Ecology

1.
Ghiselli, G. & Jardim, W. F. Interferentes endócrinos no meio ambiente. Quím. Nova 30, 695–706 (2007).
Article  Google Scholar 
2.
Vilela, C. L. S., Bassin, J. P. & Peixoto, R. S. Water contamination by endocrine disruptors: Impacts, microbiological aspects and trends for environmental protection. Environ. Poll. 235, 546–559 (2018).
CAS  Article  Google Scholar 

3.
Muller, M. et al. Occurrence of estrogens in sewage sludge and their fate during plant-scale anaerobic digestion. Chemosphere 81, 65–71 (2010).
ADS  CAS  PubMed  Article  Google Scholar 

4.
Mills, M. R. et al. Removal of ecotoxicity of 17α-ethinylestradiol using TAML/peroxide water treatment. Sci. Rep. 5, 1–10 (2015).
Article  CAS  Google Scholar 

5.
Laurenson, J. P., Bloom, R. A., Page, S. & Sadrieh, N. Ethinylestradiol and other human pharmaceutical estrogens in the aquatic environment: A review of recent risk assessment data. AAPS J. 16, 299–310 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

6.
Luzio, A., Santos, D., Fontaínhas-Fernandes, A. A., Monteiro, S. M. & Coimbra, A. M. Effects of 17α-ethinylestradiol at different water temperatures on zebrafish sex differentiation and gonad development. Aquat. Toxicol. 174, 22–35 (2016).
CAS  PubMed  Article  Google Scholar 

7.
Blewett, T., MacLatchy, D. L. & Wood, C. M. The effects of temperature and salinity on 17-α-ethynylestradiol uptake and its relationship to oxygen consumption in the model euryhaline teleost (Fundulus heteroclitus). Aquat. Toxicol. 127, 61–71 (2013).
CAS  PubMed  Article  Google Scholar 

8.
Atkinson, S., Atkinson, M. J. & Tarrant, A. M. Estrogens from sewage in coastal marine environments. Environ. Health Persp. 111, 531–535 (2003).
CAS  Article  Google Scholar 

9.
Segner, H. et al. Identification of endocrine-disrupting effects in aquatic vertebrates and invertebrates: Report from the European IDEA project. Ecotoxicol. Environ. Safe. 54, 302–314 (2003).
CAS  Article  Google Scholar 

10.
Tyler, C. R., Jobling, S. & Sumpter, J. P. Endocrine disruption in wildlife: A critical review of the evidence. Crit. Rev. Toxicol. 28, 319–361 (1998).
CAS  PubMed  Article  PubMed Central  Google Scholar 

11.
Johnson, A. C., Belfroid, A. & Di Corcia, A. Estimating steroid oestrogen inputs into activated sludge treatment works and observations on their removal from the effluent. Sci. Total Environ. 256, 163–173 (2000).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

12.
Cadwell, D. J. et al. Derivation of an aquatic predicted no-effect concentration for the synthetic hormone, 17α-ethinyl estradiol. Environ. Sci. Technol. 10, 272–283 (2008).
Google Scholar 

13.
Ternes, T. A. et al. Behavior and occurrence of estrogens in municipal sewage treatment plants—I. Investigations in Germany, Canada and Brazil. Sci. Total Environ. 225, 81–90 (1999).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

14.
Kolpin, D. W. et al. Pharmaceuticals, hormones, and other organic wastewater contaminants in U.S. streams, 1999–2000: A national reconnaissance. Environ. Sci. Technol. 36, 1202–1211 (2002).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

15.
Huang, Y., Wang, X. L., Zhang, J. W. & Wu, K. S. Impact of endocrine-disrupting chemicals on reproductive function in zebrafish (Danio rerio). Reprod. Domest. Anim. 50, 1–6 (2009).
Article  CAS  Google Scholar 

16.
Länge, R. et al. Effects of the synthetic estrogen 17α-ethinylestradiol on the life-cycle of the fathead minnow (Pimephales promelas). Environ. Toxicol. Chem. 20, 1216–1227 (2001).
PubMed  Article  PubMed Central  Google Scholar 

17.
Parrott, J. L. & Blunt, B. R. Life-cycle exposure of fathead minnows (Pimephales promelas) to an ethinylestradiol concentration below 1 ng/L reduces egg fertilization success and demasculinizes males. Environ. Toxicol. 20, 131–141 (2005).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

18.
Bloom, M. S., Micu, R. & Neamtiu, I. Female infertility and “emerging” organic pollutants of concern. Curr. Epidemiol. Rep. 3, 39–50. https://doi.org/10.1007/s40471-016-0060-1 (2016).
Article  Google Scholar 

19.
Nash, J. P. et al. Long-term exposure to environmental concentrations of the pharmaceutical ethynylestradiol causes reproductive failure in fish. Environ. Health Persp. 112, 1725–1733 (2004).
CAS  Article  Google Scholar 

20.
Wu, C., Huang, X., Lin, J. & Liu, J. Occurrence and fate of selected endocrine-disrupting chemicals in water and sediment from an urban lake. Arch. Environ. Contam. Toxicol. 68, 225–236 (2014).
PubMed  Article  CAS  PubMed Central  Google Scholar 

21.
Pennington, M. J. et al. Effects of contaminants of emerging concern on Megaselia scalaris (Lowe, Diptera: Phoridae) and its microbial community. Sci. Rep. 7, 1–12 (2017).
CAS  Article  Google Scholar 

22.
Pennington, M. J., Prager, S. M., Walton, W. E. & Trumble, J. T. Culex quinquefasciatus larval microbiomes vary with instar and exposure to common wastewater contaminants. Sci. Rep. 6, 1–9 (2016).
Article  CAS  Google Scholar 

23.
Pennington, M. J., Rivas, N. G., Prager, S. M., Walton, W. E. & Trumble, J. T. Pharmaceuticals and personal care products alter the holobiome and development of a medically important mosquito. Environ. Pollut. 203, 199–207 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

24.
Fosch, S. E. et al. Contraception: Influence on vaginal microbiota and identification of vaginal lactobacilli using MALDI-TOF MS and 16S rDNA sequencing. Open Microbiol. J. 12, 218–229 (2018).
CAS  PubMed  PubMed Central  Article  Google Scholar 

25.
Tarrant, A. M., Blomquist, C. H., Lima, P. H., Atkinson, M. J. & Atkinson, S. Metabolism of estrogens and androgens by scleractinian corals. Comp. Biochem. Phys. B. 136, 473–485 (2003).
Article  CAS  Google Scholar 

26.
Tarrant, A. M., Atkinson, M. J. & Atkinson, S. Effects of steroidal estrogens on coral growth and reproduction. Mar. Ecol. Prog. Ser. 269, 121–129 (2004).
ADS  CAS  Article  Google Scholar 

27.
Atkinson, S. & Atkinson, M. J. Detection of estradiol-17β during a mass coral spawn. Coral Reefs 11, 33–35 (1992).
ADS  Article  Google Scholar 

28.
Atkinson, S. Uptake of estrone from the water column by a coral community. Mar. Biol. 139, 321–325 (2001).
Article  Google Scholar 

29.
Rougée, L. R., Richmond, R. H. & Collier, A. C. Molecular reproductive characteristics of the reef coral Pocillopora damicornis. Comp. Biochem. Phys. A 189, 38–44 (2015).
Article  CAS  Google Scholar 

30.
Blomquist, C. H., Lima, P. H., Tarrant, A. M., Atkinson, M. J. & Atkinson, S. 17β-Hydroxysteroid dehydrogenase (17β-HSD) in scleractinian corals and zooxanthellae. Comp. Biochem. Phys. B 143, 397–403 (2006).
Article  CAS  Google Scholar 

31.
Tarrant, A. M., Atkinson, S. & Atkinson, M. J. Estrone and estradiol-17β concentration in tissue of the scleractinian coral, Montipora verrucosa. Comp. Biochem. Phys. A 122, 85–92 (1999).
CAS  Article  Google Scholar 

32.
Twan, W. et al. Hormones and reproduction in scleractinian corals. Comp. Biochem. Phys. A 144, 247–253 (2006).
Article  CAS  Google Scholar 

33.
Tarrant, A., Atkinson, M. & Atkinson, S. Uptake of estrone from the water column by a coral community. Mar. Biol. 139, 321–325 (2001).
CAS  Article  Google Scholar 

34.
Bosch, T. C. G. & McFall-Ngai, M. J. Metaorganisms as the new frontier. Zoology 114, 185–190 (2011).
PubMed  Article  PubMed Central  Google Scholar 

35.
Rosenberg, E., Koren, O., Reshef, L. & Efrony, R. The role of microorganisms in coral health, disease and evolution. Nat. Rev. Microbiol. 5, 355–362 (2007).
CAS  PubMed  Article  PubMed Central  Google Scholar 

36.
Rosenberg, E. Coral microbiology. Microb. Biotechnol. 2, 147 (2009).
PubMed  PubMed Central  Article  Google Scholar 

37.
Peixoto, R. S., Rosado, P. M., Leite, D. C., Rosado, A. S. & Bourne, D. G. Beneficial microorganisms for corals (BMC): Proposed mechanisms for coral health and resilience. Front. Mar. Sci. 8, 1–16 (2017).
CAS  Google Scholar 

38.
Ziegler, M., Seneca, F. O., Yum, L. K., Palumbi, S. R. & Voolstra, C. R. Patterns of coral heat tolerance. Nature Comm. 1, 1–8 (2017).
Google Scholar 

39.
Peixoto, R. S., Sweet, M. & Bourne, D. G. Customized medicine for corals. Front. Mar. Sci. 6, 686 (2019).
Article  Google Scholar 

40.
Rosado, P. M. et al. Marine probiotics: Increasing coral resistance to bleaching through microbiome manipulation. ISME J. 13, 921–936 (2019).
CAS  PubMed  Article  Google Scholar 

41.
Lesser, M. P., Mazel, C. H., Gorbunov, M. Y. & Falkowski, P. G. Discovery of symbiotic nitrogen-fixing cyanobacteria in corals. Science 305, 997–1000 (2004).
ADS  CAS  PubMed  Article  Google Scholar 

42.
Wegley, L., Edwards, R., Rodriguez-Brito, B., Liu, H. & Rohwer, F. Metagenomic analysis of the microbial community associated with the coral Porites astreoides. Environ. Microbiol. 9, 2707–2719 (2007).
CAS  PubMed  Article  Google Scholar 

43.
Reshef, L., Koren, O., Loya, Y., Zilber-Rosenberg, I. & Rosenberg, E. The coral probiotic hypothesis. Environ. Microbiol. 8, 2068–2073 (2006).
CAS  PubMed  Article  Google Scholar 

44.
Ritchie, K. B. Regulation of microbial populations by coral surface mucus and mucus-associated bacteria. Mar. Ecol. Prog. Ser. 322, 1–14 (2006).
ADS  CAS  Article  Google Scholar 

45.
Bourne, D. G., Morrow, K. M. & Webster, N. S. Insights into the coral microbiome: Underpinning the health and resilience of reef ecosystems. Annu. Rev. Microbiol. 70, 317–340 (2016).
CAS  PubMed  Article  Google Scholar 

46.
Santos, H. F. et al. Climate change affects key nitrogen-fixing bacterial populations on coral reefs. ISME J. 8, 2272–2279 (2014).
PubMed  PubMed Central  Article  Google Scholar 

47.
Santos, H. F. et al. Impact of oil spills on coral reefs can be reduced by bioremediation using probiotic microbiota. Sci. Rep. 5, 1–11 (2015).
Google Scholar 

48.
Röthig, T., Yum, L. K., Kremb, S. G., Roik, A. & Voolstra, C. R. Microbial community composition of deep-sea corals from the Red Sea provides insight into functional adaption to a unique environment. Sci. Rep. 7, 44714 (2017).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

49.
Meyer, J. L., Paul, V. J. & Teplitski, M. Community shifts in the surface microbiomes of the coral Porites astreoides with unusual lesions. PLoS ONE 9, e100316 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

50.
Sweet, M. J. & Bulling, M. T. On the importance of the microbiome and pathobiome in coral health and disease. Front. Mar. Sci. 4, 1–11 (2017).
Article  Google Scholar 

51.
Grottoli, A. G. et al. Coral physiology and microbiome dynamics under combined warming and ocean acidification. PLoS ONE 13, e0191156 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

52.
Webster, N. S. et al. Host-associated coral reef microbes respond to the cumulative pressures of ocean warming and ocean acidification. Sci. Rep. 6, 19324 (2016).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

53.
Ainsworth, T. D., Thurber, R. V. & Gates, R. D. The future of coral reefs: A microbial perspective. Trends Ecol. Evol. 25, 233–240 (2009).
PubMed  Article  PubMed Central  Google Scholar 

54.
Gissi, F. et al. The effect of dissolved nickel and copper on the adult coral Acropora muricata and its microbiome. Environ. Pollut. 250, 792–806 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

55.
Leite, D. C. et al. Coral bacterial-core abundance and network complexity as proxies for anthropogenic pollution. Front. Microbiol. 9, 833 (2018).
PubMed  PubMed Central  Article  Google Scholar 

56.
Vega Thurber, R. et al. Metagenomic analysis of stressed coral holobionts. Environ. Microbiol. 11, 2148–2163 (2009).
PubMed  Article  CAS  PubMed Central  Google Scholar 

57.
Meron, D. et al. The impact of reduced pH on the microbial community of the coral Acropora eurystoma. ISME J. 5, 51–60 (2011).
PubMed  Article  PubMed Central  Google Scholar 

58.
McDevitt-Irwin, J. M., Baum, J. K., Garren, M. & Vega Thurber, R. L. Responses of coral-associated bacterial communities to local and global stressors. Front. Mar. Sci. 4, 262 (2017).
Article  Google Scholar 

59.
Al-Dahash, L. M. & Mahmoud, H. M. Harboring oil-degrading bacteria: A potential mechanism of adaptation and survival in corals inhabiting oil-contaminated reefs. Mar. Pollut. Bull. 72, 364–374 (2013).
CAS  PubMed  Article  PubMed Central  Google Scholar 

60.
Wenger, A. S., Fabricius, K. E., Jones, G. P. & Brodie, J. E. Effects of sedimentation, eutrophication, and chemical pollution on coral reef fishes. In Ecology of Fishes on Coral Reefs (ed. Mora, C.) 145–153 (Cambridge University Press, Cambridge, 2015).
Google Scholar 

61.
Zaneveld, J. R. et al. Overfishing and nutrient pollution interact with temperature to disrupt coral reefs down to microbial scales. Nat. Commun. 7, 1–12 (2016).
Article  CAS  Google Scholar 

62.
Marangoni, L. F. et al. Copper effects on biomarkers associated with photosynthesis, oxidative status and calcification in the Brazilian coral Mussismilia harttii (Scleractinia, Mussidae). Mar. Environ. Res. 130, 248–257 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

63.
Ferrigno, F. et al. Corals in high diversity reefs resist human impact. Ecol. Indic. 70, 106–113 (2016).
Article  Google Scholar 

64.
Hughes, T. P. et al. Climate change, human impacts, and the resilience of coral reefs. Science 80, 929–933 (2003).
ADS  Article  CAS  Google Scholar 

65.
Pastorok, R. & Bilyard, G. Effects of sewage pollution on coral-reef communities. Mar. Ecol. Prog. Ser. 21, 175–189 (1985).
ADS  Article  Google Scholar 

66.
Tarrant, A. M. Hormonal signaling in cnidarians: Do we understand the pathways well enough to know whether they are being disrupted?. Ecotoxicology 16, 5–13 (2007).
CAS  PubMed  Article  PubMed Central  Google Scholar 

67.
Peters, E. C., Gassman, N. J., Firman, J. C., Richmond, R. H. & Power, E. A. Ecotoxicology of tropical marine ecosystems. Environ. Toxicol. Chem. 16, 12–40 (1997).
CAS  Article  Google Scholar 

68.
Holert, J. et al. Metagenomes reveal global distribution of bacterial steroid catabolism in natural, engineered, and host environments. MBio 9, e02345-e2417 (2018).
CAS  PubMed  PubMed Central  Article  Google Scholar 

69.
Winter, A. P. M., Chaloub, R. M. & Duarte, G. A. S. Photosynthetic responses of corals Mussismilia harttii (Verrill, 1867) from turbid waters to changes in temperature and presence/absence of light. Braz. J. Oceanogr. 64, 203–216 (2016).
Article  Google Scholar 

70.
Fonseca, J. S., Marangoni, L. F. B., Marques, J. A. & Bianchini, A. Effects of increasing temperature alone and combined with copper exposure on biochemical and physiological parameters in the zooxanthellate scleractinian coral Mussismilia harttii. Aquat. Toxicol. 190, 121–132 (2017).
CAS  PubMed  Article  Google Scholar 

71.
Ralph, P. J., Schreiber, U., Gademann, R., Kühl, M. & Larkum, A. W. D. Coral photobiology studied with a new imaging pulse amplitude modulated fluorometer. J. Phycol. 41, 335–342 (2005).
Article  Google Scholar 

72.
Sato, Y., Bourne, D. G. & Willis, B. L. Effects of temperature and light on the progression of black band disease on the reef coral, Montiporahispida. Coral Reefs 30, 753–761 (2011).
ADS  Article  Google Scholar 

73.
Wiedenmann, J. et al. Nutrient enrichment can increase the susceptibility of reef corals to bleaching. Nat. Clim. Change 3, 160–164 (2013).
ADS  CAS  Article  Google Scholar 

74.
Fernando, S. C. et al. Microbiota of the major south atlantic reef building. Microb. Ecol. 69, 267–280 (2015).
PubMed  Article  Google Scholar 

75.
De Castro, A. P., Dias, S. A. & Reis, A. M. M. Bacterial community associated with healthy and diseased reef coral Mussismilia hispida from eastern Brazil. Microb. Ecol. 59, 658–667 (2010).
PubMed  Article  Google Scholar 

76.
Santos, H. F. et al. Mangrove bacterial diversity and the impact of oil contamination revealed by pyrosequencing: Bacterial proxies for oil pollution. PLoS ONE 6, e14693 (2011).
Article  CAS  Google Scholar 

77.
Santos, H. F., Cury, J. C., Carmo, F. L., Rosado, A. S. & Peixoto, R. S. 18S rDNA sequences from microeukaryotes reveal oil indicators in mangrove sediment. PLoS ONE 5, e12437 (2010).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

78.
Oleynik, G. N., Yurishinets, V. I. & Starosila, Y. V. Bacterioplankton and bacteriobenthos as biological indicators of the aquatic ecosystems’ state (a review). Hydrobiol. J. 47, 37–48 (2011).
Article  Google Scholar 

79.
Jain, A., Singh, B. N., Singh, S. P., Singh, H. B. & Singh, S. Exploring biodiversity as bioindicators for water pollution. Natl. Conf. Biodivers. Dev. Poverty Alleviation, 50–56 (2010).

80.
Bloem, J. & Breure, A. M. Microbial indicators. In Bioindicators and Biomonitors (eds Markert, B. A. et al.) 257–282 (Elsevier, Amsterdam, 2003).
Google Scholar 

81.
Parmar, T. K., Rawtani, D. & Agrawal, Y. K. Bioindicators: The natural indicator of environmental pollution. Front. Life Sci. 9, 110–118 (2016).
CAS  Article  Google Scholar 

82.
Wilkins, L. G. E. et al. Host-associated microbiomes drive structure and function of marine ecosystems. PLoS Biol. 17, e3000533 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

83.
Kurisu, F., Ogura, M., Saitoh, S., Yamazoe, A. & Yagi, O. Degradation of natural estrogen and identification of the metabolites produced by soil isolates of Rhodococcus sp. and Sphingomonas sp.. J. Biosci. Bioeng. 109, 576–582 (2010).
CAS  PubMed  Article  Google Scholar 

84.
Wang, Y. et al. Degradation of 17 β-estradiol and products by a mixed culture of Rhodococcus equi DSSKP-R-001 and Comamonas testosteroni QYY20150409. Biotechnol. Biotechnol. Equip. 33, 268–277 (2019).
CAS  Article  Google Scholar 

85.
Yoshimoto, T. et al. Isolates from activated sludge in wastewater treatment plants. Appl. Environ. Microbiol. 70, 5283–5289 (2004).
CAS  PubMed  PubMed Central  Article  Google Scholar 

86.
Zhao, H. et al. Genome analysis of Rhodococcus sp. DSSKP-R-001: A highly effective β-estradiol-degrading bacterium. Int. J. Genomics 2018, 3505428 (2018).
PubMed  PubMed Central  Google Scholar 

87.
Edet, U. O. & Antai, S. P. Correlation and distribution of xenobiotics genes and metabolic activities with level of total petroleum hydrocarbon in soil, sediment and estuary water in the Niger Delta Region of Nigeria. Asian J. Biotechnol. Genet. Eng. 1(1), 1–11 (2018).
Google Scholar 

88.
Parida, S. & Sharma, D. The microbiome-estrogen connection and breast cancer risk. Cells 8, 1642 (2019).
CAS  PubMed Central  Article  PubMed  Google Scholar 

89.
Schreiber, U. Pulse-amplitude-modulation (PAM) fluorometry and saturation pulse method: An overview. In Chlorophyll a Fluorescence: A Signature of Photosynthesis (eds Papageorgiou, G. C. & Govindjee, C.) 279–319 (Springer, Berlin, 2004).
Google Scholar 

90.
Siebeck, U. E., Marshall, N. J., Klüter, A. & Hoegh-Guldberg, O. Monitoring coral bleaching using a colour reference card. Coral Reefs 25, 453–460 (2006).
ADS  Article  Google Scholar 

91.
Hammer, Ø., Harper, D. A. T. & Ryan, P. D. PAST: Paleontological statistics software package. Palaeontol. Electron. 4, 1–9 (2001).
Google Scholar 

92.
Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. U.S.A. 108, 4516–4522 (2011).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

93.
Schloss, P. D. et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).
CAS  PubMed  PubMed Central  Article  Google Scholar 

94.
McCune, B. & Mefford, M. J. PC-ORD v. 6.0. MjM Software, Gleneden Beach (2010). More

1.
Mitsch, W. J. & Gosselink, J. G. Wetlands [Elektronisk Resurs] (Wiley, Hoboken, 2015).
Google Scholar 
2.
Sieben, E. J. J., Khubeka, S. P., Sithole, S., Job, N. M. & Kotze, D. C. The classification of wetlands: Integration of top-down and bottom-up approaches and their significance for ecosystem service determination. Wetl. Ecol. Manage. 26, 441–458 (2018).
Article  Google Scholar 

3.
Seifollahi-Aghmiuni, S., Nockrach, M. & Kalantari, Z. The potential of wetlands in achieving the sustainable development goals of the 2030 Agenda. Water 11, 609 (2019).
Article  Google Scholar 

4.
Jaramillo, F. et al. Priorities and interactions of sustainable development goals (SDGs) with focus on wetlands. Water 11, 619 (2019).
Article  Google Scholar 

5.
Thorslund, J. et al. Solute evidence for hydrological connectivity of geographically isolated wetlands. Land Degrad. Dev. 29, 3954–3962 (2018).
Article  Google Scholar 

6.
Quin, A., Jaramillo, F. & Destouni, G. Dissecting the ecosystem service of large-scale pollutant retention: The role of wetlands and other landscape features. Ambio 44, 127–137 (2015).
Article  Google Scholar 

7.
Thorslund, J. et al. Wetlands as large-scale nature-based solutions: Status and challenges for research, engineering and management. Ecol. Eng. 108, 489–497 (2017).
Article  Google Scholar 

8.
Åhlén, I. et al. Wetlandscape size thresholds for ecosystem service delivery: Evidence from the Norrström drainage basin, Sweden. Sci. Total Environ. 704, 135452 (2020).
ADS  Article  Google Scholar 

9.
Moomaw, W. R. et al. Wetlands in a changing climate: Science, policy and management. Wetlands 38, 183–205 (2018).
Article  Google Scholar 

10.
Erwin, K. L. Wetlands and global climate change: The role of wetland restoration in a changing world. Wetl. Ecol. Manage. 17, 71 (2008).
Article  Google Scholar 

11.
Jaramillo, F., Prieto, C., Lyon, S. W. & Destouni, G. Multimethod assessment of evapotranspiration shifts due to non-irrigated agricultural development in Sweden. J. Hydrol. 484, 55–62 (2013).
ADS  Article  Google Scholar 

12.
Bring, A. et al. Implications of freshwater flux data from the CMIP5 multimodel output across a set of Northern Hemisphere drainage basins: Implications of freshwater flux data from the CMIP5 multimodel output across. Earths Future 3, 206–217 (2015).
ADS  Article  Google Scholar 

13.
Jarsjö, J., Asokan, S. M., Prieto, C., Bring, A. & Destouni, G. Hydrological responses to climate change conditioned by historic alterations of land-use and water-use. Hydrol. Earth Syst. Sci. 16, 1335–1347 (2012).
ADS  Article  Google Scholar 

14.
Moor, H., Hylander, K. & Norberg, J. Predicting climate change effects on wetland ecosystem services using species distribution modeling and plant functional traits. Ambio 44, 113–126 (2015).
Article  Google Scholar 

15.
Jaramillo, F. et al. Effects of hydroclimatic change and rehabilitation activities on salinity and mangroves in the Ciénaga Grande de Santa Marta, Colombia. Wetlands 38, 755–767 (2018).
Article  Google Scholar 

16.
Jarsjö, J. et al. Projecting impacts of climate change on metal mobilization at contaminated sites: Controls by the groundwater level. Sci. Total Environ. 712, 135560 (2020).
ADS  Article  Google Scholar 

17.
Ghajarnia, N. et al. Wetlandscape change information database (WetCID). Earth Syst. Sci. Data 12(2), 1083–1083. https://doi.org/10.1594/PANGAEA.907398 (2019).
ADS  Article  Google Scholar 

18.
Karlsson, J. M., Jaramillo, F. & Destouni, G. Hydro-climatic and lake change patterns in Arctic permafrost and non-permafrost areas. J. Hydrol. 529, 134–145 (2015).
ADS  Article  Google Scholar 

19.
Hofstede, R. G. M. Effects of livestock farming and recommendations for management and conservation of páramo grasslands (Colombia). Land Degrad. Dev. 6, 133–147 (1995).
Article  Google Scholar 

20.
Agudelo, C. & Fernanda, M. Ecohydrology of Paramos in Colombia: Vulnerability to Climate Change and Land Use (Universidad Nacional de Colombia, Medellín, 2019).
Google Scholar 

21.
Fallah, M. & Zamani-Ahmadmahmoodi, R. Assessment of water quality in Iran’s Anzali Wetland, using qualitative indices from 1985, 2007, and 2014. Wetl. Ecol. Manage. 25, 597–605 (2017).
CAS  Article  Google Scholar 

22.
Khazaei, B. et al. Climatic or regionally induced by humans? Tracing hydro-climatic and land-use changes to better understand the Lake Urmia tragedy. J. Hydrol. 569, 203–217 (2019).
ADS  Article  Google Scholar 

23.
Shibuo, Y., Jarsjö, J. & Destouni, G. Hydrological responses to climate change and irrigation in the Aral Sea drainage basin. Geophys. Res. Lett. https://doi.org/10.1029/2007GL031465 (2007).
Article  Google Scholar 

24.
Jarsjö, J., Törnqvist, R. & Su, Y. Climate-driven change of nitrogen retention–attenuation near irrigated fields: Multi-model projections for Central Asia. Environ. Earth Sci. 76, 117 (2017).
Article  Google Scholar 

25.
Marjani, A. & Jamali, M. Role of exchange flow in salt water balance of Urmia Lake. Dyn. Atmos. Oceans 65, 1–16 (2014).
ADS  Article  Google Scholar 

26.
Törnqvist, R. et al. Evolution of the hydro-climate system in the Lake Baikal basin. J. Hydrol. 519, 1953–1962 (2014).
ADS  Article  Google Scholar 

27.
Pietroń, J. et al. Sedimentation patterns in the Selenga River delta under changing hydroclimatic conditions. Hydrol. Process. 32, 278–292 (2018).
ADS  Article  Google Scholar 

28.
Foufoula-Georgiou, E., Takbiri, Z., Czuba, J. A. & Schwenk, J. The change of nature and the nature of change in agricultural landscapes: Hydrologic regime shifts modulate ecological transitions. Water Resour. Res. 51, 6649–6671 (2015).
ADS  Article  Google Scholar 

29.
Meter, K. J. V. & Basu, N. B. Signatures of human impact: Size distributions and spatial organization of wetlands in the Prairie Pothole landscape. Ecol. Appl. 25, 451–465 (2015).
Article  Google Scholar 

30.
McCartney, M., Morardet, S., Rebelo, L.-M., Finlayson, C. M. & Masiyandima, M. A study of wetland hydrology and ecosystem service provision: GaMampa wetland, South Africa. Hydrol. Sci. J. 56, 1452–1466 (2011).
Article  Google Scholar 

31.
Wolf, K. L., Noe, G. B. & Ahn, C. Hydrologic connectivity to streams increases nitrogen and phosphorus inputs and cycling in soils of created and natural floodplain wetlands. J. Environ. Qual. 42, 1245–1255 (2013).
CAS  Article  Google Scholar 

32.
Kasimov, N., Karthe, D. & Chalov, S. Environmental change in the Selenga River—Lake Baikal Basin. Reg. Environ. Change 17, 1945–1949 (2017).
Article  Google Scholar 

33.
Kottek, M., Grieser, J., Beck, C., Rudolf, B. & Rubel, F. World Map of the Köppen-Geiger climate classification updated. Meteorol. Z. 15, 259–263. https://doi.org/10.1127/0941-2948/2006/0130 (2006).
Article  Google Scholar 

34.
Ghajarnia, N. et al. Data for wetlandscapes and their changes around the world. Earth Syst. Sci. Data 12, 1083–1100 (2020).
ADS  Article  Google Scholar 

35.
Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7, 109 (2020).
Article  Google Scholar 

36.
Budyko, M. I. & Miller, D. H. Climate and Life (Academic Press, New York, 1974).
Google Scholar 

37.
Roderick, M. L. & Farquhar, G. D. A simple framework for relating variations in runoff to variations in climatic conditions and catchment properties. Water Resour. Res. https://doi.org/10.1029/2010WR009826 (2011).
Article  Google Scholar 

38.
Yang, H., Yang, D., Lei, Z. & Sun, F. New analytical derivation of the mean annual water-energy balance equation. Water Resour. Res. https://doi.org/10.1029/2007WR006135 (2008).
Article  Google Scholar 

39.
Zhang, D., Cong, Z., Ni, G., Yang, D. & Hu, S. Effects of snow ratio on annual runoff within the Budyko framework. Hydrol. Earth Syst. Sci. 19, 1977–1992 (2015).
ADS  Article  Google Scholar 

40.
Wen, X., Tang, G., Wang, S. & Huang, J. Comparison of global mean temperature series. Adv. Clim. Change Res. 2, 187–192 (2011).
Article  Google Scholar  More

Paraburkholderia species promote Broccoli growth in a cultivar-dependent manner
Root tip inoculation of the two Broccoli cultivars with strains of three different Paraburkholderia species led to changes in leaf color (deep green leaves), shoot biomass, root biomass and root architecture (Fig. 1a). Percent change in biomass was used as a measure to assess the growth-promoting effects of the Paraburkholderia species in the two Broccoli cultivars. Two-way analysis of variance (ANOVA) was conducted to assess the influence of the two independent variables (strains of Paraburkholderia species and Broccoli cultivars) on both shoot and root biomass. The Paraburkholderia species included three levels (Pbg, Pbh, Pbt) and the Broccoli cultivars consisted of two levels (Coronado, Malibu). For shoots, all interactions, except Pbt-Malibu, resulted in significant increases in biomass relative to the non-treated control plants, while for roots all three Paraburkholderia species significantly increased the biomass in both Broccoli cultivars (Fig. 1b). In general, the relative impact of Paraburkholderia species was up to 3 times higher for root biomass than for shoot biomass (Fig. 1b). Two-way ANOVA showed highly significant interactions between the strains of Paraburkholderia species and Broccoli cultivars regarding the percent changes in shoot and root biomass (Supplementary Table S1). Overall, for cultivar Coronado the percent change in shoot biomass was about 40% compared to the control, and not significantly different between the different strains of Paraburkholderia species, whereas in cultivar Malibu the percent change in shoot biomass was significantly higher for Pbg (~ 70%) and Pbh (~ 90%) as compared to Pbt. Furthermore, inoculation with Pbh led to a significantly higher increase in shoot biomass in cultivar Malibu than in Coronado. Regarding the percent change in root biomass, only inoculation of Pbt showed significant differences between the two Broccoli cultivars. As indicated above, the shoot biomass of cultivar Malibu inoculated with Pbt was not significantly different from the control plants (Fig. 1b). Over a period of 11 days, both Pbg and Pbh-treated Broccoli cultivars showed significantly higher shoot and root biomass from 7 days post inoculation (dpi) onwards, while Pbt-treated plants showed higher shoot biomass in Coronado from 9 dpi onwards (Fig. 1c).
Figure 1

Biomass and phenotypic changes in Broccoli cultivars in response to root tip inoculation with strains of three Paraburkholderia species. (a) Pictures of MS agar plate with two Broccoli cultivars (Coronado and Malibu) at 11 days post inoculation with strains of three Paraburkholderia species (Pbg: Paraburkholderia graminis PHS1, Pbh: P. hospita mHSR1, and Pbt: P. terricola mHS1). (b) Percent changes in shoot and root biomass (mean ± standard error, n = 4 (shoot) and n = 6 (root)) of two Broccoli cultivars inoculated with the strains of the Paraburkholderia species. Treatments sharing the same letters are not significantly different (Two-way ANOVA, Tukey’s HSD post hoc test, P  More

Focal species
The Lesser Prairie-Chicken is a medium-sized grouse endemic to, broadly, the shortgrass prairie ecosystem of the south-central United States, where it is found only in southwestern Colorado, western Kansas, northwestern Oklahoma, the Texas panhandle, and eastern New Mexico. As with almost all open-country grouse of temperate environments, the Lesser Prairie-Chicken forms leks at which a cluster of males (in this species, usually 5–12 individuals) display vigorously and female visit to assess males with an intent to secure sperm to fertilize her eggs, which she will lay and raise without male help. Outside of male lekking (mid-March to mid-May) and female nesting (late April to early July), birds congregate is small flocks to forage, at times on grain remains in farmed fields but typically, as through the rest of the life cycle, restricting themselves to native prairie. Accordingly, this species has three distinct aspects of habitat selection: general occurrence, lek placement, and nest placement.
Data
Lesser Prairie-Chicken were tracked at two study sites, one in Roosevelt County in east-central New Mexico, U.S.A., the other in Beaver, Harper, and Ellis Counties in northwestern Oklahoma, U.S.A. Birds tagged with VHF transmitters were tracked from April 1999–March 2006 in New Mexico and from March 1999–July 2013 in Oklahoma11,12,13,14. Study periods differed chiefly because of funding, which for New Mexico was insufficient after 2006. The study sites differ markedly in land tenure history. Parcel size in New Mexico averages 1300 ha versus 180 ha in Oklahoma11. The difference largely stems from settlement patterns over the past two centuries. New Mexico was part of the Spanish land grant system, which tended to yield huge parcels. In our study area, parcels approach 8 km2 ( > 1900 acres). By contrast, during the “land rush” era of the late 19th Century most of northwestern Oklahoma was parceled into 65-ha (160-acre) plots as part of the United States’ Homestead Act. Smaller parcels translate to a higher density of roads, fences, buildings, and powerlines11.
Radiotracking typically yielded a triplet of coordinate readings, from which we had to triangulate a grouse’s location. We estimated latitude and longitude using a maximum likelihood estimator (MLE), although it some cases the MLE algorithm failed to converge. If it failed, we instead used the Andrew and Huber methods15. R code for the estimation procedure can be found at https://github.com/henry-dang/triangulation/blob/master/lenth_triang.R.
From these data we used kernel density methods (R package ks16) to estimate annual home ranges (235 in New Mexico, 263 in Oklahoma). Tracking data included lek (12 in New Mexico, 23 in Oklahoma) and nest (122 in New Mexico, 128 Oklahoma) locations. For home range centroids, the outer contour of home ranges, leks, and nests, we estimated distance to seven anthropogenic features: roads (highways, primary, and secondary roads only; small farm roads or one-lane gravel roads were excluded), powerlines (overhead only, with buried or trunk lines excluded; https://hifld-geoplatform.opendata.arcgis.com/datasets/electric-power-transmission-lines), oil wells, gas wells (for both types of wells, http://www.occeweb.com and http://www.emnrd.state.nm.us/ocd), outbuildings (barns, grain silos, poultry houses, and similar large structures; chiefly the TIGER database), and fences (Bureau of Land Management) in both states, plus private houses in New Mexico and railroad tracks in Oklahoma. We placed 2000 random points on each study area to estimate distances to each of these same anthropogenic features, which provided an estimate of feature density on the landscape.
Analyses
The initial step was to estimate the probability of a grouse occurring a certain distance from a feature. We treated New Mexico and Oklahoma data separately, giving us a replicate assessment because these populations have been isolated from each other for  > 100 years17 and, as noted above, land tenure history differs strikingly between the states11. We estimated probabilities of grouse occurrence, πi, via a Bayesian model with binomial likelihood and flat prior (i.e., no assumption of the central tendency of occurrence probability at a given distance from a feature):
yi ~ binomial (πi, n) with yi the cumulative count of grouse at distance i (i.e., the data) and n the total number of home ranges, leks, or nests. Distances, i, were binned to the smallest extent possible, from 10 to 100 m, to allow the Markov chain Monte Carlo (MCMC) algorithm to converge in a reasonable number of iterations (e.g.,  More

1.
Ficetola, G. F., Miaud, C., Pompanon, F. & Taberlet, P. Species detection using environmental DNA from water samples. Biol. Lett. 4, 423–425. https://doi.org/10.1098/rsbl.2008.0118 (2008).
Article  PubMed  PubMed Central  Google Scholar 
2.
Taberlet, P., Bonin, A., Zinger, L. & Coissac, E. Environmental DNA: For Biodiversity Research and Monitoring (Oxford University Press, Oxford, 2018).
Google Scholar 

3.
Bista, I. et al. Annual time-series analysis of aqueous eDNA reveals ecologically relevant dynamics of lake ecosystem biodiversity. Nat. Commun. 8, 14087. https://doi.org/10.1038/ncomms14087 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

4.
Carraro, L., Mächler, E., Wüthrich, R. & Altermatt, F. Environmental DNA allows upscaling spatial patterns of biodiversity in freshwater ecosystems. Nat. Commun. 11, 3585. https://doi.org/10.1038/s41467-020-17337-8 (2020).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

5.
Djurhuus, A. et al. Environmental DNA reveals seasonal shifts and potential interactions in a marine community. Nat. Commun. 11, 254. https://doi.org/10.1038/s41467-019-14105-1 (2020).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

6.
Klymus, K. E. et al. Reporting the limits of detection and quantification for environmental DNA assays. Environ. DNA 2, 271–282. https://doi.org/10.1002/edn3.29 (2020).
Article  Google Scholar 

7.
Sepulveda, A. J. et al. A round-robin evaluation of the repeatability and reproducibility of environmental DNA assays for dreissenid mussels. Environ. DNA 2, 446–459. https://doi.org/10.1002/edn3.68 (2020).
Article  Google Scholar 

8.
Sepulveda, A. J., Nelson, N. M., Jerde, C. L. & Luikart, G. Are Environmental DNA methods ready for aquatic invasive species management?. Trends Ecol. Evol. 35, 668–678. https://doi.org/10.1016/j.tree.2020.03.011 (2020).
Article  PubMed  Google Scholar 

9.
Barnes, M. A. & Turner, C. R. The ecology of environmental DNA and implications for conservation genetics. Conserv. Genet. 17, 1–17. https://doi.org/10.1007/s10592-015-0775-4 (2016).
CAS  Article  Google Scholar 

10.
Lacoursière-Roussel, A., Rosabal, M. & Bernatchez, L. Estimating fish abundance and biomass from eDNA concentrations: Variability among capture methods and environmental conditions. Mol. Ecol. Resour. 16, 1401–1414. https://doi.org/10.1111/1755-0998.12522 (2016).
CAS  Article  PubMed  Google Scholar 

11.
Deiner, K., Fronhofer, E. A., Mächler, E., Walser, J. C. & Altermatt, F. Environmental DNA reveals that rivers are conveyer belts of biodiversity information. Nat. Commun. 7, 12544. https://doi.org/10.1038/ncomms12544 (2016).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

12.
Shogren, A. J. et al. Controls on eDNA movement in streams: Transport, retention, and resuspension. Sci. Rep. 7, 5065. https://doi.org/10.1038/s41598-017-05223-1 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

13.
Pont, D. et al. Environmental DNA reveals quantitative patterns of fish biodiversity in large rivers despite its downstream transportation. Sci. Rep. 8, 10361. https://doi.org/10.1038/s41598-018-28424-8 (2018).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

14.
Strickler, K. M., Fremier, A. K. & Goldberg, C. S. Quantifying effects of UV-B, temperature, and pH on eDNA degradation in aquatic microcosms. Biol. Conserv. 183, 85–92. https://doi.org/10.1016/j.biocon.2014.11.038 (2015).
Article  Google Scholar 

15.
Lance, R. F. et al. Experimental observations on the decay of environmental DNA from bighead and silver carps. Manag. Biol. Invasion 8, 343. https://doi.org/10.3391/mbi.2017.8.3.08 (2017).
Article  Google Scholar 

16.
Tsuji, S., Ushio, M., Sakurai, S., Minamoto, T. & Yamanaka, H. Water temperature-dependent degradation of environmental DNA and its relation to bacterial abundance. PLoS ONE 12, e0176608. https://doi.org/10.1371/journal.pone.0176608 (2017).
CAS  Article  PubMed  PubMed Central  Google Scholar 

17.
Cristescu, M. E. Can environmental RNA revolutionize biodiversity science?. Trends Ecol. Evol. 34, 694–697. https://doi.org/10.1016/j.tree.2019.05.003 (2019).
Article  PubMed  Google Scholar 

18.
Beng, K. C. & Corlett, R. T. Applications of environmental DNA (eDNA) in ecology and conservation: Opportunities, challenges and prospects. Biodivers. Conserv. 29, 2089–2121. https://doi.org/10.1007/s10531-020-01980-0 (2020).
Article  Google Scholar 

19.
Allan, E. A., Zhang, W. G., Lavery, A. C. & Govindarajan, A. F. Environmental DNA shedding and decay rates from diverse animal forms and thermal regimes. Environ. DNA. https://doi.org/10.1002/edn3.141 (2020).
Article  Google Scholar 

20.
Taberlet, P., Coissac, E., Pompanon, F., Brochmann, C. & Willerslev, E. Towards next-generation biodiversity assessment using DNA metabarcoding. Mol. Ecol. 21, 2045–2050. https://doi.org/10.1111/j.1365-294X.2012.05470.x (2012).
CAS  Article  PubMed  Google Scholar 

21.
Rees, H. C., Maddison, B. C., Middleditch, D. J., Patmore, J. R. M. & Gough, K. C. The detection of aquatic animal species using environmental DNA—A review of eDNA as a survey tool in ecology. J. Appl. Ecol. 51, 1450–1459. https://doi.org/10.1111/1365-2664.12306 (2014).
CAS  Article  Google Scholar 

22.
Minamoto, T. et al. Nuclear internal transcribed spacer-1 as a sensitive genetic marker for environmental DNA studies in common carp Cyprinus carpio. Mol. Ecol. Resour. 17, 324–333. https://doi.org/10.1111/1755-0998.12586 (2017).
CAS  Article  PubMed  Google Scholar 

23.
Stewart, K. A. Understanding the effects of biotic and abiotic factors on sources of aquatic environmental DNA. Biodivers. Conserv. 28, 983–1001. https://doi.org/10.1007/s10531-019-01709-8 (2019).
Article  Google Scholar 

24.
Foran, D. R. Relative degradation of nuclear and mitochondrial DNA: An experimental approach. J. Forensic Sci. 51, 766–770. https://doi.org/10.1111/j.1556-4029.2006.00176.x (2006).
CAS  Article  PubMed  Google Scholar 

25.
Dysthe, J. C., Franklin, T. W., McKelvey, K. S., Young, M. K. & Schwartz, M. K. An improved environmental DNA assay for bull trout (Salvelinus confluentus) based on the ribosomal internal transcribed spacer I. PLoS ONE 13, e0206851. https://doi.org/10.1371/journal.pone.0206851 (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

26.
Li, Y. & Breaker, R. R. Kinetics of RNA degradation by specific base catalysis of transesterification involving the 2’-hydroxyl group. J. Am. Chem. Soc. 121, 5364–5372. https://doi.org/10.1021/ja990592p (1999).
CAS  Article  Google Scholar 

27.
Fontaine, M. & Guillot, E. Study of 18S rRNA and rDNA stability by real-time RT-PCR in heat-inactivated Cryptosporidium parvum oocysts. FEMS Microbiol. Lett. 226, 237–243. https://doi.org/10.1016/S0378-1097(03)00538-X (2003).
CAS  Article  PubMed  Google Scholar 

28.
Voet, D. & Voet, J. G. Biochemistry 492–496 (Wiley, New York, 2011).
Google Scholar 

29.
Eigner, J., Boedtker, H. & Michaels, G. The thermal degradation of nucleic acids. Biochem. Biophys. Acta. 51, 165–168. https://doi.org/10.1016/0006-3002(61)91028-9 (1961).
CAS  Article  PubMed  Google Scholar 

30.
Mengoni, A. et al. Comparison of 16S rRNA and 16S rDNA T-RFLP approaches to study bacterial communities in soil microcosms treated with chromate as perturbing agent. Micro. Ecol. 50, 375–384. https://doi.org/10.1007/s00248-004-0222-4 (2005).
CAS  Article  Google Scholar 

31.
Westermann, A. J., Gorski, S. A. & Vogel, J. Dual RNA-seq of pathogen and host. Nat. Rev. Microbiol. 10, 618–630. https://doi.org/10.1038/nrmicro2852 (2012).
CAS  Article  PubMed  Google Scholar 

32.
Blanco, G. & Blanco, A. Medical Biochemistry (Academic Press, Cambridge, 2017). https://doi.org/10.1016/B978-0-12-803550-4.00006-9.
Google Scholar 

33.
Sidova, M., Tomankova, S., Abaffy, P., Kubista, M. & Sindelka, R. Effects of post-mortem and physical degradation on RNA integrity and quality. Biomol. Detect. Quantif. 5, 3–9. https://doi.org/10.1016/j.bdq.2015.08.002 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

34.
Vanderploeg, H. A., Liebig, J. R., Nalepa, T. F., Fahnenstiel, G. L. & Pothoven, S. A. Dreissena and the disappearance of the spring phytoplankton bloom in Lake Michigan. J. Great Lakes Res. 36, 50–59. https://doi.org/10.1016/j.jglr.2010.04.005 (2010).
Article  Google Scholar 

35.
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. https://doi.org/10.1021/acs.est.9b02833 (2019).
ADS  CAS  Article  PubMed  Google Scholar 

36.
Jo, T., Murakami, H., Yamamoto, S., Masuda, R. & Minamoto, T. Effect of water temperature and fish biomass on environmental DNA shedding, degradation, and size distribution. Ecol. Evol. 9, 1135–1146. https://doi.org/10.1002/ece3.4802 (2019).
Article  PubMed  PubMed Central  Google Scholar 

37.
Bylemans, J., Furlan, E. M., Gleeson, D. M., Hardy, C. M. & Duncan, R. P. Does size matter? An experimental evaluation of the relative abundance and decay rates of aquatic environmental DNA. Environ. Sci. Technol. 52, 6408–6416. https://doi.org/10.1021/acs.est.8b01071 (2018).
ADS  CAS  Article  PubMed  Google Scholar 

38.
Doi, H. et al. Use of droplet digital PCR for estimation of fish abundance and biomass in environmental DNA surveys. PLoS ONE 10, e0122763. https://doi.org/10.1371/journal.pone.0122763 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

39.
Klymus, K. E., Richter, C. A., Chapman, D. C. & Paukert, C. Quantification of eDNA shedding rates from invasive bighead carp Hypophthalmichthys nobilis and silver carp Hypophthalmichthys molitrix. Biol. Conserv. 183, 77–84. https://doi.org/10.1016/j.biocon.2014.11.020 (2015).
Article  Google Scholar 

40.
Jo, T. et al. Rapid degradation of longer DNA fragments enables the improved estimation of distribution and biomass using environmental DNA. Mol. Ecol. Resour. 17, e25–e33. https://doi.org/10.1111/1755-0998.12685 (2017).
CAS  Article  PubMed  Google Scholar 

41.
Raymaekers, M., Smets, R., Maes, B. & Cartuyvels, R. Checklist for optimization and validation of real-time PCR assays. J. Clin. Lab. Anal. 23, 145–151. https://doi.org/10.1002/jcla.20307 (2009).
CAS  Article  PubMed  PubMed Central  Google Scholar 

42.
Satoh, M. & Kuroiwa, T. Organization of multiple nucleoids and DNA molecules in mitochondria of a human cell. Exp. Cell Res. 196, 137–140. https://doi.org/10.1016/0014-4827(91)90467-9 (1991).
CAS  Article  PubMed  Google Scholar 

43.
Moushomi, R., Wilgar, G., Carvalho, G., Creer, S. & Seymour, M. Environmental DNA size sorting and degradation experiment indicates the state of Daphnia magna mitochondrial and nuclear eDNA is subcellular. Sci. Rep. 9, 12500. https://doi.org/10.1038/s41598-019-48984-7 (2019).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

44.
Jo, T., Arimoto, M., Murakami, H., Masuda, R. & Minamoto, T. Estimating shedding and decay rates of environmental nuclear DNA with relation to water temperature and biomass. Environ. DNA 2, 140–151. https://doi.org/10.1002/edn3.51 (2020).
Article  Google Scholar 

45.
Eirín-López, J. M. et al. Molecular evolutionary characterization of the mussel Mytilus histone multigene family: First record of a tandemly repeated unit of five histone genes containing an H1 subtype with “orphon” features. J. Mol. Evol. 58, 131–144. https://doi.org/10.1007/s00239-003-2531-5 (2004).
ADS  CAS  Article  PubMed  Google Scholar 

46.
Peñarrubia, L. et al. Validated methodology for quantifying infestation levels of dreissenid mussels in environmental DNA (eDNA) samples. Sci. Rep. 6, 39067. https://doi.org/10.1038/srep39067 (2016).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

47.
Gingera, T. D., Bajno, R., Docker, M. & Reist, J. Environmental DNA as a detection tool for zebra mussels Dreissena polymorpha (Pallas, 1771) at the forefront of an invasion event in Lake Winnipeg, Manitoba, Canada. Manag. Biol. Invasion 8, 287. https://doi.org/10.3391/mbi.2017.8.3.03 (2017).
Article  Google Scholar 

48.
Marshall, N. T. & Stepien, C. A. Invasion genetics from eDNA and thousands of larvae: A targeted metabarcoding assay that distinguishes species and population variation of zebra and quagga mussels. Ecol. Evol. 9, 3515–3538. https://doi.org/10.1002/ece3.4985 (2019).
Article  PubMed  PubMed Central  Google Scholar 

49.
Wood, S. A. et al. Release and degradation of environmental DNA and RNA in a marine system. Sci. Total Environ. 704, 135314. https://doi.org/10.1016/j.scitotenv.2019.135314 (2020).
ADS  CAS  Article  PubMed  Google Scholar 

50.
Osley, M. A. The regulation of histone synthesis in the cell cycle. Annu. Rev. Biochem. 60, 827–861. https://doi.org/10.1146/annurev.bi.60.070191.004143 (1991).
CAS  Article  PubMed  Google Scholar 

51.
Takeuchi, A. et al. Release of eDNA by different life history stages and during spawning activities of laboratory-reared Japanese eels for interpretation of oceanic survey data. Sci. Rep. 9, 1–9. https://doi.org/10.1038/s41598-019-42641-9 (2019).
CAS  Article  Google Scholar 

52.
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (2017). www.R-project.org.

53.
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48. https://doi.org/10.18637/jss.v067.i01. (2015).

54.
Lenth, R. et al. emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.3.3. (2020). https://CRAN.R-project.org/package=emmeans.

55.
Eichmiller, J. J., Best, S. E. & Sorensen, P. W. Effects of temperature and trophic state on degradation of environmental DNA in lake water. Environ. Sci. Technol. 50, 1859–1867. https://doi.org/10.1021/acs.est.5b05672 (2016).
ADS  CAS  Article  PubMed  Google Scholar 

56.
Collins, R. A. et al. Persistence of environmental DNA in marine systems. Commun. Biol. 1, 1–11. https://doi.org/10.1038/s42003-018-0192-6 (2018).
CAS  Article  Google Scholar 

57.
Kasai, A., Takada, S., Yamazaki, A., Masuda, R. & Yamanaka, H. The effect of temperature on environmental DNA degradation of Japanese eel. Fish. Sci. 86, 465–471. https://doi.org/10.1007/s12562-020-01409-1 (2020).
CAS  Article  Google Scholar 

58.
Williams, M. R. et al. Isothermal amplification of environmental DNA (eDNA) for direct field-based monitoring and laboratory confirmation of Dreissena sp. PLoS ONE 12, e0186462 (2019).
Article  Google Scholar  More

1.
Alasalvar, C. et al. Flavor characteristics of seven grades of black tea produced in Turkey. J. Agric. Food Chem. 60, 6323–6332 (2012).
CAS  PubMed  Article  Google Scholar 
2.
Lau, H. et al. Characterising volatiles in tea (Camellia sinensis). Part I: comparison of headspace-solid phase microextraction and solvent assisted flavour evaporation. LWT-Food Sci. Technol. 94, 178–189 (2018).
CAS  Article  Google Scholar 

3.
Yang, Y. Q. et al. Characterization of the volatile components in green tea by IRAE-HS-SPME/GC-MS combined with multivariate analysis. PLoS ONE 13, e0193393 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

4.
Magagna, F. et al. Black tea volatiles fingerprinting by comprehensive two-dimensional gas chromatography – Mass spectrometry combined with high concentration capacity sample preparation techniques: toward a fully automated sensomic assessment. Food Chem. 225, 276–287 (2017).
CAS  PubMed  Article  Google Scholar 

5.
Zhu, Y., Lv, H. P., Dai, W. D. & Li, G. Separation of aroma components in Xihu Longjing tea using simultaneous distillation extraction with comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry. Sep. Purif. Technol. 164, 146–154 (2016).
CAS  Article  Google Scholar 

6.
Baba, R. & Kumazawa, K. Characterization of the potent odorants contributing to the characteristic aroma of Chinese green tea infusions by aroma extract dilution analysis. J. Agric. Food Chem. 62, 8308–8313 (2014).
CAS  PubMed  Article  Google Scholar 

7.
Schuh, C. & Schieberle, P. Characterization of the key aroma compounds in the beverage prepared from Darjeeling black tea: quantitative differences between tea leaves and infusion. J. Agric. Food Chem. 54, 916–924 (2006).
CAS  PubMed  Article  Google Scholar 

8.
Kumazawa, K. & Masuda, H. Identification of potent odorants in different green tea varieties using flavor dilution technique. J. Agric. Food Chem. 50, 5660–5663 (2002).
CAS  PubMed  Article  Google Scholar 

9.
Nose, M., Nakatani, Y. & Yamanishi, T. Studies on flavor of green tea. 9. Identification and composition of intermediate and high boiling constituents in green tea flavor. Agric. Biol. Chem. 35, 261–271 (1971).
CAS  Google Scholar 

10.
Farre-Armengol, G., Filella, I., Llusia, J. & Penuelas, J. Bidirectional interaction between phyllospheric microbiotas and plant volatile emissions. Trends Plant Sci. 21, 854–860 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

11.
Junker, R. R. & Tholl, D. Volatile organic compound mediated interactions at the plant-microbe interface. J. Chem. Ecol. 39, 810–825 (2013).
CAS  PubMed  Article  PubMed Central  Google Scholar 

12.
Huang, M. et al. The major volatile organic compound emitted from Arabidopsis thaliana flowers, the sesquiterpene (E)-beta-caryophyllene, is a defense against a bacterial pathogen. New Phytol. 193, 997–1008 (2012).
CAS  PubMed  Article  Google Scholar 

13.
Rossi, P. G. et al. (E)-methylisoeugenol and elemicin: antibacterial components of Daucus carota L. essential oil against Campylobacter jejuni. J. Agric. Food Chem. 55, 7332–7336 (2007).
CAS  PubMed  Article  Google Scholar 

14.
Gao, Y., Jin, Y. J., Li, H. D. & Chen, H. J. Volatile organic compounds and their roles in bacteriostasis in five conifer species. J. Integr. Plant Biol. 47, 499–507 (2005).
CAS  Article  Google Scholar 

15.
Karamanoli, K., Menkissoglu-Spiroudi, U., Bosabalidis, A. M., Vokou, D. & Constantinidou, H. I. A. Bacterial colonization of the phyllosphere of nineteen plant species and antimicrobial activity of their leaf secondary metabolites against leaf associated bacteria. Chemoecology 15, 59–67 (2005).
Article  Google Scholar 

16.
Utama, I. M., Wills, R. B., Ben-Yehoshua, S. & Kuek, C. In vitro efficacy of plant volatiles for inhibiting the growth of fruit and vegetable decay microorganisms. J. Agric. Food Chem. 50, 6371–6377 (2002).
CAS  PubMed  Article  Google Scholar 

17.
Unsicker, S. B., Kunert, G. & Gershenzon, J. Protective perfumes: the role of vegetative volatiles in plant defense against herbivores. Curr. Opin. Plant Biol. 12, 479–485 (2009).
CAS  PubMed  Article  Google Scholar 

18.
Abanda-Nkpwatt, D., Musch, M., Tschiersch, J., Boettner, M. & Schwab, W. Molecular interaction between Methylobacterium extorquens and seedlings: growth promotion, methanol consumption, and localization of the methanol emission site. J. Exp. Bot. 57, 4025–4032 (2006).
CAS  PubMed  Article  Google Scholar 

19.
Sy, A., Timmers, A. C. J., Knief, C. & Vorholt, J. A. Methylotrophic metabolism is advantageous for Methylobacterium extorquens during colonization of Medicago truncatula under competitive conditions. Appl. Environ. Microbiol. 71, 7245–7252 (2005).
CAS  PubMed  PubMed Central  Article  Google Scholar 

20.
Marmulla, R. & Harder, J. Microbial monoterpene transformations-a review. Front. Microbiol. 5, 1–14 (2014).
Article  Google Scholar 

21.
Scala, A., Allmann, S., Mirabella, R., Haring, M. A. & Schuurink, R. C. Green leaf volatiles: a plant’s multifunctional weapon against herbivores and pathogens. Int. J. Mol. Sci. 14, 17781–17811 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

22.
Han, B. Y. & Chen, Z. M. Composition of the volatiles from intact and tea aphid-damaged tea shoots and their allurement to several natural enemies of the tea aphid. J. Appl. Entomol. 126, 497–500 (2002).
CAS  Article  Google Scholar 

23.
Han, B. Y., Zhang, Q. H. & Byers, J. A. Attraction of the tea aphid, Toxoptera aurantii, to combinations of volatiles and colors related to tea plants. Entomol. Exp. Appl. 144, 258–269 (2012).
Article  Google Scholar 

24.
Kubo, I., Muroi, H. & Himejima, M. Antimicrobial activity of green tea flavor components and their combination effects. J. Agric. Food Chem. 40, 245–248 (1992).
CAS  Article  Google Scholar 

25.
Cuenca Mdel, S. et al. Understanding butanol tolerance and assimilation in Pseudomonas putida BIRD-1: an integrated omics approach. Microb. Biotechnol. 9, 100–115 (2016).
PubMed  Article  CAS  PubMed Central  Google Scholar 

26.
Neumann, G. et al. Cells of Pseudomonas putida and Enterobacter sp. adapt to toxic organic compounds by increasing their size. Extremophiles 9, 163–168 (2005).
CAS  PubMed  Article  PubMed Central  Google Scholar 

27.
Heipieper, H. J., de Waard, P., van der Meer, P. & Killian, J. A. Regiospecific effect of 1-octanol on cis-trans isomerization of unsaturated fatty acids in the solvent-tolerant strain Pseudomonas putida S12. Appl. Microbiol. Biotechnol. 57, 541–547 (2001).
CAS  PubMed  Article  PubMed Central  Google Scholar 

28.
Inoue, A. & Horikoshi, K. A Pseudomonas thrives in high-concentrations of toluene. Nature 338, 264–266 (1989).
ADS  CAS  Article  Google Scholar 

29.
Fletcher, M. The effects of methanol, ethanol, propanol and butanol on bacterial attachment to surfaces. J. Gen. Microbiol. 129, 633–641 (1983).
CAS  Google Scholar 

30.
Junker, R. R. et al. Composition of epiphytic bacterial communities differs on petals and leaves. Plant Biol. 13, 918–924 (2011).
CAS  PubMed  Article  Google Scholar 

31.
Patten, C. L. & Glick, B. R. Bacterial biosynthesis of indole-3-acetic acid. Can. J. Microbiol. 42, 207–220 (1996).
CAS  PubMed  Article  Google Scholar 

32.
Duca, D., Lorv, J., Patten, C. L., Rose, D. & Glick, B. R. Indole-3-acetic acid in plant-microbe interactions. Antonie Van Leeuwenhoek 106, 85–125 (2014).
CAS  PubMed  Article  Google Scholar 

33.
Wei, K., Ruan, L., Wang, L. & Cheng, H. Auxin-induced adventitious root formation in nodal cuttings of Camellia sinensis. Int. J. Mol. Sci. 20, 4817 (2019).
CAS  PubMed Central  Article  PubMed  Google Scholar 

34.
Spaepen, S., Vanderleyden, J. & Remans, R. Indole-3-acetic acid in microbial and microorganism-plant signaling. FEMS Microbiol. Rev. 31, 425–448 (2007).
CAS  PubMed  Article  Google Scholar 

35.
Tohya, M. et al. Pseudomonas juntendi sp. nov., isolated from patients in Japan and Myanmar. Int. J. Syst. Evol. Microbiol. 69, 3377–3384 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

36.
Keshavarz-Tohid, V. et al. Genomic, phylogenetic and catabolic re-assessment of the Pseudomonas putida clade supports the delineation of Pseudomonas alloputida sp. nov., Pseudomonas inefficax sp. nov., Pseudomonas persica sp nov, and Pseudomonas shirazica sp. nov. Syst. Appl. Microbiol. 42, 468–480 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

37.
Dabboussi, F. et al. Pseudomonas mosselii sp. nov., a novel species isolated from clinical specimens. Syst. Evol. Microbiol. 52, 363–376 (2002).
CAS  Article  Google Scholar 

38.
Kieboom, J., Dennis, J. J., Zylstra, G. J. & de Bont, J. A. Active efflux of organic solvents by Pseudomonas putida S12 is induced by solvents. J. Bacteriol. 180, 6769–6772 (1998).
CAS  PubMed  PubMed Central  Article  Google Scholar 

39.
Blank, L. M., Ionidis, G., Ebert, B. E., Buhler, B. & Schmid, A. Metabolic response of Pseudomonas putida during redox biocatalysis in the presence of a second octanol phase. FEBS J. 275, 5173–5190 (2008).
CAS  PubMed  Article  Google Scholar 

40.
Halan, B., Vassilev, I., Lang, K., Schmid, A. & Buehler, K. Growth of Pseudomonas taiwanensis VLB120ΔC biofilms in the presence of n-butanol. Microb. Biotechnol. 10, 745–755 (2017).
CAS  PubMed  Article  Google Scholar 

41.
Hameed, A. et al. Draft genome sequence reveals co-occurrence of multiple antimicrobial resistance and plant probiotic traits in rice root endophytic strain Burkholderia sp. LS-044 affiliated to Burkholderia cepacia complex. J. Glob. Antimicrob. Resist. 20, 28–30 (2020).
PubMed  Article  Google Scholar 

42.
Senthilkumar, S. R., Sivakumar, T., Arulmozhi, K. T. & Mythili, N. FT-IR analysis and correlation studies on the antioxidant activity, total phenolics and total flavonoids of Indian commercial teas (Camellia sinensis L.)—a novel approach. Int. Res. J. Biol. Sci. 6, 1–7 (2017).
Google Scholar 

43.
Baker, C. N. & Tenover, F. C. Evaluation of Alamar colorimetric broth microdilution susceptibility testing method for staphylococci and enterococci. J. Clin. Microbiol. 34, 2654–2659 (1996).
CAS  PubMed  PubMed Central  Article  Google Scholar 

44.
Reguera, G. Microbial nanowires and electroactive biofilms. FEMS Microbiol. Ecol. 94, 1–13 (2018).
Article  CAS  Google Scholar 

45.
Reguera, G. et al. Extracellular electron transfer via microbial nanowires. Nature 435, 1098–1101 (2005).
ADS  CAS  PubMed  Article  Google Scholar 

46.
Prusty, R., Grisafi, P. & Fink, G. R. The plant hormone indoleacetic acid induces invasive growth in Saccharomyces cerevisiae. Proc. Natl. Acad. Sci. USA 101, 4153–4157 (2004).
ADS  CAS  PubMed  Article  Google Scholar 

47.
Bianco, C. et al. Indole-3-acetic acid improves Escherichia coli’s defences to stress. Arch. Microbiol. 185, 373–382 (2006).
CAS  PubMed  Article  Google Scholar 

48.
Council of Europe. European Pharmacopoeia 3rd edn. (Council of Europe, Strasbourg, 1997).
Google Scholar 

49.
Shahina, M. et al. Sphingomicrobium astaxanthinifaciens sp. nov., an astaxanthin-producing glycolipid-rich bacterium isolated from surface seawater and emended description of the genus Sphingomicrobium. Int. J. Syst. Evol. Microbiol. 63, 3415–3422 (2013).
CAS  PubMed  Article  Google Scholar 

50.
Yoon, S. H. et al. Introducing EzBioCloud: a taxonomically united database of 16S rRNA gene sequences and whole-genome assemblies. Int. J. Syst. Evol. Microbiol. 67, 1613–1617 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

51.
Hardy, R., Burns, R. C. & Holsten, R. D. Application of the acetylene-ethylene assay for measurement of nitrogen fixation. Soil Biol. Biochem. 5, 47–81 (1973).
CAS  Article  Google Scholar 

52.
Koch, B. & Evans, H. J. Reduction of acetylene to ethylene by soybean root nodules. Plant Physiol. 41, 1748–1750 (1966).
CAS  PubMed  PubMed Central  Article  Google Scholar 

53.
Gordon, S. A. & Weber, R. P. Colorimetric estimation of indoleacetic acid. Plant Physiol. 26, 192–195 (1951).
CAS  PubMed  PubMed Central  Article  Google Scholar 

54.
Schwyn, B. & Neilands, J. Universal chemical assay for the detection and determination of siderophores. Anal. Biochem. 160, 47–56 (1987).
CAS  PubMed  Article  PubMed Central  Google Scholar 

55.
Smibert, R. M. & Krieg, N. R. Phenotypic Characterization. In Methods for General and Molecular Bacteriology (eds Gerhardt, P. et al.) 607–654 (American Society for Microbiology, Washington, D.C, 1994).
Google Scholar 

56.
Rashid, M. H. & Kornberg, A. Inorganic polyphosphate is needed for swimming, swarming, and twitching motilities of Pseudomonas aeruginosa. Proc. Natl. Acad. Sci. USA 97, 4885–4890 (2000).
ADS  CAS  PubMed  Article  Google Scholar 

57.
Ha, D. G., Kuchma, S. L. & O’Toole, G. A. Plate-Based Assay for Swimming Motility in Pseudomonas aeruginosa. Methods Mol. Biol. 1149, 59–65 (2014).
PubMed  Article  Google Scholar  More

Reported SARS-CoV-2 case counts, mortality and testing in SSA as of December 2020
Variables and data sources for reporting data
The numbers of reported cases, deaths and tests for the 48 SSA countries studied (Supplementary Table 1) were sourced from the Africa CDC dashboard on 20 December 2020 (and previously on 23 September and 30 June 2020). The Africa CDC obtains data from the official Africa CDC Regional Collaborating Centre and member state reports. Differences in the timing of reporting by member states results in some variation in the recency of data within the centralized Africa CDC repository, but data should broadly reflect the relative scale of testing and reporting efforts across countries. For Mauritius (https://covid19.mu/) and Rwanda (https://covid19.who.int/region/afro/country/rw), reporting to the Africa CDC was confirmed by comparison to country-specific dashboards.
The countries or member states within SSA in this study follow the United Nations and Africa CDC-listed regions of Southern, Western, Central and Eastern Africa (excluding Sudan). From the Northern Africa region, Mauritania is included in SSA.
For comparison to non-SSA countries, the number of reported cases in other geographical regions were obtained from the Johns Hopkins University Coronavirus Resource Center on 23 September 2020 (https://coronavirus.jhu.edu/map.html).
Case fatality ratios (CFRs) were calculated by dividing the number of reported deaths by the number of reported cases and expressed as a percentage. Positivity was calculated by dividing the number of reported cases by the number of reported tests. Testing and case rates were calculated per 100,000 population using population size estimates for 2020 from the United Nations Population Division (https://population.un.org/wpp/Download/Standard/Population/). Since reported confirmed cases are likely to be an underestimate of the true number of infections, CFRs may be a poor proxy for the IFR, defined as the proportion of infections that result in mortality4.
Variation in testing and mortality rates
Testing rates among SSA countries varied by multiple orders of magnitude as of 30 June and remain highly variable as of 23 September and 20 December 2020. The number of tests completed per 100,000 population ranged from 19.84 in Burundi to 13,508.13 in Mauritius in June 2020; from 65.98 in the Democratic Republic of the Congo to 18,321.83 in Mauritius in September 2020; and from 100.9 in the Democratic Republic of the Congo to 23695.0 in Mauritius in December 2020 (Extended Data Fig. 1a). Tanzania (6.50 tests per 100,000 population) has not reported new tests, cases or deaths to the Africa CDC since April 2020. The number of reported infections (that is, positive tests) was strongly correlated with the number of tests completed in June 2020 (Pearson’s correlation coefficient, r = 0.9667, P 50% coincident with population >50,000) within administrative 2 units61. For some countries, estimates at administrative 2 units were unavailable (Comoros, Cape Verde, Lesotho, Mauritius, Mayotte and Seychelles); estimates at the administrative-1 unit level were used for these cases (these were all island nations, with the exception of Lesotho).
Metapopulation model methods
Once SARS-CoV-2 has been introduced into a country, the degree of spread of the infection within the country is governed by subnational mobility: the pathogen is more likely to be introduced into a location where individuals arrive more frequently than one where incoming travelers are less frequent. Large-scale consistent measures of mobility are rare. However, recently, estimates of accessibility have been produced at a global scale26. Although this is unlikely to perfectly reflect mobility within countries, especially since interventions and travel restrictions are put in place, it provides a starting point for evaluating the role of human mobility in shaping the outbreak pace across SSA. We used the inverse of a measure of the cost of travel between the centroids of administrative level 2 spatial units to describe mobility between locations (estimated by applying the costDistance function in the gdistance package v1.3-6 in R to the friction surfaces supplied in Weiss et al.26). With this, we developed a metapopulation model for each country to develop an overview of the possible range of trajectories of unchecked spread of SARS-CoV-2.
We assumed that the pathogen first arrives in each country in the administrative 2 level unit with the largest population (for example, the largest city) and the population in each administrative 2 level (of size Nj) is entirely susceptible at the time of arrival. We then tracked the spread within and between each of the administrative 2 level units of each country. Within each administrative 2 level unit, dynamics are governed by a discrete time susceptible (S), infected (I) and recovered (R) model with a time step of approximately one week, which is broadly consistent with the serial interval of SARS-CoV-2. Within the spatial unit indexed j, with total size Nj, the number of infected individuals in the next time step is defined by:

$$I_{j,t + 1} = beta I_{j,t}^alpha S_{j,t}/N_j + iota _{j,t}$$

where β captures the magnitude of transmission over the course of one discrete time step; since the discrete time step chosen is set to approximate the serial interval of the virus, this will reflect the R0 of SARS-CoV-2, and is thus set to 2.5; the exponent α = 0.97 is used to capture the effects of discretization62 and Ij,t captures the introduction of new infections into site j at time t. Susceptible and recovered individuals are updated according to:

$$begin{array}{l}S_{j,t + 1} = S_{j,t} + wR_{j,t} – I_{j,t + 1} + b\ R_{j,t + 1} = (1 – w)R_{j,t} + I_{j,t}end{array}$$

where b reflects the introduction of new susceptible individuals resulting from the birth rate, set to reflect the most recent estimates for that country from the World Bank Data (https://data.worldbank.org/indicator/SP.DYN.CBRT.IN), and w reflects the rate of waning of immunity. The population is initiated with Sj,1 = NjRj,1 = 0, and Ij,1 = 0 except for the spatial unit corresponding to the largest population size Nj for each country since this is assumed to be the location of introduction; for this spatial unit, we set Ij,1 = 1.
We made the simplifying assumption that mobility linking locations i and j, denoted as ci,j, scales with the inverse of the cost of travel between sites i and j evaluated according to the friction surface provided in Weiss et al.26. The introduction of an infected individual into location j is then defined by a draw from a Bernouilli distribution following:

$$iota _{j,t} approx {mathrm{Bernouilli}}left( {1 – {mathrm{exp}}left( { – mathop {sum }limits_1^L {c_{i,j}}{I_{i,t}}/{N_i}} right)} right)$$

where L is the total number of administrative 2 units in that country and the rate of introduction is the product of connectivity between the focal location and each other location multiplied by the proportion of population in each other location that is infected.
Some countries show rapid spread between administrative units within the country (for example, a country with parameters that broadly reflect those available for Malawi; Extended Data Fig. 7), while in others (for example, reflecting Madagascar), connectivity may be so low that the outbreak may be over in the administrative unit of the largest size (where it was introduced) before introductions successfully reach other poorly connected administrative units. Where duration of immunity is sufficiently long, the result may be a hump-shaped relationship between the proportion of the population that is infected after five years and the time to the first local extinction of the pathogen (Extended Data Fig. 7, top right). In countries with lower connectivity (for example, resembling Madagascar), local outbreaks can go extinct rapidly before traveling very far; in other countries (for example, resembling Gabon), the pathogen goes extinct rapidly because it travels rapidly and rapidly depletes susceptible individuals everywhere. The U-shaped pattern diminishes as the rate of waning of immunity increases and is replaced by a monotonic negative relationship. With sufficiently rapid waning of immunity, local extinction ceases to occur in the absence of control efforts.
The impact of the pattern of travel between centroids is echoed by the pattern of travel within administrative districts: countries where the pathogen does not reach a large fraction of the administrative 2 units within the country in five years are also those where within-administrative-unit travel is low (Extended Data Fig. 7, right).
These simulations provide a window into qualitative patterns expected for subnational spread of the pandemic virus but there is no clear way of calibrating the absolute rate of travel between regions of relevance for SARS-CoV-2; this is further complicated by the remaining uncertainties around rates of waning of immunity. Thus, the time scales of these simulations should be considered in relative, rather than absolute terms. Variation in lockdown effectiveness, or other changes in mobility for a given country, may also compromise relative comparisons as might large volumes of land border crossings in some settings, which we have not accounted for in this study. Variability in testing and case reporting complicates clarifying this (Extended Data Fig. 7, bottom left and bottom right, respectively) but we have highlighted countries with less connectivity (that is, less synchronous outbreaks expected) relative to the median among SSA countries and with older populations (that is, a greater proportion in higher-risk age groups) (Extended Data Fig. 8).
The University of Oxford’s Blavatnik School of Government generated composite scores of government response, interventions for containment and economic support provided, with each scored from 0 to 100 (Coronavirus Government Response Tracker; https://www.bsg.ox.ac.uk/research/research-projects/coronavirus-government-response-tracker). These data were compared with the day on which ten cases were exceeded in a country according to the Johns Hopkins dashboard data (Johns Hopkins Coronavirus Resource Center; https://coronavirus.jhu.edu/map.html).
While faster waning of immunity will act to increase the rate of spread of the infection, resulting in a higher proportion infected after one year, control efforts will generally act to slow the rate of spread of the infection (Extended Data Fig. 9). Since different countries are likely to have differently effective control efforts (Extended Data Fig. 9), this precludes making country-specific predictions as to the relative impact of control efforts on delay.
Modeling epidemic trajectories in scenarios where transmission rate depends on climate
Climate data sourcing: variation in humidity in SSA
Specific humidity data for selected urban centers comes from the ERA5 using an average climatology (1981–2017)53; we did not consider year-to-year climate variations. Selected cities (n = 56) were chosen to represent the major urban areas in SSA. The largest city in each SSA country was included as well as any additional cities that were among the 25 largest cities or busiest airports in SSA.
Methods for climate-driven modeling of SARS-CoV-2
We used a climate-driven susceptible-infected-recovered-susceptible model to estimate epidemic trajectories (that is, the time of peak incidence) in different cities in 2020, assuming no control measures were in place or a 10 or 20% reduction in R0 beginning 2 weeks after the total reported cases for a country exceeded 10 cases25,63. The model is given by:

$$frac{{mathrm{d}}S}{{mathrm{d}}t} = frac{{N – S – L}}{L} – frac{{beta (t)IS}}{N}$$

$$frac{{mathrm{d}}I}{{mathrm{d}}t} = frac{{beta (t)IS}}{N} – frac{I}{D}$$

where S is the susceptible population, I is the infected population and N is the total population. D is the mean infectious period, set at 5 d following ref. 25.
To investigate the effects on epidemic trajectories of a climate dependency of SARS-CoV-2 on cities with the climate patterns of the selected cities in SSA, we used parameters from the most climate-dependent scenario in ref. 25, based on the endemic betacoronavirus HKU1 in the United States. In this scenario L, the duration of immunity, was 66.25 weeks (that is, >1 year and such that waning immunity did not affect the timing of the epidemic peak). We initially selected a range where R0 declined from R0max = 2.5 to R0min = 1.5 (that is, transmission declined 40% at high humidity) since this exceeds the range observed for influenza and other coronaviruses for which data are available (from the United States). R0max = 2.5 was chosen because 2.5 is often cited as the approximate R0 for SARS-CoV-2. Thus, we initially assumed that the climate dependence of SARS-CoV-2 in SSA would not greatly exceed that of other known coronaviruses from the US context. Then, we explored the effects of different degrees of climate dependency (that is, wider ranges between R0max = 2.5 to R0min = 1.5 and scenarios where R0min approached 1) (Extended Data Fig. 10).
Transmission is governed by β(t), which is related to the basic reproduction number R0 by R0(t) = β(t)D. The basic reproduction number varies based on climate and is related to specific humidity according to the equation:

$$R_0 = {mathrm{exp}}{[a times q(t) + {mathrm{log}}(R_{0{mathrm{max}}} – R_{0{mathrm{min}}})]} + R_{0{mathrm{min}}}$$

where q(t) is specific humidity53 and a is set at −227.5 based on estimated HKU1 parameters25. We assumed the time of introduction for cities to be the date at which the total reported cases for a country exceeded 10 cases.
Sensitivity analysis
Selecting an R0min value of 1, such that epidemic growth stops at high humidities, is likely implausible since simulations indicated no outbreaks would occur in cities such as Antananarivo (countered by the observation that SARS-CoV-2 outbreaks did in fact occur) (Extended Data Fig. 10b; see Supplementary Table 1 for the reported case counts at the country level). Expanding the range between R0min and R0max by increasing R0max resulted in epidemic peaks being reached earlier after outbreak onset but did not increase the difference in timing between cities with different climates (Extended Data Fig. 10c; for example, the difference in timing between peaks in Windhoek and Lomé is similar in 10a and 10c). Finally, we explored scenarios where the R0min was between 1.0 and 1.5. When R0min  > 1.1, epidemic peaks were seen in each SSA city with the difference in timing of the peak growing larger when smaller values of R0min were selected (Extended Data Fig. 10d). However, the difference in timing, even when small values of R0min were selected, was a maximum of 25 weeks and rapidly reduced to only a few weeks when R0min approached 1.5.
Reporting Summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article. More

We selected 10 permanent plots with long-term observations from CERN to include typical forest types of various ages in the East China monsoon forest region, including tropical rainforests, subtropical evergreen coniferous and broad-leaved mixed forests, warm temperate deciduous broad-leaved forests and temperate coniferous and broad-leaved forests, with evident precipitation and temperature gradients from south to north (Fig. 1). The spatial representativeness of the selected 10 sites across the Chinese forest region was evaluated by calculating the Euclidean distance based on various environmental factors. The 10 sites performed well and represented more than 80% of the Chinese forest region (Fig. S1). Of these forests, the Xishuangbanna tropical seasonal rainforest (BNF), Dinghu Mountain subtropical evergreen coniferous and broad-leaved mixed forest (DHF), Ailao Mountain subtropical evergreen broad-leaved forest (ALF), and Changbai Mountain temperate deciduous coniferous and broad-leaved mixed forest (CBF) are mature natural forests; the Shennongjia subtropical evergreen deciduous broad-leaved mixed forest (SNF) and Huitong subtropical evergreen broad-leaved forest (HTF) are natural secondary forests; and the other sites, i.e., the Beijing warm temperate deciduous broad-leaved mixed forest (BJF), Maoxian warm temperate deciduous coniferous mixed forest (MXF), Qianyanzhou subtropical evergreen artificial coniferous mixed forest (QYF), and Heshan subtropical evergreen broad-leaved forest (HSF), are plantations or middle- and young-age forests. All 10 sites are well protected and subject to minimal human activities, thus reflecting the C cycle dynamics under global environmental change, e.g., climate change, increasing CO2 and nitrogen deposition. The detailed characteristics of each plot can be found in their profiles in the CFCCD database.
There are three main steps to create the observation-based basic dataset and assimilated dataset of typical Chinese forests C cycle dynamics:
1.
Observation-based basic data acquisition. An ensemble of daily atmospheric and water data at ten CERN sites were used as forcing datasets for MDF and future scientific analysis; biological and soil data were also collected from CERN and processed by quality control and statistical calculation as benchmark to constrain the model.

2.
Implementation of a multiple data-model fusion framework. The Markov Chain Monte Carlo (MCMC) that integrated the Data Assimilation Linked Ecosystem Carbon (DALEC) model with multiple and dynamic observational data was used to retrieve C-cycle process parameters in a realistic disequilibrium state.

3.
Key process parameters and C function data assimilation. The key parameters of the process-based C cycle model (DALEC) were determined via the model-data fusion method; then the ecosystem C sequestration datasets were simulated by forward running the DALEC model with optimized parameters and then validated based on observational data and other previous studies.

Each step is explained in more detail below.
Observation-based basic data acquisition
Atmospheric and water data
In situ observations of daily air temperature (Ta), photosynthetically active radiation (PAR), relative humidity (RH), precipitation (Precip), and soil moisture (Sw) at the 10 sites from 2005 to 2015 were obtained from the CERN scientific and technological resources service system (http://www.cnern.org.cn/). These atmospheric and water data were mostly observed by an automatic meteorological station at each site. Among them, the PAR was estimated by a LI-COR LI-190SZ Quantum Sensor; Ta and RH were measured by a QMT110 sensor; Sw was estimated by a soil moisture neutron probe or the Time-Domain Reflectometry (TDR) soil moisture probe; the associated saturated soil water capacity (Sc) was measured by the cutting ring method to sample soil in each field campaign and the oven-drying method to measure saturated moisture content after the soil was soaked in water for 48 h; and Precip was artificially observed by CERN staff using an SM1-1 rain gauge. These monitoring data were collected in keeping with CERN’s protocols of observation and quality control29,30.
There were occasional missing data in time-continuous meteorological observations; therefore, the data were processed by standardized gap filling31. Specifically, for Ta, PAR, and RH, which were applied as model driver, we used a linear interpolation method to interpolate continuous missing data with less than three observations; otherwise, we established a regression model using the CERN observations and other observations from adjacent stations of the China Meteorology Administration (756 meteorological stations; http://data.cma.cn/en) to interpolate continuous missing data with more than three observations.
Biological data
Biomass.
At each site, the diameters at breast height (DBHs) and tree heights were measured for each tree in a regular inventory performed at least once every five years. The allometric equations of the DBH and/or tree heights with the biomasses of different plant tissues (i.e., leaves, branches, stems and roots) were developed at each site for various species based on the felled standard trees in the destructive plot. Then, we calculated the biomasses for the ten ecosystems using these allometric equations (FA02 table downloaded from http://www.cnern.org.cn/), which all passed the significance test (0.01 level) and have the R2 most above 0.9 when its estimation compare to observations from standard trees. For some unfelled species under protection, the allometric equations were obtained from Luo et al.32, which were developed based on national inventories and meta analyses from the published literature.
Litterfall.
The aboveground litterfall biomass was measured monthly by ten replicates with 1 m × 1 m baskets during the growing season or once during the nongrowing season. All collected litter was dried at 70 °C for 24 h in the laboratory and then weighed. To avoid the effects of wind on the measurement of litterfall biomass within an individual month, annual litterfall biomass data were finally adopted for each site.
LAI.
The leaf area index (LAI) at each site was measured optically with an LAI-2000 plant canopy analyzer (LI-COR, Lincoln, NE, USA) at least quarterly every year.
Soil data
Soil organic matter (SOM) was measured by the potassium dichromate oxidation titrimetric method. Soil bulk density (SBD) was measured by the cutting ring method in each field campaign at 10 forest sites. Soil particle size (i.e., soil mechanical composition) was measure by the laser particle analyzer. At least three samples were collected from each of the five soil layers (0–10, 10–20, 20–40, 40–60, and 60–100 cm) once every five years.
SOC.
The soil organic C (SOC) content was calculated from SOM, SBD, and volume percentage of gravel with particle size >2 mm at 10 forest sites as follows33:

$$SOC={sum }_{i=1}^{n}0.58times {H}_{i}times {B}_{i}times {O}_{i}times (1-{rm{theta }})times 100$$
(1)

where SOC is the soil organic C density (g C/m2) of all n layers, Hi is the soil thickness (cm), Bi is the soil bulk density (g/cm3), Oi is the SOM content of the ith layer (%), and θ is the volume percentage (%) of gravel with particle size >2 mm. In the absence of soil bulk density or soil organic matter content measurements in some layers, the missing soil measurements corresponding to specific soil depths of theses forest ecosystems were supplemented according to the empirical formulas of the relationships between SOM/soil bulk density and soil depth in different layers, which were developed based on the long-term and across-site CERN soil observations34.
All these raw atmospheric, biological, and soil data mentioned above can be directly download from CERN scientific and technological resources service system (http://www.cnern.org.cn/data/initDRsearch) or obtained after online application via protocol sharing.
Auxiliary flux data
Net ecosystem exchange (NEE).
These data were obtained from ChinaFLUX (http://www.chinaflux.org/), covering CBF, QYF, and BNF. The data were aggregated to the daily time step from half-hourly CO2 flux data measured by the eddy covariance technique and processed with quality control and gap filling procedures35.
Implementation of MDF method
The assimilated data were retrieved from a multiple data-model fusion method (Fig. 2). Specifically, the long-term and dynamic observations of biomass, litterfall, LAI and SOC were used as the model constraint data; Ta, PAR, and RH were used as the meteorological driving data; and the metropolis simulated annealing algorithm, a variation in the MCMC technique36,37, was applied to retrieve the C cycle parameters (e.g., C allocation and C turnover times) against the observations and prior knowledge. Then, we forward-simulated the model to produce the dynamic and time-continuous changes in ecosystem C sequestration function.
Fig. 2

Flowchart of the generation of assimilated datasets in a multiple- and long-term data assimilation framework.

Full size image

Since the dynamic C cycle observations provided an effective solution to constrain the C cycle states without the steady state assumption (SSA), the novelty of our MDF framework involves estimating these C cycle dynamics in better agreement with the actual dynamic disequilibrium state38. Therefore, the uncertainty in allocation and turnover parameters and in C pool states have largely been reduced based on the time-series observations under the non-SSA (NSSA)21,39,40, thereby significantly enhancing the model’s ability to predict the C sequestration function19,41,42.
Carbon cycle process model description
DALEC is a box model of C pools connected via fluxes running at a daily time step and has been applied extensively to the MDF research21,43. Its main structure (i.e., C cycle, C allocation, and turnover process) is generally consistent with state-of-the-art process-based models (Fig. S2; Table S1), with five pools (i.e., foliage (Cf), fine root (Cr), woody (Cw, including branches, stems, and coarse roots), litter (Clit) and SOM (Csom)) for evergreen forests and an additional labile pool (Clab) of stored C that supports leaf flushing for deciduous forests. The C cycle was initiated with the canopy C influx: gross primary productivity (GPP), which was predicted by the Aggregated Canopy Model (ACM)44 (Appendix S1). After GPP is consumed by autotrophic respiration (Ra), the remaining photosynthate (NPP) is allocated to plant tissue pools (Cf, Cr, or Cw). The C exiting from all C reservoirs was based on a first order differential equation with various turnover rates, with temperature and moisture dependency on the turnover from the litter and soil pools. In contrast to the original DALEC model only with temperature scalar fTa, here we added a new function fSw to express soil moisture pressure on litter and soil decomposition processes (Appendix S1). In general, the C pools and fluxes in DALEC were iteratively calculated at a daily time step and determined as a function of the key turnover and allocation parameters. A detailed model description can be found in Williams et al.45 and Fox et al.46.
Multiple data-model fusion at the nonsteady state
In a realistic disequilibrium state, C pools are time-variant, i.e., the C efflux is not equal to the C influx (left(frac{dC}{dt}ne 0right)); thus, the MDF was run via the dynamic and long-term CERN observations to constrain the DALEC model at the non-steady state (Eq. 2). Here, to avoid the uncertainty arising from the spin-up process under SSA, we determined the initial state of the C pools by the initial observations of C stocks or by optimization (i.e., Clab, which cannot be directly observed). Then, the turnover and allocation parameters were retrieved under the disequilibrium state with dynamic environmental forcing. This method avoids the considerable uncertainties when invoking the SSA to estimate the initial state of C pools and the C cycle parameters(e.g., allocation coefficients and turnover rates)39,40,47, which could lead to obvious biases in C sequestration19.

$$left{begin{array}{l}Delta {C}_{i}ne 0\ {C}_{i}left({rm{t}}+1right)={C}_{i}left({rm{t}}right)+{I}_{i}left({rm{t}}right)-{k}_{i}{C}_{i}left({rm{t}}right),{rm{i}}=1,2ldots n\ {C}_{i}left({rm{t}}=0right)={C}_{i}0end{array}right.$$
(2)

where Ci, Ii, and ki represent the size, input and turnover rate of the ith C reservoir, respectively; Ci0 represents the initial state of the ith C reservoir; t represents the specific model-running time step (daily step); and ΔCi represents the ith C pool change between t day and t +1 day when applicable into actual calculation. According to the Bayesian theory, the posterior distributions of the parameters are calculated by maximizing the likelihood function (Eq. 3).

$$L={prod }_{j=1}^{m}{prod }_{i=1}^{{n}_{j}}frac{1}{sqrt{2pi }{sigma }_{j}}{e}^{-{left({x}_{j,i}-{mu }_{j,i}left({boldsymbol{P}}right)right)}^{2}/2{sigma }_{j}^{2}}$$
(3)

where L is the integrated likelihood function; m is the number of multiple data types; n is the number of data points categorized by the jth data type; xj,i is the measured value composed of dynamic C cycle observations; μj,i(P) represents the modeled fluxes and stocks based on parameters under the NSSA (P); and σj is the standard deviation of each data point classified by the jth data type. Moreover, we imposed a sequence of ecological and dynamic constraints on the model parameter inter-relationships and pool dynamics (Appendix S2), which can significantly reduce uncertainty in model parameters and simulations48. The more detailed disequilibrium method can be found in our latest study19.
Key C-cycle process parameters and C sequestration data assimilation
Key process parameter estimation
Here, we mainly focus on how the C input (i.e., the net primary productivity) partitioned into various plant pools (i.e., foliar, wood, and fine roots), i.e., allocation coefficients, which could be directly determined from the optimized parameters (Fig. S3) of the DALEC model after the step 2: MDF method. Another key process parameter, C turnover time, needs further simple statistical calculation based on the model simulations with optimized parameters. Turnover time is commonly estimated by the equation “τ = stock/flux”20,49. Since the C influx is not equal to the C efflux in the realistic dynamic disequilibrium state, the turnover time should be defined as the ratio between the mass of a C pool and its outgoing flux50. Note that with few natural and anthropogenic disturbances in these well-protected CERN sites12,18, the C efflux is approximately equivalent to the Rh from soil and litterfall (mortality) and Ra (growth) from vegetation. Hence, the turnover time for vegetation, soil, and whole ecosystem can be derived as follows:

$${tau }_{veg}=frac{{C}_{live}}{{I}_{live}-Delta {C}_{live}}=frac{{C}_{live}}{litterfall+{R}_{a}}$$
(4)

$${tau }_{soil}=frac{{C}_{dead}}{{I}_{dead}-Delta {C}_{dead}}=frac{{C}_{dead}}{{R}_{h}}$$
(5)

$${tau }_{eco}=frac{{C}_{eco}}{{I}_{eco}-Delta {C}_{eco}}=frac{{C}_{live+}{C}_{dead}}{{R}_{a}+{R}_{h}}$$
(6)

where τveɡ, τsoil, and τeco refer to the biomass, soil and whole-ecosystem turnover times, respectively; Clive, Cdead and Ceco refer to the live biomass C pool size (Cf, Cr, and Cw,), dead organic C pool size (Csoil and Clitter), and the whole-ecosystem C pool size, respectively; Ilive, Idead and Ieco refer to the C input into the live biomass C pool, dead organic C pool, and whole ecosystem C pool, respectively; ΔClive, ΔCdead and ΔCeco refer to the changes in the live biomass C pool, dead organic C pool size, and whole-ecosystem C pool size, respectively; and Ra, Rh and litterfall refer to the autotrophic and heterotrophic respiration, and turnover from all live C pools (i.e., foliage, fine root and woody pools),respectively, as calculated from the DALEC output driven with the estimated parameters during 2005–2015. Since the C reservoirs, fluxes, and turnover times are instantaneous values, we used the averages of the fluxes and reservoirs over multiple years to reflect the average turnover time during a specific period (i.e., 2005–2015).
Time-continuous C sequestration estimation
The optimized parameter values under the NSSA along with the initial observations of the corresponding C pool sizes were used in forward modeling driven by dynamic environmental variables from 2005 to 2015 to obtain the time-continuous soil and vegetation C storage51. The difference between the ecosystem C influx (GPP) and ecosystem respiration (Ra+Rh) is used to examine the ecosystem C sequestration, i.e., net ecosystem productivity (NEP). Similarly, the difference between the ecosystem C influx (GPP) and ecosystem autotrophic respiration (Ra) is used to examine the net primary ecosystem productivity (NPP). More