. 2012;62:1902–7.Article 
CAS 

Google Scholar 
Dietz R, Sonne C, Basu N, Braune B, O’Hara T, Letcher RJ, et al. What are the toxicological effects of mercury in arctic biota? Sci Total Environ. 2013;443:775–90.Article 
CAS 

Google Scholar 
Gilmour Cynthia C, Bullock Allyson L, McBurney A, Podar M, Elias Dwayne A, Lovley Derek R. Robust mercury methylation across diverse methanogenic archaea. mBio 2018;9:e02403–17.
Google Scholar 
Pan J, Chen Y, Wang Y, Zhou Z, Li M. Vertical distribution of Bathyarchaeotal communities in mangrove wetlands suggests distinct niche preference of Bathyarchaeota subgroup 6. Micro Ecol. 2019;77:417–28.Article 

Google Scholar 
Zhang C-J, Pan J, Duan C-H, Wang Y-M, Liu Y, Sun J, et al. Prokaryotic diversity in mangrove sediments across southeastern China fundamentally differs from that in other biomes. mSystems 2019;4:e00442–19.Article 
CAS 

Google Scholar 
Peng Y, Leung HC, Yiu SM, Chin FY. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 2012;28:1420–8.Article 
CAS 

Google Scholar 
Li D, Liu C-M, Luo R, Sadakane K, Lam T-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 2015;31:1674–6.Article 
CAS 

Google Scholar 
Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinforma. 2010;11:119.Article 

Google Scholar 
Zhang C-J, Pan J, Liu Y, Duan C-H, Li M. Genomic and transcriptomic insights into methanogenesis potential of novel methanogens from mangrove sediments. Microbiome. 2020;8:94.Article 
CAS 

Google Scholar 
Kang DD, Froula J, Egan R, Wang Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 2015;3:e1165.Article 

Google Scholar 
Sieber CMK, Probst AJ, Sharrar A, Thomas BC, Hess M, Tringe SG, et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat Microbiol. 2018;3:836–43.Article 
CAS 

Google Scholar 
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.Article 
CAS 

Google Scholar 
Olm MR, Brown CT, Brooks B, Banfield JF. dRep: A tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017;11:2864–8.Article 
CAS 

Google Scholar 
Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 2019;36:1925–7.
Google Scholar 
Kanehisa M, Sato Y, Morishima K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J Mol Biol. 2016;428:726–31.Article 
CAS 

Google Scholar 
Huerta-Cepas J, Forslund K, Szklarczyk D, Jensen LJ, von Mering C, Bork P. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol Biol Evol. 2017;34:2115–22.Article 
CAS 

Google Scholar 
Finn RD, Clements J, Eddy SR. HMMER web server: interactive sequence similarity searching. Nucleic Acids Res. 2011;39:W29–W37.Article 
CAS 

Google Scholar 
Eddy SR. Accelerated profile HMM searches. PLoS Comput Biol. 2011;7:e1002195.Article 
CAS 

Google Scholar 
Edgar RC. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.Article 
CAS 

Google Scholar 
Capella-Gutiérrez S, Silla-Martínez JM, Gabaldón T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 2009;25:1972–3.Article 

Google Scholar 
Nguyen L-T, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2015;32:268–74.Article 
CAS 

Google Scholar 
Price MN, Dehal PS, Arkin AP. FastTree 2 – approximately maximum-likelihood trees for large alignments. Plos ONE. 2010;5:e9490.Article 

Google Scholar 
Letunic I, Bork P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 2016;44:W242–5.Article 
CAS 

Google Scholar 
Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature 2021;596:583–9.Article 
CAS 

Google Scholar 
Trott O, Olson AJ. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31:455–61.CAS 

Google Scholar 
Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.Article 
CAS 

Google Scholar 
Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinforma (Oxf, Engl). 2010;26:841–2.Article 
CAS 

Google Scholar 
Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009;25:1754–60.Article 
CAS 

Google Scholar  More

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

Auffret, M. D. et al. The role of microbial community composition in controlling soil respiration responses to temperature. PLoS ONE 11, e0165448 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Yao, Y. et al. A data-driven global soil heterotrophic respiration dataset and the drivers of its inter‐annual variability. Glob. Biogeochem. Cycle 35, e2020GB006918 (2021).Article 
CAS 

Google Scholar 
Davidson, E. A., Janssens, I. A. & Luo, Y. On the variability of respiration in terrestrial ecosystems: moving beyond Q10. Glob. Change Biol. 12, 154–164 (2006).Article 

Google Scholar 
Wang, Q. et al. Soil microbial respiration rate and temperature sensitivity along a north–south forest transect in eastern China: patterns and influencing factors. J. Geophys. Res. Biogeosci. 121, 399–410 (2016).Article 

Google Scholar 
Sihi, D. et al. Merging a mechanistic enzymatic model of soil heterotrophic respiration into an ecosystem model in two AmeriFlux sites of northeastern USA. Agric. Meteorol. 252, 155–166 (2018).Article 

Google Scholar 
Shao, P., Zeng, X., Moore, D. J. P. & Zeng, X. Soil microbial respiration from observations and Earth system models. Environ. Res. Lett. 8, 034034 (2013).Article 
CAS 

Google Scholar 
Davidson, E. A., Samanta, S., Caramori, S. S. & Savage, K. The dual Arrhenius and Michaelis–Menten kinetics model for decomposition of soil organic matter at hourly to seasonal time scales. Glob. Change Biol. 18, 371–384 (2012).Article 

Google Scholar 
Oechel, W. C. et al. Acclimation of ecosystem CO2 exchange in the Alaskan Arctic in response to decadal climate warming. Nature 406, 978–981 (2000).Article 
CAS 
PubMed 

Google Scholar 
Alster, C. J., von Fischer, J. C., Allison, S. D. & Treseder, K. K. Embracing a new paradigm for temperature sensitivity of soil microbes. Glob. Change Biol. 26, 3221–3229 (2020).Article 

Google Scholar 
Nie, M. et al. Positive climate feedbacks of soil microbial communities in a semi-arid grassland. Ecol. Lett. 16, 234–241 (2013).Article 
PubMed 

Google Scholar 
Ji, F., Wu, Z., Huang, J. & Chassignet, E. P. Evolution of land surface air temperature trend. Nat. Clim. Change 4, 462–466 (2014).Article 

Google Scholar 
Huntingford, C., Jones, P. D., Livina, V. N., Lenton, T. M. & Cox, P. M. No increase in global temperature variability despite changing regional patterns. Nature 500, 327–330 (2013).Article 
CAS 
PubMed 

Google Scholar 
Hansen, J., Sato, M. & Ruedy, R. Perception of climate change. Proc. Natl Acad. Sci. USA 109, E2415–E2423 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Byrne, M. P. Amplified warming of extreme temperatures over tropical land. Nat. Geosci. 14, 837–841 (2021).Article 
CAS 

Google Scholar 
IPCC Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).Chan, W. P. et al. Seasonal and daily climate variation have opposite effects on species elevational range size. Science 351, 1437–1439 (2016).Article 
CAS 
PubMed 

Google Scholar 
Biederbeck, V. O. & Campbell, C. A. Soil microbial activity as influenced by temperature trends and fluctuations. Can. J. Soil Sci. 53, 363–375 (1973).Article 

Google Scholar 
Karhu, K. et al. Temperature sensitivity of soil respiration rates enhanced by microbial community response. Nature 513, 81–84 (2014).Article 
CAS 
PubMed 

Google Scholar 
Chen, H., Zhu, T., Li, B., Fang, C. & Nie, M. The thermal response of soil microbial methanogenesis decreases in magnitude with changing temperature. Nat. Commun. 11, 5733 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Allison, S. D., Wallenstein, M. D. & Bradford, M. A. Soil-carbon response to warming dependent on microbial physiology. Nat. Geosci. 3, 336–340 (2010).Article 
CAS 

Google Scholar 
Nottingham, A. T. et al. Microbial responses to warming enhance soil carbon loss following translocation across a tropical forest elevation gradient. Ecol. Lett. 22, 1889–1899 (2019).Article 
PubMed 

Google Scholar 
Alster, C. J., Robinson, J. M., Arcus, V. L. & Schipper, L. A. Assessing thermal acclimation of soil microbial respiration using macromolecular rate theory. Biogeochemistry 158, 131–141 (2022).Article 
CAS 

Google Scholar 
Moinet, G. Y. K. et al. Soil microbial sensitivity to temperature remains unchanged despite community compositional shifts along geothermal gradients. Glob. Change Biol. 27, 6217–6231 (2021).Article 

Google Scholar 
Feng, J. et al. Soil microbial trait-based strategies drive metabolic efficiency along an altitude gradient. ISME Commun. 1, 71 (2021).Article 

Google Scholar 
Li, J. et al. Key microorganisms mediate soil carbon-climate feedbacks in forest ecosystems. Sci. Bull. 66, 2036–2044 (2021).Article 
CAS 

Google Scholar 
Trivedi, P. et al. Microbial regulation of the soil carbon cycle: evidence from gene–enzyme relationships. ISME J. 10, 2593–2604 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhu, B. & Cheng, W. Constant and diurnally-varying temperature regimes lead to different temperature sensitivities of soil organic carbon decomposition. Soil Biol. Biochem. 43, 866–869 (2011).Article 
CAS 

Google Scholar 
Bradford, M. A. et al. Thermal adaptation of soil microbial respiration to elevated temperature. Ecol. Lett. 11, 1316–1327 (2008).Article 
PubMed 

Google Scholar 
Hartley, I. P., Hopkins, D. W., Garnett, M. H., Sommerkorn, M. & Wookey, P. A. Soil microbial respiration in Arctic soil does not acclimate to temperature. Ecol. Lett. 11, 1092–1100 (2008).Article 
PubMed 

Google Scholar 
Bradford, M. A. et al. Cross-biome patterns in soil microbial respiration predictable from evolutionary theory on thermal adaptation. Nat. Ecol. Evol. 3, 223–231 (2019).Article 
PubMed 

Google Scholar 
Tian, W. et al. Thermal adaptation occurs in the respiration and growth of widely distributed bacteria. Glob. Change Biol. 28, 2820–2829 (2022).Article 
CAS 

Google Scholar 
Bradford, M. A., Watts, B. W. & Davies, C. A. Thermal adaptation of heterotrophic soil respiration in laboratory microcosms. Glob. Change Biol. 16, 1576–1588 (2010).Article 

Google Scholar 
Walker, T. W. N. et al. Microbial temperature sensitivity and biomass change explain soil carbon loss with warming. Nat. Clim. Change 8, 885–889 (2018).Article 
CAS 

Google Scholar 
Chen, H. et al. Microbial respiratory thermal adaptation is regulated by r-/K-strategy dominance. Ecol. Lett. 25, 2489–2499 (2022).Article 
PubMed 

Google Scholar 
Wang, C. et al. The temperature sensitivity of soil: microbial biodiversity, growth, and carbon mineralization. ISME J. 15, 2738–2747 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ramadhin, C., Yi, C. & Hendrey, G. Temperature variance portends and indicates the extent of abrupt climate shifts. IOP SciNotes 2, 014002 (2021).Article 

Google Scholar 
Sun, Y. Q. & Ge, Y. Temporal changes in the function of bacterial assemblages associated with decomposing earthworms. Front. Microbiol. 12, 682224 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Shi, Z., Xu, J., Li, X., Li, R. & Li, Q. Links of extracellular enzyme activities, microbial metabolism, and community composition in the river-impacted coastal waters. J. Geophys. Res. Biogeosci. 124, 3507–3520 (2019).Article 

Google Scholar 
Razanamalala, K. et al. Soil microbial diversity drives the priming effect along climate gradients: a case study in Madagascar. ISME J. 12, 451–462 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Xu, M. et al. High microbial diversity stabilizes the responses of soil organic carbon decomposition to warming in the subsoil on the Tibetan Plateau. Glob. Change Biol. 27, 2061–2075 (2021).Article 
CAS 

Google Scholar 
Clemmensen, K. E. et al. Roots and associated fungi drive long-term carbon sequestration in boreal forest. Science 339, 1615–1618 (2013).Article 
CAS 
PubMed 

Google Scholar 
Qiao, N. et al. Labile carbon retention compensates for CO2 released by priming in forest soils. Glob. Change Biol. 20, 1943–1954 (2014).Article 

Google Scholar 
Ning, Q. et al. Carbon limitation overrides acidification in mediating soil microbial activity to nitrogen enrichment in a temperate grassland. Glob. Change Biol. 27, 5976–5988 (2021).Article 
CAS 

Google Scholar 
Wan, S. & Luo, Y. Substrate regulation of soil respiration in a tallgrass prairie: results of a clipping and shading experiment. Glob. Biogeochem. Cycle 17, 1054 (2003).Article 

Google Scholar 
Gillabel, J., Cebrian-Lopez, B., Six, J. & Merckx, R. Experimental evidence for the attenuating effect of SOM protection on temperature sensitivity of SOM decomposition. Glob. Change Biol. 16, 2789–2798 (2010).Article 

Google Scholar 
Xia, J. et al. Terrestrial carbon cycle affected by non-uniform climate warming. Nat. Geosci. 7, 173–180 (2014).Article 
CAS 

Google Scholar 
Balesdent, J. et al. Atmosphere–soil carbon transfer as a function of soil depth. Nature 559, 599–602 (2018).Article 
CAS 
PubMed 

Google Scholar 
Howard, D. M. & Howard, P. J. A. Relationships between CO2 evolution, moisture-content and temperature for a range of soil types. Soil Biol. Biochem. 25, 1537–1546 (1993).Article 

Google Scholar 
Hoyle, F. C., Murphy, D. V. & Brookes, P. C. Microbial response to the addition of glucose in low-fertility soils. Biol. Fertil. Soils 44, 571–579 (2008).Article 
CAS 

Google Scholar 
Mau, R. L. et al. Linking soil bacterial biodiversity and soil carbon stability. ISME J. 9, 1477–1480 (2015).Article 
CAS 
PubMed 

Google Scholar 
Tucker, C. L., Bell, J., Pendall, E. & Ogle, K. Does declining carbon-use efficiency explain thermal acclimation of soil respiration with warming? Glob. Change Biol. 19, 252–263 (2013).Article 

Google Scholar 
Billings, S. A. & Ballantyne, F. T. How interactions between microbial resource demands, soil organic matter stoichiometry, and substrate reactivity determine the direction and magnitude of soil respiratory responses to warming. Glob. Change Biol. 19, 90–102 (2013).Article 

Google Scholar 
Li, J. et al. Biogeographic variation in temperature sensitivity of decomposition in forest soils. Glob. Change Biol. 26, 1873–1885 (2020).Article 

Google Scholar 
Min, K. et al. Temperature sensitivity of biomass-specific microbial exo-enzyme activities and CO2 efflux is resistant to change across short- and long-term timescales. Glob. Change Biol. 5, 1793–1807 (2019).Article 

Google Scholar 
Dacal, M., Bradford, M. A., Plaza, C., Maestre, F. T. & Garcia-Palacios, P. Soil microbial respiration adapts to ambient temperature in global drylands. Nat. Ecol. Evol. 3, 232–238 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Field-Fote, E. E. Mediators and moderators, confounders and covariates: exploring the variables that illuminate or obscure the “active ingredients” in neurorehabilitation. J. Neurol. Phys. Ther. 43, 83–84 (2019).Article 
PubMed 

Google Scholar 
Anderson, T. H. & Domsch, K. H. Soil microbial biomass: the eco-physiological approach. Soil Biol. Biochem. 12, 2039–2043 (2010).Article 

Google Scholar 
Vance, E. D., Brookes, P. C. & Jenkinson, D. S. Microbial biomass measurements in forest soils—the use of the chloroform fumigation incubation method in strongly acid soils. Soil Biol. Biochem. 19, 697–702 (1987).Article 
CAS 

Google Scholar 
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).Article 

Google Scholar 
Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Edgar, R. C. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998 (2013).Article 
CAS 
PubMed 

Google Scholar 
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).Article 
CAS 
PubMed 
PubMed Central 

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

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 
PubMed 

Google Scholar 
Koljalg, U. et al. UNITE: a database providing web-based methods for the molecular identification of ectomycorrhizal fungi. N. Phytol. 166, 1063–1068 (2005).Article 
CAS 

Google Scholar 
German, D. P. et al. Optimization of hydrolytic and oxidative enzyme methods for ecosystem studies. Soil Biol. Biochem. 43, 1387–1397 (2011).Article 
CAS 

Google Scholar 
Mazerolle, M. Improving data analysis in herpetology: using Akaike’s information criterion (AIC) to assess the strength of biological hypotheses. Amphib. Reptil. 2, 169–180 (2006).Article 

Google Scholar 
Moinet, G. Y. K. et al. Temperature sensitivity of decomposition decreases with increasing soil organic matter stability. Sci. Total Environ. 704, 135460 (2020).Article 
CAS 
PubMed 

Google Scholar 
Moinet, G. Y. K. et al. The temperature sensitivity of soil organic matter decomposition is constrained by microbial access to substrates. Soil Biol. Biochem. 116, 333–339 (2018).Article 
CAS 

Google Scholar 
Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14, 927–930 (2003).Article 

Google Scholar  More

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

Peaucelle, M. et al. Spatial variance of spring phenology in temperate deciduous forests is constrained by background climatic conditions. Nat. Commun. 10, 5388 (2019).Article 

Google Scholar 
Hopkins, A. D. The bioclimatic law. Mon. Weather Rev. 48, 355–355 (1920).Article 

Google Scholar 
Piao, S. et al. Plant phenology and global climate change: current progresses and challenges. Glob. Change Biol. 25, 1922–1940 (2019).Article 

Google Scholar 
Ge, Q., Wang, H., Rutishauser, T. & Dai, J. Phenological response to climate change in China: a meta-analysis. Glob. Change Biol. 21, 265–274 (2015).Article 

Google Scholar 
Templ, B. et al. Pan European Phenological database (PEP725): a single point of access for European data. Int. J. Biometeorol. 62, 1109–1113 (2018).Article 

Google Scholar 
Richardson, A. D. et al. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agric. For. Meteorol. 169, 156–173 (2013).Article 

Google Scholar 
Morisette, J. T. et al. Tracking the rhythm of the seasons in the face of global change: phenological research in the 21st century. Front. Ecol. Environ. 7, 253–260 (2009).Article 

Google Scholar 
Flynn, D. F. B. & Wolkovich, E. M. Temperature and photoperiod drive spring phenology across all species in a temperate forest community. New Phytol. 219, 1353–1362 (2018).Article 
CAS 

Google Scholar 
Peñuelas, J., Rutishauser, T. & Filella, I. Phenology feedbacks on climate change. Science 324, 887–888 (2009).Article 

Google Scholar 
Körner, C. & Basler, D. Plant science. Phenol. Glob. Warm. Sci. 327, 1461–1462 (2010).
Google Scholar 
Delpierre, N. et al. Temperate and boreal forest tree phenology: from organ-scale processes to terrestrial ecosystem models. Ann. For. Sci. 73, 5–25 (2016).Article 

Google Scholar 
Klosterman, S. T. et al. Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery. Biogeosciences 11, 4305–4320 (2014).Article 

Google Scholar 
Hufkens, K. et al. Linking near-surface and satellite remote sensing measurements of deciduous broadleaf forest phenology. Remote Sens. Environ. 117, 307–321 (2012).Article 

Google Scholar 
Garrity, S. R. et al. A comparison of multiple phenology data sources for estimating seasonal transitions in deciduous forest carbon exchange. Agric. For. Meteorol. 151, 1741–1752 (2011).Article 

Google Scholar 
Fracheboud, Y. et al. The control of autumn senescence in European aspen. Plant Physiol. 149, 1982–1991 (2009).Article 
CAS 

Google Scholar 
Mariën, B. et al. Does drought advance the onset of autumn leaf senescence in temperate deciduous forest trees? Biogeosciences 18, 3309–3330 (2021).Article 

Google Scholar 
Fu, Y. H. et al. Larger temperature response of autumn leaf senescence than spring leaf-out phenology. Glob. Change Biol. 24, 2159–2168 (2018).Article 

Google Scholar 
Menzel, A., Sparks, T. H., Estrella, N. & Roy, D. B. Altered geographic and temporal variability in phenology in response to climate change. Glob. Ecol. Biogeogr. 15, 498–504 (2006).Article 

Google Scholar 
Gordo, O. & Sanz, J. J. Long-term temporal changes of plant phenology in the Western Mediterranean. Glob. Change Biol. 15, 1930–1948 (2009).Article 

Google Scholar 
Meier, M., Vitasse, Y., Bugmann, H. & Bigler, C. Phenological shifts induced by climate change amplify drought for broad-leaved trees at low elevations in Switzerland. Agric. For. Meteorol. 307, 108485 (2021).Basler, D. Evaluating phenological models for the prediction of leaf-out dates in six temperate tree species across central Europe. Agric. For. Meteorol. 217, 10–21 (2016).Article 

Google Scholar 
Keenan, T. F. et al. Terrestrial biosphere model performance for inter-annual variability of land–atmosphere CO2 exchange. Glob. Change Biol. 18, 1971–1987 (2012).Article 

Google Scholar 
Liu, G., Chen, X., Fu, Y. & Delpierre, N. Modelling leaf coloration dates over temperate China by considering effects of leafy season climate. Ecol. Modell. 394, 34–43 (2019).Article 

Google Scholar 
Keenan, T. F. & Richardson, A. D. The timing of autumn senescence is affected by the timing of spring phenology: implications for predictive models. Glob. Change Biol. 21, 2634–2641 (2015).Article 

Google Scholar 
Wu, C., Hou, X., Peng, D., Gonsamo, A. & Xu, S. Land surface phenology of China’s temperate ecosystems over 1999–2013: spatial–temporal patterns, interaction effects, covariation with climate and implications for productivity. Agric. For. Meteorol. 216, 177–187 (2016).Article 

Google Scholar 
Fu, Y. S. H. et al. Variation in leaf flushing date influences autumnal senescence and next year’s flushing date in two temperate tree species. Proc. Natl Acad. Sci. USA 111, 7355–7360 (2014).Article 
CAS 

Google Scholar 
Zani, D., Crowther, T. W., Mo, L., Renner, S. S. & Zohner, C. M. Increased growing-season productivity drives earlier autumn leaf senescence in temperate trees. Science 370, 1066–1071 (2020).Article 
CAS 

Google Scholar 
Paul, M. J. & Foyer, C. H. Sink regulation of photosynthesis. J. Exp. Bot. 52, 1383–1400 (2001).Article 
CAS 

Google Scholar 
Herold, A. Regulation of photosynthesis by sink activity—the missing link. New Phytol. 86, 131–144 (1980).Article 
CAS 

Google Scholar 
Keenan, T. F. et al. Recent pause in the growth rate of atmospheric CO2 due to enhanced terrestrial carbon uptake. Nat. Commun. 7, 13428 (2016).Campbell, J. E. et al. Large historical growth in global terrestrial gross primary production. Nature 544, 84–87 (2017).Article 
CAS 

Google Scholar 
Schimel, D., Stephens, B. B. & Fisher, J. B. Effect of increasing CO2 on the terrestrial carbon cycle. Proc. Natl Acad. Sci.USA 112, 436–441 (2015).Article 
CAS 

Google Scholar 
Walker, A. P. et al. Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO. New Phytol. 229, 2413–2445 (2021).Article 
CAS 

Google Scholar 
Liu, Q. et al. Modeling leaf senescence of deciduous tree species in Europe. Glob. Change Biol. 26, 4104–4118 (2020).Article 

Google Scholar 
Friedl, M., Gray, J. & Sulla-Menashe, D. MCD12Q2 MODIS/Terra+Aqua Land Cover Dynamics Yearly L3 Global 500m SIN Grid V006 (NASA, 2019).Zhang, X. et al. Monitoring vegetation phenology using MODIS. Remote Sens. Environ. 84, 471–475 (2003).Article 

Google Scholar 
Stocker, B. D. et al. P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production. Geosci. Model Dev. 13, 1545–1581 (2020).Article 

Google Scholar 
Pastorello, G. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data 7, 225 (2020).Article 

Google Scholar 
Sitch, S. et al. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Glob. Change Biol. 9, 161–185 (2003).Article 

Google Scholar 
Hänninen, H. & Tanino, K. Tree seasonality in a warming climate. Trends Plant Sci. 16, 412–416 (2011).Article 

Google Scholar 
Kikuzawa, K. & Lechowicz, M. J. Ecology of Leaf Longevity (Springer, 2011).Fu, Y. H. et al. Nutrient availability alters the correlation between spring leaf-out and autumn leaf senescence dates. Tree Physiol. 39, 1277–1284 (2019).Article 
CAS 

Google Scholar 
Lim, P. O., Kim, H. J. & Nam, H. G. Leaf senescence. Annu. Rev. Plant Biol. 58, 115–136 (2007).Article 
CAS 

Google Scholar 
Piao, S., Friedlingstein, P., Ciais, P., Viovy, N. & Demarty, J. Growing season extension and its impact on terrestrial carbon cycle in the Northern Hemisphere over the past 2 decades. Glob. Biogeochem. Cycles 21, GB3018 (2007).Jeong, S.-J., Ho, C.-H., Gim, H.-J. & Brown, M. E. Phenology shifts at start vs. end of growing season in temperate vegetation over the Northern Hemisphere for the period 1982–2008. Glob. Change Biol. 17, 2385–2399 (2011).Article 

Google Scholar 
Cong, N. et al. Changes in satellite-derived spring vegetation green-up date and its linkage to climate in China from 1982 to 2010: a multimethod analysis. Glob. Change Biol. 19, 881–891 (2013).Article 

Google Scholar 
Keenan, T. F. et al. Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nat. Clim. Change 4, 598–604 (2014).Article 
CAS 

Google Scholar 
Garonna, I., de Jong, R. & Schaepman, M. E. Variability and evolution of global land surface phenology over the past three decades (1982–2012). Glob. Change Biol. 22, 1456–1468 (2016).Article 

Google Scholar 
Smith, N. G. & Dukes, J. S. Plant respiration and photosynthesis in global-scale models: incorporating acclimation to temperature and CO2. Glob. Change Biol. 19, 45–63 (2013).Article 

Google Scholar 
Estiarte, M. & Peñuelas, J. Alteration of the phenology of leaf senescence and fall in winter deciduous species by climate change: effects on nutrient proficiency. Glob. Change Biol. 21, 1005–1017 (2015).Article 

Google Scholar 
Delpierre, N. et al. Modelling interannual and spatial variability of leaf senescence for three deciduous tree species in France. Agric. For. Meteorol. 149, 938–948 (2009).Article 

Google Scholar 
Chung, H. et al. Experimental warming studies on tree species and forest ecosystems: a literature review. J. Plant Res. 126, 447–460 (2013).Article 

Google Scholar 
Schaaf, C. B. et al. First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sens. Environ. 83, 135–148 (2002).Article 

Google Scholar 
Tuck, S. L. et al. MODISTools—downloading and processing MODIS remotely sensed data in R. Ecol. Evol. 4, 4658–4668 (2014).Article 

Google Scholar 
Farquhar, G. D., von Caemmerer, S. & Berry, J. A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90 (1980).Article 
CAS 

Google Scholar 
Medlyn, B. E. et al. Reconciling the optimal and empirical approaches to modelling stomatal conductance. Glob. Change Biol. 17, 2134–2144 (2011).Article 

Google Scholar 
Stocker, B. rsofun: A modelling framework that implements the P-model for leaf-level acclimation of photosynthesis. R package version 4.3 https://github.com/computationales/rsofun (2020).Weedon, G. P. et al. The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data. Water Resour. Res. 50, 7505–7514 (2014).Article 

Google Scholar 
Meek, D. W., Hatfield, J. L., Howell, T. A., Idso, S. B. & Reginato, R. J. A generalized relationship between photosynthetically active radiation and solar radiation 1. Agron. J. 76, 939–945 (1984).Article 

Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
Stocker, B. ingestr: A tool to extract environmental point data from large global files or remote data servers. R package version 1.4 https://github.com/computationales/ingestr (2020).Wang, H. et al. Towards a universal model for carbon dioxide uptake by plants. Nat. Plants 3, 734–741 (2017).Article 
CAS 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).Myneni, R., Knyazikhin, Y. & Park, T. MCD15A3H MODIS/Terra+Aqua Leaf Area Index/FPAR 4-day L4 Global 500m SIN Grid V006 (NASA EOSDIS Land Processes DAAC, 2015). More