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    Potential of indigenous crop microbiomes for sustainable agriculture

    1.Savci, S. An agricultural pollutant: chemical fertilizer. Int. J. Environ. Sci. Dev. 3, 77–80 (2012).CAS 

    Google Scholar 
    2.Guo, J. H. et al. Significant acidification in major Chinese croplands. Science 327, 1008–1010 (2010).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    3.Raza, S. et al. Dramatic loss of inorganic carbon by nitrogen‐induced soil acidification in Chinese croplands. Glob. Change Biol. 26, 3738–3751 (2020).ADS 
    Article 

    Google Scholar 
    4.Jez, J. M., Lee, S. G. & Sherp, A. M. The next green movement: plant biology for the environment and sustainability. Science 353, 1241–1244 (2016).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    5.Cordovez, V., Dini-Andreote, F., Carrion, V. J. & Raaijmakers, J. M. Ecology and evolution of plant microbiomes. Annu. Rev. Microbiol. 73, 69–88 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    6.Duran, P. et al. Microbial interkingdom interactions in roots promote Arabidopsis survival. Cell 175, 973–983.e914 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    7.Dini-Andreote, F. & Raaijmakers, J. M. Embracing community ecology in plant microbiome research. Trends Plant Sci. 23, 467–469 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    8.de Vries, F. T., Griffiths, R. I., Knight, C. G., Nicolitch, O. & Williams, A. Harnessing rhizosphere microbiomes for drought-resilient crop production. Science 368, 270–274 (2020).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    9.Hubbard, C. J. et al. The effect of rhizosphere microbes outweighs host plant genetics in reducing insect herbivory. Mol. Ecol. 28, 1801–1811 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    10.Oldroyd, G. E. D. & Leyser, O. A plant’s diet, surviving in a variable nutrient environment. Science 368, eaba0196 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    11.Tedersoo, L., Bahram, M. & Zobel, M. How mycorrhizal associations drive plant population and community biology. Science 367, eaba1223 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    12.Martín‐Robles, N. et al. Impacts of domestication on the arbuscular mycorrhizal symbiosis of 27 crop species. New Phytol. 218, 322–334 (2018).PubMed 
    Article 

    Google Scholar 
    13.Genre, A., Lanfranco, L., Perotto, S. & Bonfante, P. Unique and common traits in mycorrhizal symbioses. Nat. Rev. Microbiol. 18, 649–660 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    14.Liu, X. et al. Partitioning of soil phosphorus among arbuscular and ectomycorrhizal trees in tropical and subtropical forests. Ecol. Lett. 21, 713–723 (2018).PubMed 
    Article 

    Google Scholar 
    15.Varoquaux, N. et al. Transcriptomic analysis of field-droughted sorghum from seedling to maturity reveals biotic and metabolic responses. Proc. Natl Acad. Sci. USA 116, 27124–27132 (2019).CAS 
    Article 

    Google Scholar 
    16.Lazcano, C., Barrios-Masias, F. H. & Jackson, L. E. Arbuscular mycorrhizal effects on plant water relations and soil greenhouse gas emissions under changing moisture regimes. Soil Biol. Biochem. 74, 184–192 (2014).CAS 
    Article 

    Google Scholar 
    17.Sprent, J. I. Evolving ideas of legume evolution and diversity: a taxonomic perspective on the occurrence of nodulation. New Phytol. 174, 11–25 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    18.Soltis, D. E. et al. Chloroplast gene sequence data suggest a single origin of the predisposition for symbiotic nitrogen fixation in angiosperms. Proc. Natl Acad. Sci. USA 92, 2647–2651 (1995).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    19.Young, N. D. et al. The Medicago genome provides insight into the evolution of rhizobial symbioses. Nature 480, 520–524 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    20.van Velzen, R. et al. Comparative genomics of the nonlegume Parasponia reveals insights into evolution of nitrogen-fixing Rhizobium symbioses. Proc. Natl Acad. Sci. USA 115, E4700–E4709 (2018).PubMed 
    Article 
    CAS 

    Google Scholar 
    21.Smil, V. Nitrogen in crop production: an account of global flows. Glob. Biogeochem. Cycles 13, 647–662 (1999).ADS 
    CAS 
    Article 

    Google Scholar 
    22.O’Hara, G. W. The role of nitrogen fixation in crop production. J. Crop Prod. 1, 115–138 (1998).Article 

    Google Scholar 
    23.Remigi, P., Zhu, J., Young, J. P. W. & Masson-Boivin, C. Symbiosis within symbiosis: evolving nitrogen-fixing legume symbionts. Trends Microbiol. 24, 63–75 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    24.Garcia, K., Delaux, P. M., Cope, K. R. & Ané, J. M. Molecular signals required for the establishment and maintenance of ectomycorrhizal symbioses. New Phytol. 208, 79–87 (2015).PubMed 
    Article 

    Google Scholar 
    25.Fisher, R. F. & Long, S. R. Rhizobium–plant signal exchange. Nature 357, 655–660 (1992).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    26.Cao, Y., Halane, M. K., Gassmann, W. & Stacey, G. The role of plant innate immunity in the legume–Rhizobium symbiosis. Annu. Rev. Plant Biol. 68, 535–561 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    27.Ferguson, B. J. et al. Legume nodulation: the host controls the party. Plant Cell Environ. 42, 41–51 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    28.Remans, R. et al. Effect of Rhizobium–Azospirillum coinoculation on nitrogen fixation and yield of two contrasting Phaseolus vulgaris L. genotypes cultivated across different environments in Cuba. Plant Soil 312, 25–37 (2008).CAS 
    Article 

    Google Scholar 
    29.Cassán, F. & Diaz-Zorita, M. Azospirillum sp. in current agriculture: from the laboratory to the field. Soil Biol. Biochem. 103, 117–130 (2016).Article 
    CAS 

    Google Scholar 
    30.Han, Q. et al. Variation in rhizosphere microbial communities and its association with the symbiotic efficiency of rhizobia in soybean. ISME J. 14, 1915–1928 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    31.Saharan, B. S. & Nehra, V. Plant growth promoting rhizobacteria: a critical review. Life Sci. Med. Res. 21, 30 (2011).
    Google Scholar 
    32.Cheng, Y. T., Zhang, L. & He, S. Y. Plant–microbe interactions facing environmental challenge. Cell Host Microbe 26, 183–192 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    33.Dini-Andreote, F. Endophytes: the second layer of plant defense. Trends Plant Sci. 25, 319–322 (2020).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    34.Carrión, V. J. et al. Pathogen-induced activation of disease-suppressive functions in the endophytic root microbiome. Science 366, 606–612 (2019).ADS 
    Article 
    CAS 

    Google Scholar 
    35.Sieber, M. et al. Neutrality in the metaorganism. PLoS Biol. 17, e3000298 (2019).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    36.Trivedi, P., Leach, J. E., Tringe, S. G., Sa, T. & Singh, B. K. Plant–microbiome interactions: from community assembly to plant health. Nat. Rev. Microbiol. 18, 607–621 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    37.Burns, A. R. et al. Contribution of neutral processes to the assembly of gut microbial communities in the zebrafish over host development. ISME J. 10, 655–664 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    38.Sloan, W. T. et al. Quantifying the roles of immigration and chance in shaping prokaryote community structure. Environ. Microbiol. 8, 732–740 (2006).PubMed 
    Article 

    Google Scholar 
    39.Ning, D., Deng, Y., Tiedje, J. M. & Zhou, J. A general framework for quantitatively assessing ecological stochasticity. Proc. Natl Acad. Sci. USA 116, 16892–16898 (2019).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    40.Carlström, C. I. et al. Synthetic microbiota reveal priority effects and keystone strains in the Arabidopsis phyllosphere. Nat. Ecol. Evol. 3, 1445–1454 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    41.Purugganan, M. D. & Fuller, D. Q. The nature of selection during plant domestication. Nature 457, 843–848 (2009).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    42.Chen, Y. H., Gols, R. & Benrey, B. Crop domestication and its impact on naturally selected trophic interactions. Annu. Rev. Entomol. 60, 35–58 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    43.Szoboszlay, M. et al. Comparison of root system architecture and rhizosphere microbial communities of Balsas teosinte and domesticated corn cultivars. Soil Biol. Biochem. 80, 34–44 (2015).CAS 
    Article 

    Google Scholar 
    44.Perez-Jaramillo, J. E., Mendes, R. & Raaijmakers, J. M. Impact of plant domestication on rhizosphere microbiome assembly and functions. Plant Mol. Biol. 90, 635–644 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    45.Perez-Jaramillo, J. E., Carrion, V. J., de Hollander, M. & Raaijmakers, J. M. The wild side of plant microbiomes. Microbiome 6, 143 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    46.Emmett, B. D., Buckley, D. H., Smith, M. E. & Drinkwater, L. E. Eighty years of maize breeding alters plant nitrogen acquisition but not rhizosphere bacterial community composition. Plant Soil 431, 53–69 (2018).CAS 
    Article 

    Google Scholar 
    47.Mutch, L. A. & Young, J. P. W. Diversity and specificity of Rhizobium leguminosarum biovar viciae on wild and cultivated legumes. Mol. Ecol. 13, 2435–2444 (2004).CAS 
    PubMed 
    Article 

    Google Scholar 
    48.Kiers, E. T., Hutton, M. G. & Denison, R. F. Human selection and the relaxation of legume defences against ineffective rhizobia. Proc. R. Soc. B 274, 3119–3126 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    49.Pérez-Jaramillo, J. E. et al. Linking rhizosphere microbiome composition of wild and domesticated Phaseolus vulgaris to genotypic and root phenotypic traits. ISME J. 11, 2244–2257 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    50.Zachow, C., Müller, H., Tilcher, R. & Berg, G. Differences between the rhizosphere microbiome of Beta vulgaris ssp. maritima—ancestor of all beet crops—and modern sugar beets. Front. Microbiol. 5, 415 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    51.Coleman‐Derr, D. et al. Plant compartment and biogeography affect microbiome composition in cultivated and native Agave species. New Phytol. 209, 798–811 (2016).PubMed 
    Article 
    CAS 

    Google Scholar 
    52.Warschefsky, E., Penmetsa, R. V., Cook, D. R. & von Wettberg, E. J. Back to the wilds: tapping evolutionary adaptations for resilient crops through systematic hybridization with crop wild relatives. Am. J. Bot. 101, 1791–1800 (2014).PubMed 
    Article 

    Google Scholar 
    53.Brozynska, M., Furtado, A. & Henry, R. J. Genomics of crop wild relatives: expanding the gene pool for crop improvement. Plant Biotechnol. J. 14, 1070–1085 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    54.Zhang, H., Mittal, N., Leamy, L. J., Barazani, O. & Song, B. H. Back into the wild—apply untapped genetic diversity of wild relatives for crop improvement. Evol. Appl. 10, 5–24 (2017).PubMed 
    Article 

    Google Scholar 
    55.Maxted, N. & Kell, S. P. Establishment of a Global Network for the In Situ Conservation of Crop Wild Relatives: Status and Needs (FAO Commission on Genetic Resources for Food and Agriculture, 2009).56.Stenberg, J. A., Heil, M., Åhman, I. & Björkman, C. Optimizing crops for biocontrol of pests and disease. Trends Plant Sci. 20, 698–712 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    57.Heil, M. & Baldwin, I. T. Fitness costs of induced resistance: emerging experimental support for a slippery concept. Trends Plant Sci. 7, 61–67 (2002).CAS 
    PubMed 
    Article 

    Google Scholar 
    58.Liu, H. & Brettell, L. E. Plant defense by VOC-induced microbial priming. Trends Plant Sci. 24, 187–189 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    59.Schulz-Bohm, K. et al. Calling from distance: attraction of soil bacteria by plant root volatiles. ISME J. 12, 1252–1262 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    60.Ehlers, B. K. et al. Plant secondary compounds in soil and their role in belowground species interactions. Trends Ecol. Evol. 35, 716–730 (2020).PubMed 
    Article 

    Google Scholar 
    61.Preece, C. & Penuelas, J. A return to the wild: root exudates and food security. Trends Plant Sci. 25, 14–21 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    62.Rasmann, S. et al. Recruitment of entomopathogenic nematodes by insect-damaged maize roots. Nature 434, 732–737 (2005).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    63.Köllner, T. G. et al. A maize (E)-β-caryophyllene synthase implicated in indirect defense responses against herbivores is not expressed in most American maize varieties. Plant Cell 20, 482–494 (2008).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    64.Lebeis, S. L. et al. Salicylic acid modulates colonization of the root microbiome by specific bacterial taxa. Science 349, 860–864 (2015).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    65.Vorholt, J. A., Vogel, C., Carlstrom, C. I. & Muller, D. B. Establishing causality: opportunities of synthetic communities for plant microbiome research. Cell Host Microbe 22, 142–155 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    66.Zhang, J. et al. NRT1.1B is associated with root microbiota composition and nitrogen use in field-grown rice. Nat. Biotechnol. 37, 676–684 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    67.Hatzenpichler, R., Krukenberg, V., Spietz, R. L. & Jay, Z. J. Next-generation physiology approaches to study microbiome function at single cell level. Nat. Rev. Microbiol. 18, 241–256 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    68.Cui, L., Zhang, D., Yang, K., Zhang, X. & Zhu, Y. G. Perspective on surface-enhanced Raman spectroscopic investigation of microbial world. Anal. Chem. 91, 15345–15354 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    69.Wang, Y., Huang, W. E., Cui, L. & Wagner, M. Single cell stable isotope probing in microbiology using Raman microspectroscopy. Curr. Opin. Biotechnol. 41, 34–42 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    70.Cui, L. et al. Functional single-cell approach to probing nitrogen-fixing bacteria in soil communities by resonance Raman spectroscopy with 15N2 labeling. Anal. Chem. 90, 5082–5089 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    71.Yang, K. et al. Rapid antibiotic susceptibility testing of pathogenic bacteria using heavy-water-labeled single-cell Raman spectroscopy in clinical samples. Anal. Chem. 91, 6296–6303 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    72.Li, H. Z. et al. D2O-isotope-labeling approach to probing phosphate-solubilizing bacteria in complex soil communities by single-cell Raman spectroscopy. Anal. Chem. 91, 2239–2246 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    73.Moutia, J.-F. Y., Saumtally, S., Spaepen, S. & Vanderleyden, J. Plant growth promotion by Azospirillum sp. in sugarcane is influenced by genotype and drought stress. Plant Soil 337, 233–242 (2010).CAS 
    Article 

    Google Scholar 
    74.Bashan, Y. & De-Bashan, L. E. How the plant growth-promoting bacterium Azospirillum promotes plant growth—a critical assessment. Adv. Agron. 108, 77–136 (2010).CAS 
    Article 

    Google Scholar 
    75.Figueiredo, M. V. B., Burity, H. A., Martínez, C. R. & Chanway, C. P. Alleviation of drought stress in the common bean (Phaseolus vulgaris L.) by co-inoculation with Paenibacillus polymyxa and Rhizobium tropici. Appl. Soil Ecol. 40, 182–188 (2008).Article 

    Google Scholar 
    76.Uma, C., Sivagurunathan, P. & Sangeetha, D. Performance of bradyrhizobial isolates under drought conditions. Int. J. Curr. Microbiol. App. Sci. 2, 228–232 (2013).
    Google Scholar 
    77.Tank, N. & Saraf, M. Salinity-resistant plant growth promoting rhizobacteria ameliorates sodium chloride stress on tomato plants. J. Plant Interact. 5, 51–58 (2010).CAS 
    Article 

    Google Scholar 
    78.Tahir, H. A. et al. Plant growth promotion by volatile organic compounds produced by Bacillus subtilis SYST2. Front. Microbiol. 8, 171 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    79.Vardharajula, S., Zulfikar Ali, S., Grover, M., Reddy, G. & Bandi, V. Drought-tolerant plant growth promoting Bacillus spp.: effect on growth, osmolytes, and antioxidant status of maize under drought stress. J. Plant Interact. 6, 1–14 (2011).CAS 
    Article 

    Google Scholar 
    80.Santoyo, G., Orozco-Mosqueda, M. D. C. & Govindappa, M. Mechanisms of biocontrol and plant growth-promoting activity in soil bacterial species of Bacillus and Pseudomonas: a review. Biocontrol Sci. Technol. 22, 855–872 (2012).Article 

    Google Scholar 
    81.Leclere, V. et al. Mycosubtilin overproduction by Bacillus subtilis BBG100 enhances the organism’s antagonistic and biocontrol activities. Appl. Environ. Microbiol. 71, 4577–4584 (2005).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    82.Hu, J. et al. Probiotic Pseudomonas communities enhance plant growth and nutrient assimilation via diversity-mediated ecosystem functioning. Soil Biol. Biochem. 113, 122–129 (2017).CAS 
    Article 

    Google Scholar 
    83.Kohler, J., Hernández, J. A., Caravaca, F. & Roldán, A. Plant-growth-promoting rhizobacteria and arbuscular mycorrhizal fungi modify alleviation biochemical mechanisms in water-stressed plants. Funct. Plant Biol. 35, 141–151 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    84.Nassar, A. H., El-Tarabily, K. A. & Sivasithamparam, K. Growth promotion of bean (Phaseolus vulgaris L.) by a polyamine-producing isolate of Streptomyces griseoluteus. Plant Growth Reg. 40, 97–106 (2003).CAS 
    Article 

    Google Scholar 
    85.Gopalakrishnan, S. et al. Evaluation of Streptomyces strains isolated from herbal vermicompost for their plant growth-promotion traits in rice. Microbiol. Res. 169, 40–48 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    86.Kwak, M.-J. et al. Rhizosphere microbiome structure alters to enable wilt resistance in tomato. Nat. Biotechnol. 36, 1100–1109 (2018).CAS 
    Article 

    Google Scholar 
    87.Sang, M. K. & Kim, K. D. The volatile‐producing Flavobacterium johnsoniae strain GSE09 shows biocontrol activity against Phytophthora capsici in pepper. J. Appl. Microbiol. 113, 383–398 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    88.Naznin, H. A. et al. Systemic resistance induced by volatile organic compounds emitted by plant growth-promoting fungi in Arabidopsis thaliana. PLoS ONE 9, e86882 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    89.Kiss, L., Russell, J. C., Szentiványi, O., Xu, X. & Jeffries, P. Biology and biocontrol potential of Ampelomyces mycoparasites, natural antagonists of powdery mildew fungi. Biocontrol Sci. Technol. 14, 635–651 (2004).Article 

    Google Scholar 
    90.Lee, S., Yap, M., Behringer, G., Hung, R. & Bennett, J. W. Volatile organic compounds emitted by Trichoderma species mediate plant growth. Fungal Biol. Biotechnol. 3, 1–14 (2016).CAS 
    Article 

    Google Scholar 
    91.Zhang, S., Gan, Y. & Xu, B. Application of plant-growth-promoting fungi Trichoderma longibrachiatum t6 enhances tolerance of wheat to salt stress through improvement of antioxidative defense system and gene expression. Front. Plant Sci. 7, 1405 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    92.van der Meij, A., Worsley, S. F., Hutchings, M. I. & van Wezel, G. P. Chemical ecology of antibiotic production by Actinomycetes. FEMS Microbiol. Rev. 41, 392–416 (2017).PubMed 
    Article 
    CAS 

    Google Scholar 
    93.Bhatti, A. A., Haq, S. & Bhat, R. A. Actinomycetes benefaction role in soil and plant health. Microb. Pathog. 111, 458–467 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    94.Chaurasia, A. et al. Actinomycetes: an unexplored microorganisms for plant growth promotion and biocontrol in vegetable crops. World J. Microbiol. Biotechnol. 34, 1–16 (2018).Article 

    Google Scholar 
    95.Ercoli, L., Schüßler, A., Arduini, I. & Pellegrino, E. Strong increase of durum wheat iron and zinc content by field-inoculation with arbuscular mycorrhizal fungi at different soil nitrogen availabilities. Plant Soil 419, 153–167 (2017).CAS 
    Article 

    Google Scholar 
    96.Xu, L. et al. Arbuscular mycorrhiza enhances drought tolerance of tomato plants by regulating the 14-3-3 genes in the ABA signaling pathway. Appl. Soil Ecol. 125, 213–221 (2018).Article 

    Google Scholar 
    97.Ghorchiani, M., Etesami, H. & Alikhani, H. A. Improvement of growth and yield of maize under water stress by co-inoculating an arbuscular mycorrhizal fungus and a plant growth promoting rhizobacterium together with phosphate fertilizers. Agric. Ecosyst. Environ. 258, 59–70 (2018).CAS 
    Article 

    Google Scholar 
    98.Meeds, J. A. et al. Phosphorus deficiencies invoke optimal allocation of exoenzymes by ectomycorrhizas. ISME J. https://doi.org/10.1038/s41396-020-00864-z (2021). More

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    An integrative approach reveals a new species of flightless leaf beetle (Chrysomelidae: Suinzona) from South Korea

    Description of Suinzona borowieci sp. nov. (Figs. 1, 2 and 3)Figure 1Morphology of Suinzona borowieci sp. nov. and related species: (a,b) Holotype of S. borowieci sp. nov. (a) Dorsal habitus, (b) lateral habitus; (c–e) exposed hind wing, (c) S. borowieci sp. nov., (d) S. cyrtonoides, (e) Potaninia assamensis; (f–g) aedeagus with everted internal sac (left) and flagellum (right); (f) S. borowieci sp. nov., (g) S. cyrtonoides.Full size imageFigure 2Genitalia of Suinzona borowieci sp. nov. and related species: (a–d) S. borowieci sp. nov. (a) Aedeagus, dorsal view; (b) aedeagus, lateral view; (c) aedeagus, apical view; (d) spermatheca. (e) Aedeagus of Suinzona cyrtonoides, apical view.Full size imageFigure 3Distribution map of Suinzona and sampling sites: (a) Distribution of Suinzona species in China, South Korea and Japan, (b) type locality and collection sites of Suinzona borowieci sp. nov. in South Korea. Records of distribution are taken from Ge et al.3, Suzuki et al.21 and the results of this work. The map is redrawn and modified from National Geographic Information Institute of Korea (https://www.ngii.go.kr).Full size imageFamily Chrysomelidae Latreille, 1802Subfamily Chrysomelinae Latreille, 1802Genus Suinzona Chen, 1931Type localitySouth Korea: Gyeongbuk Province, Yeongyang County, Irwolsan Mountain, 36° 48′ 30.42″ N, 129° 5′ 23.56″ E, ca. 1135 m.Type materialHolotype: male (NMPC), South Korea: Gyeongbuk Prov., Yeongyang, Mt. Irwolsan, 36° 48′ 30.42″ N, 129° 5′ 23.56″ E, ca. 1135 m, 12.VI.2011, H.W. Cho // HOLOTYPUS Suinzona borowieci sp. n. Cho & Kim 2020. Paratype: SOUTH KOREA – Gyeongbuk Prov.: 1 female (NMPC), same data as holotype plus PARATYPUS Suinzona borowieci sp. n. Cho & Kim 2020; 1 female (HCC), same data as holotype except 31.VII.2004; 1 female (HCC), same data as holotype except 31.VII.2004; 4 males 2 females (HCC), same data as holotype except 22.V.2009; 8 males 2 females (HCC), same data as holotype except 25.VI.2010; 4 males 2 females (HCC), same data as holotype except 10.VI.2017; 1 male 1 female (HCC), same data as holotype except 17.VI.2017; 1 male (HCC), same data as holotype except 36° 48′ 11.74″ N, 129° 6′ 10.01″ E, ca. 1190 m, 17.V.2020; 3 males 1 female (HCC), same data as holotype except 7.VI.2020; 2 males (KNAE), Yeongyang, Irwol-myeon, Mt. Irwolsan, 7.VI.2014, J.K. Park // I14_KNAE483613 // I14_KNAE483649; 1 male 1 female (HCC), Bongwha, Myeongho-myeon, Bukgok-ri, Mt. Cheongnyangsan, 36° 47′ 47″ N, 128° 54′ 30″ E, 21–22.V.2015, J.S. Lee; 1 female (HCC), Daegu, Dong-gu, Mt. Palgongsan, 21.V.1998; 2 males 1 female (HCC), Gunwi, Bugye-myeon, Dongsan-ri, Mt. Palgongsan, 9.V.2009, S.S. Jung; 1 male 1 female (HCC), Yecheon, Bomun-myeon, Urae-ri, Mt. Hakgasan, 26.V.2010, Y.J. You; 1 male (HCC), Yecheon, Bomun-myeon, Mt. Hakgasan, 36° 40′ 32.16″ N, 128° 35′ 38.24″ E, ca. 330 m, 3.VI.2020, H.W. Cho; 1 female (HCC), Cheongsong, Hyeonseo-myeon, Galcheon-ri, 26.V.2004, H.W. Cho; Gangwon Prov.: 2 females (HCC), Taebaek, Hwangji-dong, Mt. Hambaeksan, 37° 9′ 53.22″ N, 128° 55′ 1.35″ E, ca. 1470 m, 6.VI.2005, H.W. Cho; 2 males 3 females (HCC), same data as preceding one except 6.VI.2006; 1 female (HCC), same data as preceding one except 29.V.2009; 1 female (HCC), same data as preceding one except 10.VI.2017; 1 female (HCC), same data as preceding one except 5.VI.2020; Chungnam Prov.: 1 male (HCC), Buyeo, Gyuam-myeon, Sumok-ri, 1–15.VI.2005, J.W. Lee.Other materialSix mature larvae (HCC), same data as holotype except 29.VI.2017; 5 mature larvae (HCC), Gangwon Prov., Taebaek, Hwangji-dong, Mt. Hambaeksan, 19.VI.2006, H.W. Cho; 8 mature larvae (HCC), Gyeongbuk Prov., Yecheon, Bomun-myeon, Mt. Hakgasan, 31.V.2020, H.W. Cho; 7 mature larvae (HCC), same data as preceding one except 3.VI.2020.DiagnosisSuinzona borowieci sp. nov. is almost identical to S. cyrtonoides in the shape of the flagellum of the aedeagus. However, it can be distinguished by its larger body size (5.5–7.0 mm vs. 4.8–6.0 mm), denser punctures on elytra (less dense punctures in S. cyrtonoides), larger and broader aedeagus with the distal tips of the flagellum quadrifurcated and slightly curved, arising from two sclerotized tubes (with a smaller and narrower aedeagus with distal tips of the flagellum quadrifurcated and almost straight, arising from a sclerotized tube in S. cyrtonoides).DescriptionMeasurements in mm (n = 5): length of body: 5.50–7.00 (mean 6.18); width of body: 3.50–4.50 (mean 3.97); height of body: 2.60–3.40 (mean 2.94); width of head: 1.65–1.95 (mean 1.81); interocular distance: 1.15–1.50 (mean 1.33); width of apex of pronotum: 1.90–2.20 (mean 2.02); width of base of pronotum: 2.70–3.25 (mean 2.94); length of pronotum along midline: 1.75–2.05 (mean 1.90); length of elytra along suture: 3.75–5.20 (mean 4.41). Body: oval and strongly convex (Fig. 1a,b). Body dark bluish-black with weak metallic lustre, rarely with a dark brass dorsum. Antenna, mouthparts and tarsus partially dark reddish-brown. Head. Vertex weakly convex, covered with sparse punctures, becoming coarser and denser towards sides, with convex area above antennal insertion. Eyes strongly transverse-oblong and protuberant. Frontal suture V-shaped, forming obtuse angle, arms bent at middle, reaching anterior margin. Frons flat, strongly depressed at anterior margin, covered with dense punctures. Clypeus narrow and trapezoidal. Anterior margin of labrum weakly concave. Mandibles with 2 blunt apical teeth and dense punctures bearing setae on outer side. Maxillary palp 4-segmented with apical palpomere fusiform, truncate apically. Antennae in males much longer than half the length of the body; antennomere 1 robust; antennomere 2 shorter than 3; antennomere 3 longer than 4; antennomeres 7–10 each moderately widened, much longer than wide; antennomere 11 longest, approximately 2.4 times as long as wide. Antennae in females less than half the length of the body. Pronotum. 1.50–1.63 times as wide as long. Lateral sides widest at or near base, roundly narrowed anteriorly, anterior angles strongly produced. Anterior and lateral margins bordered, lateral margins barely visible in dorsal view. Trichobothria present on posterior angles. Disc glabrous, covered with moderately dense punctures, becoming coarser along basal margin; interspaces covered with fine and moderately dense punctures. Scutellum much wider than long, widely rounded apically, with a few fine punctures. Elytra. 1.07–1.16 times as long as wide. Lateral sides widest near middle, roundly narrowed posteriorly. Humeral calli not developed. Disc glabrous and finely rugose, covered with rather irregular punctures arranged in longitudinal rows near suture and lateral margin, more irregular in median region; interspaces covered with fine and sparse punctures. Epipleura wholly visible in lateral view. Hind wings steno- and brachypterous (Fig. 1c). Venter. Hypomera weakly rugose, with a few punctures near anterolateral corners of prosternum. Prosternum covered with coarse and dense punctures bearing long setae; prosternal process broad and strongly expanded apicolaterally, closing procoxal cavities posteriorly. Metasternum covered with punctures bearing long setae, dense medially, sparse laterally. Abdominal ventrites covered with moderately dense punctures bearing long or short setae; apex of last visible abdominal ventrite deeply emarginate in males while rounded in females. Legs. Moderately robust. Tibiae simple without preapical tooth. Tarsomere 1 subequal in width to tarsomere 3 in males but distinctly narrower than tarsomere 3 in females. Tarsal claws simple. Genitalia. Aedeagus broad, lateral margins shallowly concave, with apex moderately produced and truncate in dorsal view (Fig. 2a,c); regularly curved, tapering from middle to apex, with apex pointed and slightly bent upward in lateral view (Fig. 2b); flagellum club-shaped with sharp, sclerotized and quadrifid tips (Fig. 1f). Spermatheca U-shaped, long and rounded at apex (Fig. 2d).EtymologyDedicated to the first author’s mentor Prof. dr hab. Lech Borowiec (University of Wrocław, Poland), the world’s leading specialist in tortoise beetles.DistributionSouth Korea: Chungnam, Gangwon, Gyeongbuk, Daegu (Fig. 3a,b).RemarksThe shape of the apical part of the male genitalia exhibits a certain degree of variation even within the same population. It is difficult to recognize a significant difference in the shape of the male genitalia between populations, but individuals from Yeongyang have a relatively large aedeagus. All specimens that we examined had a dark bluish-black dorsum with a weak metallic lustre, but a single specimen with a dark brass dorsum was found in Yecheon.Mature larva and biology of Suinzona borowieci sp. nov. (Figs. 4, 5 and 6)DiagnosisThe fourth (last) instar larva of S. borowieci sp. nov. is very similar to that of S. cyrtonoides comb. nov. in body shape, colouration and tubercular pattern. However, this species can be distinguished by the 4–5 small secondary tubercles between Dae and DLai on the meso- and metathorax and more densely setose bodies (1 large tubercle between Dae and DLai on the meso- and metathorax and less densely setose body in S. cyrtonoides).Figure 4Mature larva of Suinzona borowieci sp. nov.: (a) Dorsal habitus, (b) lateral habitus, (c) ventral habitus.Full size imageFigure 5Larval morphology of Suinzona borowieci sp. nov.: (a) Head, (b) maxillae and labium, (c) tibiotarsus and pretarsus, (d) mandible, (e) labrum and epipharynx, (f) Schematic presentation of tubercular patterns (top: prothorax; middle: mesothorax; bottom: 2nd abdominal segment).Full size imageFigure 6Host plants of Suinzona borowieci sp. nov.: (a) Arabis pendula L. from Yeongyang, (b) Urtica angustifolia Fisch. ex Hornem. from Yeongyang, (c) Aconitum pseudolaeve Nakai from Taebaek, (d) Isodon inflexus (Thunb.) Kudo from Yecheon; (e–f) A. pseudolaeve Nakai and U. angustifolia Fisch. ex Hornem. for laboratory tests (e) Adult from Yeongyang feeding on leaves, (f) larvae from Yecheon feeding on leaves.Full size imageDescriptionBody length 8.1–8.8 mm, width 2.9–3.2 mm, head width 1.75–1.80 mm (n = 3). Body elongate, rather broad, widest at abdominal segments III–IV, thence moderately narrowed posteriorly and slightly convex dorsally (Fig. 4a). Head pale yellowish-brown, densely setose, with a blackish-brown V-shaped mark along frontal arms; lateral regions of epicrania largely blackish-brown; posterior half of clypeus brown to dark brown; apex of labrum and mandibles blackish-brown. General colouration of integument yellowish-white, but dorsal integument densely covered with minute brown spinules (Fig. 4b); dorsal tubercles dark brown and ventral ones unpigmented (Fig. 4c), both densely setose; spiracles blackish-brown. Legs pale yellow with apex of tibiotarsus and pretarsus brown. Eversible glands absent. Pseudopods present on abdominal segments VI–VII. Head. Hypognathous, rounded, strongly sclerotized (Fig. 5a). Epicranium with 72–77 pairs of setae of varying length; epicranial stem distinct; frontal arms V-shaped, slightly sinuate, not extending to antennal insertions; median endocarina distinct, extending to frontoclypeal suture. Frons slightly depressed medially with 25–29 pairs of setae of varying length. Clypeus almost straight at anterior margin with 3 pairs of setae. Labrum deeply concave anteriorly with 2 pairs of setae and 2 pairs of campaniform sensilla (Fig. 5e, left); epipharynx with 6–7 pairs of setae at anterior margin (Fig. 5e, right). Mandible robust, palmate and 5-toothed, with 4–5 setae and 3 campaniform sensilla; penicillus present (Fig. 5d). Maxillary palp 3-segmented; palpomere I rectangular with 2 setae and 2 campaniform sensilla; II swollen cylindrical with 3 setae and 1 campaniform sensillum; III subconical with 1 seta, 1 digitiform sensillum and 1 campaniform sensillum on sides and a group of peg-like sensilla at the apex; palpifer well developed with 2 setae (Fig. 5b). Mala rounded with 13–14 setae and 1 campaniform sensillum; stipes distinctly longer than wide with 12–14 setae; cardo with 2–3 setae. Labial palp 2-segmented; palpomere I rectangular with 1 campaniform sensillum; II subconical with 1 seta, 1 campaniform sensillum and a group of peg-like sensilla at the apex. Hypopharynx bilobed, densely covered with minute spinules; prementum with four pairs of setae and three pairs of campaniform sensilla; postmentum basolaterally covered with minute spinules, with 8–9 pairs of setae. Six stemmata present on each side, 4 of them located above the antenna and 2 behind the antenna. Antenna 3-segmented; antenomere I wider than long with 2 campaniform sensilla; II approximately as wide as long, with a conical sensorium and 3–4 min setae; III subconical with 5–6 min setae. Thorax. Prothorax with D-DL-EP (dorsal, dorsolateral and epipleural tubercles fused together, 164–179) largest; P (pleural tubercle, 9–11) and ES-SS (eusternal and sternellar tubercles fused, 6–7) unpigmented (Fig. 5f). Meso- and metathorax with dorsal tubercles more or less arranged in 3 transverse rows; Dai (dorsal anterior interior, 6–10) on both sides separated, smaller than Dae (dorsal anterior exterior, 11–15); DLai (dorsolateral anterior interior, 4–5); Dpi (dorsal posterior interior, 12–15); Dpe (dorsal posterior exterior, 10–13) smaller than Dpi; DLpi (dorsolateral posterior interior, 17–19); DLe (dorsolateral exterior, 40–47) large; dorsal region with 8–9 secondary tubercles, 3 of them located anterior to Dai and Dae, 4–5 between Dae and DLai and 1 anterior to DLe; EPa (epipleural anterior, 17–22) larger than EPp (epipleural posterior, 8–11), both unpigmented; P (9–13), SS (1) and ES (3–4) unpigmented; sternal region with 4–5 additional setae arising from weakly sclerotized base. Mesothoracic spiracles annuliform and largest. Legs moderately long, 5-segmented; tibiotarsus with 23–25 setae; pretarsus large, strongly curved, basal tooth well developed, with 1 short seta (Fig. 5c). Abdomen. Segments I–VI with dorsal tubercles arranged in 3 transverse rows; Dai (5–8) on both sides separated, smaller than Dae (13–14); DLae (12–14) larger than DLai (7); Dpi (16–19), Dpe (15–19) and DLp (24–29) transverse, subequal in size; dorsal region with 5–10 small secondary tubercles; EP (23–27), P (12–13), PS-SS (parasternal and sternellar tubercles fused, 5–7) and ES (5–7) unpigmented; as1 (secondary tubercle on antero-exterior part of ES, 1) and as2 (secondary tubercle between P and PS, 1); sternal region with 3–4 additional setae arising from weakly sclerotized base. Segment VII with Dai and Dae fused and Dpi and Dpe fused. Segments VIII with dorsal and dorso-lateral tubercles completely fused (30–37). Segment IX with dorsal to epipleural tubercles completely fused (34–36). Segment X not visible from above, with paired pygopods. Spiracles annuliform, present on segments I–VIII.Host plantsBrassicaceae: Arabis pendula L.; Lamiaceae: Isodon inflexus (Thunb.) Kudo; Ranunculaceae: Aconitum pseudolaeve Nakai; Urticaceae: Urtica angustifolia Fisch. ex Hornem.Biological notesSuinzona borowieci sp. nov. is univoltine. Overwintered adults appear in late May. They mate and lay 15–18 eggs per cluster on the leaves of host plants in early June. Eggs are pale yellow to yellowish-orange and hatch after 8–9 days. The larvae are solitary during the instar stages and feed on the leaves. There are four larval instars, and pupation occurs in soil. The larvae take 14–16 days to pupate and then take 7–8 days to emerge as adults. Newly emerged adults are found during July. We observed larvae or adults of this species in nearby localities (~ 62 km), feeding on A. pendula L. (Fig. 6a) and U. angustifolia Fisch. ex Hornem. (Fig. 6b) from Yeongyang (at 1135 ~ 1190 m a.s.l.), A. pseudolaeve Nakai (Fig. 6c) from Taebaek (at 1,470 m a.s.l.), and I. inflexus (Thunb.) Kudo (Fig. 6d) from Yecheon (at 330 m a.s.l.). Each population showed a preference for its natural host plant but fed on other host plants and completed its life cycle in laboratory tests (Fig. 6e,f).
    Suinzona cyrtonoides (Jacoby, 1885) comb. nov. (Figs. 1, 2 and 3)Type localityJapan: Kyushu, Kumamoto Prefecture, Konose.Type materialSyntypes: 1 female (BMNH), Lectotype [mislabelled, not lectotype] // Type // DATA under card // Japan, G. Lewis, 1910–320. // Chrysomela crytonoides Jac. // Lectotype, Chrysomela crytonoides Jacoby, Designated. S. GE 2004 // Potaninia cyrtonoides Jacoby, Det. S. GE 2004 // Suinzona cyrtonoides (Jacoby, 1885) det. H.W. Cho 2014; 1 female (BMNH), Japan, G. Lewis, 1910–320. // Paralectotype // Paralectotype, Chrysomela crytonoides Jacoby, Designated. S. GE 2004 // Potaninia cyrtonoides Jacoby, Det. S. GE 2004 // Suinzona cyrtonoides (Jacoby, 1885) det. H.W. Cho 2014; 1 male (MCZC), Japan Lewis // 1st Jacoby Coll. // cyrtonoides Jac. // Type 17,474; 1 female (MCZC), Japan Lewis // 1st Jacoby Coll.Other materialJAPAN – Kyushu: 1 male (BMNH), Yuyama 1883 // Japan, G. Lewis, 1910–320. // Paralectotype [mislabelled, not type series] // Paralectotype, Chrysomela crytonoides Jacoby, Designated. S. GE 2004 // Potaninia cyrtonoides Jacoby, Det. S. GE 2004 // Suinzona cyrtonoides (Jacoby, 1885) det. H.W. Cho 2014; Honshu: 3 males 2 females (KMNH), Nippara, Okutama, Tokyo, 5.VI.1955, Y. Tominaga; 2 males 3 females (BMNH), Mt. Mitake, Ome-shi, Tokyo, 15.VII.2005, Y. Komiya; 1 male (HSC), Chichibu, Saitama Pref., 18.VI.1984, M. Minami; 1 male (HSC), Tochigi, Sano-shi, Tanuma, 4.VI.2008, H. Ohkawa; 1 male (HSC), Gumma, Fujioka-shi, Mikabo-yama rindo, 8.VI.2009, H. Ohkawa; 1 male 2 females (HSC), same data as preceding one except 21.VII.2009; 1 male 1 female (HSC), same data as preceding one except 1.V.2010; Shikoku: 1 female (HSC), Tokushima, Yoshinokawa-shi, Mt. Kotsu-zan, 18.V.1987, S. Mano; 2 females (EUMJ), Tokushima, Mt. Tsurugi, 15.VII.1984, M. Miyatake; 1 male 1 female (EUMJ), Ehime: Omogo-Sibukusa, Kamiukena-gun, 5.VI.2005, Y. Satoh; 7 males (HCC), Ehime, Kamiukena, Kumakogen, Wakayama, 33° 43′ 59.4″ N, 133° 08′ 06.5″ E, 5.VI.2019, H.W. Cho & Y. Hiroyuki; 1 male (HSC), Ehime, Saijo-shi, Mt. Ishizuchizan, 30.V.2009, H. Suenaga; 2 males (HSC), Ehime, Saijo-shi, Nishinokawa, 16.V.2010, H. Suenaga; 1 male 1 female (HSC), Ehime, Saijo-shi, Nishinokawa, 5.VI.2010, H. Suenaga; 1 female (EUMJ), Jiyoshi-toge, Ehime Pref., 26.IV.1976, A. Oda; 1 male (EUMJ), Mt. Ishizuchi, Ehime pref., 1.VI.1975, H. Kan; 1 female (EUMJ), Iwayaji, Ehime Pref., 1.VI.1969, M. Miyatake; 1 male (EUMJ), Ehime: Yokono, 750 m alt. Yanadani-mura, 7.V.1994, M. Sakai; 1 male (EUMJ), Ehime: Yokono, 660 m alt. Yanadani-mura, 6.V.1994, M. Sakai; 1 female (EUMJ), Ehime: Yokono, 700 m alt. Yanadani-mura, 15.VII.1994, M. Sakai.DistributionJapan: Honshu, Shikoku, Kyushu (Fig. 3a).Host plantsUrticaceae: Boehmeria spicata (Thunb.) Thunb., Boehmeria tricuspis (Hance) Makino.Biological notesDetailed descriptions of larvae and pupae and the life cycle have been published by Kimoto16 and Kimoto and Takizawa11. Its life cycle is similar to that of S. borowieci sp. nov., but they feed on different host plants.RemarksThe apical part of the aedeagus is highly variable, narrow to broad, apex narrowly to widely rounded or weakly truncate, mainly with two weak or strong denticles on the apicolateral margin. The aedeagus of the type specimen is narrowly rounded without apicolateral denticles (Fig. 2e). However, we were not able to find an obvious tendency in the morphological variation of the aedeagus at the intrapopulation or interpopulation level. Chrysomela cyrtonoides Jacoby, 1885 was described from Japan. Later, it was transferred to the genus Potaninia by Chûjô and Kimoto17 and then accepted by various authors until now. However, we found that all materials of P. cyrtonoides have reduced hind wings (Fig. 1d), which are the key diagnostic features of the genus Suinzona, and molecular analysis also suggests its placement in Suinzona. Therefore, Suinzona cyrtonoides (Jacoby, 1885) comb. nov. is proposed. Jacoby18 gave ‘Konose’ as the type locality and used at least two specimens collected by G. Lewis for the description. A male specimen (BMNH) from ‘Yuyama’, designated by Ge et al.3 as a lectotype, did not belong to the type series of S. cyrtonoides and thus lost its lectotype status (ICZN: Article 74.2). Indeed, a female specimen (BMNH) was mislabelled as a lectotype. We were able to find four specimens collected from Japan that might belong to the type series of S. cyrtonoides in the G. Lewis collection (BMNH, MCZC), but more precise locality data were not available. Therefore, we regard them as syntypes and defer selection of a lectotype.Molecular phylogenetic analysesIt is evident from the clarified phylogenetic inference based on mitogenomes that the genus Suinzona differs from the genus Potaninia, S. borowieci sp. nov. as the sister species of S. cyrtonoides (Fig. 7a). The phylogenetic inferences included a total of 20 mitogenomes of Chrysomelinae and outgroups of Galerucinae (Supplementary Table S1). The complete mitogenomes of the four Suinzona species and one Potaninia species (incomplete) were newly sequenced in the present study. Each mitogenome contains a typical set of mitochondrial genes (13 PCGs, 22 tRNAs and two rRNAs) and a control region. Phylogenetic trees based on ML and BI inferences revealed the presence of two well-supported clades (Chrysomelini and Doryphorini + Entomoscelini + Gonioctenini), placing the genus Suinzona as the sister group of the genus Potaninia. This result matched the morphological character of the hind wing. The COI haplotype network of the genus Suinzona complex (Fig. 7b) confirms the previous results and shows that the currently known single species is well distinguished as a species. Two independent networks were completely separated without any connection due to the existence of the mutation (62 steps) exceeding the 95% parsimony limits between them.Figure 7Phylogenetic tree and parsimonious network: (a) Bayesian consensus tree inferred from the combined mitochondrial 13 PCGs + 2 rRNA gene. Bayesian inference (left) and maximum likelihood (right) support values are shown on the branch nodes. Only the values over 70% are reported, (b) Parsimonious network of COI haplotypes. Circles correspond to haplotypes, the frequency and geographic origin of which are indicated by circle size. The geographical origins of the haplotypes are noted at the bottom right of the figure.Full size imageKey to Suinzona borowieci sp. nov. and related species1. Hind wings well developed (Fig. 1e); humeral calli present; trichobothria present on anterior angles of pronotum; lateral margins of pronotum distinctly visible from above. China, India, Laos, Myanmar, Thailand and Vietnam……………………………………………………………… Potaninia assamensis (Baly, 1879)– Hind wings reduced (Fig. 1c,d); humeral calli absent; trichobothria absent on anterior angles of pronotum; lateral margins of pronotum not or barely visible from above. China, Korea and Japan……………………… 22. Aedeagus with apex of flagellum quadrifid (Fig. 1f,g). South Korea, Japan……………. 3– Aedeagus with apex of flagellum varied in shape, but not quadrifid (see Ge et al.3 for key to 23 species). China (Sichuan, Yunnan)……………………………………………… Suinzona spp.3. Larger, body length 5.5–7.0 mm; elytra more densely punctate (Fig. 1a); aedeagus larger and broader (Fig. 2c). South Korea…………………………………. Suinzona borowieci sp. nov.– Smaller, body length 4.8–6.0 mm; elytra less densely punctate (Fig. 1d); aedeagus smaller and narrower (Fig. 2e). Japan…………………………….. Suinzona cyrtonoides (Jacoby, 1885) More

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    Non-structural carbohydrates mediate seasonal water stress across Amazon forests

    1.Martínez-Vilalta, J. et al. Dynamics of non-structural carbohydrates in terrestrial plants: A global synthesis. Ecol. Monogr. 86, 495–516 (2016).Article 

    Google Scholar 
    2.Hartmann, H. & Trumbore, S. Understanding the roles of nonstructural carbohydrates in forest trees–from what we can measure to what we want to know. N. Phytol. 211, 386–403 (2016).CAS 
    Article 

    Google Scholar 
    3.Richardson, A. D. et al. Seasonal dynamics and age of stemwood nonstructural carbohydrates in temperate forest trees. N. Phytol. 197, 850–861 (2013).CAS 
    Article 

    Google Scholar 
    4.Doughty, C. E. et al. Source and sink carbon dynamics and carbon allocation in the Amazon basin. Glob. Biogeochem. Cycles 29, 645–655 (2015).ADS 
    CAS 
    Article 

    Google Scholar 
    5.Mcdowell, N. et al. Mechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought? N. Phytol. 178, 719–739 (2008).Article 

    Google Scholar 
    6.Sala, A., Piper, F. & Hoch, G. Physiological mechanisms of drought-induced tree mortality are far from being resolved. N. Phytol. 186, 274–281 (2010).Article 

    Google Scholar 
    7.Farquhar, G. D. & Sharkey, T. D. Stomatal conductance and photosynthesis. Annu. Rev. Plant Physiol. 33, 317–345 (1982).CAS 
    Article 

    Google Scholar 
    8.Adams, H. D. et al. Nonstructural leaf carbohydrate dynamics of Pinus edulis during drought-induced tree mortality reveal role for carbon metabolism in mortality mechanism. N. Phytol. 197, 1142–1151 (2013).CAS 
    Article 

    Google Scholar 
    9.O’Brien, M. J., Leuzinger, S., Philipson, C. D., Tay, J. & Hector, A. Drought survival of tropical tree seedlings enhanced by non-structural carbohydrate levels. Nat. Clim. Chang. 4, 710–714 (2014).ADS 
    Article 
    CAS 

    Google Scholar 
    10.McDowell, N. G. Mechanisms linking drought, hydraulics, carbon metabolism, and vegetation mortality. Plant Physiol. 155, 1051–1059 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    11.Brienen, R. J. W. et al. Long-term decline of the Amazon carbon sink. Nature 519, 344–348 (2015).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    12.Phillips, O. L. et al. Drought Sensitivity of the Amazon Rainforest. Science (80-.) 323, 1344–1347 (2009).ADS 
    CAS 
    Article 

    Google Scholar 
    13.Lewis, S. L., Brando, P. M., Phillips, O. L., van der Heijden, G. M. F. & Nepstad, D. The 2010 amazon drought. Science (80-.) 331, 554–554 (2011).ADS 
    CAS 
    Article 

    Google Scholar 
    14.Jiménez-Muñoz, J. C. et al. Record-breaking warming and extreme drought in the Amazon rainforest during the course of El Niño 2015-2016. Sci. Rep. 6, 1–7 (2016).Article 
    CAS 

    Google Scholar 
    15.Duffy, P. B., Brando, P., Asner, G. P. & Field, C. B. Projections of future meteorological drought and wet periods in the Amazon. Proc. Natl Acad. Sci. U.S.A. 112, 13172–13177 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    16.Jones, S. et al. The impact of a simple representation of non-structural carbohydrates on the simulated response of tropical forests to drought. Biogeosciences https://doi.org/10.5194/bg-2019-452 (2019).17.Dünisch, O. & Puls, J. Changes in content of reserve materials in an evergreen, a semi-deciduous, and a deciduous Meliaceae species from the Amazon. J. Appl. Bot. 77, 10–16 (2003).
    Google Scholar 
    18.Würth, M. K. R., Peláez-Riedl, S., Wright, S. J. & Körner, C. Non-structural carbohydrate pools in a tropical forest. Oecologia 143, 11–24 (2005).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    19.Dickman, L. T. et al. Homoeostatic maintenance of nonstructural carbohydrates during the 2015–2016 El Niño drought across a tropical forest precipitation gradient. Plant Cell Environ. 42, 1705–1714 (2019).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    20.Rowland, L. et al. Death from drought in tropical forests is triggered by hydraulics not carbon starvation. Nature 528, 119–122 (2015).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    21.Malhi, Y. et al. Spatial patterns and recent trends in the climate of tropical rainforest regions. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 359, 311–329 (2004).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    22.Quesada, C. A. et al. Variations in chemical and physical properties of Amazon forest soils in relation to their genesis. Biogeosciences 7, 1515–1541 (2010).ADS 
    CAS 
    Article 

    Google Scholar 
    23.Fyllas, N. M. et al. Basin-wide variations in foliar properties of Amazonian forest: phylogeny, soils and climate. Biogeosciences 6, 2677–2708 (2009).ADS 
    Article 

    Google Scholar 
    24.de Barros, F. V. et al. Hydraulic traits explain differential responses of Amazonian forests to the 2015 El Niño-induced drought. N. Phytol. 223, 1253–1266 (2019).CAS 
    Article 

    Google Scholar 
    25.Coelho de Souza, F. et al. Evolutionary heritage influences Amazon tree ecology. Proc. R. Soc. B Biol. Sci. 283, 20161587 (2016).Article 

    Google Scholar 
    26.Dietze, M. C. et al. Nonstructural carbon in woody plants. Annu. Rev. Plant Biol. 65, 667–687 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    27.Tixier, A., Orozco, J., Amico Roxas, A., Earles, J. M. & Zwieniecki, M. A. Diurnal variation in non-structural carbohydrate storage in trees: remobilization and vertical mixing. Plant Physiol. 178, 1602–1613 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    28.Landhäusser, S. M. et al. Standardized protocols and procedures can precisely and accurately quantify non-structural carbohydrates. Tree Physiol. 38, 1764–1778 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    29.MacNeill, G. J. et al. Starch as a source, starch as a sink: the bifunctional role of starch in carbon allocation. J. Exp. Bot. 68, 4433–4453 (2017).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    30.Poorter, L. & Kitajima, K. Carbohydrate storage and light requirements of tropical moist and dry forest tree species. Ecology 88, 1000–1011 (2007).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    31.Nikinmaa, E. et al. Assimilate transport in phloem sets conditions for leaf gas exchange. Plant Cell Environ. 36, 655–669 (2013).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    32.Tyree, M. T. & Ewers, F. W. The hydraulic architecture of trees and other woody plants. N. Phytol. 119, 345–360 (1991).Article 

    Google Scholar 
    33.Guan, K. et al. Photosynthetic seasonality of global tropical forests constrained by hydroclimate. Nat. Geosci. 8, 284–289 (2015).ADS 
    CAS 
    Article 

    Google Scholar 
    34.Restrepo-Coupe, N. et al. What drives the seasonality of photosynthesis across the Amazon basin? A cross-site analysis of eddy flux tower measurements from the Brasil flux network. Agric. Meteorol. 182–183, 128–144 (2013).Article 

    Google Scholar 
    35.AbdElgawad, H. et al. Starch biosynthesis contributes to the maintenance of photosynthesis and leaf growth under drought stress in maize. Plant. Cell Environ. https://doi.org/10.1111/pce.13813 (2020).36.Malhi, Y. et al. The productivity, metabolism and carbon cycle of two lowland tropical forest plots in south-western Amazonia, Peru. Plant Ecol. Divers. 7, 85–105 (2014).Article 

    Google Scholar 
    37.Sánchez, F. J., Manzanares, M., De Andres, E. F., Tenorio, J. L. & Ayerbe, L. Turgor maintenance, osmotic adjustment and soluble sugar and proline accumulation in 49 pea cultivars in response to water stress. F. Crop. Res. 59, 225–235 (1998).Article 

    Google Scholar 
    38.Morgan, J. M. Osmoregulation and water stress in higher plants. Annu. Rev. Plant Physiol. 35, 299–319 (1984).Article 

    Google Scholar 
    39.Thalmann, M. & Santelia, D. Starch as a determinant of plant fitness under abiotic stress. N. Phytol. 214, 943–951 (2017).CAS 
    Article 

    Google Scholar 
    40.Guo, J. S., Gear, L., Hultine, K. R., Koch, G. W. & Ogle, K. Non-structural carbohydrate dynamics associated with antecedent stem water potential and air temperature in a dominant desert shrub. Plant Cell Environ. https://doi.org/10.1111/pce.13749 (2020).41.Kuang, Y., Xu, Y., Zhang, L., Hou, E. & Shen, W. Dominant trees in a subtropical forest respond to drought mainly via adjusting tissue soluble sugar and proline content. Front. Plant Sci. 8, 1–13 (2017).Article 

    Google Scholar 
    42.Turner, N. C. Turgor maintenance by osmotic adjustment: 40 years of progress. J. Exp. Bot. 69, 3223–3233 (2018).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    43.Kandler, O. & Hopf, H. in Carbohydrates: Structure and Function. Vol. 3, 221–270 (Elsevier, 1980).44.Deslauriers, A. et al. Impact of warming and drought on carbon balance related to wood formation in black spruce. Ann. Bot. 114, 335–345 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    45.Ford, C. W. Accumulation of low molecular weight solutes in water-stressed tropical legumes. Phytochemistry 23, 1007–1015 (1984).CAS 
    Article 

    Google Scholar 
    46.Mitchell, P. J., O’Grady, A. P., Tissue, D. T., Worledge, D. & Pinkard, E. A. Co-ordination of growth, gas exchange and hydraulics define the carbon safety margin in tree species with contrasting drought strategies. Tree Physiol. 34, 443–458 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    47.Malhi, Y. et al. An international network to monitor the structure, composition and dynamics of Amazonian forests (RAINFOR). J. Veg. Sci. 13, 439–450 (2002).Article 

    Google Scholar 
    48.Lopez-Gonzalez, G., Lewis, S. L., Burkitt, M. & Phillips, O. L. ForestPlots.net: a web application and research tool to manage and analyse tropical forest plot data. J. Veg. Sci. 22, 610–613 (2011).Article 

    Google Scholar 
    49.Sakschewski, B. et al. Resilience of Amazon forests emerges from plant trait diversity. Nat. Clim. Chang. 1, 1–5 (2016).
    Google Scholar 
    50.Sombroek, W. Spatial and temporal patterns of amazon rainfall. AMBIO A J. Hum. Environ. 30, 388–396 (2001).CAS 
    Article 

    Google Scholar 
    51.Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

    Google Scholar 
    52.Hoch, G., Popp, M. & Korner, C. Altitudinal increase of mobile carbon pools in Pinus cembra suggests sink limitation of growth at the Swiss treeline. Oikos 98, 361–374 (2002).CAS 
    Article 

    Google Scholar 
    53.Dalagnol, R., Wagner, F. H., Galvão, L. S. & Aragão, L. E. O. C. The MANVI product: MODIS (MAIAC) nadir-solar adjusted vegetation indices (EVI and NDVI) for South America. Zenodo https://doi.org/10.5281/ZENODO.3159488 (2019).54.Dalagnol, R., Wagner, F. H., Galvão, L. S., Nelson, B. W. & De Aragão, L. E. O. E. C. Life cycle of bamboo in the southwestern Amazon and its relation to fire events. Biogeosciences 15, 6087–6104 (2018).ADS 
    Article 

    Google Scholar 
    55.Fonseca, L. D. M. et al. Phenology and seasonal ecosystem productivity in an Amazonian floodplain forest. Remote Sens. 11, 1–17 (2019).Article 

    Google Scholar 
    56.Huete, A. et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83, 195–213 (2002).ADS 
    Article 

    Google Scholar 
    57.Hijmans, R. J. et al. Raster: Geographic Data Analysis And Modeling. (R package, 2020).58.Bivand, R. et al. Rgdal: Bindings for the ‘Geospatial’ Data Abstraction Library. (R package, 2020).59.R Core Team. R: A Language And Environment For Statistical Computing. URL https://www.R-project.org/. (R Foundation for Statistical Computing, 2018).60.Hull, T. E., Fairgrieve, T. F. & Tang, P. T. P. Implementing complex elementary functions using exception handling. ACM Trans. Math. Softw. 20, 215–244 (1994).MATH 
    Article 

    Google Scholar 
    61.De Mendiburu, F. Agricolae: Statistical Procedures For Agricultural Research (R package version 1.1, 2014).62.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag New York, 2016).63.Kassambara, A. ggpubr: ‘ggplot2’ Based Publication Ready Plots. (R package version 0.1.8, 2020).64.Warton, D. I., Duursma, R. A., Falster, D. S. & Taskinen, S. smatr 3- an R package for estimation and inference about allometric lines. Methods Ecol. Evol. 3, 257–259 (2012).Article 

    Google Scholar 
    65.Coelho de Souza, F. et al. Trait data from: ‘Evolutionary heritage influences Amazon tree ecology’. ForestPlots.net . https://doi.org/10.5521/FORESTPLOTS.NET/2016_4 (2016).66.Bates, D., Sarkar, D., Bates, M. D. & Matrix, L. The lme4 Package. October 2, 1–6 (2007).67.Signori-Müller, C. et al. Trait data from: ‘Non-structural carbohydrates mediate seasonal water stress across Amazon forests’. ForestPlots.net 5521 https://doi.org/10.5521/forestplots.net/2021_3 (2021).68.Boyle, B. et al. The taxonomic name resolution service: an online tool for automated standardization of plant names. BMC Bioinformatics 14, 16 (2013). 69.Esquivel-Muelbert, A. et al. Tree mode of death and mortality risk factors across Amazon forests. Nat. Commun. 11, 5515 (2020). More

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    Trophic indices for micronektonic fishes reveal their dependence on the microbial system in the North Atlantic

    1.Azam, F. et al. The ecological role of water-column microbes in the sea. Mar. Ecol. Prog. Ser. 10, 257–263 (1983).ADS 
    Article 

    Google Scholar 
    2.Legendre, L. & Le Fèvre, J. Microbial food webs and the export of biogenic carbon in oceans. Aquat. Microb. Ecol. 9, 69–77 (1995).Article 

    Google Scholar 
    3.Legendre, L. & Rivkin, R. B. Planktonic food webs: Microbial hub approach. Mar. Ecol. Prog. Ser. 365, 289–309 (2008).ADS 
    Article 

    Google Scholar 
    4.Arístegui, J., Gasol, J. M., Duarte, C. M. & Herndl, G. J. Microbial oceanography of the dark ocean’s pelagic realm. Limnol. Oceanogr. 54, 1501–1529 (2009).ADS 
    Article 

    Google Scholar 
    5.Roshan, S. & DeVries, T. Efficient dissolved organic carbon production and export in the oligotrophic ocean. Nat. Commun. 8, 2036. https://doi.org/10.1038/s41467-017-02227-3 (2017).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    6.Pauly, D. & Christensen, V. Primary production required to sustain global fisheries. Nature 374, 255–257 (1995).ADS 
    CAS 
    Article 

    Google Scholar 
    7.Sarmiento, J. L. & Gruber, N. Ocean Biogeochemical Dynamics (Princeton University Press, 2006).
    Google Scholar 
    8.Armengol, L., Calbet, A., Franchy, G., Rodríguez-Santos, A. & Hernández-León, S. Planktonic food web structure and trophic transfer efficiency along a productivity gradient in the tropical and subtropical Atlantic Ocean. Sci. Rep. 9, 2044. https://doi.org/10.1038/s41598-019-38507-9 (2019).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    9.Hernández-León, S. et al. Large deep-sea zooplankton biomass mirrors primary production in the global ocean. Nat. Commun. 11, 6048. https://doi.org/10.1038/s41467-020-19875-7 (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    10.Wilson, R. et al. Contribution of fish to the marine inorganic carbon cycle. Science 323, 359–362 (2009).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    11.Jennings, S. & van der Molen, J. Trophic levels of marine consumers from nitrogen stable isotope analysis: Estimation and uncertainty. ICES J. Mar. Sci. 72, 2289–2300 (2015).Article 

    Google Scholar 
    12.Jennings, S., Maxwell, T. A. D., Schratzberger, M. & Milligan, S. P. Body-size dependent temporal variations in nitrogen stable isotope ratios in food webs. Mar. Ecol. Prog. Ser. 370, 199–206 (2008).ADS 
    Article 

    Google Scholar 
    13.Bernal, A., Olivar, M. P., Maynou, F. & de Puelles, M. L. F. Diet and feeding strategies of mesopelagic fishes in the western Mediterranean. Prog. Oceanogr. 135, 1–17 (2015).ADS 
    Article 

    Google Scholar 
    14.Gutiérrez-Rodríguez, A., Décima, M., Popp, B. N. & Landry, M. R. Isotopic invisibility of protozoan trophic steps in marine food webs. Limnol. Oceanogr. 59, 1590–1598 (2014).ADS 
    Article 
    CAS 

    Google Scholar 
    15.Hussey, N. E. et al. Rescaling the trophic structure of marine food webs. Ecol. Lett. 17, 239–250 (2014).PubMed 
    Article 

    Google Scholar 
    16.Nielsen, J. M., Popp, B. N. & Winder, M. Meta-analysis of amino acid stable nitrogen isotope ratios for estimating trophic position in marine organisms. Oecologia 178, 631–642 (2015).ADS 
    PubMed 
    Article 

    Google Scholar 
    17.Décima, M., Landry, M. R., Bradley, C. J. & Fogel, M. L. Alanine δ15N trophic fractionation in heterotrophic protists. Limnol. Oceanogr. 62, 2308–2322 (2017).ADS 
    Article 
    CAS 

    Google Scholar 
    18.Décima, M. & Landry, M. Resilience of plankton trophic structure to an eddy-stimulated diatom bloom in the North Pacific Subtropical Gyre. Mar. Ecol. Prog. Ser. 643, 33–48 (2020).ADS 
    Article 
    CAS 

    Google Scholar 
    19.Irigoien, X. et al. Large mesopelagic fish biomass and trophic efficiency in the Open Ocean. Nat. Commun. 5, 3271. https://doi.org/10.1038/ncomms4271 (2014).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    20.Cherel, Y., Fontaine, C., Richard, P. & Labat, J.-P. Isotopic niches and trophic levels of myctophid fishes and their predators in the Southern Ocean. Limnol. Oceanogr. 55, 324–332 (2010).ADS 
    CAS 
    Article 

    Google Scholar 
    21.Young, J. W. et al. Setting the stage for a global-scale trophic analysis of marine top predators: A multi-workshop review. Rev. Fish Biol. Fish. 25, 261–272 (2015).Article 

    Google Scholar 
    22.Klevjer, T. A. et al. Large scale patterns in vertical distribution and behaviour of mesopelagic scattering layers. Sci. Rep. 6, 19873. https://doi.org/10.1038/srep19873 (2016).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    23.Olivar, M. P. et al. Mesopelagic fishes across the tropical and equatorial Atlantic: Biogeographical and vertical patterns. Prog. Oceanogr. 151, 116–137 (2017).ADS 
    Article 

    Google Scholar 
    24.Eduardo, L. N. et al. Trophic ecology, habitat, and migratory behaviour of the viperfish Chauliodus sloani reveal a key mesopelagic player. Sci. Rep. 10, 20996. https://doi.org/10.1038/s41598-020-77222-8 (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    25.Valls, M. et al. Trophic structure of mesopelagic fishes in the western Mediterranean based on stable isotopes of carbon and nitrogen. J. Mar. Syst. 138, 160–170 (2014).Article 

    Google Scholar 
    26.Choy, C. A., Popp, B. N., Hannides, C. C. S. & Drazen, J. C. Trophic structure and food resources of epipelagic and mesopelagic fishes in the North Pacific Subtropical Gyre ecosystem inferred from nitrogen isotopic compositions. Limnol. Oceanogr. 60, 1156–1171 (2015).ADS 
    Article 

    Google Scholar 
    27.Olivar, M. P., Bode, A., López-Pérez, C., Hulley, P. A. & Hernández-León, S. Trophic position of lanternfishes (Pisces: Myctophidae) of the tropical and equatorial Atlantic estimated using stable isotopes. ICES J. Mar. Sci. 76, 649–661 (2019).Article 

    Google Scholar 
    28.Richards, T. M., Sutton, T. T. & Wells, R. J. D. Trophic structure and sources of variation influencing the stable isotope signatures of meso- and bathypelagic micronekton fishes. Front. Mar. Sci. 7, 507992. https://doi.org/10.3389/fmars.2020.507992 (2020).Article 

    Google Scholar 
    29.Choy, C. A. et al. Global trophic position comparison of two dominant mesopelagic fish families (Myctophidae, Stomiidae) using amino acid nitrogen isotopic analyses. PLoS ONE 7, e50133. https://doi.org/10.1371/journal.pone.0050133 (2012).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    30.Wang, F. et al. Trophic interactions of mesopelagic fishes in the South China Sea illustrated by stable isotopes and fatty acids. Front. Mar. Sci. 5, 522. https://doi.org/10.3389/fmars.2018.00522 (2019).Article 

    Google Scholar 
    31.Czudaj, S. et al. Spatial variation in the trophic structure of micronekton assemblages from the eastern tropical North Atlantic in two regions of differing productivity and oxygen environments. Deep Sea Res. 163, 103275. https://doi.org/10.1016/j.dsr.2020.103275 (2020).CAS 
    Article 

    Google Scholar 
    32.FishBase. A Global Information System on Fishes (University of California, 2021).
    Google Scholar 
    33.Bligh, E. G. & Dyer, W. J. A rapid method of total lipid extraction and purification. Can. J. Biochem. Physiol. 37, 911–917 (1959).CAS 
    PubMed 
    Article 

    Google Scholar 
    34.Coplen, T. B. Guidelines and recommended terms for expression of stable isotope-ratio and gas-ratio measurement results. Rapid Commun. Mass Spectrom. 25, 2538–2560 (2011).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    35.Chikaraishi, Y. et al. Determination of aquatic food-web structure based on compound-specific nitrogen isotopic composition of amino acids. Limnol. Oceanogr. Methods 7, 740–750 (2009).CAS 
    Article 

    Google Scholar 
    36.McCarthy, M. D., Lehman, J. & Kudela, R. Compound-specific amino acid δ15N patterns in marine algae: Tracer potential for cyanobacterial vs. eukaryotic organic nitrogen sources in the ocean. Geochim. Cosmochim. Acta 103, 104–120 (2013).ADS 
    CAS 
    Article 

    Google Scholar 
    37.Mompeán, C., Bode, A., Gier, E. & McCarthy, M. D. Bulk vs. aminoacid stable N isotope estimations of metabolic status and contributions of nitrogen fixation to size-fractionated zooplankton biomass in the subtropical N Atlantic. Deep Sea Res. 114, 137–148 (2016).Article 
    CAS 

    Google Scholar 
    38.McClelland, J. W. & Montoya, J. P. Trophic relationships and the nitrogen isotopic composition of amino acids in plankton. Ecology 83, 2173–2180 (2002).Article 

    Google Scholar 
    39.McMahon, K. W. & McCarthy, M. D. Embracing variability in amino acid d15N fractionation: Mechanisms, implications, and applications for trophic ecology. Ecosphere 7, e01511. https://doi.org/10.1002/ecs2.1511 (2016).Article 

    Google Scholar 
    40.Swanson, H. K. et al. A new probabilistic method for quantifying n-dimensional ecological niches and niche overlap. Ecology 96, 318–324 (2015).PubMed 
    Article 

    Google Scholar 
    41.Bradley, C. J. et al. Trophic position estimates of marine teleosts using amino acid compound specific isotopic analysis. Limnol. Oceanogr. Methods 13, 476–493 (2015).Article 

    Google Scholar 
    42.Hammer, Ø., Harper, D. A. T. & Ryan, P. D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 1–9 (2001).
    Google Scholar 
    43.McCarthy, M. D., Benner, R., Lee, C. & Fogel, M. L. Amino acid nitrogen isotopic fractionation patterns as indicators of heterotrophy in plankton, particulate, and dissolved organic matter. Geochim. Cosmochim. Acta 71, 4727–4744 (2007).ADS 
    CAS 
    Article 

    Google Scholar 
    44.Hannides, C. C. S., Popp, B. N., Choy, C. A. & Drazen, J. C. Midwater zooplankton and suspended particle dynamics in the North Pacific Subtropical Gyre: A stable isotope perspective. Limnol. Oceanogr. 58, 1931–1946 (2013).ADS 
    CAS 
    Article 

    Google Scholar 
    45.Gloeckler, K. et al. Stable isotope analysis of micronekton around Hawaii reveals suspended particles are an important nutritional source in the lower mesopelagic and upper bathypelagic zones. Limnol. Oceanogr. 63, 1168–1180 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    46.Robison, B. H. Conservation of deep pelagic biodiversity. Conserv. Biol. 23, 847–858 (2009).PubMed 
    Article 

    Google Scholar 
    47.Brun, P. et al. Climate change has altered zooplankton-fuelled carbon export in the North Atlantic. Nat. Ecol. Evol. 3, 416–423 (2019).PubMed 
    Article 

    Google Scholar 
    48.Bode, M. et al. Feeding strategies of tropical and subtropical calanoid copepods throughout the eastern Atlantic Ocean: Latitudinal and bathymetric aspects. Prog. Oceanogr. 138, 268–282 (2015).ADS 
    Article 

    Google Scholar 
    49.Herndl, G. J. et al. Contribution of Archaea to total prokarytic production in the deep Atlantic Ocean. Appl. Environ. Microbiol. 71, 2303–2309 (2005).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    50.Varela, M. M., van Aken, H. M., Sintes, E., Reinthaler, T. & Herndl, G. J. Contribution of Crenarchaeota and bacteria to autotrophy in the North Atlantic interior. Environ. Microbiol. 13, 1524–1533 (2011).PubMed 
    Article 

    Google Scholar 
    51.Clifford, E. L. et al. Taurine is a major carbon and energy source for marine prokaryotes in the North Atlantic Ocean off the Iberian Peninsula. Microb. Ecol. 78, 299–312 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    52.Hoen, D. K. et al. Amino acid 15N trophic enrichment factors of four large carnivorous fishes. J. Exp. Mar. Biol. Ecol. 453, 76–83 (2014).CAS 
    Article 

    Google Scholar 
    53.McMahon, K. W. & McCarthy, M. D. Embracing variability in amino acid δ15N fractionation: Mechanisms, implications, and applications for trophic ecology. Ecosphere 7, e01511. https://doi.org/10.1002/ecs2.1511 (2016).Article 

    Google Scholar 
    54.Pauly, D., Christensen, V. & Walters, C. Ecopath, ecosim, and ecospace as tools for evaluating ecosystem impact of fisheries. ICES J. Mar. Sci. 57, 697–706 (2000).Article 

    Google Scholar 
    55.Christensen, V. & Walters, C. Ecopath with ecosim: Methods, capabilities and limitations. Ecol. Model. 172, 109–139 (2004).Article 

    Google Scholar 
    56.McClain-Counts, J. P., Demopoulos, A. W. J. & Ross, S. W. Trophic structure of mesopelagic fishes in the Gulf of Mexico revealed by gut content and stable isotope analyses. Mar. Ecol. 38, e12449. https://doi.org/10.1111/maec.12449 (2017).ADS 
    CAS 
    Article 

    Google Scholar 
    57.Olivar, M. P. et al. The contribution of migratory mesopelagic fishes to neuston fish assemblages across the Atlantic, Indian and Pacific Oceans. Mar. Freshw. Res. 67, 1114–1127 (2016).Article 

    Google Scholar 
    58.Gartner, J. V. & Musick, J. A. Feeding habits of the deep-sea fish, Scopelogadus beanii (Pisces: Melamphaide), in the western North Atlantic. Deep Sea Res. A Oceanogr. Res. Pap. 36(10), 1457–1469. https://doi.org/10.1016/0198-0149(89)90051-4 (1989).ADS 
    Article 

    Google Scholar 
    59.Clarke, L. J., Trebilco, R., Walters, A., Polanowski, A. M. & Deagle, B. E. DNA-based diet analysis of mesopelagic fish from the southern Kerguelen axis. Deep-Sea Res. II Top. Stud. Oceanogr. https://doi.org/10.1016/j.dsr2.2018.09.001 (2020).Article 

    Google Scholar 
    60.Schmoker, C., Hernández-León, S. & Calbet, A. Microzooplankton grazing in the oceans: Impacts, data variability, knowledge gaps and future directions. J. Plankton Res. 35, 691–706 (2013).Article 

    Google Scholar 
    61.Calbet, A. & Saiz, E. The ciliate-copepod link in marine ecosystems. Aquat. Microb. Ecol. 38, 157–167 (2005).Article 

    Google Scholar 
    62.Zeldis, J. R. & Décima, M. Mesozooplankton connect the microbial food web to higher trophic levels and vertical export in the New Zealand subtropical convergence zone. Deep Sea Res. 155, 103146 (2020).CAS 
    Article 

    Google Scholar 
    63.Jennings, S., Pinnegar, J. K., Polunin, N. V. C. & Boon, T. W. Weak cross-species relationships between body size and trophic level belie powerful size-based trophic structuring in fish communities. J. Anim. Ecol. 70, 934–944 (2001).Article 

    Google Scholar 
    64.Bode, A., Carrera, P. & Lens, S. The pelagic foodweb in the upwelling ecosystem of Galicia (NW Spain) during spring: Natural abundance of stable carbon and nitrogen isotopes. ICES J. Mar. Sci. 60, 11–22 (2003).CAS 
    Article 

    Google Scholar 
    65.Hunt, B. P. V. et al. A coupled stable isotope-size spectrum approach to understanding pelagic food-web dynamics: A case study from the southwest sub-tropical Pacific. Deep Sea Res. 113, 208–224 (2015).CAS 
    Article 

    Google Scholar 
    66.Romero-Romero, S., Molina-Ramírez, A., Höfer, J. & Acuña, J. L. Body size-based trophic structure of a deep marine ecosystem. Ecology 97, 171–181 (2016).PubMed 
    Article 

    Google Scholar 
    67.Barnes, C., Maxwell, D., Reuman, D. C. & Jennings, S. Global patterns in predator-prey size relationships reveal size dependency of trophic transfer efficiency. Ecology 91, 222–232 (2010).PubMed 
    Article 

    Google Scholar 
    68.Schoener, T. W. Food webs from the small to the large. Ecology 70, 1559–1589 (1989).Article 

    Google Scholar 
    69.Zhou, M. What determines the slope of a plankton biomass spectrum?. J. Plankton Res. 28, 437–448 (2006).Article 

    Google Scholar 
    70.Van der Zanden, M. J. & Fetzer, W. Global patterns of aquatic food chain length. Oikos 116, 1378–1388 (2007).Article 

    Google Scholar 
    71.Basedow, S. L., de Silva, N. A. L., Bode, A. & van Beusekorn, J. Trophic positions of mesozooplankton across the North Atlantic: estimates derived from biovolume spectrum theories and stable isotope analyses. J. Plankton Res. 38, 1364–1378 (2016).CAS 

    Google Scholar 
    72.Williams, R. J. & Martinez, N. D. Limits to trophic levels and omnivory in complex food webs: Theory and data. Am. Nat. 163, 458–468 (2004).PubMed 
    Article 

    Google Scholar 
    73.Nagata, T. et al. Emerging concepts on microbial processes in the bathypelagic ocean – ecology, biogeochemistry, and genomics. Deep Sea Res. II 57, 1519–1536 (2010).ADS 
    CAS 
    Article 

    Google Scholar  More

  • in

    Flavonoids increase melanin production and reduce proliferation, migration and invasion of melanoma cells by blocking endolysosomal/melanosomal TPC2

    Endolysosomal patch-clamp experimentsEndolysosomal patch-clamp experiments were performed as previously described6,14,23,25,29,30. In brief, for whole-LE/LY manual patch-clamp recordings, cells were treated with 1 μM vacuolin (HEK293 cells: overnight) in an incubator at 37 °C with 5% CO2. Compound was washed out before patch-clamp experimentation. Currents were recorded using an EPC-10 patch-clamp amplifier (HEKA, Lambrecht, Germany) and PatchMaster acquisition software (HEKA). Data were digitized at 40 kHz and filtered at 2.8 kHz. Fast and slow capacitive transients were cancelled by the compensation circuit of the EPC-10 amplifier. All recordings were obtained at room temperature and were analyzed using PatchMaster acquisition software (HEKA) and OriginPro 6.1 (OriginLab). Recording glass pipettes were polished and had a resistance of 4–8 MΩ. For all experiments, salt-agar bridges were used to connect the reference Ag–AgCl wire to the bath solution to minimize voltage offsets. Liquid junction potential was corrected. For the application of small molecules, compounds were added directly to the patched endolysosomes to either evoke or inhibit the current. The cytoplasmic solution was completely exchanged by cytoplasmic solution containing compound. The current amplitudes at −100 mV were extracted from individual ramp current recordings. Unless otherwise stated, cytoplasmic solution contained 140 mM K-MSA, 5 mM KOH, 4 mM NaCl, 0.39 mM CaCl2, 1 mM EGTA and 10 mM HEPES (pH was adjusted with KOH to 7.2). Luminal solution contained 140 mM Na-MSA, 5 mM K-MSA, 2 mM Ca-MSA 2 mM, 1 mM CaCl2, 10 mM HEPES and 10 mM MES (pH was adjusted with methanesulfonic acid to 4.6). In all experiments, 500-ms voltage ramps from − 100 to + 100 mV were applied every 5 s. All statistical analysis was completed using OriginPro9.0 and GraphPadPrism software.Cell cultureHEK293 cells stably expressing hTPC2-YFP or hTRPML1-YFP were used for patch-clamp experiments. Cells were maintained in DMEM supplemented with 10% FBS, 100 U penicillin/mL, and 100 μg streptomycin/mL. Cells were plated on glass cover slips 24–48 h before experimentation. Cells were transiently transfected with Turbofect (Fermentas) according to the manufacturer’s protocols and used, e.g. for confocal imaging or patch-clamp experiments 24–48 h after transfection. Cells were treated with compounds at 37 °C and 5% CO2. MNT-1 WT and TPC2−/− KO cell lines were grown in high glucose DMEM, supplemented with 20% FBS, 10% AIM-V, 1% sodium pyruvate (Thermo Fisher), and 1% penicillin–streptomycin (Sigma-Aldrich). B16F10 cells were grown in high glucose DMEM, supplemented with 10% FBS (Thermo Fisher), 1% L-glutamin, and 1% penicillin–streptomycin (Sigma-Aldrich). Cell lines were maintained at 37 °C in a 5% CO2 incubator.Melanin screening in B16F10 mouse melanoma cellsMelanin content determination was performed as described previously with some modifications31. In brief, B16F10 cells at density of 5 × 103 cells/well in 96-well plate were cultured and incubated with various plant extracts or flavonoids at a concentration of 20 µg/ml or 20 µM, respectively, for 4–5 days. Melanin content was measured using a microplate reader (Anthros, Durham, NC, USA) and calculated based on the OD ratio between treated and untreated cells.Melanin content and tyrosinase activity assaysMNT-1 WT and TPC2−/− KO cell lines were grown as described in the cell culture section. After reaching 80–90% confluency, cells were subcultured (every 2–3 days). Forskolin (Sigma-Aldrich Cas Nr. 66,575,299) was used as positive control and 4-Butyl-resorcinol (TYR-inh., Sigma-Aldrich, Cas Nr.18979-61-8) as negative control. For experiments, cells were plated in 6-well plates with 200,000 cells per well. Cells were incubated for 72 h at 37 °C and 5% CO2. After removing cell culture media, cells were washed in DPBS twice, then cells were collected using a cell scraper. Cells were centrifuged at 3000 rpm for 5 min. Pellets were lysed with RIPA buffer, supplemented with 1% protease inhibitor cocktail (Sigma-Aldrich) and 1% phosphatase inhibitor (Sigma-Aldrich) at 4 °C (on ice) for 45 min. Cells were centrifuged at 12.000 rpm for 15 min (4 °C), supernatant was subsequently removed and protein content determined using a protein dye reagent assay (Bio-Rad; protein standard curve (BSA) 0, 1, 3, 5, 8, 10, 12, 15 μg/mL). Cell pellets were dissolved in 250 μL 1 N NaOH/10% DMSO and incubated at 80 °C for 2 h. After centrifugation at 12.000 rpm for 10 min, supernatants were removed to a 96-well plate. Absorbance was measured (in triplicates, each) at 405 nm using a microplate reader (Tecan, Infinite M200 PRO). Melanin content was normalized to total protein content.To measure tyrosinase activity 100 μg protein from the supernatant after RIPA lysis were transferred into a 96-well plate and 50 μL of 15 mM L-DOPA (Sigma) were added (total volume was adjusted to 100 μL using PBS, pH 6.8 (adjusted with 1 N HCl)). After 30 min incubation at 37 °C, dopachrome formation was determined by measuring the absorbance at 475 nm using a microplate reader (Tecan, Infinite M200 PRO). Tyrosinase activity (%) was calculated as follows: OD475 (sample) × 100 / OD475 (control).Cell proliferation assayCell proliferation assay was performed in 96-well, flat-bottom microtiter plates (Sarstedt), in triplicates, and at a 5 × 103 cell density per well. Cells were seeded overnight, including cells measured as day zero control. Proliferation rate was assessed by incubation with CellTiter-Blue (Ctb, Promega, Mannheim, Germany) reagent for 3 h. Fluorescence was measured using a microplate reader at 560Ex/600Em (Tecan, Infinite M200 PRO).Wound healing/migration assayWound healing assay was performed using 12-well plates (Sarstedt) at a density of 120,000 cells/well. Cells were incubated overnight, and a scratch was performed using a yellow pipet tip. Pictures were taken at 0, 24, 48, and 72 h with an inverted microscope (Leica DM IL LED) and using a microscope camera (Leica DFC 3000 G). The wounded cell area was quantified using ImageJ 1.52a software and was subtracted from 0 h values.Invasion assayTranswell chambers in 24-well permeable support plates (Corning, #3421) were coated with Corning Matrigel basement membrane matrix (Corning, #354234) for 1.5 h. A total of 3 × 104 MNT-1 cells were seeded on top of the chambers in serum-free medium, and direct stimulation with compounds was performed. The lower compartment contained the chemotactic gradient, medium with 10% FBS. Cells were allowed to migrate for 24 h, and were then fixed and stained with crystal violet containing methanol. Non-invaded cells were removed with Q-tips and pictures were taken of the bottom side of the membrane using an inverted microscope (Olympus CKX41) and an Olympus SC50 camera (Olympus). The number of invaded cells was quantified using ImageJ 1.52a software.Western blottingWestern blot experiments were performed as described previously32. Briefly, cells were washed twice with 1 × PBS and pellets were collected. Total cell lysates were obtained by solubilizing in TRIS HCl 10 mM pH 8.0 and 0.2% SDS supplemented with protease and phosphatase inhibitors (Sigma). Protein concentrations were quantified via Bradford assay. Proteins were separated via a 10% sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE; BioRad) and transferred to polyvinylidene difluoride (PVDF; BioRad) membranes. Membranes were blocked with 5% bovine serum albumin (Sigma) or milk diluted in Tris Buffered Saline supplemented with 0.5% Tween-20 (TBS-T) for 1 h at room temperature (RT), then incubated with primary antibody at 4 °C overnight. Then, membranes were washed with TBS-T and incubated with horseradish peroxidase (HRP) conjugated anti-mouse or anti-rabbit secondary antibody (Cell Signaling Technology) at RT for 1 h. Membranes were then washed and developed by incubation with Immobilon Crescendo Western HRP substrate (Merck) and by using an Odyssey imaging system (LI-COR Biosciences). Quantification was carried out using unsaturated images on ImageJ 1.52a software. The blots were cropped prior to hybridisation with antibodies against vinculin, GAPDH, or actin. The following antibodies were used: Phospho-p44/42 MAPK (Erk1/2) (Thr202/Tyr204) (Cell Signaling Technology, 1:1000, cat. #9106), p44/42 MAPK (Erk1/2) (Thr202/Tyr204) (Cell Signaling Technology, 1:1000, cat. #9102), Phospho-Akt (Ser473) (Cell Signaling Technology, 1:1000, cat. #4058), Akt (Cell Signaling Technology, 1:1000, cat. #9272), MITF (Santa Cruz Biotechnology, 1:1000, cat. #Sc-71588), MITF (Cell Signaling Technology, 1:1000, cat. #97800), MITF (Abcam, 1:1000, cat. #ab12039), GSK-3β (Cell Signaling Technology, 1:1000, cat. #9832), CREB and pCREB (Cell Signaling Technology, 1:1000, cat. #9197S and #9198S), ß-Actin (Santa Cruz Biotechnology, 1:1000, cat. #Sc-47778), Vinculin (Cell Signaling Technology, 1:1000, cat. #4650), GAPDH (Cell Signaling Technology, 1:1000, cat. #5174S), Anti-Mouse (Cell Signaling Technology, 1:10,000, cat. #7076), and Anti-Rabbit (Cell Signaling Technology, 1:10,000, cat. #7074).RNA isolation and quantitative PCRTotal RNA was isolated from the cells using the RNeasy Mini Kit (Qiagen). Reverse Transcription was performed using the Revert First Strand cDNA Synthesis Kit (Thermo Fisher). Real-time quantitative Reverse Transcription PCR (qPCR) was performed in triplicates for each sample using the LightCycler 480 SYBR Green I Master and using the LightCycler 480 II machine (Roche Life Science), following the recommended parameters. HPRT was used as the housekeeping gene. The following human primer sets were used: Tyrosinase primers set A: fw: 5′-GTCTGTAGCCGATTGGAGGA -3′; rev: 5′- TGGGGTTCTGGATTTGTCAT -3′. Tyrosinase primers set B: fw: 5′-TGACAG TATTTTTGAGCAGTGG -3′; rev: 5′- GGTGCATTGGCTTCTGGATA-3′.Plant materialCommercially available heartwood of Dalbergia parviflora was purchased from “Chao Krom Poe” herbal medicine dispensary in Bangkok in 2004. The samples were identified as wild Dalbergia parviflora at Princess Sirindhorn Wildlife Sanctuary, known as “Pa Phru To Daeng” which is a peat swamp forest in Mueang Narathiwat, Tak Bai, Su-ngai Kolok, and Su-ngai Padi districts of Narathiwat Province in Southern Thailand (06° 04′ 33.8″ N, 101° 57′ 49.3″ E). Data collection in the area was carried out with the authorization and guidelines of the National Research Council of Thailand (NRCT), and complied with the IUCN Policy Statement on Research Involving Species at Risk of Extinction and the Conservation (1989) and the Convention on International Trade in Endangered Species of Wild Fauns and Flora (CITES, 1975). The plant was identified by Dr. Chawalit Niyomdham of the Forest Herbarium, National Park, Wildlife and Plant Conservation Department, Bangkok, Thailand. Its voucher specimen (number 68143)33,34 was deposited at The Forest Herbarium, Bangkok, Thailand.Extraction and isolation of flavonoidsThe dried heartwood of D. parviflora (2 kg) was extracted three times with MeOH (3 × 20 L) at room temperature. The extracts were combined and concentrated under reduced pressure at 60 °C to yield 910 g of a viscous mass. A part of this concentrated extract (150 g) was chromatographed on a silica gel column (12 × 40 cm) and fractionated using chloroform-MeOH (98:2, 96:4, 94:6, 90:10, 15 L each). Fractions of 500 mL were collected and pooled by TLC analysis to yield a total of 26 combined fractions. Purification of these fractions as reported previously33,34 gave various flavonoid compounds as summarized in Fig. S1. Purification of fraction 14 (8.9 g) using HPLC on a Develosil- Lop-ODS column (5 × 100 cm, flow rate, 45 mL/min with detection at 205 nm), with MeCN-H2O (30:70) as the eluent gave MT-8 (pratensein) (715 mg) (tR = 220 min). Purification of fraction 6 (3.1 g) using HPLC on a Develosil-Lop-ODS column (5 × 100 cm, flow rate: 45 mL/min with detection at 205 nm), with MeCN-H2O (32:68) as the eluent, gave UM-9 (duartin) (39 mg) (tR = 240 min). Both compounds were identified by comparison of their spectroscopic data with published values35,36.NMR analytical dataNMR spectra were measured on an JEOL alpha 400 (1H-NMR: 400 MHz, 13C-NMR: 100.4 MHz) spectrometer33,34. NMR-Spectra were measured in deuterated solvents and chemical shifts are reported in δ (ppm) relative to the internal standard tetramethylsilane (TMS) or the solvent peak at 35 °C, respectively. J values are given in hertz. Multiplicities are abbreviated as follows: s = singlet, d = doublet, t = triplet, q = quartet, m = multiplet. Signal assignments were carried out based on 1H, 13C, HMBC, HMQC and COSY spectra. Inverse-detected heteronuclear correlations were measured using HMQC (optimized for 1JC-H = 145 Hz) and HMBC (optimized for 3JC-H = 8 Hz) pulse sequences with a pulsed field gradient. FABMS spectra were obtained on a JEOL JMS-700 using a m-nitrobenzyl alcohol matrix. Optical rotation was measured on a JASCO DIP-360 digital polarimeter. Column chromatography (CC) was performed with powdered silica gel (Kieselgel 60, 230–400 mesh, Merck KGaA, Darmstadt, Germany) and styrene–divinylbenzene (Diaion HP-20, 250–800 µm particle size, Mitsubishi Chemical Co., Ltd.). Precoated glass plates of silica gel (Kieselgel 60, F254, Merck Co., Ltd., Japan) and RP-18 (F254S, Merck KGaA) were used for TLC analysis. The TLC spots were visualized under UV light at a wavelength of 254 nm and sprayed with dilute H2SO4, followed by heating. HPLC separation was mainly performed with a JASCO model 887-PU pump, and isolates were detected by an 875-UV variable-wavelength detector. Reversed-phase columns for preparative separations (Develosil Lop ODS column, 10—20 µm, 5 × 50 × 2 cm; Nomura Chemical Co. Ltd., Aichi, Japan; flow rate 45 mL/min with detection at 205 nm) and semi-preparative separations (Capcell Pak ODS, 5 µm, 2 × 25 cm, Shiseido Fine Chemiacls Co. Ltd, Tokyo, Japan; flow rate 9 mL/min with detection at 205 nm) were used. MT-8 (pratensein): Amorphous powder; 1H-NMR (400 MHz, (CD3)2CO) δ (ppm) = 13.03 (s, 1H, 5-H), 8.18 (s, 1H, 2-H), 7.13 (d, J = 2 Hz, 1H, 2′-H), 7.04 (dd, J = 9, 2 Hz, 1H, 6′-H), 6.99 (d, J = 9 Hz, 1H, 5′-H), 6.41 (d, J = 2 Hz, 1H, 8-H), 6.28 (d, J = 2 Hz, 1H, 6-H), 3.87 (s, 3H, 4′-OCH3). 13C-NMR (100.4 MHz, (CD3)2CO) δ (ppm) = 181.6 (C-4), 165.0 (C-7), 164.0 (C-5), 159.1 (C-9), 154.5 (C-2), 165.0 (C-7), 148.6 (C-4′), 147.3 (C-3′), 125.0 (C-1′), 121.3 (C-6′), 124.0 (C-3), 112.3 (C-5′), 106.3 (C-10), 99.9 (C-6), 94.5 (C-8), 56.4 (C-4′ OCH3). FABMS m/z 323 [MNa] + (calcd for C16H12O6Na). UM-9 (duartin): morphous powder; 1H-NMR (400 MHz, (CD3)2CO) δ (ppm) = 6.70 (d, J = 9 Hz, 1H, 5′-H), 6.65 (d, J = 9 Hz, 1H, 6′-H), 6.64 (d, J = 9 Hz, 1H, 5-H), 6.40 (d, J = 9 Hz, 1H, 6-H), 4.29 (ddd, J = 10, 3, 2 Hz, 1H, 2 eq-H), 3.96 (t, J = 10 Hz, 1H, 2ax-H), 3.47 (dddd, J = 11, 10, 5, 3 Hz, 1H, 3-H), 2.91 (dd, J = 16, 11 Hz, 1H, 4ax-H), 3.47 (ddd, J = 16, 5, 2 Hz, 1H, 4 eq-H), 3.87 (s, 3H, 2′-OCH3) , 3.81 (s, 3H, 4′-OCH3) , 3.77 (s, 3H, 8-OCH3). C-NMR (100.4 MHz, (CD3)2CO) δ (ppm) = 149.4 (C-7), 148.5 (C-9), 148.3 (C-4′), 146.5 (C-2′), 140.2 (C-3′), 136.6 (C-8), 128.0 (C-1′), 124.5 (C-6), 117.2 (C-6′), 115.4 (C-10), 108.4 (C-6), 107.9 (C-5′), 70.8 (C-2), 32.5 (C-2), 32.1 (C-3), 60.7 (C-8 OCH3), 60.5 (C-2′ OCH3), 56.4 (C-4′ OCH3). [α]D + 15.4° (c 1.0, CHCl3). FABMS m/z 355 [MNa] + (calcd for C18H20O6Na). More

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    A paradoxical knowledge gap in science for critically endangered fishes and game fishes during the sixth mass extinction

    1.N. United, World Population Prospects 2019. Retrived from https://population.un.org/wpp/Download/Standard/Population/ (2020) (available at https://population.un.org/wpp/Download/Standard/Population/).2.Ceballos, G., Ehrlich, P. R. & Dirzo, R. Biological annihilation via the ongoing sixth mass extinction signaled by vertebrate population losses and declines. Proc. Natl. Acad. Sci. U. S. A. 114, E6089–E6096 (2017).CAS 
    Article 

    Google Scholar 
    3.Cincotta, R. P., Wisnewski, J. & Engelman, R. Human population in the biodiversity hotspots. Nature 404, 990–992 (2000).ADS 
    CAS 
    Article 

    Google Scholar 
    4.McKee, J. K., Sciulli, P. W., David Fooce, C. & Waite, T. A. Forecasting global biodiversity threats associated with human population growth. Biol. Conserv. 115, 161–164 (2004).Article 

    Google Scholar 
    5.Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution, and protection. Science (80-) 344, 1246752–1246752 (2014).CAS 
    Article 

    Google Scholar 
    6.Malhi, Y. The concept of the anthropocene. 42 (2017).7.Crutzen, P. J. Geology of mankind. Nature 415, 23 (2002).ADS 
    CAS 
    Article 

    Google Scholar 
    8.Zalasiewicz, J. et al. When did the Anthropocene begin? A mid-twentieth century boundary level is stratigraphically optimal. Quat. Int. 1, 1. https://doi.org/10.1016/j.quaint.2014.11.045 (2014).Article 

    Google Scholar 
    9.Dirzo, R. et al. Defaunation in the anthropocene. Science (80-) 345, 401–406 (2014).ADS 
    CAS 
    Article 

    Google Scholar 
    10.Barnosky, A. D. et al. Has the Earth’s sixth mass extinction already arrived?. Nature 471, 51–57 (2011).ADS 
    CAS 
    Article 

    Google Scholar 
    11.Ceballos, G., Ehrlich, P. R., García, A. The sixth extinction crisis loss of animal populations and species conservation biology view project cost-effective conservation planning view project the sixth extinction crisis loss of animal populations and species (2010) (available at https://www.researchgate.net/publication/266231196).12.Leakey, R. E. & Lewin, R. The sixth extinction: Patterns of life and the future of Humankind (Doubleday, 1995).
    Google Scholar 
    13.Pimm, S. L., Russell, G. J., Gittleman, J. L. & Brooks, T. M. The future of biodiversity. Science (80-) 269, 347–350 (1995).ADS 
    CAS 
    Article 

    Google Scholar 
    14.Burkhead, N. M. Extinction rates in North American freshwater fishes, 1900–2010. Bioscience 62, 798–808 (2012).Article 

    Google Scholar 
    15.Bornmann, L. & Mutz, R. Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references. J. Assoc. Inf. Sci. Technol. 66, 2215–2222 (2015).CAS 
    Article 

    Google Scholar 
    16.Evans, J. A. Future science. Science (80-). 342, 44–45 (2013).ADS 
    CAS 
    Article 

    Google Scholar 
    17.Fortunato, S. et al. Science of science. Science (80-). 359, 1. https://doi.org/10.1126/science.aao0185 (2018).CAS 
    Article 

    Google Scholar 
    18.Williams, D. R., Balmford, A. & Wilcove, D. S. The past and future role of conservation science in saving biodiversity. Conserv. Lett. 13, e12720 (2020).Article 

    Google Scholar 
    19.Bolam, F. C. et al. How many bird and mammal extinctions has recent conservation action prevented?. Conserv. Lett. 1, 1 (2020).
    Google Scholar 
    20.Groves, C. R., Jensen, D. B., Valutis, L. L., Redford, K. H., Shaffer, M. L., Scott, J. M., Baumgartner, J. V., Higgins, J. V., Beck, M. W., & Anderson, M. G. Planning for biodiversity conservation: Putting conservation science into practice. A seven-step framework for developing regional plans to conserve biological diversity, based upon principles of conservation biology and ecology, is being used extensively by the nature conservancy to identify priority areas for conservation” (Oxford Academic, 2002). https://doi.org/10.1641/0006-3568(2002)052[0499:PFBCPC]2.0.CO;2.21.Syed, S., Borit, M. & Spruit, M. Narrow lenses for capturing the complexity of fisheries: A topic analysis of fisheries science from 1990 to 2016. Fish Fish. 19, 643–661 (2018).Article 

    Google Scholar 
    22.Aksnes, D. W. & Browman, H. I. An overview of global research effort in fisheries science. ICES J. Mar. Sci. 73, 1004–1011 (2016).Article 

    Google Scholar 
    23.F. Natale, G. Fiore, J. Hofherr, Mapping the research on aquaculture. A bibliometric analysis of aquaculture literature. Scientometrics. 90, 983–999 (2012).24.Donaldson, M. R. et al. Contrasting global game fish and non-game fish species. Fisheries 36, 385–397 (2011).Article 

    Google Scholar 
    25.Konno, K. et al. Ignoring non-English-language studies may bias ecological meta-analyses. Ecol. Evol. 10, 6373–6384 (2020).Article 

    Google Scholar 
    26.Nuñez, M. A. & Amano, T. Monolingual searches can limit and bias results in global literature reviews. Nat. Ecol. Evol. 4, 2000933 (2021).
    Google Scholar 
    27.Stefanoudis, P. V. et al. Turning the tide of parachute science. Curr. Biol. 31, 161–185 (2021).Article 

    Google Scholar 
    28.Gossa, C., Fisher, M. & Milner-Gulland, E. J. The research-implementation gap: How practitioners and researchers from developing countries perceive the role of peer-reviewed literature in conservation science. Oryx 49, 80–87 (2015).Article 

    Google Scholar 
    29.Bawa, K. S. et al. Opinion: Envisioning a biodiversity science for sustaining human well-being. Proc. Natl. Acad. Sci. 117, 202018436 (2020).Article 

    Google Scholar 
    30.Cooke, S. J. & Cowx, I. G. The role of recreational fishing in global fish crises. Bioscience 54, 857 (2004).Article 

    Google Scholar 
    31.Fleishman, E., Murphy, D. D. & Brussard, P. F. A new method for selection of umbrella species for conservation planning. Ecol. Appl. 10, 569–579 (2000).Article 

    Google Scholar 
    32.Runge, C. A. et al. Single species conservation as an umbrella for management of landscape threats. PLoS ONE 14, e0209619 (2019).CAS 
    Article 

    Google Scholar 
    33.van Rees, C. B. et al. Safeguarding freshwater life beyond 2020: Recommendations for the new global biodiversity framework from the European experience. Conserv. Lett. https://doi.org/10.1111/conl.12771 (2020).Article 

    Google Scholar 
    34.World Wildlife Fund for Nature, “The World’s Forgotten Fishes” (2021), (available at www.panda.org).35.Novacek, M. J. Engaging the public in biodiversity issues. Proc. Natl. Acad. Sci. U. S. A. 105, 11571–11578 (2008).ADS 
    CAS 
    Article 

    Google Scholar 
    36.Gerber, L. R. et al. Endangered species recovery: A resource allocation problem. Science (80-). 362, 284–286 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    37.Restani, M. & Marzluff, J. M. Funding extinction? Biological needs and political realities in the allocation of resources to endangered species recovery. Bioscience 52, 169–177 (2002).Article 

    Google Scholar 
    38.McClenachan, L., Cooper, A. B., Carpenter, K. E. & Dulvy, N. K. Extinction risk and bottlenecks in the conservation of charismatic marine species. Conserv. Lett. 5, 73–80 (2012).Article 

    Google Scholar 
    39.Arlettaz, R. et al. From publications to public actions: When conservation biologists bridge the gap between research and implementation. Bioscience 60, 835–842 (2010).Article 

    Google Scholar 
    40.McNie, E. C. Reconciling the supply of scientific information with user demands: An analysis of the problem and review of the literature. Environ. Sci. Policy. 10, 17–38 (2007).CAS 
    Article 

    Google Scholar 
    41.Brewer, G. D., & Stern, P. C. Decision Making for the Environment: Social and Behavioral Science Research Priorities (National Academies Press, 2005).42.Sunderland, T., Sunderland-Groves, J., Shanley, P. & Campbell, B. Bridging the gap: How can information access and exchange between conservation biologists and field practitioners be improved for better conservation outcomes?. Biotropica 41, 549–554 (2009).Article 

    Google Scholar 
    43.Steven, R., Castley, J. G. & Buckley, R. Tourism revenue as a conservation tool for threatened birds in protected areas. PLoS ONE 8, e62598 (2013).ADS 
    CAS 
    Article 

    Google Scholar 
    44.Joseph, L. N., Maloney, R. F. & Possingham, H. P. Optimal allocation of resources among threatened species: A project prioritization protocol. Conserv. Biol. 23, 328–338 (2009).Article 

    Google Scholar 
    45.Christie, A. P. et al. Poor availability of context-specific evidence hampers decision-making in conservation. Biol. Conserv. 248, 108666 (2020).Article 

    Google Scholar 
    46.International Union for Conservation of Nature (IUCN), International Union for Conservation of Nature (2018), (available at http://www.iucnredlist.org).47.International Game Fish Association (IGFA), International game fish world record list (2018), (available at http://www.igfa.org/records.asp).48.Froese, R., & Pauly, D. FishBase. World Wide Web Electron. Publ. (2019), (available at www.fishbase.org).49.R Core Team, R: a language and environment for statistical computing (2018).50.Aria, M. & Cuccurullo, C. bibliometrix: An R-tool for comprehensive science mapping analysis. J. Informetr. 11, 959–975 (2017).Article 

    Google Scholar 
    51.Sonderegger, D. L. Significant zero crossings (2020).52.Hyndam, R., Athanasopoulos, G., Caceres, G., O’Hara-Wild, M., Petropoulos, F., Razbash, S., Wang, E., & Yasmeen, F. Forecast: Forecasting functions for time series and linear models (2020).53.Hyndman, R. J. & Khandakar, Y. Automatic time series forecasting: The forecast package for R. J. Stat. Softw. 27, 1–22 (2008).Article 

    Google Scholar 
    54.Jenks, G. F. & Caspall, F. C. Error on choroplethic maps: Definition, measurement, reduction. Ann. Assoc. Am. Geogr. 61, 217–244 (1971).Article 

    Google Scholar 
    55.ESRI, ArcGIS Desktop: Release 10.7.1 (2019). More

  • in

    The hierarchy of root branching order determines bacterial composition, microbial carrying capacity and microbial filtering

    1.Vandenkoornhuyse, P., Quaiser, A., Duhamel, M., Le Van, A. & Dufresne, A. The importance of the microbiome of the plant holobiont. N. Phytol. 206, 1196–1206 (2015).Article 

    Google Scholar 
    2.Feng, H. et al. Identification of chemotaxis compounds in root exudates and their sensing chemoreceptors in plant-growth-promoting Rhizobacteria Bacillus amyloliquefaciens SQR9. Mol. Plant Microbe Interact. 31, 995–1005 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    3.Dennis, P. G., Miller, A. J. & Hirsch, P. R. Are root exudates more important than other sources of rhizodeposits in structuring rhizosphere bacterial communities? FEMS Microbiol. Ecol. 72, 313–327 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    4.Walker, T. S., Bais, H. P., Grotewold, E. & Vivanco, J. M. Root exudation and rhizosphere biology. Plant Physiol. 132, 44 (2003).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    5.Zhalnina, K. et al. Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat. Microbiol. 3, 470–480 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    6.Bulgarelli, D. et al. Revealing structure and assembly cues for Arabidopsis root-inhabiting bacterial microbiota. Nature 488, 91–95 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    7.Schreiter, S. et al. Effect of the soil type on the microbiome in the rhizosphere of field-grown lettuce. Front. Microbiol. 5, 144 (2014).8.Zhang, N. et al. Effects of different plant root exudates and their organic acid components on chemotaxis, biofilm formation and colonization by beneficial rhizosphere-associated bacterial strains. Plant Soil 374, 689–700 (2014).CAS 
    Article 

    Google Scholar 
    9.Yang, C.-H. & Crowley, D. E. Rhizosphere microbial community structure in relation to root location and plant iron nutritional status. Appl. Environ. Microbiol. 66, 345 (2000).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    10.DeAngelis, K. M. et al. Selective progressive response of soil microbial community to wild oat roots. ISME J. 3, 168–178 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    11.Peiffer, J. A. et al. Diversity and heritability of the maize rhizosphere microbiome under field conditions. Proc. Natl Acad. Sci. USA 110, 6548 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    12.Shi, S. et al. Successional trajectories of rhizosphere bacterial communities over consecutive seasons. mBio 6, e00746–00715 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    13.Lu, T. et al. Rhizosphere microorganisms can influence the timing of plant flowering. Microbiome 6, 231 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    14.Mei, C. & Flinn, B. S. The use of beneficial microbial endophytes for plant biomass and stress tolerance improvement. Recent Pat. Biotechnol. 4, 81–95 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    15.Hijri, M. Analysis of a large dataset of mycorrhiza inoculation field trials on potato shows highly significant increases in yield. Mycorrhiza 26, 209–214 (2016).PubMed 
    Article 

    Google Scholar 
    16.Waschkies, C., Schropp, A. & Marschner, H. Relations between grapevine replant disease and root colonization of grapevine (Vitis sp.) by fluorescent pseudomonads and endomycorrhizal fungi. Plant Soil 162, 219–227 (1994).Article 

    Google Scholar 
    17.Benizri, E. et al. Replant diseases: bacterial community structure and diversity in peach rhizosphere as determined by metabolic and genetic fingerprinting. Soil Biol. Biochem. 37, 1738–1746 (2005).CAS 
    Article 

    Google Scholar 
    18.Pankhurst, C. E. et al. Management practices to improve soil health and reduce the effects of detrimental soil biota associated with yield decline of sugarcane in Queensland, Australia. Soil Tillage Res. 72, 125–137 (2003).Article 

    Google Scholar 
    19.Fitzpatrick, C. R. et al. Assembly and ecological function of the root microbiome across angiosperm plant species. Proc. Natl Acad. Sci. USA 115, E1157 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    20.Zhang, Y. et al. Huanglongbing impairs the rhizosphere-to-rhizoplane enrichment process of the citrus root-associated microbiome. Microbiome 5, 97 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    21.Edwards, J. et al. Structure, variation, and assembly of the root-associated microbiomes of rice. Proc. Natl Acad. Sci. USA 112, E911 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    22.Hu, L. et al. Root exudate metabolites drive plant-soil feedbacks on growth and defense by shaping the rhizosphere microbiota. Nat. Commun. 9, 2738 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    23.Lundberg, D. S. et al. Defining the core Arabidopsis thaliana root microbiome. Nature 488, 86–90 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    24.McCormack, M. L. et al. Redefining fine roots improves understanding of below-ground contributions to terrestrial biosphere processes. N. Phytol. 207, 505–518 (2015).Article 

    Google Scholar 
    25.Pregitzer, K. S. et al. Fine root architecture of nine North American trees. Ecol. Monogr. 72, 293–309 (2002).Article 

    Google Scholar 
    26.Holdaway, R. J., Richardson, S. J., Dickie, I. A., Peltzer, D. A. & Coomes, D. A. Species- and community-level patterns in fine root traits along a 120 000-year soil chronosequence in temperate rain forest. J. Ecol. 99, 954–963 (2011).Article 

    Google Scholar 
    27.Fitter, A. H. Morphometric analysis of root systems: application of the technique and influence of soil fertility on root system development in two herbaceous species. Plant Cell Environ. 5, 313–322 (1982).
    Google Scholar 
    28.Valenzuela-Estrada, L. R., Vera-Caraballo, V., Ruth, L. E. & Eissenstat, D. M. Root anatomy, morphology, and longevity among root orders in Vaccinium corymbosum (Ericaceae). Am. J. Bot. 95, 1506–1514 (2008).PubMed 
    Article 

    Google Scholar 
    29.Hishi, T. Heterogeneity of individual roots within the fine root architecture: causal links between physiological and ecosystem functions. J. For. Res. 12, 126–133 (2007).Article 

    Google Scholar 
    30.Guo, D. et al. Anatomical traits associated with absorption and mycorrhizal colonization are linked to root branch order in twenty-three Chinese temperate tree species. N. Phytol. 180, 673–683 (2008).Article 

    Google Scholar 
    31.Makita, N. et al. Fine root morphological traits determine variation in root respiration of Quercus serrata. Tree Physiol. 29, 579–585 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    32.Guo, D., Mitchell, R. J., Withington, J. M., Fan, P.-P. & Hendricks, J. J. Endogenous and exogenous controls of root life span, mortality and nitrogen flux in a longleaf pine forest: root branch order predominates. J. Ecol. 96, 737–745 (2008).CAS 
    Article 

    Google Scholar 
    33.Gu, J., Yu, S., Sun, Y., Wang, Z. & Guo, D. Influence of root structure on root survivorship: an analysis of 18 tree species using a minirhizotron method. Ecol. Res. 26, 755–762 (2011).Article 

    Google Scholar 
    34.Wang, B. & Qiu, Y. L. Phylogenetic distribution and evolution of mycorrhizas in land plants. Mycorrhiza 16, 299–363 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    35.Tibbett, M. & Sanders, F. E. Ectomycorrhizal symbiosis can enhance plant nutrition through improved access to discrete organic nutrient patches of high resource quality. Ann. Bot. 89, 783–789 (2002).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    36.Sanders, F. E. & Tinker, P. B. Phosphate flow into mycorrhizal roots. Pestic. Sci. 4, 385–395 (1973).CAS 
    Article 

    Google Scholar 
    37.Hodge, A. & Storer, K. Arbuscular mycorrhiza and nitrogen: implications for individual plants through to ecosystems. Plant Soil 386, 1–19 (2015).CAS 
    Article 

    Google Scholar 
    38.Bending, G. D. & Read, D. J. The structure and function of the vegetative mycelium of ectomycorrhizal plants. N. Phytol. 130, 401–409 (1995).CAS 
    Article 

    Google Scholar 
    39.Chen, W. et al. Root morphology and mycorrhizal symbioses together shape nutrient foraging strategies of temperate trees. Proc. Natl Acad. Sci. USA 113, 8741 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    40.Gui, H., Hyde, K., Xu, J. & Mortimer, P. Arbuscular mycorrhiza enhance the rate of litter decomposition while inhibiting soil microbial community development. Sci. Rep. 7, 42184–42184 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    41.Svenningsen, N. B. et al. Suppression of the activity of arbuscular mycorrhizal fungi by the soil microbiota. ISME J. 12, 1296–1307 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    42.Olsson, P. A. & Wallander, H. Interactions between ectomycorrhizal fungi and the bacterial community in soils amended with various primary minerals. FEMS Microbiol. Ecol. 27, 195–205 (1998).CAS 
    Article 

    Google Scholar 
    43.Hestrin, R., Hammer, E. C., Mueller, C. W. & Lehmann, J. Synergies between mycorrhizal fungi and soil microbial communities increase plant nitrogen acquisition. Commun. Biol. 2, 233 (2019).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    44.Garbaye, J. Helper bacteria: a new dimension to the mycorrhizal symbiosis. N. Phytol. 128, 197–210 (1994).Article 

    Google Scholar 
    45.Phillips, R. P., Brzostek, E. & Midgley, M. G. The mycorrhizal-associated nutrient economy: a new framework for predicting carbon–nutrient couplings in temperate forests. N. Phytol. 199, 41–51 (2013).CAS 
    Article 

    Google Scholar 
    46.Cornelissen, J., Aerts, R., Cerabolini, B., Werger, M. & van der Heijden, M. Carbon cycling traits of plant species are linked with mycorrhizal strategy. Oecologia 129, 611–619 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    47.Reich, P. B. et al. Linking litter calcium, earthworms and soil properties: a common garden test with 14 tree species. Ecol. Lett. 8, 811–818 (2005).Article 

    Google Scholar 
    48.Minerovic, A. J., Valverde-Barrantes, O. J. & Blackwood, C. B. Physical and microbial mechanisms of decomposition vary in importance among root orders and tree species with differing chemical and morphological traits. Soil Biol. Biochem. 124, 142–149 (2018).CAS 
    Article 

    Google Scholar 
    49.Fan, P. & Guo, D. Slow decomposition of lower order roots: a key mechanism of root carbon and nutrient retention in the soil. Oecologia 163, 509–515 (2010).PubMed 
    Article 

    Google Scholar 
    50.Segal, E., Kushnir, T., Mualem, Y. & Shani, U. Water uptake and hydraulics of the root hair rhizosphere. Vadose Zone J. 7, 1027–1034 (2008).Article 

    Google Scholar 
    51.Gordon, W. S. & Jackson, R. B. Nutrient concentrations in fine roots. Ecology 81, 275–280 (2000).Article 

    Google Scholar 
    52.Ma, Z. et al. Evolutionary history resolves global organization of root functional traits. Nature 555, 94–97 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    53.Yates, C. F. et al. Tree‐induced alterations to soil properties and rhizoplane‐associated bacteria following 23 years in a common garden. Plant Soil, https://doi.org/10.1007/s11104-021-04846-8 (2021).54.Fierer, N., Bradford, M. A. & Jackson, R. B. Toward an ecological classification of soil bacteria. Ecology 88, 1354–1364 (2007).Article 
    PubMed 

    Google Scholar 
    55.Wang, N., Wang, C. & Quan, X. Variations in fine root dynamics and turnover rates in five forest types in northeastern China. J. Forestry Res. 31, 871–884 (2020).CAS 
    Article 

    Google Scholar 
    56.Kong, D. et al. Nonlinearity of root trait relationships and the root economics spectrum. Nat. Commun. 10, 2203 (2019).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    57.Jia, S., Wang, Z., Li, X., Zhang, X. & McLaughlin, N. B. Effect of nitrogen fertilizer, root branch order and temperature on respiration and tissue N concentration of fine roots in Larix gmelinii and Fraxinus mandshurica. Tree Physiol. 31, 718–726 (2011).PubMed 
    Article 

    Google Scholar 
    58.Lavely, E. K. et al. On characterizing root function in perennial horticultural crops. Am. J. Botany, https://doi.org/10.1002/ajb2.1530 (2020).59.Iffis, B., St-Arnaud, M. & Hijri, M. Bacteria associated with arbuscular mycorrhizal fungi within roots of plants growing in a soil highly contaminated with aliphatic and aromatic petroleum hydrocarbons. FEMS Microbiol. Lett. 358, 44–54 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    60.Toljander, J. F., Lindahl, B. D., Paul, L. R., Elfstrand, M. & Finlay, R. D. Influence of arbuscular mycorrhizal mycelial exudates on soil bacterial growth and community structure. FEMS Microbiol. Ecol. 61, 295–304 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    61.McCormack, M., Adams, T. S., Smithwick, E. A. H. & Eissenstat, D. M. Predicting fine root lifespan from plant functional traits in temperate trees. N. Phytol. 195, 823–831 (2012).Article 

    Google Scholar 
    62.Freschet, G. T. et al. Climate, soil and plant functional types as drivers of global fine-root trait variation. J. Ecol. 105, 1182–1196 (2017).Article 

    Google Scholar 
    63.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. Environ. Microbiol. 18, 1403–1414 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    64.Apprill, A., McNally, S., Parsons, R. J. & Weber, L. K. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat. Microb. Ecol. 75, 129–137 (2015).Article 

    Google Scholar 
    65.Trexler, R. V. & Bell, T. H. Testing sustained soil-to-soil contact as an approach for limiting the abiotic influence of source soils during experimental microbiome transfer. FEMS Microbiol. Lett. 366, https://doi.org/10.1093/femsle/fnz228 (2019).66.Schloss, P. D. et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537 (2009).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    67.Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    68.Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 
    Article 

    Google Scholar 
    69.DeSantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069 (2006).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    70.McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLOS ONE 8, e61217 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    71.Bressan, M. et al. A rapid flow cytometry method to assess bacterial abundance in agricultural soil. Appl. Soil Ecol. 88, 60–68 (2015).Article 

    Google Scholar 
    72.Oksanen, J. et al. Vegan: community ecology package. R. Package Version 2. 2-1 2, 1–2 (2015).
    Google Scholar 
    73.Bisanz, J. E. MicrobeR: Handy functions for microbiome analysis in R. (2019).74.R Foundation for Statistical Computing. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2012). More