Ecology

Field CB, Behrenfeld MJ, Randerson JT, Falkowski P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science. 1998;281:237.CAS 
Article 

Google Scholar 
Volk T, Hoffert M. Ocean carbon pumps: analysis of relative strengths and efficiencies in ocean-driven atmospheric CO2 changes. Geophys Monogr Ser. 1985;32:99–110.
Google Scholar 
Boyd PW, Claustre H, Levy M, Siegel DA, Weber T. Multi-faceted particle pumps drive carbon sequestration in the ocean. Nature. 2019;568:327–35.CAS 
Article 

Google Scholar 
Siegel DA, Buesseler KO, Doney SC, Sailley SF, Behrenfeld MJ, Boyd PW. Global assessment of ocean carbon export by combining satellite observations and food-web models. Glob Biogeochem Cycles. 2014;28:181–196.Henson SA, Sanders R, Madsen E, Morris PJ, Le Moigne F, Quartly GD. A reduced estimate of the strength of the ocean’s biological carbon pump. Geophys Res Lett. 2011;38:L04606.Article 

Google Scholar 
Boyd PW, Trull TW. Understanding the export of biogenic particles in oceanic waters: is there consensus? Prog Oceanogr. 2007;72:276–312.Article 

Google Scholar 
Werdell PJ, Behrenfeld MJ, Bontempi PS, Boss E, Cairns B, Davis GT, et al. The plankton, aerosol, cloud, ocean ecosystem mission: status, science, advances. Bull Am Meteorol Soc. 2019;100:1775–94.Article 

Google Scholar 
Picheral M, Guidi L, Stemmann L, Karl DM, Iddaoud G, Gorsky G. The Underwater Vision Profiler 5: An advanced instrument for high spatial resolution studies of particle size spectra and zooplankton. Limnol Oceanogr-Methods. 2010;8:462–73.Article 

Google Scholar 
Olson RJ, Sosik HM. A submersible imaging-in-flow instrument to analyze nano-and microplankton: imaging FlowCytobot. Limnol Oceanogr Methods. 2007;5:195–203.Article 

Google Scholar 
de Vargas C, Audic S, Henry N, Decelle J, Mahé F, Logares R, et al. Eukaryotic plankton diversity in the sunlit ocean. Science. 2015;348:1261605.Scholin C, Birch J, Jensen S, Marin R III, Massion E, Pargett D, et al. The quest to develop ecogenomic sensors: a 25-year history of the Environmental Sample Processor (ESP) as a case study. Oceanography. 2017;30:100–13.Article 

Google Scholar 
Cruz BN, Brozak S, Neuer S. Microscopy and DNA-based characterization of sinking particles at the Bermuda Atlantic Time-series Study station point to zooplankton mediation of particle flux. Limnol Oceanogr. 2021;66:3697–713.CAS 
Article 

Google Scholar 
Amacher J, Neuer S, Lomas M. DNA-based molecular fingerprinting of eukaryotic protists and cyanobacteria contributing to sinking particle flux at the Bermuda Atlantic time-series study. Deep Sea Res Part II Top Stud Oceanogr. 2013;93:71–83.CAS 
Article 

Google Scholar 
Fontanez KM, Eppley JM, Samo TJ, Karl DM, DeLong EF. Microbial community structure and function on sinking particles in the North Pacific Subtropical Gyre. Front Microbiol. 2015;6:469.Article 

Google Scholar 
Preston CM, Durkin CA, Yamahara KM DNA metabarcoding reveals organisms contributing to particulate matter flux to abyssal depths in the North East Pacific ocean. Deep Sea Res Part II Top Stud Oceanogr. 2019;173:104708.Gutierrez-Rodriguez A, Stukel MR, Lopes dos Santos A, Biard T, Scharek R, Vaulot D, et al. High contribution of Rhizaria (Radiolaria) to vertical export in the California Current Ecosystem revealed by DNA metabarcoding. ISME J. 2019;13:964–76.CAS 
Article 

Google Scholar 
Boeuf D, Edwards BR, Eppley JM, Hu SK, Poff KE, Romano AE, et al. Biological composition and microbial dynamics of sinking particulate organic matter at abyssal depths in the oligotrophic open ocean. Proc Natl Acad Sci. 2019;116:11824.CAS 
Article 

Google Scholar 
Silver MW, Gowing MM. The “particle” flux: Origins and biological components. Prog Oceanogr. 1991;26:75–113.Article 

Google Scholar 
Ebersbach F, Assmy P, Martin P, Schulz I, Wolzenburg S, Nöthig E-M. Particle flux characterisation and sedimentation patterns of protistan plankton during the iron fertilisation experiment LOHAFEX in the Southern Ocean. Deep Sea Res Part Oceanogr Res Pap. 2014;89:94–103.CAS 
Article 

Google Scholar 
Waite A, Bienfang PK, Harrison PJ. Spring bloom sedimentation in a subarctic ecosystem. II. Succession and sedimentation. Mar Biol. 1992;114:131–8.Article 

Google Scholar 
Venrick E, Lange C, Reid F, Dever EP. Temporal patterns of species composition of siliceous phytoplankton flux in the Santa Barbara Basin. J Plankton Res. 2007;30:283–97.Article 

Google Scholar 
Waite AM, Safi KA, Hall JA, Nodder SD. Mass sedimentation of picoplankton embedded in organic aggregates. Limnol Oceanogr. 2000;45:87–97.Article 

Google Scholar 
Valencia B, Stukel MR, Allen AE, McCrow JP, Rabines A, Palenik B, et al. Relating sinking and suspended microbial communities in the California Current Ecosystem: digestion resistance and the contributions of phytoplankton taxa to export. Environ Microbiol. 2021;23:6743–8.Article 

Google Scholar 
Scharek R, Tupas LM, Karl DM. Diatom fluxes to the deep sea in the oligotrophic North Pacific gyre at Station ALOHA. Mar Ecol Prog Ser. 1999;182:55–67.Article 

Google Scholar 
Beaulieu S. Accumulation and fate of phytodetritus on the sea floor. Oceanogr Mar Biol Annu Rev. 2002;40:171–232.
Google Scholar 
Ikenoue T, Kimoto K, Okazaki Y, Sato M, Honda MC, Takahashi K, et al. Phaeodaria: an important carrier of particulate organic carbon in the mesopelagic twilight zone of the North Pacific Ocean. Glob Biogeochem Cycles. 2019;33:1146–60.CAS 
Article 

Google Scholar 
Smith KL, Ruhl HA, Huffard CL, Messié M, Kahru M. Episodic organic carbon fluxes from surface ocean to abyssal depths during long-term monitoring in NE Pacific. Proc Natl Acad Sci. 2018;115:12235.CAS 
Article 

Google Scholar 
Durkin CA, Buesseler KO, Cetinić I, Estapa ML, Kelly RP, Omand M. A visual tour of carbon export by sinking particles. Glob Biogeochem Cycles. 2021;35:e2021GB006985.CAS 
Article 

Google Scholar 
Estapa ML, Valdes J, Tradd K, Sugar J, Omand M, Buesseler K. The neutrally buoyant sediment trap: two decades of progress. J Atmospheric Ocean Technol. 2020;37:957–973.Rainville L, Pinkel R. Wirewalker: An autonomous wave-powered vertical profiler. J Atmos Ocean Technol. 2001;18:1048–51.Article 

Google Scholar 
Durkin CA, Estapa ML, Buesseler KO. Observations of carbon export by small sinking particles in the upper mesopelagic. Mar Chem. 2015;175:72–81.CAS 
Article 

Google Scholar 
Malmstrom R RNAlater Recipe. protocols.io. https://doi.org/10.17504/protocols.io.c56y9d. Accessed 2 Oct 2018.Mackey MD, Mackey DJ, Higgins HW, Wright SW. CHEMTAX—a program for estimating class abundances from chemical markers: application to HPLC measurements of phytoplankton. Mar Ecol Prog Ser. 1996;144:265–83.CAS 
Article 

Google Scholar 
Massana R, Murray AE, Preston CM, DeLong EF. Vertical distribution and phylogenetic characterization of marine planktonic Archaea in the Santa Barbara Channel. Appl Environ Microbiol. 1997;63:50.CAS 
Article 

Google Scholar 
Stoeck T, Bass D, Nebel M, Christen R, Jones M, Breiner H-W, et al. Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. Mol Ecol. 2010;19:21–31.CAS 
Article 

Google Scholar 
Penna A, Casabianca S, Guerra A, Vernesi C, Scardi M. Analysis of phytoplankton assemblage structure in the Mediterranean Sea based on high-throughput sequencing of partial 18S rRNA sequences. Mar Genomics. 2017;36:49–55.Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852–7.CAS 
Article 

Google Scholar 
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
Article 

Google Scholar 
Guillou L, Bachar D, Audic S, Bass D, Berney C, Bittner L, et al. The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote Small Sub-Unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 2012;41:D597–D604.Article 

Google Scholar 
Bokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E, Knight R, et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome. 2018;6:90.Article 

Google Scholar 
Tomas CR. Identifying marine phytoplankton. Elsevier; 1997.Hasle GR, Syvertsen EE. Marine diatoms. In: Tomas CR (ed). Identifying marine phytoplankton. San Diego, CA, USA: Academic Press; 1997. pp 5–385.Godhe A, Asplund ME, Härnström K, Saravanan V, Tyagi A, Karunasagar I. Quantification of diatom and dinoflagellate biomasses in coastal marine seawater samples by real-time PCR. Appl Environ Microbiol. 2008;74:7174–82.CAS 
Article 

Google Scholar 
Smayda TJ. The suspension and sinking of phytoplankton in the sea. Oceanogr Mar Biol Annu Rev. 1970;8:353–414.
Google Scholar 
Sancetta C, Villareal T, Falkowski P. Massive fluxes of rhizosolenid diatoms: a common occurrence? Limnol Oceanogr. 1991;36:1452–7.Article 

Google Scholar 
Goldman JC. Potential role of large oceanic diatoms in new primary production. Deep Sea Res Part Oceanogr Res Pap. 1993;40:159–68.Article 

Google Scholar 
Kemp AE, Pike J, Pearce RB, Lange CB. The “Fall dump”—a new perspective on the role of a “shade flora” in the annual cycle of diatom production and export flux. Deep Sea Res Part II Top Stud Oceanogr. 2000;47:2129–54.Article 

Google Scholar 
Villareal TA, Woods S, Moore JK, CulverRymsza K. Vertical migration of Rhizosolenia mats and their significance to NO3− fluxes in the central North Pacific gyre. J Plankton Res. 1996;18:1103–21.Article 

Google Scholar 
Smayda TJ. Normal and accelerated sinking of phytoplankton in the sea. Mar Geol. 1971;11:105–22.Article 

Google Scholar 
Shiozaki T, Itoh F, Hirose Y, Onodera J, Kuwata A, Harada NA. DNA metabarcoding approach for recovering plankton communities from archived samples fixed in formalin. PLOS ONE. 2021;16:e0245936.CAS 
Article 

Google Scholar 
Omand MM, Govindarajan R, He J, Mahadevan A. Sinking flux of particulate organic matter in the oceans: sensitivity to particle characteristics. Sci Rep. 2020;10:5582.CAS 
Article 

Google Scholar 
DeVries T, Liang J-H, Deutsch C. A mechanistic particle flux model applied to the oceanic phosphorus cycle. Biogeosciences. 2014;11:5381–98.Article 

Google Scholar 
Siegel DA, Buesseler KO, Behrenfeld MJ, Benitez-Nelson CR, Boss E, Brzezinski MA, et al. Prediction of the export and fate of global ocean net primary production: the EXPORTS science plan. Front Mar Sci. 2016;3:22.Article 

Google Scholar 
NASA Ocean Biology Processing Group. MODIS-Aqua Level 3 mapped chlorophyll data version R2018.0. 2017. NASA Ocean Biology DAAC. https://doi.org/10.5067/AQUA/MODIS/L3M/CHL/2018. More

Brierley AS, Kingsford MJ. Impacts of climate change on marine organisms and ecosystems. Curr Biol. 2009;19:R602–14.CAS 
Article 

Google Scholar 
Pecl GT, Araújo MB, Bell JD, Blanchard J, Bonebrake TC, Chen I-C, et al. Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science. 2017;355:eaaai9214.Article 

Google Scholar 
Hughes TP, Barnes ML, Bellwood DR, Cinner JE, Cumming GS, Jackson JBC, et al. Coral reefs in the Anthropocene. Nature. 2017;546:82–90.CAS 
Article 

Google Scholar 
Feeley KJ, Rehm EM, Machovina B. perspective: The responses of tropical forest species to global climate change: acclimate, adapt, migrate, or go extinct? Front Biogeogr. 2012;4:69–84.Article 

Google Scholar 
Zilber-Rosenberg I, Rosenberg E. Role of microorganisms in the evolution of animals and plants: The hologenome theory of evolution. FEMS Microbiol Rev. 2008;32:723–35.CAS 
Article 

Google Scholar 
Voolstra CR, Ziegler M. Adapting with microbial help: Microbiome flexibility facilitates rapid responses to environmental change. BioEssays. 2017;42:2000004.Article 

Google Scholar 
Webster NS, Reusch TBH. Microbial contributions to the persistence of coral reefs. ISME J. 2017;11:2167–74.Article 

Google Scholar 
Wilkes Walburn J, Wemheuer B, Thomas T, Copeland E, O’Connor W, Booth M, et al. Diet and diet-associated bacteria shape early microbiome development in Yellowtail Kingfish (Seriola lalandi). Micro Biotechnol. 2019;12:275–88.CAS 
Article 

Google Scholar 
Neave MJ, Rachmawati R, Xun L, Michell CT, Bourne DG, Apprill A, et al. Differential specificity between closely related corals and abundant Endozoicomonas endosymbionts across global scales. ISME J. 2016;11:186–200.Article 

Google Scholar 
Dubé CE, Ziegler M, Mercière A, Boissin E, Planes S, Bourmaud CA-F, et al. Naturally occurring fire coral clones demonstrate a genetic and environmental basis of microbiome composition. Nat Commun. 2021;12:640.Article 

Google Scholar 
Cardini U, Bednarz VN, Naumann MS, van Hoytema N, Rix L, Foster RA, et al. Functional significance of dinitrogen fixation in sustaining coral productivity under oligotrophic conditions. Proc R Soc B: Biol Sci. 2015;282:20152257.Article 

Google Scholar 
Manzano-Marı NA, Coeur d’acier A, Clamens A-L, Orvain C, Cruaud C, Barbe V, et al. Serial horizontal transfer of vitamin-biosynthetic genes enables the establishment of new nutritional symbionts in aphids’ di-symbiotic systems. ISME J. 2020;14:259–73.Article 

Google Scholar 
Neave MJ, Michell CT, Apprill A, Voolstra CR. Endozoicomonas genomes reveal functional adaptation and plasticity in bacterial strains symbiotically associated with diverse marine hosts. Sci Rep. 2017;7:40579.CAS 
Article 

Google Scholar 
Ding JY, Shiu JH, Chen WM, Chiang YR, Tang SL. Genomic insight into the host-endosymbiont relationship of Endozoicomonas montiporae CL-33T with its coral host. Front Microbiol. 2016;7:251.
Google Scholar 
Santoro EP, Borges RM, Espinoza JL, Freire M, Messias CSMA, Villela HDM, et al. Coral microbiome manipulation elicits metabolic and genetic restructuring to mitigate heat stress and evade mortality. Sci Adv. 2021;7:eabg3088.CAS 
Article 

Google Scholar 
Cavalcanti G, Alker A, Delherbe N, Malter KE, Shikuma NJ. The influence of bacteria on animal metamorphosis. Ann Rev Microbiol. 2020;74:137–58.CAS 
Article 

Google Scholar 
Rohwer F, Seguritan V, Azam F, Knowlton N. Diversity and distribution of coral-associated bacteria. Mar Ecol Prog Ser. 2002;243:1–10.Article 

Google Scholar 
Rädecker N, Pogoreutz C, Voolstra CR, Wiedenmann J, Wild C. Nitrogen cycling in corals: the key to understanding holobiont functioning? Trends Microbiol. 2015;23:490–7.Article 

Google Scholar 
Raina JB, Clode PL, Cheong S, Bougoure J, Kilburn MR, Reeder A, et al. Subcellular tracking reveals the location of dimethylsulfoniopropionate in microalgae and visualises its uptake by marine bacteria. Elife. 2017;6:e23008.Article 

Google Scholar 
Rädecker N, Pogoreutz C, Gegner HM, Cárdenas A, Perna G, Geißler L, et al. Heat stress reduces the contribution of diazotrophs to coral holobiont nitrogen cycling. ISME J. 2021;16:1110–8.Article 

Google Scholar 
Pogoreutz C, Voolstra CR, Rädecker N, Weis V. The coral holobiont highlights the dependence of cnidarian animal hosts on their associated microbes. In: Bosch TCG, Hadfield MG, editors. Cellular Dialogues in the Holobiont. Boca Raton: CRC Press; 2020. pp. 91–118.Xiang N, Hassenrück C, Pogoreutz C, Rädecker N, Simancas-Giraldo SM, Voolstra CR, et al. Contrasting microbiome dynamics of putative denitrifying bacteria in two octocoral species exposed to dissolved organic carbon (DOC) and warming. Appl Environ Microbiol. 2021;88:e01886–21.
Google Scholar 
Nissimov J, Rosenberg E, Munn CB. Antimicrobial properties of resident coral mucus bacteria of Oculina patagonica. FEMS Microbiol Lett. 2009;292:210–5.CAS 
Article 

Google Scholar 
Pereira LB, Palermo BRZ, Carlos C, Ottoboni LMM. Diversity and antimicrobial activity of bacteria isolated from different Brazilian coral species. FEMS Microbiol Lett. 2017;364:fnx164.Article 

Google Scholar 
Dungan AM, Bulach D, Lin H, van Oppen MJH, Blackall LL. Development of a free radical scavenging bacterial consortium to mitigate oxidative stress in cnidarians. Micro Biotechnol. 2021;14:2025–40.CAS 
Article 

Google Scholar 
Neave MJ, Apprill A, Ferrier-Pagès C, Voolstra CR. Diversity and function of prevalent symbiotic marine bacteria in the genus Endozoicomonas. Appl Microbiol Biotechnol. 2016;100:8315–24.CAS 
Article 

Google Scholar 
Meyer JL, Paul VJ, Teplitski M. Community shifts in the surface microbiomes of the coral Porites astreoides with unusual lesions. PLoS One. 2014;9:e100316.Article 

Google Scholar 
Morrow KM, Bourne DG, Humphrey C, Botté ES, Laffy P, Zaneveld J, et al. Natural volcanic CO2 seeps reveal future trajectories for host–microbial associations in corals and sponges. ISME J. 2014;9:894–908.Article 

Google Scholar 
Roder C, Bayer T, Aranda M, Kruse M, Voolstra CR. Microbiome structure of the fungid coral Ctenactis echinata aligns with environmental differences. Mol Ecol. 2015;24:3501–11.Article 

Google Scholar 
Ziegler M, Grupstra CGB, Barreto MM, Eaton M, BaOmar J, Zubier K, et al. Coral bacterial community structure responds to environmental change in a host-specific manner. Nat Commun. 2019;10:e3092.Article 

Google Scholar 
Pogoreutz C, Rädecker N, Cárdenas A, Gärdes A, Wild C, Voolstra CR. Dominance of Endozoicomonas bacteria throughout coral bleaching and mortality suggests structural inflexibility of the Pocillopora verrucosa microbiome. Ecol Evol. 2018;8:2240–52.Article 

Google Scholar 
Tandon K, Lu C-Y, Chiang P-W, Wada N, Yang S-H, Chan Y-F, et al. Comparative genomics: Dominant coral-bacterium Endozoicomonas acroporae metabolizes dimethylsulfoniopropionate (DMSP). ISME J. 2020;14:1290–303.CAS 
Article 

Google Scholar 
Sweet M, Villela H, Keller-Costa T, Costa R, Romano S, Bourne DG, et al. Insights into the cultured bacterial fraction of corals. mSystems. 2021;6:e0124920.Article 

Google Scholar 
Ngugi DK, Ziegler M, Duarte CM, Voolstra CR. Genomic blueprint of glycine betaine metabolism in coral metaorganisms and their contribution to reef nitrogen budgets. iScience. 2020;23:101120.CAS 
Article 

Google Scholar 
Weber L, Gonzalez-Díaz P, Armenteros M, Apprill A. The coral ecosphere: a unique coral reef habitat that fosters coral–microbial interactions. Limnol Oceanogr. 2019;64:2373–88.CAS 
Article 

Google Scholar 
Alain K, Querellou J. Cultivating the uncultured: limits, advances and future challenges. Extremophiles. 2009;13:583–94.Article 

Google Scholar 
Robbins SJ, Singleton CM, Chan CX, Messer LF, Geers AU, Ying H, et al. A genomic view of the reef-building coral Porites lutea and its microbial symbionts. Nat Microbiol. 2019;4:2090–2100.Article 

Google Scholar 
Katharios P, Seth-Smith HMB, Fehr A, Mateos JM, Qi W, Richter D, et al. Environmental marine pathogen isolation using mesocosm culture of sharpsnout seabream: striking genomic and morphological features of novel Endozoicomonas sp. Sci Rep. 2015;5:17609.CAS 
Article 

Google Scholar 
Keller-Costa T, Eriksson D, Gonçalves JMS, Gomes NCM, Lago-Lestón A, Costa R. The gorgonian coral Eunicella labiata hosts a distinct prokaryotic consortium amenable to cultivation. FEMS Microbiol Ecol. 2017;93. https://doi.org/10.1093/femsec/fix143.Neave MJ, Michell CT, Apprill A, Voolstra CR. Whole-genome sequences of three symbiotic Endozoicomonas strains. Genome Announc. 2014;2:e00802–14.Article 

Google Scholar 
Andersson AF, Lindberg M, Jakobsson H, Bäckhed F, Nyrén P, Engstrand L. Comparative analysis of human gut microbiota by barcoded pyrosequencing. PLoS One. 2008;3:e2836.Article 

Google Scholar 
Bayer T, Neave MJ, Alsheikh-Hussain A, Aranda M, Yum LK, Mincer T, et al. The microbiome of the Red Sea coral Stylophora pistillata is dominated by tissue-associated Endozoicomonas bacteria. App Environ Microbiol. 2013;79:4759–62.CAS 
Article 

Google Scholar 
Pogoreutz C, Gore MA, Perna G, Millar C, Nestler R, Ormond RF, et al. Similar bacterial communities on healthy and injured skin of black tip reef sharks. Anim Microbiome. 2019;1:9.Article 

Google Scholar 
Pogoreutz C, Voolstra CR. Isolation, culturing, and cryopreservation of Endozoicomonas (Gammaproteobacteria: Oceanospirillales: Endozoicomonadaceae) from reef-building corals. 2018. https://doi.org/10.17504/protocols.io.t2aeqae.Lane DJ. 16S/23S rRNA sequencing. In: Stackebrandt E, Goodfellow M, editors. Nucleic acid techniques in bacterial systematics. Chichester: John Wiley & Sons; 1991. pp. 115–75.Kumar S, Stecher G, Tamura K. MEGA7: Molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol Biol Evol. 2016;33:1870–4.CAS 
Article 

Google Scholar 
Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2013;41:e1.CAS 
Article 

Google Scholar 
Koren S, Walenz BP, Berlin K, Miller JR, Bergman NH, Phillippy AM. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res. 2017;27:722–36.CAS 
Article 

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.CAS 
Article 

Google Scholar 
Overbeek R, Olson R, Pusch GD, Olsen GJ, Davis JJ, Disz T, et al. The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST). Nucleic Acids Res. 2014;42:D206–14.CAS 
Article 

Google Scholar 
Wu S, Zhu Z, Fu L, Niu B, Li W. WebMGA: a customizable web server for fast metagenomic sequence analysis. BMC Genomics. 2011;12:444.Article 

Google Scholar 
Sheu S-Y, Lin K-R, Hsu M-Y, Sheu D-S, Tang S-L, Chen W-M. Endozoicomonas acroporae sp. nov., isolated from Acropora coral. Int J Syst Evol Microbiol. 2017;67:3791–7.CAS 
Article 

Google Scholar 
Appolinario LR, Tschoeke DA, Rua CPJ, Venas T, Campeão ME, Amaral GRS, et al. Description of Endozoicomonas arenosclerae sp. nov. using a genomic taxonomy approach. Antonie Van Leeuwenhoek. 2016;109:431–8.Article 

Google Scholar 
Hyun DW, Shin NR, Kim MS, Oh SJ, Kim PS, Whon TW, et al. Endozoicomonas atrinae sp. nov., isolated from the intestine of a comb pen shell Atrina pectinata. Int J Syst Evol Microbiol. 2014;64:2312–8.CAS 
Article 

Google Scholar 
Schreiber L, Kjeldsen KU, Funch P, Jensen J, Obst M, López-Legentil S, et al. Endozoicomonas are specific, facultative symbionts of sea squirts. Front Microbiol. 2016;7:1042.Article 

Google Scholar 
Miller IJ, Weyna TR, Fong SS, Lim-Fong GE, Kwan JC. Single sample resolution of rare microbial dark matter in a marine invertebrate metagenome. Sci Rep. 2016;6:34362.CAS 
Article 

Google Scholar 
Meier-Kolthoff JP, Auch AF, Klenk H-P, Göker M. Genome sequence-based species delimitation with confidence intervals and improved distance functions. BMC Bioinform. 2013;14:60.Article 

Google Scholar 
Rodriguez-R LM, Konstantinidis KT. The enveomics collection: a toolbox for specialized analyses of microbial genomes and metagenomes. PeerJ. 2016; 4:e1900v1.Emms DM, Kelly S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 2019;20:238.Article 

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

Google Scholar 
Rambaut, A FigTree. Tree Figure Drawing Tool. http://tree.bio.ed.ac.uk/software/figtree/ 2009.Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.CAS 
Article 

Google Scholar 
Aramaki T, Blanc-Mathieu R, Endo H, Ohkubo K, Kanehisa M, Goto S, et al. KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics. 2020;36:2251–2.CAS 
Article 

Google Scholar 
Kanehisa M, Sato Y. KEGG Mapper for inferring cellular functions from protein sequences. Protein Sci. 2020;29:28–35.CAS 
Article 

Google Scholar 
UniProt Consortium. UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res. 2019;47:D506–D515.Article 

Google Scholar 
Cook CB, Davy SK. Are free amino acids responsible for the ‘host factor’ effects on symbiotic zooxanthellae in extracts of host tissue? Hydrobiologia. 2001;461:71–78.Article 

Google Scholar 
Davy S, Cook C. The relationship between nutritional status and carbon flux in the zooxanthellate sea anemone Aiptasia pallida. Mar Biol. 2001;139:999–1005.CAS 
Article 

Google Scholar 
Wiśniewski JR, Zougman A, Mann M. Combination of FASP and StageTip-based fractionation allows in-depth analysis of the hippocampal membrane proteome. J Proteome Res. 2009;8:5674–8.Article 

Google Scholar 
Tyanova S, Mann M, Cox J. MaxQuant for in-depth analysis of large SILAC datasets. Methods Mol Biol. 2014;1188:351–64.Article 

Google Scholar 
Cox J, Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol. 2008;26:1367–72.CAS 
Article 

Google Scholar 
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
Article 

Google Scholar 
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. 2012;41:D590–D596.Article 

Google Scholar 
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.CAS 
Article 

Google Scholar 
Andrews S, Krueger F, Segonds-Pichon A, Biggins L, Krueger C, Wingett S. FastQC. 2010. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/.Bushnell B. BBTools software package. 2014;578:579. https://sourceforge.net/projects/bbmap/.Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods. 2017;14:417–9.CAS 
Article 

Google Scholar 
Love M, Anders S, Huber W. Differential analysis of count data–the DESeq2 package. Genome Biol. 2014;15:10–1186.Article 

Google Scholar 
Huerta-Cepas J, Szklarczyk D, Forslund K, Cook H, Heller D, Walter MC, et al. eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Res. 2016;44:D286–93.CAS 
Article 

Google Scholar 
Tyanova S, Temu T, Sinitcyn P, Carlson A, Hein MY, Geiger T, et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat Methods. 2016;13:731–40.CAS 
Article 

Google Scholar 
Schwämmle V, Hagensen CE, Rogowska-Wrzesinska A, Jensen ON. PolySTest: robust statistical testing of proteomics data with missing values improves detection of biologically relevant features. Mol Cell Proteom. 2020;19:1396–408.Article 

Google Scholar 
Perez-Riverol Y, Csordas A, Bai J, Bernal-Llinares M, Hewapathirana S, Kundu DJ, et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 2019;47:D442–D450.CAS 
Article 

Google Scholar 
Alexa A, Rahnenfuhrer J. Others. topGO: enrichment analysis for gene ontology. R package version. 2010;2:2010.
Google Scholar 
Walter W, Sánchez-Cabo F, Ricote M. GOplot: an R package for visually combining expression data with functional analysis. Bioinformatics. 2015;31:2912–4.CAS 
Article 

Google Scholar 
Bowers RM, Kyrpides NC, Stepanauskas R, Harmon-Smith M, Doud D, Reddy TBK, et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat Biotechnol. 2017;35:725–31.CAS 
Article 

Google Scholar 
Schulz F, Martijn J, Wascher F, Lagkouvardos I, Kostanjšek R, Ettema TJG, et al. A Rickettsiales symbiont of amoebae with ancient features. Environ Microbiol. 2016;18:2326–42.CAS 
Article 

Google Scholar 
Klinges JG, Rosales SM, McMinds R, Shaver EC. Phylogenetic, genomic, and biogeographic characterization of a novel and ubiquitous marine invertebrate-associated Rickettsiales parasite, Candidatus Aquarickettsia rohweri, gen. nov., sp. nov. ISME J. 2019;13:2938–53.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 
Feehery GR, Yigit E, Oyola SO, Langhorst BW, Schmidt VT, Stewart FJ, et al. A method for selectively enriching microbial DNA from contaminating vertebrate host DNA. PLoS One. 2013;8:e76096.CAS 
Article 

Google Scholar 
Pereira-Marques J, Hout A, Ferreira RM, Weber M, Pinto-Ribeiro I, van Doorn L-J, et al. Impact of host DNA and sequencing depth on the taxonomic resolution of whole metagenome sequencing for microbiome analysis. Front Microbiol. 2019;10:1277.Article 

Google Scholar 
Nie L, Wu G, Culley DE, Scholten JCM, Zhang W. Integrative analysis of transcriptomic and proteomic data: challenges, solutions and applications. Crit Rev Biotechnol. 2007;27:63–75.CAS 
Article 

Google Scholar 
Bathke J, Konzer A, Remes B, McIntosh M, Klug G. Comparative analyses of the variation of the transcriptome and proteome of Rhodobacter sphaeroides throughout growth. BMC Genomics. 2019;20:358.Article 

Google Scholar 
Masuda T, Saito N, Tomita M, Ishihama Y. Unbiased quantitation of Escherichia coli membrane proteome using phase transfer surfactants. Mol Cell Proteom. 2009;8:2770–7.CAS 
Article 

Google Scholar 
Chaban B, Hughes HV, Beeby M. The flagellum in bacterial pathogens: For motility and a whole lot more. Semin Cell Dev Biol. 2015;46:91–103.CAS 
Article 

Google Scholar 
Gao C, Garren M, Penn K, Fernandez VI, Seymour JR, Thompson JR, et al. Coral mucus rapidly induces chemokinesis and genome-wide transcriptional shifts toward early pathogenesis in a bacterial coral pathogen. ISME J. 2021;15:3668–82.CAS 
Article 

Google Scholar 
Hentschel U, Piel J, Degnan SM, Taylor MW. Genomic insights into the marine sponge microbiome. Nat Rev Microbiol. 2012;10:641–54.CAS 
Article 

Google Scholar 
Jahn MT, Arkhipova K, Markert SM, Stigloher C, Lachnit T, Pita L, et al. A phage protein aids bacterial symbionts in eukaryote immune evasion. Cell Host Microbe. 2019;26:542–.e5.CAS 
Article 

Google Scholar 
Nguyen MTHD, Liu M, Thomas T. Ankyrin-repeat proteins from sponge symbionts modulate amoebal phagocytosis. Mol Ecol. 2014;23:1635–45.CAS 
Article 

Google Scholar 
Pitulescu ME, Adams RH. Eph/ephrin molecules–a hub for signaling and endocytosis. Genes Dev. 2010;24:2480–92.CAS 
Article 

Google Scholar 
Toth J, Cutforth T, Gelinas AD, Bethoney KA, Bard J, Harrison CJ. Crystal structure of an ephrin ectodomain. Dev Cell. 2001;1:83–92.CAS 
Article 

Google Scholar 
Kullander K, Klein R. Mechanisms and functions of Eph and ephrin signalling. Nat Rev Mol Cell Biol. 2002;3:475–86.CAS 
Article 

Google Scholar 
Duboux S, Golliard M, Muller JA, Bergonzelli G, Bolten CJ, Mercenier A, et al. Carbohydrate-controlled serine protease inhibitor (serpin) production in Bifidobacterium longum subsp. longum. Sci Rep. 2021;11:7236.CAS 
Article 

Google Scholar 
Bao J, Pan G, Poncz M, Wei J, Ran M, Zhou Z. Serpin functions in host-pathogen interactions. PeerJ. 2018;6:e4557.Article 

Google Scholar 
Rädecker N, Pogoreutz C, Gegner HM, Cárdenas A, Roth F, Bougoure J, et al. Heat stress destabilizes symbiotic nutrient cycling in corals. Proc Natl Acad Sci USA. 2021;118:e2022653118.Article 

Google Scholar 
Curson AR, Todd JD, Sullivan MJ, Johnston AW. Catabolism of dimethylsulphoniopropionate: microorganisms, enzymes and genes. Nat Reviews Microbiol. 2011;9:849–59.CAS 
Article 

Google Scholar 
Pogoreutz C, Rädecker N, Cárdenas A, Gärdes A, Voolstra CR, Wild C. Sugar enrichment provides evidence for a role of nitrogen fixation in coral bleaching. Glob Chang Biol. 2017;23:3838–48.Article 

Google Scholar 
Pogoreutz C, Rädecker N, Cárdenas A, Gärdes A, Wild C, Voolstra CR. Nitrogen fixation aligns with nifH abundance and expression in two coral trophic functional groups. Front Microbiol. 2017;8:1187.Article 

Google Scholar 
Falkowski PG, Dubinsky Z, Muscatine L, McCloskey L. Population control in symbiotic corals. Bioscience. 1993;43:606–11.Article 

Google Scholar 
Muscatine L, Cernichiari E. Assimilation of photosynthetic products of zooxanthellae by a reef coral. Biol Bull. 1969;137:506–23.CAS 
Article 

Google Scholar 
Sutton DC, Hoegh-Guldberg O. Host-zooxanthella interactions in four temperate marine invertebrate symbioses: assessment of effect of host extracts on symbionts. Biol Bull. 1990;178:175–86.CAS 
Article 

Google Scholar 
Wang JT, Douglas AE. Essential amino acid synthesis and nitrogen recycling in an alga–invertebrate symbiosis. Mar Biol. 1999;135:219–22.CAS 
Article 

Google Scholar 
Lipschultz F, Cook C. Uptake and assimilation of 15N-ammonium by the symbiotic sea anemones Bartholomea annulata and Aiptasia pallida: conservation versus recycling of nitrogen. Mar Biol. 2002;140:489–502.CAS 
Article 

Google Scholar 
Matthews JL, Oakley CA, Lutz A, Hillyer KE, Roessner U, Grossman AR, et al. Partner switching and metabolic flux in a model cnidarian–dinoflagellate symbiosis. Proc R Soc B: Biol Sci. 2018;285:20182336.CAS 
Article 

Google Scholar 
Tout J, Jeffries TC, Petrou K, Tyson GW, Webster NS, Garren M, et al. Chemotaxis by natural populations of coral reef bacteria. ISME J. 2015;9:1764–77.Article 

Google Scholar 
Raina J-B, Fernandez V, Lambert B, Stocker R, Seymour JR. The role of microbial motility and chemotaxis in symbiosis. Nat Rev Microbiol. 2019;17:284–94.CAS 
Article 

Google Scholar 
Croft MT, Lawrence AD, Raux-Deery E, Warren MJ, Smith AG. Algae acquire vitamin B12 through a symbiotic relationship with bacteria. Nature. 2005;438:90–93.CAS 
Article 

Google Scholar 
Tang YZ, Koch F, Gobler CJ. Most harmful algal bloom species are vitamin B1 and B12 auxotrophs. Proc Natl Acad Sci USA. 2010;107:20756–61.CAS 
Article 

Google Scholar 
Salem H, Bauer E, Strauss AS, Vogel H, Marz M, Kaltenpoth M. Vitamin supplementation by gut symbionts ensures metabolic homeostasis in an insect host. Proc R Soc B: Biol Sci. 2014;281:20141838.Article 

Google Scholar 
Douglas AE. The B vitamin nutrition of insects: the contributions of diet, microbiome and horizontally acquired genes. Curr Opin Insect Sci. 2017;23:65–69.Article 

Google Scholar 
Agostini S, Suzuki Y, Casareto BE, Nakano Y, Michio H, Badrun N. Coral symbiotic complex: Hypothesis through vitamin B12 for a new evaluation. Galaxea J Coral Reef Stud. 2009;11:1–11.Article 

Google Scholar 
Fitzpatrick TB, Chapman LM. The importance of thiamine (vitamin B1) in plant health: From crop yield to biofortification. J Biol Chem. 2020;295:12002–13.CAS 
Article 

Google Scholar 
Bertrand EM, Allen AE. Influence of vitamin B auxotrophy on nitrogen metabolism in eukaryotic phytoplankton. Front Microbiol. 2012;3:375.CAS 
Article 

Google Scholar 
Bourne D, Iida Y, Uthicke S, Smith-Keune C. Changes in coral-associated microbial communities during a bleaching event. ISME J. 2008;2:350–63.CAS 
Article 

Google Scholar 
Court SJ, Waclaw B, Allen RJ. Lower glycolysis carries a higher flux than any biochemically possible alternative. Nat Commun. 2015;6:8427.CAS 
Article 

Google Scholar 
Ziegler M, Seneca FO, Yum LK, Palumbi SR, Voolstra CR. Bacterial community dynamics are linked to patterns of coral heat tolerance. Nat Commun. 2017;8:14213.CAS 
Article 

Google Scholar 
Peixoto RS, Sweet M, Villela HDM, Cardoso P, Thomas T, Voolstra CR, et al. Coral probiotics: premise, promise, prospects. Annu Rev Anim Biosci. 2021;9:265–88.Article 

Google Scholar 
Jousset A, Bienhold C, Chatzinotas A, Gallien L, Gobet A, Kurm V, et al. Where less may be more: how the rare biosphere pulls ecosystems strings. ISME J. 2017;11:853–62.Article 

Google Scholar 
Santos HF, Carmo FL, Duarte G, Dini-Andreote F, Castro CB, Rosado AS, et al. Climate change affects key nitrogen-fixing bacterial populations on coral reefs. ISME J. 2014;8:2272–9.Article 

Google Scholar  More

Study system & organismsThe study was conducted in the Hengill valley, Iceland13,14,15,16,17,18 (N 64°03; W 21°18), which contains many streams of different temperature due to geothermal heating of the bedrock or soils surrounding the springs (Supplementary Fig. 1). The streams have been heated in this way for centuries33 and are otherwise similar in their physical and chemical properties13,18, providing an ideal space-for-time substitution in which to measure species responses after chronic exposure to different temperatures6,34. Fieldwork was performed in the summers of 2015–2018, between May and July. Stream temperatures were logged every 4 h using Maxim Integrated DS1921G Thermochron iButtons submerged in each stream (Supplementary Fig. 2). The average stream temperature over this study period was used as a measure of chronic temperature exposure, encompassing at least the lifetime of every invertebrate species under investigation (and potentially multiple generations6,35).Invertebrates were collected from nine streams spanning a temperature gradient of 5–20 °C across the entire study system (Supplementary Figs. 1–2). The streams exhibit some differences in the annual variability of their thermal regimes, but there are examples of both cold and warm streams that have high (IS12 and IS2) and low (IS13 and IS8) variability throughout the year. Our main finding is also robust to the inclusion of stream temperature variability as a random effect in our modelling framework (Supplementary Table 4; Supplementary Fig. 7). Note that we present temperature data from 15 streams in Supplementary Fig. 2, but it was not logistically feasible to study acute thermal responses of invertebrates collected from all of them, thus we focused on a subset of nine streams that best spanned the temperature gradient. The remaining six streams were included in other studies from the system, quantifying the biomass of all the constituent species17, describing food web structure18, and measuring whole-stream respiration15 (described in detail below).Individual organisms were stored in containers within their ‘home stream’ until the end of each collection day, when they were transported within 1 h to the University of Iceland and then transferred into 2 L aquaria filled with water from the main river in Hengill, the Hengladalsá. The water was passed through a 125 µm sieve to ensure no organisms or filamentous algae entered the aquaria, and thus limiting the potential food available to the study organisms. The aquaria were continuously aerated in temperature-controlled chambers set to the home stream temperature of the organisms during sampling, which were maintained without food for at least 24 h to standardise their digestive state prior to metabolic measurements36. While we did not observe any cannibalism or organisms feeding on dead bodies in the laboratory, we cannot rule out the possibility that organisms fed on fine algal or detrital particles in the water, thus increasing variability in our metabolic measurements due to differences in digestive state.Quantifying metabolic ratesExperiments were carried out to determine the effects of body mass, acute temperature exposure (5, 10, 15, 20 and 25 °C), and chronic temperature exposure (i.e., average stream temperature) on oxygen consumption rates as a measure of metabolic rate3,12. Before each experiment, individual organisms were confined in glass chambers in a temperature-controlled water bath and slowly adjusted to the (acute) experimental temperature over a 15 min period to avoid a shock response. Glass chambers ranged in volume from 0.8–5 ml and scaled with the size of the organism. The glass chambers were filled with water from the Hengladalsá, which was filtered through a 0.45 µm Whatman membrane after aeration to 100% oxygen saturation. A magnetic stir bar was placed at the bottom of each chamber and separated from the organism by a mesh screen. In each experiment, one individual organism was placed in each of seven chambers and the eighth chamber was used as an animal-free control to correct for potential sensor drift. The chambers were sealed with gas-tight stoppers after the 15 min acclimatisation period, ensuring there was no headspace or air bubbles.Oxygen consumption by individual organisms was measured using an oxygen microelectrode (MicroRespiration, Unisense, Denmark), fitted through a capillary in the gas-tight stopper of each chamber37. A total of 330 s measurement periods were recorded for each individual, where dissolved oxygen was measured every second. Oxygen consumption rate was calculated as the slope of the linear regression through all the data points from a single chamber, corrected for differences in chamber volume and the background rate measured from the control chamber (which was never >5% of the measured metabolic rates). We converted the units of this rate (µmol O2 h−1) to energetic equivalents (J h−1) using atomic weight (1 mol O2 = 31.9988 g), density (1.429 g L−1), and a standard conversion38 (1 ml O2 = 20.1 J). Organisms generally exhibited some activity during experiments, thus these measurements can be classified as routine metabolic rates12, which are more reflective of energy expenditure in field conditions. Nevertheless, activity levels were minimal due to the space constraints of the chambers (volume equal to 5–100 times the mass of the measured organism), indicating that the measured rates were likely to be closer to resting metabolic rates. Oxygen concentrations were never allowed to decline below 70% to minimise stress and avoid oxygen limitation. The system was cleaned with bleach at the end of each measurement day to avoid accumulation of microbial organisms on the insides of glass chambers and the water bath. In total, oxygen consumption rates were measured for 1819 individuals, none of which were ever reused in another experiment, thus every data point in the analysis corresponds to a single new individual (see below for details of how this dataset was curated to the final analysed subset of 1359 individuals based on quality-control procedures).Following each experiment, individuals were preserved in 70% ethanol and later identified to species level under a dissecting microscope, except for Chironomidae, which were identified by examining head capsules under a compound microscope39. A linear dimension was precisely measured for every individual using an eyepiece graticule and converted to dry body mass using established length-weight relationships (Supplementary Table 1).Statistical analysisAll statistical analyses were conducted in R 4.0.2 (see the Supplementary Note for full details of statistical R code). According to the Metabolic Theory of Ecology3 (MTE), metabolic rate, I, depends on body mass and temperature as:$$I={I}_{0}{M}^{b}{e}^{{E}_{A}{T}_{A}},$$
(1)
where I0 is the intercept, M is dry body mass (mg), b is an allometric exponent, EA is the activation energy (eV), and TA is a standardised Arrhenius temperature:$${T}_{A}=frac{{T}_{{acute}}-{T}_{0}}{k{T}_{{acute}}{T}_{0}}.$$
(2)
Here, Tacute is an acute temperature exposure (K), T0 sets the intercept of the relationship at 283.15 K (i.e., 10 °C), and k is the Boltzmann constant (8.618 × 10−5 eV K−1). We performed a multiple linear regression (‘lm’ function in the ‘stats’ package) on the natural logarithm of Eq. (1) to explore the main effects of temperature and body mass on the metabolic rate of each population (i.e., species × stream combination) in our dataset3. Following these analyses, we excluded populations where n < 10 individuals, r2  0.05 for any term in the model (see Supplementary Table 5, Supplementary Figs. 8–9). This excluded any poor quality species-level data and resulted in 1359 individuals from 44 populations for further analysis. Note that we find the same overall conclusion if we analyse the entire dataset (Supplementary Table 6, Supplementary Fig. 10).To determine whether chronic temperature exposure alters the size- and acute temperature-dependence of metabolic rate, we added a term for chronic temperature exposure to Eq. 1. We began our analysis by considering the natural logarithm of all possible combinations of the main and interactive effects in this model:$${ln}I= {ln}{I}_{0}+b{ln}M+{E}_{A}{T}_{A}+{E}_{C}{T}_{C}+{b}_{A}{ln}M{T}_{A}+{b}_{C}{ln}M{T}_{C}+{E}_{{AC}}{T}_{A}{T}_{C}\ +{b}_{{AC}}{ln}M{T}_{A}{T}_{C}.$$ (3) Here, TC is a standardised Arrhenius temperature with Tchronic as a chronic temperature exposure (K) substituted for Tacute in Eq. (2). To determine the optimal random effects structure for this model, we compared a generalised least squares model of Eq. 3 with linear mixed-effects models (‘gls’ and ‘lme’ functions in the ‘nlme’ package) containing all possible subsets of the following random effects structure40:$${random}={sim} 1+{ln},M+{T}_{A}+{T}_{C}|{species}.$$ (4) Here, we are accounting for the possibility that metabolic rate could be different for each species (i.e., a random intercept) and that the effect of body mass, acute temperature exposure, or chronic temperature exposure on metabolic rate could also be different for each species (i.e., random slopes).The full random structure (Eq. 4) was identified as the best model using Akaike Information Criterion (ΔAIC > 31.2; see Supplementary Table 7). We used this random structure in subsequent analyses, set ‘method = ‘ML’’ in the ‘lme’ function, and performed AIC comparison on all possible combinations of the fixed-effect structure40 (i.e., Equation 3). The optimal model was identified as follows:$${ln}I={ln}{I}_{0}+b{ln},M+{E}_{A}{T}_{A}+{E}_{C}{T}_{C}+{b}_{C}{ln}M{T}_{C}+{E}_{{AC}}{T}_{A}{T}_{C}.$$
(5)
Note that while the model with an additional interaction between ln(M) and TA performed similarly (ΔAIC = 0.2; see Supplementary Table 8), that term was not significant (t = −1.645; p = 0.1002). We set ‘method = ‘REML’’ before extracting model summaries and partial residuals from the best-fitting model40. Note that the models were always fitted to the raw metabolic rate data, with residuals only extracted for a visual representation of the best-fitting models, excluding the noise explained by the random effect of species identity (see R code in the Supplementary Note).Exploration of spatial autocorrelationA Mantel test (‘mantel’ function in the ‘vegan’ R package) was used to test for spatial autocorrelation in the temperature gradient, by comparing pairwise temperature difference between streams to the pairwise distance between streams. Pairwise distances were calculated from GPS coordinates taken at the confluence of each stream with the main river and the ‘earth.dist’ function in the ‘fossil’ R package. This analysis revealed no significant relationship between pairwise temperature and pairwise distance between sites (Mantel r = −0.1293, p = 0.780).In addition, we explored for spatial autocorrelation in the residuals of our optimal model (Table 1a) by generating an empirical semivariogram cloud, illustrating the squared difference between all pairwise residual data points as a function of the distance between the two points. We also calculated Moran’s I as a measure of spatial autocorrelation in the model residuals. The semivariogram indicated no clear patterns in the residuals as a function of the distance between data points (Supplementary Fig. 3) and there was no statistical evidence for spatial autocorrelation in the model residuals (Moran’s I = 0.1187, p = 0.453).Exploration of phylogenetic structureTo examine the influence of evolutionary relatedness on metabolic rate measurements, we reconstructed a time-calibrated phylogeny of the 16 species in our final dataset (Supplementary Table 1). To this end, we combined: (i) nucleotide sequences of the 5′ region of the cytochrome c oxidase subunit I gene (COI-5P) from the Barcode of Life Data System database41; (ii) tree topology information from the Open Tree of Life42 (OTL; v. 13.4); and (iii) previously reported divergence time estimates between pairs of genera from the TimeTree database43. More precisely, we were able to obtain COI-5P nucleotide sequences for 15 out of 16 species (Supplementary Table 2), which we aligned using the G-INS-i algorithm of MAFFT44 (v. 7.490). To constrain the topology of our phylogeny based on the results of previous studies, we queried the OTL via the ‘rotl’ R package45 (v. 3.0.11). This yielded topological information for all 16 species. Finally, we manually queried the TimeTree database to obtain node age estimates. We only used three such estimates that (a) were based on more than five previous studies and (b) did not force any tree branches to have a length of zero.We next used MrBayes46 (v. 3.2.7a) to obtain a time-calibrated phylogeny based on the sequence alignment, the OTL topology, and the node ages from TimeTree. For this, we first determined the most appropriate nucleotide substitution model using ModelTest-NG47 (v. 0.1.7). This was the General Time-Reversible model with Gamma-distributed rate variation across sites and a proportion of invariant sites. To allow branches of the phylogeny to differ in their rate of sequence evolution, we specified the Independent Gamma Rates model48 and used a normal distribution with a mean of 0.00003 and a standard deviation of 0.00001 as the prior for the mean clock rate. Finally, we executed four MrBayes runs with two chains per run for 100 million generations, sampling from the posterior distribution every 500 generations. Samples from the first ten million generations were treated as burn-in and were discarded. We examined the remaining samples to ensure that the four MrBayes runs had converged on statistically indistinguishable posterior distributions (i.e., all potential scale reduction factor values were below 1.1) and the parameter space was sufficiently explored (i.e., all effective sample size values were higher than 200). We summarised the sampled trees into a single time-calibrated phylogeny by calculating the median age estimate for each node (Supplementary Fig. 4).To investigate the influence of evolutionary and acclimatory processes on metabolic rate, we first estimated the phylogenetic heritability of metabolic rate, i.e., the extent to which closely related species have more similar trait values than species chosen at random49. This metric takes values from 0 (trait values are independent of the phylogeny) to 1 (trait values evolve similarly to a random walk in the parameter space), with intermediate values indicating deviations from a pure random walk. To estimate phylogenetic heritability, we fitted a generalised linear mixed-effects model using the ‘MCMCglmm’ R package50 (v. 2.32). We set the natural logarithm of metabolic rate as the response variable and only an intercept as a fixed effect. We also specified a phylogenetic species-level random effect on the intercept, using the phylogenetic variance-covariance matrix obtained from our time-calibrated phylogeny. We used the default (normal) prior for the fixed effect, an uninformative Cauchy prior for the random effect, and an uninformative inverse Gamma prior for the residual variance. We then executed four independent runs for 500,000 MCMC generations each, with parameter samples being obtained every 50 generations after the first 50,000. We verified that sufficient convergence was reached, based on potential scale reduction factor and effective sample size values, as described earlier. Phylogenetic heritability was calculated as the ratio of the variance captured by the species-level random effect to the sum of the random and residual variances. The mean posterior phylogenetic heritability estimate of the natural logarithm of metabolic rate was 0.48. This means that nearly half (48%) of the variation can be explained by the evolution of metabolic rate along the phylogeny (Supplementary Fig. 4), with the other half arising from other sources including (but not necessarily limited to) acclimation and measurement error.To describe the remaining unexplained variation, we fitted a series of models using MCMCglmm in R with all possible combinations of log body mass, acute temperature exposure, and chronic temperature exposure (fixed effects, as in Eq. (3) of the main text) and species-level random effects on the intercept and slopes (as in Eq. (4) of the main text). Furthermore, we specified both phylogenetic and non-phylogenetic variants of each model to understand if such a correction is warranted when the fixed effects are included. We determined the most appropriate model based on the Deviance Information Criterion51 (DIC). The optimal model (ΔDIC > 19; Supplementary Table 3; Supplementary Fig. 5) was found to include the full random effects structure (Eq. 4), the main effects of log body mass, acute temperature exposure, and chronic temperature exposure, the interaction between log body mass and chronic temperature exposure, and the interaction between acute temperature exposure and chronic temperature exposure (as for Eq. 5 in the main text), i.e., the same optimal model as that containing only species-level, rather than phylogenetic, information (Table 1a; Fig. 1). We calculated the marginal and conditional coefficients of determination to report the amounts of variance explained by the fixed and random effects, or left unexplained52. We found that the unexplained variation dropped from 52% to 8%, indicating that metabolic rate is strongly influenced by acclimatory processes in addition to evolutionary processes (see above).It should be noted, however, that a definitive empirical quantification of the relative strength of evolutionary and acclimatory processes would require population genetics (to determine evolutionary divergent populations among streams), transcriptomics (to identify the expression of genes associated with thermal adaptation), and exhaustive common garden experiments (to disentangle acclimation from adaptation in all populations). Such an undertaking was logistically unfeasible in this study, but should be a focus for follow-up research on this topic.Modelling ecosystem-level energy fluxesWe used a recently proposed approach for inferring energy fluxes through trophic links25 to predict the effects of climate warming on ecosystem-level energy fluxes. We began by assuming that each stream ecosystem is at energetic steady state, i.e., for all n consumer species in the system:$${G}_{i},=,{L}_{i},,i,=,1,,2,,ldots ,,n,$$
(6)
where Gi and Li are the energy gain and loss rates [J h−1], respectively, of the ith species in that stream. All basal species are implicitly assumed to be at energy balance. The two terms in Eq. (6) can be specified in a general way as$${G}_{i}=mathop{sum}limits_{k,in ,{{{{{{rm{R}}}}}}}_{i}}{e}_{{ki}}{w}_{{ki}}{F}_{{ki}},{{{{{rm{and}}}}}}$$
(7)
$${L}_{i}={Z}_{i}+mathop{sum}limits_{j,in ,{{{{{{rm{C}}}}}}}_{i}}{w}_{{ij}}{F}_{{ij}}.$$
(8)
Here, for the ith species, Ri and Ci are the sets of its resource and consumer species respectively, and Zi is its population-level energy loss rate stemming from mortality and metabolic expenditure on various activities realised over the timescale of the system’s dynamics. For the jth species feeding on the ith species, Fij is the maximum population-level feeding rate, eij is the assimilation efficiency (expressed as a proportion), and wij is the consumer’s preference for that species (all preferences for a given consumer sum to 1). Thus, the effective flux through a trophic link is ({e}_{{ki}}{w}_{{ki}}{F}_{{ki}}). Next, assuming the energy balance condition in Eq. 6 holds for all species, there are n linear equations (corresponding to the n consumer species) of the form:$${G}_{i}-{L}_{i}=mathop{sum}limits_{kin {{{{{{rm{R}}}}}}}_{i}}{e}_{{ki}}{w}_{{ki}}{F}_{{ki}}-left({Z}_{i}+mathop{sum}limits_{jin {{{{{{rm{C}}}}}}}_{i}}{w}_{{ij}}{F}_{{ij}}right)=0,$$
(9)
which can be solved iteratively to obtain the unknown fluxes ({F}_{{ij},{i}ne j}) of all consumer species, provided all the Zi’s, eij’s, and wij’s are known.For this, we used the ‘fluxing’ function in the ‘fluxweb’ R package, parameterised with: (1) binary predation matrices for 14 stream food webs, characterised by 49,324 directly observed feeding interactions18; (2) biomasses for every species in each food web, characterised by 13,185 individual body mass measurements17; (3) assimilation efficiencies (eij’s) based on an established temperature-dependence and resource type (i.e., plant, detritus, or invertebrate)53; (4) preferences (wij’s) depending on resource biomasses; and (5) metabolic rates estimated using Eqs. (1) and (5) (assuming that I approximates Z). We treated TA in Eqs. (1) and (5) as the short-term temperature of the streams during food web sampling17,18 and TC in Eq. (5) as the long-term average temperature of the streams measured over the current study (Supplementary Fig. 2). It is important to note that the energy balance assumption (Eq. 6) implies that Zi in Eq. (8) is a combination of basal, routine, and active metabolic rates, stemming from the combination of activities realised over the timescale of the system’s dynamics. Therefore, our use of routine metabolic rate I is an underestimate of Z, which in turn means that the fluxes (which must balance the losses) are an underestimate.Biomass and food web data were sampled in August 2008, with extensive protocols described in previous publications17,18. Briefly, this involved three stone scrapes per stream for benthic diatoms, five Surber samples per stream for macroinvertebrates, and three-run depletion electrofishing for fish. All individuals in the samples were identified to species level where possible and counted. Linear dimensions were measured for at least ten individuals of each species in each stream, with body masses estimated from length-weight relationships17. The population biomass of each species in each stream was calculated as the total abundance [individuals m−2] multiplied by the mean body mass [mg dry weight]. Food web links were largely assembled from gut content analysis of individual organisms collected from the streams ( >87% of all links in the database), but additional links were added from the literature when yield-effort curves indicated that the diet of a consumer species was incomplete18.Validation of the ecosystem flux model using field dataTo test whether our model of energy fluxes through trophic links was empirically meaningful, we calculated the sum of all energy fluxes through each stream food web to get the total energy flux, F (i.e., the sum of all ({e}_{{ki}}{w}_{{ki}}{F}_{{ki}})’s in Eq. 7). This quantity is a measure of multitrophic functioning and is expected to be positively correlated with the total respiration of each stream25. To evaluate this, we compared F to whole-ecosystem respiration rates measured in the same study streams15. The ecosystem respiration estimates were based on a modified open-system oxygen change method using two stations corrected for lateral inflows54,55. Essentially, this was an in-stream mass balance of oxygen inflows and outflows along stream reaches (17–51 m long). Oxygen concentrations were measured during 24- to 48 h periods from 6th to 16th August 2008, i.e., the exact same time period during which biomass and food web data were sampled to parameterise the energy flux model15. Dissolved oxygen concentrations were measured every minute with optic oxygen sensors (TROLL9500 Professional, In-Situ Inc. and Universal Controller SC100, Hach Lange GMBF). Hourly ecosystem respiration was calculated from the net metabolism at night, i.e., when no primary production occurs due to lack of sunlight.Modelling the consequences of metabolic plasticity for global warming impacts on ecosystem-level energy fluxIn addition to total energy flux, F, we also calculated a modified total energy flux, F*, for each food web after considering a global warming scenario, where we added 2 °C to TA in Eq. (1) and to both TA and TC in Eq. (5). We calculated the change in total energy flux as a result of the global warming scenario as ΔF = F* – F. We tested whether the (statistically optimal) model with metabolic plasticity (Eq. 5) predicted a greater ΔF across the 14 empirical stream food webs from the Hengill system than the model without metabolic plasticity using paired Wilcoxon tests (since the data did not conform to homogeneity of variance). To determine whether our results were consistent for all major trophic groupings in the system, we repeated the analysis after calculating the change in energy flux to herbivores (ΔFH = FH* – FH), detritivores (ΔFD = FD* – FD), and predators (ΔFP = FP* – FP) in each stream.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Uehlinger, U., Robinson, C. T., Hieber, M. & Zah, R. The physico-chemical habitat template for periphyton in alpine glacial streams under a changing climate. in Global Change and River Ecosystems—Implications for Structure, Function and EcosystemServices (eds. Stevenson, R. J. & Sabater, S.) 107–121 (Springer Netherlands, 2010).Battin, T. J., Wille, A., Psenner, R. & Richter, A. Large-scale environmental controls on microbial biofilms in high-alpine streams. Biogeosciences 1, 159–171 (2004).ADS 
CAS 
Article 

Google Scholar 
Kuhn, M. The nutrient cycle through snow and ice, a review. Aquat. Sci. 63, 150–167 (2001).CAS 
Article 

Google Scholar 
Milner, A. M. et al. Glacier shrinkage driving global changes in downstream systems. Proc. Natl Acad. Sci. U. S. A. 114, 9770–9778 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Tockner, K., Malard, F., Uehlinger, U. & Ward, J. V. Nutrients and organic matter in a glacial river-floodplain system (Val Roseg, Switzerland). Limnol. Oceanogr. 47, 266–277 (2002).ADS 
CAS 
Article 

Google Scholar 
Boix Canadell, M. et al. Regimes of primary production and their drivers in Alpine streams. Freshw. Biol. 66, 1449–1463 (2021).Article 

Google Scholar 
Bernhardt, E. S. et al. Control points in ecosystems: moving beyond the hot spot hot moment concept. Ecosystems 20, 665–682 (2017).Article 

Google Scholar 
Huss, M. & Hock, R. Global-scale hydrological response to future glacier mass loss. Nat. Clim. Chang. 8, 135–140 (2018).ADS 
Article 

Google Scholar 
Battin, T. J., Besemer, K., Bengtsson, M. M., Romani, A. M. & Packmann, A. I. The ecology and biogeochemistry of stream biofilms. Nat. Rev. Microbiol. 14, 251–263 (2016).CAS 
Article 
PubMed 

Google Scholar 
Roncoroni, M., Brandani, J., Battin, T. I. & Lane, S. N. Ecosystem engineers: biofilms and the ontogeny of glacier floodplain ecosystems. WIREs Water 6, e1390 (2019).Article 

Google Scholar 
Hoyle, J. T., Kilroy, C., Hicks, D. M. & Brown, L. The influence of sediment mobility and channel geomorphology on periphyton abundance. Freshw. Biol. 62, 258–273 (2017).Article 

Google Scholar 
Canadell, M. B. et al. Regimes of primary production and their drivers in Alpine streams. Freshwater Biol. https://doi.org/10.1111/fwb.13730 (2021).Fell, S. C., Carrivick, J. L. & Brown, L. E. The multitrophic effects of climate change and glacier retreat in mountain rivers. Bioscience 67, 897–911 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Cole, J. J. Interactions between bacteria and algae in aquatic ecosystems. Annu. Rev. Ecol. Syst. 13, 291–314 (1982).Article 

Google Scholar 
Seymour, J. R., Amin, S. A., Raina, J.-B. & Stocker, R. Zooming in on the phycosphere: the ecological interface for phytoplankton–bacteria relationships. Nat. Microbiol. 2, 17065 (2017).Amin, S. A. et al. Interaction and signalling between a cosmopolitan phytoplankton and associated bacteria. Nature 522, 98–101 (2015).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Christie-Oleza, J. A., Sousoni, D., Lloyd, M., Armengaud, J. & Scanlan, D. J. Nutrient recycling facilitates long-term stability of marine microbial phototroph–heterotroph interactions. Nat. Microbiol. 2, 1–10 (2017).Article 
CAS 

Google Scholar 
Haack, T. K. & McFeters, G. A. Nutritional relationships among microorganisms in an epilithic biofilm community. Microb. Ecol. 8, 115–126 (1982).CAS 
Article 
PubMed 

Google Scholar 
Kaplan, L. A. & Bott, T. L. Diel fluctuations in bacterial activity on streambed substrata during vernal algal blooms: effects of temperature, water chemistry, and habitat. Limnol. Oceanogr. 34, 718–733 (1989).ADS 
CAS 
Article 

Google Scholar 
Vincent, W. F., Downes, M. T., Castenholz, R. W. & Howard-Williams, C. Community structure and pigment organisation of cyanobacteria-dominated microbial mats in Antarctica. Eur. J. Phycol. 28, 213–221 (1993).Article 

Google Scholar 
Tolotti, M. et al. Alpine headwaters emerging from glaciers and rock glaciers host different bacterial communities: Ecological implications for the future. Sci. Total Environ. 717, 137101 (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Besemer, K., Singer, G., Hödl, I. & Battin, T. J. Bacterial community composition of stream biofilms in spatially variable-flow environments. Appl. Environ. Microbiol. 75, 7189–7195 (2009).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Risse‐Buhl, U. et al. Near streambed flow shapes microbial guilds within and across trophic levels in fluvial biofilms. Limnol. Oceanogr. 65, 2261–2277 (2020).ADS 
Article 
CAS 

Google Scholar 
Palmer, M. A., Swan, C. M., Nelson, K., Silver, P. & Alvestad, R. Streambed landscapes: evidence that stream invertebrates respond to the type and spatial arrangement of patches. Landsc. Ecol. 15, 563–576 (2000).Article 

Google Scholar 
Battin, T. J. et al. Microbial landscapes: new paths to biofilm research. Nat. Rev. Microbiol. 5, 76–81 (2007).CAS 
Article 
PubMed 

Google Scholar 
Dzubakova, K. et al. Environmental heterogeneity promotes spatial resilience of phototrophic biofilms in streambeds. Biol. Lett. 14, 20180432 (2018).Sloan, W. T. et al. Quantifying the roles of immigration and chance in shaping prokaryote community structure. Environ. Microbiol. 8, 732–740 (2006).Article 
PubMed 

Google Scholar 
Hug, L. A. et al. A new view of the tree of life. Nat. Microbiol 1, 16048 (2016).CAS 
Article 
PubMed 

Google Scholar 
Chaudhari, N. M., Overholt, W. A. & Figueroa-Gonzalez, P. A. The economical lifestyle of CPR bacteria in groundwater allows little preference for environmental drivers. bioRxiv. 16, 1–8 (2021).Vigneron, A. et al. Ultra‐small and abundant: candidate phyla radiation bacteria are potential catalysts of carbon transformation in a thermokarst lake ecosystem. Limnol. Oceanogr. Lett. 5, 212–220 (2020).Article 

Google Scholar 
Liu, Y. et al. Expanded diversity of Asgard archaea and their relationships with eukaryotes. Nature 593, 553–557 (2021).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Cai, M. et al. Ecological features and global distribution of Asgard archaea. Sci. Total Environ. 758, 143581 (2021).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Niedrist, G. H. & Füreder, L. When the going gets tough, the tough get going: The enigma of survival strategies in harsh glacial stream environments. Freshw. Biol. 63, 1260–1272 (2018).Article 

Google Scholar 
Payne, A. T. et al. Widespread cryptic viral infections in lotic biofilms. Biofilms 2, 100016 (2020).Article 

Google Scholar 
Anesio, A. M., Mindl, B., Laybourn-Parry, J., Hodson, A. J. & Sattler, B. Viral dynamics in cryoconite holes on a high Arctic glacier (Svalbard). J. Geophys. Res. 112, (2007).Bellas, C. M., Schroeder, D. C., Edwards, A., Barker, G. & Anesio, A. M. Flexible genes establish widespread bacteriophage pan-genomes in cryoconite hole ecosystems. Nat. Commun. 11, 4403 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Liu, Q. et al. Light stimulates anoxic and oligotrophic growth of glacial Flavobacterium strains that produce zeaxanthin. ISME J. 15, 1844–1857 (2021).CAS 
Article 
PubMed 

Google Scholar 
Sánchez Barranco, V. et al. Trophic position, elemental ratios and nitrogen transfer in a planktonic host-parasite-consumer food chain including a fungal parasite. Oecologia 194, 541–554 (2020).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Klawonn, I. et al. Characterizing the ‘fungal shunt’: Parasitic fungi on diatoms affect carbon flow and bacterial communities in aquatic microbial food webs. Proc. Natl. Acad. Sci. U. S. A. 118, (2021).Chróst, R. J. Microbial Enzymes in Aquatic Environments. (Springer-Verlag, 1991).Sinsabaugh, R. L., Hill, B. H. & Follstad Shah, J. J. Ecoenzymatic stoichiometry of microbial organic nutrient acquisition in soil and sediment. Nature 462, 795–798 (2009).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Stoecker, D. K. & Lavrentyev, P. J. Mixotrophic plankton in the polar seas: a pan-arctic review. Front. Mar. Sci. 5, 292 (2018).Waibel, A., Peter, H. & Sommaruga, R. Importance of mixotrophic flagellates during the ice-free season in lakes located along an elevational gradient. Aquat. Sci. 81, 45 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Avcı, B., Krüger, K., Fuchs, B. M., Teeling, H. & Amann, R. I. Polysaccharide niche partitioning of distinct Polaribacter clades during North Sea spring algal blooms. ISME J. 14, 1369–1383 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sichert, A. et al. Verrucomicrobia use hundreds of enzymes to digest the algal polysaccharide fucoidan. Nat. Microbiol 5, 1026–1039 (2020).CAS 
Article 
PubMed 

Google Scholar 
Zhou, J., Lyu, Y., Richlen, M., Anderson, D. M. & Cai, Z. Quorum sensing is a language of chemical signals and plays an ecological role in algal–bacterial interactions. CRC Crit. Rev. Plant Sci. 35, 81–105 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Croft, M. T., Lawrence, A. D., Raux-Deery, E., Warren, M. J. & Smith, A. G. Algae acquire vitamin B12 through a symbiotic relationship with bacteria. Nature 438, 90–93 (2005).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Grossman, A. Nutrient Acquisition: The Generation of Bioactive Vitamin B12 by Microalgae. Curr. Biol.: CB vol. 26, R319–R321 (2016).CAS 
Article 

Google Scholar 
Segev, E. et al. Dynamic metabolic exchange governs a marine algal-bacterial interaction. Elife 5, e17473 (2016).Hood, E., Battin, T. J., Fellman, J., O’Neel, S. & Spencer, R. G. M. Storage and release of organic carbon from glaciers and ice sheets. Nat. Geosci. 8, 91–96 (2015).ADS 
CAS 
Article 

Google Scholar 
Fellman, J. B. et al. Evidence for the assimilation of ancient glacier organic carbon in a proglacial stream food web. Limnol. Oceanogr. 60, 1118–1128 (2015).ADS 
Article 

Google Scholar 
Singer, G. A. et al. Biogeochemically diverse organic matter in Alpine glaciers and its downstream fate. Nat. Geosci. 5, 710–714 (2012).ADS 
CAS 
Article 

Google Scholar 
Boix Canadell, M., Escoffier, N., Ulseth, A. J., Lane, S. N. & Battin, T. J. Alpine glacier shrinkage drives shift in dissolved organic carbon export from quasi‐chemostasis to transport limitation. Geophys. Res. Lett. 46, 8872–8881 (2019).ADS 
CAS 
Article 

Google Scholar 
Anesio, A. M., Lutz, S., Chrismas, N. A. M. & Benning, L. G. The microbiome of glaciers and ice sheets. NPJ Biofilms Microbiomes 3, 10 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Tranter, M., Mills, R. & Raiswell, R. Chemical weathering reactions in Alpine glacial meltwaters. in International symposium on water-rock interaction. 687–690 (1989).Tranter, M., Brown, G., Raiswell, R., Sharp, M. & Gurnell, A. A conceptual model of solute acquisition by Alpine glacial meltwaters. J. Glaciol. 39, 573–581 (1993).ADS 
CAS 
Article 

Google Scholar 
St Pierre, K. A. et al. Proglacial freshwaters are significant and previously unrecognized sinks of atmospheric CO2. Proc. Natl Acad. Sci. U. S. A. 116, 17690–17695 (2019).ADS 
Article 
CAS 

Google Scholar 
Dunham, E. C., Dore, J. E., Skidmore, M. L., Roden, E. E. & Boyd, E. S. Lithogenic hydrogen supports microbial primary production in subglacial and proglacial environments. Proc. Natl. Acad. Sci. U. S. A. 118, (2021).Hernández, M. et al. Reconstructing genomes of carbon monoxide oxidisers in volcanic deposits including members of the Class Ktedonobacteria. Microorganisms 8, 1880 (2020).Quick, A. M. et al. Nitrous oxide from streams and rivers: a review of primary biogeochemical pathways and environmental variables. Earth-Sci. Rev. 191, 224–262 (2019).ADS 
CAS 
Article 

Google Scholar 
Kuypers, M. M. M., Marchant, H. K. & Kartal, B. The microbial nitrogen-cycling network. Nat. Rev. Microbiol. 16, 263–276 (2018).CAS 
Article 
PubMed 

Google Scholar 
Ren, Z., Gao, H., Elser, J. J. & Zhao, Q. Microbial functional genes elucidate environmental drivers of biofilm metabolism in glacier-fed streams. Sci. Rep. 7, 12668 (2017).ADS 
Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Gooseff, M. N., McKnight, D. M., Runkel, R. L. & Duff, J. H. Denitrification and hydrologic transient storage in a glacial meltwater stream, McMurdo Dry Valleys, Antarctica. Limnol. Oceanogr. 49, 1884–1895 (2004).ADS 
CAS 
Article 

Google Scholar 
Varin, T., Lovejoy, C., Jungblut, A. D., Vincent, W. F. & Corbeil, J. Metagenomic profiling of Arctic microbial mat communities as nutrient scavenging and recycling systems. Limnol. Oceanogr. 55, 1901–1911 (2010).ADS 
CAS 
Article 

Google Scholar 
Kohler, T. J. et al. Patterns and drivers of extracellular enzyme activity in New Zealand glacier-fed streams. Front. Microbiol. 11, 591465 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Alves, R. J. E. et al. Ammonia oxidation by the Arctic terrestrial thaumarchaeote candidatus nitrosocosmicus arcticus is stimulated by increasing temperatures. Front. Microbiol. 10, 1571 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Könneke, M. et al. Ammonia-oxidizing archaea use the most energy-efficient aerobic pathway for CO2 fixation. Proc. Natl Acad. Sci. U. S. A. 111, 8239–8244 (2014).ADS 
Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Battin, T. J., Kaplan, L. A., Denis Newbold, J. & Hansen, C. M. E. Contributions of microbial biofilms to ecosystem processes in stream mesocosms. Nature 426, 439–442 (2003).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Cockell, C. S. et al. Influence of ice and snow covers on the UV exposure of terrestrial microbial communities: dosimetric studies. J. Photochem. Photobiol. B 68, 23–32 (2002).CAS 
Article 
PubMed 

Google Scholar 
Sommaruga, R. The role of solar UV radiation in the ecology of alpine lakes. J. Photochem. Photobiol. B 62, 35–42 (2001).CAS 
Article 
PubMed 

Google Scholar 
Margesin, R. & Collins, T. Microbial ecology of the cryosphere (glacial and permafrost habitats): current knowledge. Appl. Microbiol. Biotechnol. 103, 2537–2549 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
De Maayer, P., Anderson, D., Cary, C. & Cowan, D. A. Some like it cold: understanding the survival strategies of psychrophiles. EMBO Rep. 15, 508–517 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tribelli, P. M. & López, N. I. Reporting key features in cold-adapted bacteria. Life 8, 8 (2018).Varin, T., Lovejoy, C., Jungblut, A. D., Vincent, W. F. & Corbeil, J. Metagenomic analysis of stress genes in microbial mat communities from Antarctica and the High Arctic. Appl. Environ. Microbiol. 78, 549–559 (2012).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Alonso-Sáez, L. et al. Winter bloom of a rare betaproteobacterium in the Arctic Ocean. Front. Microbiol. 5, 425 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Hornung, C. et al. The Janthinobacterium sp. HH01 genome encodes a homologue of the V. cholerae CqsA and L. pneumophila LqsA autoinducer synthases. PLoS One 8, e55045 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Maillot, N. J., Honoré, F. A., Byrne, D., Méjean, V. & Genest, O. Cold adaptation in the environmental bacterium Shewanella oneidensis is controlled by a J-domain co-chaperone protein network. Commun. Biol. 2, 323 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Konings, W. N., Albers, S.-V., Koning, S. & Driessen, A. J. M. The cell membrane plays a crucial role in survival of bacteria and archaea in extreme environments. Antonie Van. Leeuwenhoek 81, 61–72 (2002).CAS 
Article 
PubMed 

Google Scholar 
Methé, B. A. et al. The psychrophilic lifestyle as revealed by the genome sequence of Colwellia psychrerythraea 34H through genomic and proteomic analyses. Proc. Natl Acad. Sci. U. S. A. 102, 10913–10918 (2005).ADS 
Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ayala-del-Río, H. L. et al. The genome sequence of Psychrobacter arcticus 273-4, a psychroactive Siberian permafrost bacterium, reveals mechanisms for adaptation to low-temperature growth. Appl. Environ. Microbiol. 76, 2304–2312 (2010).ADS 
Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Mykytczuk, N. C. S. et al. Bacterial growth at −15 °C; molecular insights from the permafrost bacterium Planococcus halocryophilus Or1. ISME J. 7, 1211–1226 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ting, L. et al. Cold adaptation in the marine bacterium, Sphingopyxis alaskensis, assessed using quantitative proteomics. Environ. Microbiol 12, 2658–2676 (2010).CAS 
PubMed 

Google Scholar 
Tribelli, P. M. et al. Novel essential role of ethanol oxidation genes at low temperature revealed by transcriptome analysis in the Antarctic bacterium Pseudomonas extremaustralis. PLoS One 10, e0145353 (2015).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Blagojevic, D. P., Grubor-Lajsic, G. N. & Spasic, M. B. Cold defence responses: the role of oxidative stress. Front. Biosci. 3, 416–427 (2011).Article 

Google Scholar 
Busi, S. B. et al. Optimised biomolecular extraction for metagenomic analysis of microbial biofilms from high-mountain streams. PeerJ 8, e9973 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Fodelianakis, S. et al. Microdiversity characterizes prevalent phylogenetic clades in the glacier-fed stream microbiome. ISME J. https://doi.org/10.1038/s41396-021-01106-6 (2021).Stoeck, T. et al. Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. Mol. Ecol. 19, 21–31 (2010).CAS 
Article 
PubMed 

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

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

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

Google Scholar 
Foster, Z. S. L., Sharpton, T. J. & Grünwald, N. J. Metacoder: an R package for visualization and manipulation of community taxonomic diversity data. PLoS Comput. Biol. 13, e1005404 (2017).ADS 
Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Burns, A. R. et al. Contribution of neutral processes to the assembly of gut microbial communities in the zebrafish over host development. ISME J. 10, 655–664 (2016).CAS 
Article 
PubMed 

Google Scholar 
Gautreau, I. E7805 NEBNext® UltraTM II FS DNA Library Prep Kit for Illumina® Protocol for use with Inputs ≤ 100 ng. https://www.protocols.io/view/e7805-nebnext-ultra-ii-fs-dna-library-prep-kit-for-k8tczwn (2020).Narayanasamy, S. et al. IMP: a pipeline for reproducible reference-independent integrated metagenomic and metatranscriptomic analyses. Genome Biol. 17, 260 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Heintz-Buschart, A. et al. Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes. Nat. Microbiol 2, 16180 (2016).CAS 
Article 
PubMed 

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 31, 1674–1676 (2015).CAS 
Article 
PubMed 

Google Scholar 
Kang, D. D. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, e7359 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Wu, Y.-W., Simmons, B. A. & Singer, S. W. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32, 605–607 (2016).CAS 
Article 
PubMed 

Google Scholar 
Hickl, O., Queirós, P., Wilmes, P., May, P. & Heintz-Buschart, A. binny: an automated binning algorithm to recover high-quality genomes from complex metagenomic datasets. bioRxiv 2021.12.22.473795 https://doi.org/10.1101/2021.12.22.473795. (2021).Sieber, C. M. K. et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat. Microbiol 3, 836–843 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
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).CAS 
Article 
PubMed 
PubMed Central 

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. https://doi.org/10.1093/bioinformatics/btz848 (2019).Kieft, K., Zhou, Z. & Anantharaman, K. VIBRANT: automated recovery, annotation and curation of microbial viruses, and evaluation of viral community function from genomic sequences. Microbiome 8, 90 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Zablocki, O., Jang, H. B., Bolduc, B. & Sullivan, M. B. vConTACT 2: A tool to automate genome-based prokaryotic viral taxonomy. in Plant and Animal Genome XXVII Conference (January 12-16, 2019) (PAG, 2019).Nayfach, S. et al. CheckV assesses the quality and completeness of metagenome-assembled viral genomes. Nat. Biotechnol. 39, 578–585 (2021).CAS 
Article 
PubMed 

Google Scholar 
Krinos, A. I., Hu, S. K., Cohen, N. R. & Alexander, H. EUKulele: Taxonomic annotation of the unsung eukaryotic microbes. arXiv [q-bio.PE] (2020).Queirós, P., Delogu, F., Hickl, O., May, P. & Wilmes, P. Mantis: flexible and consensus-driven genome annotation. bioRxiv (2020).Zhou, Z. et al. METABOLIC: High-throughput profiling of microbial genomes for functional traits, biogeochemistry, and community-scale metabolic networks. bioRxiv 761643. https://doi.org/10.1101/761643 (2020).McDaniel, E. A., Anantharaman, K. & McMahon, K. D. metabolisHMM: Phylogenomic analysis for exploration of microbial phylogenies and metabolic pathways. bioRxiv 2019.12.20.884627. https://doi.org/10.1101/2019.12.20.884627 (2019).Eren, A. M. et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ 3, e1319 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Weissman, J. L., Hou, S. & Fuhrman, J. A. Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns. Proc. Natl. Acad. Sci. U. S. A. 118, (2021).Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bushnell, B. BBMap: A fast, accurate, splice-aware aligner. https://www.osti.gov/biblio/1241166 (2014).Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol. 20, 257 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
West, P. T., Probst, A. J., Grigoriev, I. V., Thomas, B. C. & Banfield, J. F. Genome-reconstruction for eukaryotes from complex natural microbial communities. Genome Res. 28, 569–580 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Alneberg, J. et al. CONCOCT: Clustering cONtigs on COverage and ComposiTion. arXiv [q-bio.GN] (2013).Levy Karin, E., Mirdita, M. & Söding, J. MetaEuk-sensitive, high-throughput gene discovery, and annotation for large-scale eukaryotic metagenomics. Microbiome 8, 48 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Huerta-Cepas, J. et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res 47, D309–D314 (2019).CAS 
Article 
PubMed 

Google Scholar 
Seppey, M., Manni, M. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness. Methods Mol. Biol. 1962, 227–245 (2019).CAS 
Article 
PubMed 

Google Scholar 
Saary, P., Mitchell, A. L. & Finn, R. D. Estimating the quality of eukaryotic genomes recovered from metagenomic analysis with EukCC. Genome Biol. 21, 244 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).CAS 
Article 
PubMed 

Google Scholar 
Inferring Correlation Networks from Genomic Survey Data. https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002687.Kurtz, Z. D. et al. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput. Biol. 11, e1004226 (2015).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJournal Complex. Syst. 1695, 1–9 (2006).
Google Scholar 
Dormann, C. F., Frund, J., Bluthgen, N. & Gruber, B. Indices, graphs and null models: analyzing bipartite ecological networks. Open Ecol. J. 2, 7–24 (2009).Article 

Google Scholar 
Pruitt, K. D., Tatusova, T. & Maglott, D. R. NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res 35, D61–D65 (2007).CAS 
Article 
PubMed 

Google Scholar 
Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32, 1792–1797 (2004).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2-approximately maximum-likelihood trees for large alignments. PLoS One 5, e9490 (2010).ADS 
Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lee, M. D. GToTree: a user-friendly workflow for phylogenomics. Bioinformatics 35, 4162–4164 (2019).CAS 
Article 
PubMed 
PubMed Central 

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

Google Scholar 
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinforma. 11, 119 (2010).Article 
CAS 

Google Scholar 
Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tange, O. GNU Parallel 2018. (Lulu.com, 2018).Team, R. C. & Others. R: A language and environment for statistical computing. (2013).Kahle, D. & Wickham, H. Ggmap: spatial visualization with ggplot2. R. J. 5, 144 (2013).Article 

Google Scholar 
Graham, E. D., Heidelberg, J. F. & Tully, B. J. Potential for primary productivity in a globally-distributed bacterial phototroph. ISME J. 12, 1861–1866 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 1–21 (2014).kevinblighe/EnhancedVolcano: Publication-ready volcano plots with enhanced colouring and labeling. https://github.com/kevinblighe/EnhancedVolcano.Wickham, H. ggplot2: ggplot2. Wiley Interdiscip. Rev. Comput. Stat. 3, 180–185 (2011).MATH 
Article 

Google Scholar 
Bah, T. Inkscape: guide to a vector drawing program. (Prentice Hall Press, 2007).Varrette, S., Bouvry, P., Cartiaux, H. & Georgatos, F. Management of an academic HPC cluster: The UL experience. In 2014 International Conference on High Performance Computing Simulation (HPCS) 959–967 (2014). More

Blagodatskaya E, Kuzyakov Y. Active microorganisms in soil: critical review of estimation criteria and approaches. Soil Biol Biochem. 2013;67:192–211.CAS 
Article 

Google Scholar 
Jones SE, Lennon JT. Dormancy contributes to the maintenance of microbial diversity. Proc Natl Acad Sci USA. 2010;107:5881.CAS 
Article 

Google Scholar 
Lennon JT, Jones SE. Microbial seed banks: the ecological and evolutionary implications of dormancy. Nat Rev Microbiol. 2011;9:119–30.CAS 
Article 

Google Scholar 
Couradeau E, Sasse J, Goudeau D, Nath N, Hazen TC, Bowen BP, et al. Probing the active fraction of soil microbiomes using BONCAT-FACS. Nat Commun. 2019;10:2770.Article 

Google Scholar 
Carini P, Marsden PJ, Leff JW, Morgan EE, Strickland MS, Fierer N. Relic DNA is abundant in soil and obscures estimates of soil microbial diversity. Nat Microbiol. 2016;2:16242.Article 

Google Scholar 
Sorensen JW, Shade A. Dormancy dynamics and dispersal contribute to soil microbiome resilience. Philos Trans R Soc B Biolog Sci. 2020;375:20190255.CAS 
Article 

Google Scholar 
Raina J-B, Fernandez V, Lambert B, Stocker R, Seymour JR. The role of microbial motility and chemotaxis in symbiosis. Nat Rev Microbiol. 2019;17:284–94.CAS 
Article 

Google Scholar 
Bahram M, Hildebrand F, Forslund SK, Anderson JL, Soudzilovskaia NA, Bodegom PM, et al. Structure and function of the global topsoil microbiome. Nature. 2018;560:233–7.CAS 
Article 

Google Scholar 
Lauber Christian L, Hamady M, Knight R, Fierer N. Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl Environ Microbiol. 2009;75:5111–20.CAS 
Article 

Google Scholar 
Lennon JT, Aanderud ZT, Lehmkuhl BK, Schoolmaster DR Jr. Mapping the niche space of soil microorganisms using taxonomy and traits. Ecology. 2012;93:1867–79.Article 

Google Scholar 
Albright MBN, Martiny JBH. Dispersal alters bacterial diversity and composition in a natural community. ISME J. 2018;12:296–9.Article 

Google Scholar 
Barberan A, Ladau J, Leff JW, Pollard KS, Menninger HL, Dunn RR, et al. Continental-scale distributions of dust-associated bacteria and fungi. Proc Natl Acad Sci USA. 2015;112:5756–61.CAS 
Article 

Google Scholar 
Meyer KM, Memiaghe H, Korte L, Kenfack D, Alonso A, Bohannan BJM. Why do microbes exhibit weak biogeographic patterns? ISME J. 2018;12:1404–13.Article 

Google Scholar 
Eisenlord SD, Zak DR, Upchurch RA. Dispersal limitation and the assembly of soil Actinobacteria communities in a long-term chronosequence. Ecol Evol. 2012;2:538–49.Article 

Google Scholar 
Glassman SI, Lubetkin KC, Chung JA, Bruns TD. The theory of island biogeography applies to ectomycorrhizal fungi in subalpine tree “islands” at a fine scale. Ecosphere. 2017;8:e01677.Article 

Google Scholar 
Whitaker Rachel J, Grogan Dennis W, Taylor John W. Geographic barriers isolate endemic populations of hyperthermophilic archaea. Science. 2003;301:976–8.CAS 
Article 

Google Scholar 
Amor DR, Ratzke C, Gore J. Transient invaders can induce shifts between alternative stable states of microbial communities. Sci Adv. 2020;6:eaay8676.CAS 
Article 

Google Scholar 
Zhang C, Derrien M, Levenez F, Brazeilles R, Ballal SA, Kim J, et al. Ecological robustness of the gut microbiota in response to ingestion of transient food-borne microbes. ISME J. 2016;10:2235–45.Article 

Google Scholar 
Fox JE, Gulledge J, Engelhaupt E, Burow ME, McLachlan JA. Pesticides reduce symbiotic efficiency of nitrogen-fixing rhizobia and host plants. Proc Natl Acad Sci USA. 2007;104:10282.CAS 
Article 

Google Scholar 
Ramirez KS, Craine JM, Fierer N. Consistent effects of nitrogen amendments on soil microbial communities and processes across biomes. Glob Change Biol. 2012;18:1918–27.Article 

Google Scholar 
Leff JW, Jones SE, Prober SM, Barberán A, Borer ET, Firn JL, et al. Consistent responses of soil microbial communities to elevated nutrient inputs in grasslands across the globe. Proc Natl Acad Sci USA. 2015;112:10967.CAS 
Article 

Google Scholar 
Chambers CA, Smith SE, Smith FA. Effects of ammonium and nitrate ions on mycorrhizal infection, nodulation and growth of Trifolium subterraneum. New Phytol. 1980;85:47–62.CAS 
Article 

Google Scholar 
van Diepen LTA, Lilleskov EA, Pregitzer KS, Miller RM. Simulated nitrogen deposition causes a decline of intra- and extraradical abundance of arbuscular mycorrhizal fungi and changes in microbial community structure in northern hardwood forests. Ecosystems. 2010;13:683–95.Article 

Google Scholar 
Young IM, Ritz K. Tillage, habitat space and function of soil microbes. Soil Tillage Res. 2000;53:201–13.Article 

Google Scholar 
Kabir Z. Tillage or no-tillage: impact on mycorrhizae. Can J Plant Sci. 2005;85:23–29.Article 

Google Scholar 
Bell T, Tylianakis JM. Microbes in the anthropocene: spillover of agriculturally selected bacteria and their impact on natural ecosystems. Proc R Soc B Biol Sci. 2016;283;20160896.Article 

Google Scholar 
Dang K, Gong X, Zhao G, Wang H, Ivanistau A, Feng B, Intercropping alters the soil microbial diversity and community to facilitate nitrogen assimilation: a potential mechanism for increasing proso millet grain yield. Front Microbiol. 2020;11;601054.Peay KG, Garbelotto M, Bruns TD. Evidence of dispersal limitation in soil microorganisms: Isolation reduces species richness on mycorrhizal tree islands. Ecology. 2010;91:3631–40.Article 

Google Scholar 
Mummey DL, Rillig MC. Spatial characterization of arbuscular mycorrhizal fungal molecular diversity at the submetre scale in a temperate grassland. FEMS Microbiol Ecol. 2008;64:260–70.CAS 
Article 

Google Scholar 
Hiscox J, Savoury M, Müller CT, Lindahl BD, Rogers HJ, Boddy L. Priority effects during fungal community establishment in beech wood. ISME J. 2015;9:2246–60.Article 

Google Scholar 
Song Z, Kennedy PG, Liew FJ, Schilling JS. Fungal endophytes as priority colonizers initiating wood decomposition. Funct Ecol. 2017;31:407–18.Article 

Google Scholar 
Schmidt SK, Nemergut DR, Darcy JL, Lynch R. Do bacterial and fungal communities assemble differently during primary succession? Mol Ecol. 2014;23:254–8.CAS 
Article 

Google Scholar 
Reche I, D’Orta G, Mladenov N, Winget DM, Suttle CA. Deposition rates of viruses and bacteria above the atmospheric boundary layer. ISME J. 2018;12:1154–62.CAS 
Article 

Google Scholar 
Castaño C, Bonet JA, Oliva J, Farré G, Martínez de Aragón J, Parladé J, et al. Rainfall homogenizes while fruiting increases diversity of spore deposition in Mediterranean conditions. Fungal Ecol. 2019;41:279–88.Article 

Google Scholar 
Garbelotto M, Smith T, Schweigkofler W. Variation in rates of spore deposition of Fusarium circinatum, the causal agent of pine pitch canker, over a 12-month-period at two locations in Northern California. Phytopathology®. 2007;98:137–43.Article 

Google Scholar 
Bowers RM, Clements N, Emerson JB, Wiedinmyer C, Hannigan MP, Fierer N. Seasonal variability in bacterial and fungal diversity of the near-surface atmosphere. Environ Sci Technol. 2013;47:12097–106.CAS 
Article 

Google Scholar 
Trexler RV, Bell TH. Testing sustained soil-to-soil contact as an approach for limiting the abiotic influence of source soils during experimental microbiome transfer. FEMS Microbiol Lett. 2019;366:fnz228.CAS 
Article 

Google Scholar 
King, WL, Kaminsky LM, Gannett M, Thompson GL, Kao-Kniffin J, Bell TH. Soil salinization accelerates microbiome stabilization in iterative selections for plant performance. New Phytol. 2021. https://doi.org/10.1111/nph.17774. Advance online publication.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.CAS 
Article 

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

Google Scholar 
Gardes M, Bruns TD. ITS primers with enhanced specificity for basidiomycetes – application to the identification of mycorrhizae and rusts. Mol Ecol. 1993;2:113–8.CAS 
Article 

Google Scholar 
Martin KJ, Rygiewicz PT. Fungal-specific PCR primers developed for analysis of the ITS region of environmental DNA extracts. BMC Microbiol. 2005;5:28.Article 

Google Scholar 
Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852–7.CAS 
Article 

Google Scholar 
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
Article 

Google Scholar 
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–D596.CAS 
Article 

Google Scholar 
Kõljalg U, Larsson K-H, Abarenkov K, Nilsson RH, Alexander IJ, Eberhardt U, et al. UNITE: a database providing web-based methods for the molecular identification of ectomycorrhizal fungi. New Phytol. 2005;166:1063–8.Article 

Google Scholar 
R Core Team, R: a language and environment for statistical computing. R Foundation for Statistical Computing; 2012;Vienna;AustriaMcMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.CAS 
Article 

Google Scholar 
Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin P, O’Hara B, et al. Vegan: community ecology package. R Package Version 2.2-1. 2015;2:1–2. https://cran.r-project.org/web/packages/vegan/index.html.Ogle, DH, Wheeler P, Dinno A. FSA: fisheries stock analysis; 2020. https://cran.r-project.org/web/packages/FSA/index.htmlLahti L, Shetty S. Tools for microbiome analysis in R. Microbiome package. Bioconductor; 2017. https://microbiome.github.io/tutorials/Bell T. Experimental tests of the bacterial distance–decay relationship. ISME J. 2010;4:1357–65.Article 

Google Scholar 
Boynton PJ, Peterson CN, Pringle A. Superior dispersal ability can lead to persistent ecological dominance throughout succession. Appl Environ Microbiol. 2019;85:e02421–18.CAS 
Article 

Google Scholar 
Nemergut DR, Schmidt SK, Fukami T, O’Neill SP, Bilinski TM, Stanish LF, et al. Patterns and processes of microbial community assembly. Microbiol Mol Biol Rev. 2013;77:342–56.Article 

Google Scholar 
Dickie A, N IA, Reich PB. Ectomycorrhizal fungal communities at forest edges. J Ecol. 2005;93:244–55.Article 

Google Scholar 
Vannette, RL, McMunn MS, Hall GW, Mueller TG, Munkres I, Perry D. Fungi are more dispersal limited than bacteria among flowers. https://www.biorxiv.org/content/10.1101/2020.05.19.104968v2. 2021.Zhang G, Wei G, Wei F, Chen Z, He M, Jiao S, et al., Dispersal limitation plays stronger role in the community assembly of fungi relative to bacteria in rhizosphere across the arable area of medicinal plant. Front Microbiol. 2021;12;713523.Svoboda P, Lindström ES, Ahmed Osman O, Langenheder S. Dispersal timing determines the importance of priority effects in bacterial communities. ISME J. 2018;12:644–6.Article 

Google Scholar 
Fukami T, Dickie IA, Paula Wilkie J, Paulus BC, Park D, Roberts A, et al. Assembly history dictates ecosystem functioning: evidence from wood decomposer communities. Ecol Lett. 2010;13:675–84.Article 

Google Scholar 
Rousk J, Bååth E, Brookes PC, Lauber CL, Lozupone C, Caporaso JG, et al. Soil bacterial and fungal communities across a pH gradient in an arable soil. ISME J. 2010;4:1340–51.Article 

Google Scholar 
Hartmann M, Frey B, Mayer J, Mäder P, Widmer F. Distinct soil microbial diversity under long-term organic and conventional farming. ISME J. 2015;9:1177–94.Article 

Google Scholar 
Lori M, Symnaczik S, Mäder P, De Deyn G, Gattinger A. Organic farming enhances soil microbial abundance and activity—a meta-analysis and meta-regression. PLoS ONE. 2017;12:e0180442.Article 

Google Scholar 
Blundell R, Schmidt JE, Igwe A, Cheung AL, Vannette RL, Gaudin ACM, et al. Organic management promotes natural pest control through altered plant resistance to insects. Nat Plants. 2020;6:483–91.CAS 
Article 

Google Scholar 
Riedo, J, Wettstein FE, Rösch A, Herzog C, Banerjee S, Büchi L, et al. Widespread occurrence of pesticides in organically managed agricultural soils—the ghost of a conventional agricultural past? Environ Sci Technol. 2021;55;2919–2928.Lacerda-Júnior GV, Noronha MF, Cabral L, Delforno TP, de Sousa STP, Fernandes-Júnior PI, et al., Land use and seasonal effects on the soil microbiome of a Brazilian dry forest. Front Microbiol. 2019;10;648.Article 

Google Scholar  More

High Ambition Coalition for Nature and People. 50 Countries Announce Bold Commitment to Protect at Least 30% of the World’s Land and Ocean by 2030 (Campaign for Nature, 2021).Waldron A. et al. Protecting 30% of the Planet for Nature: Costs, Benefits and Economic Implications (Campaign for Nature, 2020).Geldmann, J., Manica, A., Burgess, N. D., Coad, L. & Balmford, A. A global-level assessment of the effectiveness of protected areas at resisting anthropogenic pressures. Proc. Natl Acad. Sci. USA 116, 23209–23223 (2019).CAS 
Article 

Google Scholar 
Nelson, A. & Chomitz, K. M. Protected Area Effectiveness in Reducing Tropical Deforestation (The World Bank, 2009).Scharlemann, J. P. W. et al. Securing tropical forest carbon: the contribution of protected areas to REDD. Oryx 44, 352–357 (2010).Article 

Google Scholar 
Feng, Y. et al. Assessing the effectiveness of global protected areas based on the difference in differences model. Ecol. Indic. 130, 108078 (2021).Article 

Google Scholar 
Laurance, W. F. et al. The fate of Amazonian forest fragments: A 32-year investigation. Biol. Conserv. 144, 56–67 (2011).Article 

Google Scholar 
Laurance, W. F. et al. Averting biodiversity collapse in tropical forest protected areas. Nature 489, 290–294 (2012).CAS 
Article 

Google Scholar 
Terraube, J., Van doninck, J., Helle, P. & Cabeza, M. Assessing the effectiveness of a national protected area network for carnivore conservation. Nat. Commun. 11, 2957 (2020).CAS 
Article 

Google Scholar 
Barnes, M. D. et al. Wildlife population trends in protected areas predicted by national socio-economic metrics and body size. Nat. Commun. 7, 12747 (2016).CAS 
Article 

Google Scholar 
Amano, T. et al. Successful conservation of global waterbird populations depends on effective governance. Nature 553, 199–202 (2018).CAS 
Article 

Google Scholar 
Kleijn, D., Cherkaoui, I., Goedhart, P. W., van der Hout, J. & Lammertsma, D. Waterbirds increase more rapidly in Ramsar-designated wetlands than in unprotected wetlands. J. Appl. Ecol. 51, 289–298 (2014).Article 

Google Scholar 
Reyes-Arriagada, R. et al. Population trends of a mixed-species colony of Humboldt and Magellanic Penguins in Southern Chile after establishing a protected area. Avian Conserv. Ecol. 8, 13 (2013).
Google Scholar 
Bukart, K. Motion 101 passes at IUCN, calls for protecting 50% of Earth’s lands and seas. One Earth https://www.oneearth.org/motion-101-passes-at-iucn-calls-for-protecting-50-of-earths-lands-and-seas/ (2021).Protected Planet Report 2020 (UNEP-WCMC and IUCN, 2021).Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES Secretariat, 2019).Barnes, M. D., Glew, L., Wyborn, C. & Craigie, I. D. Prevent perverse outcomes from global protected area policy. Nat, Ecol. Evol. 2, 759–762 (2018).Article 

Google Scholar 
Pressey, R. L., Cabeza, M., Watts, M. E., Cowling, R. M. & Wilson, K. A. Conservation planning in a changing world. Trends Ecol. Evol. 22, 583–592 (2007).Article 

Google Scholar 
Geldmann, J. et al. Effectiveness of terrestrial protected areas in reducing habitat loss and population declines. Biol. Conserv. 161, 230–238 (2013).Article 

Google Scholar 
Rodrigues, A. S. L. & Cazalis, V. The multifaceted challenge of evaluating protected area effectiveness. Nat. Commun. 11, 5147 (2020).CAS 
Article 

Google Scholar 
Redford, K. H. The empty forest. BioScience 42, 412–422 (1992).Article 

Google Scholar 
Ferraro, P. J. Counterfactual thinking and impact evaluation in environmental policy. N. Direct. Eval. 2009, 75–84 (2009).Article 

Google Scholar 
Adams, V. M., Barnes, M. & Pressey, R. L. Shortfalls in conservation evidence: moving from ecological effects of interventions to policy evaluation. One Earth 1, 62–75 (2019).Article 

Google Scholar 
Wauchope, H. S. et al. Evaluating impact using time-series data. Trends Ecol. Evol. 36, 196–205 (2021).Article 

Google Scholar 
Kingsford, R. T., Roshier, D. A. & Porter, J. L. Australian waterbirds time and space travellers in dynamic desert landscapes. Mar. Freshw. Res. 61, 875–884 (2010).CAS 
Article 

Google Scholar 
The Ramsar Convention Secretariat. Managing Ramsar Sites. ramsar.org https://www.ramsar.org/sites-countries/managing-ramsar-sites (2014).European Commission. The Birds Directive. https://ec.europa.eu/environment/nature/legislation/birdsdirective/index_en.htm (accessed 3 April 2022).Zhang, W., Sheldon, B. C., Grenyer, R. & Gaston, K. J. Habitat change and biased sampling influence estimation of diversity trends. Curr. Biol. 31, 3656–3662.e3 (2021).CAS 
Article 

Google Scholar 
Bruner, A. G., Gullison, R. E., Rice, R. E. & da Fonseca, G. A. B. Effectiveness of parks in protecting tropical biodiversity. Science 291, 125–128 (2001).CAS 
Article 

Google Scholar 
Carranza, T., Balmford, A., Kapos, V. & Manica, A. Protected area effectiveness in reducing conversion in a rapidly vanishing ecosystem: the Brazilian Cerrado. Conserv. Lett. 7, 216–223 (2014).Article 

Google Scholar 
Rabinowitz, D. In The Biological Aspects of Rare Plant Conservation (ed. Synge, H.) 205–217 (John Wiley & Sons, 1981).Daskalova, G. N., Myers-Smith, I. H. & Godlee, J. L. Rare and common vertebrates span a wide spectrum of population trends. Nat. Commun. 11, 4394 (2020).CAS 
Article 

Google Scholar 
Hettiarachchi, M., Morrison, T. H. & McAlpine, C. Forty-three years of Ramsar and urban wetlands. Glob. Environ. Change 32, 57–66 (2015).Article 

Google Scholar 
Munishi, P., Chuwa, J., Kilungu, H., Moe, S. & Temu, R. Management effectiveness and conservation initiatives in the Kilombero Valley Flood Plains Ramsar Site, Tanzania. Tanzania J. For. Nat. Conserv. 81, 1–10 (2012).
Google Scholar 
Fahrig, L. Why do several small patches hold more species than few large patches? Glob. Ecol. Biogeogr. 29, 615–628 (2020).Article 

Google Scholar 
Newmark, W. D. Extinction of mammal populations in western North American National Parks. Conserv. Biol. 9, 512–526 (1995).Article 

Google Scholar 
Mascia, M. B. & Pailler, S. Protected area downgrading, downsizing, and degazettement (PADDD) and its conservation implications. Conserv. Lett. 4, 9–20 (2011).Article 

Google Scholar 
Di Marco, M. et al. Changing trends and persisting biases in three decades of conservation science. Glob. Ecol. Conserv. 10, 32–42 (2017).Article 

Google Scholar 
Wetlands International. Asian Waterbird Census. https://south-asia.wetlands.org/our-approach/healthy-wetland-nature/asian-waterbird-census/ (accessed 3 April 2022).Gill, D. A. et al. Capacity shortfalls hinder the performance of marine protected areas globally. Nature 543, 665–669 (2017).CAS 
Article 

Google Scholar 
Geldmann, J. et al. A global analysis of management capacity and ecological outcomes in terrestrial protected areas. Conserv. Lett. 11, e12434 (2018).Article 

Google Scholar 
Kingsford, R. T., Bino, G. & Porter, J. L. Continental impacts of water development on waterbirds, contrasting two Australian river basins: global implications for sustainable water use. Glob. Change Biol. 23, 4958–4969 (2017).Article 

Google Scholar 
Jia, Q., Wang, X., Zhang, Y., Cao, L. & Fox, A. D. Drivers of waterbird communities and their declines on Yangtze River floodplain lakes. Biol. Conserv. 218, 240–246 (2018).Article 

Google Scholar 
Lehikoinen, A., Rintala, J., Lammi, E. & Pöysä, H. Habitat-specific population trajectories in boreal waterbirds: alarming trends and bioindicators for wetlands. Animal Conserv. 19, 88–95 (2016).Article 

Google Scholar 
Boyd, C. et al. Spatial scale and the conservation of threatened species. Conserv. Lett. 1, 37–43 (2008).Article 

Google Scholar 
Schleicher, J. et al. Protecting half of the planet could directly affect over one billion people. Nat. Sustain. 2, 1094–1096 (2019).Article 

Google Scholar 
Wauchope, H. et al. Quantifying the impact of protected areas on near-global waterbird population trends, a pre-analysis plan. Preprint at https://doi.org/10.7287/peerj.preprints.27741v2 (2019).Nosek, B. A., Ebersole, C. R., DeHaven, A. C. & Mellor, D. T. The preregistration revolution. Proc. Natl Acad. Sci. USA 115, 2600–2606 (2018).CAS 
Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).QGIS Geographic Information System (QGIS, 2021).Hadley Wickham. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S (Springer, 2002).The World Database on Protected Areas (WDPA)/The Global Database on Protected Areas Management Effectiveness (GD-PAME) www.protectedplanet.net (UNEP-WCMC and IUCN, 2019).Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHG) (NOAA, 2017).Coetzer, K. L., Witkowski, E. T. F. & Erasmus, B. F. N. Reviewing Biosphere Reserves globally: effective conservation action or bureaucratic label? Biol. Rev. 89, 82–104 (2014).Article 

Google Scholar 
Ament, J. M. & Cumming, G. S. Scale dependency in effectiveness, isolation, and social-ecological spillover of protected areas. Conserv. Biol. 30, 846–855 (2016).Article 

Google Scholar 
Naimi, B., Hamm, N. A. S., Groen, T. A., Skidmore, A. K. & Toxopeus, A. G. Where is positional uncertainty a problem for species distribution modelling? Ecography 37, 191–203 (2014).Article 

Google Scholar 
Salmerón Gómez, R., García, Pérez, J., López Martín, M. D. M. & García, C. G. Collinearity diagnostic applied in ridge estimation through the variance inflation factor. J. Appl. Stat. 43, 1831–1849 (2016).MathSciNet 
Article 

Google Scholar 
Gu, X. S. & Rosenbaum, P. R. Comparison of multivariate matching methods: structures, distances, and algorithms. J. Comput. Graph. Stat. 2, 405–420 (1993).
Google Scholar 
Stuart, E. A. Matching methods for causal inference: a review and a look forward. Stat. Sci. 25, 1–21 (2010).MathSciNet 
Article 

Google Scholar 
King, G. & Nielsen, R. Why propensity scores should not be used for matching. Pol. Anal. 27, 435–454 (2019).Article 

Google Scholar 
Rosenbaum, P. R. DOS: design of observational studies. https://cran.r-project.org/web/packages/DOS/index.html (2018).Linden, A. A matching framework to improve causal inference in interrupted time-series analysis. J. Eval. Clin. Pract. 24, 408–415 (2018).Article 

Google Scholar 
Simmons, B. I., Hoeppke, C. & Sutherland, W. J. Beware greedy algorithms. J. Anim. Ecol. 88, 804–807 (2019).Article 

Google Scholar 
Austin, P. C. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Stat. Med. 28, 3083–3107 (2009).MathSciNet 
Article 

Google Scholar 
Rubin, D. B. Using propensity scores to help design observational studies: application to the tobacco litigation. Health Serv. Outcomes Res. Methodol. 2, 169–188 (2001).Article 

Google Scholar 
Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage, 2019).Hartig, F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. https://cran.r-project.org/web/packages/DHARMa/index.html (2021).Zeileis, A. & Hothorn, T. Diagnostic checking in regression relationships. R News 2, 7–10 (2002).
Google Scholar 
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 48 (2015).Article 

Google Scholar 
Christensen, R. Ordinal–regression models for ordinal data. https://cran.r-project.org/web/packages/ordinal/index.html (2019).Lüdecke, D. ggeffects: tidy data frames of marginal effects from regression models. J. Op. Source Softw. 3, 772 (2018).Article 

Google Scholar 
McKay, M. D., Beckman, R. J. & Conover, W. J. Comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21, 239–245 (1979).MathSciNet 

Google Scholar 
Carnell, R. lhs: latin hypercube samples. https://cran.r-project.org/web/packages/lhs/index.html (2020).Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 Dataset. Int. J. Climatol. 34, 623–642 (2014).Article 

Google Scholar 
Lu, C. & Tian, H. Global nitrogen and phosphorus fertilizer use for agriculture production in the past half century: Shifted hot spots and nutrient imbalance. Earth Syst. Sci. Data 9, 181–192 (2017).Article 

Google Scholar 
Joppa, L. N. & Pfaff, A. High and far: biases in the location of protected areas. PLoS ONE 4, e8273 (2009).Article 

Google Scholar 
Hurtt, G. C. et al. Harmonization of land-use scenarios for the period 1500-2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Clim. Change 109, 117–161 (2011).Article 

Google Scholar 
Lloyd, C. T., Sorichetta, A. & Tatem, A. J. High resolution global gridded data for use in population studies. Sci. Data 4, 17001 (2017).Article 

Google Scholar 
Pekel, J.-F., Cottam, A., Gorelick, N. & Belward, A. S. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016).CAS 
Article 

Google Scholar 
Sandvik, B. World Borders Dataset. Thematic Mapping http://thematicmapping.org/downloads/world_borders.php (2009).BirdLife International. Species Distribution Data Download http://www.birdlife.org/datazone/info/spcdownload (accessed 25 February 2020).Wilman, H. et al. EltonTraits 1.0: species-level foraging attributes of the world’s birds and mammals. Ecology 95, 2027 (2014).Article 

Google Scholar 
WWF International. Management Effectiveness Tracking Tool https://wwfeu.awsassets.panda.org/downloads/mett2_final_version_july_2007.pdf (2007). More

Lister, B. C. & Garcia, A. Climate-driven declines in arthropod abundance restructure a rainforest food web. Proc. Natl Acad. Sci. USA 115, E10397–E10406 (2018).CAS 
Article 

Google Scholar 
Soroye, P., Newbold, T. & Kerr, J. Climate change contributes to widespread declines among bumble bees across continents. Science 367, 685–688 (2020).CAS 
Article 

Google Scholar 
Outhwaite, C. L., Gregory, R. D., Chandler, R. E., Collen, B. & Isaac, N. J. B. Complex long-term biodiversity change among invertebrates, bryophytes and lichens. Nat. Ecol. Evol. 4, 384–392 (2020).Article 

Google Scholar 
Crossley, M. S. et al. No net insect abundance and diversity declines across US long term ecological research sites. Nat. Ecol. Evol. 4, 1368–1376 (2020).Article 

Google Scholar 
Janzen, D. H. & Hallwachs, W. To us insectometers, it is clear that insect decline in our Costa Rican tropics is real, so let’s be kind to the survivors. Proc. Natl Acad. Sci. USA 118, e2002546117 (2021).CAS 
Article 

Google Scholar 
Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).CAS 
Article 

Google Scholar 
Spooner, F. E. B., Pearson, R. G. & Freeman, R. Rapid warming is associated with population decline among terrestrial birds and mammals globally. Glob. Change Biol. 24, 4521–4531 (2018).Article 

Google Scholar 
Powney, G. D. et al. Widespread losses of pollinating insects in Britain. Nat. Commun. 10, 1018 (2019).Article 

Google Scholar 
van Klink, R. et al. Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances. Science 368, 417–420 (2020).Article 

Google Scholar 
Yang, L. H. & Gratton, C. Insects as drivers of ecosystem processes. Curr. Opin. Insect Sci. 2, 26–32 (2014).Article 

Google Scholar 
Dainese, M. et al. A global synthesis reveals biodiversity-mediated benefits for crop production. Sci. Adv. 5, eaax0121 (2019).Article 

Google Scholar 
Halsch, C. A. et al. Insects and recent climate change. Proc. Natl. Acad. Sci. USA 118, e2002543117 (2021).CAS 
Article 

Google Scholar 
Wagner, D. L., Fox, R., Salcido, D. M. & Dyer, L. A. A window to the world of global insect declines: moth biodiversity trends are complex and heterogeneous. Proc. Natl Acad. Sci. USA 118, e2002549117 (2021).CAS 
Article 

Google Scholar 
Maxwell, S. L., Fuller, R. A., Brooks, T. M. & Watson, J. E. M. Biodiversity: the ravages of guns, nets and bulldozers. Nature 536, 143–145 (2016).CAS 
Article 

Google Scholar 
Kerr, J. T. et al. Climate change impacts on bumblebees converge across continents. Science 349, 177–180 (2015).CAS 
Article 

Google Scholar 
Uhler, J. et al. Relationship of insect biomass and richness with land use along a climate gradient. Nat. Commun. 12, 5946 (2021).CAS 
Article 

Google Scholar 
Oliver, T. H. & Morecroft, M. D. Interactions between climate change and land use change on biodiversity: attribution problems, risks, and opportunities. Wiley Interdiscip. Rev. Clim. Change 5, 317–335 (2014).Article 

Google Scholar 
Williams, J. J. & Newbold, T. Local climatic changes affect biodiversity responses to land use: a review. Divers. Distrib. 26, 76–92 (2020).Article 

Google Scholar 
González del Pliego, P. et al. Thermally buffered microhabitats recovery in tropical secondary forests following land abandonment. Biol. Conserv. 201, 385–395 (2016).Article 

Google Scholar 
Senior, R. A., Hill, J. K., González del Pliego, P., Goode, L. K. & Edwards, D. P. A pantropical analysis of the impacts of forest degradation and conversion on local temperature. Ecol. Evol. 7, 7897–7908 (2017).Article 

Google Scholar 
Peters, M. K. et al. Climate–land-use interactions shape tropical mountain biodiversity and ecosystem functions. Nature 568, 88–92 (2019).CAS 
Article 

Google Scholar 
Mantyka-Pringle, C. S., Martin, T. G. & Rhodes, J. R. Interactions between climate and habitat loss effects on biodiversity: a systematic review and meta-analysis. Glob. Change Biol. 18, 1239–1252 (2012).Article 

Google Scholar 
Northrup, J. M., Rivers, J. W., Yang, Z. & Betts, M. G. Synergistic effects of climate and land-use change influence broad-scale avian population declines. Glob. Change Biol. 25, 1561–1575 (2019).Article 

Google Scholar 
Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. Proc. Natl Acad. Sci. USA 105, 6668–6672 (2008).CAS 
Article 

Google Scholar 
Newbold, T., Oppenheimer, P., Etard, A. & Williams, J. J. Tropical and Mediterranean biodiversity is disproportionately sensitive to land-use and climate change. Nat. Ecol. Evol. 4, 1630–1638 (2020).Article 

Google Scholar 
Perez, T. M., Stroud, J. T. & Feeley, K. J. Thermal trouble in the tropics. Science 351, 1392–1393 (2016).CAS 
Article 

Google Scholar 
Betts, M. G., Phalan, B., Frey, S. J. K., Rousseau, J. S. & Yang, Z. Old-growth forests buffer climate-sensitive bird populations from warming. Divers. Distrib. 24, 439–447 (2018).Article 

Google Scholar 
Hendershot, J. N. et al. Intensive farming drives long-term shifts in avian community composition. Nature 579, 393–396 (2020).CAS 
Article 

Google Scholar 
Tscharntke, T. et al. Landscape moderation of biodiversity patterns and processes – eight hypotheses. Biol. Rev. 87, 661–685 (2012).Article 

Google Scholar 
Suggitt, A. J. et al. Extinction risk from climate change is reduced by microclimatic buffering. Nat. Clim. Change 8, 713–717 (2018).Article 

Google Scholar 
Hudson, L. N. et al. The database of the PREDICTS (Projecting Responses of Ecological Diversity in Changing Terrestrial Systems) project. Ecol. Evol. 7, 145–188 (2017).Article 

Google Scholar 
Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7, 109 (2020).Article 

Google Scholar 
Johansson, F., Orizaola, G. & Nilsson-Örtman, V. Temperate insects with narrow seasonal activity periods can be as vulnerable to climate change as tropical insect species. Sci. Rep. 10, 8822 (2020).CAS 
Article 

Google Scholar 
Hoskins, A. J. et al. Downscaling land-use data to provide global 30′′ estimates of five land-use classes. Ecol. Evol. 6, 3040–3055 (2016).Article 

Google Scholar 
Grab, H. et al. Agriculturally dominated landscapes reduce bee phylogenetic diversity and pollination services. Science 363, 282–284 (2019).CAS 
Article 

Google Scholar 
Rusch, A. et al. Agricultural landscape simplification reduces natural pest control: a quantitative synthesis. Agric. Ecosyst. Environ. 221, 198–204 (2016).Article 

Google Scholar 
Oliver, T. H. et al. Declining resilience of ecosystem functions under biodiversity loss. Nat. Commun. 6, 10122 (2015).Article 

Google Scholar 
Sunday, J. M. et al. Thermal-safety margins and the necessity of thermoregulatory behavior across latitude and elevation. Proc. Natl Acad. Sci. USA. 111, 5610–5615 (2014).CAS 
Article 

Google Scholar 
Balmford, A. Extinction filters and current resilience: the significance of past selection pressures for conservation biology. Trends Ecol. Evol. 11, 193–196 (1996).CAS 
Article 

Google Scholar 
Garibaldi, L. A. et al. Stability of pollination services decreases with isolation from natural areas despite honey bee visits. Ecol. Lett. 14, 1062–1072 (2011).Article 

Google Scholar 
Carvalheiro, L. G., Seymour, C. L., Veldtman, R. & Nicolson, S. W. Pollination services decline with distance from natural habitat even in biodiversity-rich areas. J. Appl. Ecol. 47, 810–820 (2010).Article 

Google Scholar 
Dainese, M., Luna, D. I., Sitzia, T. & Marini, L. Testing scale-dependent effects of seminatural habitats on farmland biodiversity. Ecol. Appl. 25, 1681–1690 (2015).Article 

Google Scholar 
Fourcade, Y. et al. Habitat amount and distribution modify community dynamics under climate change. Ecol. Lett. 24, 950–957 (2021).Article 

Google Scholar 
Alexander, L. V. et al. Global observed changes in daily climate extremes of temperature and precipitation. J. Geophys. Res. 111, D05109 (2006).
Google Scholar 
Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574, 671–674 (2019).CAS 
Article 

Google Scholar 
De Palma, A. et al. Dimensions of biodiversity loss: spatial mismatch in land-use impacts on species, functional and phylogenetic diversity of European bees. Divers. Distrib. 23, 1435–1446 (2017).Article 

Google Scholar 
Collen, B., Ram, M., Zamin, T. & McRae, L. The tropical biodiversity data gap: addressing disparity in global monitoring. Trop. Conserv. Sci. 1, 75–88 (2008).Article 

Google Scholar 
Menéndez, R. How are insects responding to global warming? Tijdschr. Entomol. 150, 355 (2007).
Google Scholar 
Bale, J. S. et al. Herbivory in global climate change research: direct effects of rising temperature on insect herbivores. Glob. Change Biol. 8, 1–16 (2002).Article 

Google Scholar 
Hudson, L. N. et al. The 2016 release of the PREDICTS database. Natural History Museum Data Portal, https://doi.org/10.5519/0066354 (2016).Hudson, L. N. et al. The PREDICTS database: a global database of how local terrestrial biodiversity responds to human impacts. Ecol. Evol. 4, 4701–4735 (2014).Article 

Google Scholar 
De Palma, A. et al. Annual changes in the Biodiversity Intactness Index in tropical and subtropical forest biomes, 2001–2012. Sci. Rep. 11, 20249 (2021).Article 

Google Scholar 
Chao, A., Chazdon, R. L., Colwell, R. K. & Shen, T.-J. A new statistical approach for assessing similarity of species composition with incidence and abundance data. Ecol. Lett. 8, 148–159 (2005).Article 

Google Scholar 
Ndakidemi, B., Mtei, K. & Ndakidemi, P. A. Impacts of synthetic and botanical pesticides on beneficial insects. Agric. Sci. 07, 364–372 (2016).CAS 

Google Scholar 
Wang, X., Hua, F., Wang, L., Wilcove, D. S. & Yu, D. W. The biodiversity benefit of native forests and mixed‐species plantations over monoculture plantations. Divers. Distrib. 25, 1721–1735 (2019).Article 

Google Scholar 
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 
Warszawski, L. et al. The inter-sectoral impact model intercomparison project (ISI-MIP): project framework. Proc. Natl Acad. Sci. USA 111, 3228–3232 (2014).CAS 
Article 

Google Scholar 
van Vuuren, D. P. et al. The representative concentration pathways: an overview. Clim. Change 109, 5–31 (2011).Article 

Google Scholar 
New, M., Hulme, M. & Jones, P. Representing twentieth-century space–time climate variability. Part I: development of a 1961–90 mean monthly terrestrial climatology. J. Clim. 12, 829–856 (1999).Article 

Google Scholar 
Hijmans, R. J. Raster: Geographic data analysis and modelling. R package version 2.8-42018, https://CRAN.R-project.org/package=raster (2018).Rigby, R. A., Stasinopoulos, D. M. & Akantziliotou, C. A framework for modelling overdispersed count data, including the Poisson-shifted generalized inverse Gaussian distribution. Comput. Stat. Data Anal. 53, 381–393 (2008).MathSciNet 
Article 

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

Google Scholar  More

To help to achieve ecological recovery worldwide, more multinational corporations are making commitments to biodiversity conservation1–3. According to the most recent assessment in 2018, 31 of the 100 largest companies by revenue worldwide (the global Fortune 100) have done so, from the retail corporation Walmart to the insurance company AXA4.To deliver real gains — in the population sizes of endangered species, say, or in the number of hectares of restored forests, grasslands or wetlands — large organizations need to determine which of their activities have the greatest impacts on biodiversity5. And they need to disclose and mitigate those impacts. Currently, methods for doing this are lacking (see ‘Promises are hard to keep’). (By large organizations, we mean formal entities composed of hundreds of people or more that act towards a certain purpose, whether in the public, private or non-profit sectors.)
Promises are hard to keep

A lack of consensus on methods and metrics means companies are struggling to clearly define — and deliver on — commitments relating to biodiversity.
So far, most studies of the environmental impacts of organizations, such as multinational corporations and universities, have focused on greenhouse-gas emissions.
The G7 group of the world’s largest economies endorsed the new Taskforce on Nature-related Financial Disclosures (TNFD) only last year. This builds on a similar approach used for climate change — the Taskforce on Climate-related Financial Disclosures. The TNFD aims to guide organizations on how to disclose environmental harms tied to their activities, but is still being developed.
The number of corporations making commitments to achieve ‘net gain’ or ‘no net loss’ outcomes in relation to biodiversity has risen steadily in the past two decades3. But some of these promises have subsequently been retracted. In 2016, for example, the mining corporation Rio Tinto moved away from its 2006 agenda-setting ‘net positive impact’ biodiversity commitment, reportedly to focus on minimizing impacts3 (see also go.nature.com/3xtjggo).
Many other commitments are not quantitative. As of 2018, only 5 of the 31 global Fortune 100 companies making biodiversity-related commitments had provided ones that were SMART — specific, measurable, ambitious, realistic and time-bound4 (the global Fortune 100 is an annual list of the 100 largest firms worldwide by revenue, as ranked by Fortune magazine).
When quantitative analyses have been done, they tend to be of limited use, mainly because of inconsistencies in the biodiversity metrics used, and limitations in the scope of the assessment made. Disclosure of results is also limited.

When quantitative analyses have been done, a variety of metrics have been used to quantify impacts. These range from the proportion of local species that would be lost as a result of an activity, to factors such as hectares of habitat affected, or the amount of sustainably sourced paper, fish or palm oil that is used4. But the choice of metric can radically alter the results of an impact assessment, so it is difficult to compare organizations. Likewise, few analyses consider the impacts of activities that are not under the direct control of the organization, such as those associated with supply chains6.As a proof of principle, we conducted a comprehensive assessment of biodiversity losses associated with activities at the University of Oxford, UK. We used data on purchasing, travel bookings, utility bills and other information from the 2018–19 and 2019–20 academic years. The 60 activities we assessed included the day-to-day running of buildings and transport services; travel (including flights) for students and researchers; construction of laboratories and other buildings; consumption of food and beverages at restaurants and cafeterias; and use of medical supplies and other materials in research labs.Our aim was to demonstrate what it would take for a large organization such as the University of Oxford to bring about a net gain in biodiversity — meaning that, thanks to its actions, the world’s biodiversity is left in a better state than it was before. As part of our analysis, we assessed how the university’s various activities and operations also affect greenhouse-gas emissions, and how those, in turn, affect biodiversity by driving climate change.We are confident that the approach we’ve developed for Oxford could be applied more broadly. Indeed, we hope that such a well-known institution disclosing a full assessment of its biodiversity footprint will offer powerful inspiration for others. (All seven of us have a current or previous affiliation with the university.)What we didThe University of Oxford launched an ambitious environmental sustainability strategy in March 2021. Its two main goals are to achieve biodiversity net gain and net-zero carbon, both by 2035. (The latter means that the university will remove as much carbon from the atmosphere as it adds.)To understand how challenging these goals might be to fulfil, we assessed the environmental impacts of the university’s various activities. This covered all those to do with research, education and operations during an academic year for staff and students (see ‘Upstream effects’). For our purposes, operations includes the university transport fleet, consumption of departmental food and utilities, waste disposal and the operational supply chain, including for paper.

Source: J. W. Bull et al.

As a first step, we defined a conceptual framework to systematically categorize the environmental impacts. We grouped activities in research, education and operations according to whether they involved any of five features: travel; food; the built environment (university buildings); the natural environment (any green space or land owned by the university, including managed parks and gardens); and resource use and waste (see ‘What we left out’). Each of these is associated with five general environmental impacts: greenhouse-gas emissions, the use of land and water, and pollution of water and air.
What we left out

Other organizations could assess different types of impact on biodiversity.
Our biodiversity analysis of the University of Oxford, UK, included most upstream impacts — those resulting from consumption of goods and services created outside the university, such as food or medical supplies. We excluded the downstream impacts of research and education, such as those of a discovery in gene editing or chemistry, because it would be impossible to comprehensively account for all of the environmental impacts of knowledge generation. Also not included in our analysis were the university’s 39 colleges, 6 permanent private halls and more than 260 commercial buildings. These are independent legal entities that manage sustainability issues separately.
Other analyses in different sectors might well be able to include downstream impacts. The effects of discarded plastic bottles or clothes could be included for a soft-drinks company or clothing manufacturer, for example.

To further categorize the environmental impacts, we assigned each activity to one of two groups: those under direct university control or influence (through staff and key contractors), and those that the university can influence only indirectly (through students and supply chains). We deemed students buying tuna sandwiches from a university-owned cafe as direct control, for instance, because the university could decide to serve only vegetarian food. However, it can influence only indirectly what happens up the supply chain, before materials are used in a research lab, for example.Using this organizational framework, we worked with administrators to obtain the relevant information, such as travel bookings for staff and students, electricity and water bills, and purchasing records for goods, services and materials used in construction projects.Next, we used various tools to convert all the activities data into estimates of ‘mid-point environmental impacts’ (amount of carbon dioxide emitted, land or water used, and air or water pollutants produced). The database Exiobase 3 is one of the most extensive sources of international supply-chain impacts worldwide7. It shows, for instance, that the roughly US$3.5 million the university spent on paper and paper products in 2019–20 contributed to atmospheric acidification by releasing 2,448 kilograms of sulfur dioxide equivalent. Similarly, the UK Higher Education Supply Chain Emissions Tool uses spending data on goods and services to estimate greenhouse-gas emissions. The roughly $23 million Oxford spent on personal computers, printers and calculators in 2019–20, for example, produced an estimated 20,105 tonnes of CO2 equivalent.We then needed to estimate the extent of biodiversity loss associated with this wide range of broad environmental impacts. So we converted the mid-point environmental impacts into ‘end-point impacts’ that are specifically concerned with biodiversity. To do this, we used an established conversion methodology called ReCiPe8. The output metric ultimately linked to each activity is based on the proportion of local species that would be lost as a result of that activity, relative to the number that exists currently (see Supplementary information for all results and conversion factors).CaveatsWe refined our methods slightly when analysing data from the 2019–20 academic year. This, combined with the disruption caused by the COVID-19 pandemic, makes it difficult to compare years. So for simplicity, we report our results only from the 2019–20 academic year.The biodiversity metric we obtain using ReCiPe is based on strong evidence: the conversion tool is derived from the results of hundreds of studies of the impacts of human pressures on biodiversity8. But, in general, we weren’t able to factor in fine-level variables, such as whether the beef steaks in a university-owned restaurant are sourced from a UK or Brazilian farm. As such, our approach is best seen as a way to evaluate relative impacts, rather than as an indicator of precise absolute impacts.This difficulty aside, it is hard to compare the impact of the University of Oxford on biodiversity with that of similarly sized organizations. As yet, and as far as we know, no other organization has comprehensively evaluated and disclosed its impact on biodiversity, and then had its assessment independently validated. (Ecologists and other stakeholders at the University of Jyväskylä in Finland have begun to explore the impacts of that university’s activities on biodiversity using a similar approach to ours.)Using the greenhouse-gas metric, however, we can compare the impacts of the University of Oxford on emissions (which are related to its impacts on biodiversity) with those of comparably sized organizations.What we foundThe absolute size of the university’s greenhouse-gas footprint is astonishingly large — comparable to that of the eastern Caribbean island nation of Saint Lucia. It is two orders of magnitude smaller than Microsoft’s greenhouse-gas footprint, but one order of magnitude larger than that of the London Stock Exchange, according to estimates publicly disclosed by those organizations.Perhaps the most striking finding in our assessment of impacts specifically on biodiversity is that most of the harms are tied to university activities that are not under its direct control. In fact, the activities with the five biggest impacts on biodiversity are (from biggest to smallest): the supply chain for research activities (such as for chemicals, medical products, organic tissue and plastics); the supply chain for the day-to-day running of buildings (for paper, information technology and so on); food consumption; electricity consumption; and the supply chain for construction. All of these activities are associated with resource use and waste, food and the built environment.

The University of Oxford’s use of laboratory materials has a large impact on biodiversity because of the upstream supply chain.Credit: Peter Nicholls/Reuters

In short, supplies of lab equipment have much greater impacts on biodiversity overall than do international flights, the university’s consumption of electricity or its use of construction materials. (Personal protective equipment used in the lab, for example, requires the extraction and industrial processing of hydrocarbons, often from areas that are rich in biodiversity.)This observation is in line with the results of a handful of studies that suggest that supply chains, not transport or the day-to-day running of buildings, are the main contributors to greenhouse-gas emissions for universities (see, for example, ref. 9). It also aligns with the results of assessments by the fashion giant Kering since 2012, using its Environmental Profit & Loss account — a tool designed to quantify the environmental impacts of the company’s activities. These have revealed that Kering’s procurements of commodities, such as leather, wool and metals, have much more impact on greenhouse-gas emissions, particularly on those from land use, than does the day-to-day running of its factories and offices10.Yet the sustainability strategies of large organizations typically focus not on supply chains, but on recycling, reducing the number of flights people take or the amount of electricity used11–13 (see also Nature 546, 565–567; 2017).Another important finding is the scale of intervention needed. Restoring the university’s owned land (around 1,000 hectares) to native woodland or some other natural habitat would make little difference when it comes to compensating for the impacts on biodiversity that result from just one year of activity. The university colleges own much more land than the university itself — some 50,000 hectares — but we excluded them from our analysis because they are independent legal entities that manage sustainability issues separately.Biodiversity boostHow could the university reverse the biodiversity losses stemming from its activities and operations?Here we consider three options. It could pursue its current environmental sustainability strategy. This entails (among other steps) setting quantitative targets to reduce flights, limiting consumption of all single-use products, making university-catered food vegetarian by default, and achieving 20% net gain for biodiversity in new construction projects. Alternatively, it could focus more heavily on preventing harms to biodiversity. We model a scenario in which all staff flights are prevented, all use of paper and any further construction is stopped, and the purchasing of lab materials is halved. Or the university could focus on compensating for the impacts that its activities and operations have on the planet, by taking steps to increase biodiversity in other places (see ‘Oxford’s options’).

Source: J. W. Bull et al.

Using the 2018–19 academic year results (selected because the COVID-19 pandemic made 2019–20 so unusual), we estimated how far these mitigation strategies might take the university towards biodiversity net gain.Our analysis indicates that the set of preventive measures proposed under the university’s environmental sustainability strategy get it about one-third of the way towards net gain. The findings also indicate that focusing mainly on the prevention of impacts is operationally unfeasible. Activities that have most effect on biodiversity, such as purchasing lab consumables, are central to the university’s existence and cannot simply stop.To achieve net gain, preventive measures, such as reducing flights and paper use, will have to be accompanied by additional and extensive actions to compensate for the remaining impacts on biodiversity.Such actions could include investing in reforestation, wetland restoration, sustainable land-management programmes and prevention of habitat loss caused by independent parties. For example, those directing the Oyu Tolgoi mining project in Mongolia are seeking to achieve biodiversity net gain by spending around 0.6% of the total project cost on actions that benefit biodiversity, such as sustainable grazing practices (see go.nature.com/3tkkbjh). Similarly, the Ambatovy metals mine in Madagascar is on course to offset its impacts on biodiverse eastern rainforests by preventing deforestation of those same habitats through small-scale agriculture14.Achieving true biodiversity net gain will require substantial offsetting that does not necessarily contribute to the university’s reductions in greenhouse-gas emissions. But whatever mix of approaches the institution pursues, it should strive for win–wins on both biodiversity and climate.Many types of action can simultaneously increase biodiversity and reduce greenhouse-gas emissions. For example, restoring mangroves in Bangladesh increased populations of wintering water birds 20-fold in just three years from 2004. And these restored mangroves can absorb carbon four times faster than land-based forests can15. But in other cases, there are trade-offs. Constructing wind turbines and solar photovoltaics to produce renewable energy, for instance, requires extensive mining of metals in places that can be rich in biodiversity16.Net gain for other organizationsOur calculations are likely to be comparable to results for other universities. In our analysis, we do not include the impacts of individual colleges. But because similar kinds of activity occur in colleges as in the rest of the university, their inclusion — or of halls of residence at other universities — is unlikely to qualitatively change our main findings. In fact, because of the colleges’ unusually large land holdings, including them would arguably result in an assessment that doesn’t so easily compare with those of other universities.Crucially, however, the analytical framework we have developed can be applied to a wide range of large organizations — whether they be universities, multinational corporations or government institutions.

Restoring mangroves in western Bangladesh increased populations of wintering water birds, such as this oriental darter (Anhinga melanogaster).Credit: Muhammad Mostafigur Rahman/Alamy

Governments, intergovernmental organizations and multinational corporations are increasingly recognizing that it will not be enough to simply slow the loss of the world’s biodiversity. Damaged habitats and depleted natural resources must be restored to prevent the collapse of ecosystems.Last year, the United Nations called for the urgent revival of nature in farmlands, forests and other ecosystems, declaring 2021–30 to be the Decade on Ecosystem Restoration. Later this year, at a meeting in Kunming, China, it is hoped that 196 nations will agree to the Post-2020 Global Biodiversity Framework of the Convention on Biological Diversity. Among the goals listed in the draft document are a “net gain in the area, connectivity and integrity of natural systems of at least 5 per cent”17.We urge all large organizations, academic or otherwise, to commit to strategies for a net gain in biodiversity — and to adopt formalized approaches that quantify current impacts and allow transparent tracking of progress. Otherwise, the degree of worldwide recovery of natural resources increasingly recognized as crucial for resilient societies to function will not happen.A key challenge is the lack of traceability for commodities. Both our assessment of the University of Oxford and those of others have revealed that large organizations often don’t know which country their commodities (such as cotton, flour or cement) come from — let alone which supplier or what kinds of biodiversity are being affected as a result.According to its 2022 report, for example, even a sector leader such as Kering could trace the source of only about three-quarters of its cotton. Supply chains for other commodities, such as sand, are even more opaque18.Encouragingly, various initiatives are being developed to provide more transparency about environmental impacts across supply chains. These include the supply-chain mapping tool TRASE, which aims to address deforestation.A related challenge, covered extensively elsewhere19,20, is how to ensure that biodiversity offsets are effectively and appropriately implemented such that they lead to conservation outcomes that are truly additional.Currently, there are uncertainties around how long it takes for a restored forest to start delivering biodiversity gains, whether promises to offset harms to biodiversity are actually met, what level of biodiversity gain is delivered by the restoration of a particular habitat, and so on. Take the Ambatovy mine in Madagascar. Its directors began protecting areas of eastern rainforest in 2009 to offset the impacts of deforestation directly caused by the mine. Yet forest gains are not estimated to balance losses until sometime between 2018 and 203314.Despite such challenges, however, we think that a commitment to full transparency, and to improving data collection over time, will enable organizations to compare performance and drive change — both in their own operations and throughout supply chains.Time is too short to let the perfect be the enemy of the good, or to claim that biodiversity net gain is too hard to achieve because there is no universal biodiversity metric. Individual metrics are imperfect but improving, and their limitations should not be a reason to delay measuring, disclosing and tackling impacts on biodiversity. More