Philippa Kaur

1.
Hutchinson, G. E. Cold spring harbor symposium on quantitative biology. Cold Spring Harb. Symp. Quant. Biol. 22, 415–427 (1957).
Article  Google Scholar 
2.
Chown, S., Gaston, K. & Robinson, D. Macrophysiology: large‐scale patterns in physiological traits and their ecological implications. Funct. Ecol. 18, 159–167 (2004).
Article  Google Scholar 

3.
Spicer, J. I., Morley, S. A. & Bozinovic, F. Physiological diversity, biodiversity patterns and global climate change: testing key hypotheses involving temperature and oxygen. Philos. Trans. R. Soc. B 374 (2019).

4.
Chown, S. L. & Gaston, K. J. Macrophysiology—progress and prospects. Funct. Ecol. 30, 330–344 (2016).
Article  Google Scholar 

5.
Sunday, J. M., Bates, A. E. & Dulvy, N. K. Thermal tolerance and the global redistribution of animals. Nat. Clim. Change 2, 686–690 (2012).
Article  Google Scholar 

6.
Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. PNAS 105, 6668–6672 (2008).
CAS  Article  Google Scholar 

7.
Waldock, C., Stuart‐Smith, R. D., Edgar, G. J., Bird, T. J. & Bates, A. E. The shape of abundance distributions across temperature gradients in reef fishes. Ecol. Lett. 22, 685–696 (2019).
Article  Google Scholar 

8.
Payne, N. L. et al. Temperature dependence of fish performance in the wild: links with species biogeography and physiological thermal tolerance. Funct. Ecol. 30, 903–912 (2016).
Article  Google Scholar 

9.
Sunday, J. M., Bates, A. E. & Dulvy, N. K. Global analysis of thermal tolerance and latitude in ectotherms. Proc. R. Soc. B. 278, 1823–1830 (2011).
Article  Google Scholar 

10.
Sunday, J. M. et al. Thermal-safety margins and the necessity of thermoregulatory behavior across latitude and elevation. PNAS 111, 5610–5615 (2014).
CAS  Article  Google Scholar 

11.
Araujo, M. B. et al. Heat freezes niche evolution. Ecol. Lett. 16, 1206–1219 (2013).
Article  Google Scholar 

12.
Payne, N. L. & Smith, J. A. An alternative explanation for global trends in thermal tolerance. Ecol. Lett. 20, 70–77 (2017).
Article  Google Scholar 

13.
Sinclair, B. J. et al. Can we predict ectotherm responses to climate change using thermal performance curves and body temperatures? Ecol. Lett. 19, 1372–1385 (2016).
Article  Google Scholar 

14.
Rezende, E. L. & Bozinovic, F. Thermal performance across levels of biological organization. Philos. Trans. R. Soc. B 374, 20180549 (2019).
CAS  Article  Google Scholar 

15.
Barnes, D. K., Peck, L. S. & Morley, S. A. Ecological relevance of laboratory determined temperature limits: colonization potential, biogeography and resilience of Antarctic invertebrates to environmental change. Glob. Change Biol. 16, 3164–3169 (2010).
Article  Google Scholar 

16.
Clark, T. D., Sandblom, E. & Jutfelt, F. Aerobic scope measurements of fishes in an era of climate change: respirometry, relevance and recommendations. J. Exp. Biol. 216, 2771–2782 (2013).
Article  Google Scholar 

17.
Norin, T., Malte, H. & Clark, T. D. Aerobic scope does not predict the performance of a tropical eurythermal fish at elevated temperatures. J. Exp. Biol. 217, 244–251 (2014).
Article  Google Scholar 

18.
Kearney, M. & Porter, W. Mechanistic niche modelling: combining physiological and spatial data to predict species’ ranges. Ecol. Lett. 12, 334–350 (2009).
Article  Google Scholar 

19.
Buckley, L. B. et al. Can mechanism inform species’ distribution models? Ecol. Lett. 13, 1041–1054 (2010).
Article  Google Scholar 

20.
Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).
Article  Google Scholar 

21.
Peck, L. S., Clark, M. S., Morley, S. A., Massey, A. & Rossetti, H. Animal temperature limits and ecological relevance: effects of size, activity and rates of change. Funct. Ecol. 23, 248–256 (2009).
Article  Google Scholar 

22.
Richard, J., Morley, S. A., Thorne, M. A. & Peck, L. S. Estimating long-term survival temperatures at the assemblage level in the marine environment: towards macrophysiology. PLoS ONE 7, e34655 (2012).
CAS  Article  Google Scholar 

23.
Dillon, M. E., Wang, G. & Huey, R. B. Global metabolic impacts of recent climate warming. Nature 467, 704–706 (2010).
CAS  Article  Google Scholar 

24.
Gillooly, J. F., Brown, J. H., West, G. B., Savage, V. M. & Charnov, E. L. Effects of size and temperature on metabolic rate. Science 293, 2248–2251 (2001).
CAS  Article  Google Scholar 

25.
Stuart-Smith, R. D., Edgar, G. J. & Bates, A. E. Thermal limits to the geographic distributions of shallow-water marine species. Nat. Ecol. Evol. 1, 1846–1852 (2017).
Article  Google Scholar 

26.
Janzen, D. H. Why mountain passes are higher in the tropics. Am. Nat. 101, 233–249 (1967).
Article  Google Scholar 

27.
Martin, T. L. & Huey, R. B. Why “Suboptimal” is optimal: Jensen’s inequality and ectotherm thermal preferences. Am. Nat. 171, E102–E118 (2008).
Article  Google Scholar 

28.
Pörtner, H.-O. et al. Cod and climate in a latitudinal cline: physiological analyses of climate effects in marine fishes. Clim. Res. 37, 253–270 (2008).
Article  Google Scholar 

29.
Sylvestre, E.-L., Lapointe, D., Dutil, J.-D. & Guderley, H. Thermal sensitivity of metabolic rates and swimming performance in two latitudinally separated populations of cod, Gadus morhua L. J. Comp. Physiol. B Biochem. Syst. Environ. Physiol. 177, 447–460 (2007).
Article  Google Scholar 

30.
Claireaux, G., Webber, D., Lagardère, J.-P. & Kerr, S. Influence of water temperature and oxygenation on the aerobic metabolic scope of Atlantic cod (Gadus morhua). J. Sea Res. 44, 257–265 (2000).
Article  Google Scholar 

31.
Björnsson, B. & Steinarsson, A. The food-unlimited growth rate of Atlantic cod (Gadus morhua). Can. J. Fish. Aquat. Sci. 59, 494–502 (2002).
Article  Google Scholar 

32.
Morley, S., Peck, L., Sunday, J., Heiser, S. & Bates, A. Physiological acclimation and persistence of ectothermic species under extreme heat events. Glob. Ecol. Biogeogr. 28, 1018–1037 (2019).
Article  Google Scholar 

33.
Edgar, G. J. & Stuart-Smith, R. D. Systematic global assessment of reef fish communities by the Reef Life Survey program. Sci. Data 1, 140007 (2014).
Article  Google Scholar 

34.
Grafen, A. The phylogenetic regression. Philos. Trans. R. Soc. Lond. Ser. B, Biol. Sci. 326, 119–157 (1989).
CAS  Google Scholar 

35.
Orme, D., Freckleton, R., Thomas, G., Petzoldt, T. & Fritz, S. The caper package: comparative analysis of phylogenetics and evolution in R. R. Package Version 5, 1–36 (2013).
Google Scholar 

36.
Halsey, L. G., Butler, P. J. & Blackburn, T. M. A phylogenetic analysis of the allometry of diving. Am. Nat. 167, 276–287 (2006).
Article  Google Scholar 

37.
Michonneau, F., Brown, J. W. & Winter, D. J. rotl: an R package to interact with the Open Tree of Life data. Methods Ecol. Evol. 7, 1476–1481 (2016).
Article  Google Scholar 

38.
Freckleton, R. The seven deadly sins of comparative analysis. J. Evol. Biol. 22, 1367–1375 (2009).
CAS  Article  Google Scholar 

39.
Freckleton, R. P., Harvey, P. H. & Pagel, M. Phylogenetic analysis and comparative data: a test and review of evidence. Am. Nat. 160, 712–726 (2002).
CAS  Article  Google Scholar  More

1.
Roberts, C. M. et al. Marine biodiversity hotspots and conservation priorities for tropical reefs. Science 295, 1280–1284 (2002).
CAS  Article  Google Scholar 
2.
Kenchington, R. & Hutchings, P. Some implications of high biodiversity for management of tropical marine ecosystems—an Australian perspective. Diversity 10, 1 (2017).
Article  Google Scholar 

3.
Field, C. et al. Mangrove biodiversity and ecosystem function. Glob. Ecol. Biogeogr. Lett. 7, 3–14 (1998).
Article  Google Scholar 

4.
Honda, K., Nakamura, Y., Nakaoka, M., Uy, W. H. & Fortes, M. D. Habitat use by fishes in coral reefs, seagrass beds and mangrove habitats in the Philippines. PLoS ONE 8, e65735 (2013).
CAS  Article  Google Scholar 

5.
Nagelkerken, I. et al. Importance of mangroves, seagrass beds and the shallow coral reef as a nursery for important coral reef fishes, using a visual census technique. Estuar. Coast. Shelf Sci. 51, 31–44 (2000).
Article  Google Scholar 

6.
Unsworth, R. K. et al. High connectivity of Indo-Pacific seagrass fish assemblages with mangrove and coral reef habitats. Mar. Ecol. Prog. Ser. 353, 213–224 (2008).
Article  Google Scholar 

7.
Hoeksema, B. W. in Biogeography, Time, and Place: Distributions, Barriers, and Islands. Topics in Geobiology (ed. Renema, W.) 117–178 (Springer, 2007).

8.
Pitcher, C. R. et al. Seabed Biodiversity on the Continental Shelf of the Great Barrier Reef World Heritage Area AIMS/CSIRO/QM/QDPI Final Report to CRC Reef Research (CSIRO Marine and Atmospheric Research, 2007); http://www.frdc.com.au/Archived-Reports/FRDC%20Projects/2003-021-DLD.pdf

9.
Richards, Z. T. & Day, J. C. Biodiversity of the Great Barrier Reef—how adequately is it protected? PeerJ 6, e4747 (2018).
Article  Google Scholar 

10.
Harris, P. T. et al. Submerged banks in the Great Barrier Reef, Australia, greatly increase available coral reef habitat. ICES J. Mar. Sci. 70, 284–293 (2013).
Article  Google Scholar 

11.
Chin, A. in State of the Great Barrier Reef Report 2003 (ed. Chin, A.) 1–16 (Great Barrier Reef Marine Park Authority, 2003); https://hdl.handle.net/11017/669

12.
Whiteway, T., Smithers, S., Potter, A. & Brooke, B. Geological and Geomorphological Features of Outstanding Universal Value in the Great Barrier Reef World Heritage Area. Report prepared for SEWPaC (Coastal Marine and Climate Change Group, Geoscience Australia and School of Earth and Environmental Sciences, James Cook Univ., 2013).

13.
Mathews, E., Heap, A. & Woods, M. Inter-Reefal Seabed Sediments and Geomorphology of the Great Barrier Reef: A Spatial Analysis (Geoscience Australia, 2007).

14.
Huang, Z. et al. A conceptual surrogacy framework to evaluate the habitat potential of submarine canyons. Prog. Oceanogr. 169, 199–213 (2018).
Article  Google Scholar 

15.
McNeil, M. A., Webster, J. M., Beaman, R. J. & Graham, T. L. New constraints on the spatial distribution and morphology of the Halimeda bioherms of the Great Barrier Reef, Australia. Coral Reefs 35, 1343–1355 (2016).
Article  Google Scholar 

16.
Cumings, E. R. Reefs or bioherms? Geol. Soc. Am. Bull. 43, 331–352 (1932).
Article  Google Scholar 

17.
Klement, K. W. Practical classification of reefs and banks, bioherms and biostromes. Am. Assoc. Pet. Geol. Bull. 51, 167–168 (1967).

18.
Beaman, R. J. High-Resolution Depth Model for the Great Barrier Reef—30 m (Geoscience Australia 2017); https://doi.org/10.4225/25/5a207b36022d2

19.
Orme, G. The sedimentological importance of Halimeda in the development of back reef lithofacies, northern Great Barrier Reef (Australia). In Proc. 5th International Coral Reef Symposium 31–37 (1985).

20.
Orme, G. R. & Salama, M. S. Form and seismic stratigraphy of Halimeda banks in part of the northern Great Barrier Reef Province. Coral Reefs 6, 131–137 (1988).
Article  Google Scholar 

21.
Davies, P. in Encyclopaedia of Modern Coral Reefs—Structure, Form and Process (ed. Hopley, D.) 539–549 (Springer, 2011).

22.
Marshall, J. F. & Davies, P. J. Halimeda bioherms of the northern Great Barrier Reef. Coral Reefs 6, 139–148 (1988).
Article  Google Scholar 

23.
McNeil, M. A., Nothdurft, L. D., Dyriw, N. J., Webster, J. M. & Beaman, R. J. Morphotype differentiation in the Great Barrier Reef Halimeda bioherm carbonate factory: internal architecture and surface geomorphometrics. Depos. Rec. https://doi.org/10.1002/dep2.122 (2020).

24.
Great Barrier Reef Outlook Report 2009 (Great Barrier Reef Marine Park Authority, 2009).

25.
Great Barrier Reef Outlook Report 2014 (Great Barrier Reef Marine Park Authority, 2014).

26.
Ferrari, R. et al. Habitat structural complexity metrics improve predictions of fish abundance and distribution. Ecography 41, 1077–1091 (2017).
Article  Google Scholar 

27.
Ferrari, R. et al. Quantifying the response of structural complexity and community composition to environmental change in marine communities. Glob. Change Biol. 22, 1965–1975 (2016).
Article  Google Scholar 

28.
Dustan, P., Doherty, O. & Pardede, S. Digital reef rugosity estimates coral reef habitat complexity. PLoS ONE 8, e57386 (2013).
CAS  Article  Google Scholar 

29.
Pyle, R. L. & Copus, J. M. in Mesophotic Coral Ecosystems (eds Loya, Y. et al.) 3–27 (Springer International Publishing, 2019).

30.
Hopley, D., Smithers, S. G. & Parnell, K. E. The Geomorphology of the Great Barrier Reef: Development, Diversity, and Change (Cambridge Univ. Press, 2007).

31.
Colwell, R. K. & Coddington, J. A. Estimating terrestrial biodiversity through extrapolation. Philos. Trans. R. Soc. Lond. B Biol. Sci 345, 101–118 (1994).
CAS  Article  Google Scholar 

32.
Gotelli, N. J. & Colwell, R. K. Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecol. Lett. 4, 379–391 (2001).
Article  Google Scholar 

33.
Chao, A. Estimating the population size for capture–recapture data with unequal catchability. Biometrics 43, 783–791 (1987).
CAS  Article  Google Scholar 

34.
IUCN Red List of Threatened Species. Version 2020-2 (IUCN, 2020); https://www.iucnredlist.org

35.
Obura, D., Fenner, D., Hoeksema, B., Devantier, L. & Sheppard, C. Tubipora musica. IUCN Red List of Threatened Species 2008: e.T133065A3589084 (IUCN, 2008); https://doi.org/10.2305/IUCN.UK.2008.RLTS.T133065A3589084.en

36.
Turak, E., Sheppard, C. & Wood, E. Catalaphyllia jardinei. IUCN Red List of Threatened Species 2008: e.T132890A3479919 (IUCN, 2008); https://doi.org/10.2305/IUCN.UK.2008.RLTS.T132890A3479919.en

37.
Cappo, M. & Kelley, R. in Oceanographic Processes of Coral Reefs: Physical and Biological Links in the Great Barrier Reef (ed. Wolanski, E.) 161–187(CRC Press, 2000).

38.
Cappo, M., De’ath, G. & Speare, P. Inter-reef vertebrate communities of the Great Barrier Reef Marine Park determined by baited remote underwater video stations. Mar. Ecol. Prog. Ser. 350, 209–221 (2007).
Article  Google Scholar 

39.
Sambrook, K. et al. Beyond the reef: the widespread use of non-reef habitats by coral reef fishes. Fish Fish. (Oxf.) 20, 903–920 (2019).
Article  Google Scholar 

40.
Hurrey, L. P., Pitcher, C. R., Lovelock, C. E. & Schmidt, S. Macroalgal species richness and assemblage composition of the Great Barrier Reef seabed. Mar. Ecol. Prog. Ser. 492, 69–83 (2013).
Article  Google Scholar 

41.
Kämpf, J. & Chapman, P. Upwelling Systems of the World: A Scientific Journey to the Most Productive Marine Ecosystems (Springer International Publishing, 2016).

42.
Wolanski, E., Drew, E., Abel, K. M. & O’Brien, J. Tidal jets, nutrient upwelling and their influence on the productivity of the alga Halimeda in the Ribbon Reefs, Great Barrier Reef. Estuar. Coast. Shelf Sci. 26, 169–201 (1988).
CAS  Article  Google Scholar 

43.
Andrews, J. C. & Gentien, P. Upwelling as a source of nutrients for the Great Barrier Reef ecosystems: a solution to Darwin’s question?. Mar. Ecol. Prog. Ser. 8, 257–269 (1982).
Article  Google Scholar 

44.
Benthuysen, J. A., Tonin, H., Brinkman, R., Herzfeld, M. & Steinberg, C.Intrusive upwelling in the Central Great Barrier Reef. J. Geophys. Res. Oceans 121, 8395–8416 (2016).
Article  Google Scholar 

45.
Berkelmans, R., Weeks, S. J. & Steinberg, C. R. Upwelling linked to warm summers and bleaching on the Great Barrier Reef. Limnol. Oceanogr. 55, 2634–2644 (2010).
Article  Google Scholar 

46.
Hughes, T. P. et al. Global warming transforms coral reef assemblages. Nature 556, 492–496 (2018).
CAS  Article  Google Scholar 

47.
Bridge, T. C. L., Hughes, T. P., Guinotte, J. M. & Bongaerts, P. Call to protect all coral reefs. Nat. Clim. Change 3, 528–530 (2013).
Article  Google Scholar 

48.
Slattery, M., Lesser, M. P., Brazeau, D., Stokes, M. D. & Leichter, J. J.Connectivity and stability of mesophotic coral reefs. J. Exp. Mar. Biol. Ecol. 408, 32–41(2011).
Article  Google Scholar 

49.
Campbell, J. E., Fisch, J., Langdon, C. & Paul, V. J. Increased temperature mitigates the effects of ocean acidification in calcified green algae (Halimeda spp.). Coral Reefs 35, 357–368 (2016).
Article  Google Scholar 

50.
Mongin, M. et al. The exposure of the Great Barrier Reef to ocean acidification. Nat. Commun. 7, 10732 (2016).
CAS  Article  Google Scholar 

51.
Price, N. N., Hamilton, S. L., Tootell, J. S. & Smith, J. E. Species-specific consequences of ocean acidification for the calcareous tropical green algae Halimeda. Mar. Ecol. Prog. Ser. 440, 67–78 (2011).
CAS  Article  Google Scholar 

52.
Sinutok, S., Hill, R., Doblin, M. A., Kühl, M. & Ralph, P. J. Microenvironmental changes support evidence of photosynthesis and calcification inhibition in Halimeda under ocean acidification and warming. Coral Reefs 31, 1201–1213 (2012).
Article  Google Scholar 

53.
Wizemann, A., Meyer, F. W., Hofmann, L. C., Wild, C. & Westphal, H. Ocean acidification alters the calcareous microstructure of the green macro-alga Halimeda opuntia. Coral Reefs 34, 941–954 (2015).
Article  Google Scholar 

54.
Smithers, S., Harvey, N., Hopley, D. & Woodroffe, C. D. in Climate Change and the Great Barrier Reef: A Vulnerability Assessment (eds Johnson, J. E. & Marshall, P. A.) 667–716 (Great Barrier Reef Marine Park Authority, 2007).

55.
Cappo, M., Speare, P. & De’ath, G. Comparison of baited remote underwater video stations (BRUVS) and prawn (shrimp) trawls for assessments of fish biodiversity in inter-reefal areas of the Great Barrier Reef Marine Park. J. Exp. Mar. Biol. Ecol. 302, 123–152 (2004).
Article  Google Scholar 

56.
Pitcher, C. R. GBR Seabed Biodiversity Mapping Project: Phase 1. Draft Report to CRC-Reef (Australian Institute of Marine Science, 2002).

57.
Ugland, K. I., Gray, J. S. & Ellingsen, K. E. The species–accumulation curve and estimation of species richness. J. Anim. Ecol. 72, 888–897 (2003).
Article  Google Scholar 

58.
Clarke, K. & Gorley, R. PRIMER v7: User Manual/Tutorial (PRIMER-e, 2015).

59.
Clarke, K. R., Gorley, R. N., Somerfield, P. J. & Warwick, R. M. Change in Marine Communities: An Approach to Statistical Analysis and Interpretation 3rd edn (PRIMER-e, 2014).

60.
Ridgway, K. R., Dunn, J. R. & Wilkin, J. L. Ocean interpolation by four-dimensional weighted least squares—application to the waters around Australasia. J. Atmos. Ocean. Technol. 19, 1357–1375 (2002).
Article  Google Scholar  More

1.
Merlino G, Barozzi A, Michoud G, Ngugi DK, Daffonchio D. Microbial ecology of deep-sea hypersaline anoxic basins. FEMS Microbiol Ecol. 2018;94:1–15.
Article  CAS  Google Scholar 
2.
Antunes A, Ngugi DK, Stingl U. Microbiology of the Red Sea (and other) deep-sea anoxic brine lakes. Environ Microbiol Rep. 2011;3:416–33.
PubMed  Article  PubMed Central  Google Scholar 

3.
La Cono V, Smedile F, Bortoluzzi G, Arcadi E, Maimone G, Messina E, et al. Unveiling microbial life in new deep-sea hypersaline Lake Thetis. Part I: prokaryotes and environmental settings. Environ Microbiol. 2011;13:2250–68.
PubMed  Article  PubMed Central  Google Scholar 

4.
Daffonchio D, Borin S, Brusa T, Brusetti L, van der Wielen PWJJ, Bolhuis H, et al. Stratified prokaryote network in the oxic-anoxic transition of a deep-sea halocline. Nature. 2006;440:203–7.
CAS  PubMed  Article  PubMed Central  Google Scholar 

5.
van der Wielen PWJJ, Bolhuis H, Borin S, Daffonchio D, Corselli C, Giuliano L, et al. The enigma of prokaryotic life in deep hypersaline anoxic basins. Science. 2005;307:121–3.
PubMed  Article  CAS  PubMed Central  Google Scholar 

6.
Borin S, Brusetti L, Mapelli F, D’Auria G, Brusa T, Marzorati M, et al. Sulfur cycling and methanogenesis primarily drive microbial colonization of the highly sulfidic Urania deep hypersaline basin. Proc Natl Acad Sci USA. 2009;106:9151–6.
CAS  PubMed  Article  PubMed Central  Google Scholar 

7.
Joye SB, Samarkin VA, Orcutt BN, MacDonald IR, Hinrichs K-U, Elvert M, et al. Metabolic variability in seafloor brines revealed by carbon and sulphur dynamics. Nat Geosci. 2009;2:349–54.
CAS  Article  Google Scholar 

8.
Guan Y, Hikmawan T, Antunes A, Ngugi DK, Stingl U. Diversity of methanogens and sulfate-reducing bacteria in the interfaces of five deep-sea anoxic brines of the Red Sea. Res Microbiol. 2015;166:688–99.
PubMed  Article  PubMed Central  Google Scholar 

9.
Pachiadaki MG, Yakimov M, LaCono V, Leadbetter E, Edgcomb V. Unveiling microbial activities along the halocline of Thetis, a deep-sea hypersaline anoxic basin. ISME J. 2014;8:1–12.
Article  CAS  Google Scholar 

10.
Borin S, Mapelli F, Rolli E, Song B, Tobias C, Schmid MC, et al. Anammox bacterial populations in deep marine hypersaline gradient systems. Extremophiles. 2013;17:289–99.
CAS  PubMed  Article  PubMed Central  Google Scholar 

11.
Yakimov MM, La Cono V, Spada GL, Bortoluzzi G, Messina E, Smedile F, et al. Microbial community of the deep-sea brine Lake Kryos seawater-brine interface is active below the chaotropicity limit of life as revealed by recovery of mRNA. Environ Microbiol. 2015;17:364–82.
CAS  PubMed  Article  PubMed Central  Google Scholar 

12.
Ngugi DK, Blom J, Alam I, Rashid M, Ba-Alawi W, Zhang G, et al. Comparative genomics reveals adaptations of a halotolerant thaumarchaeon in the interfaces of brine pools in the Red Sea. ISME J. 2015;9:396–411.
Article  CAS  Google Scholar 

13.
Ngugi DK, Blom J, Stepanauskas R, Stingl U. Diversification and niche adaptations of Nitrospina-like bacteria in the polyextreme interfaces of Red Sea brines. ISME J. 2016;10:1383–99.
CAS  PubMed  Article  PubMed Central  Google Scholar 

14.
Zhang W, Ding W, Yang B, Tian RM, Gu S, Luo H, et al. Genomic and transcriptomic evidence for carbohydrate consumption among microorganisms in a cold seep brine pool. Front Microbiol. 2016;7:1825.
PubMed  PubMed Central  Google Scholar 

15.
Bougouffa S, Yang JK, Lee OO, Wang Y, Batang Z, Al-Suwailem A, et al. Distinctive microbial community structure in highly stratified deep-sea brine water columns. Appl Environ Microbiol. 2013;79:3425–37.
CAS  PubMed  PubMed Central  Article  Google Scholar 

16.
Abdallah RZ, Adel M, Ouf A, Sayed A, Ghazy MA, Alam I, et al. Aerobic methanotrophic communities at the Red Sea brine-seawater interface. Front Microbiol. 2014;5:1–16.
Article  Google Scholar 

17.
Erguder TH, Boon N, Wittebolle L, Marzorati M, Verstraete W. Environmental factors shaping the ecological niches of ammonia-oxidizing archaea. FEMS Microbiol Rev. 2009;33:855–69.
CAS  PubMed  Article  PubMed Central  Google Scholar 

18.
Gunde-Cimerman N, Plemenitaš A, Oren A. Strategies of adaptation of microorganisms of the three domains of life to high salt concentrations. FEMS Microbiol Rev. 2018;42:353–75.
CAS  PubMed  Article  PubMed Central  Google Scholar 

19.
Yakimov M, La Cono V, Denaro R, D’Auria G, Decembrini F, Timmis KN, et al. Primary producing prokaryotic communities of brine, interface and seawater above the halocline of deep anoxic lake L’Atalante, Eastern Mediterranean Sea. ISME J. 2007;1:743–55.
CAS  PubMed  Article  PubMed Central  Google Scholar 

20.
Brune A, Frenzel P, Cypionka H. Life at the oxic–anoxic interface: microbial activities and adaptations. FEMS Microbiol Rev. 2000;24:691–710.
CAS  PubMed  Article  Google Scholar 

21.
Oren A. Thermodynamic limits to microbial life at high salt concentrations. Environ Microbiol. 2011;13:1908–23.
CAS  PubMed  Article  Google Scholar 

22.
Baumann A, Richter H, Schoell M. Suakin deep: brines and hydrothermal sediments in the deepest part of the Red Sea. Geol Rundsch. 1973;62:684–97.
CAS  Article  Google Scholar 

23.
Backer H, Schoell M. New deeps with brines and metalliferous sediments in the red sea. Nat Phys Sci. 1972;240:153–8.
Article  Google Scholar 

24.
Schmidt, M, Al-Farawati R, Botz R. Geochemical classification of brine-filled Red Sea Deeps. In: Rasul NMA, Stewart ICF, editors. The Red Sea. Springer; 2015. p. 219–233.

25.
Calleja ML, Al-Otaibi N, Morán XAG. Dissolved organic carbon contribution to oxygen respiration in the central Red Sea. Sci Rep. 2019;9:4690.
PubMed  PubMed Central  Article  CAS  Google Scholar 

26.
Duarte CM, Røstad A, Michoud G, Barozzi A, Merlino G, Delgado-Huertas A, et al. Discovery of Afifi, the shallowest and southernmost brine pool reported in the Red Sea. Sci Rep. 2020;10:910.
CAS  PubMed  PubMed Central  Article  Google Scholar 

27.
Wood SN. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J R Stat Soc Ser B Stat Methodol. 2011;73:3–36.
Article  Google Scholar 

28.
Salata GG, Roelke LA, Cifuentes LA. A rapid and precise method for measuring stable carbon isotope ratios of dissolved inorganic carbon. Mar Chem. 2000;69:153–61.
CAS  Article  Google Scholar 

29.
McIlvin MR, Altabet MA. Chemical conversion of nitrate and nitrite to nitrous oxide for nitrogen and oxygen isotopic analysis in freshwater and seawater. Anal Chem. 2005;77:5589–95.
CAS  PubMed  Article  Google Scholar 

30.
Green MR, Sambrook J. Isolation of high-molecular-weight DNA using organic solvents. Cold Spring Harb Protoc. 2017;2017:pdb.prot093450.

31.
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014;30:2114–20.
CAS  PubMed  PubMed Central  Article  Google Scholar 

32.
Bushnell B. BBMap short read aligner. https://sourceforge.net/projects/bbmap/. 2016. Accessed 03 Feb 2021.

33.
Andrews S. FastQC A quality control tool for high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/. 2010. Accessed 30 Jan 2021.

34.
Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil P-A, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36:996–1004.
CAS  PubMed  Article  PubMed Central  Google Scholar 

35.
Parks DH, Beiko RG. Measures of phylogenetic differentiation provide robust and complementary insights into microbial communities. ISME J. 2013;7:173–83.
CAS  PubMed  Article  PubMed Central  Google Scholar 

36.
Paradis E, Schliep K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics. 2019;35:526–8.
CAS  PubMed  Article  PubMed Central  Google Scholar 

37.
Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27:824–34.
CAS  PubMed  PubMed Central  Article  Google Scholar 

38.
Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 2010;11:119.
PubMed  PubMed Central  Article  CAS  Google Scholar 

39.
Huang Y, Niu B, Gao Y, Fu L, Li W. CD-HIT Suite: a web server for clustering and comparing biological sequences. Bioinformatics. 2010;26:680–2.
CAS  PubMed  PubMed Central  Article  Google Scholar 

40.
Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.
CAS  PubMed  PubMed Central  Article  Google Scholar 

41.
Nayfach S, Pollard KS. Average genome size estimation improves comparative metagenomics and sheds light on the functional ecology of the human microbiome. Genome Biol. 2015;16:51.
PubMed  PubMed Central  Article  CAS  Google Scholar 

42.
Alam I, Antunes A, Kamau AA, Alawi WB, Kalkatawi M, Stingl U, et al. INDIGO – Integrated data warehouse of microbial genomes with examples from the red sea extremophiles. PLoS ONE. 2013;8:e82210.
PubMed  PubMed Central  Article  CAS  Google Scholar 

43.
Llorens-Marès T, Yooseph S, Goll J, Hoffman J, Vila-Costa M, Borrego CM, et al. Connecting biodiversity and potential functional role in modern euxinic environments by microbial metagenomics. ISME J. 2015;9:1648–61.
PubMed  PubMed Central  Article  CAS  Google Scholar 

44.
Lüke C, Speth DR, Kox MAR, Villanueva L, Jetten MSM. Metagenomic analysis of nitrogen and methane cycling in the Arabian Sea oxygen minimum zone. PeerJ. 2016;4:e1924.
PubMed  PubMed Central  Article  CAS  Google Scholar 

45.
Wickham H. ggplot2: elegant graphics for data analysis. Springer New York; 2016.

46.
Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: community ecology package. https://github.com/vegandevs/vegan, https://cran.r-project.org/package=vegan. 2017. Accessed 28 November 2020.

47.
Blanchet FG, Legendre P, Borcard D. Forward selection of explanatory variables. Ecology. 2008;89:2623–32.
PubMed  Article  PubMed Central  Google Scholar 

48.
Kang DD, Froula J, Egan R, Wang Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ. 2015;3:e1165.
PubMed  PubMed Central  Article  CAS  Google Scholar 

49.
Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2014;12:59–60.
PubMed  Article  CAS  PubMed Central  Google Scholar 

50.
Olm MR, Brown CT, Brooks B, Banfield JF. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017;11:2864–8.
CAS  PubMed  PubMed Central  Article  Google Scholar 

51.
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  PubMed  PubMed Central  Article  Google Scholar 

52.
Woodcroft BJ, Singleton CM, Boyd JA, Evans PN, Emerson JB, Zayed AAF, et al. Genome-centric view of carbon processing in thawing permafrost. Nature. 2018;560:49–54.
CAS  PubMed  Article  PubMed Central  Google Scholar 

53.
Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.
CAS  Article  Google Scholar 

54.
Matsen FA, Kodner RB, Armbrust EV. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinformatics. 2010;11:538.
PubMed  PubMed Central  Article  Google Scholar 

55.
Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2020;36:1925–27.
CAS  Google Scholar 

56.
Castresana J. Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Mol Biol Evol. 2000;17:540–52.
CAS  PubMed  Article  PubMed Central  Google Scholar 

57.
Darriba D, Taboada GL, Doallo R, Posada D. ProtTest 3: fast selection of best-fit models of protein evolution. Bioinformatics. 2011;27:1164–5.
CAS  PubMed  PubMed Central  Article  Google Scholar 

58.
Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3.
CAS  PubMed  PubMed Central  Article  Google Scholar 

59.
Rambaut A. FigTree. http://tree.bio.ed.ac.uk/software/figtree/. 2009. Accessed 04 Jan 2011.

60.
Contreras-Moreira B, Vinuesa P. GET_HOMOLOGUES, a versatile software package for scalable and robust microbial pangenome analysis. Appl Environ Microbiol. 2013;79:7696–701.
CAS  PubMed  PubMed Central  Article  Google Scholar 

61.
Li L, Stoeckert CJ, Roos DS. OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res. 2003;13:2178–89.
CAS  PubMed  PubMed Central  Article  Google Scholar 

62.
Warnes GR, Bolker B, Bonebakker L, Gentleman R, Huber W, Liaw A, et al. gplots: various r programming tools for plotting data. 2019. https://cran.r-project.org/package=gplots. Accessed 28 Nov 2020.

63.
Long A, Heitman J, Tobias C, Philips R, Song B. Co-occurring anammox, denitrification, and codenitrification in agricultural soils. Appl Environ Microbiol. 2013;79:168–76.
CAS  PubMed  PubMed Central  Article  Google Scholar 

64.
Shu D, He Y, Yue H, Wang Q. Metagenomic and quantitative insights into microbial communities and functional genes of nitrogen and iron cycling in twelve wastewater treatment systems. Chem Eng J. 2016;290:21–30.
CAS  Article  Google Scholar 

65.
Augustin N, Devey CW, van der Zwan FM. A Modern view on the Red Sea Rift: tectonics, volcanism and salt blankets. In: Rasul NMA, Stewart ICF, editors. Geological setting, palaeoenvironment and archaeology of the Red Sea. Springer International Publishing, 2019. p. 37–52.

66.
Bristow LA, Dalsgaard T, Tiano L, Mills DB, Bertagnolli AD, Wright JJ, et al. Ammonium and nitrite oxidation at nanomolar oxygen concentrations in oxygen minimum zone waters. Proc Natl Acad Sci USA. 2016;113:10601–6.
CAS  PubMed  Article  Google Scholar 

67.
Ward BB, Kilpatrick KA. Relationship between substrate concentration and oxidation of ammonium and methane in a stratified water column. Cont Shelf Res. 1990;10:1193–208.
Article  Google Scholar 

68.
Stedmon CA, Thomas DN, Papadimitriou S, Granskog MA, Dieckmann GS. Using fluorescence to characterize dissolved organic matter in Antarctic sea ice brines. J Geophys Res. 2011;116:G03027.
Google Scholar 

69.
Taylor PG, Townsend AR. Stoichiometric control of organic carbon–nitrate relationships from soils to the sea. Nature. 2010;464:1178–81.
CAS  PubMed  Article  Google Scholar 

70.
Granger J, Sigman DM, Lehmann MF, Tortell PD. Nitrogen and oxygen isotope fractionation during dissimilatory nitrate reduction by denitrifying bacteria. Limnol Oceanogr. 2008;53:2533–45.
CAS  Article  Google Scholar 

71.
Nigro LM, Hyde AS, MacGregor BJ. Teske A. Phylogeography, salinity adaptations and metabolic potential of the candidate division kb1 bacteria based on a partial single cell genome. Front Microbiol. 2016;7:1266.

72.
Mwirichia R, Alam I, Rashid M, Vinu M, Ba-Alawi W, Anthony Kamau A, et al. Metabolic traits of an uncultured archaeal lineage -MSBL1- from brine pools of the Red Sea. Sci Rep. 2016;6:19181.
CAS  PubMed  PubMed Central  Article  Google Scholar 

73.
Adam PS, Borrel G, Brochier-Armanet C, Gribaldo S. The growing tree of Archaea: new perspectives on their diversity, evolution and ecology. ISME J. 2017;11:2407–25.
PubMed  PubMed Central  Article  Google Scholar 

74.
Pereira AD, Leal CD, Dias MF, Etchebehere C, Chernicharo CAL, de Araújo JC. Effect of phenol on the nitrogen removal performance and microbial community structure and composition of an anammox reactor. Bioresour Technol. 2014;166:103–11.
CAS  PubMed  Article  PubMed Central  Google Scholar 

75.
Yamada T, Sekiguchi Y. Anaerolineaceae. In: Trujillo ME, Dedysh S, DeVos P, Hedlund B, Kämpfer P, Rainey FA et al, editors. Bergey’s manual of systematics of archaea and bacteria. Wiley, 2018. p. 1–5.

76.
Browne P, Tamaki H, Kyrpides N, Woyke T, Goodwin L, Imachi H, et al. Genomic composition and dynamics among Methanomicrobiales predict adaptation to contrasting environments. ISME J. 2017;11:87–99.
CAS  PubMed  Article  PubMed Central  Google Scholar 

77.
Youssef NH, Ashlock-Savage KN, Elshahed MS. Phylogenetic diversities and community structure of members of the extremely halophilic archaea (order Halobacteriales) in multiple saline sediment habitats. Appl Environ Microbiol. 2012;78:1332–44.
CAS  PubMed  PubMed Central  Article  Google Scholar 

78.
Sorokin DY, Merkel AY, Abbas B, Makarova KS, Rijpstra WIC, Koenen M, et al. Methanonatronarchaeum thermophilum gen. nov., sp. nov. and ‘Candidatus Methanohalarchaeum thermophilum’, extremely halo(natrono)philic methyl-reducing methanogens from hypersaline lakes comprising a new euryarchaeal class Methanonatronarchaeia classis nov. Int J Syst Evol Microbiol. 2018;68:2199–208.
CAS  PubMed  PubMed Central  Article  Google Scholar 

79.
van Niftrik L, Geerts WJC, van Donselaar EG, Humbel BM, Webb RI, Fuerst JA, et al. Linking ultrastructure and function in four genera of anaerobic ammonium-oxidizing bacteria: cell plan, glycogen storage, and localization of cytochrome c proteins. J Bacteriol. 2008;190:708–17.
PubMed  Article  CAS  PubMed Central  Google Scholar 

80.
Muck S, De Corte D, Clifford EL, Bayer B, Herndl GJ, Sintes E. Niche differentiation of aerobic and anaerobic ammonia oxidizers in a high latitude deep oxygen minimum zone. Front Microbiol. 2019;10:2141.
PubMed  PubMed Central  Article  Google Scholar 

81.
Jensen MM, Lam P, Revsbech NP, Nagel B, Gaye B, Jetten MS, et al. Intensive nitrogen loss over the Omani Shelf due to anammox coupled with dissimilatory nitrite reduction to ammonium. ISME J. 2011;5:1660–70.
CAS  PubMed  PubMed Central  Article  Google Scholar 

82.
Tian R, Ning D, He Z, Zhang P, Spencer SJ, Gao S, et al. Small and mighty: adaptation of superphylum Patescibacteria to groundwater environment drives their genome simplicity. Microbiome. 2020;8:51.
PubMed  PubMed Central  Article  Google Scholar 

83.
Yakimov MM, La Cono V, Slepak VZ, La Spada G, Arcadi E, Messina E, et al. Microbial life in the Lake Medee, the largest deep-sea salt-saturated formation. Sci Rep. 2013;3:3554.
PubMed  PubMed Central  Article  Google Scholar 

84.
Jayakumar A, Chang BX, Widner B, Bernhardt P, Mulholland MR, Ward BB. Biological nitrogen fixation in the oxygen-minimum region of the eastern tropical North Pacific ocean. ISME J. 2017;11:2356–67.
CAS  PubMed  PubMed Central  Article  Google Scholar 

85.
Woebken D, Lam P, Kuypers MMM, Naqvi SWA, Kartal B, Strous M, et al. A microdiversity study of anammox bacteria reveals a novel Candidatus scalindua phylotype in marine oxygen minimum zones. Environ Microbiol. 2008;10:3106–19.
CAS  PubMed  Article  PubMed Central  Google Scholar 

86.
Speth DR, Lagkouvardos I, Wang Y, Qian P-Y, Dutilh BE, Jetten MSM. Draft genome of Scalindua rubra, obtained from the interface above the discovery deep brine in the red sea, sheds light on potential salt adaptation strategies in anammox bacteria. Micro Ecol. 2017;74:1–5.
CAS  Article  Google Scholar 

87.
Ali M, Shaw DR, Saikaly PE. Application of an enrichment culture of the marine anammox bacterium “Ca. Scalindua” for nitrogen removal under moderate salinity and in the presence of organic carbon. Water Res. 2020;170:115345.
CAS  PubMed  Article  PubMed Central  Google Scholar 

88.
Ren M, Feng X, Huang Y, Wang H, Hu Z, Clingenpeel S, et al. Phylogenomics suggests oxygen availability as a driving force in Thaumarchaeota evolution. ISME J. 2019;13:2150–61.
PubMed  PubMed Central  Article  Google Scholar 

89.
Awata T, Goto Y, Kindaichi T, Ozaki N, Ohashi A. Nitrogen removal using an anammox membrane bioreactor at low temperature. Water Sci Technol. 2015;72:2148–53.
CAS  PubMed  Article  PubMed Central  Google Scholar 

90.
Pappalardo RT. Seeking Europa’s ocean. Proc Int Astron Union. 2010;6:101–14.
Article  Google Scholar 

91.
Martínez GM, Renno NO. Water and brines on mars: current evidence and implications for MSL. Space Sci Rev. 2013;175:29–51.
Article  CAS  Google Scholar 

92.
Jokinen SA, Virtasalo JJ, Jilbert T, Kaiser J, Dellwig O, Arz HW, et al. A 1500-year multiproxy record of coastal hypoxia from the northern Baltic Sea indicates unprecedented deoxygenation over the 20th century. Biogeosciences. 2018;15:3975–4001.
CAS  Article  Google Scholar  More

1.
Behrenfeld MJ, Randerson JT, McClain CR, Feldman GC, Los SO, Tucker CJ, et al. Biospheric primary production during an ENSO transition. Science. 2001;291:2594–7.
CAS  PubMed  Article  PubMed Central  Google Scholar 
2.
Buchan A, LeCleir GR, Gulvik CA, González JM. Master recyclers: features and functions of bacteria associated with phytoplankton blooms. Nat Rev Microbiol. 2014;12:686–98.
CAS  PubMed  Article  PubMed Central  Google Scholar 

3.
Field CB, Behrenfeld MJ, Randerson JT, Falkowski P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science. 1998;281:237–40.
CAS  PubMed  Article  PubMed Central  Google Scholar 

4.
Needham DM, Fuhrman JA. Pronounced daily succession of phytoplankton, archaea and bacteria following a spring bloom. Nat Microbiol. 2016;1:16005.
CAS  PubMed  Article  PubMed Central  Google Scholar 

5.
Teeling H, Fuchs BM, Bennke CM, Krüger K, Chafee M, Kappelmann L, et al. Recurring patterns in bacterioplankton dynamics during coastal spring algae blooms. eLife. 2016;5:e11888.
PubMed  PubMed Central  Article  Google Scholar 

6.
Teeling H, Fuchs BM, Becher D, Klockow C, Gardebrecht A, Bennke CM, et al. Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom. Science. 2012;336:608–11.
CAS  PubMed  Article  PubMed Central  Google Scholar 

7.
Williams TJ, Wilkins D, Long E, Evans F, DeMaere MZ, Raftery MJ, et al. The role of planktonic Flavobacteria in processing algal organic matter in coastal East Antarctica revealed using metagenomics and metaproteomics. Environ Microbiol. 2013;15:1302–17.
CAS  PubMed  Article  PubMed Central  Google Scholar 

8.
Chafee M, Fernàndez-Guerra A, Buttigieg PL, Gerdts G, Eren AM, Teeling H, et al. Recurrent patterns of microdiversity in a temperate coastal marine environment. ISME J. 2018;12:237–52.
PubMed  Article  PubMed Central  Google Scholar 

9.
Francis TB, Krüger K, Fuchs BM, Teeling H, Amann RI. CandidatusProsiliicoccus vernus, a spring phytoplankton bloom associated member of the Flavobacteriaceae. Syst Appl Microbiol. 2019;42:41–53.
CAS  PubMed  Article  PubMed Central  Google Scholar 

10.
Krüger K, Chafee M, Francis TB, Glavina del Rio T, Becher D, Schweder T, et al. In marine Bacteroidetes the bulk of glycan degradation during algae blooms is mediated by few clades using a restricted set of genes. ISME J. 2019;13:2800–16.
PubMed  PubMed Central  Article  CAS  Google Scholar 

11.
Needham DM, Fichot EB, Wang E, Berdjeb L, Cram JA, Fichot CG, et al. Dynamics and interactions of highly resolved marine plankton via automated high-frequency sampling. ISME J. 2018;12:2417–32.
CAS  PubMed  PubMed Central  Article  Google Scholar 

12.
Cottrell MT, Kirchman DL. Natural assemblages of marine Proteobacteria and members of the Cytophaga-Flavobacter cluster consuming low- and high-molecular-weight dissolved organic matter. Appl Environ Microbiol. 2000;66:1692–7.
CAS  PubMed  PubMed Central  Article  Google Scholar 

13.
Fernández-Gomez B, Richter M, Schüler M, Pinhassi J, Acinas SG, González JM, et al. Ecology of marine Bacteroidetes: a comparative genomics approach. ISME J. 2013;7:1026–37.
PubMed  PubMed Central  Article  CAS  Google Scholar 

14.
Grondin JM, Tamura K, Déjean G, Abbott DW, Brumer H. Polysaccharide utilization loci: fueling microbial communities. J Bacteriol. 2017;199:e00860–16.
CAS  PubMed  PubMed Central  Article  Google Scholar 

15.
Kappelmann L, Krüger K, Hehemann J-H, Harder J, Markert S, Unfried F, et al. Polysaccharide utilization loci of North Sea Flavobacteriia as basis for using SusC/D-protein expression for predicting major phytoplankton glycans. ISME J. 2019;13:76–91.
CAS  PubMed  Article  Google Scholar 

16.
Kirchman DL. The ecology of Cytophaga–Flavobacteria in aquatic environments. FEMS Microbiol Ecol. 2002;39:91–100.
CAS  PubMed  Google Scholar 

17.
Thomas F, Hehemann J-H, Rebuffet E, Czjzek M, Michel G. Environmental and gut Bacteroidetes: the food connection. Front Microbiol. 2011;2:93–93.
PubMed  PubMed Central  Article  Google Scholar 

18.
Glenwright AJ, Pothula KR, Bhamidimarri SP, Chorev DS, Baslé A, Firbank SJ, et al. Structural basis for nutrient acquisition by dominant members of the human gut microbiota. Nature. 2017;541:407–11.
CAS  PubMed  PubMed Central  Article  Google Scholar 

19.
Joglekar P, Sonnenburg ED, Higginbottom SK, Earle KA, Morland C, Shapiro-Ward S, et al. Genetic variation of the SusC/SusD homologs from a polysaccharide utilization locus underlies divergent fructan specificities and functional adaptation in Bacteroides thetaiotaomicron strains. mSphere. 2018;3:e00185–18.
CAS  PubMed  PubMed Central  Article  Google Scholar 

20.
Cuskin F, Lowe EC, Temple MJ, Zhu Y, Cameron EA, Pudlo NA, et al. Human gut Bacteroidetes can utilize yeast mannan through a selfish mechanism. Nature. 2015;517:165–9.
CAS  PubMed  PubMed Central  Article  Google Scholar 

21.
Reintjes G, Arnosti C, Fuchs BM, Amann R. An alternative polysaccharide uptake mechanism of marine bacteria. ISME J. 2017;11:1640–50.
CAS  PubMed  PubMed Central  Article  Google Scholar 

22.
Hehemann J-H, Truong LV, Unfried F, Welsch N, Kabisch J, Heiden SE, et al. Aquatic adaptation of a laterally acquired pectin degradation pathway in marine Gammaproteobacteria. Environ Microbiol. 2017;19:2320–33.
CAS  PubMed  Article  Google Scholar 

23.
Neumann AM, Balmonte JP, Berger M, Giebel H-A, Arnosti C, Voget S, et al. Different utilization of alginate and other algal polysaccharides by marine Alteromonas macleodii ecotypes. Environ Microbiol. 2015;17:3857–68.
CAS  PubMed  Article  Google Scholar 

24.
Mirus O, Strauss S, Nicolaisen K, von Haeseler A, Schleiff E. TonB-dependent transporters and their occurrence in Cyanobacteria. BMC Biol. 2009;7:68.
PubMed  PubMed Central  Article  CAS  Google Scholar 

25.
Gudmundsdottir A, Bell PE, Lundrigan MD, Bradbeer C, Kadner RJ. Point mutations in a conserved region (TonB box) of Escherichia coli outer membrane protein BtuB affect vitamin B12 transport. J Bacteriol. 1989;171:6526–33.
CAS  PubMed  PubMed Central  Article  Google Scholar 

26.
Köster W, Braun V. Iron (III) hydroxamate transport into Escherichia coli. Substrate binding to the periplasmic FhuD protein. J Biol Chem. 1990;265:21407–10.
PubMed  Article  Google Scholar 

27.
Schauer K, Gouget B, Carrière M, Labigne A, Reuse HD. Novel nickel transport mechanism across the bacterial outer membrane energized by the TonB/ExbB/ExbD machinery. Mol Microbiol. 2007;63:1054–68.
CAS  PubMed  Article  Google Scholar 

28.
Reeves AR, D’Elia JN, Frias J, Salyers AA. A Bacteroides thetaiotaomicron outer membrane protein that is essential for utilization of maltooligosaccharides and starch. J Bacteriol. 1996;178:823–30.
CAS  PubMed  PubMed Central  Article  Google Scholar 

29.
Cheng Q, Yu MC, Reeves AR, Salyers AA. Identification and characterization of a Bacteroides gene, csuF, which encodes an outer membrane protein that is essential for growth on chondroitin sulfate. J Bacteriol. 1995;177:3721–7.
CAS  PubMed  PubMed Central  Article  Google Scholar 

30.
Neugebauer H, Herrmann C, Kammer W, Schwarz G, Nordheim A, Braun V. ExbBD-dependent transport of maltodextrins through the novel MalA protein across the outer membrane of Caulobacter crescentus. J Bacteriol. 2005;187:8300–11.
CAS  PubMed  PubMed Central  Article  Google Scholar 

31.
Noinaj N, Guillier M, Barnard TJ, Buchanan SK. TonB-dependent transporters: regulation, structure, and function. Annu Rev Microbiol. 2010;64:43–60.
CAS  PubMed  PubMed Central  Article  Google Scholar 

32.
Schauer K, Rodionov DA, de Reuse H. New substrates for TonB-dependent transport: do we only see the ‘tip of the iceberg’? Trends Biochem Sci. 2008;33:330–8.
CAS  PubMed  Article  PubMed Central  Google Scholar 

33.
Lapébie P, Lombard V, Drula E, Terrapon N, Henrissat B. Bacteroidetes use thousands of enzyme combinations to break down glycans. Nat Commun. 2019;10:2043.
PubMed  PubMed Central  Article  CAS  Google Scholar 

34.
Cantarel BL, Coutinho PM, Rancurel C, Bernard T, Lombard V, Henrissat B. The Carbohydrate-Active EnZymes database (CAZy): an expert resource for Glycogenomics. Nucleic Acids Res. 2009;37:D233–8.
CAS  PubMed  Article  PubMed Central  Google Scholar 

35.
Foley MH, Cockburn DW, Koropatkin NM. The Sus operon: a model system for starch uptake by the human gut Bacteroidetes. Cell Mol Life Sci. 2016;73:2603–17.
CAS  PubMed  PubMed Central  Article  Google Scholar 

36.
Terrapon N, Lombard V, Gilbert HJ, Henrissat B. Automatic prediction of polysaccharide utilization loci in Bacteroidetes species. Bioinformatics. 2015;31:647–55.
CAS  PubMed  Article  PubMed Central  Google Scholar 

37.
Terrapon N, Lombard V, Drula E, Lapébie P, Al-Masaudi S, Gilbert HJ, et al. PULDB: the expanded database of polysaccharide utilization loci. Nucleic Acids Res. 2017;46:D677–83.
PubMed Central  Article  CAS  Google Scholar 

38.
Bergauer K, Fernandez-Guerra A, Garcia JA, Sprenger RR, Stepanauskas R, Pachiadaki MG, et al. Organic matter processing by microbial communities throughout the Atlantic water column as revealed by metaproteomics. Proc Natl Acad Sci USA. 2018;115:E400–8.
CAS  PubMed  Article  PubMed Central  Google Scholar 

39.
Dong H-P, Hong Y-G, Lu S, Xie L-Y. Metaproteomics reveals the major microbial players and their biogeochemical functions in a productive coastal system in the northern South China Sea. Environ Microbiol Rep. 2014;6:683–95.
CAS  PubMed  Article  PubMed Central  Google Scholar 

40.
McCarren J, Becker JW, Repeta DJ, Shi Y, Young CR, Malmstrom RR, et al. Microbial community transcriptomes reveal microbes and metabolic pathways associated with dissolved organic matter turnover in the sea. Proc Natl Acad Sci USA. 2010;107:16420–7.
CAS  PubMed  Article  Google Scholar 

41.
Morris RM, Nunn BL, Frazar C, Goodlett DR, Ting YS, Rocap G. Comparative metaproteomics reveals ocean-scale shifts in microbial nutrient utilization and energy transduction. ISME J. 2010;4:673–85.
CAS  PubMed  Article  Google Scholar 

42.
Williams TJ, Long E, Evans F, DeMaere MZ, Lauro FM, Raftery MJ, et al. A metaproteomic assessment of winter and summer bacterioplankton from Antarctic Peninsula coastal surface waters. ISME J. 2012;6:1883–900.
CAS  PubMed  PubMed Central  Article  Google Scholar 

43.
Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27:824–34.
CAS  PubMed  PubMed Central  Article  Google Scholar 

44.
Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 2010;11:119.
Article  CAS  Google Scholar 

45.
Besemer J, Lomsadze A, Borodovsky M. GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions. Nucleic Acids Res. 2001;29:2607–18.
CAS  PubMed  PubMed Central  Article  Google Scholar 

46.
Orellana LH, Francis TB, Krüger K, Teeling H, Müller M-C, Fuchs BM, et al. Niche differentiation among annually recurrent coastal Marine Group II Euryarchaeota. ISME J. 2019;13:3024–36.
CAS  PubMed  PubMed Central  Article  Google Scholar 

47.
Eren AM, Esen ÖC, Quince C, Vineis JH, Morrison HG, Sogin ML, et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ. 2015;3:e1319.
PubMed  PubMed Central  Article  Google Scholar 

48.
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  PubMed  PubMed Central  Article  Google Scholar 

49.
Deusch S, Seifert J. Catching the tip of the iceberg—evaluation of sample preparation protocols for metaproteomic studies of the rumen microbiota. Proteomics. 2015;15:3590–5.
CAS  PubMed  Article  PubMed Central  Google Scholar 

50.
Li W, Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006;22:1658–9.
CAS  PubMed  Article  Google Scholar 

51.
Nesvizhskii AI, Keller A, Kolker E, Aebersold R. A statistical model for identifying proteins by tandem mass spectrometry. Anal Chem. 2003;75:4646–58.
CAS  PubMed  Article  Google Scholar 

52.
Florens L, Carozza MJ, Swanson SK, Fournier M, Coleman MK, Workman JL, et al. Analyzing chromatin remodeling complexes using shotgun proteomics and normalized spectral abundance factors. Methods. 2006;40:303–11.
CAS  PubMed  PubMed Central  Article  Google Scholar 

53.
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:D442–50.
CAS  PubMed  Article  Google Scholar 

54.
Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25:3389–402.
CAS  PubMed  PubMed Central  Article  Google Scholar 

55.
Ondov BD, Treangen TJ, Melsted P, Mallonee AB, Bergman NH, Koren S, et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol. 2016;17:132.
PubMed  PubMed Central  Article  CAS  Google Scholar 

56.
Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.
CAS  PubMed  Article  Google Scholar 

57.
Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil P-A, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36:996–1004.
CAS  PubMed  PubMed Central  Article  Google Scholar 

58.
Matsen FA, Kodner RB, Armbrust EV. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinform. 2010;11:538.
Article  Google Scholar 

59.
El-Gebali S, Mistry J, Bateman A, Eddy SR, Luciani A, Potter SC, et al. The Pfam protein families database in 2019. Nucleic Acids Res. 2019;47:D427–32.
CAS  Article  Google Scholar 

60.
Saier MH, Reddy VS, Tsu BV, Ahmed MS, Li C, Moreno-Hagelsieb G. The Transporter Classification Database (TCDB): recent advances. Nucleic Acids Res. 2016;44:D372–9.
CAS  PubMed  Article  Google Scholar 

61.
Eddy SR. Accelerated profile HMM searches. PLoS Comput Biol. 2011;7:e1002195.
CAS  PubMed  PubMed Central  Article  Google Scholar 

62.
Yin Y, Mao X, Yang J, Chen X, Mao F, Xu Y. dbCAN: a web resource for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 2012;40:W445–51.
CAS  PubMed  PubMed Central  Article  Google Scholar 

63.
Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12:59–60.
CAS  PubMed  Article  Google Scholar 

64.
Lombard V, Golaconda Ramulu H, Drula E, Coutinho PM, Henrissat B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 2013;42:D490–5.
PubMed  PubMed Central  Article  CAS  Google Scholar 

65.
Tang K, Jiao N, Liu K, Zhang Y, Li S. Distribution and functions of TonB-dependent transporters in marine bacteria and environments: implications for dissolved organic matter utilization. PLoS ONE. 2012;7:e41204.
CAS  PubMed  PubMed Central  Article  Google Scholar 

66.
Gómez-Santos N, Glatter T, Koebnik R, Świątek-Połatyńska MA, Søgaard-Andersen L. A TonB-dependent transporter is required for secretion of protease PopC across the bacterial outer membrane. Nat Commun. 2019;10:1360.
PubMed  PubMed Central  Article  CAS  Google Scholar 

67.
Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.
CAS  PubMed  PubMed Central  Article  Google Scholar 

68.
Price MN, Dehal PS, Arkin AP. FastTree 2–approximately maximum-likelihood trees for large alignments. PLoS ONE. 2010;5:e9490.
PubMed  PubMed Central  Article  CAS  Google Scholar 

69.
Letunic I, Bork P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 2016;44:W242–5.
CAS  PubMed  PubMed Central  Article  Google Scholar 

70.
Engel A, Händel N. A novel protocol for determining the concentration and composition of sugars in particulate and in high molecular weight dissolved organic matter (HMW-DOM) in seawater. Mar Chem. 2011;127:180–91.
CAS  Article  Google Scholar 

71.
Reintjes G, Fuchs BM, Scharfe M, Wiltshire KH, Amann R, Arnosti C. Short-term changes in polysaccharide utilization mechanisms of marine bacterioplankton during a spring phytoplankton bloom. Environ Microbiol. 2020;22:1884–900.
CAS  PubMed  Article  PubMed Central  Google Scholar 

72.
Avcı B, Krüger K, Fuchs BM, Teeling H, Amann RI. Polysaccharide niche partitioning of distinct Polaribacter clades during North Sea spring algal blooms. ISME J. 2020;14:1369–83.
PubMed  PubMed Central  Article  CAS  Google Scholar 

73.
Sperling M, Piontek J, Engel A, Wiltshire KH, Niggemann J, Gerdts G, et al. Combined carbohydrates support rich communities of particle-associated marine bacterioplankton. Front Microbiol. 2017;8:65.
PubMed  PubMed Central  Article  Google Scholar 

74.
Koch H, Dürwald A, Schweder T, Noriega-Ortega B, Vidal-Melgosa S, Hehemann J-H, et al. Biphasic cellular adaptations and ecological implications of Alteromonas macleodii degrading a mixture of algal polysaccharides. ISME J. 2019;13:92–103.
CAS  PubMed  Article  PubMed Central  Google Scholar 

75.
Görke B, Stülke J. Carbon catabolite repression in bacteria: many ways to make the most out of nutrients. Nat Rev Microbiol. 2008;6:613–24.
PubMed  Article  CAS  PubMed Central  Google Scholar 

76.
Becker S, Scheffel A, Polz MF, Hehemann J-H. Accurate quantification of laminarin in marine organic matter with enzymes from marine microbes. Appl Environ Microbiol. 2017;83:e03389–16.
CAS  PubMed  PubMed Central  Article  Google Scholar 

77.
Becker S, Tebben J, Coffinet S, Wiltshire K, Iversen MH, Harder T, et al. Laminarin is a major molecule in the marine carbon cycle. Proc Natl Acad Sci USA. 2020;117:6599–607.
CAS  PubMed  Article  PubMed Central  Google Scholar  More

In natural ecosystems, the efficiency of energy transfer from resources to consumers determines the biomass structure of food webs. As a general rule, about 10% of the energy produced in one trophic level makes it up to the next1–3. Recent theory suggests this energy transfer could be further constrained if rising temperatures increase metabolic growth costs4, although experimental confirmation in whole ecosystems is lacking. We quantified nitrogen transfer efficiency (a proxy for overall energy transfer) in freshwater plankton in artificial ponds exposed to 7 years of experimental warming. We provide the first direct experimental evidence that, relative to ambient conditions, 4 °C of warming can decrease trophic transfer efficiency by up to 56%. In addition, both phytoplankton and zooplankton biomass were lower in the warmed ponds, indicating major shifts in energy uptake, transformation and transfer5,6. These new findings reconcile observed warming-driven changes in individual-level growth costs and carbon-use efficiency across diverse taxa4,7–10 with increases in the ratio of total respiration to gross primary production at the ecosystem level11–13. Our results imply that an increasing proportion of the carbon fixed by photosynthesis will be lost to the atmosphere as the planet warms, impairing energy flux through food chains, with negative implications for larger consumers and the functioning of entire ecosystems. More

Natural soundscapes have always called to me. As an eco- and electro-acoustics researcher, with a background in sound engineering and electronic music composition, I have always tried to strike a balance between art and science in my work.
In 1998, when I first heard about the extinction crisis — more than 35,500 species of flora and fauna are endangered — the idea for the Fragments of Extinction project came to me very quickly. My vision was to build a collection of 24-hour-long ‘acoustic fragments’, recorded at the highest definition possible, capturing the sonic heritage of ancient, biodiverse, untouched tropical rainforests — before climate change damages them irreversibly.
In these forests, some species vocalize from the canopy, some from the ground and others from big tree trunks that act like sound diffusers. To capture a 3D acoustic portrait of the forest, we simultaneously record on 38 audio channels and microphones.
In this photograph, I am standing in the Sonosfera, a geodesic theatre in Pesaro, Italy, in which audiences can experience rainforest soundscapes captured in the Amazon, Africa and Borneo. Forty-five high-definition loudspeakers are positioned in an isolated, acoustically perfect space, realistically reproducing the ecosystems’ natural sounds.
For the first 15 minutes of the performance, the Sonosfera is completely dark. Sound helps listeners to ‘build’ the forest space around them — the position of every insect and amphibian; the birds and mammals moving through the canopy. My team then projects the spectrograms shown here to explain the sounds, and present data showing that these ecosystems are disappearing.
We have captured the deep infrasound calls of elephants and have recorded insects that sound exactly like violins or trumpets. Our ecosystem recordings are very different. But I don’t have a favourite — they’re a collection. More

1.
Wilkinson, C. Status of Coral Reefs of the World: 2008 (Global Coral Reef Monitoring Network and Reef and Rainforest Research Centre, 2008).
2.
Jackson, J. B. C., Donovan, M. K., Cramer, K. L. & Lam, V. V. Status and Trends of Caribbean Coral Reefs: 1970–2012 (Global Coral Reef Monitoring Network, 2014).

3.
Eakin, C. M. et al. Caribbean corals in crisis: record thermal stress, bleaching, and mortality in 2005. PLoS ONE 5, e13969 (2010).
PubMed  PubMed Central  Article  CAS  Google Scholar 

4.
Baker, A. C., Glynn, P. W. & Riegl, B. Climate change and coral reef bleaching: an ecological assessment of long-term impacts, recovery trends and future outlook. Estuar. Coast. Shelf Sci. 80, 435–471 (2008).
Article  Google Scholar 

5.
Hughes, T. P. et al. Global warming transforms coral reef assemblages. Nature 556, 492–496 (2018).
CAS  Article  PubMed  Google Scholar 

6.
Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80–83 (2018).
CAS  PubMed  Article  Google Scholar 

7.
De’ath, G., Fabricius, K. E., Sweatman, H. & Puotinen, M. The 27-year decline of coral cover on the Great Barrier Reef and its causes. Proc. Natl Acad. Sci. USA 109, 17995–17999 (2012).
PubMed  Article  Google Scholar 

8.
Gardner, T. A. Long-term region-wide declines in Caribbean corals. Science 301, 958–960 (2003).
CAS  PubMed  Article  Google Scholar 

9.
Carpenter, K. E. et al. One-third of reef-building corals face elevated extinction risk from climate change and local impacts. Science 321, 560–563 (2008).
CAS  PubMed  Article  Google Scholar 

10.
ter Steege, H. et al. Estimating the global conservation status of more than 15,000 Amazonian tree species. Sci. Adv. 1, e1500936 (2015).
PubMed  PubMed Central  Article  Google Scholar 

11.
Fauset, S. et al. Hyperdominance in Amazonian forest carbon cycling. Nat. Commun. 6, 6857 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

12.
Crowther, T. W. et al. Mapping tree density at a global scale. Nature 525, 201–205 (2015).
CAS  PubMed  Article  Google Scholar 

13.
Connell, J., Hughes, T. & Wallace, C. A 30-year study of coral abundance, recruitment, and disturbance at several scales in space and time. Ecol. Monogr. 67, 461–488 (1997).
Article  Google Scholar 

14.
Hughes, T. P. & Jackson, J. B. C. Population dynamics and life histories of foliaceous corals. Ecol. Monogr. 55, 141–166 (1985).
Article  Google Scholar 

15.
ter Steege, H. et al. Hyperdominance in the Amazonian tree flora. Science 342, 1243092 (2013).
PubMed  Article  CAS  Google Scholar 

16.
Gaston, K. J. & Blackburn, T. M. How many birds are there? Biodivers. Conserv. 6, 615–625 (1997).
Article  Google Scholar 

17.
Kerry, J. T. & Bellwood, D. R. Do tabular corals constitute keystone structures for fishes on coral reefs? Coral Reefs 34, 41–50 (2015).
Article  Google Scholar 

18.
Connolly, S. R., Hughes, T. P., Bellwood, D. R. & Karlson, R. H. Community structure of corals and reef fishes at multiple scales. Science 309, 1363–1365 (2005).
CAS  PubMed  Article  Google Scholar 

19.
Connolly, S. R., Hughes, T. P. & Bellwood, D. R. A unified model explains commonness and rarity on coral reefs. Ecol. Lett. 20, 477–486 (2017).
PubMed  Article  Google Scholar 

20.
Hubbell, S. P. Estimating the global number of tropical tree species, and Fisher’s paradox. Proc. Natl Acad. Sci. USA 112, 7343–7344 (2015).
CAS  PubMed  Article  Google Scholar 

21.
Hughes, T. P., Bellwood, D. R. & Connolly, S. R. Biodiversity hotspots, centres of endemicity, and the conservation of coral reefs. Ecol. Lett. 5, 775–784 (2002).
Article  Google Scholar 

22.
Hughes, T. P., Bellwood, D. R., Connolly, S. R. & Cornell, H. V. Double jeopardy and global extinction risk in corals and reef fishes. Curr. Biol. 24, 2946–2951 (2014).
CAS  PubMed  Article  Google Scholar 

23.
Kinlan, B. P. & Gaines, S. D. Propagule dispersal in marine and terrestrial environments: a community perspective. Ecology 84, 2007–2020 (2003).
Article  Google Scholar 

24.
Hull, P. M., Darroch, S. A. F. & Erwin, D. H. Rarity in mass extinctions and the future of ecosystems. Nature 528, 345–351 (2015).
CAS  PubMed  Article  Google Scholar 

25.
Cardoso, P., Borges, P. A. V., Triantis, K. A., Ferrández, M. A. & Martín, J. L. Adapting the IUCN Red List criteria for invertebrates. Biol. Conserv. 144, 2432–2440 (2011).
Article  Google Scholar 

26.
Cardoso, P., Borges, P. A. V., Triantis, K. A., Ferrández, M. A. & Martín, J. L. The underrepresentation and misrepresentation of invertebrates in the IUCN Red List. Biol. Conserv. 149, 147–148 (2012).
Article  Google Scholar 

27.
Estes, J. A., Duggins, D. O. & Rathbun, G. B. The ecology of extinctions in kelp forest communities. Conserv. Biol. 3, 252–264 (1989).
Article  Google Scholar 

28.
Oliver, J. & Babcock, R. Aspects of the fertilization ecology of broadcast spawning corals: sperm dilution effects and in situ measurements of fertilization. Biol. Bull. 183, 409–417 (1992).
CAS  PubMed  Article  Google Scholar 

29.
Knowlton, N., Lang, J. C. & Keller, B. D. Case study of natural population collapse: post-hurricane predation on Jamaican staghorn corals. Smithson. Contrib. Mar. Sci. 31, 1–25 (1990).
Google Scholar 

30.
Gaston, K. J. & Fuller, R. A. Commonness, population depletion and conservation biology. Trends Ecol. Evol. 23, 14–19 (2008).
PubMed  Article  Google Scholar 

31.
Säterberg, T., Sellman, S. & Ebenman, B. High frequency of functional extinctions in ecological networks. Nature 499, 468–470 (2013).
PubMed  Article  CAS  Google Scholar 

32.
Pratchett, M. S. Dietary overlap among coral-feeding butterflyfishes (Chaetodontidae) at Lizard Island, northern Great Barrier Reef. Mar. Biol. 148, 373–382 (2005).
Article  Google Scholar 

33.
Huang, D., Licuanan, W. Y., Baird, A. H. & Fukami, H. Cleaning up the ‘Bigmessidae’: molecular phylogeny of scleractinian corals from Faviidae, Merulinidae, Pectiniidae and Trachyphylliidae. BMC Evol. Biol. 11, 37 (2011).
PubMed  PubMed Central  Article  Google Scholar 

34.
Knowlton, N. & Jackson, J. B. C. New taxonomy and niche partitioning on coral reefs: jack of all trades or master of some? Trends Ecol. Evol. 9, 7–9 (1994).
CAS  PubMed  Article  Google Scholar 

35.
Gilpin, M. E. & Soulé, M. E. in Conservation Biology: The Science of Scarcity and Diversity (ed, Soulé, M. E.) 19–34 (Sinauer Associates, 1986).

36.
Bak, R. P. M. & Meesters, E. H. Population structure as a response of coral communities to global change. Am. Zool. 39, 56–65 (1999).
Article  Google Scholar 

37.
McClanahan, T. R., Ateweberhan, M. & Omukoto, J. Long-term changes in coral colony size distributions on Kenyan reefs under different management regimes and across the 1998 bleaching event. Mar. Biol. 153, 755–768 (2008).
Article  Google Scholar 

38.
Riegl, B. M., Bruckner, A. W., Rowlands, G. P., Purkis, S. J. & Renaud, P. Red Sea coral reef trajectories over 2 decades suggest increasing community homogenization and decline in coral size. PLoS ONE 7, e38396 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

39.
Hughes, T. P. et al. Coral reefs in the Anthropocene. Nature 546, 82–90 (2017).
CAS  Article  Google Scholar 

40.
Global Distribution of Coral Reefs (UNEP-WCMC, WorldFish Centre, WRI & TNC, 2018); https://data.unep-wcmc.org/datasets/

41.
Bruno, J. F. & Valdivia, A. Coral reef degradation is not correlated with local human population density. Sci. Rep. 6, 29778 (2016).

42.
Bruno, J. Data from: Coral reef degradation is not correlated with local human population density. Dryad Digital Repository https://doi.org/10.5061/dryad.48r68 (2016).

43.
Karlson, R. H., Cornell, H. V. & Hughes, T. P. Coral communities are regionally enriched along an oceanic biodiversity gradient. Nature 429, 867–870 (2004).
CAS  PubMed  Article  Google Scholar 

44.
Cornell, H. V., Karlson, R. H. & Hughes, T. P. Scale-dependent variation in coral community similarity across sites, islands, and island groups. Ecology 88, 1707–1715 (2007).
PubMed  Article  Google Scholar 

45.
Cornell, H. V., Karlson, R. H. & Hughes, T. P. Local-regional species richness relationships are linear at very small to large scales in west-central Pacific corals. Coral Reefs 27, 145–151 (2008).
Article  Google Scholar 

46.
Connolly, S. R., Dornelas, M., Bellwood, D. R. & Hughes, T. P. Testing species abundance models: a new bootstrap approach applied to Indo-Pacific coral reefs. Ecology 90, 3138–3149 (2009).
PubMed  Article  Google Scholar 

47.
Reef Habitat Maps (NOAA-NCCOS, accessed 10 November 2017); https://products.coastalscience.noaa.gov/collections/benthic/default.aspx

48.
Purkis, S. J. et al. High-resolution habitat and bathymetry maps for 65,000 sq. km of Earth’s remotest coral reefs. Coral Reefs 38, 467–488 (2019).
Article  Google Scholar 

49.
Roelfsema, C., Phinn, S., Jupiter, S., Comley, J. & Albert, S. Mapping coral reefs at reef to reef-system scales, 10s–1000s km2, using object-based image analysis. Int. J. Remote Sens. 34, 6367–6388 (2013).
Article  Google Scholar 

50.
Bürkner, P.-C. brms: an R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).
Article  Google Scholar 

51.
Warton, D. I. & Hui, F. K. C. The arcsine is asinine: the analysis of proportions in ecology. Ecology 92, 3–10 (2011).
PubMed  Article  Google Scholar 

52.
Marsh, L. M., Bradbury, R. H. & Reichelt, R. E. Determination of the physical parameters of coral distributions using line transect data. Coral Reefs 2, 175–180 (1984).
Google Scholar 

53.
Hughes, T. P. Population dynamics based on individual size rather than age: a general model with a reef coral example. Am. Nat. 123, 778–795 (1984).
Article  Google Scholar 

54.
Hall, V. R. & Hughes, T. P. Reproductive strategies of modular organisms: comparative studies of reef-building corals. Ecology 77, 950–963 (1996).
Article  Google Scholar 

55.
Hughes, T. P., Connolly, S. R. & Keith, S. A. Geographic ranges of reef corals (Cnidaria: Anthozoa: Scleractinia) in the Indo-Pacific. Ecology 94, 1659 (2013).
Article  Google Scholar 

56.
Bar-On, Y. M., Phillips, R. & Milo, R. The biomass distribution on Earth. Proc. Natl Acad. Sci. USA 115, 6506–6511 (2018).
CAS  PubMed  Article  Google Scholar 

57.
van den Hoogen, J. et al. Soil nematode abundance and functional group composition at a global scale. Nature 572, 194–198 (2019).
PubMed  Article  CAS  Google Scholar 

58.
Hubbell, S. P. et al. How many tree species are there in the Amazon and how many of them will go extinct? Proc. Natl Acad. Sci. USA 105, 11498–11504 (2008).
CAS  PubMed  Article  Google Scholar 

59.
Atkinson, A., Siegel, V., Pakhomov, E. A., Jessopp, M. J. & Loeb, V. A re-appraisal of the total biomass and annual production of Antarctic krill. Deep-Sea Res. I 56, 727–740 (2009).
Article  Google Scholar 

60.
Current World Population (Worldometer, accessed 13 May 2020); https://www.worldometers.info/world-population/

61.
California Condor Recovery Program: 2017 Annual Population Status (US Fish and Wildlife Service, 2017).

62.
Goodrich, J. M. et al. Panthera tigris. The IUCN Red List of Threatened Species 2015 Report number e.T15955A50659951 (IUCN, 2015). More

1.
Suess E. Marine cold seeps and their manifestations: geological control, biogeochemical criteria and environmental conditions. Int J Earth Sci. 2014;103:1889–916.
CAS  Article  Google Scholar 
2.
Joye SB. The geology and biogeochemistry of hydrocarbon seeps. Annu Rev Earth Planet Sci. 2020;48:205–31.
CAS  Article  Google Scholar 

3.
Etiope G, Panieri G, Fattorini D, Regoli F, Vannoli P, Italiano F, et al. A thermogenic hydrocarbon seep in shallow Adriatic Sea (Italy): Gas origin, sediment contamination and benthic foraminifera. Mar Pet Geol. 2014;57:283–93.
CAS  Article  Google Scholar 

4.
Kennicutt, MC Habitats and biota of the Gulf of Mexico: before the deepwater horizon oil spill. Ward CH, editor. New York, NY: Springer New York; 2017. p. 275–358.

5.
Ruppel CD, Kessler JD. The interaction of climate change and methane hydrates. Rev Geophys. 2017;55:126–68.
Article  Google Scholar 

6.
Kniemeyer O, Musat F, Sievert SM, Knittel K, Wilkes H, Blumenberg M, et al. Anaerobic oxidation of short-chain hydrocarbons by marine sulphate-reducing bacteria. Nature. 2007;449:898–901.
CAS  PubMed  Article  PubMed Central  Google Scholar 

7.
Jaekel U, Musat N, Adam B, Kuypers M, Grundmann O, Musat F. Anaerobic degradation of propane and butane by sulfate-reducing bacteria enriched from marine hydrocarbon cold seeps. ISME J. 2013;7:885–95.
CAS  PubMed  Article  PubMed Central  Google Scholar 

8.
Teske A, Carvalho V. Marine hydrocarbon seeps: microbiology and biogeochemistry of a global marine habitat. Cham, Switzerland: Springer Nature; 2020.

9.
Kellogg CA. Enumeration of viruses and prokaryotes in deep-sea sediments and cold seeps of the Gulf of Mexico. Deep Sea Res Part II Top Stud Oceanogr. 2010;57:2002–7.
Article  Google Scholar 

10.
Bryson SJ, Thurber AR, Correa AM, Orphan VJ, Vega Thurber R. A novel sister clade to the enterobacteria microviruses (family Microviridae) identified in methane seep sediments. Environ Microbiol. 2015;17:3708–21.
CAS  PubMed  Article  PubMed Central  Google Scholar 

11.
Paul BG, Bagby SC, Czornyj E, Arambula D, Handa S, Sczyrba A, et al. Targeted diversity generation by intraterrestrial archaea and archaeal viruses. Nat Commun. 2015;6:6585.
CAS  PubMed  PubMed Central  Article  Google Scholar 

12.
Pan D, Morono Y, Inagaki F, Takai K. An improved method for extracting viruses from sediment: detection of far more viruses in the subseafloor than previously reported. Front Microbiol. 2019;10:878.
PubMed  PubMed Central  Article  Google Scholar 

13.
Emerson JB, Roux S, Brum JR, Bolduc B, Woodcroft BJ, Jang HB, et al. Host-linked soil viral ecology along a permafrost thaw gradient. Nat Microbiol. 2018;3:870–80.
CAS  PubMed  PubMed Central  Article  Google Scholar 

14.
Jin M, Guo X, Zhang R, Qu W, Gao B, Zeng R. Diversities and potential biogeochemical impacts of mangrove soil viruses. Microbiome. 2019;7:58.
PubMed  PubMed Central  Article  Google Scholar 

15.
Labbe M, Girard C, Vincent WF, Culley AI. Extreme viral partitioning in a marine-derived high arctic lake. mSphere. 2020;5:e00334–00320.
CAS  PubMed  PubMed Central  Article  Google Scholar 

16.
Okazaki Y, Nishimura Y, Yoshida T, Ogata H, Nakano SI. Genome-resolved viral and cellular metagenomes revealed potential key virus-host interactions in a deep freshwater lake. Environ Microbiol. 2019;21:4740–54.
CAS  PubMed  Article  PubMed Central  Google Scholar 

17.
Backstrom D, Yutin N, Jorgensen SL, Dharamshi J, Homa F, Zaremba-Niedwiedzka K, et al. Virus genomes from deep sea sediments expand the ocean megavirome and support independent origins of viral gigantism. mBio. 2019;10:e02497–02418.
PubMed  PubMed Central  Article  Google Scholar 

18.
Daly RA, Roux S, Borton MA, Morgan DM, Johnston MD, Booker AE, et al. Viruses control dominant bacteria colonizing the terrestrial deep biosphere after hydraulic fracturing. Nat Microbiol. 2019;4:352–61.
CAS  PubMed  Article  PubMed Central  Google Scholar 

19.
Daly RA, Borton MA, Wilkins MJ, Hoyt DW, Kountz DJ, Wolfe RA, et al. Microbial metabolisms in a 2.5-km-deep ecosystem created by hydraulic fracturing in shales. Nat Microbiol. 2016;1:16146.
CAS  PubMed  Article  PubMed Central  Google Scholar 

20.
Roux S, Brum JR, Dutilh BE, Sunagawa S, Duhaime MB, Loy A, et al. Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses. Nature. 2016;537:689–93.
CAS  PubMed  Article  PubMed Central  Google Scholar 

21.
Gregory AC, Zayed AA, Conceicao-Neto N, Temperton B, Bolduc B, Alberti A, et al. Marine DNA viral macro- and microdiversity from pole to pole. Cell. 2019;177:1109–23.
CAS  PubMed  PubMed Central  Article  Google Scholar 

22.
Coutinho FH, Silveira CB, Gregoracci GB, Thompson CC, Edwards RA, Brussaard CPD, et al. Marine viruses discovered via metagenomics shed light on viral strategies throughout the oceans. Nat Commun. 2017;8:15955.
CAS  PubMed  PubMed Central  Article  Google Scholar 

23.
Breitbart M, Bonnain C, Malki K, Sawaya NA. Phage puppet masters of the marine microbial realm. Nat Microbiol. 2018;3:754–66.
CAS  PubMed  Article  PubMed Central  Google Scholar 

24.
Chen LX, Meheust R, Crits-Christoph A, McMahon KD, Nelson TC, Slater GF, et al. Large freshwater phages with the potential to augment aerobic methane oxidation. Nat Microbiol. 2020;5:1504–15.
CAS  PubMed  PubMed Central  Article  Google Scholar 

25.
Cai L, Jorgensen BB, Suttle CA, He M, Cragg BA, Jiao N, et al. Active and diverse viruses persist in the deep sub-seafloor sediments over thousands of years. ISME J. 2019;13:1857–64.
CAS  PubMed  PubMed Central  Article  Google Scholar 

26.
Danovaro R, Dell’Anno A, Corinaldesi C, Magagnini M, Noble R, Tamburini C, et al. Major viral impact on the functioning of benthic deep-sea ecosystems. Nature. 2008;454:1084–7.
CAS  PubMed  Article  PubMed Central  Google Scholar 

27.
Middelboe M, Glud RN, Wenzhöfer F, Oguri K, Kitazato H. Spatial distribution and activity of viruses in the deep-sea sediments of Sagami Bay. Jpn Deep Sea Res Part 1 Oceanogr Res Pap. 2006;53:1–13.
Article  Google Scholar 

28.
Danovaro R, Serresi M. Viral density and virus-to-bacterium ratio in deep-sea sediments of the Eastern Mediterranean. Appl Environ Microbiol. 2000;66:1857–61.
CAS  PubMed  PubMed Central  Article  Google Scholar 

29.
Hewson I, Fuhrman JA. Viriobenthos production and virioplankton sorptive scavenging by suspended sediment particles in coastal and pelagic waters. Micro Ecol. 2003;46:337–47.
CAS  Article  Google Scholar 

30.
Corinaldesi C, Dell’Anno A, Danovaro R. Viral infection plays a key role in extracellular DNA dynamics in marine anoxic systems. Limnol Oceanogr. 2007;52:508–16.
CAS  Article  Google Scholar 

31.
Dong X, Greening C, Rattray JE, Chakraborty A, Chuvochina M, Mayumi D, et al. Metabolic potential of uncultured bacteria and archaea associated with petroleum seepage in deep-sea sediments. Nat Commun. 2019;10:1816.
PubMed  PubMed Central  Article  CAS  Google Scholar 

32.
Dong X, Rattray JE, Campbell DC, Webb J, Chakraborty A, Adebayo O, et al. Thermogenic hydrocarbon biodegradation by diverse depth-stratified microbial populations at a Scotian Basin cold seep. Nat Commun. 2020;11:5825.
CAS  PubMed  PubMed Central  Article  Google Scholar 

33.
Gruber-Vodicka HR, Seah BKB, Pruesse E. phyloFlash: rapid small-subunit rRNA profiling and targeted assembly from metagenomes. mSystems. 2020;5:e00920–00920.
CAS  PubMed  PubMed Central  Article  Google Scholar 

34.
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.
CAS  PubMed  Article  PubMed Central  Google Scholar 

35.
Uritskiy GV, DiRuggiero J, Taylor J. MetaWRAP-a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome. 2018;6:158.
PubMed  PubMed Central  Article  Google Scholar 

36.
Li D, Luo R, Liu CM, Leung CM, Ting HF, Sadakane K, et al. MEGAHIT v1.0: A fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods. 2016;102:3–11.
CAS  PubMed  Article  PubMed Central  Google Scholar 

37.
Olm MR, Brown CT, Brooks B, Banfield JF. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017;11:2864–8.
CAS  PubMed  PubMed Central  Article  Google Scholar 

38.
Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the genome taxonomy database. Bioinformatics. 2019;36:1925–7.
PubMed  PubMed Central  Google Scholar 

39.
Parks DH, Chuvochina M, Chaumeil PA, Rinke C, Mussig AJ, Hugenholtz P. A complete domain-to-species taxonomy for Bacteria and Archaea. Nat Biotechnol. 2020;38:1079–86.
CAS  PubMed  Article  PubMed Central  Google Scholar 

40.
Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3.
CAS  PubMed  PubMed Central  Article  Google Scholar 

41.
Federhen S. The NCBI taxonomy database. Nucleic Acids Res. 2012;40:D136–43.
CAS  PubMed  Article  PubMed Central  Google Scholar 

42.
Roux S, Enault F, Hurwitz BL, Sullivan MB. VirSorter: mining viral signal from microbial genomic data. PeerJ. 2015;3:e985.
PubMed  PubMed Central  Article  CAS  Google Scholar 

43.
Ren J, Ahlgren NA, Lu YY, Fuhrman JA, Sun F. VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data. Microbiome. 2017;5:69.
PubMed  PubMed Central  Article  Google Scholar 

44.
Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics. 2012;28:3150–2.
CAS  PubMed  PubMed Central  Article  Google Scholar 

45.
Marquet M, Hölzer M, Pletz MW, Viehweger A, Makarewicz O, Ehricht R, et al. What the phage: a scalable workflow for the identification and analysis of phage sequences. 2020. https://www.biorxiv.org/content/10.1101/2020.07.24.219899v1.

46.
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. 2020;8:90.
PubMed  PubMed Central  Article  Google Scholar 

47.
Nayfach S, Camargo AP, Schulz F, Eloe-Fadrosh E, Roux S, Kyrpides NC. CheckV assesses the quality and completeness of metagenome-assembled viral genomes. Nat Biotechnol. 2020. https://doi.org/10.1101/2020.1105.1106.081778.

48.
Dalcin Martins P, Danczak RE, Roux S, Frank J, Borton MA, Wolfe RA, et al. Viral and metabolic controls on high rates of microbial sulfur and carbon cycling in wetland ecosystems. Microbiome. 2018;6:138.
PubMed  PubMed Central  Article  Google Scholar 

49.
Hyatt D, Chen GL, Locascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 2010;11:119.
Article  CAS  Google Scholar 

50.
Bin Jang H, Bolduc B, Zablocki O, Kuhn JH, Roux S, Adriaenssens EM, et al. Taxonomic assignment of uncultivated prokaryotic virus genomes is enabled by gene-sharing networks. Nat Biotechnol. 2019;37:632–9.
Article  CAS  Google Scholar 

51.
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504.
CAS  PubMed  PubMed Central  Article  Google Scholar 

52.
Roux S, Paez-Espino D, Chen IA, Palaniappan K, Ratner A, Chu K, et al. IMG/VR v3: an integrated ecological and evolutionary framework for interrogating genomes of uncultivated viruses. Nucleic Acids Res. 2020;49:D764–75.

53.
Roux S, Adriaenssens EM, Dutilh BE, Koonin EV, Kropinski AM, Krupovic M, et al. Minimum information about an uncultivated virus genome (MIUViG). Nat Biotechnol. 2019;37:29–37.
CAS  PubMed  Article  PubMed Central  Google Scholar 

54.
Castelan-Sanchez HG, Lopez-Rosas I, Garcia-Suastegui WA, Peralta R, Dobson ADW, Batista-Garcia RA, et al. Extremophile deep-sea viral communities from hydrothermal vents: structural and functional analysis. Mar Genom. 2019;46:16–28.
Article  Google Scholar 

55.
Huson DH, Auch AF, Qi J, Schuster SC. MEGAN analysis of metagenomic data. Genome Res. 2007;17:377–86.
CAS  PubMed  PubMed Central  Article  Google Scholar 

56.
Tominaga K, Morimoto D, Nishimura Y, Ogata H, Yoshida T. In silico prediction of virus-host interactions for marine bacteroidetes with the use of metagenome-assembled genomes. Front Microbiol. 2020;11:738.
PubMed  PubMed Central  Article  Google Scholar 

57.
Ahlgren NA, Ren J, Lu YY, Fuhrman JA, Sun F. Alignment-free d2*oligonucleotide frequency dissimilarity measure improves prediction of hosts from metagenomically-derived viral sequences. Nucleic Acids Res. 2017;45:39–53.
CAS  PubMed  Article  PubMed Central  Google Scholar 

58.
Laslett D, Canback B. ARAGORN, a program to detect tRNA genes and tmRNA genes in nucleotide sequences. Nucleic Acids Res. 2004;32:11–16.
CAS  PubMed  PubMed Central  Article  Google Scholar 

59.
Skennerton CT, Imelfort M, Tyson GW. Crass: identification and reconstruction of CRISPR from unassembled metagenomic data. Nucleic Acids Res. 2013;41:e105.
CAS  PubMed  PubMed Central  Article  Google Scholar 

60.
Dong X, Strous M. An integrated pipeline for annotation and visualization of metagenomic contigs. Front Genet. 2019;10:999.
CAS  PubMed  PubMed Central  Article  Google Scholar 

61.
Zhou Z, Tran PQ, Breister AM, Liu Y, Kieft K, Cowley ES, et al. METABOLIC: a scalable high-throughput metabolic and biogeochemical functional trait profiler based on microbial genomes. 2020. https://www.biorxiv.org/content/10.1101/761643v1.

62.
Edgar RC. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinform. 2004;5:113.
Article  CAS  Google Scholar 

63.
Kumar S, Stecher G, Li M, Knyaz C, Tamura K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol. 2018;35:1547–9.
CAS  PubMed  PubMed Central  Article  Google Scholar 

64.
Shaffer M, Borton MA, McGivern BB, Zayed AA, La Rosa SL, Solden LM, et al. DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic Acids Res. 2020;48:8883–900.
CAS  PubMed  PubMed Central  Article  Google Scholar 

65.
Guo J, Bolduc B, Zayed AA, Varsani A, Dominguez-Huerta G, Delmont TO, et al. VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses. Microbiome. 2021;9:37.
PubMed  PubMed Central  Article  Google Scholar 

66.
Vik D, Gazitua MC, Sun CL, Zayed AA, Aldunate M, Mulholland MR et al. Genome-resolved viral ecology in a marine oxygen minimum zone. Environ Microbiol. 2020. https://doi.org/10.1111/1462-2920.15313.

67.
ter Horst AM, Santos-Medellin C, Sorensen JW, Zinke LA, Wilson RM, Johnston ER, et al. Minnesota peat viromes reveal terrestrial and aquatic niche partitioning for local and global viral populations. 2020. https://www.biorxiv.org/content/10.1101/2020.12.15.422944v1.full.

68.
Lu S, Wang J, Chitsaz F, Derbyshire MK, Geer RC, Gonzales NR, et al. CDD/SPARCLE: the conserved domain database in 2020. Nucleic Acids Res. 2020;48:D265–8.
CAS  PubMed  Article  PubMed Central  Google Scholar 

69.
Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJ. The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc. 2015;10:845–58.
CAS  PubMed  PubMed Central  Article  Google Scholar 

70.
Dixon P. VEGAN, a package of R functions for community ecology. J Veg Sci. 2003;14:927–30.
Article  Google Scholar 

71.
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  PubMed  PubMed Central  Article  Google Scholar 

72.
Jain C, Rodriguez RL, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:5114.
PubMed  PubMed Central  Article  CAS  Google Scholar 

73.
Al-Shayeb B, Sachdeva R, Chen LX, Ward F, Munk P, Devoto A, et al. Clades of huge phages from across Earth’s ecosystems. Nature. 2020;578:425–31.
CAS  PubMed  PubMed Central  Article  Google Scholar 

74.
Ruff SE, Biddle JF, Teske AP, Knittel K, Boetius A, Ramette A. Global dispersion and local diversification of the methane seep microbiome. Proc Natl Acad Sci USA. 2015;112:4015–20.
CAS  PubMed  Article  PubMed Central  Google Scholar 

75.
Trubl G, Jang HB, Roux S, Emerson JB, Solonenko N, Vik DR, et al. Soil viruses are underexplored players in ecosystem carbon processing. mSystems. 2018;3:e00076–00018.
CAS  PubMed  PubMed Central  Article  Google Scholar 

76.
Paez-Espino D, Eloe-Fadrosh EA, Pavlopoulos GA, Thomas AD, Huntemann M, Mikhailova N, et al. Uncovering Earth’s virome. Nature. 2016;536:425–30.
CAS  PubMed  Article  PubMed Central  Google Scholar 

77.
Roux S, Hallam SJ, Woyke T, Sullivan MB. Viral dark matter and virus-host interactions resolved from publicly available microbial genomes. elife. 2015;4:e08490.
PubMed Central  Article  Google Scholar 

78.
Castelle CJ, Brown CT, Anantharaman K, Probst AJ, Huang RH, Banfield JF. Biosynthetic capacity, metabolic variety and unusual biology in the CPR and DPANN radiations. Nat Rev Microbiol. 2018;16:629–45.
CAS  PubMed  Article  PubMed Central  Google Scholar 

79.
Jarett JK, Dzunkova M, Schulz F, Roux S, Paez-Espino D, Eloe-Fadrosh E, et al. Insights into the dynamics between viruses and their hosts in a hot spring microbial mat. ISME J. 2020;14:2527–41.
CAS  PubMed  PubMed Central  Article  Google Scholar 

80.
Orsi WD. Ecology and evolution of seafloor and subseafloor microbial communities. Nat Rev Microbiol. 2018;16:671–83.
CAS  PubMed  Article  PubMed Central  Google Scholar 

81.
Hurwitz BL, Brum JR, Sullivan MB. Depth-stratified functional and taxonomic niche specialization in the ‘core’ and ‘flexible’ Pacific Ocean Virome. ISME J. 2015;9:472–84.
CAS  PubMed  Article  PubMed Central  Google Scholar 

82.
Brum JR, Sullivan MB. Rising to the challenge: accelerated pace of discovery transforms marine virology. Nat Rev Microbiol. 2015;13:147–59.
CAS  PubMed  Article  PubMed Central  Google Scholar 

83.
Mara P, Vik D, Pachiadaki MG, Suter EA, Poulos B, Taylor GT, et al. Viral elements and their potential influence on microbial processes along the permanently stratified Cariaco Basin redoxcline. ISME J. 2020;14:3079–92.
CAS  PubMed  PubMed Central  Article  Google Scholar 

84.
Anderson CL, Sullivan MB, Fernando SC. Dietary energy drives the dynamic response of bovine rumen viral communities. Microbiome. 2017;5:155.
PubMed  PubMed Central  Article  Google Scholar 

85.
Gao SM, Schippers A, Chen N, Yuan Y, Zhang MM, Li Q, et al. Depth-related variability in viral communities in highly stratified sulfidic mine tailings. Microbiome. 2020;8:89.
PubMed  PubMed Central  Article  Google Scholar 

86.
Zhao R, Summers ZM, Christman GD, Yoshimura KM, Biddle JF. Metagenomic views of microbial dynamics influenced by hydrocarbon seepage in sediments of the Gulf of Mexico. Sci Rep. 2020;10:5772.
CAS  PubMed  PubMed Central  Article  Google Scholar 

87.
Dekas AE, Poretsky RS, Orphan VJ. Deep-sea archaea fix and share nitrogen in methane-consuming microbial consortia. Science. 2009;326:422–6.
CAS  PubMed  Article  PubMed Central  Google Scholar 

88.
Zheng, X, Liu, W, Dai, X, Zhu, Y, Wang, J, Zhu, Y et al. Extraordinary diversity of viruses in deep-sea sediments as revealed by metagenomics without prior virion separation. Environ Microbiol. 2020. https://doi.org/10.1111/1462-2920.15154. More