Ecology

Pienta, K. J., Hammarlund, E. U., Austin, R. H., Axelrod, R., Brown, J. S. & Amend, S. R. Cancer cells employ an evolutionarily conserved polyploidization program to resist therapy. In Seminars in Cancer Biology, 1–15 (2020).Siegel, R. L., Miller, K. D. & Jemal, A. Cancer statistics, 2020. CA A Cancer J. Clin. 70(1), 7–30 (2020).Article 

Google Scholar 
Duesberg, P. & Rasnick, D. Aneuploidy, the somatic mutation that makes cancer a species of its own. Cell Motil. Cytoskelet. 47(2), 81–107 (2000).CAS 
Article 

Google Scholar 
Hanahan, D. & Weinberg, R. A. Leading edge review hallmarks of cancer: The next generation. Cell 144, 646–674 (2011).CAS 
PubMed 
Article 

Google Scholar 
Amend, S. R. et al. Polyploid giant cancer cells: Unrecognized actuators of tumorigenesis, metastasis, and resistance. Prostate 79(13), 1489–1497 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Pienta, K. J. et al. Convergent evolution, evolving evolvability, and the origins of lethal cancer. Mol. Cancer Res. 18(6), 801–810 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pienta, K. J., Hammarlund, E. U., Axelrod, R., Brown, J. S. & Amend, S. R. Poly-aneuploid cancer cells promote evolvability, generating lethal cancer. Evol. Appl. 13(7), 1626–1634 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Roychowdhury, S. et al. Personalized oncology through integrative high-throughput sequencing: A pilot study. Sci. Transl. Med. 3(111), 1–12 (2011).Article 
CAS 

Google Scholar 
Kuczler, M. D., Olseen, A. M., Pienta, K. J. & Amend, S. R. ROS-induced cell cycle arrest as a mechanism of resistance in polyaneuploid cancer cells (PACCs). Prog. Biophys. Mol. Biol. 165, 3–7 (2021).CAS 
PubMed 
Article 

Google Scholar 

Brown, R. L. What evolvability really is. Br. J. Philos. Sci.65(3), 549–572 (2014).MathSciNet 
Article 

Google Scholar 
Crother, B. I. & Murray, C. M. Early usage and meaning of evolvability. Ecol. Evol. 9(7), 3784–3793 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Payne, J. L. & Wagner, A. The causes of evolvability and their evolution. Nat. Rev. Genet. 20, 24–38 (2019).CAS 
PubMed 
Article 

Google Scholar 
Pigliucci, M. Is evolvability evolvable?. Genetics 9, 75–82 (2008).CAS 
PubMed 

Google Scholar 
Sniegowski, P. D. & Murphy, H. A. Evolvability. Curr. Biol. 16, R831–R834 (2006).CAS 
PubMed 
Article 

Google Scholar 
Kostecka, L. G., Pienta, K. J. & Amend, S. R. Polyaneuploid cancer cell dormancy: Lessons from evolutionary phyla. Front. Ecol. Evol. 9, 439 (2021).Article 

Google Scholar 
Rajaraman, R., Rajaraman, M. M., Rajaraman, S. R. & Guernsey, D. L. Neosis–a paradigm of self-renewal in cancer. Cell Biol. Int. 29(12), 1084–1097 (2005).CAS 
PubMed 
Article 

Google Scholar 
Rajaraman, R., Guernsey, D. L., Rajaraman, M. M. & Rajaraman, S. R. Neosis–A parasexual somatic reduction division in cancer. Int. J. Hum. Genet. 7(1), 29–48 (2007).CAS 
Article 

Google Scholar 
Sundaram, M., Guernsey, D. L., Rajaraman, M. M. & Rajaraman, R. Neosis: A novel type of cell division in cancer. Cancer Biol. Ther. 3(2), 207–218 (2004).CAS 
PubMed 
Article 

Google Scholar 
Gatenby, R. A., Cunningham, J. J. & Brown, J. S. Evolutionary triage governs fitness in driver and passenger mutations and suggests targeting never mutations. Nat. Commun. 5(1), 1–9 (2014).Article 

Google Scholar 
Bukkuri, A. & Brown, J. S. Evolutionary game theory: Darwinian dynamics and the G function approach. MDPI Games 12(4), 1–19 (2021).MathSciNet 
MATH 

Google Scholar 
Lopez-Sánchez, L. M. et al. CoCl2, a mimic of hypoxia, induces formation of polyploid giant cells with stem characteristics in colon cancer. PLoS ONE 9(6), e99143 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Mittal, K. et al. Multinucleated polyploidy drives resistance to Docetaxel chemotherapy in prostate cancer. Br. J. Cancer 116(9), 1186–1194 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Niu, N., Mercado-Uribe, I. & Liu, J. Dedifferentiation into blastomere-like cancer stem cells via formation of polyploid giant cancer cells. Oncogene 36(34), 4887–4900 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ogden, A., Rida, P. C. G., Knudsen, B. S., Kucuk, O. & Aneja, R. Docetaxel-induced polyploidization may underlie chemoresistance and disease relapse. Cancer Lett. 367, 89–92 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Puig, P. E. et al. Tumor cells can escape DNA-damaging cisplatin through DNA endoreduplication and reversible polyploidy. Cell Biol. Int. 32(9), 1031–1043 (2008).CAS 
PubMed 
Article 

Google Scholar 
Zhang, S. et al. Generation of cancer stem-like cells through the formation of polyploid giant cancer cells. Oncogene 33(1), 116–128 (2014).CAS 
PubMed 
Article 

Google Scholar 
Lin, K. C. et al. The role of heterogeneous environment and docetaxel gradient in the emergence of polyploid, mesenchymal and resistant prostate cancer cells. Clin. Exp. Metastasis 36(2), 97–108 (2019).PubMed 
Article 

Google Scholar 
Lin, K.-C. et al. Epithelial and mesenchymal prostate cancer cell population dynamics on a complex drug landscape. Converg. Sci. Phys. Oncol. 3(4), 045001 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Boe, L. Mechanism for induction of adaptive mutations in Escherichia coli. Mol. Microbiol. 4(4), 597–601 (1990).CAS 
PubMed 
Article 

Google Scholar 
Cairns, J. Mutation and cancer: The antecedents to our studies of adaptive mutation. Genetics 148(4), 1433–1440 (1998).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hall, B. G. Adaptive mutagenesis: A process that generates almost exclusively beneficial mutations. Genetica 102, 109 (1998).PubMed 
Article 

Google Scholar 
Waddington, C. H. Genetic assimilation of an acquired character. Evolution 7(2), 118–126 (1953).Article 

Google Scholar 
Waddington, C. H. Genetic assimilation. Adv. Genet. 10, 257–293 (1961).CAS 
PubMed 
Article 

Google Scholar 
Jablonka, E. V. A. & Raz, G. A. L. Transgenerational epigenetic inheritance: Prevalence, mechanisms, and implications for the study of heredity and evolution. Q. Rev. Biol. 84(2), 131–176 (2009).PubMed 
Article 

Google Scholar 
Steele, E. J. & Pollard, J. W. Hypothesis: Somatic hypermutation by gene conversion via the error prone DNA(longrightarrow )RNA(longrightarrow )DNA information loop. Mol. Immunol. 24(6), 667–673 (1987).CAS 
PubMed 
Article 

Google Scholar 
Steele, E. J. Somatic hypermutation in immunity and cancer: Critical analysis of strand-biased and codon-context mutation signatures. DNA Repair 45, 1–24 (2016).CAS 
PubMed 
Article 

Google Scholar 
Steele, E. J. Somatic Selection and Adaptive Evolution (Springer, US, 1979).
Google Scholar 
Steele, E. J., Lindley, R. A. & Blanden, R. V. Lamarck’s Signature (Perseus Books, 1998).
Google Scholar 
Foster, P. L. Adaptive mutation: Implications for evolution. Bioessays 22, 1067–1074 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
McCutcheon, J. P. & Moran, N. A. Extreme genome reduction in symbiotic bacteria. Nat. Rev. Microbiol. 10(1), 13–26 (2012).CAS 
Article 

Google Scholar 
Badyaev, A. V. Stress-induced variation in evolution: From behavioural plasticity to genetic assimilation. Proc. R. Soc. B Biol. Sci. 272, 877–886 (2005).Article 

Google Scholar 
Bateman, K. G. The genetic assimilation of four venation phenocopies. J. Genet. 56(3), 443–474 (1959).Article 

Google Scholar 
Milkman, R. D. The genetic basis of natural variation. VI. Selection of a crossveinless strain of Drosophila by phenocopying at high temperature. Genetics 51(1), 87 (1965).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Waddington, C. H. Genetic assimilation of the bithorax phenotype. Evolution 10(1), 1–13 (1956).Article 

Google Scholar 
Godoy, O., Saldaña, A., Fuentes, N., Valladares, F. & Gianoli, E. Forests are not immune to plant invasions: Phenotypic plasticity and local adaptation allow Prunella vulgaris to colonize a temperate evergreen rainforest. Biol. Invasions 13(7), 1615–1625 (2011).Article 

Google Scholar 
Schlichting, C. D. & Wund, M. A. Phenotypic plasticity and epigenetic marking: An assessment of evidence for genetic accommodation. Evolution 68(3), 656–672 (2014).PubMed 
Article 

Google Scholar 
Otaki, J. M., Hiyama, A., Iwata, M. & Kudo, T. Phenotypic plasticity in the range-margin population of the lycaenid butterfly Zizeeria maha. BMC Evol. Biol. 10(1), 1–13 (2010).Article 

Google Scholar 
Aubret, F. & Shine, R. Genetic assimilation and the postcolonization erosion of phenotypic plasticity in island tiger snakes. Curr. Biol. 19(22), 1932–1936 (2009).CAS 
PubMed 
Article 

Google Scholar 
Losos, J. B., Irschick, D. J. & Schoener, T. W. Adaptation and constraint in the evolution of specialization of Bahamian Anolis lizards. Evolution 48(6), 1786–1798 (1994).PubMed 
Article 

Google Scholar 
Losos, J. B. et al. Evolutionary implications of phenotypic plasticity in the hindlimb of the lizard Anolis sagrei. Evolution 54(1), 301–305 (2000).CAS 
PubMed 

Google Scholar 
Sword, G. A. Density-dependent warning coloration. Nature 397(6716), 217 (1999).ADS 
CAS 
Article 

Google Scholar 
Sword, G. A. A role for phenotypic plasticity in the evolution of aposematism. Proc. R. Soc. B Biol. Sci. 269(1501), 1639–1644 (2002).Article 

Google Scholar 
Clausen, J. & Hiesey, W. M. The balance between coherence and variation in evolution. Proc. Natl. Acad. Sci. 46(4), 494–506 (1960).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gurevitch, J. Variation in leaf dissection and leaf energy budgets among populations of Achillea from an altitudinal gradient. Am. J. Bot. 75(9), 1298–1306 (1988).Article 

Google Scholar 
Gurevitch, J. & Schuepp, P. H. Boundary layer properties of highly dissected leaves: An investigation using an electrochemical fluid tunnel. Plant Cell Environ. 13(8), 783–792 (1990).Article 

Google Scholar 
Gurevitch, J. Sources of variation in leaf shape among two populations of Achillea lanulosa. Genetics 130(2), 385–394 (1992).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Foster, P. L. Stress-induced mutagenesis in bacteria. Crit. Rev. Biochem. Mol. Biol. 42(5), 373–397 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Soppa, J. Polyploidy in archaea and bacteria: About desiccation resistance, giant cell size, long-term survival, enforcement by a eukaryotic host and additional aspects. Microb. Physiol. 24, 409–419 (2014).ADS 
CAS 
Article 

Google Scholar 
Bastide, A. & David, A. The ribosome, (slow) beating heart of cancer (stem) cell. Oncogenesis 7(4), 1–13 (2018).CAS 
Article 

Google Scholar 
Cairns, J., Overbaugh, J. & Miller, S. The origin of mutants. Nature 335, 142–145 (1988).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Foster, P. L. Adaptive mutation: The uses of adversity. Annu. Rev. Microbiol. 47, 467–504. https://doi.org/10.1146/annurev.mi.47.100193.002343 (2003).Article 

Google Scholar 
Lenski, R. E. & Mittler, J. E. The directed mutation controversy and neo-Darwinism. Science 259(5092), 188–194 (1993).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Lenski, R. E. & Sniegowski, P. D. “Adaptive mutation’’: The debate goes on. Science 269, 285–288 (1995).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Noller, H. F., Hoffarth, V. & Zimniak, L. Unusual resistance of peptidyl transferase to protein extraction procedures. Science 256(5062), 1416–1419 (1992).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Pribis, J. P. et al. Gamblers: An antibiotic-induced evolvable cell subpopulation differentiated by reactive-oxygen-induced general stress response. Mol. Cell 74(4), 785–800 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Silvera, D., Formenti, S. C. & Schneider, R. J. Translational control in cancer. Nat. Rev. Cancer 10(4), 254–266 (2010).CAS 
PubMed 
Article 

Google Scholar 
Shcherbakov, D. et al. Ribosomal mistranslation leads to silencing of the unfolded protein response and increased mitochondrial biogenesis. Commun. Biol. 2(1), 1–16 (2019).CAS 
Article 

Google Scholar 
Truitt, M. L. & Ruggero, D. New frontiers in translational control of the cancer genome. Nat. Rev. Cancer 16(5), 288–304 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Alphey, L. S., Crisanti, A., Randazzo, F. & Akbari, O. S. Opinion: Standardizing the definition of gene drive. Proc. Natl. Acad. Sci. USA 117(49), 30864 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Champer, J., Buchman, A. & Akbari, O. S. Cheating evolution: Engineering gene drives to manipulate the fate of wild populations. Nat. Rev. Genet. 17, 146–159 (2016).CAS 
PubMed 
Article 

Google Scholar 
Champer, S. E. et al. Modeling CRISPR gene drives for suppression of invasive rodents using a supervised machine learning framework. PLOS Comput. Biol. 17(12), e1009660 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Deredec, A., Burt, A. & Godfray, H. C. J. The population genetics of using homing endonuclease genes in vector and pest management. Genetics 179(4), 2013–2026 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
Heffel, M. G. & Finnigan, G. C. Mathematical modeling of self-contained CRISPR gene drive reversal systems. Sci. Rep. 9(1), 1–10 (2019).Article 
CAS 

Google Scholar 
Leftwich, P. T. et al. Recent advances in threshold-dependent gene drives for mosquitoes. Biochem. Soc. Trans. 46, 1203–1212 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nijhout, H. F., Kudla, A. M. & Hazelwood, C. C. Genetic assimilation and accommodation: Models and mechanisms. Curr. Top. Dev. Biol. 141, 337–369 (2021).PubMed 
Article 

Google Scholar 
Noble, C., Adlam, B., Church, G. M., Esvelt, K. M. & Nowak, M. A. Current CRISPR gene drive systems are likely to be highly invasive in wild populations. eLife 7, e33423 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Novozhilov, A. S., Karev, G. P. & Koonin, E. V. Mathematical modeling of evolution of horizontally transferred genes. Mol. Biol. Evol. 22(8), 1721–1732 (2005).CAS 
PubMed 
Article 

Google Scholar 
Pigliucci, M. & Murren, C. J. Perspective: Genetic assimilation and a possible evolutionary paradox: Can macroevolution sometimes be so fast as to pass us by?. Evolution 57, 1455–1464 (2003).PubMed 
Article 

Google Scholar 
Hammerstein, P. Darwinian adaptation, population genetics and the streetcar theory of evolution. J. Math. Biol. 34(5–6), 511–532 (1996).CAS 
PubMed 
MATH 
Article 

Google Scholar 
Dieckmann, U. Coevolutionary Dynamics of Stochastic Replicator Systems (Central Library of the Research Center Jülich, 1994).
Google Scholar 
Dieckmann, U., Marrow, P. & Law, R. Evolutionary cycling in predator-prey interactions: population dynamics and the red queen. J. Theor. Biol. 176(1), 91–102 (1995).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Dieckmann, U. & Law, R. The dynamical theory of coevolution: a derivation from stochastic ecological processes. J. Math. Biol. 34, 579–612 (1996).MathSciNet 
CAS 
PubMed 
MATH 
Article 

Google Scholar 
Metz, J. A. J., Nisbet, R. M. & Geritz, S. A. H. How should we define ‘fitness’ for general ecological scenarios?. Trends Ecol. Evol. 7(6), 198–202 (1992).CAS 
PubMed 
Article 

Google Scholar 
Goldschmidt, R. Some aspects of evolution. Science 78(2033), 539–547 (1933).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Vincent, T. L., Cohen, Y. & Brown, J. S. Evolution via strategy dynamics. Theor. Popul. Biol. 44(2), 149–176 (1993).MATH 
Article 

Google Scholar 
Bell, G. Evolutionary rescue. Annu. Rev. Ecol. Evol. Syst. 48, 605–627 (2017).Article 

Google Scholar  More

Lombard, F. et al. Consistent quantitative observations of planktonic ecosystems. Front. Mar. Sci. 6, 196. https://doi.org/10.3389/fmars.2019.00196 (2019).Article 

Google Scholar 
Sieracki, M. E., et al. Optical plankton imaging and analysis systems for ocean observation. Proceedings of OceanObs’09: Sustained Ocean Observations and Information for Society, 878–885 (2010). https://doi.org/10.5270/OceanObs09.cwp.81.Irisson, J.-O., Ayata, S.-D., Lindsay, D. J., Karp-Boss, L. & Stemmann, L. Machine learning for the study of plankton and marine snow from images. Ann. Rev. Mar. Sci. 14(1), 277. https://doi.org/10.1146/annurev-marine-041921-013023 (2022).Article 
PubMed 

Google Scholar 
Mars Brisbin, M., Brunner, O. D., Grossmann, M. M. & Mitarai, S. Paired high-throughput, in situ imaging and high-throughput sequencing illuminate acantharian abundance and vertical distribution. Limnol. Oceanogr. 65(12), 2953–2965. https://doi.org/10.1002/lno.11567 (2020).ADS 
CAS 
Article 

Google Scholar 
Benfield, M. et al. RAPID: Research on automated plankton identification. Oceanography 20(2), 172–187. https://doi.org/10.5670/oceanog.2007.63 (2007).Article 

Google Scholar 
Colin, S. et al. Quantitative 3D-imaging for cell biology and ecology of environmental microbial eukaryotes. Elife 6, e26066. https://doi.org/10.7554/eLife.26066 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Kim, M. K. Principles and techniques of digital holographic microscopy. J. Photonics Energy. 1, 018005. https://doi.org/10.1117/6.0000006 (2010).Article 

Google Scholar 
Tahara, T., Quan, X., Otani, R., Takaki, Y. & Matoba, O. Digital holography and its multidimensional imaging applications: A review. Microscopy 67(2), 55–67. https://doi.org/10.1093/jmicro/dfy007 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Jericho, S. K., Garcia-Sucerquia, J. F. W., Jericho, M. H. & Kreuzer, H. J. Submersible digital in-line holographic microscope. Rev. Sci. Instrum. 77(4), 043706. https://doi.org/10.1063/1.2193827 (2006).ADS 
CAS 
Article 

Google Scholar 
Bochdansky, A. B., Jericho, M. H. & Herndl, G. J. Development and deployment of a point-source digital inline holographic microscope for the study of plankton and particlesto a depth of 6000 m. Limnol. Oceanogr: Methods 11, 28–40 (2013).Article 

Google Scholar 
Yourassowsky, C. & Dubois, F. High throughput holographic imaging-in-flow for the analysis of a wide plankton size range. Opt. Express 22(6), 6661. https://doi.org/10.1364/OE.22.006661 (2014).ADS 
Article 
PubMed 

Google Scholar 
Jericho, M. H. & Kreuzer, H. J. Point source digital in-line holographic microscopy. In Coherent Light Microscopy (eds Ferraro, P. et al.) 3–30 (Springer, 2011).Chapter 

Google Scholar 
Kanka, M., Riesenberg, R. & Kreuzer, H. J. Reconstruction of high-resolution holographic microscopic images. Opt. Lett. 34(8), 1162. https://doi.org/10.1364/OL.34.001162 (2009).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Jericho, M. H., Kreuzer, H. J., Kanka, M. & Riesenberg, R. Quantitative phase and refractive index measurements with point-source digital in-line holographic microscopy. Appl. Opt. 51(10), 1503. https://doi.org/10.1364/AO.51.001503 (2012).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Wu, Y. & Ozcan, A. Lensless digital holographic microscopy and its applications in biomedicine and environmental monitoring. Methods 136, 4–16 (2018).CAS 
Article 

Google Scholar 
Sun, H. et al. digital holography for studies of marine plankton. Philos. Trans. R. Soc. A. 366, 1789–1806 (2008).ADS 
CAS 
Article 

Google Scholar 
Bianco, V. et al. microplastic identification via holographic imaging and machine learning. Adv. Intell. Syst. 2(2), 1900153. https://doi.org/10.1002/aisy.201900153 (2020).Article 

Google Scholar 
Guo, B. et al. Automated plankton classification from holographic imagery with deep convolutional neural networks. Limnol. Oceanogr. 19(1), 21–36. https://doi.org/10.1002/lom3.10402 (2021).Article 

Google Scholar 
Nayak, A. R., Malkiel, E., McFarland, M. N., Twardowski, M. S. & Sullivan, J. M. A Review of holography in the aquatic sciences: In situ characterization of particles, plankton, and small scale biophysical interactions. Front. Mar. Sci. 7, 572147. https://doi.org/10.3389/fmars.2020.572147 (2021).Article 

Google Scholar 
Di Bella, J. M., Bao, Y., Gloor, G. B., Burton, J. P. & Reid, G. High throughput sequencing methods and analysis for microbiome research. J. Microbiol. Methods 95(3), 401–414. https://doi.org/10.1016/j.mimet.2013.08.011 (2013).CAS 
Article 
PubMed 

Google Scholar 
Stoeck, T. et al. Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. Mol. Ecol. 19, 21–31. https://doi.org/10.1111/j.1365-294X.2009.04480.x (2010).CAS 
Article 
PubMed 

Google Scholar 
de Vargas, C. et al. Eukaryotic plankton diversity in the sunlit ocean. Science 348(6237), 1261605–1261605. https://doi.org/10.1126/science.1261605 (2015).CAS 
Article 
PubMed 

Google Scholar 
Lima-Mendez, G. et al. Determinants of community structure in the global plankton interactome. Science 348(6237), 1262073–1262073. https://doi.org/10.1126/science.1262073 (2015).CAS 
Article 
PubMed 

Google Scholar 
Santoferrara, L. et al. Perspectives from ten years of protist studies by high-throughput metabarcoding. J. Eukaryot. Microbiol. 67(5), 612–622. https://doi.org/10.1111/jeu.12813 (2020).Article 
PubMed 

Google Scholar 
Eickbush, T. H. & Eickbush, D. G. Finely orchestrated movements: evolution of the ribosomal RNA genes. Genetics 175(2), 477–485. https://doi.org/10.1534/genetics.107.071399 (2007).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kirkham, A. R. et al. Basin-scale distribution patterns of photosynthetic picoeukaryotes along an Atlantic Meridional Transect: Marine photosynthetic picoeukaryote community structure. Environ. Microbiol. 13(4), 975–990. https://doi.org/10.1111/j.1462-2920.2010.02403.x (2011).CAS 
Article 
PubMed 

Google Scholar 
Decelle, J. et al. PhytoREF: A reference database of the plastidial 16S rRNA gene of photosynthetic eukaryotes with curated taxonomy. Mol. Ecol. Resour. 15(6), 1435–1445. https://doi.org/10.1111/1755-0998.12401 (2015).CAS 
Article 
PubMed 

Google Scholar 
Leray, M. & Knowlton, N. Censusing marine eukaryotic diversity in the twenty-first century. Phil. Trans. R. Soc. B. 371(1702), 20150331. https://doi.org/10.1098/rstb.2015.0331 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Cowart, D. A. et al. Metabarcoding is powerful yet still blind: A comparative analysis of morphological and molecular surveys of seagrass communities. PLoS ONE 10(2), e0117562. https://doi.org/10.1371/journal.pone.0117562 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Stefanni, S. et al. Multi-marker metabarcoding approach to study mesozooplankton at basin scale. Sci. Rep. 8(1), 12085. https://doi.org/10.1038/s41598-018-30157-7 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Pappalardo, P. et al. The role of taxonomic expertise in interpretation of metabarcoding studies. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsab082 (2021).Article 

Google Scholar 
Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: And this is not optional. Front. Microbiol. 8, 2224. https://doi.org/10.3389/fmicb.2017.02224 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Zhu, F., Massana, R., Not, F., Marie, D. & Vaulot, D. Mapping of picoeucaryotes in marine ecosystems with quantitative PCR of the 18S rRNA gene. FEMS Microbiol. Ecol. 52(1), 79–92. https://doi.org/10.1016/j.femsec.2004.10.006 (2005).CAS 
Article 
PubMed 

Google Scholar 
Sargent, E. C. et al. Evidence for polyploidy in the globally important diazotroph Trichodesmium. FEMS Microbiol. Lett. 363(21), 244. https://doi.org/10.1093/femsle/fnw244 (2016).CAS 
Article 

Google Scholar 
Gong, W. & Marchetti, A. Estimation of 18S gene copy number in marine eukaryotic plankton using a next-generation sequencing approach. Front. Mar. Sci. 6, 219. https://doi.org/10.3389/fmars.2019.00219 (2019).Article 

Google Scholar 
Biard, T. et al. Biogeography and diversity of collodaria (radiolaria) in the global ocean. ISME J. 11, 1331–1344 (2017).Article 

Google Scholar 
Callahan, B. J., McMurdie, P. J. & Holmes, S. P. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 11(12), 2639–2643. https://doi.org/10.1038/ismej.2017.119 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Behrenfeld, M. J. et al. The North Atlantic aerosol and marine ecosystem study (NAAMES): Science motive and mission overview. Front. Mar. Sci. 6, 122. https://doi.org/10.3389/fmars.2019.00122 (2019).Article 

Google Scholar 
Bolaños, L. M. et al. Seasonality of the microbial community composition in the North Atlantic. Front. Mar. Sci. 8, 624164. https://doi.org/10.3389/fmars.2021.624164 (2021).Article 

Google Scholar 
Aitchison, J. The statistical analysis of compositional data. J. R. Stat. Soc. B 44(2), 139–160. https://doi.org/10.1111/j.2517-6161.1982.tb01195.x (1982).MathSciNet 
Article 
MATH 

Google Scholar 
Decelle, J. & Not, F. Acantharia. ELS, 1–10 (2015). https://doi.org/10.1002/9780470015902.a0002102.pub2.Yu, L., An, Y. & Cai, L. Numerical reconstruction of digital holograms with variable viewing angles. Opt. Express 10(22), 1250. https://doi.org/10.1364/OE.10.001250 (2002).ADS 
Article 
PubMed 

Google Scholar 
Della Penna, A. & Gaube, P. Overview of (sub)mesoscale Ocean dynamics for the NAAMES field program. Front. Mar. Sci. 6, 384. https://doi.org/10.3389/fmars.2019.00384 (2019).Article 

Google Scholar 
Sverdrup, H. U. Oceanography for Meteorologists (Prentice Hall, 1942).Book 

Google Scholar 
Mahadevan, A. The impact of submesoscale physics on primary productivity of plankton. Annu. Rev. Mar. Sci. 8(1), 161–184. https://doi.org/10.1146/annurev-marine-010814-015912 (2016).ADS 
Article 

Google Scholar 
Fratantoni, P. S. & Pickart, R. S. The Western North Atlantic shelfbreak current system in summer. J. Phys. Oceanogr. 37(10), 2509–2533. https://doi.org/10.1175/JPO3123.1 (2007).ADS 
Article 

Google Scholar 
Bolaños, L. M. et al. Small phytoplankton dominate western North Atlantic biomass. ISME J. 14(7), 1663–1674. https://doi.org/10.1038/s41396-020-0636-0 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kramer, S. J., Siegel, D. A. & Graff, J. R. Phytoplankton community composition determined from co-variability among phytoplankton pigments from the NAAMES field campaign. Front. Mar. Sci. 7, 215. https://doi.org/10.3389/fmars.2020.00215 (2020).Article 

Google Scholar 
Faure, E. et al. Mixotrophic protists display contrasted biogeographies in the global ocean. ISME J. 13(4), 1072–1083. https://doi.org/10.1038/s41396-018-0340-5 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Fratantoni, P. S. & McCartney, M. S. Freshwater export from the labrador current to the North Atlantic Current at the tail of the grand banks of Newfoundland. Deep Sea Res. I. 57(2), 258–283. https://doi.org/10.1016/j.dsr.2009.11.006 (2010).Article 

Google Scholar 
Torti, A., Lever, M. A. & Jørgensen, B. B. Origin, dynamics, and implications of extracellular DNA pools in marine sediments. Mar. Genom. 24, 185–196. https://doi.org/10.1016/j.margen.2015.08.007 (2015).Article 

Google Scholar 
Jian, C., Salonen, A. & Korpela, K. Commentary: How to count our microbes? The effect of different quantitative microbiome profiling approaches. Front. Cell. Infect. Microbiol. 11, 627910. https://doi.org/10.3389/fcimb.2021.627910 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Djurhuus, A. et al. Evaluation of marine zooplankton community structure through environmental DNA metabarcoding: Metabarcoding zooplankton from eDNA. Limnol. Oceanogr. Methods 16(4), 209–221. https://doi.org/10.1002/lom3.10237 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
del Campo, J. et al. The others: Our biased perspective of eukaryotic genomes. Trends Ecol. Evol. 29(5), 252–259. https://doi.org/10.1016/j.tree.2014.03.006 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Karst, S. M. et al. Retrieval of a million high-quality, full-length microbial 16S and 18S rRNA gene sequences without primer bias. Nat. Biotech. 36(2), 190–195. https://doi.org/10.1038/nbt.4045 (2018).CAS 
Article 

Google Scholar 
Johnson, J. S. et al. Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis. Nat. Commun. 10(1), 5029. https://doi.org/10.1038/s41467-019-13036-1 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Callahan, B. J. et al. High-throughput amplicon sequencing of the full-length 16S rRNA gene with single-nucleotide resolution. Nucleic Acids Res. 47(18), e103–e103. https://doi.org/10.1093/nar/gkz569 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Lin, Y., Gifford, S., Ducklow, H., Schofield, O. & Cassar, N. Towards quantitative microbiome community profiling using internal standards. Appl. Environ. Microbiol. 85(5), 18. https://doi.org/10.1128/AEM.02634-18 (2019).Article 

Google Scholar 
Vogt, M. et al. Global marine plankton functional type biomass distributions: Phaeocystis spp. Earth Syst. Sci. Data 5, 405–443. https://doi.org/10.5194/essdd-5-405-2012 (2012).ADS 
Article 

Google Scholar 
MacNeil, L., Missan, S., Luo, J., Trappenberg, T. & LaRoche, J. Plankton classification with high-throughput submersible holographic microscopy and transfer learning. BMC Ecol. Evol. 21(1), 123. https://doi.org/10.1186/s12862-021-01839-0 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Pan, J., del Campo, J. & Keeling, P. J. Reference tree and environmental sequence diversity of labyrinthulomycetes. J. Eukary. Microbiol. 64(1), 88–96. https://doi.org/10.1111/jeu.12342 (2017).Article 

Google Scholar 
Bochdansky, A. B., Clouse, M. A. & Herndl, G. J. Eukaryotic microbes, principally fungi and labyrinthulomycetes, dominate biomass on bathypelagic marine snow. ISME J. 11(2), 362–373. https://doi.org/10.1038/ismej.2016.113 (2017).Article 
PubMed 

Google Scholar 
Xie, N., Hunt, D. E., Johnson, Z. I., He, Y. & Wang, G. Annual partitioning patterns of Labyrinthulomycetes protists reveal their multifaceted role in marine microbial food webs. Appl. Environ. Microbiol. https://doi.org/10.1128/AEM.01652-20 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Walcutt, N. L. et al. Assessment of holographic microscopy for quantifying marine particle size and concentration. Limnol. Oceanogr. Methods 3, 10379. https://doi.org/10.1002/lom3.10379 (2020).Article 

Google Scholar 
Axler, K. et al. Fine-scale larval fish distributions and predator-prey dynamics in a coastal river-dominated ecosystem. Mar. Ecol. Prog. Ser. 650, 37–61. https://doi.org/10.3354/meps13397 (2020).ADS 
Article 

Google Scholar 
Trudnowska, E. et al. Marine snow morphology illuminates the evolution of phytoplankton blooms and determines their subsequent vertical export. Nat. Commun. 12(1), 2816. https://doi.org/10.1038/s41467-021-22994-4 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
González, P. et al. Automatic plankton quantification using deep features. J. Plankton Res. 41(4), 449–463. https://doi.org/10.1093/plankt/fbz023 (2019).Article 

Google Scholar 
Briseño-Avena, C. et al. Three-dimensional cross-shelf zooplankton distributions off the Central Oregon Coast during anomalous oceanographic conditions. Prog. Oceanogr. 188, 102436. https://doi.org/10.1016/j.pocean.2020.102436 (2020).Article 

Google Scholar 
Biard, T. et al. In situ imaging reveals the biomass of giant protists in the global ocean. Nature 532, 504–507 (2016).ADS 
CAS 
Article 

Google Scholar 
Orenstein, E. C. et al. The scripps plankton camera system: A framework and platform for in situ microscopy. Limnol. Oceanogr. Methods 18(11), 681–695. https://doi.org/10.1002/lom3.10394 (2020).Article 

Google Scholar 
Fowler, B. L. et al. Dynamics and functional diversity of the smallest phytoplankton on the Northeast US Shelf. PNAS 117(22), 12215–12221. https://doi.org/10.1073/pnas.1918439117 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Tréguer, P. et al. Influence of diatom diversity on the ocean biological carbon pump. Nat. Geosci. 11(1), 27–37. https://doi.org/10.1038/s41561-017-0028-x (2018).ADS 
CAS 
Article 

Google Scholar 
Ryabov, A. et al. Shape matters: The relationship between cell geometry and diversity in phytoplankton. Ecol. Lett. 24(4), 847–861. https://doi.org/10.1111/ele.13680 (2021).MathSciNet 
Article 
PubMed 

Google Scholar 
Keeling, P. J. & del Campo, J. marine protists are not just big bacteria. Curr. Biol. 27(11), R541–R549. https://doi.org/10.1016/j.cub.2017.03.075 (2017).CAS 
Article 
PubMed 

Google Scholar 
Sgubin, G., Swingedouw, D., Drijfhout, S., Mary, Y. & Bennabi, A. Abrupt cooling over the North Atlantic in modern climate models. Nat. Commun. 8(1), 14375. https://doi.org/10.1038/ncomms14375 (2017).CAS 
Article 
PubMed Central 

Google Scholar 
Desbruyères, D., Chafik, L. & Maze, G. A shift in the ocean circulation has warmed the subpolar North Atlantic Ocean since 2016. Commun. Earth Environ. 2(1), 48. https://doi.org/10.1038/s43247-021-00120-y (2021).ADS 
Article 

Google Scholar 
Mitchell, M. R. et al. Atlantic zone monitoring program protocol. Can. Tech. Rep. Hydrogr. Ocean Sci. 223, 1–23 (2002).
Google Scholar 
Li, W. K. W., Glen Harrison, W. & Head, E. J. H. Coherent assembly of phytoplankton communities in diverse temperate ocean ecosystems. Proc. R. Soc. B. 273(1596), 1953–1960. https://doi.org/10.1098/rspb.2006.3529 (2006).Article 
PubMed 
PubMed Central 

Google Scholar 
Richardson, P. L. Florida current, gulf stream, and labrador current. In Encyclopedia of Ocean Sciences (ed. Steele, J. H.) 1054–1064 (Academic Press, 2001). https://doi.org/10.1006/rwos.2001.0357.Chapter 

Google Scholar 
Henson, S. A., Dunne, J. P. & Sarmiento, J. L. Decadal variability in North Atlantic phytoplankton blooms. J. Geophys. Res. 114(C4), C04013. https://doi.org/10.1029/2008JC005139 (2009).ADS 
CAS 
Article 

Google Scholar 
Han, G., Lu, Z., Wang, Z., Helbig, J. & Chen, N. Seasonal variability of the labrador current and shelf circulation off Newfoundland. J. Geophys. Res. 113, 10. https://doi.org/10.1029/2007JC004376 (2008).Article 

Google Scholar 
Pante, E. & Simon-Bouhet, B. marmap: A package for importing, plotting and analyzing bathymetric and topographic data in R. PLoS ONE 8(9), e73051. https://doi.org/10.1371/journal.pone.0073051 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kelley, D. “The Oce Package” In Oceanographic Analysis with R 91–101 (Springer, 2018).Book 

Google Scholar 
Oksanen, J., et al. vegan: Community Ecology Package. R package version 2.5-7 (2020). https://CRAN.R-project.org/package=vegan.Tomas, C. R. Identifying Marine Phytoplankton (Academic Press Inc, 1997).
Google Scholar 
Comeau, A. M., Li, W. K. W., Tremblay, J. -É., Carmack, E. C. & Lovejoy, C. Arctic ocean microbial community structure before and after the 2007 record sea ice minimum. PLoS ONE 6(11), e27492. https://doi.org/10.1371/journal.pone.0027492 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: Assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples: Primers for marine microbiome studies. Environ. Microbiol. 18(5), 1403–1414. https://doi.org/10.1111/1462-2920.13023 (2016).CAS 
Article 
PubMed 

Google Scholar 
Walters, W. et al. Improved bacterial 16S rRNA gene (V4 and V4–5) and fungal internal transcribed spacer marker gene primers for microbial community surveys. MSystems https://doi.org/10.1128/mSystems.00009-15 (2016).Article 
PubMed 

Google Scholar 
Comeau, A. M., Douglas, G. M. & Langille, M. G. I. Microbiome helper: A custom and streamlined workflow for microbiome research. MSystems 2(1), e00127-e216. https://doi.org/10.1128/mSystems.00127-16 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotech. 37(8), 852–857. https://doi.org/10.1038/s41587-019-0209-9 (2019).CAS 
Article 

Google Scholar 
Amir, A. et al. Deblur rapidly resolves single-nucleotide community sequence patterns. MSystems 2(2), e00191-e216. https://doi.org/10.1128/mSystems.00191-16 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Guillou, L. et al. The protist ribosomal reference database (PR2): A catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 41(D1), D597–D604. https://doi.org/10.1093/nar/gks1160 (2013).CAS 
Article 
PubMed 

Google Scholar 
Mohsen, A., Park, J., Chen, Y.-A., Kawashima, H. & Mizuguchi, K. Impact of quality trimming on the efficiency of reads joining and diversity analysis of Illumina paired-end reads in the context of QIIME1 and QIIME2 microbiome analysis frameworks. BMC Bioinform. 20(1), 581. https://doi.org/10.1186/s12859-019-3187-5 (2019).Article 

Google Scholar 
Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6(1), 90. https://doi.org/10.1186/s40168-018-0470-z (2018).MathSciNet 
Article 
PubMed 
PubMed Central 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41(D1), D590–D596. https://doi.org/10.1093/nar/gks1219 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2021). https://www.R-project.org/.McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8(4), e61217. https://doi.org/10.1371/journal.pone.0061217 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Willis, A. & Bunge, J. Estimating diversity via frequency ratios: estimating diversity via ratios. Biometrics 71(4), 1042–1049. https://doi.org/10.1111/biom.12332 (2015).MathSciNet 
Article 
PubMed 
MATH 

Google Scholar 
Willis, A. D. Rarefaction, alpha diversity, and statistics. Front. Microbiol. 10, 2407. https://doi.org/10.3389/fmicb.2019.02407 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Quinn, T. P. et al. A field guide for the compositional analysis of any-omics data. GigaScience 8(9), 107. https://doi.org/10.1093/gigascience/giz107 (2019).CAS 
Article 

Google Scholar 
Silverman, J. D., Roche, K., Mukherjee, S. & David, L. A. Naught all zeros in sequence count data are the same. Comput. Struct. Biotech. J. 18, 2789–2798. https://doi.org/10.1016/j.csbj.2020.09.014 (2020).CAS 
Article 

Google Scholar 
Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral. Ecol. 26, 32–46 (2001).
Google Scholar  More

Sun, C., Zhang, J., Ma, Q., Chen, Y. & Ju, H. Polycyclic aromatic hydrocarbons (PAHs) in water and sediment from a river basin: Sediment–water partitioning, source identification and environmental health risk assessment. Environ. Geochem. Health 39, 63–74 (2017).CAS 
PubMed 
Article 

Google Scholar 
Savari, M. & Shokati Amghani, M. Factors influencing farmers’ adaptation strategies in confronting the drought in Iran. Environ. Dev. Sustain. 2020 234 23, 4949–4972 (2020).Article 

Google Scholar 
Kumar Singh, P., Dey, P., Kumar Jain, S. & Mujumdar, P. P. Hydrology and water resources management in ancient India. Hydrol. Earth Syst. Sci. 24, 4691–4707 (2020).ADS 
Article 

Google Scholar 
Warner, L. A. & Diaz, J. M. Amplifying the Theory of Planned behavior with connectedness to water to inform impactful water conservation program planning and evaluation. J. Agric. Educ. Ext. 27, 229–253 (2021).Article 

Google Scholar 
Warner, L. A. Who conserves and who approves? Predicting water conservation intentions in urban landscapes with referent groups beyond the traditional ‘important others’. Urban For. Urban Green. 60, 127070 (2021).Article 

Google Scholar 
Savari, M., Eskandari Damaneh, H. & Eskandari Damaneh, H. Drought vulnerability assessment: Solution for risk alleviation and drought management among Iranian farmers. Int. J. Disaster Risk Reduct. 67, 102654 (2022).Article 

Google Scholar 
Eskandari Damaneh, H. et al. Testing possible scenario-based responses of vegetation under expected climatic changes in Khuzestan Province. Air Soil Water Res. https://doi.org/10.1177/1178622121101333214 (2021).Article 

Google Scholar 
Eskandari Damaneh, H., Khosravi, H., Habashi, K., Eskandari Damaneh, H. & Tiefenbacher, J. P. The impact of land use and land cover changes on soil erosion in western Iran. Nat. Hazards 110, 2185–2205 (2022).Article 

Google Scholar 
Savari, M., Abdeshahi, A., Gharechaee, H. & Nasrollahian, O. Explaining farmers’ response to water crisis through theory of the norm activation model: Evidence from Iran. Int. J. Disaster Risk Reduct. 60, 102284 (2021).Article 

Google Scholar 
Liu, J., Scanlon, B. R., Zhuang, J. & Varis, O. Food-energy-water nexus for multi-scale sustainable development. Resour. Conserv. Recycl. 154, 104565 (2020).Article 

Google Scholar 
Araya, F., Osman, K. & Faust, K. M. Perceptions versus reality: Assessing residential water conservation efforts in the household. Resour. Conserv. Recycl. 162, 105020 (2020).Article 

Google Scholar 
Omer, A., Elagib, N. A., Zhuguo, M., Saleem, F. & Mohammed, A. Water scarcity in the Yellow River Basin under future climate change and human activities. Sci. Total Environ. 749, 141446 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Aslam, S. et al. Sustainable model: Recommendations for water conservation strategies in a developing country through a psychosocial wellness program. Water (Switzerland) 13, 1–20 (2021).
Google Scholar 
Diaz, J., Odera, E. & Warner, L. Delving deeper: Exploring the influence of psycho-social wellness on water conservation behavior. J. Environ. Manag. 264, 110404 (2020).Article 

Google Scholar 
Fader, M., Shi, S., Von Bloh, W., Bondeau, A. & Cramer, W. Mediterranean irrigation under climate change: More efficient irrigation needed to compensate for increases in irrigation water requirements. Hydrol. Earth Syst. Sci. 20, 953–973 (2016).ADS 
CAS 
Article 

Google Scholar 
Brown, T. C., Mahat, V. & Ramirez, J. A. Adaptation to future water shortages in the United States caused by population growth and climate change. Earth’s Future 7, 219–234 (2019).ADS 
Article 

Google Scholar 
Lall, U., Josset, L. & Russo, T. A snapshot of the world’s groundwater challenges. Annu. Rev. Environ. Resour. 45, 171–194 (2020).Article 

Google Scholar 
Jin, J. et al. Impacts of climate change on hydrology in the Yellow River Source Region, China. J. Water Clim. Change 11, 916–930 (2020).Article 

Google Scholar 
Cochand, F., Brunner, P., Hunkeler, D., Rössler, O. & Holzkämper, A. Cross-sphere modelling to evaluate impacts of climate and land management changes on groundwater resources. Sci. Total Environ. 798, 148759 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Waha, K. et al. Climate change impacts in the Middle East and Northern Africa (MENA) region and their implications for vulnerable population groups. Reg. Environ. Change 17, 1623–1638 (2017).Article 

Google Scholar 
Boretti, A. & Rosa, L. Reassessing the projections of the World Water Development Report. npj Clean Water 2, 1–6 (2019).Article 

Google Scholar 
Fragaszy, S. R. et al. Drought monitoring in the Middle East and North Africa (MENA) region. Bull. Am. Meteorol. Soc. 101, 1148–1173 (2020).Article 

Google Scholar 
Tajeri moghadam, M., Raheli, H., Zarifian, S. & Yazdanpanah, M. The power of the health belief model (HBM) to predict water demand management: A case study of farmers’ water conservation in Iran. J. Environ. Manag. 263, 110388 (2020).Article 

Google Scholar 
Marston, L., Ao, Y., Konar, M., Mekonnen, M. M. & Hoekstra, A. Y. High-resolution water footprints of production of the United States. Water Resour. Res. 54, 2288–2316 (2018).ADS 
Article 

Google Scholar 
Savari, M. & Shokati Amghani, M. SWOT-FAHP-TOWS analysis for adaptation strategies development among small-scale farmers in drought conditions. Int. J. Disaster Risk Reduct. 67, 102695 (2022).Article 

Google Scholar 
Savari, M. & Moradi, M. The effectiveness of drought adaptation strategies in explaining the livability of Iranian rural households. Habitat Int. 124, 102560 (2022).Article 

Google Scholar 
Warner, L., Chaudhary, A. K., Rumble, J., Lamm, A. & Momol, E. Using audience segmentation to tailor residential irrigation water conservation programs. J. Agric. Educ. 58, 313–333 (2017).Article 

Google Scholar 
Tapsuwan, S., Cook, S. & Moglia, M. Willingness to pay for rainwater tank features: A post-drought analysis of Sydney water users. Water (Switzerland) 10, 1199 (2018).
Google Scholar 
Chubaka, C. E., Whiley, H., Edwards, J. W. & Ross, K. E. A review of roof harvested rainwater in Australia. J. Environ. Public Health 2018, 6471324 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Smith, H. M., Brouwer, S., Jeffrey, P. & Frijns, J. Public responses to water reuse—Understanding the evidence. J. Environ. Manag. 207, 43–50 (2018).CAS 
Article 

Google Scholar 
Addo, I. B., Thoms, M. C. & Parsons, M. Barriers and drivers of household water-conservation behavior: A profiling approach. Water (Switzerland) 10, 1794 (2018).
Google Scholar 
Jarrett, W. B. A survey of the influences on water conservation behavior in Pickens and Oconee counties (2015).Yazdanpanah, M., Forouzani, M., Abdeshahi, A. & Jafari, A. Investigating the effect of moral norm and self-identity on the intention toward water conservation among Iranian young adults. Water Policy 18, 73–90 (2016).Article 

Google Scholar 
Sabzali Parikhani, R., Sadighi, H. & Bijani, M. Ecological consequences of nanotechnology in agriculture: Researchers’ perspective. J. Agric. Sci. Technol. 20, 205–219 (2018).
Google Scholar 
Moglia, M., Cook, S. & Tapsuwan, S. Promoting water conservation: Where to from here?. Water (Switzerland) 10, 1510 (2018).
Google Scholar 
Savari, M. & Zhoolideh, M. The role of climate change adaptation of small-scale farmers on the households food security level in the west of Iran. Dev. Pract. 31, 650–664 (2021).Article 

Google Scholar 
Bennett, N. J. et al. Conservation social science: Understanding and integrating human dimensions to improve conservation. Biol. Conserv. 205, 93–108 (2017).Article 

Google Scholar 
Kumar Chaudhary, A., Lamm, A. & Warner, L. Using cognitive dissonance to theoretically explain water conservation intentions. J. Agric. Educ. 59, 194–210 (2018).Article 

Google Scholar 
Russell, S. V. & Knoeri, C. Exploring the psychosocial and behavioural determinants of household water conservation and intention. Int. J. Water Resour. Dev. 36, 940–955 (2020).Article 

Google Scholar 
Savari, M., Yazdanpanah, M. & Rouzaneh, D. Factors affecting the implementation of soil conservation practices among Iranian farmers. Sci. Rep. 12, 1–13 (2022).Article 
CAS 

Google Scholar 
Savari, M., Zhoolideh, M. & Khosravipour, B. Explaining pro-environmental behavior of farmers: A case of rural Iran. Curr. Psychol. https://doi.org/10.1007/S12144-021-02093-9 (2021).Article 

Google Scholar 
Lee, M. & Tansel, B. Water conservation quantities vs customer opinion and satisfaction with water efficient appliances in Miami, Florida. J. Environ. Manag. 128, 683–689 (2013).Article 

Google Scholar 
Yazdanpanah, M., Klein, K., Zobeidi, T., Sieber, S. & Löhr, K. Why have economic incentives failed to convince farmers to adopt drip irrigation in southwestern Iran?. Sustainability 14, 1–15 (2022).Article 

Google Scholar 
Zobeidi, T., Yaghoubi, J. & Yazdanpanah, M. Developing a paradigm model for the analysis of farmers’ adaptation to water scarcity. Environ. Dev. Sustain. 24, 5400–5425 (2022).Article 

Google Scholar 
Russell, S. & Fielding, K. Water demand management research: A psychological perspective. Water Resour. Res. 46, 1–12 (2010).Article 

Google Scholar 
Shahangian, S. A., Tabesh, M., Yazdanpanah, M., Zobeidi, T. & Raoof, M. A. Promoting the adoption of residential water conservation behaviors as a preventive policy to sustainable urban water management. J. Environ. Manag. 313, 115005 (2022).Article 

Google Scholar 
Onwezen, M. C., Antonides, G. & Bartels, J. The Norm Activation Model: An exploration of the functions of anticipated pride and guilt in pro-environmental behaviour. J. Econ. Psychol. 39, 141–153 (2013).Article 

Google Scholar 
Shahangian, S. A., Tabesh, M. & Yazdanpanah, M. Psychosocial determinants of household adoption of water-efficiency behaviors in Tehran capital, Iran: Application of the social cognitive theory. Urban Clim. 39, 100935 (2021).Article 

Google Scholar 
Yazdanpanah, M., Feyzabad, F. R., Forouzani, M., Mohammadzadeh, S. & Burton, R. J. F. Predicting farmers’ water conservation goals and behavior in Iran: A test of social cognitive theory. Land Use Policy 47, 401–407 (2015).Article 

Google Scholar 
Valizadeh, N., Bijani, M., Hayati, D. & Fallah Haghighi, N. Social-cognitive conceptualization of Iranian farmers’ water conservation behavior. Hydrogeol. J. 27, 1131–1142 (2019).ADS 
Article 

Google Scholar 
Greaves, M., Zibarras, L. D. & Stride, C. Using the theory of planned behavior to explore environmental behavioral intentions in the workplace. J. Environ. Psychol. 34, 109–120 (2013).Article 

Google Scholar 
Wang, Y. et al. Analysis of the environmental behavior of farmers for non-point source pollution control and management: An integration of the theory of planned behavior and the protection motivation theory. J. Environ. Manag. 237, 15–23 (2019).Article 

Google Scholar 
Savari, M. & Gharechaee, H. Application of the extended theory of planned behavior to predict Iranian farmers’ intention for safe use of chemical fertilizers. J. Clean. Prod. 263, 121512 (2020).CAS 
Article 

Google Scholar 
Strydom, W. F. Applying the theory of planned behavior to recycling behavior in South Africa. Recycling 3, 43 (2018).Article 

Google Scholar 
Lam, S. P. Predicting intention to save water: Theory of planned behavior, response efficacy, vulnerability, and perceived efficiency of alternative solutions. J. Appl. Soc. Psychol. 36, 2803–2824 (2006).Article 

Google Scholar 
Abdulkarim, B., Yacob, M. R., Abdullahi, A. M. & Radam, A. Farmers’ perceptions and attitudes toward forest watershed conservation of the North Selangor Peat Swamp Forest. J. Sustain. For. 36, 309–323 (2017).
Google Scholar 
Yuriev, A., Dahmen, M., Paillé, P., Boiral, O. & Guillaumie, L. Pro-environmental behaviors through the lens of the theory of planned behavior: A scoping review. Resour. Conserv. Recycl. 155, 104660 (2020).Article 

Google Scholar 
Bosnjak, M., Ajzen, I. & Schmidt, P. Editorial the theory of planned behavior: Selected recent advances and applications (1841).Ajzen, I. Consumer attitudes and behavior: The theory of planned behavior applied to food consumption decisions. Ital. Rev. Agric. Econ. 70(2), 121–138. https://doi.org/10.13128/REA-18003 (2015).Article 

Google Scholar 
Soorani, F. & Ahmadvand, M. Determinants of consumers’ food management behavior: Applying and extending the theory of planned behavior. Waste Manag. 98, 151–159 (2019).PubMed 
Article 

Google Scholar 
Popa, B., Niță, M. D. & Hălălișan, A. F. Intentions to engage in forest law enforcement in Romania: An application of the theory of planned behavior. For. Policy Econ. 100, 33–43 (2019).Article 

Google Scholar 
Tam, K. P. Understanding the psychology X politics interaction behind environmental activism: The roles of governmental trust, density of environmental NGOs, and democracy. J. Environ. Psychol. 71, 101330 (2020).Article 

Google Scholar 
Ajzen, I. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50, 179–211 (1991).Article 

Google Scholar 
Icek, A. From intentions to actions: A theory of planned behavior. in Action Control 11–39 (1985).Empidi, A. V. A. & Emang, D. Understanding public intentions to participate in protection initiatives for forested watershed areas using the theory of planned behavior: A case study of Cameron highlands in Pahang, Malaysia. Sustainability 13, 4399 (2021).Article 

Google Scholar 
Holt, J. R. et al. Using the theory of planned behavior to understand family forest owners’ intended responses to invasive forest insects. Soc. Nat. Resour. 34, 1001–1018 (2021).Article 

Google Scholar 
Marcos, K. J., Moersidik, S. S. & Soesilo, T. E. B. Extended theory of planned behavior on utilizing domestic rainwater harvesting in Bekasi, West Java, Indonesia. IOP Conf. Ser. Earth Environ. Sci. 716, 012054 (2021).Article 

Google Scholar 
Sánchez, M., López-Mosquera, N., Lera-López, F. & Faulin, J. An extended planned behavior model to explain the willingness to pay to reduce noise pollution in road transportation. J. Clean. Prod. 177, 144–154 (2018).Article 

Google Scholar 
Fernandez, M. E., Ruiter, R. A. C., Markham, C. M. & Kok, G. Intervention mapping: Theory-and evidence-based health promotion program planning: Perspective and examples. Front. Public Health 7, 209 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Zhong, F. et al. Quantifying the influence path of water conservation awareness on water-saving irrigation behavior based on the theory of planned behavior and structural equation modeling: A case study from Northwest China. Sustainability 11, 1–16 (2019).
Google Scholar 
Ullah, S. et al. Predicting behavioral intention of rural inhabitants toward economic incentive for deforestation in Gilgit-Baltistan, Pakistan. Sustainability 13, 1–17 (2021).
Google Scholar 
Koop, S. H. A., Van Dorssen, A. J. & Brouwer, S. Enhancing domestic water conservation behaviour: A review of empirical studies on influencing tactics. J. Environ. Manag. 247, 867–876 (2019).CAS 
Article 

Google Scholar 
Goh, E., Ritchie, B. & Wang, J. Non-compliance in national parks: An extension of the theory of planned behaviour model with pro-environmental values. Tour. Manag. 59, 123–127 (2017).Article 

Google Scholar 
Liang, Y., Kee, K. F. & Henderson, L. K. Towards an integrated model of strategic environmental communication: Advancing theories of reactance and planned behavior in a water conservation context. J. Appl. Commun. Res. 46, 135–154 (2018).CAS 
Article 

Google Scholar 
Gkargkavouzi, A., Halkos, G. & Matsiori, S. Environmental behavior in a private-sphere context: Integrating theories of planned behavior and value belief norm, self-identity and habit. Resour. Conserv. Recycl. 148, 145–156 (2019).Article 

Google Scholar 
Vaske, J. J., Landon, A. C. & Miller, C. A. Normative influences on farmers’ intentions to practice conservation without compensation. Environ. Manag. 66, 191–201 (2020).Article 

Google Scholar 
Nguru, W. M., Gachene, C. K., Onyango, C. M., Ng’ang’a, S. K. & Girvetz, E. H. Factors constraining the adoption of soil organic carbon enhancing technologies among small-scale farmers in Ethiopia. Heliyon 7, e08497 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Møller, M., Haustein, S. & Bohlbro, M. S. Adolescents’ associations between travel behaviour and environmental impact: A qualitative study based on the Norm-Activation Model. Travel Behav. Soc. 11, 69–77 (2018).Article 

Google Scholar 
Savari, M., Naghibeiranvand, F. & Asadi, Z. Modeling environmentally responsible behaviors among rural women in the forested regions in Iran. Glob. Ecol. Conserv. 35, e02102 (2022).Article 

Google Scholar 
van Valkengoed, A. M. & Steg, L. Meta-analyses of factors motivating climate change adaptation behaviour. Nat. Clim. Chang. 9, 158–163 (2019).ADS 
Article 

Google Scholar 
Maduku, D. K. Water conservation campaigns in an emerging economy: How effective are they?. Int. J. Advert. 40, 452–472 (2021).Article 

Google Scholar 
Thøgersen, J. & Grønhøj, A. Electricity saving in households—A social cognitive approach. Energy Policy 38, 7732–7743 (2010).Article 

Google Scholar 
Ouellette, J. A. & Wood, W. Habit and intention in everyday life: The multiple processes by which past behavior predicts future behavior. Psychol. Bull. 124, 54–74 (1998).Article 

Google Scholar 
Ajzen, I. The theory of planned behavior: Frequently asked questions. Hum. Behav. Emerg. Technol. 2, 314–324 (2020).Article 

Google Scholar 
Hofmann, W., Gschwendner, T., Friese, M., Wiers, R. W. & Schmitt, M. Working memory capacity and self-regulatory behavior: toward an individual differences perspective on behavior determination by automatic versus controlled processes. J. Pers. Soc. Psychol. 95, 962–977 (2008).PubMed 
Article 

Google Scholar 
Jorgensen, B. S., Martin, J. F., Pearce, M. W. & Willis, E. M. Aligning theory and measurement in behavioral models of water conservation. Water Policy 17, 762–776 (2015).Article 

Google Scholar 
Barr, S. & Gilg, A. W. A conceptual framework for understanding and analyzing attitudes towards environmental behaviour. Geogr. Ann. Ser. B Hum. Geogr. 89 B, 361–379 (2007).Article 

Google Scholar 
Hansmann, R., Bernasconi, P., Smieszek, T., Loukopoulos, P. & Scholz, R. W. Justifications and self-organization as determinants of recycling behavior: The case of used batteries. Resour. Conserv. Recycl. 47, 133–159 (2006).Article 

Google Scholar 
Tang, Z., Chen, X. & Luo, J. Determining socio-psychological drivers for rural household recycling behavior in developing countries: A case study from Wugan, Hunan, China. Environ. Behav. 43, 848–877 (2011).Article 

Google Scholar 
Krejcie, R. V. & Morgan, W. D. (1970) “Determining sample size for research activities”, educational and psychological measurement. Int. J. Employ. Stud. 18, 89–123 (1996).
Google Scholar 
Gregory, G. D. & Di Leo, M. Repeated behavior and environmental psychology: The role of personal involvement and habit formation in explaining water consumption. J. Appl. Soc. Psychol. 33, 1261–1296 (2003).Article 

Google Scholar 
Keramitsoglou, K. M. & Tsagarakis, K. P. Raising effective awareness for domestic water saving: Evidence from an environmental educational programme in Greece. Water Policy 13, 828–844 (2011).Article 

Google Scholar 
Chaudhary, A. K. et al. Using the theory of planned behavior to encourage water conservation among extension clients. J. Agric. Educ. 58, 185–202 (2017).Article 

Google Scholar 
Pradhananga, A. K., Davenport, M. A., Fulton, D. C., Maruyama, G. M. & Current, D. An integrated moral obligation model for landowner conservation norms. Soc. Nat. Resour. 30, 212–227 (2017).Article 

Google Scholar 
Heath, Y. & Gifford, R. Extending the theory of planned behavior: Predicting the use of public transportation. J. Appl. Soc. Psychol. 32, 2154–2189 (2002).Article 

Google Scholar 
Bodimeade, H. et al. Testing the direct, indirect, and interactive roles of referent group injunctive and descriptive norms for sun protection in relation to the theory of planned behavior. J. Appl. Soc. Psychol. 44, 739–750 (2014).Article 

Google Scholar 
Veisi, K., Bijani, M. & Abbasi, E. A human ecological analysis of water conflict in rural areas: Evidence from Iran. Glob. Ecol. Conserv. 23, e01050 (2020).Article 

Google Scholar 
Botetzagias, I., Dima, A. F. & Malesios, C. Extending the Theory of Planned Behavior in the context of recycling: The role of moral norms and of demographic predictors. Resour. Conserv. Recycl. 95, 58–67 (2015).Article 

Google Scholar 
Martínez-Espiñeira, R., García-Valiñas, M. A. & Nauges, C. Households’ pro-environmental habits and investments in water and energy consumption: Determinants and relationships. J. Environ. Manag. 133, 174–183 (2014).Article 

Google Scholar 
Dolnicar, S., Hurlimann, A. & Grün, B. Water conservation behavior in Australia. J. Environ. Manag. 105, 44–52 (2012).Article 

Google Scholar 
Untaru, E. N., Ispas, A., Candrea, A. N., Luca, M. & Epuran, G. Predictors of individuals’ intention to conserve water in a lodging context: The application of an extended Theory of Reasoned Action. Int. J. Hosp. Manag. 59, 50–59 (2016).Article 

Google Scholar 
Khoshmaram, M., Shiri, N., Shinnar, R. S. & Savari, M. Environmental support and entrepreneurial behavior among Iranian farmers: The mediating roles of social and human capital. J. Small Bus. Manag. https://doi.org/10.1111/jsbm.1250158,1064-1088 (2020).Article 

Google Scholar 
Benitez, J., Henseler, J., Castillo, A. & Schuberth, F. How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research. Inf. Manag. 57, 103168 (2020).Article 

Google Scholar 
Sarstedt, M., Ringle, C. M. & Hair, J. F. Partial least squares structural equation modeling. in Handbook of Market Research 1–47. https://doi.org/10.1007/978-3-319-05542-8_15-2 (2021).Clark, W. A. & Finley, J. C. Determinants of water conservation intention in Blagoevgrad, Bulgaria. Soc. Nat. Resour. 20, 613–627 (2007).Article 

Google Scholar 
De Dominicis, S., Sokoloski, R., Jaeger, C. M. & Schultz, P. W. Making the smart meter social promotes long-term energy conservation. Palgrave Commun. 5, 1–8 (2019).Article 

Google Scholar 
Wang, S., Hung, K. & Huang, W.-J. Motivations for entrepreneurship in the tourism and hospitality sector: A social cognitive theory perspective. Int. J. Hosp. Manag. https://doi.org/10.1016/j.ijhm.2018.11.018 (2018).Article 

Google Scholar 
Ramirez, E., Kulinna, P. H. & Cothran, D. Constructs of physical activity behaviour in children: The usefulness of Social Cognitive Theory. Psychol. Sport Exerc. 13, 303–310 (2012).Article 

Google Scholar 
Glanz, K., Rimer, B. K. & Viswanath, K. Health and Health (2002). More

Chu, D. M. et al. Maturation of the infant microbiome community structure and function across multiple body sites and in relation to mode of delivery. Nat. Med. 23, 314–326 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ward, T. L. et al. Development of the human mycobiome over the first month of life and across body sites. mSystems 3, e00140–17 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Oh, J. et al. Biogeography and individuality shape function in the human skin metagenome. Nature 514, 59–64 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Abeles, S. R. et al. Human oral viruses are personal, persistent and gender-consistent. ISME J. 8, 1753–1767 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Grice, E. A. & Segre, J. A. The human microbiome: our second genome. Annu. Rev. Genomics Hum. Genet. 13, 151–170 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lauber, C. L., Hamady, M., Knight, R. & Fierer, N. Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl. Environ. Microbiol. 75, 5111–5120 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zengler, K. & Zaramela, L. S. The social network of microorganisms – how auxotrophies shape complex communities. Nat. Rev. Microbiol. 16, 383–390 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Smits, S. A. et al. Seasonal cycling in the gut microbiome of the Hadza hunter-gatherers of Tanzania. Science 357, 802–806 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rasko, D. A. Changes in microbiome during and after travellers’ diarrhea: what we know and what we do not. J. Travel. Med. 24, S52–S56 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Zheng, D., Liwinski, T. & Elinav, E. Interaction between microbiota and immunity in health and disease. Cell Res. 30, 492–506 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Zaneveld, J. R., McMinds, R. & Vega Thurber, R. Stress and stability: applying the Anna Karenina principle to animal microbiomes. Nat. Microbiol. 2, 17121 (2017).CAS 
PubMed 
Article 

Google Scholar 
Dini-Andreote, F., Stegen, J. C., van Elsas, J. D. & Salles, J. F. Disentangling mechanisms that mediate the balance between stochastic and deterministic processes in microbial succession. Proc. Natl Acad. Sci. USA 112, E1326–E1332 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dominguez-Bello, M. G. et al. Delivery mode shapes the acquisition and structure of the initial microbiota across multiple body habitats in newborns. Proc. Natl Acad. Sci. USA 107, 11971–11975 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
Yassour, M. et al. Natural history of the infant gut microbiome and impact of antibiotic treatment on bacterial strain diversity and stability. Sci. Transl. Med. 8, 343ra81 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bokulich, N. A. et al. Antibiotics, birth mode, and diet shape microbiome maturation during early life. Sci. Transl. Med. 8, 343ra82 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
David, L. A. et al. Host lifestyle affects human microbiota on daily timescales. Genome Biol. 15, R89 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Vangay, P. et al. US immigration westernizes the human gut microbiome. Cell 175, 962–972.e10 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yatsunenko, T. et al. Human gut microbiome viewed across age and geography. Nature 486, 222–227 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gregory, A. C. et al. The gut virome database reveals age-dependent patterns of virome diversity in the human gut. Cell Host Microbe 28, 724–740.e8 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Faith, J. J. et al. The long-term stability of the human gut microbiota. Science 341, 1237439 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Thaiss, C. A. et al. Microbiota diurnal rhythmicity programs host transcriptome oscillations. Cell 167, 1495–1510.e12 (2016).CAS 
PubMed 
Article 

Google Scholar 
Zaura, E. et al. Same exposure but two radically different responses to antibiotics: resilience of the salivary microbiome versus long-term microbial shifts in feces. mBio 6, e01693–15 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dethlefsen, L. & Relman, D. A. Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proc. Natl Acad. Sci. USA 108 (Suppl. 1), 4554–4561 (2011).CAS 
PubMed 
Article 

Google Scholar 
Hsiao, A. et al. Members of the human gut microbiota involved in recovery from Vibrio cholerae infection. Nature 515, 423–426 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chng, K. R. et al. Metagenome-wide association analysis identifies microbial determinants of post-antibiotic ecological recovery in the gut. Nat. Ecol. Evol. 4, 1256–1267 (2020).PubMed 
Article 

Google Scholar 
Gibbons, S. M. Keystone taxa indispensable for microbiome recovery. Nat. Microbiol. 5, 1067–1068 (2020).CAS 
PubMed 
Article 

Google Scholar 
Rizzatti, G., Lopetuso, L. R., Gibiino, G., Binda, C. & Gasbarrini, A. Proteobacteria: a common factor in human diseases. Biomed. Res. Int. 2017, 9351507 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Biagi, E. et al. Gut microbiota and extreme longevity. Curr. Biol. 26, 1480–1485 (2016).CAS 
PubMed 
Article 

Google Scholar 
Lim, A. I. et al. Prenatal maternal infection promotes tissue-specific immunity and inflammation in offspring. Science 373, eabf3002 (2021).CAS 
PubMed 
Article 

Google Scholar 
Al Nabhani, Z. & Eberl, G. Imprinting of the immune system by the microbiota early in life. Mucosal Immunol. 13, 183–189 (2020).CAS 
PubMed 
Article 

Google Scholar 
Lynn, M. A. et al. Early-life antibiotic-driven dysbiosis leads to dysregulated vaccine immune responses in mice. Cell Host Microbe 23, 653–660.e5 (2018).CAS 
PubMed 
Article 

Google Scholar 
Blaser, M. J. The theory of disappearing microbiota and the epidemics of chronic diseases. Nat. Rev. Immunol. 17, 461–463 (2017).CAS 
PubMed 
Article 

Google Scholar 
Thorburn, A. N. et al. Evidence that asthma is a developmental origin disease influenced by maternal diet and bacterial metabolites. Nat. Commun. 6, 7320 (2015).CAS 
PubMed 
Article 

Google Scholar 
Gomez de Agüero, M. et al. The maternal microbiota drives early postnatal innate immune development. Science 351, 1296–1302 (2016).PubMed 
Article 
CAS 

Google Scholar 
Macpherson, A. J., de Agüero, M. G. & Ganal-Vonarburg, S. C. How nutrition and the maternal microbiota shape the neonatal immune system. Nat. Rev. Immunol. 17, 508–517 (2017).CAS 
PubMed 
Article 

Google Scholar 
Nakajima, A. et al. Maternal high fiber diet during pregnancy and lactation influences regulatory T cell differentiation in offspring in mice. J. Immunol. 199, 3516–3524 (2017).CAS 
PubMed 
Article 

Google Scholar 
Jamalkandi, S. A. et al. Oral and nasal probiotic administration for the prevention and alleviation of allergic diseases, asthma and chronic obstructive pulmonary disease. Nutr. Res. Rev. 34, 1–16 (2021).CAS 
PubMed 
Article 

Google Scholar 
Örtqvist, A. K., Lundholm, C., Halfvarson, J., Ludvigsson, J. F. & Almqvist, C. Fetal and early life antibiotics exposure and very early onset inflammatory bowel disease: a population-based study. Gut 68, 218–225 (2019).PubMed 
Article 
CAS 

Google Scholar 
Munyaka, P. M., Eissa, N., Bernstein, C. N., Khafipour, E. & Ghia, J.-E. Antepartum antibiotic treatment increases offspring susceptibility to experimental colitis: a role of the gut microbiota. PLoS ONE 10, e0142536 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Kiss, E. A. et al. Natural aryl hydrocarbon receptor ligands control organogenesis of intestinal lymphoid follicles. Science 334, 1561–1565 (2011).CAS 
PubMed 
Article 

Google Scholar 
Lee, J. S. et al. AHR drives the development of gut ILC22 cells and postnatal lymphoid tissues via pathways dependent on and independent of Notch. Nat. Immunol. 13, 144–151 (2011).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Qiu, J. et al. The aryl hydrocarbon receptor regulates gut immunity through modulation of innate lymphoid cells. Immunity 36, 92–104 (2012).CAS 
PubMed 
Article 

Google Scholar 
Schulfer, A. F. et al. Intergenerational transfer of antibiotic-perturbed microbiota enhances colitis in susceptible mice. Nat. Microbiol. 3, 234–242 (2018).CAS 
PubMed 
Article 

Google Scholar 
Ma, J. et al. High-fat maternal diet during pregnancy persistently alters the offspring microbiome in a primate model. Nat. Commun. 5, 3889 (2014).CAS 
PubMed 
Article 

Google Scholar 
Torres, J. et al. Infants born to mothers with IBD present with altered gut microbiome that transfers abnormalities of the adaptive immune system to germ-free mice. Gut 69, 42–51 (2020).CAS 
PubMed 
Article 

Google Scholar 
Milliken, S., Allen, R. M. & Lamont, R. F. The role of antimicrobial treatment during pregnancy on the neonatal gut microbiome and the development of atopy, asthma, allergy and obesity in childhood. Expert. Opin. Drug. Saf. 18, 173–185 (2019).CAS 
PubMed 
Article 

Google Scholar 
Santacruz, A. et al. Gut microbiota composition is associated with body weight, weight gain and biochemical parameters in pregnant women. Br. J. Nutr. 104, 83–92 (2010).CAS 
PubMed 
Article 

Google Scholar 
Trevisanuto, D. et al. Fetal placental inflammation is associated with poor neonatal growth of preterm infants: a case-control study. J. Matern. Fetal Neonatal Med. 26, 1484–1490 (2013).PubMed 
Article 

Google Scholar 
Song, S. J. et al. Naturalization of the microbiota developmental trajectory of Cesarean-born neonates after vaginal seeding. Med 2, 951–964.e5 (2021).CAS 
PubMed 
Article 

Google Scholar 
Abu-Raya, B., Michalski, C., Sadarangani, M. & Lavoie, P. M. Maternal immunological adaptation during normal pregnancy. Front. Immunol. 11, 575197 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hanson, L. A. et al. The transfer of immunity from mother to child. Ann. NY. Acad. Sci. 987, 199–206 (2003).CAS 
PubMed 
Article 

Google Scholar 
Dominguez-Bello, M. G. et al. Partial restoration of the microbiota of cesarean-born infants via vaginal microbial transfer. Nat. Med. 22, 250–253 (2016). This study demonstrates that ‘seeding’ infants born by caesarean delivery with the vaginal microbiota of the mother at birth partially naturalizes development of the microbial community.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ferretti, P. et al. Mother-to-infant microbial transmission from different body sites shapes the developing infant gut microbiome. Cell Host Microbe 24, 133–145.e5 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Helve, O. et al. 2843. Maternal fecal transplantation to infants born by cesarean section: safety and feasibility. Open. Forum Infect. Dis. 6, S68 (2019).PubMed Central 
Article 

Google Scholar 
Subramanian, S. et al. Persistent gut microbiota immaturity in malnourished Bangladeshi children. Nature 510, 417–421 (2014). This study shows that severe acute malnutrition leads to immature microbial development and introduces a metric for the measure of microbiota maturity.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Palmer, C., Bik, E. M., DiGiulio, D. B., Relman, D. A. & Brown, P. O. Development of the human infant intestinal microbiota. PLoS Biol. 5, e177 (2007).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Groer, M. W. et al. Development of the preterm infant gut microbiome: a research priority. Microbiome 2, 38 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Henrick, B. M. et al. Bifidobacteria-mediated immune system imprinting early in life. Cell 184, 3884–3898.e11 (2021). This report describes the immune development driven by microbial interactions and the negative impact of lack of HMO-utilizing microorganisms on the immune system.CAS 
PubMed 
Article 

Google Scholar 
Sela, D. A. & Mills, D. A. Nursing our microbiota: molecular linkages between bifidobacteria and milk oligosaccharides. Trends Microbiol. 18, 298–307 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Seppo, A. E. et al. Infant gut microbiome is enriched with Bifidobacterium longum ssp. infantis in old order mennonites with traditional farming lifestyle. Allergy 76, 3489–3503 (2021).CAS 
PubMed 
Article 

Google Scholar 
Triantis, V., Bode, L. & van Neerven, R. J. J. Immunological effects of human milk oligosaccharides. Front. Pediatr. 6, 190 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Yu, Z.-T., Chen, C. & Newburg, D. S. Utilization of major fucosylated and sialylated human milk oligosaccharides by isolated human gut microbes. Glycobiology 23, 1281–1292 (2013).CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
McDonald, D. et al. American gut: an open platform for citizen science microbiome research. mSystems 3, e00031–18 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Odamaki, T. et al. Age-related changes in gut microbiota composition from newborn to centenarian: a cross-sectional study. BMC Microbiol. 16, 90 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Schei, K. et al. Early gut mycobiota and mother-offspring transfer. Microbiome 5, 107 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Alonso, R., Pisa, D., Fernández-Fernández, A. M. & Carrasco, L. Infection of fungi and bacteria in brain tissue from elderly persons and patients with Alzheimer’s disease. Front. Aging Neurosci. 10, 159 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Nagpal, R. et al. Gut mycobiome and its interaction with diet, gut bacteria and Alzheimer’s disease markers in subjects with mild cognitive impairment: a pilot study. EBioMedicine 59, 102950 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ahmad, H. F. et al. Gut mycobiome dysbiosis is linked to hypertriglyceridemia among home dwelling elderly Danes. Preprint at bioRxiv https://doi.org/10.1101/2020.04.16.044693 (2020).Article 

Google Scholar 
Wampach, L. et al. Colonization and succession within the human gut microbiome by archaea, bacteria, and microeukaryotes during the first year of life. Front. Microbiol. 8, 738 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Breitbart, M. et al. Metagenomic analyses of an uncultured viral community from human feces. J. Bacteriol. 185, 6220–6223 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Liang, G. et al. The stepwise assembly of the neonatal virome is modulated by breastfeeding. Nature 581, 470–474 (2020). This study describes the assembly of the human virome during development.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lim, E. S. et al. Early life dynamics of the human gut virome and bacterial microbiome in infants. Nat. Med. 21, 1228–1234 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Liang, G. et al. Dynamics of the stool virome in very early-onset inflammatory bowel disease. J. Crohns. Colitis 14, 1600–1610 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Koren, O. & Rautava, S. The Human Microbiome in Early Life: Implications to Health and Disease (Academic, 2020).Reyes, A. et al. Gut DNA viromes of Malawian twins discordant for severe acute malnutrition. Proc. Natl Acad. Sci. USA 112, 11941–11946 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Liang, G. & Bushman, F. D. The human virome: assembly, composition and host interactions. Nat. Rev. Microbiol. 19, 514–527 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Oude Munnink, B. B. & van der Hoek, L. Viruses causing gastroenteritis: the known, the new and those beyond. Viruses 8, 42 (2016).PubMed Central 
Article 
CAS 

Google Scholar 
Woolhouse, M., Scott, F., Hudson, Z., Howey, R. & Chase-Topping, M. Human viruses: discovery and emergence. Phil. Trans. R. Soc. B 367, 2864–2871 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Rascovan, N., Duraisamy, R. & Desnues, C. Metagenomics and the human virome in asymptomatic individuals. Annu. Rev. Microbiol. 70, 125–141 (2016).CAS 
PubMed 
Article 

Google Scholar 
Mason, M. R., Chambers, S., Dabdoub, S. M., Thikkurissy, S. & Kumar, P. S. Characterizing oral microbial communities across dentition states and colonization niches. Microbiome 6, 67 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Dzidic, M. et al. Oral microbiome development during childhood: an ecological succession influenced by postnatal factors and associated with tooth decay. ISME J. 12, 2292–2306 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Merglova, V. & Polenik, P. Early colonization of the oral cavity in 6- and 12-month-old infants by cariogenic and periodontal pathogens: a case-control study. Folia Microbiol. 61, 423–429 (2016).CAS 
Article 

Google Scholar 
Gomez, A. & Nelson, K. E. The oral microbiome of children: development, disease, and implications beyond oral health. Microb. Ecol. 73, 492–503 (2017).CAS 
PubMed 
Article 

Google Scholar 
Cephas, K. D. et al. Comparative analysis of salivary bacterial microbiome diversity in edentulous infants and their mothers or primary care givers using pyrosequencing. PLoS ONE 6, e23503 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Crielaard, W. et al. Exploring the oral microbiota of children at various developmental stages of their dentition in the relation to their oral health. BMC Med. Genomics 4, 22 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Darwazeh, A. M. & al-Bashir, A. Oral candidal flora in healthy infants. J. Oral. Pathol. Med. 24, 361–364 (1995).CAS 
PubMed 
Article 

Google Scholar 
Stecksén-Blicks, C., Granström, E., Silfverdal, S. A. & West, C. E. Prevalence of oral Candida in the first year of life. Mycoses 58, 550–556 (2015).PubMed 
Article 
CAS 

Google Scholar 
Ghannoum, M. A. et al. Characterization of the oral fungal microbiome (mycobiome) in healthy individuals. PLoS Pathog. 6, e1000713 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Brusa, T., Conca, R., Ferrara, A., Ferrari, A. & Pecchioni, A. The presence of methanobacteria in human subgingival plaque. J. Clin. Periodontol. 14, 470–471 (1987).CAS 
PubMed 
Article 

Google Scholar 
Ferrari, A., Brusa, T., Rutili, A., Canzi, E. & Biavati, B. Isolation and characterization ofMethanobrevibacter oralis sp. nov. Curr. Microbiol. 29, 7–12 (1994).CAS 
Article 

Google Scholar 
Nguyen-Hieu, T., Khelaifia, S., Aboudharam, G. & Drancourt, M. Methanogenic archaea in subgingival sites: a review. APMIS 121, 467–477 (2013).PubMed 
Article 

Google Scholar 
Abeles, S. R., Ly, M., Santiago-Rodriguez, T. M. & Pride, D. T. Effects of long term antibiotic therapy on human oral and fecal viromes. PLoS ONE 10, e0134941 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Pérez-Brocal, V. & Moya, A. The analysis of the oral DNA virome reveals which viruses are widespread and rare among healthy young adults in Valencia (Spain). PLoS ONE 13, e0191867 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Dye, B. A., Li, X. & Thornton-Evans, G. Oral health disparities as determined by selected healthy people 2020 oral health objectives for the United States, 2009–2010. NCHS Data Brief. 104, 1–8 (2012).
Google Scholar 
Baker, J. L., Bor, B., Agnello, M., Shi, W. & He, X. Ecology of the oral microbiome: beyond bacteria. Trends Microbiol. 25, 362–374 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gaitanis, G. et al. Variation of cultured skin microbiota in mothers and their infants during the first year postpartum. Pediatr. Dermatol. 36, 460–465 (2019).PubMed 

Google Scholar 
Lee, Y. W., Yim, S. M., Lim, S. H., Choe, Y. B. & Ahn, K. J. Quantitative investigation on the distribution of Malassezia species on healthy human skin in Korea. Mycoses 49, 405–410 (2006).CAS 
PubMed 
Article 

Google Scholar 
Byrd, A. L., Belkaid, Y. & Segre, J. A. The human skin microbiome. Nat. Rev. Microbiol. 16, 143–155 (2018).CAS 
PubMed 
Article 

Google Scholar 
Sugita, T. et al. Quantitative analysis of the cutaneous Malassezia microbiota in 770 healthy Japanese by age and gender using a real-time PCR assay. Med. Mycol. 48, 229–233 (2010).CAS 
PubMed 
Article 

Google Scholar 
Probst, A. J., Auerbach, A. K. & Moissl-Eichinger, C. Archaea on human skin. PLoS ONE 8, e65388 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hulcr, J. et al. A jungle in there: bacteria in belly buttons are highly diverse, but predictable. PLoS ONE 7, e47712 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Caporaso, J. G. et al. Moving pictures of the human microbiome. Genome Biol. 12, R50 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
Moya, A. & Brocal, V. P. The Human Virome: Methods and Protocols (Springer, 2018).Foulongne, V. et al. Human skin microbiota: high diversity of DNA viruses identified on the human skin by high throughput sequencing. PLoS ONE 7, e38499 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Turnbaugh, P. J. et al. Organismal, genetic, and transcriptional variation in the deeply sequenced gut microbiomes of identical twins. Proc. Natl Acad. Sci. USA 107, 7503–7508 (2010). This study shows that cohabitating identical twins result in different microbial communities, highlighting the many unknown processes that lead to the unique human microbiota.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Shao, Y. et al. Stunted microbiota and opportunistic pathogen colonization in caesarean-section birth. Nature 574, 117–121 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Stewart, C. J. et al. Temporal development of the gut microbiome in early childhood from the TEDDY study. Nature 562, 583–588 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ainonen, S. et al. Antibiotics at birth and later antibiotic courses: effects on gut microbiota. Pediatr. Res. 91, 154–162 (2022).CAS 
PubMed 
Article 

Google Scholar 
Chen, X., Lu, Y., Chen, T. & Li, R. The female vaginal microbiome in health and bacterial vaginosis. Front. Cell. Infect. Microbiol. 11, 631972 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wells, J. S., Chandler, R., Dunn, A. & Brewster, G. The vaginal microbiome in U.S. black women: a systematic review. J. Womens Health 29, 362–375 (2020).Article 

Google Scholar 
Martino, C. et al. Context-aware dimensionality reduction deconvolutes gut microbial community dynamics. Nat. Biotechnol. 39, 165–168 (2021).CAS 
PubMed 
Article 

Google Scholar 
Furman, O. et al. Stochasticity constrained by deterministic effects of diet and age drive rumen microbiome assembly dynamics. Nat. Commun. 11, 1904 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Henderickx, J. G. E., Zwittink, R. D., van Lingen, R. A., Knol, J. & Belzer, C. The preterm gut microbiota: an inconspicuous challenge in nutritional neonatal care. Front. Cell. Infect. Microbiol. 9, 85 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Malamitsi-Puchner, A. et al. The influence of the mode of delivery on circulating cytokine concentrations in the perinatal period. Early Hum. Dev. 81, 387–392 (2005).CAS 
PubMed 
Article 

Google Scholar 
Stokholm, J. et al. Maturation of the gut microbiome and risk of asthma in childhood. Nat. Commun. 9, 141 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Andersen, V., Möller, S., Jensen, P. B., Møller, F. T. & Green, A. Caesarean delivery and risk of chronic inflammatory diseases (inflammatory bowel disease, rheumatoid arthritis, coeliac disease, and diabetes mellitus): a population based registry study of 2,699,479 births in Denmark during 1973–2016. Clin. Epidemiol. 12, 287–293 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Blustein, J. et al. Association of caesarean delivery with child adiposity from age 6 weeks to 15 years. Int. J. Obes. 37, 900–906 (2013).CAS 
Article 

Google Scholar 
Ardic, C., Usta, O., Omar, E., Yıldız, C. & Memis, E. Caesarean delivery increases the risk of overweight or obesity in 2-year-old children. J. Obstet. Gynaecol. 41, 374–379 (2021).PubMed 
Article 

Google Scholar 
Cox, L. M. et al. Altering the intestinal microbiota during a critical developmental window has lasting metabolic consequences. Cell 158, 705–721 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Martinez, K. A. 2nd et al. Increased weight gain by C-section: functional significance of the primordial microbiome. Sci. Adv. 3, eaao1874 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Olszak, T. et al. Microbial exposure during early life has persistent effects on natural killer T cell function. Science 336, 489–493 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Livanos, A. E. et al. Antibiotic-mediated gut microbiome perturbation accelerates development of type 1 diabetes in mice. Nat. Microbiol. 1, 16140 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Moya-Pérez, A. et al. Intervention strategies for cesarean section–induced alterations in the microbiota-gut-brain axis. Nutr. Rev. 75, 225–240 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Braniste, V. et al. The gut microbiota influences blood-brain barrier permeability in mice. Sci. Transl. Med. 6, 263ra158 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Forbes, J. D. et al. Association of exposure to formula in the hospital and subsequent infant feeding practices with gut microbiota and risk of overweight in the first year of life. JAMA Pediatr. 172, e181161 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Shenhav, L. & Azad, M. B. Using community ecology theory and computational microbiome methods to study human milk as a biological system. mSystems 7, e01132–21 (2022).PubMed Central 
Article 

Google Scholar 
Kaetzel, C. S. Cooperativity among secretory IgA, the polymeric immunoglobulin receptor, and the gut microbiota promotes host-microbial mutualism. Immunol. Lett. 162, 10–21 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Munblit, D., Verhasselt, V. & Warner, J. O. Human Milk Composition and Health Outcomes in Children (Frontiers Media, 2019).Mastromarino, P. et al. Correlation between lactoferrin and beneficial microbiota in breast milk and infant’s feces. Biometals 27, 1077–1086 (2014).CAS 
PubMed 
Article 

Google Scholar 
Agus, A., Planchais, J. & Sokol, H. Gut microbiota regulation of tryptophan metabolism in health and disease. Cell Host Microbe 23, 716–724 (2018).CAS 
PubMed 
Article 

Google Scholar 
Coats, S. R., Pham, T.-T. T., Bainbridge, B. W., Reife, R. A. & Darveau, R. P. MD-2 mediates the ability of tetra-acylated and penta-acylated lipopolysaccharides to antagonize Escherichia coli lipopolysaccharide at the TLR4 signaling complex. J. Immunol. 175, 4490–4498 (2005).CAS 
PubMed 
Article 

Google Scholar 
Denou, E. et al. Defective NOD 2 peptidoglycan sensing promotes diet‐induced inflammation, dysbiosis, and insulin resistance. EMBO Mol. Med. 7, 259–274 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Quinn, R. A. et al. Global chemical effects of the microbiome include new bile-acid conjugations. Nature 579, 123–129 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rooks, M. G. & Garrett, W. S. Gut microbiota, metabolites and host immunity. Nat. Rev. Immunol. 16, 341–352 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vatanen, T. et al. Variation in microbiome LPS immunogenicity contributes to autoimmunity in humans. Cell 165, 1551 (2016).CAS 
PubMed 
Article 

Google Scholar 
Fan, Y. & Pedersen, O. Gut microbiota in human metabolic health and disease. Nat. Rev. Microbiol. 19, 55–71 (2021).CAS 
PubMed 
Article 

Google Scholar 
Xiao, J., Fiscella, K. A. & Gill, S. R. Oral microbiome: possible harbinger for children’s health. Int. J. Oral. Sci. 12, 12 (2020).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Zhao, S. et al. Adaptive evolution within gut microbiomes of healthy people. Cell Host Microbe 25, 656–667.e8 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhang, R., Lahens, N. F., Ballance, H. I., Hughes, M. E. & Hogenesch, J. B. A circadian gene expression atlas in mammals: implications for biology and medicine. Proc. Natl Acad. Sci. USA 111, 16219–16224 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Allaband, C. et al. Intermittent hypoxia and hypercapnia alter diurnal rhythms of luminal gut microbiome and metabolome. mSystems 6, e00116–e00121 (2021).CAS 
PubMed Central 
Article 

Google Scholar 
Marotz, C. et al. Quantifying live microbial load in human saliva samples over time reveals stable composition and dynamic load. mSystems 6, e01182–20 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bouslimani, A. et al. The impact of skin care products on skin chemistry and microbiome dynamics. BMC Biol. 17, 47 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Costello, E. K. et al. Bacterial community variation in human body habitats across space and time. Science 326, 1694–1697 (2009). This study demonstrates the important variability between body habitats and between individuals across the same body habitat.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kolodziejczyk, A. A., Zheng, D. & Elinav, E. Diet–microbiota interactions and personalized nutrition. Nat. Rev. Microbiol. 17, 742–753 (2019).CAS 
PubMed 
Article 

Google Scholar 
Zaramela, L. S. et al. Gut bacteria responding to dietary change encode sialidases that exhibit preference for red meat-associated carbohydrates. Nat. Microbiol. 4, 2082–2089 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Zmora, N., Suez, J. & Elinav, E. You are what you eat: diet, health and the gut microbiota. Nat. Rev. Gastroenterol. Hepatol. 16, 35–56 (2019).CAS 
PubMed 
Article 

Google Scholar 
Etemadi, A. et al. Mortality from different causes associated with meat, heme iron, nitrates, and nitrites in the NIH-AARP Diet and Health Study: population based cohort study. BMJ 357, j1957 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Koeth, R. A. et al. Intestinal microbiota metabolism of L-carnitine, a nutrient in red meat, promotes atherosclerosis. Nat. Med. 19, 576–585 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gilbert, J. A. et al. Current understanding of the human microbiome. Nat. Med. 24, 392–400 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Durack, J. & Lynch, S. V. The gut microbiome: relationships with disease and opportunities for therapy. J. Exp. Med. 216, 20–40 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lai, Y. et al. Commensal bacteria regulate Toll-like receptor 3–dependent inflammation after skin injury. Nat. Med. 15, 1377–1382 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chng, K. R. et al. Whole metagenome profiling reveals skin microbiome-dependent susceptibility to atopic dermatitis flare. Nat. Microbiol. 1, 16106 (2016).CAS 
PubMed 
Article 

Google Scholar 
Li, H. et al. Skin commensal Malassezia globosa secreted protease attenuates Staphylococcus aureus biofilm formation. J. Invest. Dermatol. 138, 1137–1145 (2018).CAS 
PubMed 
Article 

Google Scholar 
Shirtliff, M. E., Peters, B. M. & Jabra-Rizk, M. A. Cross-kingdom interactions: Candida albicans and bacteria. FEMS Microbiol. Lett. 299, 1–8 (2009).CAS 
PubMed 
Article 

Google Scholar 
Santus, W., Devlin, J. R. & Behnsen, J. Crossing kingdoms: how the mycobiota and fungal-bacterial interactions impact host health and disease. Infect. Immun. 89, e00648–20 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Taur, Y. et al. Reconstitution of the gut microbiota of antibiotic-treated patients by autologous fecal microbiota transplant. Sci. Transl. Med. 10, eaap9489 (2018). This study shows that autologous faecal microbiota transplantation helps to restore the microbiota of patients who underwent antibiotic treatment.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
van Nood, E., Dijkgraaf, M. G. W. & Keller, J. J. Duodenal infusion of feces for recurrent Clostridium difficile. N. Engl. J. Med. 368, 2145 (2013).PubMed 
Article 
CAS 

Google Scholar 
Tariq, R., Pardi, D. S., Bartlett, M. G. & Khanna, S. Low cure rates in controlled trials of fecal microbiota transplantation for recurrent Clostridium difficile infection: a systematic review and meta-analysis. Clin. Infect. Dis. 68, 1351–1358 (2019).CAS 
PubMed 
Article 

Google Scholar 
Panigrahi, P. et al. Corrigendum: a randomized synbiotic trial to prevent sepsis among infants in rural India. Nature 553, 238 (2018).CAS 
PubMed 
Article 

Google Scholar 
Halkjær, S. I. et al. Faecal microbiota transplantation alters gut microbiota in patients with irritable bowel syndrome: results from a randomised, double-blind placebo-controlled study. Gut 67, 2107–2115 (2018).PubMed 
Article 
CAS 

Google Scholar 
Korpela, K. et al. Maternal fecal microbiota transplantation in cesarean-born infants rapidly restores normal gut microbial development: a proof-of-concept study. Cell 183, 324–334.e5 (2020).CAS 
PubMed 
Article 

Google Scholar 
Morton, J. T. et al. Learning representations of microbe–metabolite interactions. Nat. Methods 16, 1306–1314 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kehe, J. et al. Positive interactions are common among culturable bacteria. Sci. Adv. 7, eabi7159 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Strandwitz, P. et al. GABA-modulating bacteria of the human gut microbiota. Nat. Microbiol. 4, 396–403 (2019).CAS 
PubMed 
Article 

Google Scholar 
Rubin, B. E. et al. Species- and site-specific genome editing in complex bacterial communities. Nat. Microbiol. 7, 34–47 (2022).CAS 
PubMed 
Article 

Google Scholar 
Zmora, N. et al. Personalized gut mucosal colonization resistance to empiric probiotics is associated with unique host and microbiome features. Cell 174, 1388–1405.e21 (2018).CAS 
PubMed 
Article 

Google Scholar 
Zeevi, D. et al. Personalized nutrition by prediction of glycemic responses. Cell 163, 1079–1094 (2015).CAS 
PubMed 
Article 

Google Scholar 
Schooley, R. T. et al. Development and use of personalized bacteriophage-based therapeutic cocktails to treat a patient with a disseminated resistant Acinetobacter baumannii infection. Antimicrob. Agents Chemother. 61, e00954–17 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mu, A. et al. Effects on the microbiome during treatment of a staphylococcal device infection. Preprint at Research Square https://doi.org/10.21203/rs.3.rs-969336/v1 (2021).Article 

Google Scholar 
Claesson, M. J. et al. Gut microbiota composition correlates with diet and health in the elderly. Nature 488, 178–184 (2012). This study reports microbial community alterations between older individuals (aged 65 years and older) dependent on whether they live in the company of others or alone, the latter of which was correlated to worse outcomes (that is, frailty and co-morbidity).CAS 
PubMed 
Article 

Google Scholar 
Wu, L. et al. A cross-sectional study of compositional and functional profiles of gut microbiota in Sardinian centenarians. mSystems 4, e00325–19 (2019).PubMed 
PubMed Central 

Google Scholar 
Kong, F. et al. Gut microbiota signatures of longevity. Curr. Biol. 26, R832–R833 (2016).CAS 
PubMed 
Article 

Google Scholar 
Claesson, M. J. et al. Composition, variability, and temporal stability of the intestinal microbiota of the elderly. Proc. Natl Acad. Sci. USA 108 (Suppl. 1), 4586–4591 (2011).CAS 
PubMed 
Article 

Google Scholar 
O’Toole, P. W. & Jeffery, I. B. Gut microbiota and aging. Science 350, 1214–1215 (2015).PubMed 
Article 
CAS 

Google Scholar 
Shibagaki, N. et al. Aging-related changes in the diversity of women’s skin microbiomes associated with oral bacteria. Sci. Rep. 7, 10567 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Liu, S., Wang, Y., Zhao, L., Sun, X. & Feng, Q. Microbiome succession with increasing age in three oral sites. Aging 12, 7874–7907 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Schwartz, J. L. et al. Old age and other factors associated with salivary microbiome variation. BMC Oral. Health 21, 490 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Strati, F. et al. Age and gender affect the composition of fungal population of the human gastrointestinal tract. Front. Microbiol. 7, 01227 (2016).Article 

Google Scholar 
Wu, L. et al. Age-related variation of bacterial and fungal communities in different body habitats across the young, elderly, and centenarians in Sardinia. mSphere 5, e00558–19 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Nagpal, R. et al. Gut microbiome and aging: physiological and mechanistic insights. Nutr. Healthy Aging 4, 267–285 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Wilmanski, T. et al. Gut microbiome pattern reflects healthy ageing and predicts survival in humans. Nat. Metab. 3, 274–286 (2021).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Sato, Y. et al. Novel bile acid biosynthetic pathways are enriched in the microbiome of centenarians. Nature 599, 458–464 (2021). This study finds that centenarians often had high abundances of microorganisms that produced unique secondary bile acids, namely various isoforms of lithocholic acid.CAS 
PubMed 
Article 

Google Scholar 
Gill-King, H. in Forensic Taphonomy: the Postmortem Fate of Human Remains 93–108 (CRC, 1997).Janaway, R. C., Percival, S. L. & Wilson, A. S. in Microbiology and Aging (ed. Percival, S. L) 313–334 (Humana, 2009).Forbes, S. L., Perrault, K. A. & Comstock, J. L. in Taphonomy of Human Remains: Forensic Analysis of the Dead and the Depositional Environment (eds Schotsmans, E. M. J., Márquez-Grant, N. & Forbes, S. L.) 26–38 (Wiley, 2017).Heimesaat, M. M. et al. Comprehensive postmortem analyses of intestinal microbiota changes and bacterial translocation in human flora associated mice. PLoS ONE 7, e40758 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Parkinson, R. A. et al. in Criminal and Environmental Soil Forensics (eds Ritz, K., Dawson, L. & Miller, D.) 379–394 (Springer, 2009).Metcalf, J. L. et al. Microbial community assembly and metabolic function during mammalian corpse decomposition. Science 351, 158–162 (2016). This study finds that the time since death was predictable through the microbial community composition independent of the soil type and season.CAS 
PubMed 
Article 

Google Scholar 
DeBruyn, J. M. & Hauther, K. A. Postmortem succession of gut microbial communities in deceased human subjects. PeerJ 5, e3437 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Pechal, J. L., Schmidt, C. J., Jordan, H. R. & Benbow, M. E. A large-scale survey of the postmortem human microbiome, and its potential to provide insight into the living health condition. Sci. Rep. 8, 5724 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Kodama, W. A. et al. Trace evidence potential in postmortem skin microbiomes: from death scene to morgue. J. Forensic Sci. 64, 791–798 (2019).PubMed 
Article 

Google Scholar 
Hauther, K. A., Cobaugh, K. L., Jantz, L. M., Sparer, T. E. & DeBruyn, J. M. Estimating time since death from postmortem human gut microbial communities. J. Forensic Sci. 60, 1234–1240 (2015).PubMed 
Article 

Google Scholar 
Burcham, Z. M. et al. Fluorescently labeled bacteria provide insight on post-mortem microbial transmigration. Forensic Sci. Int. 264, 63–69 (2016).CAS 
PubMed 
Article 

Google Scholar 
Burcham, Z. M. et al. Bacterial community succession, transmigration, and differential gene transcription in a controlled vertebrate decomposition model. Front. Microbiol. 10, 745 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Balzan, S., de Almeida Quadros, C., de Cleva, R., Zilberstein, B. & Cecconello, I. Bacterial translocation: overview of mechanisms and clinical impact. J. Gastroenterol. Hepatol. 22, 464–471 (2007).CAS 
PubMed 
Article 

Google Scholar 
Metcalf, J. L. et al. A microbial clock provides an accurate estimate of the postmortem interval in a mouse model system. eLife 2, e01104 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Hyde, E. R., Haarmann, D. P., Petrosino, J. F., Lynne, A. M. & Bucheli, S. R. Initial insights into bacterial succession during human decomposition. Int. J. Leg. Med. 129, 661–671 (2015).Article 

Google Scholar 
Javan, G. T., Finley, S. J., Smith, T., Miller, J. & Wilkinson, J. E. Cadaver thanatomicrobiome signatures: the ubiquitous nature of Clostridium species in human decomposition. Front. Microbiol. 8, 2096 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Johnson, H. R. et al. A machine learning approach for using the postmortem skin microbiome to estimate the postmortem interval. PLoS ONE 11, e0167370 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Belk, A. et al. Microbiome data accurately predicts the postmortem interval using random forest regression models. Genes 9, 104 (2018).PubMed Central 
Article 
CAS 

Google Scholar 
Metcalf, J. L. Estimating the postmortem interval using microbes: knowledge gaps and a path to technology adoption. Forensic Sci. Int. Genet. 38, 211–218 (2019).CAS 
PubMed 
Article 

Google Scholar 
Deel, H. et al. A pilot study of microbial succession in human rib skeletal remains during terrestrial decomposition. mSphere 6, e0045521 (2021).PubMed 
Article 

Google Scholar 
Metcalf, J. L. et al. Microbiome tools for forensic science. Trends Biotechnol. 35, 814–823 (2017).CAS 
PubMed 
Article 

Google Scholar 
Nguyen, T. T., Hathaway, H., Kosciolek, T., Knight, R. & Jeste, D. V. Gut microbiome in serious mental illnesses: a systematic review and critical evaluation. Schizophr. Res. 234, 24–40 (2021).PubMed 
Article 

Google Scholar 
Jeste, D. V., Koh, S. & Pender, V. B. Perspective: social determinants of mental health for the new decade of healthy aging. Am. J. Geriatr. Psychiatry 30, 733–736 (2022).PubMed 
Article 

Google Scholar 
Matijašić, M. et al. Gut microbiota beyond bacteria-mycobiome, virome, archaeome, and eukaryotic parasites in IBD. Int. J. Mol. Sci. 21, 2668 (2020).PubMed Central 
Article 
CAS 

Google Scholar 
Morton, J. T. et al. Establishing microbial composition measurement standards with reference frames. Nat. Commun. 10, 2719 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Gerber, G. K. The dynamic microbiome. FEBS Lett. 588, 4131–4139 (2014).CAS 
PubMed 
Article 

Google Scholar 
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 
Vázquez-Baeza, Y. et al. Guiding longitudinal sampling in IBD cohorts. Gut 67, 1743–1745 (2018).PubMed 
Article 

Google Scholar 
Kane, P. B., Bittlinger, M. & Kimmelman, J. Individualized therapy trials: navigating patient care, research goals and ethics. Nat. Med. 27, 1679–1686 (2021).CAS 
PubMed 
Article 

Google Scholar 
Huang, S. et al. Human skin, oral, and gut microbiomes predict chronological age. mSystems 5, e00630–19 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Thompson, L. R. et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 551, 457–463 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Franzosa, E. A. et al. Identifying personal microbiomes using metagenomic codes. Proc. Nat. Acad. Sci. USA 112, E2930–E2938 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vangay, P. et al. Microbiome metadata standards: report of the national microbiome data collaborative’s workshop and follow-on activities. mSystems 6, e01194–20 (2021).PubMed 
PubMed Central 

Google Scholar  More

Körner, C. The cold range limit of trees. Trends Ecol. Evo. 36, 979–989 (2021).Article 

Google Scholar 
Körner, C. Alpine Treelines (Springer, 2012).Miehe, G., Miehe, S., Vogel, J., Co, S. & Duo, L. Highest treeline in the northern hemisphere found in southern Tibet. Mt. Res. Dev. 27, 169–173 (2007).Article 

Google Scholar 
Hoch, G. & Körner, C. Growth, demography and carbon relations of Polylepis trees at the world’s highest treeline. Funct. Ecol. 19, 941–951 (2005).Article 

Google Scholar 
von Humboldt, A. & Bonpland, A. Ideen zu einer Geographie der Pflanzen nebst einem Naturgemälde der Tropenländer: auf Beobachtungen und Messungen gegründet, welche vom 10ten Grade nördlicher bis zum 10ten Grade südlicher Breite, in den Jahren 1799, 1800, 1801, 1802 und 1803 angestellt worden sind. Tübingen, Bey F.G. Cotta (1807).Körner, C. Climatic treelines: conventions, global patterns, causes. Erdkunde 61, 315–324 (2007).Article 

Google Scholar 
Piermattei, A., Crivellaro, A., Carrer, M. & Urbinati, C. The “blue ring”: anatomy and formation hypothesis of a new tree-ring anomaly in conifers. Trees Struct. Funct. 29, 613–620 (2015).CAS 
Article 

Google Scholar 
Körner, C. et al. Life at 0 °C: the biology of the alpine snowbed plant Soldanella pulsatilla. Alp. Bot. 129, 63–80 (2019).Article 

Google Scholar 
Crivellaro, A. & Büntgen, U. New evidence of thermally-constraint plant cell wall lignification. Trends Plant Sci. 24, 322–324 (2020).Article 
CAS 

Google Scholar 
Büntgen, U. et al. Temperature-induced recruitment pulses of Arctic dwarf shrub communities. J. Ecol. 103, 489–501 (2015).Article 

Google Scholar 
Dolezal, J. et al. Vegetation dynamics at the upper elevational limit of vascular plants in Himalaya. Sci. Rep. 6, 24881 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ryan, M. G. & Yoder, B. J. Hydraulic limits to tree height and tree growth. Biosci 47, 235–242 (1997).Article 

Google Scholar 
Koch, G. W., Sillett, S. C., Jennings, G. M. & Davis, S. D. The limits to tree height. Nature 428, 851–854 (2004).CAS 
PubMed 
Article 

Google Scholar 
Körner, C. Alpine Plant Life: Functional Plant Ecology of High Mountain Ecosystems (Springer, 2003).Scherrer, D. & Körner, C. Infra-red thermometry of alpine landscapes challenges climatic warming projections. Glob. Change Biol. 16, 2602–2613 (2010).
Google Scholar 
Begum, S., Nakaba, S., Yamagishi, Y., Oribe, Y. & Funada, R. Regulation of cambial activity in relation to environmental conditions: understanding the role of temperature in wood formation of trees. Physiol. Planta 147, 46–54 (2013).CAS 
Article 

Google Scholar 
Plomion, C., Leprovost, G. & Stokes, A. Wood formation in trees. Plant Physiol. 127, 1513–1523 (2001).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rossi, S., Deslauriers, A., Anfodillo, T. & Carraro, V. Evidence of threshold temperatures for xylogenesis in conifers at high altitudes. Oecologia 152, 1–12 (2007).PubMed 
Article 

Google Scholar 
Moura, J. C. M. S., Bonine, C. A. V., Viana, J. O. F., Dornelas, M. C. & Mazzafera, P. Abiotic and biotic stresses and changes in the lignin content and composition in plants. J. Integr. Plant Biol. 52, 360–376 (2010).CAS 
PubMed 
Article 

Google Scholar 
Weng, J. K. & Chapple, C. The origin and evolution of lignin biosynthesis. N. Phytol. 187, 273–285 (2010).CAS 
Article 

Google Scholar 
Niklas, K. J., Cobb, E. D. & Matas, A. J. The evolution of hydrophobic cell wall biopolymers: from algae to angiosperms. J. Exp. 68, 5261–5269 (2017).CAS 

Google Scholar 
Popper, Z. A. et al. Evolution and diversity of plant cell walls: from algae to flowering plants. Annu. Rev. Plant Biol. 62, 567–590 (2011).CAS 
PubMed 
Article 

Google Scholar 
Piquemal, J. et al. Down regulation of cinnamoyl CoA reductase induces significant changes of lignin profiles in transgenic tobacco plants. Plant J. 13, 71–83 (1998).CAS 
Article 

Google Scholar 
Renault, H., Werck-Reichhart, D. & Weng, J.-K. Harnessing lignin evolution for biotechnological applications. Curr. Opin. Biotechnol. 56, 105–111 (2019).CAS 
PubMed 
Article 

Google Scholar 
Schenk, H. J., Espino, S., Rich-Cavazos, S. M. & Jansen, S. From the sap’s perspective: The nature of vessel surfaces in angiosperm xylem. Am. J. Bot. 105, 172–185 (2018).CAS 
PubMed 
Article 

Google Scholar 
Polo, C. C. et al. Correlations between lignin content and structural robustness in plants revealed by X-ray ptychography. Sci. Rep. 10, 6023 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Meents, M. J., Watanabe, Y. & Samuels, A. L. The cell biology of secondary cell wall biosynthesis. Ann. Bot. 121, 1107–1125 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Campbell, M. M. & Sederoff, R. R. Variation in lignin content and composition (mechanisms of control and implications for the genetic improvement of plants). Plant Physiol. 110, 3–13 (1996).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Schweingruber, F. H. & Büntgen, U. What is ‘wood’ – An anatomical re-definition. Dendrochronologia 31, 187–191 (2013).Article 

Google Scholar 
Ellenberg, H. & Mueller-Dombois, D. A key to Raunkiaer plant life forms with revised subdivisions. Ber. Geobot. Inst. ETH Z.ürich. 37, 56–73 (1967).
Google Scholar 
Kim, W. J., Campbell, A. G. & Koch, P. Chemical variation in Lodgepole pine with latitude, elevation, and diameter class. Prod. J. 39, 7–12 (1989).CAS 

Google Scholar 
Gindl, W., Grabner, M. & Wimmer, R. The influence of temperature on latewood lignin content in treeline Norway spruce compared with maximum density and ring width. Trees, Struct. Funct. 14, 409–414 (2000).Article 

Google Scholar 
Schenker, G., Lens, A., Körner, C. & Hoch, G. Physiological minimum temperatures for root growth in seven common European broad-leaved tree species. Tree Physiol. 34, 302–313 (2014).PubMed 
Article 

Google Scholar 
Nagelmüller, S., Hiltbrunner, E. & Körner, C. Low temperature limits for root growth in alpine species are set by cell differentiation. AoB Plants 9, plx054 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Ji, H. et al. The Arabidopsis RCC1 family protein TCF1 regulates freezing tolerance and cold acclimation through modulating lignin biosynthesis. PLoS Gen. 11, e1005471 (2015).Article 
CAS 

Google Scholar 
Büntgen, U. Re-thinking the boundaries of dendrochronology. Dendrochronologia 53, 1–4 (2019).Article 

Google Scholar 
Piermattei, A. et al. A millennium-long ‘Blue-Ring’ chronology from the Spanish Pyrenees reveals sever ephemeral summer cooling after volcanic eruptions. Environ. Res. Lett. 15, 124016 (2020).Article 

Google Scholar 
Montwé, D., Isaac-Rentin, M., Hamman, A. & Spiecker, H. Cold adaptation recorded in tree rings highlights risks associated with climate change and assisted migration. Nat. Comm. 9, 1574 (2018).Article 
CAS 

Google Scholar 
Barros, J., Serk, H., Granlund, I. & Pesquet, E. The cell biology of lignification in higher plants. Ann. Bot. 115, 1053–1074 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hao, Z. & Mohnen, D. A review of xylan and lignin biosynthesis: Foundation for studying Arabidopsis irregular xylem mutants with pleiotropic phenotypes. Cri. Rev. Biochem. Mol. Biol. 49, 212–241 (2014).CAS 
Article 

Google Scholar 
Liu, Q., Luo, L. & Zheng, L. Lignins: biosynthesis and biological functions in plants. Int. J. Mol. Sci. 19, 335 (2018).PubMed Central 
Article 
CAS 

Google Scholar 
Kumar, M., Campbell, L. & Turner, S. Secondary cell walls: biosynthesis and manipulation. J. Exp. Bot. 67, 515–531 (2016).CAS 
PubMed 
Article 

Google Scholar 
Mellerowicz, E. J., Baucher, M., Sundberg, B. & Boerjan, W. Unravelling cell wall formation in the woody dicot stem. Plant Mol. Biol. 47, 239–274 (2001).CAS 
PubMed 
Article 

Google Scholar 
Petit, G., Anfodillo, T., Carraro, V., Grani, F. & Carrer, M. Hydraulic constraints limit height growth in trees at high altitude. N. Phytol. 189, 241–252 (2010).Article 

Google Scholar 
Li, L. et al. Combinatorial modification of multiple lignin traits in trees through multigene co-transformation. Proc. Natl Acad. Sci. USA 100, 4939–4944 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Baldacci-Cresp, F. et al. A rapid and quantitative safranin-based fluorescent microscopy method to evaluate cell wall lignification. Plant J. 102, 1074–1089 (2020).CAS 
PubMed 
Article 

Google Scholar 
Körner, C. A re-assessment of high elevation treeline positions and their explanation. Oecologia 115, 445–459 (1998).PubMed 
Article 

Google Scholar 
Landolt, E. et al. Flora indicativa: Okologische Zeigerwerte und biologische Kennzeichen zur Flora der Schweiz und der Alpen (Haupt, 2010).Büntgen, U., Psomas, A. & Schweingruber, F. H. Introducing wood anatomical and dendrochronological aspects of herbaceous plants: applications of the Xylem Database to vegetation science. J. Veg. Sci. 25, 967–977 (2014).Article 

Google Scholar 
Körner, C. Coldest places on earth with angiosperm plant life. Alp. Bot. 121, 11–22 (2011).Article 

Google Scholar 
GBIF.org. GBIF Occurrence Download. https://doi.org/10.15468/dl.ms4hjt (2018).Chamberlain, S., Ram, K. & Hart, T. Spocc: Interface to Specie Occurrence Data Sources, R package v.0.9.0. http://CRAN.R-project.org/package=spocc (2018).Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high-resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).Article 

Google Scholar 
Hijmans, R. J. Raster: geographic data analysis and modelling, R package v.2.2-12. http://CRAN.R-project.org/package=raster (2014).Gärtner, H. et al. A technical perspective in modern tree-ring research – How to overcome dendroecological and wood anatomical challenges. J. Vis. Exp. 97, e52337 (2015).
Google Scholar 
Gärtner, H. & Schweingruber, F. H. Microscopic Preparation Techniques for Plant Stem Analysis (Verlag Kessel, 2013).Ghislan, B., Engel, J. & Clair, B. Diversity of anatomical structure of tension wood among 242 tropical tree species. IAWA J. 40, 1–20 (2019).Article 

Google Scholar 
Schweingruber, F. H., Börner, A. & Schulze, E. D. Atlas of Stem Anatomy in Herbs, Shrubs and Trees Vol. 1 (Springer, 2011).Schweingruber, F. H., Börner, A. & Schulze, E. D. Atlas of Stem Anatomy in Herbs, Shrubs and Trees Vol. 2 (Springer, 2013).Dolezal, J., Dvorsky, M., Börner, A., Wild, J. & Schweingruber, F. H. Anatomy, Age and Ecology of High Mountain Plants in Ladakh, the Western Himalaya (Springer International Publishing, 2018).Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH image to imageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ter Braak, C. J. F. & Šmilauer, P. Canoco Reference Manual and User’s Guide: Software 559 for Ordination, Version 5.0 (Cambridge Univ. Press, 2012).Šmilauer, P. & Lepš, J. Multivariate Analysis of Ecological Data Using Canoco 5 (Cambridge Univ. Press, 2014). More

Douglas, A. E. Multiorganismal insects: diversity and function of resident microorganisms. Annu. Rev. Entomol. 60, 17–34 (2015).CAS 
PubMed 
Article 

Google Scholar 
Ma, Q. et al. Gut bacterial communities of Lymantria xylina and their associations with host development and diet. Microorganisms 9(9), 1860 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yuan, X. et al. Comparison of gut bacterial communities of Grapholita molesta (Lepidoptera: Tortricidae) reared on different host plants. Int. J. Mol. Sci. 22(13), 6843 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Liu, Y. et al. Comparison of gut bacterial communities and their associations with host diets in four fruit borers. Pest Manag. Sci. 76(4), 1353–1362 (2020).CAS 
PubMed 
Article 

Google Scholar 
Lauzon, C. R., Sjogren, R. E. & Prokopy, R. J. Enzymatic capabilities of bacteria associated with apple maggot flies: A postulated role in attraction. J. Chem. Ecol. 26, 953–967 (2000).CAS 
Article 

Google Scholar 
Douglas, A. E. The microbial dimension in insect nutritional ecology. Funct. Ecol. 23, 38–47 (2009).Article 

Google Scholar 
Kaltenpoth, M. & Engl, T. Defensive microbial symbionts in Hymenoptera. Funct. Ecol. 28(2), 315–327 (2014).Article 

Google Scholar 
Bruner-Montero, G., Wood, M., Horn, H. A., Gemperline, E., Li, L. & Currie, C. R. Symbiont-mediated protection of acromyrmex leaf-cutter ants from the entomopathogenic fungus Metarhizium anisopliae. mBio 12(6), e0188521 (2021).Zhang, Q. et al. Enterobacter hormaechei in the intestines of housefly larvae promotes host growth by inhibiting harmful intestinal bacteria. Parasit. Vector. 14(1), 598 (2021).CAS 
Article 

Google Scholar 
Zhang, S., et al. The gut microbiota in Camellia weevils are influenced by plant secondary metabolites and contribute to saponin degradation. mSystems 5(2), e00692–19 (2020).Sato, Y. et al. Insecticide resistance by a host-symbiont reciprocal detoxification. Nat. Commun. 12(1), 6432 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jordan, H. R. & Tomberlin, J. K. Microbial influence on reproduction, conversion, and growth of mass produced insects. Curr. Opin. Insect Sci. 48, 57–63 (2021).PubMed 
Article 

Google Scholar 
Strano, C. P., Malacrinò, A., Campolo, O. & Palmeri, V. Influence of host plant on Thaumetopoea pityocampa gut bacterial community. Microb. Ecol. 75(2), 487–494 (2018).PubMed 
Article 

Google Scholar 
Mason, C. J. et al. Diet influences proliferation and stability of gut bacterial populations in herbivorous lepidopteran larvae. PLoS ONE 15(3), e0229848 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hammer, T. J., Janzen, D. H., Hallwachs, W., Jaffe, S. P. & Fierer, N. Caterpillars lack a resident gut microbiome. Proc. Natl. Acad. Sci. USA 114, 9641–9646 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Scully, E. D. et al. Host-plant induced changes in microbial community structure and midgut gene expression in an invasive polyphage (Anoplophora glabripennis). Sci. Rep. 8(1), 9620 (2018).ADS 
MathSciNet 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Goergen, G., Kumar, P. L., Sankung, S. B., Togola, A. & Tamò, M. F. irst report of outbreaks of the fall armyworm Spodoptera frugiperda (J E Smith) (Lepidoptera, Noctuidae), a new alien invasive pest in west and central Africa. PLoS ONE 11(10), e0165632 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Nagoshi, R. N. et al. Southeastern Asia fall armyworms are closely related to populations in Africa and India, consistent with common origin and recent migration. Sci. Rep. 10, 1–10 (2020).Article 
CAS 

Google Scholar 
Beuzelin, J. M., Larsen, D. J., Roldán, E. L. & Schwan Resende, E. Susceptibility to chlorantraniliprole in fall armyworm (Lepidoptera: Noctuidae) populations infesting sweet corn in southern florida. J. Econ. Entomol. 115(1), 224–232 (2022).Montezano, D. G. et al. Host plants of Spodoptera frugiperda (Lepidoptera: Noctuidae) in the Americas. Afr. Entomol. 26, 286–300 (2018).Article 

Google Scholar 
Jones, A. G., Mason, C. J., Felton, G. W. & Hoover, K. Host plant and population source drive diversity of microbial gut communities in two polyphagous insects. Sci. Rep. 9(1), 2792 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Mason, C. J., Hoover, K. & Felton, G. W. Effects of maize (Zea mays) genotypes and microbial sources in shaping fall armyworm (Spodoptera frugiperda) gut bacterial communities. Sci. Rep. 119(1), 4429 (2021).ADS 
Article 
CAS 

Google Scholar 
Lv, D. et al. Comparison of gut bacterial communities of fall armyworm (Spodoptera frugiperda) reared on different host plants. Int. J. Mol. Sci. 22(20), 11266 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chen, Y. P. et al. Effects of host plants on bacterial community structure in larvae midgut of Spodoptera frugiperda. Insects 13(4), 373 (2022).PubMed 
PubMed Central 
Article 

Google Scholar 
Chen, J. et al. Cabbage cultivars influence transfer and toxicity of cadmium in soil-Chinese flowering cabbage Brassica campestris-cutworm Spodoptera litura larvae. Ecotoxicol. Environ. Saf. 213, 112076 (2021).CAS 
PubMed 
Article 

Google Scholar 
Abdullah, A., Ullah, M. I., Raza, A. M., Arshad, M. & Afzal, M. Host plant selection affects biological parameters in armyworm, Spodoptera litura (Lepidoptera: Noctuidae). Pak. J. Zool. 51(6), 2117–2123 (2019).Article 

Google Scholar 
Gopalakrishnan, R. & Kalia, V. K. Biology and biometric characteristics of Spodoptera frugiperda (Lepidoptera: Noctuidae) reared on different host plants with regard to diet. Pest Manag. Sci. 78(5), 2043–2051 (2022).CAS 
PubMed 
Article 

Google Scholar 
He, L. et al. Larval diet affects development and reproduction of East Asian strain of the fall armyworm Spodoptera frugiperda. J. Integr. Agr. 20(3), 736–744 (2021).Article 

Google Scholar 
He, L., Wu, Q., Gao, X. & Wu, K. Population life tables for the invasive fall armyworm, Spodoptera frugiperda fed on major oil crops planted in China. J. Integr. Agr. 20(3), 745–754 (2021).Article 

Google Scholar 
Xie, W. et al. Age-stage, two-sex life table analysis of Spodoptera frugiperda (JE Smith) (Lepidoptera: Noctuidae) reared on maize and kidney bean. Chem. Biol. Technol. Ag. 8, 44 (2021).CAS 
Article 

Google Scholar 
Gopalakrishnan, R. & Kalia, V. K. Biology and biometric characteristics of Spodoptera frugiperda (Lepidoptera: Noctuidae) reared on different host plants with regard to diet. Pest Manag. Sci. 78(5), 2043–2051 (2022).CAS 
PubMed 
Article 

Google Scholar 
Wang, P. et al. Host selection and adaptation of the invasive pest Spodoptera frugiperda to indica and japonica rice cultivars. Entomol. Gen. https://doi.org/10.1127/entomologia/2022/1330 (2022).Article 

Google Scholar 
Wu, L. et al. Fitness of fall armyworm, Spodoptera frugiperda to three solanaceous vegetables. J. Integr. Agr. 20(3), 755–763 (2021).Article 

Google Scholar 
Wu, F. et al. Population development, fecundity, and flight of Spodoptera frugiperda (Lepidoptera: Noctuidae) reared on three green manure crops: implications for an ecologically based pest management approach in China. J. Econ. Entomol. 115(1), 124–132 (2022).PubMed 
Article 

Google Scholar 
Hou, M. L. & Sheng, C. F. Effects of different foods on growth, development and reproduction of cotton bollworm, Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae). Acta Entomol. Sin. 43, 168–175 (2000).CAS 

Google Scholar 
Wang, X. L. et al. Variability of gut microbiota across the life cycle of Grapholita molesta (Lepidoptera: Tortricidae). Front. Microbiol. 11, 1366 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Näsvall, K. et al. Host plant diet affects growth and induces altered gene expression and microbiome composition in the wood white (Leptidea sinapis) butterfly. Mol. Ecol. 30(2), 499–516 (2021).PubMed 
Article 
CAS 

Google Scholar 
Ort, B. S., Bantay, R. M., Pantoja, N. A. & O’Grady, P. M. Fungal diversity associated with Hawaiian Drosophila host plants. PLoS ONE 7(7), e40550 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Majumder, R., Sutcliffe, B., Taylor, P. W. & Chapman, T. A. Fruit host-dependent fungal communities in the microbiome of wild Queensland fruit fly larvae. Sci. Rep. 10(1), 16550 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zeng, J. Y. et al. Avermectin stress varied structure and function of gut microbial community in Lymantria dispar asiatica (Lepidoptera: Lymantriidae) larvae. Pestic. Biochem Physiol. 164, 196–202 (2020).CAS 
PubMed 
Article 

Google Scholar 
Chen, C., Zhang, J., Tan, H., Fu, Z. & Wang, X. Characterization of the gut microbiome in the beet armyworm Spodoptera exigua in response to the short-term thermal stress. J. Asia-Pac. Entomol. 25, 101863 (2022).Article 

Google Scholar 
Rozadilla, G., Cabrera, N. A., Virla, E. G., Greco, N. M. & McCarthy, C. B. Gut microbiota of Spodoptera frugiperda (J.E. Smith) larvae as revealed by metatranscriptomic analysis. J. Appl. Entomol. 144, 351–363 (2020).CAS 
Article 

Google Scholar 
Ugwu, J. A., Liu, M., Sun, H. & Asiegbu, F. O. Microbiome of the larvae of Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae) from maize plants. J. Appl. Entomol. 144, 764–776 (2020).CAS 
Article 

Google Scholar 
Wang, X. et al. Variability of gut microbiota across the life cycle of Grapholita molesta (Lepidoptera: Tortricidae). Front. Microbiol. 11, 1366 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Yang, F. Y. et al. Differential profiles of gut microbiota and metabolites associated with host shift of Plutella xylostella. Int. J. Mol. Sci. 21, 6283 (2020).CAS 
PubMed Central 
Article 

Google Scholar 
Shao, Y. et al. Crystallization of alpha- and beta-carotene in the foregut of Spodoptera larvae feeding on a toxic food plant. Insect Biochem. Mol. Biol. 41, 273–281 (2011).CAS 
PubMed 
Article 

Google Scholar 
Santos, T. A., Scorzoni, L., Correia, R., Junqueira, J. C. & Anbinder, A. L. Interaction between Lactobacillus reuteri and periodontopathogenic bacteria using in vitro and in vivo (G mellonella) approaches. Pathog. Dis. 78(8), ftaa044 (2020).CAS 
PubMed 
Article 

Google Scholar 
Biedermann, P. & Vega, F. E. Ecology and evolution of insect-fungus mutualisms. Annu. Rev. Entomol. 65, 431–455 (2020).CAS 
PubMed 
Article 

Google Scholar 
Guo, Q., Yao, Z., Cai, Z., Bai, S. & Zhang, H. Gut fungal community and its probiotic effect on Bactrocera dorsalis. Insect Sci. https://doi.org/10.1111/1744-7917.12986 (2021).Article 
PubMed 

Google Scholar 
Bing, X. L., Gerlach, J., Loeb, G. & Buchon, N. Nutrient-dependent impact of microbes on Drosophila suzukii development. MBio 9, e02199-e2117 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Keebaugh, E. S., Ryuichi, Y., Benjamin, O., Ludington, W. B. & Ja, W. W. Microbial quantity impacts Drosophila nutrition, development, and lifespan. Iscience 4, 247–259 (2018).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Deutscher, A. T., Chapman, T. A., Shuttleworth, L. A., Riegler, M. & Reynolds, O. L. Tephritid-microbial interactions to enhance fruit fly performance in sterile insect technique programs. BMC Microbiol. 19, 287 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Gurung, K., Wertheim, B. & Falcao Salles, J. The microbiome of pest insects: it is not just bacteria. Entomol. Exp. Appl. 167, 156–170 (2019).Article 

Google Scholar 
Sun, J., Xia, Y. & Ming, D. Whole-genome sequencing and bioinformatics analysis of Apiotrichum mycotoxinivorans: Predicting putative zearalenone-degradation enzymes. Front. Microbiol. 11, 1866 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Qian, X. J. et al. Bioconversion of volatile fatty acids into lipids by the oleaginous yeast Apiotrichum porosum DSM27194. Fuel 290, 119811 (2021).CAS 
Article 

Google Scholar 
Passos, D. F., Pereira, N. & Castro, A. M. A comparative review of recent advances in cellulases production by Aspergillus, Penicillium and Trichoderma strains and their use for lignocellulose deconstruction. Curr. Opin. Green Sustain Chem. 14, 60–66 (2018).Article 

Google Scholar 
Višňovská, D. et al. Caterpillar gut and host plant phylloplane mycobiomes differ: a new perspective on fungal involvement in insect guts. FEMS Microbiol. Ecol. 96(9), fiaa116 (2020).PubMed 
Article 
CAS 

Google Scholar 
Shu, B. et al. Growth inhibition of Spodoptera frugiperda larvae by camptothecin correlates with alteration of the structures and gene expression profiles of the midgut. BMC Genomics 22, 391 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

Case study areaThe studied catchment (2 621 km2) is located in the central-western part of Poland, and constitutes a part of the Oder River basin. The Wełna River (118 km) discharges to the Warta River near the town of Oborniki18, with an average flow rate of 8.1 m3s−1 (1980–2019) in this profile19. The natural conditions in this catchment favour the development of intensive agriculture, which covers almost 72% of this area (1888 km2) and contributes to the high consumption of mineral fertilizers20. Forest areas cover another 22% of this catchment (589 km2), while urbanised ones only 4% (93 km2) (Fig. 1). The Wełna River catchment is inhabited by approx. 230,000 people, of which only approx. 74% is served by wastewater treatment facilities21.Figure 1Localisation of the Wełna River catchment with its land use forms and nutrient sources. This figure was created using ArcGIS 10.2.1 for Desktop available at https://www.esri.com/en-us/home. Licence granted to Institute of Meteorology and Water Management.Full size imageInput dataBoth the mass-balance method and the modelling method require a similar amount and type of input data (Supplementary Table S1). Basic information on the Wełna River daily flow rates and nutrient concentrations in the closing profile of the catchment (Oborniki) has been obtained from the state monitoring services (Institute of Meteorology and Water Management—National Research Institute—IMGW-PIB13 and State Environmental Monitoring22—SEM) (Supplementary Table S1). They have formed the basis for the estimation of the share of individual sources in the mass-balance method, as well as for the calibration of the Macromodel DNS/SWAT in the modelling method. Other data, such as maps of elevation, river network and soil maps, as well as meteorological data, necessary for the development of an accurate representation of the studied catchment area on the Macromodel DNS/SWAT digital platform, were also obtained from state repositories. Data on the land use comes from the Corine Land Cover8, while detailed information on nutrient sources has been obtained mostly from the Local Data Bank of statistical information. The utilisation of the collected database has been presented in Fig. 2, and described in the following text. The comparison of the results for nutrient loads from both method was based on the year 2017, which was characterised by the maximum amount of monitoring data for both flows (365 measurements) (IMGW-PIB) and total nitrogen (TN) and total phosphorus (TP) (12 measurements–SEM). The average air temperature in 2017 in Poland was 1.5 °C higher than the long-term average (1971–2000) and was over 10 °C which resulted from the warm autumn and the end of the year. The time of the snow cover presence was shorter than the long-term data, and the rest of the year was classified as thermally normal.Figure 2Methodology diagram with relevant chapters marked in grey ovals (green—steps and data used for Mass Balance method, blue—steps data used for Modelling method, green/blue—steps and data used for both methods).Full size imageIn terms of precipitation, 2017 was assessed as wet, similarly due to rainy autumn and summer. In the Wełna River catchment area, the annual rainfall was about 770 mm, however high variability of precipitation conditions in particular months, from extremely wet to very dry, should be noted23. Therefore hydrologically, 2017 was considered normal with the flows only slightly lower than the long-term average.Mass-balance methodThe first method used for the quantification of sources and loads in the studied area was the static mass-balance method. It is widely used by the Polish administration authorities responsible for water management17. This method is based on the assumption that the sum of the nutrient loads in the river’s closing profile (selected based on access to the monitoring data) and its retention in the catchment equals the emission of nutrients in a given time. Such assumption allows the apportioning of the river loads among identified sources and the estimation of their contribution to the total loads, based on known or assumed values of their retention.River load calculationThe total load of nutrients discharged from the catchment was calculated using the daily flow rate and nutrient concentrations in the closing profile of the catchment area (Oborniki, Fig. 1) from the SEM (Supplementary Table S1). The daily river load was calculated using the following Eq. 5:$${L}_{river}=0.0864sum_{t=1}^{n=365}{({Q}_{t}cdot {C}_{t})}_{t}$$
(1)
where: Lriver is the annual load [kga-1], n is the number of days, t is the consecutive day, Ct is the concentration [mg L-1], Qt is the mean daily flow rate [Ls-1], and 0.0864 is the unit conversion.Due to the fact that the flow rate is measured daily and nutrient concentrations only 12 times a year, the linear interpolation method5 was used to obtain the daily concentration values:$${x}_{k}={x}_{a}+kcdot frac{{x}_{b}-{x}_{a}}{n+1}$$
(2)
where: xk is the interpolated concentration value, xa is the first of the two measured concentration values between which the concentrations are interpolated, xb is the second of the two measured concentration values between which the concentrations are interpolated, k is the consecutive number of missing value and n is number of missing values.Source apportionmentFor the mass-balance method, data on nutrient loads for source apportionment (emission inventory) was collected in order to proceed with further calculations. The calculations were performed for 2017, due to the availability of river monitoring data and the nutrient sources were divided into 7 categories, based on the HELCOM guidelines5: municipal (MWS) and industrial (INS) point sources, municipal diffuse sources (SCS), forestry (MFS), agriculture (AGS), natural background (NBS) and atmospheric deposition (ATS). The category of “unknown sources” (UKS) was taken into account, in order to include possible discrepancies in nutrients load apportionment, and to cover eventual differences between calculated river load and inventoried emission.The MWS loads were calculated on the basis of the number of inhabitants served by the wastewater treatment plants (WWTPs)21. In the Wełna River catchment, 151 771 inhabitants were served by the 12 WWTPs covered by the National Wastewater Treatment Program (NWTP)24, which provides information on the total discharge volume from each facility. For 5 of these plants, information on influent/effluent nutrient concentrations was also available, allowing the direct calculation of discharged loads. For the remaining seven facilities, the loads were calculated on the basis of the mean influent concentration information, available for the WWTPs covered by the NWTP (80 mgL−1 and 12 mgL−1 for TN and TP, respectively), and approximated nutrient reduction level in non-biological WWTPs. This reduction level, based on data from the NWTP, was set at 65% for TN and 35% for TP24. Another 19 350 inhabitants of this catchment were connected to the small WWTPs, not included in the NWTP. This part of the MWS load was calculated using the mean daily wastewater outflow (0.12 m3day−1 per person), the same mean nutrient concentrations and reduction levels as presented above. Additionally, the remaining 25% of the catchment’s population (58,000) is not connected to any WWTP and uses septic tanks and other types of individual wastewater treatment systems. The load from this source was expressed as SCS, and calculated using unit loads set on 11 gday−1 per person and 1.6 gday-1 per person for TN and TP, respectively17. The industrial nutrient input information (INS) was gathered directly from the Statistics Poland office database21.The AGS load was calculated using nitrate and phosphate concentrations in shallow groundwater (90 cm below the ground surface), from 22 sampling points located on agricultural areas in the Wełna River catchment17. Concentrations were recalculated to TN and TP respectively and averaged. Thus, the obtained mean concentrations were 8.25 mgL−1 of TN and 1.92 mgL−1 of TP. Subsequently, load values were calculated by multiplying these concentrations by the outflow from agricultural areas, calculated as a share of the total catchment outflow respective to the agricultural use of the area. The calculated loads were multiplied by coefficients reflecting the share of monitored outflow (groundwaters and tile drainage) from the agricultural runoff (1.11 for TN and 4.17 for TP)25. Subsequently, the natural background (NBS) was subtracted from the AGS load.The load corresponding to NBS was calculated using the total catchment outflow and nutrient concentration values reflecting conditions in undisturbed areas of pre-human activity, set as 0.15 mgL−1 and 0.02 mgL−1, for TN and TP respectively17. The MFS load was also calculated in a similar way, using nutrient concentrations set to represent forest catchment as 0.31 mgL−1 and 0.038 mgL−117 and the outflow calculated as the share of the total catchment area, respective for the catchment part covered by forest. Also in this case, the NBS load has been subtracted. As for the ATS load, data on pollutant deposition into the ground from precipitation was taken from the SEM network26. This data was based on precipitation and its chemistry measurements taken from 22 monitoring stations covering the entire territory of Poland. The total load from the point and diffuse sources was calculated by adding the loads mentioned above. The eventual difference between the river load (“River load calculation” Section) and inventoried emission (“Mass-balance method” Section) accounted for the other sources (UKS).Load apportionmentThe contribution of each source to the calculated river load was calculated based on a simplified equation modified from HELCOM5:$${L}_{river}=DP+LOD-RP-RD$$
(3)
where: Lriver is the river load [kga−1], DP is the load from point sources (MWS and INS) [kga−1], LOD is the load from diffuse sources (SCS, ATS, MFS, AGS and, NBS) [kga−1], RP is the point source retention [kga−1] and RD is the diffuse source retention [kga−1].In the adopted mass-balance method, it is assumed that nutrient load from the point sources (DP) is introduced directly into the river bed phase, while load from the diffuse sources (LOD) is discharged into both phases of the catchment, land and river bed ones. In both phases, self-purification processes are taking place, resulting in the reduction of nutrient loads on the way from the source to the catchment closing profile. However, due to the limited amount of data, the self-purification processes in the river have been omitted, therefore the point source retention (RP) equalled 0 kga−1. Subsequently, the diffuse source retention (RD) has been estimated on the basis of the difference between each nutrient load of the river (Lriver) and the point sources (LOD). The remaining river load has been then attributed proportionally to the contribution of the particular diffuse sources to the total source apportionment (emission inventory).Modelling methodThe digital platform, the Macromodel DNS with the SWAT module27,28,29,30,31,32, was used for comparison for the nutrient balancing method described in “Mass-balance method” Section. This advanced dynamic tool tracks nitrogen and phosphorus migration paths in the river basin taking into account their spatial and temporal variability. For this purpose, it takes into account a very extensive input database, similar to that used in the mass balance method (Supplementary Table S1). Natural and anthropogenic processes that affect the transport and transformation of nutrients, are also part of this platform. The SWAT module (version 2012) is a tool which operates in the spatial information system (GIS) and is fully integrated with it. Using the digital elevation model (DEM), the SWAT module divided the entire analysed Wełna River catchment into a total of 225 sub-catchments of an average area of 11.5 km2. The subsequent use of data on land use (forests, agriculture and urbanised areas) and the types of soils (31 classes) allowed the authors to identify a total of 2824 hydrological response units (HRUs), homogeneous in terms of vegetation, soil and topography33. Afterwards, a simulation of soil water content, evapotranspiration, surface runoff, primary and underground flows was carried out in accordance with the water balance Eq. (4), which represents the basis for the quantitative component and the HRU.$${SW}_{t}={SW}_{0}+sum_{i=1}^{t}({R}_{day}-{Q}_{surf}-{E}_{a}-{W}_{seep}-{Q}_{gw})$$
(4)
where: SWt is the final soil water content (mm H2O), SW0 is the initial soil water content (mm H2O), Rday is the amount of precipitation (mm H2O), Qsurf is amount of surface runoff (mm H2O), Ea is the amount of evapotranspiration (mm H2O), Wseep is the amount of water entering the vadose zone from the soil profile (mm H2O), Qgw is the amount of return flow (mm H2O).The model directs all runoff flows generated by each HRU through the channel network, thus simulating a catchment. The water balance equation also represents a basis for the simulation, transport and transformation of nutrients required for the quantitative component of the model. This tool models forms of nitrogen, organic and inorganic , different forms of phosphorus in soil34, as well as organic nitrogen and phosphorus forms associated with plant residues, microbial biomass and soil humus35,36,37,38. Final results of simulations are produced by the SWAT model as all the forms of nitrogen and phosphorus (in kilograms of N and P per a time unit, respectively) are then summed up to give TN and TP values. To verify that the model properly predicts TN and TP values its results are calibrated with the TN and TP values resulting from SEM, as described in Sect. 2.4.1. Moreover, the particular forms of nitrogen and phosphorus have also been compared with the modelling results (Supplementary Table S4). A detailed overview of the migration and transformation pathways of nitrogen and phosphorus forms in the catchment has been presented in the Supplementary Information (Sect. S1), while mathematical description of these processes is included in the theoretical documentation of the SWAT model39.Similarly, as in the case of the mass-balance method, diffuse sources of nutrients from agriculture (AGS), forestry (MFS) or urban areas (URB) in SWAT were simulated in the land phase of the catchment. In the land phase, the model simulates both the infiltration of nutrients into the soil (fertilization, plant biomass, precipitation) and their removal from it (volatilization, denitrification, erosion, surface runoff). Additionally, changes in the distribution of nutrients in the soil (uptake by plants) and the low mobility of phosphorus itself are also taken into account39,40,41.Pollutants from municipal and industrial point sources (MWS, INS) are introduced directly into the river bed phase. The exception here is the nutrient load from municipal diffuse sources (SCS) which, reduced as a result of the self-purification processes taking place in the land phase, is also treated in the model as point sources. The SCS nutrient load mainly derives from leaking or illegally emptied septic tanks. For this purpose, septic tanks have been divided into three types: leaky, partially illegally emptied, and sealed septic tanks, legally emptied. The loads from the legally emptied tanks are included in the effluents from WWTPs reported in the catchment. While for the remaining types, their loads are calculated using factors depending on their effectiveness in nutrient removal (15 – 50%). The final nutrient load derived from these types of facilities is then calculated, taking into consideration the number of inhabitants using the different types of septic tanks and the average chemical composition of wastewater21.The load of nutrients from the atmospheric deposition (ATS) affects both land and river phases due to the presence of two deposition mechanisms in the SWAT module, i.e., wet and dry deposition. The model also allows for the determination of nutrient loads generated as a result of natural processes of nitrogen and phosphorus transformation and transport in the soil, with the omission of all anthropogenic pressure—natural background (NBS).Calibration, verification and validationThe SWAT module for the Wełna River has been calibrated, verified and validated using the SWAT-CUP software42. For the quantitative component (water circulation in the catchment), the implemented daily flow data (source: IMGW-PIB) for the period of 18 years (2001–2018) came from the water gauge stations on the Wełna River (Pruśce and Kowanówko) and its tributary (the Flinta River-Ryczywół) (Fig. 1). The qualitative component (nitrogen and phosphorus concentration in the catchment) was gathered from the SEM stations localised at the Wełna River (Oborniki and Rogoźno) (Fig. 1) and covered a period of 13 years (2005–2018). Three statistical measures, coefficient of determination (R2)43, percent bias (PBIAS)44, and Kling-Gupta efficiency (KGE)45, have been used to indicate the Wełna River model performance (Supplementary Tables S2 and S3). In terms of the quantitative component, the calibration and verification coefficients R2, KGE and PBIAS classified the model performance generally as good and very good for the main river (Wełna), and satisfactory and good for its tributary (Flinta). During the validation procedure, all coefficient values rated the model performance for daily flow simulations as very good. In terms of qualitative components, the model performance for TN simulations can be considered as very good or good, according to the all-applied coefficients. Lower model performance, mostly satisfactory, was observed for TP mainly due to the variability of phosphorus temporal distribution patterns (Supplementary Table S2). The entire process was described in detail in Orlińska-Woźniak et al46.Variant scenariosIn order to determine the contribution of individual sources to the total load of nutrients in the profile closing the analysed catchment, a final simulation of the model was used and subjected to calibration, verification and validation procedures, and called the baseline scenario (A0). Subsequent so-called variant scenarios (A1–A5), i.e. model simulations, were developed. In variant scenarios the values of selected parameters were changed in relation to the A0 scenario. This was used both in the river bed phase for point sources (A1) and for individual diffuse sources (A2–5), thus imitating surface changes for particular types of land use in the land phase of the catchment (Fig. 3).Figure 3Variant analysis diagram for assessment of nutrient loads (L) for particular modelling scenarios and sources described in the text: MWS, INS, SCS—point sources, URB—urban, AGS—agricultural, MFS—forest.Full size imageIn the A1 scenario, all parameterized and aggregated point sources (MWS, INS, SCS) for each relevant sub-basin (LMWS,INS,SCS), were removed from the model to calculate their contribution to the total nutrient loads in the closing profile of the studied catchment (LA1).In the next two scenarios (A2 and A3), concerning urban and agricultural land use, their surface areas (5 663 ha and 192 917 ha, respectively) were successively replaced by the forest land use. This procedure was based on the assumption that the forest is the primary type of land use for this catchment area47. In order to completely eliminate the influence of these areas, the nutrient loads from the relevant surface area occupied by forest land use were subtracted, in order to estimate the contribution of urban and agricultural land (LURB and LAGS, respectively).The change in land use from urbanised and agricultural areas to forest areas increased their percentage of the catchment area to almost 100%, thus the original image of the catchment area and the nutrient load at its mouth. On this basis, in scenario A4, the nutrient load from forests LMFS, which currently occupy only 20% of the catchment area (A0), flowing to the closing profile, was calculated from the proportion.The A5 scenario is the difference between the nutrient load from the A0 scenario and the sum of the remaining loads from the subsequent variant scenarios (A1–A4). In this way, both the natural background (NBS) and atmospheric deposition (ATS) were taken into account. More

Tedersoo L, Bahram M, Põlme S, Kõljalg U, Yorou NS, Wijesundera R, et al. Fungal biogeography. Global diversity and geography of soil fungi. Science (80-). 2014;346:1256688.Article 
CAS 

Google Scholar 
Oliverio AM, Geisen S, Delgado Baquerizo M, Maestre FT, Turner BL, Fierer N. The global-scale distributions of soil protists and their contributions to belowground systems. Sci Adv. 2020;6:eaax8787.Article 
CAS 

Google Scholar 
Delgado Baquerizo M, Oliverio AM, Brewer TE, Benavent-Gonzalez A, Eldridge DJ, Bardgett RD, et al. A global atlas of the dominant bacteria found in soil. Science. 2018;359:320–5.CAS 
PubMed 
Article 

Google Scholar 
Bates ST, Clemente JC, Flores GE, Walters WA, Parfrey LW, Knight R, et al. Global biogeography of highly diverse protistan communities in soil. ISME J. 2012;7:652–9.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Crowther TW, van den Hoogen J, Wan J, Mayes MA, Keiser AD, Mo L, et al. The global soil community and its influence on biogeochemistry. Science. 2019;365:eaav0550.CAS 
PubMed 
Article 

Google Scholar 
Xiong W, Jousset A, Li R, Delgado-Baquerizo M, Bahram M, Logares R, et al. A global overview of the trophic structure within microbiomes across ecosystems. Environ Int. 2021;151:106438.PubMed 
Article 

Google Scholar 
Singh BK, Bardgett RD, Smith P, Reay DS. Microorganisms and climate change: terrestrial feedbacks and mitigation options. Nat Rev Microbiol. 2010;8:779–90.CAS 
PubMed 
Article 

Google Scholar 
Nowicka B, Kruk J. Powered by light: Phototrophy and photosynthesis in prokaryotes and its evolution. Microbiol Res. 2016;186-7:99–118.Article 
CAS 

Google Scholar 
Hamard S, Céréghino R, Barret M, Sytiuk A, Lara E, Dorrepaal E, et al. Contribution of microbial photosynthesis to peatland carbon uptake along a latitudinal gradient. J Ecol. 2021;109:3424–41.CAS 
Article 

Google Scholar 
Seppey CVW, Singer D, Dumack K, Fournier B, Belbahri LL, Mitchell EAD, et al. Distribution patterns of soil microbial eukaryotes suggests widespread algivory by phagotrophic protists as an alternative pathway for nutrient cycling. Soil Biol Biochem. 2017;112:68–76.CAS 
Article 

Google Scholar 
Schmidt O, Dyckmans J, Schrader S. Photoautotrophic microorganisms as a carbon source for temperate soil invertebrates. Biol Lett. 2016;12:20150646.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Halvorson HM, Barry JR, Lodato MB, Findlay RH, Francoeur SN, Kuehn KA. Periphytic algae decouple fungal activity from leaf litter decomposition via negative priming. Funct Ecol. 2019;33:188–201.PubMed 
Article 

Google Scholar 
Wyatt KH, Turetsky MR. Algae alleviate carbon limitation of heterotrophic bacteria in a boreal peatland. J Ecol. 2015;103:1165–71.CAS 
Article 

Google Scholar 
Elbert W, Weber B, Burrows S, Steinkamp J, Büdel B, Andreae MO, et al. Contribution of cryptogamic covers to the global cycles of carbon and nitrogen. Nat Geosci. 2012;5:459–62.CAS 
Article 

Google Scholar 
Jassey VEJ, Walcker R, Kardol P, Geisen S, Heger T, Lamentowicz M, et al. Contribution of soil algae to the global carbon cycle. New Phytol. 2022;234:64–76.CAS 
PubMed 
Article 

Google Scholar 
Tahon G, Tytgat B, Willems A. Diversity of phototrophic genes suggests multiple bacteria may be able to exploit sunlight in exposed soils from the Sør Rondane Mountains, East Antarctica. Front Microbiol. 2016;7:2026.PubMed 
PubMed Central 
Article 

Google Scholar 
Maier S, Tamm A, Wu D, Caesar J, Grube M, Weber B. Photoautotrophic organisms control microbial abundance, diversity, and physiology in different types of biological soil crusts. ISME J. 2018;12:1032–46.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Büdel B. Ecology and diversity of rock-inhabiting cyanobacteria in tropical regions. Eur J Phycol. 1999;34:361–70.Article 

Google Scholar 
Hamard S, Küttim M, Céréghino R, Jassey VEJ. Peatland microhabitat heterogeneity drives phototrophic microbes distribution and photosynthetic activity. Environ Microbiol. 2021;23:6811–27.CAS 
PubMed 
Article 

Google Scholar 
Cano-Díaz C, Maestre FT, Eldridge DJ, Singh BK, Bardgett RD, Fierer N, et al. Contrasting environmental preferences of photosynthetic and non-photosynthetic soil cyanobacteria across the globe. Glob Ecol Biogeogr. 2020;29:2025–38.Article 

Google Scholar 
Rodriguez-Caballero E, Belnap J, Büdel B, Crutzen PJ, Andreae MO, Pöschl U, et al. Dryland photoautotrophic soil surface communities endangered by global change. Nat Geosci. 2018;11:185–9.CAS 
Article 

Google Scholar 
Pointing SB, Belnap J. Microbial colonization and controls in dryland systems. Nat Rev Microbiol. 2012;10:551–62.CAS 
PubMed 
Article 

Google Scholar 
Bates ST, Clemente JC, Flores GE, Walters WA, Parfrey LW, Knight R, et al. Global biogeography of highly diverse protistan communities in soil. ISME J. 2013;7:652–9.CAS 
PubMed 
Article 

Google Scholar 
Küttim L, Küttim M, Puusepp L, Sugita S. The effects of ecotope, microtopography and environmental variables on diatom assemblages in hemiboreal bogs in Northern Europe. Hydrobiologia. 2017;792:137–49.Article 
CAS 

Google Scholar 
Mahé F, de Vargas C, Bass D, Czech L, Stamatakis A, Lara E, et al. Parasites dominate hyperdiverse soil protist communities in Neotropical rainforests. Nat Ecol Evol. 2017;1:91.PubMed 
Article 

Google Scholar 
Lindo Z, Gonzalez A. The Bryosphere: An Integral and Influential Component of the Earth’s Biosphere. Ecosystems. 2010;13:612–27.Article 

Google Scholar 
Sporn SG, Bos MM, Kessler M, Gradstein SR. Vertical distribution of epiphytic bryophytes in an Indonesian rainforest. Biodivers Conserv. 2010;19:745–60.Article 

Google Scholar 
Cornelissen JHC, Lang SI, Soudzilovskaia NA, During HJ. Comparative cryptogam ecology: a review of bryophyte and lichen traits that drive biogeochemistry. Ann Bot. 2007;99:987–1001.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Van Breemen N. How Sphagnum bogs down other plants. Trends Ecol Evol. 1995;10:270–5.PubMed 
Article 

Google Scholar 
Jonsson M, Kardol P, Gundale MJ, Bansal S, Nilsson M-C, Metcalfe DB, et al. Direct and Indirect Drivers of Moss Community Structure, Function, and Associated Microfauna Across a Successional Gradient. Ecosystems. 2014;18:1–16.
Google Scholar 
Bragina A, Berg C, Cardinale M, Shcherbakov A, Chebotar V, Berg G. Sphagnum mosses harbour highly specific bacterial diversity during their whole lifecycle. ISME J. 2012;6:802–13.CAS 
PubMed 
Article 

Google Scholar 
Bay G, Nahar N, Oubre M, Whitehouse MJ, Wardle DA, Zackrisson O, et al. Boreal feather mosses secrete chemical signals to gain nitrogen. New Phytol. 2013;200:54–60.CAS 
PubMed 
Article 

Google Scholar 
Kip N, van Winden JF, Pan Y, Bodrossy L, Reichart G-J, Smolders AJP, et al. Global prevalence of methane oxidation by symbiotic bacteria in peat-moss ecosystems. Nat Geosci. 2010;3:617–21.CAS 
Article 

Google Scholar 
Lindo Z, Nilsson M-C, Gundale MJ. Bryophyte-cyanobacteria associations as regulators of the northern latitude carbon balance in response to global change. Glob Chang Biol. 2013;19:2022–35.PubMed 
Article 

Google Scholar 
Jassey VEJ, Shimano S, Dupuy C, Toussaint M-L, Gilbert D. Characterizing the feeding habits of the testate amoebae Hyalosphenia papilio and Nebela tincta along a narrow ‘fen-bog’ gradient using digestive vacuole content and 13C and 15N isotopic analyses. Protist. 2012;163:451–64.PubMed 
Article 

Google Scholar 
Raanan H, Oren N, Treves H, Keren N, Ohad I, Berkowicz SM, et al. Towards clarifying what distinguishes cyanobacteria able to resurrect after desiccation from those that cannot: The photosynthetic aspect. Biochim Biophys Acta – Bioenerg. 2016;1857:715–22.CAS 
Article 

Google Scholar 
Puente-Sánchez F, Arce-Rodríguez A, Oggerin M, García-Villadangos M, Moreno-Paz M, Blanco Y, et al. Viable cyanobacteria in the deep continental subsurface. Proc Natl Acad Sci USA. 2018;115:10702–7.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Küttim M, Küttim L, Ilomets M, Laine AM. Controls of Sphagnum growth and the role of winter. Ecol Res. 2020;35:219–34.Article 
CAS 

Google Scholar 
Jassey VEJ, Chiapusio G, Mitchell EAD, Binet P, Toussaint M-L, Gilbert D. Fine-scale horizontal and vertical micro-distribution patterns of testate amoebae along a narrow Fen/Bog gradient. Microb Ecol. 2011;61:374–85.PubMed 
Article 

Google Scholar 
Wilken S, Huisman J, Naus-Wiezer S, Van Donk E. Mixotrophic organisms become more heterotrophic with rising temperature. Ecol Lett. 2012;16:225–33.PubMed 
Article 

Google Scholar 
Jassey VEJ, Signarbieux C. Effects of climate warming on Sphagnumphotosynthesis in peatlands depend on peat moisture and species‐specific anatomical traits. Glob Chang Biol. 2019;182:12–65.
Google Scholar 
McDonald D, Price MN, Goodrich J, Nawrocki EP, Desantis TZ, Probst A, et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 2011;6:610–8. 2012 63PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Lozupone CA, Stombaugh J, Gonzalez A, Ackermann G, Wendel D, Vázquez-Baeza Y, et al. Meta-analyses of studies of the human microbiota. Genome Res. 2013;23:1704–14.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pawluczyk M, Weiss J, Links MG, Egaña Aranguren M, Wilkinson MD, Egea-Cortines M. Quantitative evaluation of bias in PCR amplification and next-generation sequencing derived from metabarcoding samples. Anal Bioanal Chem. 2015;407:1841–8.CAS 
PubMed 
Article 

Google Scholar 
Ramirez KS, Knight CG, de Hollander M, Brearley FQ, Constantinides B, Cotton A, et al. Detecting macroecological patterns in bacterial communities across independent studies of global soils. Nat Microbiol. 2018;3:189–96.CAS 
PubMed 
Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2019.Opelt K, Berg C, Schönmann S, Eberl L, Berg G. High specificity but contrasting biodiversity of Sphagnum-associated bacterial and plant communities in bog ecosystems independent of the geographical region. ISME J. 2007;1:502–16.CAS 
PubMed 
Article 

Google Scholar 
Hamard S, Robroek BJM, Allard P-M, Signarbieux C, Zhou S, Saesong T, et al. Effects of Sphagnum Leachate on Competitive Sphagnum Microbiome Depend on Species and Time. Front Microbiol. 2019;10:3317.Article 

Google Scholar 
Holland-Moritz H, Stuart J, Lewis LR, Miller S, Mack MC, McDaniel SF, et al. Novel bacterial lineages associated with boreal moss species. Environ Microbiol. 2018;20:2625–38.CAS 
PubMed 
Article 

Google Scholar 
Singer D, Metz S, Unrein F, Shimano S, Mazei Y, Mitchell EAD, et al. Contrasted Micro-Eukaryotic Diversity Associated with Sphagnum Mosses in Tropical, Subtropical and Temperate Climatic Zones. Microb Ecol. 2019;78:714–24.CAS 
PubMed 
Article 

Google Scholar 
Holland-Moritz H, Stuart JEM, Lewis LR, Miller SN, Mack MC, Ponciano JM, et al. The bacterial communities of Alaskan mosses and their contributions to N2-fixation. Microbiome. 2021;9:1–14.Article 
CAS 

Google Scholar 
Righetti D, Vogt M, Gruber N, Psomas A, Zimmermann NE. Global pattern of phytoplankton diversity driven by temperature and environmental variability. Sci Adv. 2019;5:eaau6253.PubMed 
PubMed Central 
Article 

Google Scholar 
Amend AS, Cobian GM, Laruson AJ, Remple K, Tucker SJ, Poff KE, et al. Phytobiomes are compositionally nested from the ground up. PeerJ. 2019;2019:e6609.Article 

Google Scholar 
Dedysh SN, Pankratov TA, Belova SE, Kulichevskaya IS, Liesack W. Phylogenetic analysis and in situ identification of Bacteria community composition in an acidic Sphagnum peat bog. Appl Environ Microbiol. 2006;72:2110–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Robroek BJM, Martí M, Svensson BH, Dumont MG, Veraart AJ, Jassey VEJ. Rewiring of peatland plant–microbe networks outpaces species turnover. Oikos. 2021;303:605–15.
Google Scholar 
Holland-Moritz H, Stuart J, Lewis LR, Miller S, Mack MC, Mcdaniel SF, et al. Novel bacterial lineages associated with boreal moss species. Environ Microbiol. 2018;20:2625–38.CAS 
PubMed 
Article 

Google Scholar 
Sytiuk A, Céréghino R, Hamard S, Delarue F, Guittet A, Barel JM, et al. Predicting the structure and functions of peatland microbial communities from Sphagnum phylogeny, anatomical and morphological traits and metabolites. J Ecol. 2021;1365-2745:13728.
Google Scholar 
Rudolph H, Samland J. Occurrence and metabolism of sphagnum acid in the cell walls of bryophytes. Phytochemistry. 1985;24:745–9.CAS 
Article 

Google Scholar 
Chiapusio G, Jassey VEJ, Bellvert F, Comte G, Weston LA, Delarue F, et al. Sphagnum species modulate their phenolic profiles and mycorrhizal colonization of surrounding Andromeda polifolia along peatland microhabitats. J Chem Ecol. 2018;27:1–12.
Google Scholar 
Rasmussen S, Wolff C, Rudolph H. Compartmentalization of phenolic constituents in sphagnum. Phytochemistry. 1995;38:35–39.CAS 
Article 

Google Scholar 
Sytiuk A, Céréghino R, Hamard S, Delarue F, Dorrepaal E, Küttim M, et al. Biochemical traits enhance the trait concept in Sphagnum ecology. Oikos 2022;00:00.Hájek T, Ballance S, Limpens J, Zijlstra M, Verhoeven JTA. Cell-wall polysaccharides play an important role in decay resistance of Sphagnum and actively depressed decomposition in vitro. Biogeochemistry. 2011;103:45–57.Article 
CAS 

Google Scholar 
Bengtsson F, Rydin Hå, Hájek T. Biochemical determinants of litter quality in 15 species of Sphagnum. Plant Soil. 2018;425:161–76.CAS 
Article 

Google Scholar 
Fudyma JD, Lyon J, AminiTabrizi R, Gieschen H, Chu RK, Hoyt DW, et al. Untargeted metabolomic profiling of Sphagnum fallax reveals novel antimicrobial metabolites. Plant Direct. 2019;3:e00179–17.Article 

Google Scholar 
He L, Mazza Rodrigues JL, Soudzilovskaia NA, Barceló M, Olsson PA, Song C, et al. Global biogeography of fungal and bacterial biomass carbon in topsoil. Soil Biol Biochem. 2020;151:108024.CAS 
Article 

Google Scholar 
Hanson CA. Beyond biogeographic patterns: processes shaping the microbial landscape. Nat Rev Microbiol. 2012;10:497–506.CAS 
PubMed 
Article 

Google Scholar 
Waddington JM, Morris PJ, Kettridge N, Granath G, Thompson DK, Moore PA. Hydrological feedbacks in northern peatlands. Ecohydrology. 2015;8:113–27.Article 

Google Scholar 
Reczuga MK, Lamentowicz M, Mulot M, Mitchell EAD, Buttler A, Chojnicki B, et al. Predator–prey mass ratio drives microbial activity under dry conditions in Sphagnum peatlands. Ecol Evol. 2018;8:5752–64.PubMed 
PubMed Central 
Article 

Google Scholar 
Ritchie RJ. Consistent sets of spectrophotometric chlorophyll equations for acetone, methanol and ethanol solvents. Photosynth Res. 2006;89:27–41.CAS 
PubMed 
Article 

Google Scholar 
Perrine Z, Negi S, Sayre RT. Optimization of photosynthetic light energy utilization by microalgae. Algal Res. 2012;1:134–42.Article 

Google Scholar 
Carey CC, Ibelings BW, Hoffmann EP, Hamilton DP, Brookes JD. Eco-physiological adaptations that favour freshwater cyanobacteria in a changing climate. Water Res. 2012;46:1394–407.CAS 
PubMed 
Article 

Google Scholar 
Gorbunov MY, Falkowski PG. Using chlorophyll fluorescence kinetics to determine photosynthesis in aquatic ecosystems. Limnol Ocean. 2020;66:1–13.Article 
CAS 

Google Scholar 
MacIntyre HL, Kana TM, Anning T, Geider RJ. Photoacclimation of photosynthesis irradiance response curves and photosynthetic pigments in microalgae and cyanobacteria. J Phycol. 2002;38:17–38.Article 

Google Scholar 
Grote EE, Belnap J, Housman DC, Sparks JP. Carbon exchange in biological soil crust communities under differential temperatures and soil water contents: implications for global change. Glob Chang Biol. 2010;16:2763–74.Article 

Google Scholar 
Robarts RD, Zohary T. Temperature effects on photosynthetic capacity, respiration, and growth rates of bloom‐forming cyanobacteria. New Zealand Journal of Marine and Freshwater Research. 1987;21:391–9.CAS 
Article 

Google Scholar 
Le Quéré C, Andrew RM, Friedlingstein P, Sitch S, Pongratz J, Manning AC, et al. Global Carbon Budget 2017. Earth Syst Sci Data. 2018;10:405–48.Article 

Google Scholar  More