Ecology

Stearns, S. C. The Evolution of Life Histories (Oxford University, 1992).
Google Scholar 
Roff, D. A., Mostowy, S. & Fairbairn, D. J. The evolution of trade-offs: Testing predictions on response to selection and environmental variation. Evolution (N. Y.). 56, 84–95 (2002).
Google Scholar 
Lack, D. The Significance of Clutch-size. Ibis (Lond. 1859). 89, 302–352 (1947).Article 

Google Scholar 
Drent, R. H. & Daan, S. The prudent parent: Energetic adjustments in avian breeding. Adrea 68, 225–263 (1980).
Google Scholar 
Harshman, L. G. & Zera, A. J. The cost of reproduction: The devil in the details. Trends Ecol. Evol. 22, 80–86 (2007).Article 

Google Scholar 
Dijkstra, C., Daan, S. & Tinbergen, J. M. Family planning in the Kestrel (Falco Tinnunculus): The ultimate control of covariation of laying date and clutch size. Behaviour 114, 83–116 (1990).Article 

Google Scholar 
Cox, R. M. et al. Experimental evidence for physiological costs underlying the trade-off between reproduction and survival. Funct. Ecol. 24, 1262–1269 (2010).Article 

Google Scholar 
Marshall, K. E. & Sinclair, B. J. Repeated stress exposure results in a survival-reproduction trade-off in Drosophila melanogaster. Proc. R. Soc. B Biol. Sci. 277, 963–969 (2010).Article 

Google Scholar 
Rivalan, P. et al. Trade-off between current reproductive effort and delay to next reproduction in the leatherback sea turtle. Oecologia 145, 564–574 (2005).ADS 
Article 

Google Scholar 
Perrins, C. M. Population Fluctuations and Clutch-Size in the Great Tit, Parus major. J. Anim. Ecol. 34, 601 (1965).Article 

Google Scholar 
Walker, R. S., Gurven, M., Burger, O. & Hamilton, M. J. The trade-off between number and size of offspring in humans and other primates. Proc. R. Soc. B Biol. Sci. 275, 827–833 (2008).Article 

Google Scholar 
Williams, G. C. Natural selection, the costs of reproduction, and a refinement of Lack’s principle. Am. Nat. 100, 687–690 (1966).Article 

Google Scholar 
Charnov, E. L. & Krebs, J. R. On clutch-size and fitness. Ibis (Lond. 1859). 116, 217–219 (1974).Article 

Google Scholar 
Ricklefs, R. E. On the evolution of reproductive strategies in birds: Reproductive effort. Am. Nat. 111, 453–478 (1977).Article 

Google Scholar 
Martin, T. E. Food as a limit on breeding birds: A life-history perspective. Annu. Rev. Ecol. Syst. 18, 435–487 (1987).Article 

Google Scholar 
Santangeli, A., Hakkarainen, H., Laaksonen, T. & Korpimäki, E. Home range size is determined by habitat composition but feeding rate by food availability in male Tengmalm’s owls. Anim. Behav. 83, 1115–1123 (2012).Article 

Google Scholar 
Kouba, M., Bartoš, L., Sindelář, J. & St’astny, K. Alloparental care and adoption in Tengmalm’s Owl (Aegolius funereus). J. Ornithol. 158, 185–191 (2017).Article 

Google Scholar 
Redpath, S. M. Habitat fragmentation and the individual: Tawny owls Strix aluco in woodland patches. J. Anim. Ecol. 64, 652 (1995).Article 

Google Scholar 
Bruun, M. & Smith, H. G. Landscape composition affects habitat use and foraging flight distances in breeding European starlings. Biol. Conserv. 114, 179–187 (2003).Article 

Google Scholar 
Frey-Roos, F., Brodmann, P. A. & Reyer, H. U. Relationships between food resources, foraging patterns, and reproductive success in the water pipit, Anthus sp. Spinoletta. Behav. Ecol. 6, 287–295 (1995).Article 

Google Scholar 
Saïd, S. et al. What shapes intra-specific variation in home range size? A case study of female roe deer. Oikos 118, 1299–1306 (2009).Article 

Google Scholar 
Van Beest, F. M., Rivrud, I. M., Loe, L. E., Milner, J. M. & Mysterud, A. What determines variation in home range size across spatiotemporal scales in a large browsing herbivore?. J. Anim. Ecol. 80, 771–785 (2011).Article 

Google Scholar 
Hakkarainen, H., Koivunen, V. & Korpimäki, E. Reproductive success and parental effort of Tengmalm’s owls: Effects of spatial and temporal variation in habitat quality. Ecoscience 4, 35–42 (1997).Article 

Google Scholar 
Kittle, A. M. et al. Wolves adapt territory size, not pack size to local habitat quality. J. Anim. Ecol. 84, 1177–1186 (2015).Article 

Google Scholar 
Trembley, I., Thomas, D., Blondel, J., Perret, P. & Lambrechts, M. M. The effect of habitat quality on foraging patterns, provisioning rate and nestling growth in Corsican Blue Tits Parus caeruleus. Ibis (Lond. 1859). 147, 17–24 (2004).Article 

Google Scholar 
Turcotte, Y. & Desrochers, A. Landscape-dependent response to predation risk by forest birds. Oikos 100, 614–618 (2003).Article 

Google Scholar 
Hinsley, S. A., Rothery, P. & Bellamy, P. E. Influence of woodland area on breeding success in great tits parus major and blue tits Parus caeruleus. J. Avian Biol. 30, 271 (1999).Article 

Google Scholar 
Hinam, H. L. & Clair, C. C. S. High levels of habitat loss and fragmentation limit reproductive success by reducing home range size and provisioning rates of Northern saw-whet owls. Biol. Conserv. 141, 524–535 (2008).Article 

Google Scholar 
Daan, S., Deerenberg, C. & Dijkstra, C. Increased daily work precipitates natural death in the Kestrel. J. Anim. Ecol. 65, 539 (1996).Article 

Google Scholar 
Slagsvold, T., Sandvik, J., Rofstad, G., Lorentsen, O. & Husby, M. On the adaptive value of intraclutch egg-size variation in birds. Auk 101, 685–697 (1984).Article 

Google Scholar 
Tripet, F., Richner, H. & Tripet, F. Host responses to ectoparasites: Food compensation by parent blue tits. Oikos 78, 557 (1997).Article 

Google Scholar 
Budden, A. E. & Beissinger, S. R. Resource allocation varies with parental sex and brood size in the asynchronously hatching green-rumped parrotlet (Forpus passerinus). Behav. Ecol. Sociobiol. 63, 637–647 (2009).Article 

Google Scholar 
Bókony, V. et al. Stress response and the value of reproduction: Are birds prudent parents?. Am. Nat. 173, 589–598 (2009).Article 

Google Scholar 
McGinley, M. A., Temme, D. H. & Geber, M. A. Parental investment in offspring in variable environments: Theoretical and empirical considerations. Am. Nat. 130, 370–398 (1987).Article 

Google Scholar 
Ghalambor, C. K. & Martin, T. E. Fecundity-survival trade-offs and parental risk-taking in birds. Science (80-.). 292, 494–497 (2001).ADS 
CAS 
Article 

Google Scholar 
Caro, S. M., Griffin, A. S., Hinde, C. A. & West, S. A. Unpredictable environments lead to the evolution of parental neglect in birds. Nat. Commun. 7, 1–10 (2016).Article 

Google Scholar 
Roulin, A. Barn Owls: Evolution and Ecology (Cambridge University Press, 2020).
Google Scholar 
Romano, A., Séchaud, R. & Roulin, A. Global biogeographical patterns in the diet of a cosmopolitan avian predator. J. Biogeogr. 47, 1467–1481 (2020).Article 

Google Scholar 
Arlettaz, R., Krähenbühl, M., Almasi, B., Roulin, A. & Schaub, M. Wildflower areas within revitalized agricultural matrices boost small mammal populations but not breeding Barn Owls. J. Ornithol. 151, 553–564 (2010).Article 

Google Scholar 
Hindmarch, S., Elliott, J. E., Mccann, S. & Levesque, P. Habitat use by barn owls across a rural to urban gradient and an assessment of stressors including, habitat loss, rodenticide exposure and road mortality. Landsc. Urban Plan. 164, 132–143 (2017).Article 

Google Scholar 
Castañeda, X. A., Huysman, A. E. & Johnson, M. D. Barn Owls select uncultivated habitats for hunting in a winegrape growing region of California. Ornithol. Appl. 123, 1–15 (2021).
Google Scholar 
Séchaud, R. et al. Behaviour-specific habitat selection patterns of breeding barn owls. Mov. Ecol. 9, 18 (2021).Article 

Google Scholar 
Roulin, A., Ducrest, A.-L. & Dijkstra, C. Effect of brood size manipulations on parents and offspring in the barn owl Tyto alba. Ardea 87, 91–100 (1999).
Google Scholar 
Béziers, P. & Roulin, A. Double brooding and offspring desertion in the barn owl Tyto alba. J. Avian Biol. 47, 235–244 (2016).Article 

Google Scholar 
Laaksonen, T., Hakkarainen, H. & Korpimäki, E. Lifetime reproduction of a forest-dwelling owl increases with age and area of forests. Proc. R. Soc. B Biol. Sci. 271, 10058 (2004).Article 

Google Scholar 
Bryant, D. M. Energy expenditure and body mass changes as measures of reproductive costs in birds. Funct. Ecol. 2, 23 (1988).Article 

Google Scholar 
Merilä, J. & Wiggins, D. A. Mass loss in breeding blue tits: The role of energetic stress. J. Anim. Ecol. 66, 452 (1997).Article 

Google Scholar 
Frey, C., Sonnay, C., Dreiss, A. & Roulin, A. Habitat, breeding performance, diet and individual age in Swiss Barn Owls (Tyto alba). J. Ornithol. 152, 279–290 (2010).Article 

Google Scholar 
Aschwanden, J., Holzgang, O. & Jenni, L. Importance of ecological compensation areas for small mammals in intensively farmed areas. Wildlife Biol. 13, 150–158 (2007).Article 

Google Scholar 
Roulin, A. Tyto alba Barn Owl. BWP Updat. 4, 115–138 (2002).
Google Scholar 
Calabrese, J. M., Fleming, C. H. & Gurarie, E. ctmm: An R package for analyzing animal relocation data as a continuous-time stochastic process. Methods Ecol. Evol. 7, 1124–1132 (2016).Article 

Google Scholar 
Fleming, C. H. et al. Rigorous home range estimation with movement data: A new autocorrelated kernel density estimator. Ecology 96, 1182–1188 (2015).CAS 
Article 

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

Google Scholar 
Taylor, I. Barn Owls: Predator-Prey Relationships and Conservation (Cambridge University Press, 1994).
Google Scholar 
van den Brink, V., Dreiss, A. N. & Roulin, A. Melanin-based coloration predicts natal dispersal in the barn owl, Tyto alba. Anim. Behav. 84, 805–812 (2012).Article 

Google Scholar 
Dreiss, A. N. & Roulin, A. Divorce in the barn owl: Securing a compatible or better mate entails the cost of re-pairing with a less ornamented female mate. J. Evol. Biol. 27, 1114–1124 (2014).CAS 
Article 

Google Scholar 
Garriga, J., Palmer, J. R. B., Oltra, A. & Bartumeus, F. Expectation-maximization binary clustering for behavioural annotation. PLoS One 11, e0151984 (2016).Article 

Google Scholar 
San-Jose, L. M. et al. Differential fitness effects of moonlight on plumage colour morphs in barn owls. Nat. Ecol. Evol. 3, 1331–1340 (2019).Article 

Google Scholar 
Bracis, C., Bildstein, K. L. & Mueller, T. Revisitation analysis uncovers spatio-temporal patterns in animal movement data. Ecography (Cop.) 41, 1801–1811 (2018).Article 

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

Google Scholar 
Lüdecke, D. Data Visualization for Statistics in Social Science [R package sjPlot version 2.8.9]. (2021).Rutz, C. & Bijlsma, R. G. Food-limitation in a generalist predator. Proc. R. Soc. B Biol. Sci. 273, 2069–2076 (2006).Article 

Google Scholar 
Altmann, S. A. The impact of locomotor energetics on mammalian foraging. J. Zool. 211, 215–225 (1987).Article 

Google Scholar 
Evens, R. et al. Proximity of breeding and foraging areas affects foraging effort of a crepuscular, insectivorous bird. Sci. Rep. 8, 1–11 (2018).
Google Scholar 
Pfeiffer, T. & Meyburg, B. U. GPS tracking of Red Kites (Milvus milvus) reveals fledgling number is negatively correlated with home range size. J. Ornithol. 156, 963–975 (2015).Article 

Google Scholar 
Romano, A. et al. Nestling sex and plumage color predict food allocation by barn swallow parents. Behav. Ecol. 27, 1198–1205 (2016).Article 

Google Scholar 
Bryant, D. M. & Tatner, P. Hatching asynchrony, sibling competition and siblicide in nestling birds: Studies of swiftlets and bee-eaters. Anim. Behav. 39, 657–671 (1990).Article 

Google Scholar 
Mock, D. W. & Parker, G. A. Advantages and disadvantages of egret and heron brood reduction. Evolution (N. Y.). 40, 459–470 (1986).
Google Scholar 
Stenning, M. J. Hatching asynchrony, brood reduction and other rapidly reproducing hypotheses. Trends Ecol. Evol. 11, 243–246 (1996).CAS 
Article 

Google Scholar 
Roulin, A., Colliard, C., Russier, F., Fleury, M. & Grandjean, V. Sib-sib communication and the risk of prey theft in the barn owl Tyto alba. J. Avian Biol. 39, 593–598 (2008).Article 

Google Scholar 
Korpimaki, E. Costs of reproduction and success of manipulated broods under varying food conditions in Tengmalm’s owl. J. Anim. Ecol. 57, 1879 (1988).
Google Scholar 
Tolonen, P. & Korpimäki, E. Do kestrels adjust their parental effort to current or future benefit in a temporally varying environment?. Écoscience 3, 165–172 (1996).Article 

Google Scholar 
Harrison, F., Barta, Z., Cuthill, I. & Székely, T. How is sexual conflict over parental care resolved? A meta-analysis. J. Evol. Biol. 22, 1800–1812 (2009).CAS 
Article 

Google Scholar 
Osorno, J. L. & Székely, T. Sexual conflict and parental care in magnificent frigatebirds: Full compensation by deserted females. Anim. Behav. 68, 337–342 (2004).Article 

Google Scholar 
Paredes, R., Jones, I. L. & Boness, D. J. Parental roles of male and female thick-billed murres and razorbills at the Gannet Islands, Labrador. Behaviour 143, 451–481 (2006).Article 

Google Scholar 
Kleijn, D. et al. Mixed biodiversity benefits of agri-environment schemes in five European countries. Ecol. Lett. 9, 243–254 (2006).CAS 
Article 

Google Scholar 
Zingg, S., Ritschard, E., Arlettaz, R. & Humbert, J. Y. Increasing the proportion and quality of land under agri-environment schemes promotes birds and butterflies at the landscape scale. Biol. Conserv. 231, 39–48 (2019).Article 

Google Scholar  More

Rook, G. A. W. Review series on helminths, immune modulation and the hygiene hypothesis: The broader implications of the hygiene hypothesis. Immunology 126, 3–11 (2009).CAS 
Article 

Google Scholar 
Von Hertzen, L., Hanski, I. & Haahtela, T. Natural immunity. Biodiversity loss and inflammatory diseases are two global megatrends that might be related. EMBO Rep. 12, 1089–1093 (2011).Article 

Google Scholar 
Von Hertzen, L. & Haahtela, T. Disconnection of man and the soil: Reason for the asthma and atopy epidemic?. J. Allergy Clin. Immunol. 117, 334–344 (2006).Article 

Google Scholar 
Hanski, I. et al. Environmental biodiversity, human microbiota, and allergy are interrelated. Proc. Natl. Acad. Sci. U. S. A. 109, 8334–8339 (2012).ADS 
CAS 
Article 

Google Scholar 
Haahtela, T. et al. Immunological resilience and biodiversity for prevention of allergic diseases and asthma. Allergy Eur. J. Allergy Clin. Immunol. https://doi.org/10.1111/all.14895 (2021).Article 

Google Scholar 
Rook, G. A. W. et al. Mycobacteria and other environmental organisms as immunomodulators for immunoregulatory disorders. Springer Semin. Immunopathol. 25, 237–255 (2004).CAS 
Article 

Google Scholar 
Fyhrquist, N. et al. Acinetobacter species in the skin microbiota protect against allergic sensitization and inflammation. J. Allergy Clin. Immunol. 134, 1301-1309.e11 (2014).CAS 
Article 

Google Scholar 
Ottman, N. et al. Soil exposure modifies the gut microbiota and supports immune tolerance in a mouse model. J. Allergy Clin. Immunol. 143, 1198-1206.e12 (2019).CAS 
Article 

Google Scholar 
Nurminen, N. et al. Nature-derived microbiota exposure as a novel immunomodulatory approach. Fut. Microbiol. 13, 737–744 (2018). CAS 
Article 

Google Scholar 
Shaffer, M. & Lozupone, C. Prevalence and source of fecal and oral bacteria on infant, child, and adult hands. mSystems 3, 1–12 (2018).Article 

Google Scholar 
Grönroos, M. et al. Short-term direct contact with soil and plant materials leads to an immediate increase in diversity of skin microbiota. MicrobiologyOpen https://doi.org/10.1002/mbo3.645 (2019).Article 

Google Scholar 
Roslund, M. I. et al. Biodiversity intervention enhances immune regulation and health-associated commensal microbiota among daycare children. Sci. Adv. 6, 7–105 (2020).Article 

Google Scholar 
Roslund, M. I. et al. Long-term biodiversity intervention shapes health-associated commensal microbiota among urban day-care children. Environ. Int. 157, 7008 (2021).Article 

Google Scholar 
Lax, S. et al. Longitudinal analysis of microbial interaction between humans and the indoor environment. Science (80-.). 345, 1048–1052 (2014).ADS 
CAS 
Article 

Google Scholar 
Flies, E. J., Clarke, L. J., Brook, B. W. & Jones, P. Urbanisation reduces the abundance and diversity of airborne microbes-but what does that mean for our health? A systematic review. Sci. Total Environ. 738, 140337 (2020).ADS 
CAS 
Article 

Google Scholar 
Ege, M. J. et al. Exposure to environmental microorganisms and childhood asthma.. Science 364, 701–709 (2011).CAS 

Google Scholar 
Li, H. et al. Spatial and seasonal variation of the airborne microbiome in a rapidly developing city of China. Sci. Total Environ. 665, 61–68 (2019).ADS 
CAS 
Article 

Google Scholar 
Chase, J. et al. Geography and location are the primary drivers of office microbiome composition. mSystems 1, 1–18 (2016).
Google Scholar 
Danko, D. et al. A global metagenomic map of urban microbiomes and antimicrobial resistance. Cell 184, 3376-3393.e17 (2021).CAS 
Article 

Google Scholar 
Hui, N. et al. Soil microbial communities are shaped by vegetation type and park age in cities under cold climate. Environ. Microbiol. 19, 1281–1295 (2017).Article 

Google Scholar 
Mhuireach, G. et al. Urban greenness influences airborne bacterial community composition. Sci. Total Environ. 571, 680–687 (2016).ADS 
CAS 
Article 

Google Scholar 
Franzetti, A., Gandolfi, I., Gaspari, E., Ambrosini, R. & Bestetti, G. Seasonal variability of bacteria in fine and coarse urban air particulate matter. Appl. Microbiol. Biotechnol. 90, 745–753 (2011).CAS 
Article 

Google Scholar 
Mhuireach, G., Wilson, H. & Johnson, B. R. Urban aerobiomes are influenced by season, vegetation, and individual site characteristics. EcoHealth 18, 331–344 (2021).Article 

Google Scholar 
Mahnert, A., Moissl-Eichinger, C. & Berg, G. Microbiome interplay: Plants alter microbial abundance and diversity within the built environment. Front. Microbiol. 6, 1–11 (2015).Article 

Google Scholar 
Ruokolainen, L. et al. Green areas around homes reduce atopic sensitization in children. Allergy Eur. J. Allergy Clin. Immunol. 70, 195–202 (2015).CAS 
Article 

Google Scholar 
Kirjavainen, P. V. et al. Farm-like indoor microbiota in non-farm homes protects children from asthma development. Nat. Med. 25, 1089–1095 (2019).CAS 
Article 

Google Scholar 
Nurminen, N. et al. Land cover of early-life environment modulates the risk of type 1 diabetes. Diabetes Care 44, 1506–1514 (2021).Article 

Google Scholar 
Parajuli, A. et al. Yard vegetation is associated with gut microbiota composition. Sci. Total Environ. 713, 136707 (2020).ADS 
CAS 
Article 

Google Scholar 
Köberl, M., Dita, M., Martinuz, A., Staver, C. & Berg, G. Members of Gammaproteobacteria as indicator species of healthy banana plants on Fusarium wilt-infested fields in Central America. Sci. Rep. 7, 1–9 (2017).Article 

Google Scholar 
Delanghe, L. et al. The role of lactobacilli in inhibiting skin pathogens. Biochem. Soc. Trans. 5, 617–627. https://doi.org/10.1042/bst20200329 (2021).CAS 
Article 

Google Scholar 
George, F. et al. Occurrence and dynamism of lactic acid bacteria in distinct ecological niches: A multifaceted functional health perspective. Front. Microbiol. 9, 1–15 (2018).CAS 
Article 

Google Scholar 
Yu, A. O., Leveau, J. H. J. & Marco, M. L. Abundance, diversity and plant-specific adaptations of plant-associated lactic acid bacteria. Environ. Microbiol. Rep. 12, 16–29 (2020).CAS 
Article 

Google Scholar 
Parajuli, A. et al. Urbanization reduces transfer of diverse environmental microbiota indoors. Front. Microbiol. 9, 1405 (2018).Article 

Google Scholar 
Parajuli, A. et al. The abundance of health-associated bacteria is altered in PAH polluted soils—Implications for health in urban areas?. PLoS One 7, 1–18. https://doi.org/10.1371/journal.pone.0187852 (2017).CAS 
Article 

Google Scholar 
Vari, H. K. et al. Associations between land cover categories, gaseous PAH levels in ambient air and endocrine signaling predicted from gut bacterial metagenome of the elderly. Chemosphere 265, 1559 (2021).Article 

Google Scholar 
Orsini, F., Kahane, R., Nono-Womdim, R. & Gianquinto, G. Urban agriculture in the developing world: A review. Agron. Sustain. Dev. 33, 695–720 (2013).Article 

Google Scholar 
Hui, N. et al. Diverse environmental microbiota as a tool to augment biodiversity in urban landscaping materials. Front. Microbiol. 10, 1–10 (2019).CAS 
Article 

Google Scholar 
Puhakka, R. et al. Greening of daycare yards with biodiverse materials affords well-being, play and environmental relationships. Int. J. Environ. Res. Public Health 16, 2948 (2019).Article 

Google Scholar 
Burmeister, A. R. & Marriott, I. The interleukin-10 family of cytokines and their role in the CNS. Front. Cell. Neurosci. 12, 1–13 (2018).Article 

Google Scholar 
Opal, S. M. & DePalo, V. A. Anti-inflammatory cytokines. Chest 117, 1162–1172 (2000).CAS 
Article 

Google Scholar 
Kuwabara, T., Ishikawa, F., Kondo, M. & Kakiuchi, T. The role of IL-17 and related cytokines in inflammatory autoimmune diseases. Mediators Inflamm. 2017, 4598 (2017).Article 

Google Scholar 
Li, M. O., Wan, Y. Y., Sanjabi, S., Robertson, A. K. L. & Flavell, R. A. Transforming growth factor-β regulation of immune responses. Annu. Rev. Immunol. 24, 99–146 (2006).CAS 
Article 

Google Scholar 
Prudhomme, G. J. & Piccirillo, C. A. The inhibitory effects of transforming growth factor-beta-1 (TGF-β1) in autoimmune diseases. J. Autoimmun. 14, 23–42 (2000).CAS 
Article 

Google Scholar 
Esebanmen, G. E. & Langridge, W. H. R. The role of TGF-beta signaling in dendritic cell tolerance. Immunol. Res. 65, 987–994 (2017).CAS 
Article 

Google Scholar 
Honkanen, J. et al. IL-17 immunity in human type 1 diabetes. J. Immunol. 185, 1959–1967 (2010).CAS 
Article 

Google Scholar 
Torpy, F. et al. Testing the single-pass VOC removal efficiency of an active green wall using methyl ethyl ketone (MEK). Air Qual. Atmos. Heal. 11, 163–170 (2018).CAS 
Article 

Google Scholar 
Roslund, M. I. et al. Endocrine disruption and commensal bacteria alteration associated with gaseous and soil PAH contamination among daycare children. Environ. Int. 130, 104894 (2019).CAS 
Article 

Google Scholar 
Schloss, P. D., Gevers, D. & Westcott, S. L. Reducing the effects of PCR amplification and sequencing Artifacts on 16s rRNA-based studies. PLoS One 6, 1789 (2011).
Google Scholar 
Kozich, J., Westcott, S., Baxter, N., Highlander, S. & Schloss, P. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl. Environ. Microbiol. 79, 5112–5120 (2013).ADS 
CAS 
Article 

Google Scholar 
Soininen, L., Grönroos, M., Roslund, M. I. & Sinkkonen, A. Long-term storage affects resource availability and occurrence of bacterial taxa linked to pollutant degradation and human health in landscaping materials. Urban For. Urban Green. 60, 1789 (2021).Article 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, 590–596 (2013).Article 

Google Scholar 
Huse, S. M., Welch, D. M., Morrison, H. G. & Sogin, M. L. Ironing out the wrinkles in the rare biosphere through improved OTU clustering. Environ. Microbiol. 12, 1889–1898 (2010).CAS 
Article 

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

Google Scholar 
Wang, Q., Garrity, G. M., Tiedje, J. M., Cole, J. R. & Al, W. E. T. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).ADS 
CAS 
Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. (2020).Oksanen, J. et al. vegan: Community Ecology Package. (2019).Huang, F. L. Alternatives to multilevel modeling for the analysis of clustered data. J. Exp. Educ. 84, 175–196 (2016).Article 

Google Scholar 
Moen, E. L., Fricano-Kugler, C. J., Luikart, B. W. & O’Malley, A. J. Analyzing clustered data: Why and how to account for multiple observations nested within a study participant?. PLoS ONE 11, 1–17 (2016).
Google Scholar 
Twisk, J. et al. Different ways to estimate treatment effects in randomised controlled trials. Contemp. Clin. Trials Commun. 10, 80–85 (2018).Chapat, L., Chemin, K., Dubois, B., Bourdet-Sicard, R. & Kaiserlian, D. Lactobacillus casei reduces CD8+ T cell-mediated skin inflammation. Eur. J. Immunol. 34, 2520–2528 (2004).CAS 
Article 

Google Scholar 
Kaur, K. & Rath, G. Formulation and evaluation of UV protective synbiotic skin care topical formulation. J. Cosmet. Laser Ther. 21, 332–342 (2019).Article 

Google Scholar 
Rong, J. et al. Skin resistance to UVB-induced oxidative stress and hyperpigmentation by the topical use of Lactobacillus helveticus NS8-fermented milk supernatant. J. Appl. Microbiol. 123, 511–523 (2017).CAS 
Article 

Google Scholar 
Yuan, J. et al. Microbial volatile compounds alter the soil microbial community. Environ. Sci. Pollut. Res. 24, 22485–22493 (2017).CAS 
Article 

Google Scholar 
Abis, L. et al. Reduced microbial diversity induces larger volatile organic compound emissions from soils. Sci. Rep. 10, 1–15 (2020).Article 

Google Scholar 
Duffy, E. & Morrin, A. Endogenous and microbial volatile organic compounds in cutaneous health and disease. TrAC Trends Anal. Chem. 111, 163–172 (2019).CAS 
Article 

Google Scholar 
Lemfack, M. C. et al. Novel volatiles of skin-borne bacteria inhibit the growth of Gram-positive bacteria and affect quorum-sensing controlled phenotypes of Gram-negative bacteria. Syst. Appl. Microbiol. 39, 503–515 (2016).CAS 
Article 

Google Scholar 
Ahmed, M. & Gaffen, S. L. IL-17 in obesity and adipogenesis. Cytokine Growth Factor Rev. 21, 449–453 (2010).CAS 
Article 

Google Scholar  More

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

Biogeographic pattern of LOSSThe original forest plot data aggregated at 0.25 degree show large spatial variations (Fig. 1a) across the continents, with the greatest LOSS in Asia & Australia (mean ± 1 SE; 6.5 ± 0.5 Mg ha−1 y−1) > South America (4.9 ± 0.2 Mg ha−1 y−1) and Africa (4.6 ± 0.2 Mg ha−1 y−1) > North America (2.3 ± 0.1 Mg ha−1 y−1 in boreal and 2 ± 0.1 Mg ha−1 y−1 in temperate)36 (Fig. 1b; Supplementary Fig. 5a). This pattern was robust to bootstrapping (1000 iterations) to randomly select 90% of plots for estimating the probability distribution of the mean continental values (Supplementary Fig. 5b). The upscaled gridded LOSS maps generated by our random forest algorithm (see Methods) over the spatial domain of our full datasets shows hotspots of high LOSS in Southern Asia & Australia ( > 6 Mg ha−1 y−1), Northwestern South America (Amazon) ( > 5 Mg ha−1 y−1), and the western coast of North America ( >3 Mg ha−1 y−1)10,36,37,38 (Supplementary Fig. 6a). These patterns were robust to two bootstrapping approaches – based on the sampled biomes of each point feature and also randomly sampling 90% data with replacement (see Methods) (Fig. 2a; Supplementary Fig. 6b). The uncertainty (coefficient of variance – CV; %mean) was generally low ( 10%), despite the larger sample size (n  > 500 at 0.25 degree) (Fig. 2b; Supplementary Fig. 6c), likely because of potential effects of forest recovery or regrowth following past disturbance16 as well as the small plot size (i.e., 0.067 ha in each plot)39.Fig. 1: Map of sample locations and biomass loss to mortality (LOSS) data.a Sampling sites. A total of 2676 samples were collected and aggregated into 814 grids at 0.25 degree that were used for geospatial modeling. b The median and interquartile range of LOSS across continents—North America, South America, Africa, and Asia & Australia.Full size imageFig. 2: Map of biomass loss to mortality (LOSS) and its uncertainty across continents.a, b Ensemble mean of LOSS a and its uncertainty (coefficient of variation, b across continents at 0.25 degree derived from forest plot data using the bootstrapped (10 iterations) approach by randomly sampling 90% plots with replacement. c, d Ensemble mean of LOSS c and its uncertainty (coefficient of variation, d across continents at 0.5 degree derived from six dynamic vegetation models (DGVMs, ORCHIDEE, CABLE-POP, JULES, LPJ-GUESS, LPJmL, and SEIB-DGVM). Coefficient of variation was quantified as the standard deviation divided by the mean predicted value as a measure of prediction accuracy. e The difference of LOSS between ensemble mean of DGVMs and ensemble mean of LOSS derived from forest plots data across continents at 0.5 degree, quantified as difference between c and a, whereby LOSS in Fig. 2a is resampled at 0.5 degree.Full size imageDrivers of LOSSMean annual temperature (MAT), aridity index (the ratio of precipitation to potential evapotranspiration), and precipitation seasonality were identified as the dominant predictors of LOSS across continents (Supplementary Fig. 7a), with positive relationships with LOSS (Fig. 3a)10,36. In contrast to local-scale studies40,41, wood density, forest stand density, and soil conditions were poor predictors of LOSS when all data were used. These relationships were largely driven by the spatial pattern of LOSS and climate gradients, whereby LOSS and MAT, aridity index, and precipitation seasonality were high in tropical forests (Supplementary Fig. 8). This motivated us to examine the drivers of LOSS in tropical vs non-tropical biomes (Supplementary Fig. 7b, c; Fig. 3b–d). With a smaller gradient in climate within wet tropical forests, soil properties such as nutrient content and cation exchange capacity (CEC) were significant predictors of LOSS (Supplementary Fig. 7b; Fig. 3b)42. In wet tropical forests, the relationships between soil nutrient content and CEC and LOSS were positive (Fig. 3b) and thus appeared to support the pattern of higher mortality in more productive tropical forests growing over nutrient rich soils42,43. In non-tropical regions, basal area or a competition index based on the degree of crowding within stocked areas44 (see Methods) were the dominant predictors of LOSS, especially in extra-tropical North America (Supplementary Fig. 7c; Fig. 3c, d). This result highlights the role of stand competition in driving the spatial patterns of LOSS44,45. This pattern also supports the existence of a spatial tradeoff between faster growth and higher mortality because of resource limitations or younger death, whereby competition plays the fundamental role13,45. In contrast to other studies15,46, forest age (available in boreal and temperate forests in North America) was not a good predictor of LOSS (Supplementary Fig. 9), likely because of our focus on mature and old-growth forests (i.e., age > 80 and 100 years in boreal and temperate forests, respectively).Fig. 3: Standardized response coefficients (mean ± 95% CIs) between LOSS and dominant environmental drivers.The scales analyzed were at continents a, tropical regions b vs non-tropical regions c, d. The response coefficients were quantified by linear mixed model which account for each plot as a random effect. Panels c and d used basal area and stand density index (SDI) as competition index, respectively. SDI was defined as the degree of crowding within stocked areas and quantified as a function of tree density and the quadratic mean diameter in centimeters. Basal area is strongly correlated with total biomass and higher LOSS in higher basal area may be merely because of its correlations. Thus, we used another competition metrics – SDI to further confirm the role of competition in LOSS. The error bars denote the 95% confidence interval. *P  130%) in western boreal forests in North America (Fig. 2d).Conventional emergent constraintWe first used the conventional emergent constraint approach27 to constrain the projected (2015–2099) NPP and HR across continents. This approach was conducted by building the statistic (linear) relationship between the historical LOSS averaged at forest-plot scale (derived from original plot data of LOSS) or continental scale (derived from the map of LOSS) and projected NPP and HR summed across continents (see Methods and Supplementary Fig. 4 for details). We found that the emergent constraint approach worked well in North America, where the relationship between historical LOSS and projected NPP and HR was significant (the scenario of using original plot data of LOSS: R2 = 0.68 and P = 0.04 for grid-level NPP; R2 = 0.97 and P = 0.0001 for grid-level HR; the scenario of using map of LOSS at continent scale: R2 = 0.7 and P = 0.04 for grid-level NPP; R2 = 0.95 and P = 0.0008 for grid-level HR) (Supplementary Fig. 11a; Supplementary Fig. 12a). This emergent constraint approach was less effective, however, for other continents, where tropical forests are predominant (all P  > 0.05; Supplementary Fig. 11b, c, d; Supplementary Fig. 12b, c, d). These results suggest a weak linear relationships when observations are lumped or averaged at broad continental scales for tropical continents, thus highlighting the importance of spatial scale and non-linear relationships in emergent constraint25. We interpret the result that this LOSS emergent constraint works better in North America than in the tropical forests, by a better representation of forest plot distribution and couplings of LOSS and NPP and HR across space in North America.Machine learning constraintTo overcome this limitation, we trained a machine learning algorithm34 to reproduce the emerging relationship between historical LOSS and projected NPP and HR at grid level in each DGVM by incorporating all grid values without or with climate predictors, expressed as NPPpro or HRpro = f(LOSShis) or f(LOSShis, MATpro, MAPpro), respectively, where pro refers to projected variables, his refers to historical variables, and MAT and MAP is mean annual temperature precipitation, respectively (see Methods). The results show consistently positive non-linear relationships between LOSShis and NPPpro or HRpro across DGVMs (Supplementary Fig. 3). Our machine learning algorithms can surrogate well the results of process-based models between the historical LOSS and the projected NPP and HR (R  > 0.65 and R  > 0.9 in both scenarios without climate effects and with climate effects, respectively; see Methods) (Supplementary Fig. 13). After including the observed LOSShis (derived from LOSS) in the machine learning algorithm, we were able to generate spatially explicit constrained estimates34 of projected NPP and HR, and then compare them with the scenario without the constraint (Supplementary Fig. 14; Supplementary Fig. 15). These patterns essentially show a lower NPPpro or HRpro in locations of overestimated LOSShis in DGVMs, consistent with the positive relationship between LOSShis and NPPpro or HRpro (Supplementary Fig. 3).Our results show that most DGVMs overestimate tree mortality, particularly in tropical regions (Fig. 2c, e). Thus, if modeled mortality is over-estimated, we expect that NPP is over-estimated as well. Ultimately, we used a bootstrap approach to generate 100 maps of mean value of LOSS with its distribution following the values of the average and 2 times of standard deviation of LOSS maps as a conservative constraint (see Methods). Then the 100 maps of mean value of LOSS were used to constrain the projected NPP or HR as ensemble means in our ML constraint and the uncertainty of the constraint was assessed. Our bootstrapping constraint approach by LOSS reduces this common bias of models and decreases projected NPP down to 7.9, 2.3, 2 Pg C y−1 in South America, Africa and Asia & Australia, compared to original NPP values of 9, 2.4, 2.3 Pg C y−1 (Fig. 4a). The reason for this is that NPP or growth is strongly positively correlated with LOSS across space in both inventory data and DGVMs (Supplementary Figs. 2 and 3; Supplementary Fig. 16). The constant mortality parameter used in most models may be too large if modelers have tuned this parameter to obtain reasonable biomass stocks, thus compensating for an overestimate of NPP in absence of modeled competition between individuals and nutrients (e.g. phosphorus) limitations in tropical forests13. Likewise, HRpro showed similar patterns with NPPpro because of coupling of HR and NPP and LOSS at broad spatial and long term scales20,21, despite the likely decoupling of the instantaneous rate of HR and NPP and LOSS at local and short-term scales22,23. Thus, we also constrained a decrease in projected grid-level HR with values of 6.5, 1.9, 1.7 Pg C y−1 in South America, Africa and Asia & Australia compared to 7, 1.9, 1.8 Pg C y−1 in the original model ensemble (Fig. 4b). Taken together, our results constrain a weaker future tropical forest carbon sink from observation-based LOSS estimates down to 1.4, 0.4, 0.3 Pg C y−1 in South America, Africa and Asia & Australia as compared to 2, 0.5, 0.5 Pg C y−1 in the original models. The projected sink is reduced in particular over the Amazon basin, while North America showed an enhanced future carbon sink (1.1 and 0.8 Pg C y−1 after and before constraint, respectively). The constraint by the machine learning approach significantly reduced the model spread in grid-level NPPpro and HRpro generally in tropical regions and particularly in South America (Fig. 4; Table 1). This was in contrast to the case of constraint at the whole North America scale (Fig. 4; Table 1), presumably because of spatial trade-off or compensation from regions of mortality overestimation (i.e., eastern North America—temperate zones) vs underestimation (i.e., boreal zones). To this end, we further divided the whole North America into temperate and boreal forests and found the significant effects of the ML constraint (Supplementary Fig. 17). These results highlight the importance of spatial scale in the ML constraint approach. We thus recommend accounting for the role of spatial trade-off in our ML constraint approach or using our ML constraint approach at broad spatial scales whereby the effect of spatial trade-off is minimal. We also caution that the bootstrapping (100 times) approach used in our ML constraint increases the sample size and could have increased the significant difference with and without LOSS constraint. Overall, the uncertainty of the ML constraint was low in the bootstrapping approach (Supplementary Fig. 18).Fig. 4: Projected grid-level NPP and grid heterotrophic respiration (HR) across continents.a, b Projected (2015–2099) grid-level NPP a and grid-level HR b across continents quantified by six dynamic vegetation models—DGVMs (ORCHIDEE, CABLE-POP, JULES, LPJ-GUESS, LPJmL, and SEIB-DGVM). The y axes are the minimum, mean, and maximum values in six DGVMs. ‘DGVMs’ refers to the scenario before constraint and ‘DGVMs + Observation’ refers to the scenario after constraint without climate predictors. The constraint was achieved by using the observational maps (n = 100; through a bootstrapping approach; see Methods for details) of LOSS derived from forest plots data to feed into the trained ML (random forest) model. Reported are ensemble means of constraint. The constraint effect was significant when North America were divided into temperate and boreal forests (see results of Supplementary Fig. 17). *P  More

Mann, J. Behavioral sampling methods for cetaceans: A review and critique. Mar. Mammal Sci. 15, 102–122 (1999).Article 

Google Scholar 
Pompanon, F. et al. Who is eating what: Diet assessment using next generation sequencing. Mol. Ecol. https://doi.org/10.1111/j.1365-294X.2011.05403.x (2012).Article 

Google Scholar 
Deagle, B. E. et al. Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data?. Mol. Ecol. 28, 391–406 (2019).Article 

Google Scholar 
Berry, T. E. et al. DNA metabarcoding for diet analysis and biodiversity: A case study using the endangered Australian sea lion (Neophoca cinerea). Ecol. Evol. 7, 5435–5453 (2017).Article 

Google Scholar 
Brassea-Pérez, E., Schramm, Y., Heckel, G., Chong-Robles, J. & Lago-Lestón, A. Metabarcoding analysis of the Pacific harbor seal diet in Mexico. Mar. Biol. 166, 1–14 (2019).Article 

Google Scholar 
Ford, M. J. et al. Estimation of a killer whale (Orcinus orca) population’s diet using sequencing analysis of DNA from feces. PLoS ONE 11, e0144956 (2016).Article 

Google Scholar 
Thomas, A. C., Deagle, B. E., Eveson, J. P., Harsch, C. H. & Trites, A. W. Quantitative DNA metabarcoding: Improved estimates of species proportional biomass using correction factors derived from control material. Mol. Ecol. Resour. 16, 714–726 (2016).CAS 
Article 

Google Scholar 
Deagle, B. E., Chiaradia, A., Mcinnes, J. & Jarman, S. N. Pyrosequencing faecal DNA to determine diet of little penguins: is what goes in what comes out? https://doi.org/10.1007/s10592-010-0096-6.Ando, H. et al. Methodological trends and perspectives of animal dietary studies by noninvasive fecal DNA metabarcoding. Environ. DNA 2, 391–406 (2020).Article 

Google Scholar 
Günther, B., Fromentin, J., Metral, L. & Arnaud-haond, S. Metabarcoding confirms the opportunistic foraging behaviour of Atlantic bluefin tuna and reveals the importance of gelatinous prey. PeerJ 9, e11757. https://doi.org/10.7717/peerj.11757 (2021).Article 

Google Scholar 
Simon, M., Hanson, M. B., Murrey, L., Tougaard, J. & Ugarte, F. From captivity to the wild and back: An attempt to release keiko the killer whale. Mar. Mammal Sci. 25, 693–705 (2009).Article 

Google Scholar 
Moore, M. et al. Rehabilitation and release of marine mammals in the United States: Risks and benefits. Mar. Mammal Sci. 23, 731–750 (2007).Article 

Google Scholar 
Leray, M. et al. A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: Application for characterizing coral reef fish gut contents. Front. Zool. 10, 1–14 (2013).Article 

Google Scholar 
Geller, J., Meyer, C. & Parker, M. Redesign of PCR primers for mitochondrial cytochrome c oxidase subunit I for marine invertebrates and application in all-taxa biotic surveys. Mol. Ecol. Resour. 13(5), 851–861. https://doi.org/10.1111/1755-0998.12138 (2013).CAS 
Article 

Google Scholar 
Blaxter, M. L. et al. A molecular evolutionary framework for the phylum Nematoda. Nature https://doi.org/10.1038/32160 (1998).Article 

Google Scholar 
Sinniger, F. et al. Worldwide analysis of sedimentary DNA reveals major gaps in taxonomic knowledge of deep-sea benthos. Front. Mar. Sci. 3, 1–14 (2016).Article 

Google Scholar 
Brandt, M. I. et al. Bioinformatic pipelines combining denoising and clustering tools allow for more comprehensive prokaryotic and eukaryotic metabarcoding. Mol. Ecol. Resour. 21(6), 1904–1921 (2021).Article 

Google Scholar 
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal https://doi.org/10.14806/ej.17.1.200 (2011).Article 

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

Google Scholar 
Antich, A., Palacin, C., Wangensteen, O. S. & Turon, X. To denoise or to cluster, that is not the question: Optimizing pipelines for COI metabarcoding and metaphylogeography. BMC Bioinform. 22, 1–25 (2021).Article 

Google Scholar 
Mahé, F., Rognes, T., Quince, C., de Vargas, C. & Dunthorn, M. Swarmv2: Highly-scalable and high-resolution amplicon clustering. PeerJ 2015, 1–12 (2015).
Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. https://doi.org/10.1093/nar/gks1219 (2013).Article 

Google Scholar 
Machida, R. J., Leray, M., Ho, S.-L. & Knowlton, N. Metazoan mitochondrial gene sequence reference datasets for taxonomic assignment of environmental samples. Sci. Data 4, 170027 (2017).CAS 
Article 

Google Scholar 
Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naıve Bayesian classifier for rapid assignment of rRNA sequences.pdf. Appl. Environ. Microbiol. 73, 5261–5267 (2007).ADS 
CAS 
Article 

Google Scholar 
Davis, N. M., Di Proctor, M., Holmes, S. P., Relman, D. A. & Callahan, B. J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome https://doi.org/10.1186/s40168-018-0605-2 (2018).Article 

Google Scholar 
Wangensteen, O. S., Palacín, C., Guardiola, M. & Turon, X. DNA metabarcoding of littoral hardbottom communities: High diversity and database gaps revealed by two molecular markers. PeerJ 2018, 1–30 (2018).
Google Scholar 
Schnell, I. B., Bohmann, K. & Gilbert, M. T. P. Tag jumps illuminated – reducing sequence-to-sample misidentifications in metabarcoding studies. Mol. Ecol. Resour. 15, 1289–1303 (2015).CAS 
Article 

Google Scholar 
Song, X. et al. A new deep-sea hydroid (Cnidaria:Hydrozoa ) from the Bering Sea Basin reveals high genetic relevance to Arctic and adjacent shallow-water species. Polar Biol. 39, 461–471 (2016).Article 

Google Scholar 
Frøslev, T. G. et al. Algorithm for post-clustering curation of DNA amplicon data yields reliable biodiversity estimates. Nat. Commun. 8, 1–11 (2017).Article 

Google Scholar 
Vacquié-Garcia, J., Lydersen, C., Ims, R. A. & Kovacs, K. M. Habitats and movement patterns of white whales Delphinapterus leucas in Svalbard, Norway in a changing climate. Mov. Ecol. 6, 1–12 (2018).Article 

Google Scholar 
Kastelein, R. A., Nieuwstraten, S. H. & Verstegen, M. W. A. Passage time of carmine red dye through the digestion tract . In The Biology of the Harbour Porpoise 235–245 (1997).Lesage, V., Lair, S., Turgeon, S. & Beland, P. Diet of St. Lawrence Estuary Beluga (Delphinapterus leucas) in a changing ecosystem. Can. Field-Nat. 134, 21–35 (2020).Article 

Google Scholar 
Bluhm, B. A. & Gradinger, R. Regional variability in food availability for arctic marine mammals. Ecol. Appl. 18, S77–S96 (2008).Article 

Google Scholar 
Quakenbush, L. T. et al. Diet of beluga whales, Delphinapterus leucas, in Alaska from stomach contents, March-November. Mar. Fish. Rev. 77, 70–84 (2015).Article 

Google Scholar 
Choy, E. S. et al. Variation in the diet of beluga whales in response to changes in prey availability: Insights on changes in the Beaufort Sea ecosystem. Mar. Ecol. Prog. Ser. 647, 195–210 (2020).ADS 
CAS 
Article 

Google Scholar 
Mychek-Londer, J. G., Chaganti, S. R. & Heath, D. D. Metabarcoding of native and invasive species in stomach contents of Great Lakes fishes. PLoS ONE 15, 1–22 (2020).Article 

Google Scholar 
Nedreaas, K. Food and feeding habits of young saithe, Pollachius virens (L.), on the coast of Western Norway. Fisk. Skr. Ser. Havundersokelser 18, 263–301 (1987).
Google Scholar 
Højgaard, D. P. Food and parasitic nematodes of saithe, Pollachius virens (L.), from the Faroe Islands. Sarsia 84, 473–478 (1999).Article 

Google Scholar 
Ekbaum, E. Notes on the occurrence of Acanthocephala in Pacific fishes: I. Echinorhynchus gadi (Zoega) Müller in salmon and E. lageniformis sp. nov. and Corynosoma strumosum (Rudolphi) in two species of flounder. Parasitology 30, 267–274 (1938).Article 

Google Scholar 
Baptista-Fernandes, T. et al. Human gastric hyperinfection by Anisakis simplex: A severe and unusual presentation and a brief review. Int. J. Infect. Dis. 64, 38–41 (2017).Article 

Google Scholar 
Hubert, B., Bacou, J. & Belveze, H. Epidemiology of human anisakiasis: Incidence and sources in France. Am. J. Trop. Med. Hyg. 40, 301–303 (1989).CAS 
Article 

Google Scholar 
Hays, R., Measures, L. N. & Huot, J. Capelin (Mallotus villosus) and herring (Clupea harengus) as paratenic hosts of Anisakis simplex, a parasite of beluga (Delphinapterus leucas) in the St. Lawrence estuary. Can. J. Zool. 78, 1411–1417 (1998).Article 

Google Scholar 
Yanong, R. P. E. Nematode (Roundworm) Infections in Fish Vol. 1, 1–9 (2002).Jauniaux, T. et al. Post-mortem findings and causes of death of harbour porpoises (Phocoena phocoena) stranded from 1990 to 2000 along the coastlines of Belgium and Northern France. J. Compar. Pathol. 126, 243–253 (2002).CAS 
Article 

Google Scholar  More