Philippa Kaur

1.
Bardgett RD, Van Der Putten WH. Belowground biodiversity and ecosystem functioning. Nature. 2014;515:505–11.
CAS  PubMed  Article  Google Scholar 
2.
Fierer N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat Rev Microbiol. 2017;15:579–90.
CAS  PubMed  Article  Google Scholar 

3.
De Vries FT, Shade A. Controls on soil microbial community stability under climate change. Front Microbiol. 2013;4:1–16.
Article  Google Scholar 

4.
Allison SD, Martiny JBH. Resistance, resilience, and redundancy in microbial communities. Proc Natl Acad Sci. 2008;105:11512–9.
CAS  PubMed  Article  Google Scholar 

5.
Leifeld J, Zimmermann M, Fuhrer J, Conen F. Storage and turnover of carbon in grassland soils along an elevation gradient in the Swiss Alps. Glob Chang Biol. 2009;15:668–79.
Article  Google Scholar 

6.
Schirpke U, Leitinger G, Tasser E, Schermer M, Steinbacher M, Tappeiner U. Multiple ecosystem services of a changing Alpine landscape: past, present and future. Int J Biodivers Sci Ecosyst Serv Manag. 2013;9:123–35.
PubMed  Article  Google Scholar 

7.
Beniston M. Is snow in the Alps receding or disappearing? Wiley Interdiscip Rev Clim Chang. 2012;3:349–58.
Article  Google Scholar 

8.
Beniston M, Keller F, Koffi B, Goyette S. Estimates of snow accumulation and volume in the Swiss Alps under changing climatic conditions. Theor Appl Climatol. 2003;76:125–40.
Article  Google Scholar 

9.
Monson RK, Burns SP, Williams MW, Delany AC, Weintraub M, Lipson DA. The contribution of beneath-snow soil respiration to total ecosystem respiration in a high-elevation, subalpine forest. Glob Biogeochem Cycles. 2006;20:1–13.
Article  CAS  Google Scholar 

10.
Zhang Y, Wang S, Barr AG, Black TA. Impact of snow cover on soil temperature and its simulation in a boreal aspen forest. Cold Reg Sci Technol. 2008;52:355–70.
Article  Google Scholar 

11.
Campbell JL, Ollinger SV, Flerchinger GN, Wicklein H, Hayhoe K, Bailey AS. Past and projected future changes in snowpack and soil frost at the Hubbard Brook Experimental Forest, New Hampshire, USA. Hydrol Process. 2010;24:2465–80.
Google Scholar 

12.
Pederson GT, Gray ST, Woodhouse CA, Betancourt JL, Fagre DB, Littell JS, et al. The unusual nature of recent snowpack declines in the North American Cordillera. Science. 2011;333:332–5.
CAS  PubMed  Article  Google Scholar 

13.
Gavazov K, Ingrisch J, Hasibeder R, Mills RTE, Buttler A, Gleixner G, et al. Winter ecology of a subalpine grassland: effects of snow removal on soil respiration, microbial structure and function. Sci Total Environ. 2017;590–591:316–324.
PubMed  Article  CAS  Google Scholar 

14.
Buckeridge KM, Banerjee S, Siciliano SD, Grogan P. The seasonal pattern of soil microbial community structure in mesic low arctic tundra. Soil Biol Biochem. 2013;65:338–47.
CAS  Article  Google Scholar 

15.
Puissant J, Cécillon L, Mills RTE, Robroek BJM, Gavazov K, De Danieli S, et al. Seasonal influence of climate manipulation on microbial community structure and function in mountain soils. Soil Biol Biochem. 2015;80:296–305.
CAS  Article  Google Scholar 

16.
Bardgett RD, Bowman WD, Kaufmann R, Schmidt SK. A temporal approach to linking aboveground and belowground ecology. Trends Ecol Evol. 2005;20:634–41.
PubMed  Article  Google Scholar 

17.
Schmidt SK, Costello EK, Nemergut DR, Cleveland CC, Reed SC, Weintraub MN, et al. Biogeochemical consequences of rapid microbial turnover and seasonal succession in soil. Ecology. 2007;88:1379–85.
CAS  PubMed  Article  Google Scholar 

18.
Schadt CW, Martin AP, Lipson DA, Schmidt SK. Seasonal dynamics of previously unknown fungal lineages in Tundra soils. Science. 2003;301:1359–61.
CAS  PubMed  Article  Google Scholar 

19.
Jefferies RL, Walker NA, Edwards KA, Dainty J. Is the decline of soil microbial biomass in late winter coupled to changes in the physical state of cold soils? Soil Biol Biochem. 2010;42:129–35.
CAS  Article  Google Scholar 

20.
Buckeridge KM, Grogan P. Deepened snow increases late thaw biogeochemical pulses in mesic low arctic tundra. Biogeochemistry. 2010;101:105–21.
Article  Google Scholar 

21.
Schimel J, Balser TC, Wallenstein M. Microbial stress-response physiology and its implications for ecosystem function. Ecology. 2007;88:1386–94.
PubMed  Article  Google Scholar 

22.
Buckeridge KM, Grogan P. Deepened snow alters soil microbial nutrient limitations in arctic birch hummock tundra. Appl Soil Ecol. 2008;39:210–22.
Article  Google Scholar 

23.
Väisänen M, Gavazov K, Krab EJ, Dorrepaal E. The legacy effects of winter climate on microbial functioning after snowmelt in a subarctic Tundra. Micro Ecol. 2019;77:186–90.
Article  Google Scholar 

24.
Darrouzet-Nardi A, Steltzer H, Sullivan PF, Segal A, Koltz AM, Livensperger C, et al. Limited effects of early snowmelt on plants, decomposers, and soil nutrients in Arctic Tundra soils. Ecol Evol. 2019;9:1820–44.
PubMed  PubMed Central  Article  Google Scholar 

25.
Ernakovich JG, Hopping KA, Berdanier AB, Simpson RT, Kachergis EJ, Steltzer H, et al. Predicted responses of arctic and alpine ecosystems to altered seasonality under climate change. Glob Chang Biol. 2014;20:3256–69.
PubMed  Article  Google Scholar 

26.
Li W, Wu J, Bai E, Jin C, Wang A, Yuan F, et al. Response of terrestrial carbon dynamics to snow cover change: a meta-analysis of experimental manipulation (II). Soil Biol Biochem. 2016;103:388–93.
CAS  Article  Google Scholar 

27.
Neuwinger I Bodenökologische. Untersuchungen im Gebiet Obergurgler Zirbenwald—Hohe Mut. In: Patzelt G (Hrsg.. (ed). MaB-Projekt Obergurgl. 1987. Universitätsverlag Wagner, Innsbruck, Austria, pp 173-90.

28.
Bligh EG, Dyer WJ. A rapid method of total lipid extraction and purification. Can J Biochem Physiol. 1959;37:911–917.
CAS  Article  Google Scholar 

29.
Bardgett RD, Hobbs PJ, Frostegard A. Changes in soil fungal:bacterial biomass ratios following reductions in the intensity of management of an upland grassland. Biol Fertil Soils. 1996;22:261–4.
Article  Google Scholar 

30.
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.
PubMed  PubMed Central  Article  CAS  Google Scholar 

31.
Arenz BE, Schlatter DC, Bradeen JM, Kinkel LL. Blocking primers reduce co-amplification of plant DNA when studying bacterial endophyte communities. J Microbiol Methods. 2015;117:1–3.
CAS  PubMed  Article  Google Scholar 

32.
Ihrmark K, Bödeker ITM, Cruz-Martinez K, Friberg H, Kubartova A, Schenck J, et al. New primers to amplify the fungal ITS2 region—evaluation by 454-sequencing of artificial and natural communities. FEMS Microbiol Ecol. 2012;82:666–77.
CAS  PubMed  Article  Google Scholar 

33.
White TJ, Bruns T, Lee S, Taylor J. PCR protocols. 1990. Academic Press.

34.
Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the miseq illumina sequencing platform. Appl Environ Microbiol. 2013;79:5112–20.
CAS  PubMed  PubMed Central  Article  Google Scholar 

35.
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  PubMed  PubMed Central  Article  Google Scholar 

36.
R Core Team. R: a language and environment for statistical computing. 2019. R Foundation for Statistical Computing.

37.
DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006;72:5069–72.
CAS  PubMed  PubMed Central  Article  Google Scholar 

38.
Kõljalg U, Larsson KH, Abarenkov K, Nilsson RH, Alexander IJ, Eberhardt U, et al. UNITE: A database providing web-based methods for the molecular identification of ectomycorrhizal fungi. N. Phytol. 2005;166:1063–8.
Article  CAS  Google Scholar 

39.
McMurdie PJ, Holmes S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.
CAS  PubMed  PubMed Central  Article  Google Scholar 

40.
Illumina. bcl2fastq and bcl2fastq2 Conversion software. 2020. https://support.illumina.com/sequencing/sequencing

41.
Sáenz JS, Marques TV, Barone RSC, Cyrino JEP, Kublik S, Nesme J, et al. Oral administration of antibiotics increased the potential mobility of bacterial resistance genes in the gut of the fish Piaractus mesopotamicus. Microbiome. 2019;7:1–14.
Article  Google Scholar 

42.
Schubert M, Lindgreen S, Orlando L. AdapterRemoval v2: rapid adapter trimming, identification, and read merging. BMC Res Notes. 2016;9:1–7.
Article  Google Scholar 

43.
Schmieder R, Edwards R. Fast identification and removal of sequence contamination from genomic and metagenomic datasets. PLoS ONE. 2011;6:e17288.
CAS  PubMed  PubMed Central  Article  Google Scholar 

44.
Menzel P, Ng KL, Krogh A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat Commun. 2016;7:11257.
CAS  PubMed  PubMed Central  Article  Google Scholar 

45.
Tu Q, Lin L, Cheng L, Deng Y, He Z. NCycDB: a curated integrative database for fast and accurate metagenomic profiling of nitrogen cycling genes. Bioinformatics. 2019;35:1040–8.
CAS  PubMed  Article  Google Scholar 

46.
First Y, Job P. GNU parallel: the command-line power tool | USENIX. 3: 42–47.

47.
Jackson CR, Tyler HL, Millar JJ. Determination of microbial extracellular enzyme activity in waters, soils, and sediments using high throughput microplate assays. J Vis Exp. 2013;80:e50399.
Google Scholar 

48.
De Long JR, Semchenko M, Pritchard WJ, Cordero I, Fry EL, Jackson BG, et al. Drought soil legacy overrides maternal effects on plant growth. Funct Ecol. 2019;33:1400–10.
PubMed  PubMed Central  Article  Google Scholar 

49.
Kandeler E, Gerber H. Short-term assay of soil urease activity using colorimetric determination of ammonium article in biology and fertility of soils. Biol Fertil Soils. 1988;6:68–72.
CAS  Article  Google Scholar 

50.
Jones DL, Willett VB. Experimental evaluation of methods to quantify dissolved organic nitrogen (DON) and dissolved organic carbon (DOC) in soil. Soil Biol Biochem. 2006;38:991–9.
CAS  Article  Google Scholar 

51.
Ross DJ. Influence of sieve mesh size on estimates of microbial carbon and nitrogen by fumigation-extraction procedures in soils under pasture. Soil Biol Biochem. 1992;24:343–50.
Article  Google Scholar 

52.
De Boer W, Folman LB, Summerbell RC, Boddy L. Living in a fungal world: Impact of fungi on soil bacterial niche development. FEMS Microbiol Rev. 2005;29:795–811.
PubMed  Article  CAS  Google Scholar 

53.
Moorhead DDL, Sinsabaugh RRL. A theoretical model of litter decay and microbial interaction. Ecol Monogr. 2006;76:151–74.
Article  Google Scholar 

54.
Zhou Y, Pope PB, Li S, Wen B, Tan F, Cheng S, et al. Omics-based interpretation of synergism in a soil-derived cellulose-degrading microbial community. Sci Rep. 2014;4:1–6.
Google Scholar 

55.
Lynd LR, Weimer PJ, van Zyl WH, Pretorius IS. Microbial cellulose utilization: fundamentals and biotechnology. Microbiol Mol Biol Rev. 2002;66:506–77.
CAS  PubMed  PubMed Central  Article  Google Scholar 

56.
Bhatnagar JM, Peay KG, Treseder KK. Litter chemistry influences decomposition through activity of specific microbial functional guilds. Ecol Monogr. 2018;88:429–44.
Article  Google Scholar 

57.
Sinsabaugh RL, Lauber CL, Weintraub MN, Ahmed B, Allison SD, Crenshaw C, et al. Stoichiometry of soil enzyme activity at global scale. Ecol Lett. 2008;11:1252–64.
PubMed  Article  Google Scholar 

58.
Fierer N, Lauber CL, Ramirez KS, Zaneveld J, Bradford MA, Knight R. Comparative metagenomic, phylogenetic and physiological analyses of soil microbial communities across nitrogen gradients. ISME J. 2012;6:1007–17.
CAS  PubMed  Article  Google Scholar 

59.
Broadbent AAD, Orwin KH, Peltzer DA, Dickie IA, Mason NWH, Ostle NJ, et al. Invasive N-fixer impacts on litter decomposition driven by changes to soil properties not litter quality. Ecosystems. 2017;20:1–13.
Article  CAS  Google Scholar 

60.
Prosser JI, Nicol GW. Archaeal and bacterial ammonia-oxidisers in soil: the quest for niche specialisation and differentiation. Trends Microbiol. 2012;20:523–31.
CAS  PubMed  Article  Google Scholar 

61.
Verhamme DT, Prosser JI, Nicol GW. Ammonia concentration determines differential growth of ammonia-oxidising archaea and bacteria in soil microcosms. ISME J. 2011;5:1067–71.
CAS  PubMed  PubMed Central  Article  Google Scholar 

62.
Brooks PD, Williams MW, Schmidt SK. Inorganic nitrogen and microbial biomass dynamics before and during spring snowmelt. Biogeochemistry. 1998;43:1–15.
Article  Google Scholar 

63.
Jaeger CH, Monson RK, Fisk MC, Schmidt SK. Seasonal partitioning of nitrogen by plants and soil microorganisms in an alpine ecosystem. Ecology. 1999;80:1883–91.
Article  Google Scholar 

64.
Ashton IW, Miller AE, Bowman WD, Suding KN. Niche complementarity due to plasticity in resource use: plant partitioning of chemical N forms. Ecology. 2010;91:3252–60.
PubMed  Article  Google Scholar 

65.
Bilbrough CJ, Welker JM, Bowman WD. Early spring nitrogen uptake by snow-covered plants: a comparison of Arctic and alpine plant function under the snowpack. Arct, Antarct Alp Res. 2000;32:404–11.
Article  Google Scholar 

66.
Michelsen A, Schmidt IK, Jonasson S, Quarmby C, Sleep D. Leaf 15N abundance of subarctic plants provides field evidence that ericoid, ectomycorrhizal and non-and arbuscular mycorrhizal species access different sources of soil nitrogen. Oecologia. 1996;105:53–63.
PubMed  Article  Google Scholar 

67.
Wookey PA, Aerts R, Bardgett RD, Baptist F, Bråthen K, Cornelissen JHC, et al. Ecosystem feedbacks and cascade processes: understanding their role in the responses of Arctic and alpine ecosystems to environmental change. Glob Chang Biol. 2009;15:1153–72.
Article  Google Scholar  More

1.
Diaz-Martin, Z., Swamy, V., Terborgh, J., Alvarez-Loayza, P. & Cornejo, F. Identifying keystone plant resources in an Amazonian forest using a long-term fruit-fall record. J. Trop. Ecol. 30, 291–301. https://doi.org/10.1017/S0266467414000248 (2014).
Article  Google Scholar 
2.
Terborgh, J. & Andresen, E. The composition of Amazonian forests: Patterns at local and regional scales. J. Trop. Ecol. 14, 645–664 (1998).
Article  Google Scholar 

3.
Wright, J. S. Plant diversity in tropical forests: A review of mechanisms of species coexistence. Oecologia 130, 1–14. https://doi.org/10.1007/s004420100809 (2002).
ADS  Article  Google Scholar 

4.
Bascompte, J. & Jordano, P. Plant-animal mutualistic networks: The architecture of biodiversity. Annu. Rev. Ecol. Evol. Syst. 38, 567–593. https://doi.org/10.1146/annurev.ecolsys.38.091206.095818 (2007).
Article  MATH  Google Scholar 

5.
Chapman, C. A., Wrangham, R. & Chapman, L. J. Indexes of habitat-wide fruit abundance in tropical forests. Biotropica 26, 160–171. https://doi.org/10.2307/2388805 (1994).
Article  Google Scholar 

6.
White, L. J. T. Patterns of fruit-fall phenology in the Lopé Reserve, Gabon. J. Trop. Ecol. 10, 289–312. https://doi.org/10.1017/S0266467400007975 (1994).
Article  Google Scholar 

7.
Bello, C. et al. Defaunation affects carbon storage in tropical forests. Sci. Adv. https://doi.org/10.1126/sciadv.1501105 (2015).
Article  PubMed  PubMed Central  Google Scholar 

8.
Peres, C. A., Emilio, T., Schietti, J., Desmoulière, S. J. & Levi, T. Dispersal limitation induces long-term biomass collapse in overhunted Amazonian forests. Proc. Natl. Acad. Sci. 113, 892–897 (2016).
ADS  CAS  Article  Google Scholar 

9.
Dee, L. E. et al. When do ecosystem services depend on rare species?. Trends Ecol. Evol. 34, 746–758. https://doi.org/10.1016/j.tree.2019.03.010 (2019).
Article  PubMed  Google Scholar 

10.
Pinho, B. X., Peres, C. A., Leal, I. R. & Tabarelli, M. In Tropical Ecosystems in the 21st Century (eds Alex, J. D., Edgar, C. T., & Tom, M. F.) Ch. 7, 253–294 (Academic Press, Cambridge, 2020).

11.
Bastin, J.-F. et al. Pan-tropical prediction of forest structure from the largest trees. Glob. Ecol. Biogeogr. 27, 1366–1383. https://doi.org/10.1111/geb.12803 (2018).
Article  Google Scholar 

12.
Lutz, J. A. et al. Global importance of large-diameter trees. Glob. Ecol. Biogeogr. 27, 849–864. https://doi.org/10.1111/geb.12747 (2018).
Article  Google Scholar 

13.
Sist, P., Mazzei, L., Blanc, L. & Rutishauser, E. Large trees as key elements of carbon storage and dynamics after selective logging in the Eastern Amazon. For. Ecol. Manag. 318, 103–109. https://doi.org/10.1016/j.foreco.2014.01.005 (2014).
Article  Google Scholar 

14.
Schulze, M., Grogan, J., Landis, R. M. & Vidal, E. How rare is too rare to harvest? Management challenges posed by timber species occurring at low densities in the Brazilian Amazon. For. Ecol. Manag. 256, 1443–1457. https://doi.org/10.1016/j.foreco.2008.02.051 (2008).
Article  Google Scholar 

15.
SFB. Florestas do Brasil em resumo 2013: dados de 2007–2012. (2013).

16.
Azevedo-Ramos, C., Silva, J. N. M. & Merry, F. The evolution of Brazilian forest concessions. Elem. Sci. Anth. https://doi.org/10.12952/journal.elementa.000048 (2015).
Article  Google Scholar 

17.
Golden Kroner, R. E. et al. The uncertain future of protected lands and waters. Science 364, 881. https://doi.org/10.1126/science.aau5525 (2019).
ADS  CAS  Article  Google Scholar 

18.
Degen, B. et al. Impact of selective logging on genetic composition and demographic structure of four tropical tree species. Biol. Cons. 131, 386–401. https://doi.org/10.1016/j.biocon.2006.02.014 (2006).
Article  Google Scholar 

19.
Richardson, V. A. & Peres, C. A. Temporal decay in timber species composition and value in Amazonian logging concessions. PLoS ONE 11, e0159035. https://doi.org/10.1371/journal.pone.0159035 (2016).
CAS  Article  PubMed  PubMed Central  Google Scholar 

20.
Aleixo, I. et al. Amazonian rainforest tree mortality driven by climate and functional traits. Nat. Clim. Change 9, 384–388. https://doi.org/10.1038/s41558-019-0458-0 (2019).
ADS  Article  Google Scholar 

21.
Nepstad, D. et al. Amazon drought and its implications for forest flammability and tree growth: A basin-wide analysis. Glob. Change Biol. 10, 704–717 (2004).
ADS  Article  Google Scholar 

22.
Vidal, E., West, T. A. & Putz, F. E. Recovery of biomass and merchantable timber volumes twenty years after conventional and reduced-impact logging in Amazonian Brazil. For. Ecol. Manag. 376, 1–8. https://doi.org/10.1016/j.foreco.2016.06.003 (2016).
Article  Google Scholar 

23.
Varty, N. & Guadagnin, D. L. Vouacapoua americana. The IUCN Red List of Threatened Species: e.T33918A9820054, https://doi.org/10.2305/IUCN.UK.1998.RLTS.T33918A9820054.en (1998).

24.
Dutech, C., Maggia, L., Tardy, C., Joly, H. I. & Jarne, P. Tracking a genetic signal of extinction-recolonization events in a neotropical tree species: Vouacapoua americana aublet in french guiana. Evolution 57, 2753–2764 (2003).
Article  Google Scholar 

25.
Guimarães, P. R. Jr., Galetti, M. & Jordano, P. Seed dispersal anachronisms: Rethinking the fruits extinct megafauna ate. PLoS ONE 3, e1745. https://doi.org/10.1371/journal.pone.0001745 (2008).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

26.
Traissac, S. & Pascal, J. P. Birth and life of tree aggregates in tropical forest: Hypotheses on population dynamics of an aggregated shade-tolerant species. J. Veg. Sci. 25, 491–502. https://doi.org/10.1111/jvs.12080 (2014).
Article  Google Scholar 

27.
Forget, P.-M. Seed-dispersal of Vouacapoua americana (Caesalpiniaceae) by caviomorph rodents in French Guiana. J. Trop. Ecol. 6, 459–468. https://doi.org/10.1017/S0266467400004867 (1990).
Article  Google Scholar 

28.
Jansen, P. A., Bongers, F. & van der Meer, P. J. Is farther seed dispersal better? Spatial patterns of offspring mortality in three rainforest tree species with different dispersal abilities. Ecography 31, 43–52. https://doi.org/10.1111/j.2007.0906-7590.05156.x (2008).
Article  Google Scholar 

29.
MMA. Vol. 18/12/2014 (ed Ministério do Meio Ambiente—MMA) 110–121 (Diário Oficial da União, Brasilia, 2014).

30.
Avitabile, V. et al. An integrated pan-tropical biomass map using multiple reference datasets. Glob. Change Biol. 22, 1406–1420. https://doi.org/10.1111/gcb.13139 (2016).
ADS  Article  Google Scholar 

31.
Baccini, A. et al. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nat. Clim. Change 2, 182–185. https://doi.org/10.1038/nclimate1354 (2012).
ADS  CAS  Article  Google Scholar 

32.
Saatchi, S. S. et al. Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl. Acad. Sci. U.S.A. 108, 9899–9904. https://doi.org/10.1073/pnas.1019576108 (2011).
ADS  Article  PubMed  PubMed Central  Google Scholar 

33.
Saatchi, S. S., Houghton, R. A., Dos Santos AlvalÁ, R. C., Soares, J. V. & Yu, Y. Distribution of aboveground live biomass in the Amazon basin. Glob. Change Biol. 13, 816–837. https://doi.org/10.1111/j.1365-2486.2007.01323.x (2007).
ADS  Article  Google Scholar 

34.
Muller-Landau, H. C., Wright, S. J., Calderon, O., Condit, R. & Hubbell, S. P. Interspecific variation in primary seed dispersal in a tropical forest. J. Ecol. 96, 653–667. https://doi.org/10.1111/j.1365-2745.2008.01399.x (2008).
Article  Google Scholar 

35.
Mendoza, I. et al. Does masting result in frugivore satiation? A test with Manilkara trees in French Guiana. J. Trop. Ecol. 31, 553–556. https://doi.org/10.1017/S0266467415000425 (2015).
Article  Google Scholar 

36.
Kelly, D. The evolutionary ecology of mast seeding. Trends Ecol. Evol. 9, 465–470. https://doi.org/10.1016/0169-5347(94)90310-7 (1994).
CAS  Article  PubMed  Google Scholar 

37.
Kelly, D. & Sork, V. L. Mast seeding in perennial plants: Why, how, where?. Annu. Rev. Ecol. Syst. 33, 427–447. https://doi.org/10.1146/annurev.ecolsys.33.020602.095433 (2002).
Article  Google Scholar 

38.
Johnson, M. O. et al. Variation in stem mortality rates determines patterns of above-ground biomass in Amazonian forests: Implications for dynamic global vegetation models. Glob. Change Biol. 22, 3996–4013 (2016).
ADS  Article  Google Scholar 

39.
Batista, A. P. B. et al. Caracterização estrutural em uma floresta de terra firme no estado do Amapá, Brasil. Pesq. flor. bras 35, 21–33 (2015).
Article  Google Scholar 

40.
Charles-Dominique, P. et al. Les mammiferes frugivores arboricoles nocturnes d’une foret guyanaise: Inter-relations plantes-animaux. La Terre et la Vie: Revue d’Ecologie Appliquée 35, 341–435 (1981).
Google Scholar 

41.
de Oliveira, A. N. & do Amaral, I. L. ,. Florística e fitossociologia de uma floresta de vertente na Amazônia Central, Amazonas, Brasil. Acta Amazonica 34, 21–34 (2004).
Article  Google Scholar 

42.
Pereira, L. A., Pinto Sobrinho, F. D. A. & Costa Neto, S. V. D. Florística e estrutura de uma mata de terra firme na reserva de desenvolvimento sustentável rio Iratapuru, Amapá, Amazônia Oriental, Brasil. (2011).

43.
Pereira, L. A., Sena, K. S., dos Santos, M. R. & Neto, S. V. C. Aspectos florísticos da FLONA do Amapá e sua importância na conservação da biodiversidade. Revista Brasileira de Biociências 5, 693–695 (2007).
Google Scholar 

44.
Sabatier, D. Saisonnalité et déterminisme du pic de fructification en forêt guyanaise. Revue d’Ecologie (Terrre et Vie) 40, 89–320 (1985).
Google Scholar 

45.
ter Steege, H. et al. An analysis of the floristic composition and diversity of Amazonian forests including those of the Guiana Shield. J. Trop. Ecol. 16, 801–828 (2000).
Article  Google Scholar 

46.
Hanya, G. et al. Seasonality in fruit availability affects frugivorous primate biomass and species richness. Ecography 34, 1009–1017. https://doi.org/10.1111/j.1600-0587.2010.06775.x (2011).
Article  Google Scholar 

47.
Situmorang, J. P. & Sugianto, S. Estimation of carbon stock stands using EVI and NDVI vegetation index in production forest of Lembah Seulawah Sub-District, Aceh Indonesia. Aceh Int. J. Sci. Technol. 5 (2016).

48.
Asner, G. P. et al. Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation. Science 355, 385–389. https://doi.org/10.1126/science.aaj1987 (2017).
ADS  CAS  Article  PubMed  Google Scholar 

49.
Bhardwaj, D., Banday, M., Pala, N. A. & Rajput, B. S. Variation of biomass and carbon pool with NDVI and altitude in sub-tropical forests of northwestern Himalaya. Environ. Monit. Assess. 188, 635 (2016).
CAS  Article  Google Scholar 

50.
Dubayah, R. O. et al. Estimation of tropical forest height and biomass dynamics using lidar remote sensing at La Selva, Costa Rica. J. Geophys. Res. Biogeosci. https://doi.org/10.1029/2009JG000933 (2010).
Article  Google Scholar 

51.
Holly, K. G., Sandra, B., John, O. N. & Jonathan, A. F. Monitoring and estimating tropical forest carbon stocks: Making REDD a reality. Environ. Res. Lett. 2, 045023 (2007).
Article  Google Scholar 

52.
Asner, G. P. et al. High-resolution forest carbon stocks and emissions in the Amazon. Proc. Natl. Acad. Sci. 107, 16738–16742 (2010).
ADS  CAS  Article  Google Scholar 

53.
Magnusson, W. et al. Biodiversidade e monitoramento ambiental integrado (Biodiversity and Integrated Environmental Monitoring). 335 (PPBio INPA, 2013).

54.
Kottek, M., Grieser, J., Beck, C., Rudolf, B. & Rubel, F. World map of the Koppen–Geiger climate classification updated. Meteorol. Z. 15, 259–263. https://doi.org/10.1127/0941-2948/2006/0130 (2006).
Article  Google Scholar 

55.
ANA. Sistema de Monitoramento Hidrológico (Hydrological Monitoring System). Agência Nacional de Águas[[nl]]National Water Agency. http://www.hidroweb.ana.gov.br, 2016).

56.
ICMBio. Vol. I (ed MINISTÉRIO DO MEIO AMBIENTE) 222 (Instituto Chico Mendes de Conservação da Biodiversidade, Macapá, Amapá, 2014).

57.
Eswaran, H., Ahrens, R., Rice, T. J. & Stewart, B. A. Soil Classification: A Global Desk Reference. (CRC Press, Boca Raton, 2002).

58.
Dutech, C., Maggia, L. & Joly, H. I. Chloroplast diversity in Vouacapoua americana (Caesalpiniaceae), a neotropical forest tree. Mol. Ecol. 9, 1427–1432. https://doi.org/10.1046/j.1365-294x.2000.01027.x (2000).
CAS  Article  PubMed  Google Scholar 

59.
ter Steege, H. et al. Hyperdominance in the Amazonian Tree Flora. Science https://doi.org/10.1126/science.1243092 (2013).
Article  PubMed  Google Scholar 

60.
Kido, T., Taniguchi, M. & Baba, K. Diterpenoids from Amazonian crude drug of Fabaceae. Chem. Pharm. Bull. 51, 207–208. https://doi.org/10.1248/cpb.51.207 (2003).
CAS  Article  Google Scholar 

61.
Maurya, R., Ravi, M., Singh, S. & Yadav, P. P. A review on cassane and norcassane diterpenes and their pharmacological studies. Fitoterapia 83, 272–280. https://doi.org/10.1016/j.fitote.2011.12.007 (2012).
CAS  Article  PubMed  Google Scholar 

62.
Alves, J. C. Z. O. & Miranda, I. D. S. Análise da estrutura de comunidades arbóreas de uma floresta amazônica de Terra Firme aplicada ao manejo florestal. Acta Amazonica 38, 657–666 (2008).
Article  Google Scholar 

63.
Forget, P. M., Mercier, F. & Collinet, F. Spatial patterns of two rodent-dispersed rain forest trees Carapa procera (Meliaceae) and Vouacapoua americana (Caesalpiniaceae) at Paracou, French Guiana. J. Trop. Ecol. 15, 301–313. https://doi.org/10.1017/s0266467499000838 (1999).
Article  Google Scholar 

64.
Forget, P.-M. Ten-year seedling dynamics in Vouacapoua americana in French Guiana: A hypothesis. Biotropica 29, 124–126 (1997).
Article  Google Scholar 

65.
Forget, P. M. Recruitment pattern of Vouacapoua-Americana (Caesalpiniaceae), a rodent-dispersed tree specie in French-Guiana. Biotropica 26, 408–419. https://doi.org/10.2307/2389235 (1994).
Article  Google Scholar 

66.
Forget, P. M. Effect of microhabitat on seed fate and seedling performance in two rodent-dispersed tree species in rain forest in French Guiana. J. Ecol. 85, 693–703. https://doi.org/10.2307/2960539 (1997).
Article  Google Scholar 

67.
Zhang, S. Y. & Wang, L. X. Comparison of 3 fruit census methods in French-Guiana. J. Trop. Ecol. 11, 281–294 (1995).
Article  Google Scholar 

68.
Stevenson, P. R. The relationship between fruit production and primate abundance in Neotropical communities. Biol. J. Lin. Soc. 72, 161–178. https://doi.org/10.1006/bijl.2000.049 (2001).
Article  Google Scholar 

69.
Norris, D., Rodriguez Chuma, V. J. U., Arevalo-Sandi, A. R., Landazuri Paredes, O. S. & Peres, C. A. Too rare for non-timber resource harvest? Meso-scale composition and distribution of arborescent palms in an Amazonian sustainable-use forest. For. Ecol. Manag. 377, 182–191. https://doi.org/10.1016/j.foreco.2016.07.008 (2016).
Article  Google Scholar 

70.
Paredes, O. S. L., Norris, D., Oliveira, T. G. D. & Michalski, F. Water availability not fruitfall modulates the dry season distribution of frugivorous terrestrial vertebrates in a lowland Amazon forest. PLoS ONE 12, e0174049. https://doi.org/10.1371/journal.pone.0174049 (2017).
CAS  Article  PubMed  PubMed Central  Google Scholar 

71.
Magnusson, W. E. et al. RAPELD: A modification of the Gentry method for biodiversity surveys in long-term ecological research sites. Biota. Neotrop. 5, 19–24. https://doi.org/10.1590/s1676-06032005000300002 (2005).
Article  Google Scholar 

72.
Norris, D., Fortin, M.-J. & Magnusson, W. E. Towards monitoring biodiversity in Amazonian forests: How regular samples capture meso-scale altitudinal variation in 25 km(2) plots. PLoS ONE https://doi.org/10.1371/journal.pone.0106150 (2014).
Article  PubMed  PubMed Central  Google Scholar 

73.
The Angiosperm Phylogeny Group. An update of the Angiosperm Phylogeny Group classification for the orders and families of flowering plants: APG III. Bot. J. Linn. Soc. 161, 105–121. https://doi.org/10.1111/j.1095-8339.2009.00996.x (2009).
Article  Google Scholar 

74.
Guisan, A. & Zimmermann, N. E. Predictive habitat distribution models in ecology. Ecol. Model. 135, 147–186. https://doi.org/10.1016/S0304-3800(00)00354-9 (2000).
Article  Google Scholar 

75.
Platts, P. J., McClean, C. J., Lovett, J. C. & Marchant, R. Predicting tree distributions in an East African biodiversity hotspot: Model selection, data bias and envelope uncertainty. Ecol. Model. 218, 121–134. https://doi.org/10.1016/j.ecolmodel.2008.06.028 (2008).
Article  Google Scholar 

76.
Camarero, J. J., Albuixech, J., López-Lozano, R., Casterad, M. A. & Montserrat-Martí, G. An increase in canopy cover leads to masting in Quercus ilex. Trees 24, 909–918. https://doi.org/10.1007/s00468-010-0462-5 (2010).
Article  Google Scholar 

77.
Fernández-Martínez, M., Garbulsky, M., Peñuelas, J., Peguero, G. & Espelta, J. M. Temporal trends in the enhanced vegetation index and spring weather predict seed production in Mediterranean oaks. Plant Ecol. 216, 1061. https://doi.org/10.1007/s11258-015-0489-1 (2015).
Article  Google Scholar 

78.
Fortin, M.-J. & Dale, M. R. T. Spatial Analysis: A Guide for Ecologists. 365 (Cambridge University Press, Cambridge, 2005).

79.
Hastie, T. J. & Tibshirani, R. J. Generalized Additive Models. Vol. 43 (CRC Press, Boca Raton, 1990).

80.
Wood, S. Generalized Additive Models: An Introduction with R. (CRC Press, Boca Raton, 2006).

81.
Wood, S. N. & Augustin, N. H. GAMs with integrated model selection using penalized regression splines and applications to environmental modelling. Ecol. Model. 157, 157–177. https://doi.org/10.1016/S0304-3800(02)00193-X (2002).
Article  Google Scholar 

82.
R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2016).

83.
Burnham, K. P. & Anderson, D. R. Model Selection and Multi-model Inference: A Practical Information-Theoretic Approach. (Springer, New York, 2002).

84.
Pebesma, E. J. Multivariable geostatistics in S: The gstat package. Comput. Geosci. 30, 683–691. https://doi.org/10.1016/j.cageo.2004.03.012 (2004).
ADS  Article  Google Scholar 

85.
Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 18–22 (2002).
Google Scholar 

86.
e1071: Misc Functions of the Department of Statistics, Probability Theory Group v. 1.6-8 (2017). More

1.
Cao, M. & Woodward, F. I. Dynamic responses of terrestrial ecosystem carbon cycling to global climate change. Nature 393, 249–252 (1998).
CAS  Article  Google Scholar 
2.
Heimann, M. & Reichstein, M. Terrestrial ecosystem carbon dynamics and climate feedbacks. Nature 451, 289–292 (2008).
CAS  Article  Google Scholar 

3.
Allen, A. P., Gillooly, J. F. & Brown, J. H. Linking the global carbon cycle to individual metabolism. Funct. Ecol. 19, 202–213 (2005).
Article  Google Scholar 

4.
Enquist, B. J. et al. Scaling metabolism from organisms to ecosystems. Nature 423, 639–642 (2003).
CAS  Article  Google Scholar 

5.
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 

6.
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 

7.
Friedlingstein, P. et al. Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks. J. Clim. 27, 511–526 (2014).
Article  Google Scholar 

8.
Davidson, E. A. & Janssens, I. A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440, 165–173 (2006).
CAS  Article  Google Scholar 

9.
Lenton, T. M. & Huntingford, C. Global terrestrial carbon storage and uncertainties in its temperature sensitivity examined with a simple model. Glob. Change Biol. 9, 1333–1352 (2003).
Article  Google Scholar 

10.
Song, B. et al. Divergent apparent temperature sensitivity of terrestrial ecosystem respiration. J. Plant Ecol. 7, 419–428 (2014).
Article  Google Scholar 

11.
Lloyd, J. & Taylor, J. A. On the temperature dependence of soil respiration. Funct. Ecol. 8, 315–323 (1994).

12.
Mahecha, M. D. et al. Global convergence in the temperature sensitivity of respiration at ecosystem level. Science 329, 838–840 (2010).
CAS  Article  Google Scholar 

13.
Yvon-Durocher, G. et al. Reconciling the temperature dependence of respiration across timescales and ecosystem types. Nature 487, 472–476 (2012).
CAS  Article  Google Scholar 

14.
Johnston, A. S. A. & Sibly, R. M. The influence of soil communities on the temperature sensitivity of soil respiration. Nat. Ecol. Evol. 2, 1597–1602 (2018).
Article  Google Scholar 

15.
Dell, A. I., Pawar, S. & Savage, V. M. Systematic variation in the temperature dependence of physiological and ecological traits. Proc. Natl Acad. Sci. USA 108, 10591–10596 (2011).
CAS  Article  Google Scholar 

16.
Buckley, L. B. & Huey, R. B. Temperature extremes: geographic patterns, recent changes, and implications for organismal vulnerabilities. Glob. Change Biol. 22, 3829–3842 (2016).
Article  Google Scholar 

17.
Gill, A. L. & Finzi, A. C. Belowground carbon flux links biogeochemical cycles and resource-use efficiency at the global scale. Ecol. Lett. 19, 1419–1428 (2016).
Article  Google Scholar 

18.
Green, J. K. et al. Large influence of soil moisture on long-term terrestrial carbon uptake. Nature 565, 476–479 (2019).
CAS  Article  Google Scholar 

19.
Allison, S. D., Wallenstein, M. D. & Bradford, M. A. Soil-carbon response to warming dependent on microbial physiology. Nat. Geosci. 3, 336–340 (2010).
CAS  Article  Google Scholar 

20.
Michaletz, S. T., Cheng, D., Kerkhoff, A. J. & Enquist, B. J. Convergence of terrestrial plant production across global climate gradients. Nature 512, 39–43 (2014).
CAS  Article  Google Scholar 

21.
Pastorello, G. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data 7, 225 (2020).
Article  Google Scholar 

22.
Monson, R. K. et al. Winter forest soil respiration controlled by climate and microbial community composition. Nature 439, 711–714 (2006).
CAS  Article  Google Scholar 

23.
Mauder, M. et al. A strategy for quality and uncertainty assessment of long-term eddy-covariance measurements. Agric. Meteorol. 169, 122–135 (2013).
Article  Google Scholar 

24.
Kim, D.-G., Vargas, R., Bond-Lamberty, B. & Turetsky, M. R. Effects of soil rewetting and thawing on soil gas fluxes: a review of current literature and suggestions for future research. Biogeosciences 9, 2459–2483 (2012).
CAS  Article  Google Scholar 

25.
Du, E. et al. Winter soil respiration during soil-freezing process in a boreal forest in Northeast China. J. Plant Ecol. 6, 349–357 (2013).
Article  Google Scholar 

26.
Schuur, E. A. et al. Climate change and the permafrost carbon feedback. Nature 520, 171–179 (2015).
CAS  Article  Google Scholar 

27.
Koven, C. D., Hugelius, G., Lawrence, D. M. & Wieder, W. R. Higher climatological temperature sensitivity of soil carbon in cold than warm climates. Nat. Clim. Change 7, 817–822 (2017).
CAS  Article  Google Scholar 

28.
Bond-Lamberty, B. P. & Thomson, A. M. A Global Database of Soil Respiration Data Version 4.0 (ORNL DAAC, 2018); https://doi.org/10.3334/ORNLDAAC/1578

29.
Zhang, Z. et al. A temperature threshold to identify the driving climate forces of the respiratory process in terrestrial ecosystems. Eur. J. Soil Biol. 89, 1–8 (2018).
Article  Google Scholar 

30.
Yang, Y., Donohue, R. J., McVicar, T. R., Roderick, M. L. & Beck, H. E. Long-term CO2 fertilization increases vegetation productivity and has little effect on hydrological partitioning in tropical rainforests. J. Geophys. Res. Biogeosci. 121, 2125–2140 (2016).
Article  Google Scholar 

31.
Fleischer, K. et al. Amazon forest response to CO2 fertilization dependent on plant phosphorus acquisition. Nat. Geosci. 12, 736–741 (2019).
CAS  Article  Google Scholar 

32.
Padfield, D. et al. Metabolic compensation constrains the temperature dependence of gross primary production. Ecol. Lett. 20, 1250–1260 (2017).
Article  Google Scholar 

33.
Atkin, O. K. & Tjoelker, M. G. Thermal acclimation and the dynamic response of plant respiration to temperature. Trends Plant Sci. 8, 343–351 (2003).
CAS  Article  Google Scholar 

34.
Huntingford, C. et al. Implications of improved representations of plant respiration in a changing climate. Nat. Commun. 8, 1602 (2017).
Article  Google Scholar 

35.
Niu, S. et al. Thermal optimality of net ecosystem exchange of carbon dioxide and underlying mechanisms. New Phytol. 194, 775–783 (2012).
Article  Google Scholar 

36.
Rind, D. The consequences of not knowing low- and high-latitude climate sensitivity. Bull. Am. Meteorol. Soc. 89, 855–864 (2008).
Article  Google Scholar 

37.
Liu, Z. et al. Increased high-latitude photosynthetic carbon gain offset by respiration carbon loss during an anomalous warm winter to spring transition. Glob. Change Biol. 26, 682–696 (2020).
Article  Google Scholar 

38.
Haverd, V. et al. Higher than expected CO2 fertilization inferred from leaf to global observations. Glob. Change Biol. 26, 2390–2402 (2020).
Article  Google Scholar 

39.
Tagesson, T. et al. Recent divergence in the contributions of tropical and boreal forests to the terrestrial carbon sink. Nat. Ecol. Evol. 4, 202–209 (2020).
Article  Google Scholar 

40.
Climate Research Unit, University of East Anglia Average Annual Temperature. Atlas Biosphere (Center for Sustainability and the Global Environment, accessed 6 February 2020); https://nelson.wisc.edu/sage/data-and-models/atlas/maps.php More

1.
Taberlet, P., Bonin, A., Zinger, L, & Coissac, E. Environmental DNA, for Biodiversity Research and Monitoring (Oxford Univ. Press, 2018).
2.
Jo, T., Arimoto, M., Murakami, H., Masuda, R. & Minamoto, T. Particle size distribution of environmental DNA from the nuclei of marine fish. Environ. Sci. Technol. 53, 9947–9956 (2019).
CAS  PubMed  Article  Google Scholar 

3.
Wilcox, T. M., McKelvey, K. S., Young, M. K., Lowe, W. H. & Schwartz, M. K. Environmental DNA particle size distribution from Brook Trout (Salvelinus fontinalis). Conserv. Genet. Resour. 7, 639–641 (2015).
Article  Google Scholar 

4.
Thomsen, P. F. & Willerslev, E. Environmental DNA – an emerging tool in conservation for monitoring past and present biodiversity. Biol. Conserv. 183, 4–18 (2015).
Article  Google Scholar 

5.
Seymour, M. et al. Executing multi-taxa eDNA ecological assessment via traditional metrics and interactive networks. Sci. Total Environ. 729, 138801 (2020).
CAS  PubMed  Article  Google Scholar 

6.
Jarman, S. N., Berry, O. & Bunce, M. The value of environmental DNA biobanking for long-term biomonitoring. Nat. Ecol. Evol. 2, 1192–1193 (2018).
PubMed  Article  Google Scholar 

7.
Jeunen, G.-J. et al. Species-level biodiversity assessment using marine environmental DNA metabarcoding requires protocol optimization and standardization. Ecol. Evol. 9, 1323–1335 (2019).
PubMed  PubMed Central  Article  Google Scholar 

8.
Turner, C. R. et al. Particle size distribution and optimal capture of aqueous microbial eDNA. Methods Ecol. Evol. 5, 676–684 (2014).
Article  Google Scholar 

9.
Koziol, A. et al. Environmental DNA metabarcoding studies are critically affected by substrate selection. Mol. Ecol. Resour. 19, 366–376 (2019).
CAS  PubMed  Article  Google Scholar 

10.
Tsuji, S., Takahara, T., Doi, H., Shibata, N. & Yamanaka, H. The detection of aquatic macroorganisms using environmental DNA analysis – a review of methods for collection, extraction, and detection. Environ. DNA 1, 99–108 (2019).
Article  Google Scholar 

11.
Shu, L., Ludwig, A. & Peng, Z. Standards for methods utilizing environmental DNA for detection of fish species. Genes 11, 296 (2020).
CAS  PubMed Central  Article  PubMed  Google Scholar 

12.
Deiner, K., Walser, J.-C., Mächler, E. & Altermatt, F. Choice of capture and extraction methods affect detection of freshwater biodiversity from environmental DNA. Biol. Conserv. 183, 53–63 (2015).
Article  Google Scholar 

13.
Jeunen, G.-J. et al. Environmental DNA (eDNA) metabarcoding reveals strong discrimination among diverse marine habitats connected by water movement. Mol. Ecol. Resour. 19, 426–438 (2019).
CAS  PubMed  Article  Google Scholar 

14.
Thomas, A. C., Howard, J., Nguyen, P. L., Seimon, T. A. & Goldberg, C. S. ANDeTM: a fully integrated environmental DNA sampling system. Methods Ecol. Evol. 9, 1379–1385 (2018).
Article  Google Scholar 

15.
Schumer, G. et al. Utilizing environmental DNA for fish eradication effectiveness monitoring in streams. Biol. Invasions 21, 3415–3426 (2019).
Article  Google Scholar 

16.
Zinger, L. et al. DNA metabarcoding – need for robust experimental designs to draw sound ecological conclusions. Mol. Ecol. 28, 1857–1862 (2019).
PubMed  Article  Google Scholar 

17.
Bessey, C. et al. Maximizing fish detection with eDNA metabarcoding. Environ. DNA 2, 493–504, https://doi.org/10.1002/edn3.74 (2020).
Article  Google Scholar 

18.
Harrison, J. B., Sunday, J. M. & Rogers, S. M. Predicting the fate of eDNA in the environment and implications for studying biodiversity. Proc. R. Soc. Ser. B 286, 20191409 (2019).
CAS  Article  Google Scholar 

19.
Seymour, M. et al. Acidity promotes degradation of multi-species environmental DNA in lotic mesocosms. Commun. Biol. 1, https://doi.org/10.1038/s42003-017-0005-3 (2018).

20.
Deiner, K. & Altermatt, F. Transport distance of invertebrate environmental DNA in a natural river. PLoS ONE 9, e88786 (2014).
PubMed  PubMed Central  Article  CAS  Google Scholar 

21.
Mächler, E., Deiner, K., Spahn, F. & Altermatt, F. Fishing in the water: effect of sampled water volume on environmental DNA-based detection of macroinvertebrates. Environ. Sci. Technol. 50, 305–312 (2016).
PubMed  Article  CAS  Google Scholar 

22.
Hanfling, B. et al. Environmental DNA metabarcoding of lake fish communities reflects long-term data from established survey methods. Mol. Ecol. 25, 3101–3119 (2016).
PubMed  Article  CAS  Google Scholar 

23.
Cantera, I. et al. Optimizing environmental DNA sampling effort for fish inventories in tropical streams and rivers. Sci. Rep. 9, 3085 (2019).
PubMed  PubMed Central  Article  CAS  Google Scholar 

24.
McQuillan, J. S. & Robidart, J. C. Molecular-biological sensing in aquatic environments: recent developments and emerging capabilities. Curr. Opin. Biotechnol. 45, 43–50 (2017).
CAS  PubMed  Article  Google Scholar 

25.
Schabacker, J. C. et al. Increased eDNA detection sensitivity using a novel high-volume water sampling method. Environ. DNA 2, 244–251 (2020).
Article  Google Scholar 

26.
Mariani, S., Baillie, C., Colosimo, G. & Riesgo, A. Sponges as natural environmental DNA samples. Curr. Biol. 29, R395–R402 (2019).
Article  CAS  Google Scholar 

27.
Keesing, J., Webber, B.L. & Hardiman, L. Ashmore Reef Marine Park Environmental Assessment. Final report to director of National Park (2020).

28.
Kirtane, A., Atkinson, J. D. & Sassoubre, L. Design and validation of passive environmental DNA samplers using granular activated carbon and montmorillonite clay. Environ. Sci. Technol. https://doi.org/10.1021/acs.est.0c01863 (2020).
Article  PubMed  Google Scholar 

29.
Taberlet, P., Coissac, E., Hajibabaei, M. & Rieseberg, L. H. Environmental DNA. Mol. Ecol. Resour. 21, 1789–1793 (2012).
CAS  Article  Google Scholar 

30.
Fonseca, V. G. Pitfalls in relative abundance estimation using eDNA metabarcoding. Mol. Ecol. Resour. 18, 923–926 (2018).
CAS  Article  Google Scholar 

31.
Lamb, P. D. et al. How quantitative is metabarcoding: a meta-analytical approach. Mol. Ecol. 28, 420–430 (2019).
PubMed  Article  Google Scholar 

32.
Derocles, S. A. P. et al. Biomonitoring for the 21st century: integrating next-generation sequencing into ecological network analysis. Adv. Ecol. Res. 58, 1–62 (2018).
Article  Google Scholar 

33.
Prosser, J. I. Replicate or lie. Environ. Microbiol. 12, 1806–1810 (2010).
CAS  PubMed  Article  Google Scholar 

34.
MacKenzie, D. I. What are the issues with presence-absence data for wildlife managers? J. Wildl. Manag. 69, 849–860 (2005).
Article  Google Scholar 

35.
Liang, Z. & Keeley, A. Filtration recovery of extracellular DNA from environmental water samples. Environ. Sci. Technol. 47, 9324–9331 (2013).
CAS  PubMed  Article  Google Scholar 

36.
Renshaw, M. A., Olds, B. P., Jerde, C. L., McVeigh, M. M. & Lodge, D. M. The room temperature preservation of filtered environmental DNA samples and assimilation into a phenol-chloroform-isoamyl alcohol DNA extraction. Mol. Ecol. Resour. 15, 168–176 (2015).
CAS  PubMed  Article  Google Scholar 

37.
Eichmiller, J. J., Miller, L. M. & Sorensen, P. W. Optimizing techniques to capture and extract environmental DNA for detection and quantification of fish. Mol. Ecol. Resour. 16, 56–68 (2016).
CAS  PubMed  Article  Google Scholar 

38.
Majaneva, M. et al. Environmental DNA filtration techniques affect recovered biodiversity. Sci. Rep. 8, 4682 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

39.
Stier, A. C., Bolker, B. M. & Osenberg, C. W. Using rarefaction to isolate the effects of patch size and sampling effort on beta diversity. Ecosphere 7, e01612 (2016).
Article  Google Scholar 

40.
Yates, M. C., Fraser, D. J. & Derry, A. M. Meta-analysis supports further refinement of eDNA for monitoring aquatic species-specific abundance in nature. Environ. DNA 1, 5–13 (2019).
Article  Google Scholar 

41.
Strickland, G. J. & Roberts, J. H. Utility of eDNA and occupancy models for monitoring an endangered fish across diverse riverine habitats. Hydrobiologia 826, 129–144 (2019).
CAS  Article  Google Scholar 

42.
Deagle, B. E. et al. Counting with DNA metabarcoding studies: how should we convert sequence reads to dietary data? Mol. Ecol. 28, 391–406 (2019).

43.
Shogren, A. J. et al. Controls on eDNA movement in streams: transport, retention, and resuspension. Sci. Rep. 7, 5065 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

44.
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).
PubMed  PubMed Central  Article  Google Scholar 

45.
Deagle, B. E. et al. Studying seabird diet through genetic analysis of faeces: a case study on Macaroni penguins (Eudyptes chrysolophus). PLoS ONE 2, e831 (2007).
PubMed  PubMed Central  Article  CAS  Google Scholar 

46.
Murray, D. C., Coghlan, M. L. & Bunce, M. From benchtop to desktop: important considerations when designing amplicon sequencing workflows. PLoS ONE 10, e0124671 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

47.
Benson, D. A. et al. GenBank. Nucleic Acids Res. 42, D32–D37 (2014).
CAS  PubMed  Article  Google Scholar 

48.
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  PubMed  PubMed Central  Article  Google Scholar 

49.
Paradis, E. APE 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).
CAS  Article  PubMed  Google Scholar 

50.
Baselga, A. & Orme, C. D. L. Betapart: an R package for the study of beta diversity. Methods Ecol. Evol. 3, 808–812 (2012).
Article  Google Scholar 

51.
Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14, 927–930 (2003).
Article  Google Scholar 

52.
Herve, M. RVAideMemoire, testing and plotting procedures for biostatistics. https://cran.r-project.org/web/packages/RVAideMemoire/index.html (2018). More

The chemical components analysis of different extracts of A. argyi
In our preliminary study, we accidentally found that A. argyi powder significantly inhibited the germination and reduced the varieties and biomass of weeds in the field, when it was applied as a fertilizer originally. Therefore, we speculated that certain allelochemicals present in A. argyi might inhibit the growth of weeds. To investigated the possible allelochemicals in A. argyi, three solvents (water, 50% ethanol and pure ethanol) were used to extract the metabolites in A. argyi leaves. The three type of extracts were analysed by UPLC-Q-TOF-MS and the components were confirmed by comparison with synthetic standards and MS data in literatures9,10,11. As shown in Table 1 and supplement Fig. 1, we have identified a total of 29 components in A. argyi. Six main compound mass signals were identified in the water extract: caffeic acid, schaftoside, 4-caffeoylquinic acid, 5-caffeoylquinic acid, 3,5-dicaffeoylquinic acid and 3-caffeoylquinic acid. The main compounds of the 50% ethanol extract were 4,5-dicaffeoylquinic acid, 3-caffeoylquinic acid, schaftoside, rutin, kaempferol 3-rutinoside, 3,4-dicaffeoylquinic acid, 3,5-dicaffeoylquinic acid, 3-caffeoy,1-p-coumaroylquinic acid, 1,3,4-tri-caffeoylquinic acid and eupatilin. The metabolites with higher contents in the pure ethanol extract were eupatilin, jaceosidin and casticin. Among these compounds, caffeic acid is very unique in water extract. Higher contents of schaftoside, 4-caffeoylquinic acid and 3-caffeoylquinic acid were observed in water extract and 50% ethanol extract, but very low concentrations were detected in the pure ethanol extract. 3,4-dicaffeoylquinic acid, jaceosidin, eupatilin and casticin were present at higher concentrations in the 50% ethanol extract and pure ethanol extract, but were detected at very low concentrations or were absent in the water-soluble extract. In a word, we have preliminarily identified the chemical components of different extracts.
Table 1 The chemical composition of different solvent extracts of A. argyi.
Full size table

Comparison of the allelopathic effects of different extracts of A. argyi
To explore the allelopathic effects of three different extracts of A. argyi, seed germination and seedling growth of B. pekinensis, L. sativa and O. sativa were investigated after treatment of A. argyi powder extracts. The results showed that the allelopathic inhibition increased in a concentration dependent manner. When seeds were incubated with extracts in a range of concentrations, the water-soluble extract of A. argyi powder exerted an extremely significant inhibitory effect on the germination index of all the three plants (Fig. 1a,b). While the 50% ethanol extract also showed striking allelopathic inhibitory effects on the germination index of B. pekinensis and L. sativa, but moderately inhibitory effects on O. sativa (Fig. 1c,d). Similarly, the pure ethanol extract only showed powerful inhibitory effects on the germination index of B. pekinensis and L. sativa, but no effects on O. sativa (Fig. 1e,f). Additionally, the water-soluble extract of A. argyi powder displayed extremely inhibition of the biomass of the three plants (Fig. 2a), while the 50% ethanol extract also exerted extremely significant allelopathic inhibitory effects on the biomass of B. pekinensis and L. sativa but inhibited O. sativa moderately (Fig. 2b). However, the pure ethanol extract exerted inhibitory effects on the biomass of these three plants only in high concentrations (Fig. 2c). Based on these results, the allelopathic intensity of the three different extracts of A. argyi was in the order of water-soluble extract  > 50% ethanol extract  > pure ethanol extract.
Figure 1

The different solvent exracts of A. argyi: (a,b) the water-soluble extract, (c,d) the 50% ethanol extract,and (e,f) the pure ethanol extract exert allelopathic effects on germination index of different plants. (n = 3,*P  germination index  > biomass  > germination rate  > root length  > stem length. All allelopathy response indexes reached -1.00 when plants were treated with 150 mg/ml extract. For O. sativa, the six physiological indexes also could be inhibited by a low concentration of extract (50 mg/ml), but the changes were not as obvious as the changes in B. pekinensis and L. sativa. The intensity of inhibition on the six indexes was root length  > stem length  > biomass  > germination index  > germination speed index  > germination rate. When O. sativa seeds treated with 100 mg/ml of extract, the allelopathic response index of root length and stem length were -1.00. The germination rate, germination speed index, germination index and biomass were -0.79, -0.91, -0.91 and -0.84, respectively, under the treatment with 150 mg/ml of extract. In brief, according to the comprehensive allelopathy index of the 6 indicators , the order in which they were sensitive to water-soluble extract of A. argyi were B. pekinensis (Cruciferae)  > L. sativa (Compositae)  > O. sativa (Gramineae).
Figure 3

The water-soluble extract of A. argyi inhibits the germination and growth of Brassica pekinensis, Lactuca sativa and Oryza sativa. Specific performance is in a series of indicators: (a) the germination rate, (b) the germination speed index, (c) the root length, (d) the stem length. (n = 3,*P  More

Viromes outperform total metagenomes in the recovery of viral sequences from complex soil communities
To determine the extent to which viral sequences were enriched and bacterial and archaeal sequences were depleted in viromes, relative to total metagenomes, we performed a series of analyses to compare these two approaches. After quality filtering, total metagenomes yielded an average of 8,741,015 paired reads per library for April samples and 14,551,631 paired reads for August samples, while viromes yielded an average of 9,519,518 and 5,770,419 paired reads in April and August, respectively (Fig. 1A and Supplementary Table 2). Viromes displayed a significant depletion of bacterial and archaeal sequences, as evidenced by fewer reads classified as 16S rRNA gene fragments: 0.006% of virome reads, compared to 0.042% of reads in total metagenomes (Fig. 1B). Moreover, taxonomic classification of the recovered 16S rRNA gene reads revealed clear differences in the microbial profiles associated with each approach: total metagenomes were significantly enriched in Acidobacteria, Actinobacteria, Firmicutes, and Thaumarchaeota, whereas viromes were significantly enriched in Armatimonadetes, Saccharibacteria, and Parcubacteria (Supplementary Fig. 2A, B). These last two taxa belong to the candidate phyla radiation and are typified by small cells [59,60,61], which would be more likely to pass through the 0.22-um filter that we used for viral particle purification [37, 62]. Although we acknowledge that taxon-specific differences in 16S rRNA gene copy numbers could theoretically account for some of the observed differences in absolute numbers of reads assigned to 16S rRNA genes between viromes and total metagenomes [63], in the context of subsequent analyses (see below), the most parsimonious interpretation is that both the abundances and types of bacterial and archaeal genomic content differed between the two datasets.
Fig. 1: Differences in sequence composition and assembly performance between total metagenomes and viromes.

A Sequencing depth distribution across profiling methods and time points. The y-axis displays the number of paired reads in each library after quality trimming and adapter removal. Boxes display the median and interquartile range (IQR), and data points further than 1.5x IQR from box hinges are plotted as outliers. B Percent of reads classified as 16S rRNA gene fragments in the set of quality trimmed reads; the distribution of data within boxes, whiskers, and outliers is as in A. C Sequence complexity as measured by the frequency distribution of a representative set of k-mers (k = 31) detected in each library. The x-axis displays occurrence, i.e., the number of times a particular k-mer was found in a library, while the y-axis shows the number of k-mers that exhibited a specific occurrence. D Length distribution of contigs assembled from each library (min. length = 2Kbp). White dots represent the N50 of each assembly, and green squares display the viral enrichment, as measured by the percent of contigs classified as putatively viral by DeepVirFinder and/or VirSorter. Total MG = total metagenome.

Full size image

To assess differences in sequence complexity between the two profiling methods, we calculated the k-mer frequency spectrum for each library (Fig. 1C). Relative to viromes, total metagenomes displayed an increased number of singletons (k-mers observed only once) and an overall tendency toward lower k-mer occurrences, indicating that size-fractionating our soil communities reduced sequence complexity. These differences in sequence complexity translated into notable contrasts in the quality of de novo assemblies obtained from individual libraries (Fig. 1D), while viromes yielded 800 Mbp of assembled sequences across 169,421 contigs (250 Mbp assembled in ≥10 Kbp contigs), total metagenomes produced only 65 Mbp across 22,951 contigs (1.5 Mbp assembled in ≥10 Kbp contigs). The improved assembly quality from the viromes was despite lower sequencing throughput relative to total metagenomes, particularly for the August samples (Fig. 1A). Using DeepVirFinder [48] and VirSorter [47] to mine assemblies for viral contigs, we found that 52.4% of virome contigs and only 2.2% of total metagenome contigs were identified as viral. Together, these results show that our laboratory methods for removing contamination from cells and free DNA reduced genomic signatures from cellular organisms, substantially improved sequence assembly, and successfully enriched the viral signal in soil viromes relative to total metagenomes.
Viromes facilitate exploration of the rare virosphere
To remove redundancy in our assemblies, we clustered all 192,372 contigs into a set of 105,909 representative contigs (global identity threshold = 0.95). Following current standards to define viral populations (vOTUs) [51, 52], we then screened all nonredundant ≥10 Kbp contigs for viral signatures. We identified 4065 vOTUs with a median sequence length of 17,870 bp (max = 259,025 bp) and a median gene content of 27 predicted ORFs (max = 421 ORFs). To profile the viral communities in our samples, we mapped reads against this database of nonredundant vOTU sequences (≥90% average nucleotide identity, ≥75% coverage over the length of the contig). On average, 0.04% of total metagenomic reads and 23.4% of viromic reads were mapped to vOTUs (Supplementary Fig. 3A). One August virome sample (CS-H) had particularly low sequencing throughput and low vOTU recovery (Fig. 1A and Supplementary Fig. 3B) and was discarded from downstream analyses.
In total, 2961 vOTUs were detected through read mapping in at least one sample. Of these, 2864 were exclusively found in viromes, 94 in both viromes and total metagenomes, and three in total metagenomes alone. Thus, viromes were able to recover 30 times as many viral populations as total metagenomes, even when vOTUs assembled from viromes were part of the reference set for read mapping. Notably, the three vOTUs exclusively detected in total metagenomes were only present in one metagenome from April that did not have a successful paired virome (Supplementary Fig. 4). Considering that all other vOTUs detected in total metagenomes were detected in at least one virome, it seems possible that the corresponding virome could have contained these vOTUs if sequencing had been successful. Consistent with capturing a representative amount of viral diversity from the viromes but not total metagenomes, our sampling effort was sufficient to approach a richness asymptote in vOTU accumulation curves derived from viromes but not total metagenomes (Fig. 2A).
Fig. 2: Viral richness, abundance, and occupancy patterns captured by viromes compared to total metagenomes.

A Accumulation curves of vOTUs in total metagenomes (red, n = 16) and viromes (blue, n = 14). Dots represent cumulative richness at each sampling effort across 100 permutations of sample order; the overlaid line displays the mean cumulative richness. The right graph includes the same total metagenomic data as the left graph, zoomed in along the y-axis. B Abundance-occupancy data based on vOTU profiles derived from viromes. Data in blue are from vOTUs detected only in viromes, and data in red are from vOTUs detected in both viromes and total metagenomes. Bottom left: dots represent the mean relative abundance (x-axis) and occupancy (percent of samples in which a given vOTU was detected, y-axis) that individual vOTUs displayed in viromes within a collection time point (April or August). Thus, vOTUs detected in both time points are represented twice. Red dots highlight the set of vOTUs shared between total metagenomes and viromes. Top: density curves showing the distribution of relative abundances for all vOTUs detected in viromes (blue) or the subset of vOTUs detected in viromes and total metagenomes (red). Bottom right: percent of vOTUs (x-axis) found at each occupancy level (y-axis). Red bars highlight the percent of vOTUs detected in both profiling methods. C Euler diagram displaying the overlap in detection for each vOTU (n = 2961) across profiling methods. Red vOTUs were detected by both profiling methods, and three vOTUs were detected exclusively in total metagenomes. Total MG = total metagenome.

Full size image

To examine the distribution of vOTUs along the abundance-occupancy spectrum, we compared mean relative abundances of vOTUs against the number of samples in which each vOTU was detected. Given the contrasting experimental conditions between the April and August collections, we performed this analysis within each time point. In viromes, highly abundant vOTUs tended to be recovered in the majority of samples (i.e., they displayed high occupancies), while rare vOTUs were typically recovered in only a few samples (Fig. 2B, C and Supplementary Fig. 5A), a trend usually observed in microbial communities [64]. Furthermore, more than 30% of vOTUs were found in all sampled plots, indicating the presence of a sizable core virosphere distributed throughout the field. In contrast, the distribution of 16S rRNA gene OTUs in viromes leaned toward lower occupancies (Supplementary Fig. 5B) as expected from the significant depletion of cellular genomes upon size fractionation (Fig. 1B). On the other hand, more than 80% of vOTUs in total metagenomes were detected only once (Supplementary Fig. 5C), despite the widespread distribution displayed by the 16S rRNA gene OTUs identified in the same samples (Supplementary Fig. 5D), suggesting a sparse recovery of viral diversity compared to a more complete recovery of bacterial and archaeal diversity in total metagenomes.
Inspecting the abundance-occupancy patterns for the 94 vOTUs detected in both viromes and total metagenomes revealed that vOTUs recovered from total metagenomes were among the most abundant and ubiquitous in virome profiles (Fig. 2B), indicating that total soil metagenomes were more likely to miss the rare virosphere. Notably, comparing the relative abundances of vOTUs across paired total metagenomes and viromes showed that their abundance-based ranks were not always preserved (Supplementary Fig. 4). While this discrepancy could stem from methodological challenges associated with virome preparations (e.g., differential adsorption of viruses to the soil matrix could have impacted their resuspension and recovery, therefore affecting the relative abundances of the associated vOTUs), it is also likely that total metagenomes were more susceptible to subsampling biases as evidenced by the sparse and inconsistent recovery of vOTUs exhibited by this profiling method (Supplementary Fig. 5C).
Viromes reveal a diverse taxonomic landscape
To examine the taxonomic spread covered by our vOTUs, we compared them against the RefSeq prokaryotic virus database using vConTACT2, a network-based method to classify viral contigs [49]. Under this approach, vOTUs are grouped by shared predicted protein content into taxonomically informative viral clusters (VCs) that approximate viral genera. Of the 2961 vOTUs, 1712 were confidently assigned to VCs, while the rest were only weakly connected to other clusters (outliers, 784 vOTUs) or shared no genus-level predicted protein content with any other contigs (singletons, 465 vOTUs) (Supplementary Table 3). Only 130 vOTUs were grouped with RefSeq genomes, indicating that this dataset has substantially expanded known viral taxonomic diversity (Fig. 3A). Subsetting the vOTUs detected by each profiling method showed that viromes captured a more taxonomically diverse set of viruses: 1711 vOTUs detected in viromes were assigned to 533 VCs, while 68 vOTUs detected in total metagenomes (67 of which were also detected in viromes) were assigned to 54 VCs (53 of which were also detected in viromes). Thus, any potential biases in the types of viruses recovered through soil viromics (e.g., through preferential recovery of certain viral taxonomic groups from the soil matrix) were not immediately obvious. Any such biases were either eclipsed by the much greater taxonomic diversity of viruses recovered in viromes, relative to total metagenomes, and/or they also apply to total metagenomes, at least for the viruses and soils examined here.
Fig. 3: Taxonomic diversity and predicted hosts of viral populations (vOTUs) identified in viromes compared to total metagenomes.

A Gene-sharing network of vOTUs detected in viromes alone (blue nodes), total metagenomes (red nodes; nodes outlined in white were also detected in viromes, nodes outlined in black were detected exclusively in total metagenomes), and RefSeq prokaryotic virus genomes (gray nodes). Edges connect contigs or genomes with a significant overlap in predicted protein content. Only vOTUs and genomes assigned to a viral cluster (VC) are shown. Accompanying bar plots indicate the number of distinct VCs detected in total metagenomes and viromes (VCs detected in both profiling methods are counted twice, once per bar plot). B, C Subnetwork of all RefSeq genomes and co-clustered vOTUs. Colored nodes indicate the virus family (B) or the associated host phylum (C) of each RefSeq genome. Bar plots display the number of vOTUs classified as each predicted family (B) or host phylum (C) across total metagenomes and viromes (vOTUs detected in both profiling methods are counted twice, once per bar plot). Total MG = total metagenome.

Full size image

Most of the 130 vOTUs clustered with RefSeq viral genomes could be taxonomically classified at the family level (Fig. 3B). Podoviridae was the most highly represented family, followed by Siphoviridae and Myoviridae. Myoviridae were only detected in viromes, not total metagenomes, further confirming that viromes do not seem to exclude viral groups relative to total metagenomes—if anything, the opposite seems to be true. Among the Siphoviridae clusters, we could further identify three vOTUs as belonging to the genus Decurrovirus, which are phages of Arthrobacter, a genus of Actinobacteria common in soil [65,66,67]. Because the genome network was highly structured by host taxonomy (Fig. 3A, [68]), we used consistent host signatures among RefSeq viruses in the same VC to assign putative hosts to vOTUs in VCs shared with RefSeq genomes. Most such vOTUs were putatively assigned to Proteobacteria, Actinobacteria, or Bacteroidetes hosts, and a few were linked to Firmicutes. Interestingly, these bacterial phyla were among the most abundant taxa in the 16S rRNA gene profiles derived from the total metagenomes from these soils (Supplementary Fig. 2A).
Although soil viromes and total metagenomes have been compared [62] and their presumed advantages and disadvantages have been reviewed [10], here a comprehensive comparison of results from both profiling approaches applied to the same samples showed that soil viromes recover richer (Fig. 2A) and more taxonomically diverse (Fig. 3) soil viral communities than total metagenomes.
Compositional patterns of agricultural soil viral communities and their ecological drivers
Since viromes vastly outperformed total metagenomes in capturing the viral diversity in our samples, we focused on viromes to explore the compositional relationships among viral communities. To assess the impact of each individual experimental factor on beta diversity, we performed separate permutational multivariate analyses of variance (PERMANOVA) on Bray–Curtis dissimilarities (Supplementary Table 4). Collection time point had a significant effect (R2 = 0.50, p = 0.001), but biochar (R2 = 0.19, p = 0.58) and nitrogen (R2 = 0.12, p = 0.75) treatments did not (only samples from the August time points, after nitrogen amendments, were considered for the nitrogen analysis). Additionally, to determine if the location of each sampled plot had an impact on community composition, we tested the effect of plot position along the West–East (W–E) and South–North (S–N) axes of the field (Supplementary Table 4). Viral communities displayed a significant spatial gradient along the W–E axis (R2 = 0.17, p = 0.046) but not the S–N axis (R2 = 0.10, p = 0.20). Given the significant spatiotemporal structuring in our samples, we performed an additional PERMANOVA to examine the effect of biochar while accounting for these factors (Supplementary Table 5) and detected a significant effect (R2 = 0.19, p = 0.012) only when both collection time point and W–E gradient were part of the model. We did not detect a significant effect of nitrogen treatment, even after accounting for the W–E gradient in the August samples (Supplementary Table 5).
To assess whether the bacterial and archaeal communities displayed similar compositional trends and could therefore potentially explain patterns in viral community composition, we attempted to generate metagenome assembled genomes (MAGs) from our total metagenomes. However, the low quality of total metagenomic assemblies (Fig. 1D) precluded MAG reconstruction (19 MAGs with a median completeness of 30.3), so instead we used 16S rRNA gene profiles recovered from total metagenomes (Supplementary Fig. 2A). Although 16S rRNA genes accounted for More

1.
Saunders JK, Fuchsman CA, McKay C, Rocap G. Complete arsenic-based respiratory cycle in the marine microbial communities of pelagic oxygen-deficient zones. Proc Natl Acad Sci USA. 2019;116:9925–30.
CAS  PubMed  Article  PubMed Central  Google Scholar 
2.
Callbeck CM, Lavik G, Ferdelman TG, Fuchs B, Gruber-Vodicka HR, Hach PF, et al. Oxygen minimum zone cryptic sulfur cycling sustained by offshore transport of key sulfur oxidizing bacteria. Nat Commun. 2018;9:1729.
PubMed  PubMed Central  Article  CAS  Google Scholar 

3.
Garcia-Robledo E, Padilla CC, Aldunate M, Stewart FJ, Ulloa O, Paulmier A, et al. Cryptic oxygen cycling in anoxic marine zones. Proc Natl Acad Sci USA. 2017;114:8319–24.
CAS  PubMed  Article  PubMed Central  Google Scholar 

4.
Sun X, Kop LFM, Lau MCY, Frank J, Jayakumar A, Lücker S, et al. Uncultured Nitrospina-like species are major nitrite oxidizing bacteria in oxygen minimum zones. ISME J. 2019;13:2391–402.
CAS  PubMed  PubMed Central  Article  Google Scholar 

5.
Tsementzi D, Wu J, Deutsch S, Nath S, Rodriguez-R LM, Burns AS, et al. SAR11 bacteria linked to ocean anoxia and nitrogen loss. Nature. 2016;536:179–83.
CAS  PubMed  PubMed Central  Article  Google Scholar 

6.
Thamdrup B, Steinsdóttir HGR, Bertagnolli AD, Padilla CC, Patin NV, Garcia-Robledo E, et al. Anaerobic methane oxidation is an important sink for methane in the ocean’s largest oxygen minimum zone. Limnol Oceanogr. 2019;64:2569–85.
CAS  Article  Google Scholar 

7.
Breitburg D, Levin LA, Oschlies A, Grégoire M, Chavez FP, Conley DJ, et al. Declining oxygen in the global ocean and coastal waters. Science. 2018;359:eaam7240.
PubMed  Article  CAS  PubMed Central  Google Scholar 

8.
Brown CT, Hug LA, Thomas BC, Sharon I, Castelle CJ, Singh A, et al. Unusual biology across a group comprising more than 15% of domain Bacteria. Nature. 2015;523:208–U173.
CAS  PubMed  Article  PubMed Central  Google Scholar 

9.
Castelle CJ, Wrighton KC, Thomas BC, Hug LA, Brown CT, Wilkins MJ, et al. Genomic expansion of domain archaea highlights roles for organisms from new phyla in anaerobic carbon cycling. Curr Biol. 2015;25:690–701.
CAS  PubMed  Article  PubMed Central  Google Scholar 

10.
Hug LA, Baker BJ, Anantharaman K, Brown CT, Probst AJ, Castelle CJ, et al. A new view of the tree of life. Nat Microbiol. 2016;1:16048.

11.
Mylroie JE, Carew JL, Moore AI. Blue holes: definition and genesis. Carbonates Evaporates. 1995;10:225–33.
CAS  Article  Google Scholar 

12.
Canganella F, Bianconi G, Kato C, Gonzalez J. Microbial ecology of submerged marine caves and holes characterized by high levels of hydrogen sulphide. Rev Environ Sci Biotechnol. 2007;6:61–70.

13.
Gischler E, Shinn EA, Oschmann W, Fiebig J, Buster NA. A 1500-year holocene caribbean climate archive from the blue hole, Lighthouse Reef, Belize. J Coast Res. 2008;246:1495–505.
Article  CAS  Google Scholar 

14.
Pohlman JW. The biogeochemistry of anchialine caves: progress and possibilities. Hydrobiologia. 2011;677:33–51.
CAS  Article  Google Scholar 

15.
Davis MC, Garey JR. Microbial function and hydrochemistry within a stratified anchialine sinkhole: A window into coastal aquifer interactions. Water. 2018;10:972–972.
Article  CAS  Google Scholar 

16.
Garman KM, Rubelmann H, Karlen DJ, Wu T, Garey JR. Comparison of an inactive submarine spring with an active nearshore anchialine spring in Florida. Hydrobiologia. 2011;677:65–87.

17.
Gonzalez BC, Iliffe TM, Macalady JL, Schaperdoth I, Kakuk B. Microbial hotspots in anchialine blue holes: Initial discoveries from the Bahamas. Hydrobiologia. 2011;677:149–56.
CAS  Article  Google Scholar 

18.
Seymour JR, Humphreys WF, Mitchell JG. Stratification of the microbial community inhabiting an anchialine sinkhole. Aquat Microb Ecol. 2007;50:11–24.

19.
Yao P, Wang XC, Bianchi TS, Yang ZS, Fu L, Zhang XH, et al. Carbon cycling in the world’s deepest blue hole. J Geophys Res. 2020;125:e2019JG005307.

20.
He H, Fu L, Liu Q, Fu L, Bi N, Yang Z, et al. Community Structure, abundance and potential functions of bacteria and archaea in the Sansha Yongle blue hole, Xisha, South China Sea. Front Microbiol. 2019;10:2404–2404.
PubMed  PubMed Central  Article  Google Scholar 

21.
He P, Xie L, Zhang X, Li J, Lin X, Pu X, et al. Microbial diversity and metabolic potential in the stratified Sansha Yongle Blue Hole in the South China Sea. Sci Rep. 2020;10:5949–5949.
CAS  PubMed  PubMed Central  Article  Google Scholar 

22.
DeWitt D. Submarine springs and other Karst features in offshore waters of the Gulf of Mexico and Tampa Bay, Southwest Florida Water Management District. 2003.

23.
Hu C, Muller-Karger FE, Swarzenski PW. Hurricanes, submarine groundwater discharge, and Florida’s red tides. Geophys Res Lett. 2006;33:L11601.
Google Scholar 

24.
Smith CG, Swarzenski PW. An investigation of submarine groundwater-borne nutrient fluxes to the west Florida shelf and recurrent harmful algal blooms. Limnol Oceanogr. 2012;57:471–85.
CAS  Article  Google Scholar 

25.
Vargo GA, Heil CA, Fanning KA, Dixon LK, Neely MB, Lester K, et al. Nutrient availability in support of Karenia brevis blooms on the central West Florida Shelf: What keeps Karenia blooming? Continental Shelf Res. 2008;28:73–98.
Article  Google Scholar 

26.
Walsh DA, Zaikova E, Howes CG, Song YC, Wright JJ, Tringe SG, et al. Metagenome of a versatile chemolithoautotroph from expanding oceanic dead zones. Science. 2009;326:578–82.
CAS  PubMed  Article  PubMed Central  Google Scholar 

27.
Weisberg RH, Liu YG, Lembke C, Hu CM, Hubbard K, Garrett M. The coastal ocean circulation influence on the 2018 West Florida Shelf K. brevis Red Tide Bloom. J Geophys Res Oceans. 2019;124:2501–12.
Article  Google Scholar 

28.
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 

29.
Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil PA, 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 

30.
Rodriguez RLM, Gunturu S, Tiedje JM, Cole JR, Konstantinidis KT. Nonpareil 3: fast estimation of metagenomic coverage and sequence diversity. Msystems. 2018;3: e00039-18.

31.
Baker BJ, De Anda V, Seitz KW, Dombrowski N, Santoro AE, Lloyd KG. Diversity, ecology and evolution of Archaea. Nat Microbiol. 2020;5:887–900.
PubMed  Article  CAS  PubMed Central  Google Scholar 

32.
Thiel V, Costas AMG, Fortney NW, Martinez JN, Tank M, Roden EE, et al. “Candidatus Thermonerobacter thiotrophicus,” a non-phototrophic member of the Bacteroidetes/Chlorobi with dissimilatory sulfur metabolism in hot spring mat communities. Front Microbiol. 2019;9:3159–3159.
PubMed  PubMed Central  Article  Google Scholar 

33.
Helly JJ, Levin LA. Global distribution of naturally occurring marine hypoxia on continental margins. Deep-Sea Res Part I. 2004;51:1159–68.
CAS  Article  Google Scholar 

34.
Xie LP, Wang BD, Pu XM, Xin M, He PQ, Li CX, et al. Hydrochemical properties and chemocline of the Sansha Yongle blue hole in the South China Sea. Sci Total Environ. 2019;649:1281–92.
CAS  PubMed  Article  PubMed Central  Google Scholar 

35.
Thamdrup B, Dalsgaard T, Revsbech NP. Widespread functional anoxia in the oxygen minimum zone of the Eastern South Pacific. Deep-Sea Res Part I. 2012;65:36–45.
CAS  Article  Google Scholar 

36.
Wyrtki K. The oxygen minima in relation to ocean circulation. Deep-Sea Res Oceanographic Abstr. 1962;9:11–23.
CAS  Article  Google Scholar 

37.
Ghosh W, Dam B. Biochemistry and molecular biology of lithotrophic sulfur oxidation by taxonomically and ecologically diverse bacteria and archaea. FEMS Microbiol Rev. 2009;33:999–1043.
CAS  PubMed  Article  PubMed Central  Google Scholar 

38.
Luther GW, Findlay AJ, MacDonald DJ, Owings SM, Hanson TE, Beinart RA, et al. Thermodynamics and kinetics of sulfide oxidation by oxygen: a look at inorganically controlled reactions and biologically mediated processes in the environment. Front Microbiol. 2011;2:1–9.
Article  CAS  Google Scholar 

39.
Houghton JL, Foustoukos DI, Flynn TM, Vetriani C, Bradley AS, Fike DA. Thiosulfate oxidation by Thiomicrospira thermophila: metabolic flexibility in response to ambient geochemistry. Environ Microbiol. 2016;18:3057–72.
CAS  PubMed  PubMed Central  Article  Google Scholar 

40.
Kelly DP, Shergill JK, Lu WP, Wood AP. Oxidative metabolism of inorganic sulfur compounds by bacteria. Antonie Van Leeuwenhoek Int J Gen Mol Microbiol. 1997;71:95–107.
CAS  Article  Google Scholar 

41.
Grimm F, Franz B, Dahl C. Thiosulfate and sulfur oxidation in purple sulfur bacteria. In: Dahl C, Friedrich C, editors. Microbial sulfur metabolism. Springer: Heidelberg, Germany; 2008. p. 101–16.

42.
Zopfi J, Ferdelman TG, Fossing H. Distribution and fate of sulfur intermediates – sulfite, tetrathionate, thiosulfate, and elemental sulfur – in marine sediments. In: Amend JP, Edwards KJ, Lyons TW, editors. Sulfur biogeochemistry: past and present. The Geological Society of America: Boulder, Colorado; 2004. p. 97–116.

43.
Wright JJ, Konwar KM, Hallam SJ. Microbial ecology of expanding oxygen minimum zones. Nat Rev Microbiol. 2012;10:381–94.
CAS  PubMed  Article  PubMed Central  Google Scholar 

44.
Bertagnolli AD, Stewart FJ. Microbial niches in marine oxygen minimum zones. Nat Rev Microbiol. 2018;16:723–9.
CAS  PubMed  Article  PubMed Central  Google Scholar 

45.
Hawley AK, Brewer HM, Norbeck AD, Pasǎ-Tolić L, Hallam SJ. Metaproteomics reveals differential modes of metabolic coupling among ubiquitous oxygen minimum zone microbes. Proc Natl Acad Sci USA. 2014;111:11395–400.
CAS  PubMed  Article  PubMed Central  Google Scholar 

46.
Anantharaman K, Hausmann B, Jungbluth SP, Kantor RS, Lavy A, Warren LA, et al. Expanded diversity of microbial groups that shape the dissimilatory sulfur cycle. ISME J. 2018;12:1715–28.
CAS  PubMed  PubMed Central  Article  Google Scholar 

47.
Murillo AA, Ramírez-Flandes S, DeLong EF, Ulloa O. Enhanced metabolic versatility of planktonic sulfur-oxidizing gamma-proteobacteria in an oxygen-deficient coastal ecosystem. Front Mar Sci. 2014;1:1–13.

48.
Shah V, Chang BX, Morris RM. Cultivation of a chemoautotroph from the SUP05 clade of marine bacteria that produces nitrite and consumes ammonium. ISME J. 2017;11:263–71.
CAS  PubMed  Article  PubMed Central  Google Scholar 

49.
Wirsen CO, Sievert SM, Cavanaugh CM, Molyneaux SJ, Ahmad A, Taylor LT, et al. Characterization of an autotrophic sulfide-oxidizing marine Arcobacter sp. that produces filamentous sulfur. Appl Environ Microbiol. 2002;68:316–25.
CAS  PubMed  PubMed Central  Article  Google Scholar 

50.
Luther GW, Glazer BT, Hohmann L, Popp JI, Tailefert M, Rozan TF, et al. Sulfur speciation monitored in situ with solid state gold amalgam voltammetric microelectrodes: polysulfides as a special case in sediments, microbial mats and hydrothermal vent waters. J Environ Monit. 2001;3:61–6.
CAS  PubMed  Article  PubMed Central  Google Scholar 

51.
Rozan TF, Theberge SM, Luther G. Quantifying elemental sulfur (S0), bisulfide (HS-) and polysulfides (S(x)2-) using a voltammetric method. Analyt Chim Acta. 2000;415:175–84.
CAS  Article  Google Scholar 

52.
Sievert SM, Wieringa EBA, Wirsen CO, Taylor CD. Growth and mechanism of filamentous-sulfur formation by Candidatus Arcobacter sulfidicus in opposing oxygen-sulfide gradients. Environ Microbiol. 2007;9:271–6.
CAS  PubMed  Article  PubMed Central  Google Scholar 

53.
Moussard H, Corre E, Cambon-Bonavita MA, Fouquet Y, Jeanthon C. Novel uncultured Epsilonproteobacteria dominate a filamentous sulphur mat from the 13 degrees N hydrothermal vent field, East Pacific Rise. FEMS Microbiol Ecol. 2006;58:449–63.
CAS  PubMed  Article  PubMed Central  Google Scholar 

54.
Heylen K, Vanparys B, Wittebolle L, Verstraete W, Boon N, De PV. Cultivation of denitrifying bacteria: optimization of isolation conditions and diversity study. Appl Environ Microbiol. 2006;72:2637–43.
CAS  PubMed  PubMed Central  Article  Google Scholar 

55.
Taillefert M, Bono AB, Luther GW. Reactivity of freshly formed Fe(III) in synthetic solutions and (pore)waters: voltammetric evidence of an aging process. Environ Sci Technol. 2000;34:2169–77.
CAS  Article  Google Scholar 

56.
Barco RA, Emerson D, Sylvan JB, Orcutt BN, Jacobson Meyers ME, Ramírez GA, et al. New insight into microbial iron oxidation as revealed by the proteomic profile of an obligate iron-oxidizing chemolithoautotroph. Appl Environ Microbiol. 2015;81:5927–37.
CAS  PubMed  PubMed Central  Article  Google Scholar 

57.
Garber AI, Nealson KH, Okamoto A, McAllister SM, Chan CS, Barco RA, et al. FeGenie: a comprehensive tool for the identification of iron genes and iron gene neighborhoods in genome and metagenome assemblies. Front Microbiol. 2020;11:37.
PubMed  PubMed Central  Article  Google Scholar 

58.
Canfield DE, Stewart FJ, Thamdrup B, De Brabandere L, Dalsgaard T, Delong EF, et al. A cryptic sulfur cycle in oxygen-minimum-zone waters off the Chilean Coast. Science. 2010;330:1375–8.
CAS  PubMed  Article  PubMed Central  Google Scholar 

59.
Ding J, Zhang Y, Wang H, Jian H, Leng H, Xiao X. Microbial community structure of deep-sea hydrothermal vents on the ultraslow spreading Southwest Indian Ridge. Front Microbiol. 2017;8:1012.

60.
Leon-Zayas R, Peoples L, Biddle JF, Podell S, Novotny M, Cameron J, et al. The metabolic potential of the single cell genomes obtained from the Challenger Deep, Mariana Trench within the candidate superphylum Parcubacteria (OD1). Environ Microbiol. 2017;19:2769–84.
CAS  PubMed  PubMed Central  Article  Google Scholar 

61.
Liu X, Li M, Castelle CJ, Probst AJ, Zhou Z, Pan J, et al. Insights into the ecology, evolution, and metabolism of the widespread Woesearchaeotal lineages. Microbiome. 2018;6:102–102.
PubMed  PubMed Central  Article  Google Scholar 

62.
Ortiz-Alvarez R, Casamayor EO. High occurrence of Pacearchaeota and Woesearchaeota (Archaea superphylum DPANN) in the surface waters of oligotrophic high-altitude lakes. Environ Microbiol Rep. 2016;8:210–7.
CAS  PubMed  Article  PubMed Central  Google Scholar 

63.
Suominen S, Dombrowski N, Damste JSS, Villanueva L. A diverse uncultivated microbial community is responsible for organic matter degradation in the Black Sea sulphidic zone. Environ Microbiol. 2021. https://doi.org/10.1111/1462-2920.14902.

64.
Castelle CJ, Banfield JF. Major new microbial groups expand diversity and alter our understanding of the tree of life. Cell. 2018;172:1181–97.
CAS  PubMed  Article  PubMed Central  Google Scholar 

65.
Dombrowski N, Lee JH, Williams TA, Offre P, Spang A. Genomic diversity, lifestyles and evolutionary origins of DPANN archaea. FEMS Microbiol Lett. 2019;366:fnz008.

66.
Tian RM, Ning DL, He ZL, Zhang P, Spencer SJ, Gao SH, et al. Small and mighty: adaptation of superphylum Patescibacteria to groundwater environment drives their genome simplicity. Microbiome. 2020;8:51.

67.
Vigneron A, Cruaud P, Langlois V, Lovejoy C, Culley AI, Vincent WF. Ultra-small and abundant: Candidate phyla radiation bacteria are potential catalysts of carbon transformation in a thermokarst lake ecosystem. Limnol Oceanogr Lett. 2020;5:212–20.
Article  Google Scholar 

68.
Beam JP, Becraft ED, Brown JM, Schulz F, Jarett JK, Bezuidt O, et al. Ancestral absence of electron transport chains in Patescibacteria and DPANN. Front Microbiol. 2020;11:1848.

69.
Luef B, Frischkorn KR, Wrighton KC, Holman HYN, Birarda G, Thomas BC, et al. Diverse uncultivated ultra-small bacterial cells in groundwater. Nat Commun. 2015;6:6372.

70.
Wrighton KC, Castelle CJ, Wilkins MJ, Hug LA, Sharon I, Thomas BC, et al. Metabolic interdependencies between phylogenetically novel fermenters and respiratory organisms in an unconfined aquifer. ISME J. 2014;8:1452–63.
CAS  PubMed  PubMed Central  Article  Google Scholar 

71.
Konstantinidis KT, Tiedje JM. Trends between gene content and genome size in prokaryotic species with larger genomes. Proc Natl Acad Sci USA. 2004;101:3160–5.
CAS  PubMed  Article  PubMed Central  Google Scholar 

72.
Moya A, Pereto J, Gil R, Latorre A. Learning how to live together: genomic insights into prokaryote-animal symbioses. Nat Rev Genet. 2008;9:218–29.
CAS  PubMed  Article  PubMed Central  Google Scholar 

73.
Moran NA, Plague GR. Genomic changes following host restriction in bacteria. Curr Opin Genet Dev. 2004;14:627–33.
CAS  PubMed  Article  PubMed Central  Google Scholar 

74.
Chaudhury P, Quax TEF, Albers SV. Versatile cell surface structures of archaea. Mol Microbiol. 2018;107:298–311.
CAS  PubMed  Article  PubMed Central  Google Scholar 

75.
Pohlschroder M, Esquivel RN. Archaeal type IV pili and their involvement in biofilm formation. Front Microbiol. 2015;6:190.

76.
Aylward FO, Santoro AE. Heterotrophic Thaumarchaea with small genomes are widespread in the dark ocean. mSystems. 2020;5:e00415–20.
CAS  PubMed  PubMed Central  Article  Google Scholar 

77.
Reji L, Francis CA. Metagenome-assembled genomes reveal unique metabolic adaptations of a basal marine Thaumarchaeota lineage. ISME J. 2020;14:2105–15.
CAS  PubMed  Article  PubMed Central  Google Scholar 

78.
Santoro AE, Richter RA, Dupont CL. Planktonic marine Archaea. Annu Rev Mar Sci. 2019;11:131–58.
Article  Google Scholar 

79.
Rinke C, Rubino F, Messer LF, Youssef N, Parks DH, Chuvochina M, et al. A phylogenomic and ecological analysis of the globally abundant Marine Group II archaea (Ca. Poseidoniales ord. nov.). ISME J. 2019;13:663–75.
CAS  PubMed  Article  PubMed Central  Google Scholar 

80.
Pereira O, Hochart C, Auguet JC, Debroas D, Galand PE. Genomic ecology of Marine Group II, the most common marine planktonic Archaea across the surface ocean. Microbiol Open. 2019;8:e00852.
Google Scholar 

81.
Martin-Cuadrado AB, Garcia-Heredia I, Moltó AG, López-Úbeda R, Kimes N, López-García P, et al. A new class of marine Euryarchaeota group II from the mediterranean deep chlorophyll maximum. ISME J. 2015;9:1619–34.
CAS  PubMed  Article  PubMed Central  Google Scholar 

82.
Martin-Cuadrado AB, Rodriguez-Valera F, Moreira D, Alba JC, Ivars-Martínez E, Henn MR, et al. Hindsight in the relative abundance, metabolic potential and genome dynamics of uncultivated marine archaea from comparative metagenomic analyses of bathypelagic plankton of different oceanic regions. ISME J. 2008;2:865–86.
CAS  PubMed  Article  PubMed Central  Google Scholar 

83.
Moreira D, Rodríguez-Valera F, López-García P. Analysis of a genome fragment of a deep-sea uncultivated Group II euryarchaeote containing 16S rDNA, a spectinomycin-like operon and several energy metabolism genes. Environ Microbiol. 2004;6:959–69.
CAS  PubMed  Article  PubMed Central  Google Scholar 

84.
Sforna MC, Philippot P, Somogyi A, Van Zuilen MA, Medjoubi K, Schoepp-Cothenet B, et al. Evidence for arsenic metabolism and cycling by microorganisms 2.7 billion years ago. Nat Geosci. 2014;7:811–5.
CAS  Article  Google Scholar 

85.
Meheust R, Burstein D, Castelle CJ, Banfield JF. The distinction of CPR bacteria from other bacteria based on protein family content. Nat Commun. 2019;10:4173.

86.
Luther GW, Glazer BT, Ma S, Trouwborst RE, Moore TS, Metzger E, et al. Use of voltammetric solid-state (micro)electrodes for studying biogeochemical processes: laboratory measurements to real time measurements with an in situ electrochemical analyzer (ISEA). Mar Chem. 2008;108:221–35.
CAS  Article  Google Scholar 

87.
Brendel PJ, Luther GW. Development of a gold amalgam voltammetric microelectrode for the determination of dissolved Fe, Mn, O2, and S(-II) in porewaters of marine and freshwater sediments. Environ Sci Technol. 1995;29:751–61.
CAS  PubMed  Article  PubMed Central  Google Scholar 

88.
Arar EJ, Collins GB. Method 445.0 in vitro determination of chlorophyll a and pheophytin a in marine and freshwater algae by fluorescence. Washington, DC: U.S. Environmental Protection Agency; 1997.

89.
Bran+Luebbe/Seal. Ammonia in water and seawater, in Method No G-171-96. 2005. Norderstedt, Germany.

90.
Bran+Luebbe/Seal. Nitrate and nitrite in water and seawater; total nitrogen in persulfate digests, in Metho No G-172-96. 2010. Norderstedt, Germany.

91.
Solórzano L, Sharp JH. Determination of total dissolved nitrogen in natural waters. Limnol Oceanogr. 1980;25:751–4.
Article  Google Scholar 

92.
Solórzano L, Sharp JH. Determination of total dissolved phosphorus and particulate phosphorus in natural waters. Limnol Oceanogr. 1980;25:754–8.
Article  Google Scholar 

93.
Dickson AG, Sabine CL, Christian JR. Guide to best practices for ocean CO2 measurements. PICES Special Publication 3. 2007.

94.
Murphy J, Riley JP. A modified single solution method for the determination of phosphate in natural waters. Analy Chim Acta. 1962;27:31–6.
CAS  Article  Google Scholar 

95.
Stookey LL. Ferrozine—a new spectrophotometric reagent for iron. Anal Chem. 1970;42:779–81.
CAS  Article  Google Scholar 

96.
Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, et al. Fiji: an open-source platform for biological-image analysis. Nat Methods. 2012;9:676–82.
CAS  PubMed  Article  PubMed Central  Google Scholar 

97.
Padilla CC, Bertagnolli AD, Bristow LA, Sarode N, Glass JB, Thamdrup B, et al. Metagenomic binning recovers a transcriptionally active gammaproteobacterium linking methanotrophy to partial denitrification in an anoxic oxygen minimum zone. Front Mar Sci. 2017;4:23–23.
Article  Google Scholar 

98.
Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq illumina sequencing platform. Appl Environ Microbiol. 2013;79:5112–20.
CAS  PubMed  PubMed Central  Article  Google Scholar 

99.
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  PubMed  PubMed Central  Article  Google Scholar 

100.
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  PubMed  PubMed Central  Article  Google Scholar 

101.
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550–550.
PubMed  PubMed Central  Article  CAS  Google Scholar 

102.
McMurdie PJ, Holmes S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217–e61217.
CAS  PubMed  PubMed Central  Article  Google Scholar 

103.
McMurdie PJ, Holmes S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput Biol. 2014;10:e1003531.

104.
Willis AD, Martin BD. DivNet: estimating diversity in networked communities. bioRxiv. 2018. Available from https://www.biorxiv.org/content/10.1101/305045v1.

105.
Wickham H. Elegant graphics for data analysis. New York: Springer-Verlag; 2016.
Google Scholar 

106.
Oksanen J, Blanchet F, Friendly M, Kindt R, Legendre P, Mcglinn D, et al. vegan: Community Ecology package, in R package version 2.5-5. 2019. https://cran.r-project.org/package=vegan.

107.
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;16:51–51.
PubMed  PubMed Central  Article  CAS  Google Scholar 

108.
Nayfach S, Bradley PH, Wyman SK, Laurent TJ, Williams A, Eisen JA, et al. Automated and accurate estimation of gene family abundance from shotgun metagenomes. PLoS Comput Biol. 2015;11:e1004573.

109.
Nayfach S, Pollard KS. Toward accurate and quantitative comparative metagenomics. Cell. 2016;166:1103–16.
CAS  PubMed  PubMed Central  Article  Google Scholar 

110.
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 

111.
Li D, Liu CM, Luo R, Sadakane K, Lam TW. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015;31:1674–6.
CAS  PubMed  Article  PubMed Central  Google Scholar 

112.
Mikheenko A, Saveliev V, Gurevich A. MetaQUAST: evaluation of metagenome assemblies. Bioinformatics. 2016;32:1088–90.
CAS  PubMed  Article  PubMed Central  Google Scholar 

113.
Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.
CAS  PubMed  Article  PubMed Central  Google Scholar 

114.
Hyatt D, Chen GL, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 2010;11:119–119.
PubMed  PubMed Central  Article  CAS  Google Scholar 

115.
James BT, Luczak BB, Girgis HZ. MeShClust: an intelligent tool for clustering DNA sequences. Nucleic Acids Res. 2018;46:e83.
PubMed  PubMed Central  Article  CAS  Google Scholar 

116.
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. 2019;36:2251–2.
PubMed Central  Article  CAS  Google Scholar 

117.
Boratyn GM, Thierry-Mieg J, Thierry-Mieg D, Busby B, Madden TL. Magic-BLAST, an accurate RNA-seq aligner for long and short reads. BMC Bioinformatics. 2019;20:405–405.
PubMed  PubMed Central  Article  CAS  Google Scholar 

118.
Dunivin TK, Yeh SY, Shade A. A global survey of arsenic-related genes in soil microbiomes. BMC Biol. 2019;17:45–45.
CAS  PubMed  PubMed Central  Article  Google Scholar 

119.
Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol. 2019;20:257.

120.
Lu J, Breitwieser FP, Thielen P, Salzberg SL. Bracken: estimating species abundance in metagenomics data. PeerJ Comput Sci. 2017;3:e104.

121.
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 

122.
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 

123.
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 Central  Google Scholar 

124.
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 

125.
Letunic I, Bork P. Interactive Tree of Life (iTOL) v4: Recent updates and new developments. Nucleic Acids Res. 2019;47:p. W256–9.
Article  CAS  Google Scholar  More

Distribution, frequency, and functional implications of mutations during laboratory evolution of obligate syntrophy
We evaluated whether the selection of mutations in the same genes (i.e., “parallel evolution” [17]) had contributed to improvements in syntrophic growth of Dv and Mm across independent evolution lines, all of which started with the same ancestral clone of each organism. The goal of this analysis was to focus on generalized strategies for adaptation to syntrophy, irrespective of the culturing condition so we investigated parallelism across both U and H lines. Based on the number of mutations (normalized to gene length and genome size) in Dv and Mm across 13 evolved lines (six lines designated U for “uniform” conditions with continuous shaking and seven H lines for “heterogenous” conditions without shaking), we calculated a G-score [18] (“goodness-of-fit”, see “Methods” section [18]) to assess if the observed parallel evolution rate was higher than expected by chance. The “observed G-score” was calculated as the sum of G-scores for all genes in the genome of each organism; mean and standard deviation of “expected G-scores” were calculated through 1000 simulations of randomizing locations of observed numbers of mutations across the genome of each organism. The observed total G-score for Dv (1092.617) and Mm (805.02) was significantly larger than the expected mean G-score (Dv: 798.19 ± 14.99, Z = 19.63 and Mm: 564.83 ± 15.95, Z = 15.06), demonstrating significant parallel evolution across lines.
With the exception of five high G-score genes (DVU0597, DVU1862, DVU0436, DVU0013, and DVU2394), which were mutated during long term salt adaptation of Dv [19], mutations in other high G-score genes appeared to be putatively specific to syntrophic interactions. Altogether, 24 genes in Dv and 16 genes in Mm associated with core processes had accumulated function modulating mutations across at least 2 or more evolution lines (Fig. 2 and Supplementary Table S1). Signal transduction and regulatory gene mutations (seven in Dv and six in Mm) represented 19.9% and 27.2% of all mutations in Dv and Mm, respectively, similar to long term laboratory evolution of E. coli [18], potentially because their influence on the functions of many genes [20, 21]. We also observed missense and nonsense mutations in outer membrane and transport functions (four genes in Dv and three genes in Mm). For example, the highest G-score gene in Dv, DVU0799—an abundant outer membrane porin for the uptake of sulfate and other solutes in low-sulfate conditions [22], was mutated early across all lines, with at least two missense mutations in UE3 (S223Y) and UA3 (T242P). Notably, the regulator of the archaellum operon (MMP1718) had the highest G-score with frameshift (11 lines) and nonsynonymous coding (2 lines) mutations [23]. Similarly, two motility-associated genes of Dv (DVU1862 and DVU3227) also accumulated frameshift, nonsense and nonsynonymous mutations across 4 H and 3 U lines. Together, these observations were consistent with other laboratory evolution experiments performed in liquid media [24], suggesting that retaining motility has a fitness cost during syntrophy [25, 26].
Fig. 2: Frequency and location of high G-score mutations in Dv (A) and Mm (B) across 13 independent evolution lines.

SnpEff predicted impact of mutations* are indicated as moderate (orange circles) or high (red circles) with the frequency of mutations indicated by node size. Expected number of mutations for each gene was calculated based on the gene length and the total number of mutations in a given evolution line. Genes with parallel changes were ranked by calculating a G (goodness of fit) score between observed and expected values and indicated inside each panel. Mutations for each gene are plotted along their genomic coordinates (vertical axes) across 13 evolution lines (horizontal axes). Total number of mutations for a given gene is shown as horizontal bar plots. [*HIGH impact mutations: gain or loss of start and stop codons and frameshift mutations; MODERATE impact mutations: codon deletion, nonsynonymous in coding sequence, change or insertion of codon; low impact mutations: synonymous coding and nonsynonymous start codon].

Full size image

Consistent with our previous observation that obligate mutual interdependence drove the erosion of metabolic independence of Dv [5, 27], mutations in SR genes were among the top contributors to the total G-score in Dv (DVU2776 (74.7), DVU1295 (46.5), DVU0846 (42.9), and DVU0847 (22.3)). However, it was intriguing that DVU2776 (DsrC), which catalyzes the conversion of sulfite to sulfide, the final step in SR, accumulated function modulating but not loss-of-function mutations across six lines. The functional impact of these mutations is not clear but it is possible that these changes might alter previously suggested alternative roles for this gene, including electron confurcation for the oxidation of lactate [28], sulfite reduction, 2-thiouridine biosynthesis and possibly gene regulation [29].
Analysis of temporal appearance and combinations of mutations across evolution lines
Growth characteristics of all evolution lines improved by the 300th generation [4], and in some lines even before the appearance of SR− mutations, indicating that mutations in other genes had also contributed to improvements in syntrophy. Each evolution line had at least 8 and up to 13 out of 24 high G-score mutations in Dv, while Mm had mutations in at least 5 and up to 10 out of 16 high G-score genes. We interrogated the temporal order in which high G-score mutations were selected and the combinations in which they co-existed in each evolution line to uncover evidence for epistatic interactions in improving obligate syntrophy. Indeed, missense mutations in DsrC (DVU2776) were fixed simultaneously with the appearance of loss of function mutations in one of two sigma 54 type regulators (DVU2894, DVU2394) in lines HA2, and UR1 (P = 5.40 × 10−5). In rare instances, we also observed that some high G-score mutations co-occurred across evolution lines, e.g., two U- and one H-line consistently showed for at least two time points a mutation in DVU1283 (GalU) coexisting with mutations in DVU2394 (P = 5.04 × 10−3). More commonly, the combinations of high G-score gene mutations varied across multiple lines. In fact, no two lines possessed identical combination of high G-score gene mutations (Fig. 3A, B), and many high-frequency mutations were uniquely present or absent in different lines (Fig. 3C, D).
Fig. 3: Frequency and time of appearance of mutations through 1 K generations of laboratory evolution lines of Dv and Mm cocultures.

The heat maps display frequency of mutations in genes (rows) in Dv (A) and Mm (B) in each evolution line, ordered from early to later generations (horizontal axis). High G-score genes are shown in red font and their G-score rank is shown to the left in gray shaded box, also in red font. Bar plots above heat maps indicate total number of mutations in each generation and the color indicates impact of mutation. Use “Frequency”, “Generations”, and “Mutation impact” key below the heat maps for interpretation. Mutations that were unique to each evolution line is shown in (C, D) for Dv and Mm, respectively. E The heatmap illustrates a selective sweep across both organisms in line HS3.

Full size image

Mutations in high G-score genes appeared consistently in all evolution lines (P 80% EPD-03 vs, More