Ecology

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

1.
Alerstam, T., Hedenström, A. & Åkesson, S. Long-distance migration: evolution and determinants. Oikos 103, 247–260 (2003).
Article  Google Scholar 
2.
Gilroy, J. J., Gill, J. A., Butchart, S. H. M., Jones, V. R. & Franco, A. M. A. Migratory diversity predicts population declines in birds. Ecol. Lett. 19, 308–317 (2016).
Article  Google Scholar 

3.
Gill, J. A., Alves, J. A. & Gunnarsson, T. G. Mechanisms driving phenological and range change in migratory species. Philos. Trans. R. Soc. B 374, 20180047 (2019).
Article  Google Scholar 

4.
Méndez, V., Gill, J. A., Alves, J. A., Burton, N. H. K. & Davies, R. G. Consequences of population change for local abundance and site occupancy of wintering waterbirds. Divers. Distrib. 24, 24–35 (2018).
Article  Google Scholar 

5.
Finch, T., Butler, S. J., Franco, A. M. A. & Cresswell, W. Low migratory connectivity is common in long-distance migrant birds. J. Anim. Ecol. 86, 662–673 (2017).
Article  Google Scholar 

6.
Phillips, R. A., Silk, J. R. D., Croxall, J. P., Afanasyev, V. & Bennett, V. J. Summer distribution and migration of nonbreeding albatrosses: Individual consistencies and implications for conservation. Ecology 86, 2386–2396 (2005).
Article  Google Scholar 

7.
Newton, I. The Migration Ecology of Birds (Academic Press, New York, 2008).
Google Scholar 

8.
Grist, H. et al. Site fidelity and individual variation in winter location in partially migratory European shags. PLoS ONE 9, e98562 (2014).
ADS  Article  Google Scholar 

9.
Alves, J. A. et al. Costs, benefits, and fitness consequences of different migratory strategies. Ecology 94, 11–17 (2013).
ADS  Article  Google Scholar 

10.
Evans, D. R. et al. Individual condition, but not fledging phenology, carries over to affect post-fledging survival in a neotropical migratory songbird. Ibis (Lond. 1859) 162, 331–344 (2020).
Article  Google Scholar 

11.
Gill, J. A. et al. Why is timing of bird migration advancing when individuals are not?. Proc. Biol. Sci. 281, 20132161 (2014).
PubMed  PubMed Central  Google Scholar 

12.
Meyburg, B.-U. et al. Orientation of native versus translocated juvenile lesser spotted eagles (Clanga pomarina) on the first autumn migration. J. Exp. Biol. 220, 2765–2776 (2017).
Article  Google Scholar 

13.
Gill, J. A. Does competition really drive population distributions?. Wader Study 126, 166–168 (2019).
Article  Google Scholar 

14.
Berthold, P. Control of Bird Migration (Chapman & Hall, Boca Raton, 1996).
Google Scholar 

15.
Sutherland, W. J. Evidence for flexibility and constraint in migration systems. J. Avian Biol. 29, 441 (1998).
Article  Google Scholar 

16.
Harrison, X. A. et al. Cultural inheritance drives site fidelity and migratory connectivity in a long-distance migrant. Mol. Ecol. 19, 5484–5496 (2010).
Article  Google Scholar 

17.
Piersma, T., Loonstra, A. H. J., Verhoeven, M. A. & Oudman, T. Rethinking classic starling displacement experiments: evidence for innate or for learned migratory directions?. J. Avian Biol. 51, jav.02337 (2020).
Article  Google Scholar 

18.
Þórisson, B. et al. Population size of oystercatchers Haematopus ostralegus wintering in Iceland. Bird Study 65, 274–278 (2018).
Article  Google Scholar 

19.
Méndez, V. et al. Individual variation in migratory behaviour in a sub-Arctic partial migrant shorebird. Behav. Ecol. https://doi.org/10.1093/beheco/araa010 (2020).
Article  Google Scholar 

20.
van de Pol, M. et al. A global assessment of the conservation status of the nominate subspecies of Eurasian oystercatcher Haematopus ostralegus ostralegus. Int. Wader Stud. 20, 47–61 (2014).
Google Scholar 

21.
Méndez, V. et al. Effects of migratory behaviour on breeding phenology and success in a sub-arctic partially migratory shorebird. J. Anim. Ecol. (under review).

22.
Ens, B. J., Safriel, U. N. & Harris, M. P. Divorce in the long-lived and monogamous oystercatcher, Haematopus ostralegus: incompatibility or choosing the better option?. Anim. Behav. 45, 1199–1217 (1993).
Article  Google Scholar 

23.
Ens, B. J., Choudhury, S. & Black, J. M. Mate fidelity and divorce in monogamous birds. In Partnerships in Birds: The Study of Monogamy (ed. Black, J. M.) 344–401 (Oxford University Press, Oxford, 1996).
Google Scholar 

24.
Winger, B. M., Auteri, G. G., Pegan, T. M. & Weeks, B. C. A long winter for the Red Queen: rethinking the evolution of seasonal migration. Biol. Rev. 94, 737–752 (2019).
Article  Google Scholar 

25.
Bulla, M. et al. Unexpected diversity in socially synchronized rhythms of shorebirds. Nature 540, 109–113 (2016).
ADS  CAS  Article  Google Scholar 

26.
Nol, E. Sex roles in the American oystercatcher. Behaviour 95, 232–260 (1985).
Article  Google Scholar 

27.
Reynolds, J. D. & Székely, T. The evolution of parental care in shorebirds: life histories, ecology, and sexual selection. Behav. Ecol. 8, 126–134 (1997).
Article  Google Scholar 

28.
Lazarus, J. The logic of mate desertion. Anim. Behav. 39, 672–684 (1990).
Article  Google Scholar 

29.
Safriel, U. N., Ens, B. J., Kaiser, A. & Goss-Custard, J. D. Rearing to independence. In The Oystercatcher: From Individuals to Populations (ed. Goss-Custard, J. D.) 219–250 (Oxford University Press, Oxford, 1996).
Google Scholar 

30.
Gunnarsson, T. G., Gill, J. A., Sigurbjörnsson, T. & Sutherland, W. J. Pair bonds: arrival synchrony in migratory birds. Nature 431, 646 (2004).
ADS  CAS  Article  Google Scholar 

31.
Gunnarsson, T. G., Gill, J. A., Newton, J., Potts, P. M. & Sutherland, W. J. Seasonal matching of habitat quality and fitness in a migratory bird. Proc. R. Soc. B 272, 2319–2323 (2005).
Article  Google Scholar 

32.
Gunnarsson, T. G. Monitoring wader productivity during autumn passage in Iceland. Wader Study Gr. Bull. 110, 21–29 (2006).
Google Scholar 

33.
Cramp, S. & Simmons, K. E. L. Birds of the Western Palearctic (Oxford University Press, Oxford, 1983).
Google Scholar 

34.
Alves, J. A., Gunnarsson, T. G., Sutherland, W. J., Potts, P. M. & Gill, J. A. Linking warming effects on phenology, demography, and range expansion in a migratory bird population. Ecol. Evol. 9, 2365–2375 (2019).
Article  Google Scholar 

35.
Liebezeit, J. R. et al. Assessing the development of shorebird eggs using the flotation method: species-specific and generalized regression models. Condor 109, 32–47 (2007).
Article  Google Scholar 

36.
Burnham, K. & Anderson, D. Model Selection and Multi-model Inference: A Practical Information-Theoretic Approach (Springer, Berlin, 2002).
Google Scholar 

37.
R Core Team. A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2020).
Google Scholar  More

1.
Litchman, E. et al. Global biogeochemical impacts of phytoplankton: a trait-based perspective. J. Ecol. 103, 1384–1396 (2015).
CAS  Article  Google Scholar 
2.
De Senerpont Domis, L. N. et al. Plankton dynamics under different climatic conditions in space and time. Freshw. Biol. 58, 463–482 (2013).
Article  Google Scholar 

3.
Reynolds, C. Ecology of Phytoplankton. (Cambridge University Press, 2006).

4.
Phillips, G. et al. Water Framework Directive Intercalibration: Central Baltic Lake Phytoplankton Ecological Assessment Methods. 189 (Join Research Center, 2014).

5.
Ptacnik, R., Solimini, A. & Brettum, P. Performance of a new phytoplankton composition metric along a eutrophication gradient in Nordic lakes. Hydrobiologia 633, 75–82 (2009).
CAS  Article  Google Scholar 

6.
Pollard, A. I., Hampton, S. E. & Leech, D. M. The promise and potential of continental-scale limnology using the U.S. Environmental Protection Agency’s National Lakes. Assessment. Limnol. Oceanogr. Bull. 27, 36–41 (2018).
Article  Google Scholar 

7.
de Hoyos, C. et al. Water Framework Directive Intercalibration: Mediterranean Lake Phytoplankton Ecological Assessment Methods. 189 (Join Research Center, 2014).

8.
Mischke, U., Riedmüller, U., Hoehn, E., Schönfelder, I. & Nixdorf, B. Description of the German System for Phytoplankton-Based Assessment of Lakes for Implementation of the EU Water Framework Directive (WFD). 31 (Univ. Cottbus, 2008).

9.
Laplace-Treyture, C. & Feret, T. Performance of the Phytoplankton Index for Lakes (IPLAC): A multimetric phytoplankton index to assess the ecological status of water bodies in France. Ecol. Indic. 69, 686–698 (2016).
CAS  Article  Google Scholar 

10.
Xue, Y. et al. Distinct patterns and processes of abundant and rare eukaryotic plankton communities following a reservoir cyanobacterial bloom. ISME J. 12, 2263–2277 (2018).
CAS  Article  Google Scholar 

11.
Barbe, J. et al. Actualisation de la Méthode de Diagnose Rapide des Plans d’Eau: Analyse Critique des Indices de Qualité des Lacs et Propositions d’Indices de Fonctionnement de l’Écosystème Lacustre. 107 (Cemagref, 2003).

12.
Marchetto, A., Padedda, B., Mariani, M., Luglie, A. & Sechi, N. A numerical index for evaluating phytoplankton response to changes in nutrient levels in deep mediterranean reservoirs. J. Limnol. 68, 106–121 (2009).
Article  Google Scholar 

13.
Kruk, C., Mazzeo, N., Lacerot, G. & Reynolds, C. S. Classification schemes for phytoplankton: A local validation of a functional approach to the analysis of species temporal replacement. J. Plankton Res. 24, 901–912 (2002).
Article  Google Scholar 

14.
Reynolds, C. S. Phytoplankton designer – or how to predict compositional responses to trophic-state change. Hydrobiologia 424, 123–132 (2000).
Article  Google Scholar 

15.
Reynolds, C. S., Huszar, V., Kruk, C., Naselli-Flores, L. & Melo, S. Towards a functional classification of the freshwater phytoplankton. J. Plankton Res. 24, 417–428 (2002).
Article  Google Scholar 

16.
Mieleitner, J. & Reichert, P. Modelling functional groups of phytoplankton in three lakes of different trophic state. Ecol. Model. 211, 279–291 (2008).
Article  Google Scholar 

17.
Rangel, L. M., Soares, M. C. S., Paiva, R. & Silva, L. H. S. Morphology-based functional groups as effective indicators of phytoplankton dynamics in a tropical cyanobacteria-dominated transitional river–reservoir system. Ecol. Indic. 64, 217–227 (2016).
Article  Google Scholar 

18.
Salmaso, N., Naselli-Flores, L. & Padisák, J. Functional classifications and their application in phytoplankton ecology. Freshw. Biol. 60, 603–619 (2015).
Article  Google Scholar 

19.
Padisák, J., Borics, G., Grigorszky, I. & Soróczki-Pintér, É. Use of phytoplankton assemblages for monitoring ecological status of lakes within the water framework directive: The assemblage index. Hydrobiologia 553, 1–14 (2006).
Article  Google Scholar 

20.
Borics, G. et al. A new evaluation technique of potamo-plankton for the assessment of the ecological status of rivers. Large Rivers 17, 465–486 (2007).
Google Scholar 

21.
European Parliament. Directive 2000/60/CE du Parlement Européen et du Conseil du 23 Octobre 2000 Établissant un Cadre pour une Politique Communautaire dans le Domaine de l’Eau. 72 (Communauté Européenne, 2000).

22.
Padisák, J., Crossetti, L. O. & Naselli-Flores, L. Use and misuse in the application of the phytoplankton functional classification: A critical review with updates. Hydrobiologia 621, 1–19 (2009).
Article  Google Scholar 

23.
Kruk, C. et al. Classification of Reynolds phytoplankton functional groups using individual traits and machine learning techniques. Freshw. Biol. 62, 1681–1692 (2017).
CAS  Article  Google Scholar 

24.
Wentzky, V. C., Tittel, J., Jäger, C. G., Bruggeman, J. & Rinke, K. Seasonal succession of functional traits in phytoplankton communities and their interaction with trophic state. J. Ecol. 108, 1649–1663 (2020).
CAS  Article  Google Scholar 

25.
Olenina, I. et al. Biovolumes and Size-Classes of Phytoplankton in the Baltic Sea. 144 (Baltic Marine Environnment Protection Commission, 2006).

26.
Kremer, C. T., Gillette, J. P., Rudstam, L. G., Brettum, P. & Ptacnik, R. A compendium of cell and natural unit biovolumes for >1200 freshwater phytoplankton species. Ecology 95, 2984–2984 (2014).
Article  Google Scholar 

27.
Druart, J. C. & Rimet, F. Protocole d’Analyse du Phytoplancton de l’INRA: Prélèvement, Dénombrement et Biovolume. 96 (INRA, 2008).

28.
Rimet, F. & Druart, J.-C. A trait database for phytoplankton of temperate lakes. Ann. Limnol. – Int. J. Limnol. 54, 18 (2018).
Article  Google Scholar 

29.
John, D. M., Whitton, B. A. & Brook, A. J. The Freshwater Algal Flora of the British Isles: an Identification Guide to Freshwater and Terrestrial Algae. Second Edition. (Cambridge University Press, 2011).

30.
Wehr, J. D., Sheath, R. G. & Kociolek, P. Freshwater Algae of North America: Ecology and Classification. (Academic press, 2015).

31.
Laplace-Treyture, C., Hadoux, E., Plaire, M., Dubertrand, A. & Esmieu, P. PHYTOBS v3.0: Outil de Comptage du Phytoplancton en Laboratoire et de Calcul de l’IPLAC. Version 3.0. Application JAVA. https://hydrobio-dce.inrae.fr/phytobs-software/ (2017).

32.
Hillebrand, H., Dürselen, C. D., Kirschtel, D., Pollinger, U. & Zohary, T. Biovolume calculation for pelagic and benthic microalgae. J. Phycol. 35, 403–424 (1999).
Article  Google Scholar 

33.
Hutorowicz, A. Opracowanie Standardowych Objętości Komórek do Szacowania Biomasy w Wybranych Taksonów Glonów Planktonowych Wraz z Określeniem Sposobu Pomiarów i Szacowania. 42 (Instytutu Rybactwa Śródlądowego, 2005).

34.
Padisak, J. & Adrian, R. In Methoden der Biologischen Wasseruntersuchung 2. Biologische Gewässeruntersuchung (ed. Friedrich, W. und G.) Biovolumen und Biomasse (Gustav Fischer Verlag, 1999).

35.
NF EN 16695. Qualité de l’eau – Lignes Directrices pour l’Estimation du Biovolume des Microalgues. 106 (2015).

36.
Menden-Deuer, S. & Lessard, E. J. Carbon to volume relationships for dinoflagellates, diatoms, and other protist plankton. Limnol. Oceanogr. 45, 569–579 (2000).
ADS  CAS  Article  Google Scholar 

37.
Sieburth, J. M., Smetacek, V. & Lenz, J. Pelagic ecosystem structure: Heterotrophic compartments of the plankton and their relationship to plankton size fractions. Limnol. Oceanogr. 23, 1256–1263 (1978).
ADS  Article  Google Scholar 

38.
Ignatiades, L. Redefinition of cell size classification of phytoplankton – a potential tool for improving the quality and assurance of data interpretation. Mediterr. Mar. Sci. 17, 56 (2015).
Article  Google Scholar 

39.
Whitton, B. A. Ecology of Cyanobacteria II. Their Diversity in Space and Time. (Springer Verlag, 2012).

40.
Dittmann, E., Gugger, M., Sivonen, K. & Fewer, D. P. Natural product biosynthetic diversity and comparative genomics of the Cyanobacteria. Trends Microbiol. 23, 642–652 (2015).
CAS  Article  Google Scholar 

41.
Sanseverino, I., Conduto, D., Pozzoli, L., Dobricic, S. & Lettieri, T. Algal Bloom and its Economic Impact. 48 (Join Research Center, 2016).

42.
Sanseverino, I., Conduto Antonio, D., Loos, R. & Lettieri, T. Cyanotoxins: Methods and Approaches for their Analysis and Detection. 64 (Join Research Center, 2017).

43.
Meriluoto, J., Spoof, L. & Codd, G. A. Handbook of Cyanobacterial Monitoring and Cyanotoxin Analysis. (John Wiley & Sons, 2017).

44.
Lwoff, A., Van Niel, C. B., Ryan, P. J. & Tatum, E. L. Nomenclature of Nutritional Types of Microorganisms. In Cold Spring Harbor Symposia on Quantitative Biology. XI (5th ed.) 302–303 (1946).

45.
Morris, J. Biology: How Life Works. (W. H. Freeman/Macmillan Learning, 2018).

46.
Laplace-Treyture, C. et al. Phytoplankton morpho-functional trait dataset from French water-bodies. Portail Data INRAE https://doi.org/10.15454/GJGIAH (2020).

47.
Morabito, G., Oggioni, A., Caravati, E. & Panzani, P. Seasonal morphological plasticity of phytoplankton in Lago Maggiore (N. Italy). Hydrobiologia 578, 47–57 (2007).
Article  Google Scholar 

48.
Naselli-Flores, L., Padisák, J. & Albay, M. Shape and size in phytoplankton ecology: Do they matter? Hydrobiologia 578, 157–161 (2007).
Article  Google Scholar 

49.
Strathmann, R. R. Estimating the organic carbon content of phytoplankton from cell volume or plasma volume. Limnol. Oceanogr. 12, 411–418 (1967).
ADS  CAS  Article  Google Scholar 

50.
Chaffin, J. D., Stanislawczyk, K., Kane, D. D. & Lambrix, M. M. Nutrient addition effects on chlorophyll a, phytoplankton biomass, and heterocyte formation in Lake Erie’s central basin during 2014–2017: Insights into diazotrophic blooms in high nitrogen water. Freshw. Biol. 00, 1–15 (2020).
Google Scholar 

51.
Hadoux, E. & Laplace-Treyture, C. PHYTOBS: Phytoplankton Counting Tool in Laboratory. Version 1.0. JAVA Application. https://hydrobio-dce.inrae.fr/phytobs-software/ (2009).

52.
Huber Pestalozzi, G. & Thienemann, A. Das Phytoplankton des Susswassers Systematik und Biologie: 5 Teil Chlorophyceae (Grünalgen) Ordnung: Volvocales. (E. Schweizerbart’sche verlagsbuchhandlung, 1974).

53.
Komarek, J., Fott, B. & Huber Pestalozzi, G. Das Phytoplankton des Susswassers Systematik und Biologie: 7 Teil 1 Halfte Chlorophyceae (Grunalgen) Ordnung: Chlorococcales. (E. Schweizerbart’sche verlagsbuchhandlung, 1983).

54.
Coesel, P. F. M. & Meesters, K. J. Desmids of the Lowlands: Mesotaeniaceae and Desmidiaceae of the European Lowlands. (KNNV Publishing, 2007).

55.
Coesel, P. F. M. & Meesters, K. European Flora of the Desmid Genera Staurastrum and Staurodesmus. (KNNV Publishing, 2013).

56.
Starmach, K. Chrysophyceae und Haptophyceae. (VEB Gustav Fischer Verlag, 1985).

57.
Komarek, J. & Anagnostidis, K. Cyanoprokaryota 1.Teil: Chroococcales. (Gustav Fischer, 1999).

58.
Komarek, J. & Anagnostidis, K. Cyanoprokaryota 2.Teil: Oscillatoriales. (Elsevier, 2005).

59.
Komarek, J. Cyanoprokaryota: 3. Teil/Part 3: Heterocytous Genera. (Springer Spektrum Verlag, 2013).

60.
Anses. Evaluation des Risques Liés aux Cyanobactéries et leurs Toxines dans les Eaux Douces. Avis de l’Anses. 438 (Anses, 2020).

61.
Bey, M.-Y. & Ector, L. Atlas des Diatomées des Cours d’Eau de la Région Rhône-Alpes. (DREAL Rhône-Alpes, 2013).

62.
Cox, E. J. Identification of Freshwater Diatoms from Live Material. (Chapman & Hall, 1996).

63.
Druart, J. C. & Straub, F. Description de deux nouvelles Cyclotelles (Bacillariophyceae) de milieux alcalins et eutrophes: Cyclotella costei nov. sp. et Cyclotella wuethrichiana nov. sp. Swiss J. Hydrol. 50, 182–188 (1988).
Article  Google Scholar 

64.
Houk, V. Atlas of Freshwater Centric Diatoms with a Brief Key and Descriptions Part I Melosiraceae, Orthoseiraceae, Paraliaceae and Aulacoseiraceae. vol. 1 (Czech Phycological Society, Prague & Palacký University Olomouc, 2003).

65.
Houk, V. & Klee, R. Atlas of freshwater centric diatoms with a brief key and descriptions Part II Melosiraceae and Aulacoseiraceae (Supplement to Part I). Fottea J. Czech Phycol. Soc. 7, 85–255 (2007).
Google Scholar 

66.
Houk, V., Klee, R. & Tanaka, H. Atlas of Freshwater Centric Diatoms with a Brief Key and Descriptions Part IV Stephanodiscaceae B. vol. 14 (Czech Phycological Society, Prague & Palacký University Olomouc, 2014).

67.
Houk, V., Klee, R. & Tanaka, H. Atlas of Freshwater Centric Diatoms with a Brief Key and Descriptions Part III Steogabiduscaceae A Cyclotella, Tertiarius, Discostella. vol. 10 (Czech Phycological Society, Prague & Palacký University Olomouc, 2010).

68.
Houk, V., Klee, R. & Tanaka, H. Atlas of freshwater centric diatoms with a brief key and descriptions: second emended edition of Part I and II Melosiraceae, Orthoseiraceae, Paraliaceae and Aulacoseiraceae. Fottea J. Czech Phycol. Soc. 17, 1–615 (2017).
Google Scholar 

69.
Krammer, K. & Lange Bertalot, H. Bacillariophyceae. 1. Teil: Naviculaceae. (Specktrum Akademischer Verlag GmbH Heidelberg, 1999).

70.
Krammer, K. & Lange Bertalot, H. Bacillariophyceae. 4. Teil: Achnanthaceae Kritische Ergänzungen zu Achnanthes s.l., Bavicula s. str., Gomphonema. (Spektrum, 2004).

71.
Krammer, K. & Lange Bertalot, H. Bacillariophyceae. 2. Teil: Bacillariaceae, Epithemiaceae, Surirellaceae. (Elsevier, 2007).

72.
Krammer, K. & Lange-Bertalot, H. Bacillariophyceae. 3. Teil: Centrales, Fragilariaceae, Eunotiaceae. (Gustav Fischer Verlag, 2004).

73.
Lange-Bertalot, H., Hofmann, G., Werum, M. & Cantonati, M. Freshwater Benthic Diatoms of Central Europe: Over 800 Common Species Used in Ecological Assessment. (Koeltz Botanical Books, 2017).

74.
Siver, P. A. et al. Observations on Fragilaria longifusiformis comb. nov. et nom. nov. (Bacillariophyceae), a widespread planktic diatom documented from North America and Europe. Phycol. Res. 54, 183–192 (2006).
Article  Google Scholar 

75.
Popovsky, J. & Pfiester, L. A. Dinophyceae (Dinoflagellida). (Gustav Fischer Verlag, 1990).

76.
Moestrup, Ø. & Calado, A. J. Dinophyceae. vol. 6 (Spektrum Akademischer Verlag, 2018).

77.
Huber Pestalozzi, G. Das Phytoplankton des Susswassers Systematik und Biologie: 4 Teil Euglenophyceen. (E. Schweizerbart’sche verlagsbuchhandlung, 1969).

78.
Ettl, H. Xanthophyceae: 1. Teil. (Gustav Fischer Verlag, 1978).

79.
Rieth, A. Xanthophyceae: 2. Teil. (Gustav Fischer Verlag, 1980). More

1.
Sender, R., Fuchs, S. & Milo, R. Revised estimates for the number of human and bacteria cells in the body. PLOS Biol. 14, e1002533 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 
2.
Kho, Z. Y. & Lal, S. K. The human gut microbiome—A potential controller of wellness and disease. Front. Microbiol. 9, 1835 (2018).
PubMed  PubMed Central  Article  Google Scholar 

3.
Kostic, A. D., Xavier, R. J. & Gevers, D. The microbiome in inflammatory bowel disease: Current status and the future ahead. Gastroenterology 146, 1489–1499 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

4.
Thaiss, C. A., Zmora, N., Levy, M. & Elinav, E. The microbiome and innate immunity. Nature 535, 65–74 (2016).
CAS  PubMed  Article  ADS  PubMed Central  Google Scholar 

5.
Das, B. & Nair, G. B. Homeostasis and dysbiosis of the gut microbiome in health and disease. J. Biosci. 44, 117 (2019).
PubMed  Article  PubMed Central  Google Scholar 

6.
Shreiner, A. B., Kao, J. Y. & Young, V. B. The gut microbiome in health and in disease. Curr. Opin. Gastroenterol. 31, 69–75 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

7.
Petersen, C. & Round, J. L. Defining dysbiosis and its influence on host immunity and disease: How changes in microbiota structure influence health. Cell. Microbiol. 16, 1024–1033 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

8.
Karlsson, F. H. et al. Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature 498, 99–103 (2013).
CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

9.
Koren, O. et al. Human oral, gut, and plaque microbiota in patients with atherosclerosis. Proc. Natl. Acad. Sci. 108, 4592–4598 (2011).
CAS  PubMed  Article  ADS  PubMed Central  Google Scholar 

10.
Karlsson, F. H. et al. Symptomatic atherosclerosis is associated with an altered gut metagenome. Nat. Commun. 3, 1245 (2012).
PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

11.
Chatelier, L. M. et al. Richness of human gut microbiome correlates with metabolic markers. Nature 500, 541–546 (2013).
PubMed  Article  CAS  PubMed Central  Google Scholar 

12.
Franzosa, E. A. et al. Gut microbiome structure and metabolic activity in inflammatory bowel disease. Nat. Microbiol. 4, 293–305 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

13.
Becker, C., Neurath, M. F. & Wirtz, S. The intestinal microbiota in inflammatory bowel disease. ILAR J. 56, 192–204 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

14.
Kostic, A. D. et al. Fusobacterium nucleatum potentiates intestinal tumorigenesis and modulates the tumor-immune microenvironment. Cell Host Microbe 14, 207–215 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

15.
The Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).
PubMed Central  Article  ADS  CAS  Google Scholar 

16.
Johnson, A. J. et al. Daily sampling reveals personalized diet–microbiome associations in humans. Cell Host Microbe 25, 789-802.e5 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

17.
Villmones, H. C. et al. Species level description of the human ileal bacterial microbiota. Sci. Rep. 8, 1–9 (2018).
CAS  Article  Google Scholar 

18.
Gevers, D. et al. The treatment-naive microbiome in new-onset Crohn’s disease. Cell Host Microbe 15, 382–392 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

19.
David, L. A. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014).
CAS  PubMed  Article  ADS  PubMed Central  Google Scholar 

20.
Zhou, J., Deng, Y., Luo, F., He, Z. & Yang, Y. Phylogenetic molecular ecological network of soil microbial communities in response to elevated CO2. MBio 2, e00122-e211 (2011).
PubMed  PubMed Central  Article  Google Scholar 

21.
Lupatini, M. et al. Network topology reveals high connectance levels and few key microbial genera within soils. Front. Environ. Sci. 2, 10 (2014).
Article  Google Scholar 

22.
Eiler, A., Heinrich, F. & Bertilsson, S. Coherent dynamics and association networks among lake bacterioplankton taxa. ISME J. 6, 330–342 (2012).
CAS  PubMed  Article  Google Scholar 

23.
Kara, E. L., Hanson, P. C., Hu, Y. H., Winslow, L. & McMahon, K. D. A decade of seasonal dynamics and co-occurrences within freshwater bacterioplankton communities from eutrophic Lake Mendota, WI, USA. ISME J. 7, 680–684 (2013).
PubMed  Article  Google Scholar 

24.
Shetty, S. A., Hugenholtz, F., Lahti, L., Smidt, H. & de Vos, W. M. Intestinal microbiome landscaping: Insight in community assemblage and implications for microbial modulation strategies. FEMS Microbiol. Rev. 41, 182–199 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

25.
Gould, A. L. et al. Microbiome interactions shape host fitness. Proc. Natl. Acad. Sci. 115, E11951–E11960 (2018).
CAS  PubMed  Article  Google Scholar 

26.
Hibbing, M. E., Fuqua, C., Parsek, M. R. & Peterson, S. B. Bacterial competition: Surviving and thriving in the microbial jungle. Nat. Rev. Microbiol. 8, 15–25 (2010).
CAS  PubMed  PubMed Central  Article  Google Scholar 

27.
Fox, G. E., Magrum, L. J., Balcht, W. E., Wolfef, R. S. & Woese, C. R. Classification of methanogenic bacteria by 16S ribosomal RNA characterization (comparative oligonucleotide cataloging/phylogeny/molecular evolution). Evolution (N.Y.) 74, 4537–4541 (1977).
CAS  Google Scholar 

28.
Venter, J. C. et al. Environmental genome shotgun sequencing of the Sargasso Sea. Science 304, 66 (2004).
CAS  PubMed  Article  ADS  Google Scholar 

29.
Větrovský, T. & Baldrian, P. The variability of the 16S rRNA gene in bacterial genomes and its consequences for bacterial community analyses. PLoS ONE 8, e57923 (2013).
PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

30.
Edgar, R. C. Accuracy of taxonomy prediction for 16S rRNA and fungal ITS sequences. PeerJ 6, 1–29 (2018).
Google Scholar 

31.
Ranjan, R., Rani, A., Metwally, A., McGee, H. S. & Perkins, D. L. Analysis of the microbiome: Advantages of whole genome shotgun versus 16S amplicon sequencing. Biochem. Biophys. Res. Commun. 469, 967–977 (2016).
CAS  PubMed  Article  Google Scholar 

32.
Laudadio, I. et al. Quantitative assessment of shotgun metagenomics and 16S rDNA amplicon sequencing in the study of human gut microbiome. Omi. A J. Integr. Biol. 22, 248–254 (2018).
CAS  Article  Google Scholar 

33.
Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: And this is not optional. Front. Microbiol. 8, 1–6 (2017).
Article  Google Scholar 

34.
Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8, e1002687 (2012).
CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

35.
Layeghifard, M., Hwang, D. M. & Guttman, D. S. Disentangling interactions in the microbiome: A network perspective. Trends Microbiol. 25, 217–228 (2017).
CAS  PubMed  Article  Google Scholar 

36.
Aitchison, J. The statistical analysis of compositional data. J. R. Stat. Soc. Ser. B 44, 40 (1982).
MathSciNet  MATH  Google Scholar 

37.
Kurtz, Z. D. et al. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput. Biol. 11, 1–25 (2015).
Article  CAS  Google Scholar 

38.
Friedman, J., Hastie, T. & Tibshirani, R. Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9, 432–441 (2008).
PubMed  MATH  Article  Google Scholar 

39.
Falony, G. et al. Population-level analysis of gut microbiome variation. Science 352, 560–564 (2016).
CAS  PubMed  Article  ADS  Google Scholar 

40.
Efron, B. & Tibshirani, R. Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Stat. Sci. 1, 54–75 (1986).
MathSciNet  MATH  Article  Google Scholar 

41.
Su, W., Bogdan, M., Candès, E. & Candes, E. False discoveries occur early on the lasso path. Ann. Stat. 45, 2133–2150 (2017).
MathSciNet  MATH  Article  Google Scholar 

42.
Saunders, A. M., Albertsen, M., Vollertsen, J. & Nielsen, P. H. The activated sludge ecosystem contains a core community of abundant organisms. ISME J. 10, 11–20 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

43.
Tsvetovat, M. & Kouznetsov, A. Social network analysis for startups. Zhurnal Eksperimental’noi i Teoreticheskoi Fiziki (2011).

44.
Stadtfeld, C., Takács, K. & Vörös, A. The emergence and stability of groups in social networks. Soc. Netw. 60, 129–145 (2020).
Article  Google Scholar 

45.
Cordasco, G. & Gargano, L. Community detection via semi-synchronous label propagation algorithms. 2010 IEEE Int. Work. Bus. Appl. Soc. Netw. Anal. BASNA 2010 (2010). https://doi.org/10.1109/BASNA.2010.5730298.

46.
Prettejohn, B. J., Berryman, M. J. & McDonnell, M. D. Methods for generating complex networks with selected structural properties for simulations: A review and tutorial for neuroscientists. Front. Comput. Neurosci. 5, 11 (2011).
PubMed  PubMed Central  Article  Google Scholar 

47.
Guimerà, R., Sales-Pardo, M. & Amaral, L. A. N. Modularity from fluctuations in random graphs and complex networks. Phys. Rev. E 70, 25101 (2004).
Article  ADS  CAS  Google Scholar 

48.
Trosvik, P. & de Muinck, E. J. Ecology of bacteria in the human gastrointestinal tract—Identification of keystone and foundation taxa. Microbiome 3, 44 (2015).
PubMed  PubMed Central  Article  Google Scholar 

49.
Verster, A. J. & Borenstein, E. Competitive lottery-based assembly of selected clades in the human gut microbiome. Microbiome 6, 186 (2018).
PubMed  PubMed Central  Article  Google Scholar 

50.
Berry, D. & Widder, S. Deciphering microbial interactions and detecting keystone species with co-occurrence networks. Front. Microbiol. 5, 219 (2014).
PubMed  PubMed Central  Article  Google Scholar 

51.
Foster, K. R. & Bell, T. Competition, not cooperation, dominates interactions among culturable microbial species. Curr. Biol. 22, 1845–1850 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

52.
Nemergut, D. R. et al. Patterns and processes of microbial community assembly. Microbiol. Mol. Biol. Rev. 77, 342–356 (2013).
PubMed  PubMed Central  Article  Google Scholar 

53.
Hamilton, W. D. The genetical evolution of social behaviour. I. J. Theor. Biol. 7, 1–16 (1964).
CAS  PubMed  Article  PubMed Central  Google Scholar 

54.
Hardin, G. The competitive exclusion principle. Science 131, 1292–1297 (1960).
CAS  PubMed  Article  ADS  Google Scholar 

55.
Jackson, M. A. et al. Detection of stable community structures within gut microbiota co-occurrence networks from different human populations. PeerJ 6, e4303 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

56.
Darcy, J. L. et al. A phylogenetic model for the recruitment of species into microbial communities and application to studies of the human microbiome. ISME J. 14, 1359–1368 (2020).
PubMed  Article  Google Scholar 

57.
Pacheco, A. R., Moel, M. & Segrè, D. Costless metabolic secretions as drivers of interspecies interactions in microbial ecosystems. Nat. Commun. 10, 103 (2019).
PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

58.
Chase, J. M. & Leibold, M. A. Spatial scale dictates the productivity–biodiversity relationship. Nature 416, 427–430 (2002).
CAS  PubMed  Article  ADS  PubMed Central  Google Scholar 

59.
Zarrinpar, A., Chaix, A., Yooseph, S. & Panda, S. Diet and feeding pattern affect the diurnal dynamics of the gut microbiome. Cell Metab. 20, 1006–1017 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

60.
Mark Welch, J. L., Hasegawa, Y., McNulty, N. P., Gordon, J. I. & Borisy, G. G. Spatial organization of a model 15-member human gut microbiota established in gnotobiotic mice. Proc. Natl. Acad. Sci. 114, E9105–E9114 (2017).
CAS  PubMed  Article  Google Scholar 

61.
Fung, T. C., Artis, D. & Sonnenberg, G. F. Anatomical localization of commensal bacteria in immune cell homeostasis and disease. Immunol. Rev. 260, 35–49 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

62.
Donaldson, G. P., Lee, S. M. & Mazmanian, S. K. Gut biogeography of the bacterial microbiota. Nat. Rev. Microbiol. 14, 20–32 (2016).
CAS  PubMed  Article  Google Scholar 

63.
Stachowicz, J. J. Mutualism, facilitation, and the structure of ecological communities. Bioscience 51, 235 (2001).
Article  Google Scholar 

64.
Lozupone, C. A., Stombaugh, J. I., Gordon, J. I., Jansson, J. K. & Knight, R. Diversity, stability and resilience of the human gut microbiota. Nature 489, 220–230 (2012).
CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

65.
Lynd, L. R., Weimer, P. J., van Zyl, W. H. & Pretorius, I. S. Microbial cellulose utilization: Fundamentals and biotechnology. Microbiol. Mol. Biol. Rev. 66, 72 (2002).
Article  Google Scholar 

66.
Turroni, F. et al. Glycan cross-feeding activities between bifidobacteria under in vitro conditions. Front. Microbiol. 6, 1030 (2015).
PubMed  PubMed Central  Article  Google Scholar 

67.
Hall, C. V. et al. Co-existence of network architectures supporting the human gut microbiome. iScience 22, 380–391 (2019).
PubMed  PubMed Central  Article  ADS  Google Scholar 

68.
Fisher, C. K. & Mehta, P. Identifying keystone species in the human gut microbiome from metagenomic timeseries using sparse linear regression. PLoS ONE 9, e102451 (2014).
PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

69.
Jones, M. B. et al. Library preparation methodology can influence genomic and functional predictions in human microbiome research. Proc. Natl. Acad. Sci. 112, 14024–14029 (2015).
CAS  PubMed  Article  ADS  Google Scholar 

70.
Lahti, L., Salojärvi, J., Salonen, A., Scheffer, M. & de Vos, W. M. Tipping elements in the human intestinal ecosystem. Nat. Commun. 5, 4344 (2014).
CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

71.
Dhakan, D. B. et al. The unique composition of Indian gut microbiome, gene catalogue, and associated fecal metabolome deciphered using multi-omics approaches. Gigascience 8, giz004 (2019).
PubMed  PubMed Central  Article  CAS  Google Scholar 

72.
Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010).
CAS  PubMed  PubMed Central  Article  Google Scholar 

73.
Yachida, S. et al. Metagenomic and metabolomic analyses reveal distinct stage-specific phenotypes of the gut microbiota in colorectal cancer. Nat. Med. 25, 968–976 (2019).
CAS  PubMed  Article  Google Scholar 

74.
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

75.
Langmead, B. & Salzberg, S. Bowtie2. Nat. Methods 9, 357–359 (2013).
Article  CAS  Google Scholar 

76.
O’Leary, N. A. et al. Reference sequence (RefSeq) database at NCBI: Current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 44, D733–D745 (2016).
PubMed  Article  CAS  Google Scholar 

77.
Xia, L. C., Cram, J. A., Chen, T., Fuhrman, J. A. & Sun, F. Accurate genome relative abundance estimation based on shotgun metagenomic reads. PLoS ONE 6, e27992 (2011).
CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

78.
Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: Assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

79.
Hyatt, D. et al. Prodigal: Prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 11, 119 (2010).
Article  CAS  Google Scholar 

80.
Jones, P. et al. InterProScan 5: Genome-scale protein function classification. Bioinformatics 30, 1236–1240 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

81.
Haft, D. H. TIGRFAMs: A protein family resource for the functional identification of proteins. Nucleic Acids Res. 29, 41–43 (2001).
CAS  PubMed  PubMed Central  Article  Google Scholar 

82.
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

83.
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2017).
Google Scholar 

84.
Zhao, T., Liu, H., Roeder, K., Lafferty, J. & Wasserman, L. The huge package for high-dimensional undirected graph estimation in R. J. Mach. Learn. Res. 13, 6 (2016).
MathSciNet  MATH  Google Scholar 

85.
Liu, H., Roeder, K. & Wasserman, L. Stability approach to regularization selection (stars) for high dimensional graphical models. Advances in Neural Information Processing Systems (2010).

86.
Hagberg, A., Swart, P. & Chult, D. S. Exploring network structure, dynamics, and function using NetworkX. No. LA-UR-08-05495; LA-UR-08-5495 (Los Alamos National Lab. (LANL), Los Alamos, 2008).

87.
Newman, M. E. J. Networks: An Introduction 168–234 (Oxford University Press, Oxford, 2010).
Google Scholar 

88.
Newman, M. E. J. Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103, 8577–8582 (2006).
CAS  PubMed  Article  ADS  PubMed Central  Google Scholar 

89.
Newman, M. E. J. Mixing patterns in networks. Phys. Rev. E 67, 26126 (2003).
MathSciNet  CAS  Article  ADS  Google Scholar 

90.
Fortunato, S. Community detection in graphs. Phys. Rep. 486, 75–174 (2010).
MathSciNet  Article  ADS  Google Scholar 

91.
Brandes, U. A faster algorithm for betweenness centrality*. J. Math. Sociol. 25, 163–177 (2001).
MATH  Article  Google Scholar  More

1.
Keller, R. P., Geist, J., Jeschke, J. M. & Kühn, I. Invasive species in Europe: ecology, status, and policy. Environ. Sci. Eur. 23, 1–17 (2011).
Article  Google Scholar 
2.
Parker, M., Thompson, J. N. & Weller, S. G. The population biology of invasive species. Annu. Rev. Ecol. Syst. 32, 305–332 (2001).
Article  Google Scholar 

3.
Allendorf, F. W. & Lundquist, L. L. Introduction: population biology, evolution, and control of invasive species. Conserv. Biol. 17, 24–30 (2003).
Article  Google Scholar 

4.
Crowl, T. A., Crist, T. O., Parmenter, R. R., Belovsky, G. & Lugo, A. E. The spread of invasive species and infectious disease as drivers of ecosystem change. Front. Ecol. Environ. 6, 238–246 (2008).
Article  Google Scholar 

5.
van der Veer, G. & Nentwig, W. Environmental and economic impact assessment of alien and invasive fish species in Europe using the generic impact scoring system. Ecol. Freshw. Fish 24, 646–656 (2015).
Article  Google Scholar 

6.
Clavero, M. & García-Berthou, E. Invasive species are a leading cause of animal extinctions. Trends Ecol. Evol. 20, 110 (2005).
PubMed  Article  PubMed Central  Google Scholar 

7.
Scalera, R. How much is Europe spending on invasive alien species?. Biol. Invasions 12, 173–177 (2010).
Article  Google Scholar 

8.
Sala, O. E. et al. Global biodiversity scenarios for the year 2100. Science 287, 1770–1774 (2000).
CAS  PubMed  Article  PubMed Central  Google Scholar 

9.
McLellan, R., Iyengar, L., Jeffries, B. & Oerlemans, N. Living Planet Report 2014: Species and Spaces, People and Places (WWF International, Gland, 2014).
Google Scholar 

10.
García-Berthou, E. et al. Introduction pathways and establishment rates of invasive aquatic species in Europe. Can. J. Fish. Aquat. Sci. 62, 453–463 (2005).
Article  Google Scholar 

11.
Karatayev, A. Y., Burlakova, L. E., Padilla, D. K., Mastitsky, S. E., & Olenin, S. Invaders are not a random selection of species. Biol. Invasions, 11, 2009. https://doi.org/10.1007/s10530-009-9498-0 (2009).
Article  Google Scholar 

12.
Verdonschot, R. C. M., Vos, J. H., & Verdonschot, P. F. M. Exotische macrofauna en macrofyten in de Nederlandse zoete wateren: voorkomen en beleid in 2012. (WOt-werkdocument 334) (Wettelijke Onderzoekstaken Natuur & Milieu, 2013).

13.
Holdich, D. M., Reynolds, J. D., Souty-Grosset, C. & Sibley, P. J. A review of the ever increasing threat to European crayfish from non-indigenous crayfish species. Knowl. Manag. Aquat. Ecosyst. 394–395, 11 (2009).
Article  Google Scholar 

14.
Chucholl, C. Invaders for sale: trade and determinants of introduction of ornamental freshwater crayfish. Biol. Invasions 15, 125–141 (2013).
Article  Google Scholar 

15.
Barbaresi, S. & Gherardi, F. The invasion of the alien crayfish Procambarus clarkii in Europe, with particular reference to Italy. Biol. Invasions 2, 259–264 (2000).
Article  Google Scholar 

16.
Gherardi, F. Crayfish invading Europe: the case study of Procambarus clarkii. Mar. Freshw. Behav. Physiol. 39, 175–191 (2006).
Article  Google Scholar 

17.
Kouba, A., Petrusek, A. & Kozák, P. Continental-wide distribution of crayfish species in Europe: update and maps. Knowl. Manag. Aquat. Ecosyst. 413, 5 (2014).
Article  Google Scholar 

18.
Lowe, S., Browne, M., Boudjelas, S., & De Poorter, M. 100 of the world’s worst invasive alien species: a selection from the global invasive species database in Aliens vol. 12 (Invasive Species Specialist Group, 2000).

19.
Padilla, D. K. & Williams, S. L. Beyond ballast water: aquarium and ornamental trades as sources of invasive species in aquatic ecosystems. Front. Ecol. Environ. 2, 131–138 (2004).
Article  Google Scholar 

20.
Faulkes, Z. The global trade in crayfish as pets. Crustacean Res. 44, 75–92 (2015).
Article  Google Scholar 

21.
Soes, D. M., & Koese, B. Invasive Crayfish in the Netherlands: A Preliminary Risk Analysis. (Bureau Waardenburg bv, Stichting EIS-Nederland, Invasive Alien Species Team, 2010).

22.
Chucholl, C. & Wendler, F. Positive selection of beautiful invaders: long-term persistence and bio-invasion risk of freshwater crayfish in the pet trade. Biol. Invasions 19, 197–208 (2017).
Article  Google Scholar 

23.
Zeng, Y., Chong, K. Y., Grey, E. K., Lodge, D. M. & Yeo, D. C. Disregarding human pre-introduction selection can confound invasive crayfish risk assessments. Biol. Invasions 17, 2373–2385 (2015).
Article  Google Scholar 

24.
Blackburn, T. M. et al. A proposed unified framework for biological invasions. Trends Ecol. Evol. 26, 333–339 (2011).
PubMed  Article  Google Scholar 

25.
Statzner, B., Bonada, N. & Dolédec, S. Biological attributes discriminating invasive from native European stream macroinvertebrates. Biol. Invasions 10, 517–530 (2008).
Article  Google Scholar 

26.
Whitney, K. D. & Gabler, C. A. Rapid evolution in introduced species, ‘invasive traits’ and recipient communities: challenges for predicting invasive potential. Divers. Distrib. 14, 569–580 (2008).
Article  Google Scholar 

27.
Kolar, C. S. & Lodge, D. M. Progress in invasion biology: predicting invaders. Trends Ecol. Evol. 16, 199–204 (2001).
PubMed  Article  Google Scholar 

28.
Marchetti, M. P., Moyle, P. B. & Levine, R. Invasive species profiling? Exploring the characteristics of non-native fishes across invasion stages in California. Freshw. Biol. 49, 646–661 (2004).
Article  Google Scholar 

29.
Grabowski, M., Bacela, K. & Konopacka, A. How to be an invasive gammarid (Amphipoda: Gammaroidea)-comparison of life history traits. Hydrobiologia 590, 75–84 (2007).
Article  Google Scholar 

30.
Thiébaut, G. Invasion success of non-indigenous aquatic and semi-aquatic plants in their native and introduced ranges. A comparison between their invasiveness in North America and in France. Biol. Invasions 9, 1–12 (2007).
Article  Google Scholar 

31.
Swart, C., Visser, V. & Robinson, T. B. Patterns and traits associated with invasions by predatory marine crabs. NeoBiota 39, 79 (2018).
Article  Google Scholar 

32.
Larson, E. R. & Olden, J. D. Latent extinction and invasion risk of crayfishes in the southeastern United States. Conserv. Biol. 24, 1099–1110 (2010).
PubMed  Article  PubMed Central  Google Scholar 

33.
Tricarico, E., Vilizzi, L., Gherardi, F. & Copp, G. H. Calibration of FI-ISK, an invasiveness screening tool for nonnative freshwater invertebrates. Risk Anal. Int. J. 30, 285–292 (2010).
Article  Google Scholar 

34.
Larson, E. R. & Olden, J. D. Using avatar species to model the potential distribution of emerging invaders. Glob Ecol. Biogeogr. 21, 1114–1125 (2012).
Article  Google Scholar 

35.
Veselý, L., Buřič, M. & Kouba, A. Hardy exotics species in temperate zone: can “warm water” crayfish invaders establish regardless of low temperatures?. Sci. Rep. 5, 16340 (2015).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

36.
Jaklič, M. & Vrezec, A. The first tropical alien crayfish species in European waters: the redclaw Cherax quadricarinatus (Von Martens, 1868) (Decapoda, Parastacidae). Crustaceana 84, 651–665 (2011).
Article  Google Scholar 

37.
Colautti, R. I., Grigorovich, I. A. & MacIsaac, H. J. Propagule pressure: a null model for biological invasions. Biol. Invasions 8, 1023–1037 (2006).
Article  Google Scholar 

38.
Marchetti, M. P., Moyle, P. B. & Levine, R. Alien fishes in California watersheds: characteristics of successful and failed invaders. Ecol. Appl. 14, 587–596 (2004).
Article  Google Scholar 

39.
Bennett, S. N., Olson, J. R., Kershner, J. L. & Corbett, P. Propagule pressure and stream characteristics influence introgression: cutthroat and rainbow trout in British Columbia. Ecol. Appl. 20, 263–277 (2010).
PubMed  Article  PubMed Central  Google Scholar 

40.
Cruz, M. J. & Rebelo, R. Colonization of freshwater habitats by an introduced crayfish, Procambarus clarkii Southwest Iberian Peninsula. Hydrobiologia 575, 191–201 (2007).
Article  Google Scholar 

41.
Lynas, J., Storey, A. W. & Knott, B. Aggressive interactions between three species of freshwater crayfish of the genus Cherax (Decapoda: Parastacidae). Mar. Freshw. Behav. Physiol. 40, 105–116 (2007).
Article  Google Scholar 

42.
Corey, S. Comparative fecundity of four species of crayfish in southwestern Ontario, Canada (Decapoda, Astacidea). Crustaceana 52(3), 276–286 (1987).
Article  Google Scholar 

43.
Somers, K. M. Characterizing size-specific fecundity in crustaceans. Crustacean Egg Prod. 7, 357–378 (1991).
Google Scholar 

44.
Maguire, I., Klobučar, G. I. V. & Erben, R. The relationship between female size and egg size in the freshwater crayfish Austropotamobius torrentium. Bulletin Français de la Pêche et de la Pisciculture 376–377, 777–785 (2005).
Article  Google Scholar 

45.
Pilotto, F. et al. The invasive crayfish Faxonius limosus in Lake Varese: estimating abundance and population size structure in the context of habitat and methodological constraints. J. Crustacean Biol. 28, 633–640 (2008).
Article  Google Scholar 

46.
Hobbs Jr, H. H. A checklist of the North and Middle American crayfishes (Decapoda: Astacidae and Cambaridae). Smithsonian Contrib. Zool. 166, 1–161 (1974).
Google Scholar 

47.
Mrugała, A. et al. Trade of ornamental crayfish in Europe as a possible introduction pathway for important crustacean diseases: crayfish plague and white spot syndrome. Biol. Invasions 17, 1313–1326 (2015).
Article  Google Scholar 

48.
Svoboda, J., Mrugała, A., Kozubíková-Balcarová, E. & Petrusek, A. Hosts and transmission of the crayfish plague pathogen Aphanomyces astaci: a review. J. Fish Dis. 40, 127–140 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

49.
Grandjean, F. et al. Status of Pacifastacus leniusculus and its role in recent crayfish plague outbreaks in France: improving distribution and crayfish plague infection patterns. Aquat. Invasions, 12, 541–549 (2017).
Article  Google Scholar 

50.
Crandall, K. A. & De Grave, S. An updated classification of the freshwater crayfishes (Decapoda: Astacidea) of the world, with a complete species list. J. Crustacean Biol. 37, 615–653 (2017).
Article  Google Scholar 

51.
Freshwater Crayfish: A Global Overview. (ed. Kawai, T., Faulkes, Z., & Scholtz, G.) (CRC Press, Boca Raton, 2015).

52.
Buřič, M., Kouba, A. & Kozak, P. Reproductive plasticity in freshwater invader: from long-term sperm storage to parthenogenesis. PLoS ONE 8, e77597. https://doi.org/10.1371/journal.pone.0077597 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

53.
Kaldre, K., Meženin, A., Paaver, T., & Kawai, T. A preliminary study on the tolerance of marble crayfish Procambarus fallax f. virginalis to low temperature in Nordic climate in Freshwater crayfish: global overview, 54–62 (2016).

54.
Vogt, G. Marmorkrebs: natural crayfish clone as emerging model for various biological disciplines. J. Biosci. 36, 377–382 (2011).
PubMed  Article  Google Scholar 

55.
Chucholl, C. Predicting the risk of introduction and establishment of an exotic aquarium animal in Europe: insights from one decade of Marmorkrebs (Crustacea, Astacida, Cambaridae) releases. Biol. Invasions 5, 309–318 (2014).
Article  Google Scholar 

56.
Chucholl, C., Morawetz, K. & Groß, H. The clones are coming–strong increase in Marmorkrebs [Procambarus fallax (Hagen, 1870) f. virginalis] records from Europe. Aquat. Invasions 7, 511–519 (2012).
Article  Google Scholar 

57.
Soes, D. M. & van Eekelen, R. Rivierkreeften, een oprukkend probleem?. De Levende Natuur 107, 56–59 (2006).
Google Scholar 

58.
Mauvisseau, Q., Tönges, S., Andriantsoa, R., Lyko, F. & Sweet, M. Early detection of an emerging invasive species: eDNA monitoring of a parthenogenetic crayfish in freshwater systems. Manag. Biol. Invasions 10, 461 (2019).
Article  Google Scholar 

59.
Strand, D. A. et al. Monitoring a Norwegian freshwater crayfish tragedy: eDNA snapshots of invasion, infection and extinction. J. Appl. Ecol. 56, 1661–1673 (2019).
CAS  Article  Google Scholar 

60.
Beentjes, K. K., Speksnijder, A. G., Schilthuizen, M., Schaub, B. E. & van der Hoorn, B. B. The influence of macroinvertebrate abundance on the assessment of freshwater quality in The Netherlands. Metabarcoding Metagenom. 2, e26744 (2018).
Article  Google Scholar 

61.
Melo-Merino, S. M., Reyes-Bonilla, H. & Lira-Noriega, A. Ecological niche models and species distribution models in marine environments: a literature review and spatial analysis of evidence. Ecol. Model. 415, 108837 (2020).
Article  Google Scholar 

62.
Zhang, Z. et al. Impacts of climate change on the global potential distribution of two notorious invasive crayfishes. Freshw. Biol. 65, 353–365 (2020).
Article  Google Scholar 

63.
Capinha, C., Leung, B. & Anastácio, P. Predicting worldwide invasiveness for four major problematic decapods: an evaluation of using different calibration sets. Ecography 34, 448–459 (2011).
Article  Google Scholar 

64.
Havel, J. E., Kovalenko, K. E., Thomaz, S. M., Amalfitano, S. & Kats, L. B. Aquatic invasive species: challenges for the future. Hydrobiologia 750, 147–170 (2015).
PubMed  PubMed Central  Article  Google Scholar 

65.
Früh, D., Stoll, S. & Haase, P. Physicochemical and morphological degradation of stream and river habitats increases invasion risk. Biol. Invasions 14, 2243–2253 (2012).
Article  Google Scholar 

66.
Ghalambor, C. K., McKay, J. K., Carroll, S. P. & Reznick, D. N. Adaptive versus non-adaptive phenotypic plasticity and the potential for contemporary adaptation in new environments. Funct. Ecol. 21, 394–407 (2007).
Article  Google Scholar 

67.
Scalici, M. et al. The new threat to Italian inland waters from the alien crayfish “gang”: the Australian Cherax destructor Clark, 1936. Hydrobiologia 632, 341–345 (2009).
Article  Google Scholar 

68.
Koese, B. & Evers, C. H. M. A National Inventory of Invasive Freshwater Crayfish in the Netherlands in 2010 (EIS, Stichting European Invertebrate Survey Nederland, 2011).
Google Scholar 

69.
Clement, J., & van Puijenbroek, P. Basiskaart Aquatisch: de Watertypenkaart Het oppervlaktewater in de TOP10NL geclassificeerd naar watertype (No. 500067004). (Planbureau voor de Leefomgeving 2010).

70.
Peel, M. C., Finlayson, B. L. & McMahon, T. A. Updated world map of the Köppen-Geiger climate classification. Hydrol. Earth Syst. Sci. Discuss. 4, 439–473 (2007).
ADS  Google Scholar 

71.
Lyko, F. The marbled crayfish (Decapoda: Cambaridae) represents an independent new species. Zootaxa 4363(4), 544–552 (2017).
PubMed  Article  PubMed Central  Google Scholar 

72.
Usseglio-Polatera, P. & Tachet, H. Theoretical habitat templets, species traits, and species richness: Plecoptera and Ephemeroptera in the Upper Rhône River and its floodplain. Freshw. Biol. 31, 357–375 (1994).
Article  Google Scholar 

73.
Poff, N. L. et al. Functional trait niches of North American lotic insects: traits-based ecological applications in light of phylogenetic relationships. J. North Am. Benthological. Soc. 25, 730–755 (2006).
Article  Google Scholar 

74.
Wyse, S. V. et al. A quantitative assessment of shoot flammability for 60 tree and shrub species supports rankings based on expert opinion. Int. J. Wildland Fire 25, 466–477 (2016).
Article  Google Scholar 

75.
Hill, M. O. TWINSPAN. A FORTRAN program for arranging multivariate data in an ordered two-way table by classification of the individuals and attributes. (Ecology and Systematics, Cornell University, 1979).

76.
Hu, G. et al. Regeneration of different plant functional types in a Masson pine forest following pine wilt disease. PLoS ONE 7, e36432. https://doi.org/10.1371/journal.pone.0036432 (2012).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

77.
Agir, S. U., Kutbay, H. G. & Surmen, B. Plant diversity along coastal dunes of the Black Sea (North of Turkey). Rendiconti Lincei 27, 443–453 (2016).
Article  Google Scholar 

78.
Andrej, P. & Andraž, Č. Functional response traits and plant community strategy indicate the stage of secondary succession. Hacquetia 11, 209–225 (2012).
Article  Google Scholar 

79.
Hill, M.O. & Šmilauer, P. TWINSPAN for Windows version 2.3. (Centre for Ecology and Hydrology & University of South Bohemia, Huntingdon & Ceske Budejovice, 2005).

80.
Roleček, J., Tichý, L., Zelený, D. & Chytrý, M. Modified TWINSPAN classification in which the hierarchy respects cluster heterogeneity. J. Veg. Sci. 20, 596–602 (2009).
Article  Google Scholar  More