More stories

  • in

    Prebiotic effects of yeast mannan, which selectively promotes Bacteroides thetaiotaomicron and Bacteroides ovatus in a human colonic microbiota model

    1.
    Liu, H. Z., Liu, L., Hui, H. & Wang, Q. Structural characterization and antineoplastic activity of Saccharomyces cerevisiae mannoprotein. Int. J. Food Prop. 18, 359–371 (2015).
    CAS  Google Scholar 
    2.
    Kocourek, J. & Ballou, C. E. Method for fingerprinting yeast cell wall mannans. J. Bacteriol. 100, 1175–1181 (1969).
    CAS  PubMed  PubMed Central  Google Scholar 

    3.
    Scheller, H. V. & Ulvskov, P. Hemicelluloses. Annu. Rev. Plant Biol. 61, 263–289 (2010).
    CAS  PubMed  Google Scholar 

    4.
    Jin, X., Zhang, M., Cao, G. F. & Yang, Y. F. Saccharomyces cerevisiae mannan induces sheep beta-defensin-1 expression via Dectin-2-Syk-p38 pathways in ovine ruminal epithelial cells. Vet. Res. (Faisalabad) 50, 8 (2019).
    Google Scholar 

    5.
    Michael, C. F. et al. Airway epithelial repair by a prebiotic mannan derived from Saccharomyces cerevisiae. J. Immunol. Res. 2017, 8903982 (2017).
    PubMed  PubMed Central  Google Scholar 

    6.
    Lew, D. B. et al. Beneficial effects of prebiotic Saccharomyces cerevisiae mannan on allergic asthma mouse models. J. Immunol. Res. 2017, 3432701 (2017).
    PubMed  PubMed Central  Google Scholar 

    7.
    Cuskin, F. et al. Human gut Bacteroidetes can utilize yeast mannan through a selfish mechanism. Nature 517, 165–169 (2015).
    ADS  CAS  PubMed  PubMed Central  Google Scholar 

    8.
    Flint, H. J., Scott, K. P., Louis, P. & Duncan, S. H. The role of the gut microbiota in nutrition and health. Nat. Rev. Gastroenterol. Hepatol. 9, 577–589 (2012).
    CAS  PubMed  Google Scholar 

    9.
    Cani, P. D. et al. Microbial regulation of organismal energy homeostasis. Nat. Metab. 1, 34–46 (2019).
    CAS  PubMed  Google Scholar 

    10.
    Hooper, L. V. & Macpherson, A. J. Immune adaptations that maintain homeostasis with the intestinal microbiota. Nat. Rev. Immunol. 10, 159–169 (2010).
    CAS  PubMed  Google Scholar 

    11.
    Pickard, J. M., Zeng, M. Y., Caruso, R. & Núñez, G. Gut microbiota: Role in pathogen colonization, immune responses, and inflammatory disease. Immunol. Rev. 279, 70–89 (2017).
    CAS  PubMed  PubMed Central  Google Scholar 

    12.
    Arora, T. & Bäckhed, F. The gut microbiota and metabolic disease: Current understanding and future perspectives. J. Intern. Med. 280, 339–349 (2016).
    CAS  PubMed  Google Scholar 

    13.
    Wang, Z. et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 472, 57–63 (2011).
    ADS  CAS  PubMed  PubMed Central  Google Scholar 

    14.
    Wong, S. H. & Yu, J. Gut microbiota in colorectal cancer: Mechanisms of action and clinical applications. Nat. Rev. Gastroenterol. Hepatol. 16, 690–704 (2019).
    CAS  PubMed  PubMed Central  Google Scholar 

    15.
    Vogt, N. M. et al. Gut microbiome alterations in Alzheimer’s disease. Sci. Rep. 7, 13537 (2017).
    ADS  PubMed  PubMed Central  Google Scholar 

    16.
    The Human Microbiome Project Consortium. Structure, function, and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).
    ADS  PubMed Central  Google Scholar 

    17.
    Bolam, D. N. & Koropatkin, N. M. Glycan recognition by the Bacteroidetes Sus-like systems. Curr. Opin. Struct. Biol. 22, 563–569 (2012).
    CAS  PubMed  Google Scholar 

    18.
    Foley, M. H., Cockburn, D. W. & Koropatkin, N. M. The Sus operon: A model system for starch uptake by the human gut Bacteroidetes. Cell. Mol. Life. Sci. 73, 2603–2617 (2016).
    CAS  PubMed  PubMed Central  Google Scholar 

    19.
    Bågenholm, V. et al. Galactomannan catabolism conferred by a polysaccharide utilization locus of Bacteroides ovatus. J. Biol. Chem. 292, 229–243 (2017).
    PubMed  Google Scholar 

    20.
    Martens, E. C., Koropatkin, N. M., Smith, T. J. & Gordon, J. I. Complex glycan catabolism by the human gut microbiota: The Bacteroidetes Sus-like paradigm. J. Biol. Chem. 284, 24673–24677 (2009).
    CAS  PubMed  PubMed Central  Google Scholar 

    21.
    Larsbrink, J. et al. A discrete genetic locus confers xyloglucan metabolism in select human gut Bacteroidetes. Nature 506, 498–502 (2014).
    ADS  CAS  PubMed  PubMed Central  Google Scholar 

    22.
    Martens, E. C. et al. Recognition and degradation of plant cell wall polysaccharides by two human gut symbionts. PLoS Biol. 9, e1001221 (2011).
    CAS  PubMed  PubMed Central  Google Scholar 

    23.
    Rakoff-Nahoum, S., Foster, K. R. & Comstock, L. E. The evolution of cooperation within the gut microbiota. Nature 533, 255–259 (2016).
    ADS  CAS  PubMed  PubMed Central  Google Scholar 

    24.
    Flint, H. J., Bayer, E. A., Rincon, M. T., Lamed, R. & White, B. A. Polysaccharide utilization by gut bacteria: Potential for new insights from genomic analysis. Nat. Rev. Microbiol. 6, 121–131 (2008).
    CAS  PubMed  Google Scholar 

    25.
    Koropatkin, N. M., Cameron, E. A. & Martens, E. C. How glycan metabolism shapes the human gut microbiota. Nat. Rev. Microbiol. 10, 323–335 (2012).
    CAS  PubMed  PubMed Central  Google Scholar 

    26.
    Varyukhina, S. et al. Glycan-modifying bacteria-derived soluble factors from Bacteroides thetaiotaomicron and Lactobacillus casei inhibit rotavirus infection in human intestinal cells. Microbes Infect. 14, 273–278 (2012).
    CAS  PubMed  Google Scholar 

    27.
    López-Boado, Y. S. et al. Bacterial exposure induces and activates matrilysin in mucosal epithelial cells. J. Cell Biol. 148, 1305–1315 (2000).
    PubMed  PubMed Central  Google Scholar 

    28.
    Delday, M., Mulder, I., Logan, E. T. & Grant, G. Bacteroides thetaiotaomicron ameliorates colon inflammation in preclinical models of Crohn’s disease. Inflamm. Bowel Dis. 25, 85–96 (2019).
    PubMed  Google Scholar 

    29.
    Hansen, R. et al. A phase I randomized, double-blind, placebo-controlled study to assess the safety and tolerability of (Thetanix) Bacteroides thetaiotaomicron in adolescents with stable Crohn’s disease. https://www.4dpharmaplc.com/application/files/1815/5824/8886/Thetanix_DDW_poster_2019.pdf. Accessed 15 July 2020 (2019).

    30.
    Salyers, A. A., Vercellotti, J. R., West, S. E. & Wilkins, T. D. Fermentation of mucin and plant polysaccharides by strains of Bacteroides from the human colon. Appl. Environ. Microbiol. 33, 319–322 (1977).
    CAS  PubMed  PubMed Central  Google Scholar 

    31.
    Rawi, M. H., Zaman, S. A., Pa’ee, K. F., Leong, S. S. & Sarbini, S. R. Prebiotics metabolism by gut-isolated probiotics. J. Food Sci. Technol. 57, 1–14 (2020).
    Google Scholar 

    32.
    Oba, S. et al. Yeast mannan increases Bacteroides thetaiotaomicron abundance and suppresses putrefactive compound production in in vitro fecal microbiota fermentation. Biosci. Biotechnol. Biochem. 84, 2174–2178 (2020).
    CAS  PubMed  Google Scholar 

    33.
    Sasaki, D. et al. Low amounts of dietary fibre increase in vitro production of short-chain fatty acids without changing human colonic microbiota structure. Sci. Rep. 8, 435 (2018).
    ADS  PubMed  PubMed Central  Google Scholar 

    34.
    Takagi, R. et al. A single-batch fermentation system to simulate human colonic microbiota for high-throughput evaluation of prebiotics. PLoS ONE 11, e0160533 (2016).
    PubMed  PubMed Central  Google Scholar 

    35.
    Sender, R., Fuchs, S. & Milo, R. Revised estimates for the number of human and bacteria cells in the body. PLoS Biol. 14, e1002533 (2016).
    PubMed  PubMed Central  Google Scholar 

    36.
    Wexler, H. M. Bacteroides: The good, the bad, and the nitty-gritty. Clin. Microbiol. Rev. 20, 593–621 (2007).
    CAS  PubMed  PubMed Central  Google Scholar 

    37.
    Tong, J., Liu, C., Summanen, P., Xu, H. & Finegold, S. M. Application of quantitative real-time PCR for rapid identification of Bacteroides fragilis group and related organisms in human wound samples. Anaerobe 17, 64–68 (2011).
    CAS  PubMed  Google Scholar 

    38.
    Slavin, J. Fiber and prebiotics: Mechanisms and health benefits. Nutrients 5, 1417–1435 (2013).
    CAS  PubMed  PubMed Central  Google Scholar 

    39.
    Koh, A., De Vadder, F., Kovatcheva-Datchary, P. & Bäckhed, F. From dietary fiber to host physiology: Short-chain fatty acids as key bacterial metabolites. Cell 165, 1332–1345 (2016).
    CAS  PubMed  Google Scholar 

    40.
    den Besten, G. et al. The role of short-chain fatty acids in the interplay between diet, gut microbiota, and host energy metabolism. J. Lipid Res. 54, 2325–2340 (2013).
    Google Scholar 

    41.
    Gibson, G. R. et al. The International Scientific Association for Probiotics and Prebiotics (ISAPP) consensus statement on the definition and scope of prebiotics. Nat. Rev. Gastroenterol. Hepatol. 14, 491–502 (2017).
    PubMed  Google Scholar 

    42.
    Holscher, H. D. Dietary fiber and prebiotics and the gastrointestinal microbiota. Gut Microbes 8, 172–184 (2017).
    CAS  PubMed  PubMed Central  Google Scholar 

    43.
    Chang, C. J. et al. Next generation probiotics in disease amelioration. J. Food Drug Anal. 27, 615–622 (2019).
    CAS  PubMed  Google Scholar 

    44.
    Tan, H. et al. Pilot safety evaluation of a novel strain of Bacteroides ovatus. Front. Genet. 9, 539 (2018).
    CAS  PubMed  PubMed Central  Google Scholar 

    45.
    Tzianabos, A. O., Onderdonk, A. B., Rosner, B., Cisneros, R. L. & Kasper, D. L. Structural features of polysaccharides that induce intra-abdominal abscesses. Science 262, 416–419 (1993).
    ADS  CAS  PubMed  Google Scholar 

    46.
    Bamba, T., Matsuda, H., Endo, M. & Fujiyama, Y. The pathogenic role of Bacteroides vulgatus in patients with ulcerative colitis. J Gastroenterol. 30(Suppl 8), 45–47 (1995).
    PubMed  Google Scholar 

    47.
    Ulsemer, P. et al. Specific humoral immune response to the Thomsen-Friedenreich tumor antigen (CD176) in mice after vaccination with the commensal bacterium Bacteroides ovatus D-6. Cancer Immunol. Immunother. 62, 875–887 (2013).
    CAS  PubMed  Google Scholar 

    48.
    Tan, H., Zhao, J., Zhang, H., Zhai, Q. & Chen, W. Novel strains of Bacteroides fragilis and Bacteroides ovatus alleviate the LPS-induced inflammation in mice. Appl. Microbiol. Biotechnol. 103, 2353–2365 (2019).
    CAS  PubMed  Google Scholar 

    49.
    Luis, A. S. et al. Dietary pectic glycans are degraded by coordinated enzyme pathways in human colonic Bacteroides. Nat. Microbiol. 3, 210–219 (2018).
    CAS  PubMed  Google Scholar 

    50.
    Rakoff-Nahoum, S., Coyne, M. J. & Comstock, L. E. An ecological network of polysaccharide utilization among human intestinal symbionts. Curr. Biol. 24, 40–49 (2014).
    CAS  PubMed  Google Scholar 

    51.
    Rogowski, A. et al. Glycan complexity dictates microbial resource allocation in the large intestine. Nat. Commun. 6, 7481 (2015).
    ADS  CAS  PubMed  PubMed Central  Google Scholar 

    52.
    Le Poul, E. et al. Functional characterization of human receptors for short chain fatty acids and their role in polymorphonuclear cell activation. J. Biol. Chem. 278, 25481–25489 (2003).
    PubMed  Google Scholar 

    53.
    Okubo, T. et al. Effects of partially hydrolyzed guar gum intake on human intestinal microflora and its metabolism. Biosci. Biotechnol. Biochem. 58, 1364–1369 (1994).
    CAS  Google Scholar 

    54.
    Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, e1 (2013).
    CAS  PubMed  Google Scholar 

    55.
    Magoč, T. & Salzberg, S. L. FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).
    PubMed  PubMed Central  Google Scholar 

    56.
    Li, W., Fu, L., Niu, B., Wu, S. & Wooley, J. Ultrafast clustering algorithms for metagenomic sequence analysis. Brief. Bioinform. 13, 656–668 (2012).
    PubMed  PubMed Central  Google Scholar 

    57.
    Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).
    CAS  Google Scholar 

    58.
    Maidak, B. L. et al. The RDP-II (ribosomal database project). Nucleic Acids Res. 29, 173–174 (2001).
    ADS  CAS  PubMed  PubMed Central  Google Scholar 

    59.
    Lozupone, C. & Knight, R. UniFrac: A new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005).
    CAS  PubMed  PubMed Central  Google Scholar 

    60.
    Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).
    CAS  PubMed  PubMed Central  Google Scholar 

    61.
    Furet, J. P. et al. Comparative assessment of human and farm animal faecal microbiota using real-time quantitative PCR. FEMS Microbiol. Ecol. 68, 351–362 (2009).
    CAS  PubMed  Google Scholar 

    62.
    Goubet, F., Jackson, P., Deery, M. J. & Dupree, P. Polysaccharide analysis using carbohydrate gel electrophoresis: A method to study plant cell wall polysaccharides and polysaccharide hydrolases. Anal. Biochem. 300, 53–68 (2002).
    CAS  PubMed  Google Scholar 

    63.
    Terrapon, N. et al. PULDB: The expanded database of polysaccharide utilization loci. Nucleic Acids Res. 46, D677–D683 (2018).
    CAS  PubMed  Google Scholar  More

  • in

    Different types of agricultural land use drive distinct soil bacterial communities

    1.
    Marschner, P., Crowley, D. & Yang, C. H. Development of specific rhizosphere bacterial communities in relation to plant species, nutrition and soil type. Plant Soil 261, 199–208 (2004).
    CAS  Article  Google Scholar 
    2.
    Osler, G. H. R. & Sommerkorn, M. Toward a complete soil C and N cycle: Incorporating the soil fauna. Ecology 88, 1611–1621 (2007).
    PubMed  Article  PubMed Central  Google Scholar 

    3.
    Kennedy, A. C. Bacterial diversity in agroecosystems. Agric. Ecosyst. Environ. 74, 65–76 (1999).
    Article  Google Scholar 

    4.
    Mendes, R., Garbeva, P. & Raaijmakers, J. M. The rhizosphere microbiome: Significance of plant beneficial, plant pathogenic, and human pathogenic microorganisms. FEMS Microbiol. Rev. 37, 634–663 (2013).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    5.
    van der Heijden, M. G. A., Bardgett, R. D. & van Straalen, N. M. The unseen majority: soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. Ecol. Lett. 11, 296–310 (2008).
    PubMed  Article  PubMed Central  Google Scholar 

    6.
    Drenovsky, R. E., Steenwerth, K. L., Jackson, L. E. & Scow, K. M. Land use and climatic factors structure regional patterns in soil microbial communities. Glob. Ecol. Biogeogr. 19, 27–39 (2010).
    PubMed  PubMed Central  Article  Google Scholar 

    7.
    Ma, B. et al. Distinct biogeographic patterns for archaea, bacteria, and fungi along the vegetation gradient at the continental scale in Eastern China. mSystems 2, e00174-e1116 (2017).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    8.
    Wang, X. B. et al. Habitat-specific patterns and drivers of bacterial beta-diversity in China’s drylands. ISME J. 11, 1345–1358 (2017).
    PubMed  PubMed Central  Article  Google Scholar 

    9.
    Sala, O. E. et al. Global biodiversity scenarios for the year 2100. Science 287, 1770–1774 (2000).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    10.
    Singh, J. S., Pandey, V. C. & Singh, D. P. Efficient soil microorganisms: A new dimension for sustainable agriculture and environmental development. Agric. Ecosyst. Environ. 140, 339–353 (2011).
    Article  Google Scholar 

    11.
    Verhulst, N. et al. Soil quality as affected by tillage-residue management in a wheat-maize irrigated bed planting system. Plant Soil 340, 453–466 (2011).
    CAS  Article  Google Scholar 

    12.
    Aziz, I., Mahmood, T. & Islam, K. R. Effect of long term no-till and conventional tillage practices on soil quality. Soil Tillage Res. 131, 28–35 (2013).
    Article  Google Scholar 

    13.
    Navarro-Noya, Y. E. et al. Relative impacts of tillage, residue management and crop-rotation on soil bacterial communities in a semi-arid agroecosystem. Soil Biol. Biochem. 65, 86–95 (2013).
    CAS  Article  Google Scholar 

    14.
    Meriles, J. M. et al. Soil microbial communities under different soybean cropping systems: Characterization of microbial population dynamics, soil microbial activity, microbial biomass, and fatty acid profiles. Soil Tillage Res. 103, 271–281 (2009).
    Article  Google Scholar 

    15.
    Chaudhry, V., Rehman, A., Mishra, A., Chauhan, P. S. & Nautiyal, C. S. Changes in bacterial community structure of agricultural land due to long-term organic and chemical amendments. Microb. Ecol. 64, 450–460 (2012).
    PubMed  Article  PubMed Central  Google Scholar 

    16.
    Hartmann, M., Frey, B., Mayer, J., Mader, P. & Widmer, F. Distinct soil microbial diversity under long-term organic and conventional farming. ISME J. 9, 1177–1194 (2015).
    PubMed  Article  PubMed Central  Google Scholar 

    17.
    Fierer, N. & Jackson, R. B. The diversity and biogeography of soil bacterial communities. Proc. Natl. Acad. Sci. USA. 103, 626–631 (2006).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    18.
    Bartram, A. K. et al. Exploring links between pH and bacterial community composition in soils from the Craibstone experimental farm. FEMS Microbiol. Ecol. 87, 403–415 (2014).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    19.
    Min, W. et al. Response of soil microbial community and diversity to increasing water salinity and nitrogen fertilization rate in an arid soil. Acta Agric. Scand. B Soil Plant Sci. 66, 117–126 (2016).
    CAS  Google Scholar 

    20.
    Lauber, C. L., Strickland, M. S., Bradford, M. A. & Fierer, N. The influence of soil properties on the structure of bacterial and fungal communities across land-use types. Soil Biol. Biochem. 40, 2407–2415 (2008).
    CAS  Article  Google Scholar 

    21.
    Lozupone, C. A. & Knight, R. Global patterns in bacterial diversity. Proc. Natl. Acad. Sci. USA. 104, 11436–11440 (2007).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    22.
    Zhang, X.-Y., Sui, Y.-Y., Zhang, X.-D., Meng, K. & Herbert, S. J. Spatial variability of nutrient properties in black soil of Northeast China. Pedosphere 17, 19–29 (2007).
    Article  Google Scholar 

    23.
    Griffiths, R. I. et al. The bacterial biogeography of British soils. Environ. Microbiol. 13, 1642–1654 (2011).
    PubMed  Article  PubMed Central  Google Scholar 

    24.
    Liu, J. et al. High throughput sequencing analysis of biogeographical distribution of bacterial communities in the black soils of northeast China. Soil Biol. Biochem. 70, 113–122 (2014).
    CAS  Article  Google Scholar 

    25.
    Kim, M. et al. Highly heterogeneous soil bacterial communities around Terra Nova Bay of northern Victoria, Land Antarctica. PLoS ONE 10, e0119966 (2015).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    26.
    Hermans, S. M. et al. Bacteria as emerging indicators of soil condition. Appl. Environ. Microbiol. 83, e02826-e12816 (2017).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    27.
    Barberan, A., Bates, S. T., Casamayor, E. O. & Fierer, N. Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J. 6, 343–351 (2012).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    28.
    Faust, K. & Raes, J. Microbial interactions: from networks to models. Nat. Rev. Microbiol. 10, 538–550 (2012).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    29.
    van der Heijden, M. G. & Hartmann, M. Networking in the plant microbiome. PLoS Biol. 14, e1002378 (2016).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    30.
    Shi, S. et al. The interconnected rhizosphere: High network complexity dominates rhizosphere assemblages. Ecol. Lett. 19, 926–936 (2016).
    PubMed  Article  PubMed Central  Google Scholar 

    31.
    Kim, J. M. et al. Soil pH and electrical conductivity are key edaphic factors shaping bacterial communities of greenhouse soils in Korea. J. Microbiol. 54, 838–845 (2016).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    32.
    Tripathi, B. et al. Spatial scaling effects on soil bacterial communities in Malaysian tropical forests. Microb. Ecol. 68, 247–258 (2014).
    PubMed  Article  PubMed Central  Google Scholar 

    33.
    Feng, M. et al. Interpreting distance-decay pattern of soil bacteria via quantifying the assembly processes at multiple spatial scales. MicrobiologyOpen 8, e00851 (2019).
    PubMed  PubMed Central  Google Scholar 

    34.
    Morlon, H. et al. A general framework for the distance-decay of similarity in ecological communities. Ecol. Lett. 11, 904–917 (2008).
    PubMed  PubMed Central  Article  Google Scholar 

    35.
    Figuerola, E. L. M. et al. Bacterial indicator of agricultural management for soil under no-till crop production. PLoS ONE 7, e51075 (2012).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    36.
    Jimenez-Bueno, N. G. et al. Bacterial indicator taxa in soils under different long-term agricultural management. J. Appl. Microbiol. 120, 921–933 (2016).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    37.
    Liesack, W., Schnell, S. & Revsbech, N. P. Microbiology of flooded rice paddies. FEMS Microbiol. Rev. 24, 625–645 (2000).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    38.
    Kang, S. S. et al. Status and change in chemical properties of polytunnel soil in Korea from 2000 to 2012. Korean J. Soil Sci. Fertil. 46, 641–646 (2013).
    CAS  Article  Google Scholar 

    39.
    Handley, K. M. et al. High-density PhyloChip profiling of stimulated aquifer microbial communities reveals a complex response to acetate amendment. FEMS Microbiol. Ecol. 81, 188–204 (2012).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    40.
    Ma, J. C., Ibekwe, A. M., Yang, C. H. & Crowley, D. E. Bacterial diversity and composition in major fresh produce growing soils affected by physiochemical properties and geographic locations. Sci. Total Environ. 563, 199–209 (2016).
    ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

    41.
    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 

    42.
    Sridevi, G. et al. Soil bacterial communities of a calcium-supplemented and a reference watershed at the Hubbard Brook experimental forest (HBEF), New Hampshire, USA. FEMS Microbiol. Ecol. 79, 728–740 (2012).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    43.
    Singh, D., Shi, L. & Adams, J. M. Bacterial diversity in the mountains of South-West China: Climate dominates over soil parameters. J. Microbiol. 51, 439–447 (2013).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    44.
    Miethling, R., Wieland, G., Backhaus, H. & Tebbe, C. C. Variation of microbial rhizosphere communities in response to crop secies, soil origin, and inoculation with Sinorhizobium meliloti L33. Microb. Ecol. 40, 43–56 (2000).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    45.
    De Caceres, M. & Legendre, P. Associations between species and groups of sites: Indices and statistical inference. Ecology 90, 3566–3574 (2009).
    PubMed  Article  PubMed Central  Google Scholar 

    46.
    Oyaizu, H., Debrunner-Vossbrinck, B., Mandelco, L., Studier, J. A. & Woese, C. R. The green non-sulfur bacteria: A deep branching in the eubacterial line of descent. Syst. Appl. Microbiol. 9, 47–53 (1987).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    47.
    Rappe, M. S. & Giovannoni, S. J. The uncultured microbial majority. Annu. Rev. Microbiol. 57, 369–394 (2003).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    48.
    Krzmarzick, M. J. et al. Natural niche for organohalide-respiring Chloroflexi. Appl. Environ. Microbiol. 78, 393–401 (2012).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    49.
    Speirs, L. B. M., Rice, D. T. F., Petrovski, S. & Seviour, R. J. The phylogeny, biodiversity, and ecology of the chloroflexi in activated sludge. Front. Microbiol.10 (2019).

    50.
    Janssen, P. H. Identifying the dominant soil bacterial taxa in libraries of 16S rRNA and 16S rRNA genes. Appl. Environ. Microbiol. 72, 1719–1728 (2006).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    51.
    Will, C. et al. Horizon-specific bacterial community composition of German grassland soils, as revealed by pyrosequencing-based analysis of 16S rRNA genes. Appl. Environ. Microbiol. 76, 6751–6759 (2010).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    52.
    Costello, E. K. & Schmidt, S. K. Microbial diversity in alpine tundra wet meadow soil: novel Chloroflexi from a cold, water-saturated environment. Environ. Microbiol. 8, 1471–1486 (2006).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    53.
    Lee, H. J., Jeong, S. E., Kim, P. J., Madsen, E. & Jeon, C. O. High resolution depth distribution of Bacteria, Archaea, methanotrophs, and methanogens in the bulk and rhizosphere soils of a flooded rice paddy. Front. Microbiol. 6, 639 (2015).
    PubMed  PubMed Central  Google Scholar 

    54.
    Ahn, J. H. et al. Dynamics of bacterial communities in rice field soils as affected by different long-term fertilization practices. J. Microbiol. 54, 724–731 (2016).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    55.
    Hernández, M., Conrad, R., Klose, M., Ma, K. & Lu, Y. Structure and function of methanogenic microbial communities in soils from flooded rice and upland soybean fields from Sanjiang plain NE China. Soil Biol. Biochem. 105, 81–91 (2017).
    Article  CAS  Google Scholar 

    56.
    Fierer, N., Bradford, M. A. & Jackson, R. B. Toward an ecological classification of soil bacteria. Ecology 88, 1354–1364 (2007).
    PubMed  Article  PubMed Central  Google Scholar 

    57.
    Jones, R. T. et al. A comprehensive survey of soil acidobacterial diversity using pyrosequencing and clone library analyses. ISME J. 3, 442–453 (2009).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    58.
    Navarrete, A. A. et al. Differential response of Acidobacteria subgroups to forest-to-pasture conversion and their biogeographic patterns in the western Brazilian Amazon. Front. Microbiol. 6, 1443 (2015).
    PubMed  PubMed Central  Article  Google Scholar 

    59.
    Leff, J. W. et al. Consistent responses of soil microbial communities to elevated nutrient inputs in grasslands across the globe. Proc. Natl. Acad. Sci. USA. 112, 10967–10972 (2015).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    60.
    Trivedi, P., Delgado-Baquerizo, M., Anderson, I. C. & Singh, B. K. Response of soil properties and microbial communities to agriculture: Implications for primary productivity and soil health indicators. Front. Plant Sci. 7, 990 (2016).
    PubMed  PubMed Central  Google Scholar 

    61.
    Radhakrishnan, R., Hashem, A. & Abd Allah, E. F. Bacillus: A biological tool for crop improvement through bio-molecular changes in adverse environments. Front. Physiol. 8, 667 (2017).
    PubMed  PubMed Central  Article  Google Scholar 

    62.
    Pujalte, M. J., Lucena, T., Ruvira, M. A., Arahal, D. R. & Macián, M. C. The Family Rhodobacteraceae. In The Prokaryotes (eds Rosenberg, E. et al.) 439–512 (Springer, Berlin, 2014).
    Google Scholar 

    63.
    Baldani, J. I. et al. The Family Rhodospirillaceae. In The Prokaryotes (eds Rosenberg, E. et al.) 533–618 (Springer, Berlin, 2014).
    Google Scholar 

    64.
    Karimi, B. et al. Biogeography of soil bacterial networks along a gradient of cropping intensity. Sci. Rep. 9, 3812 (2019).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    65.
    Hartman, K. et al. Cropping practices manipulate abundance patterns of root and soil microbiome members paving the way to smart farming. Microbiome 6, 14 (2018).
    PubMed  PubMed Central  Article  Google Scholar 

    66.
    Nielsen, M. N. & Winding, A. Microorganisms as indicators of soil health. National Environmental Research Institute, Denmark, Technical Report no. 388 (2002).

    67.
    Allison, L. E. Organic carbon in Methods of Soil Analysis, Part 2, Chemical and Microbiological Properties (ed Black, C. A.) 1367–1378 (American Society of Agronomy, 1965).

    68.
    National Institute of Agricultural Science and Technology (NIAST). Methods of analysis of soil and plant. (NIAST, 2000)

    69.
    Edgar, R. C. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998 (2013).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    70.
    Schloss, P. D. et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    71.
    R Core Team. R: a language and environment for statistical computing. https://www.R-project.org/ (2016).

    72.
    Dray, S. et al. Community ecology in the age of multivariate multiscale spatial analysis. Ecol. Monogr. 82, 257–275 (2012).
    Article  Google Scholar 

    73.
    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R Stat. Soc. Series B Stat. Methodol. 57, 289–300 (1995).
    MathSciNet  MATH  Google Scholar 

    74.
    Ogle, D. H. FSA: Fisheries stock analysis. R package version 0.8.13. (2017).

    75.
    Bastian, M., Heymann, S. & Jacomy, M. Gephi: an open source software for exploring and manipulating networks. International AAAI Conference on Weblogs and Social Media (2009).

    76.
    Deng, Y. et al. Molecular ecological network analyses. BMC Bioinform. 13, 113 (2012).
    Article  Google Scholar  More

  • in

    Experimentally-validated correlation analysis reveals new anaerobic methane oxidation partnerships with consortium-level heterogeneity in diazotrophy

    1.
    Knittel K, Boetius A. Anaerobic oxidation of methane: progress with an unknown process. Annu Rev Microbiol. 2009;63:311–34.
    CAS  PubMed  Article  Google Scholar 
    2.
    Reeburgh WS. Oceanic Methane Biogeochemistry. Chem Rev. 2007;107:486–513.
    CAS  PubMed  Article  Google Scholar 

    3.
    Orphan VJ, House CH, Hinrichs K-U, McKeegan KD, DeLong EF. Methane-consuming archaea revealed by directly coupled isotopic and phylogenetic analysis. Science. 2001;293:484–7.
    CAS  PubMed  Article  Google Scholar 

    4.
    Boetius A, Ravenschlag K, Schubert CJ, Rickert D, Widdel F, Gieseke A, et al. A marine microbial consortium apparently mediating anaerobic oxidation of methane. Nature. 2000;407:623.
    CAS  PubMed  Article  Google Scholar 

    5.
    McGlynn SE, Chadwick GL, Kempes CP, Orphan VJ. Single cell activity reveals direct electron transfer in methanotrophic consortia. Nature. 2015;526:531–5.
    CAS  PubMed  Article  Google Scholar 

    6.
    Scheller S, Yu H, Chadwick GL, McGlynn SE, Orphan VJ. Artificial electron acceptors decouple archaeal methane oxidation from sulfate reduction. Science. 2016;351:703–7.
    CAS  PubMed  Article  Google Scholar 

    7.
    Wegener G, Krukenberg V, Riedel D, Tegetmeyer HE, Boetius A. Intercellular wiring enables electron transfer between methanotrophic archaea and bacteria. Nature. 2015;526:587–90.
    CAS  PubMed  Article  Google Scholar 

    8.
    Dekas AE, Connon SA, Chadwick GL, Trembath-Reichert E, Orphan VJ. Activity and interactions of methane seep microorganisms assessed by parallel transcription and FISH-NanoSIMS analyses. ISME J. 2016;10:678–92.
    CAS  PubMed  Article  Google Scholar 

    9.
    Dekas AE, Poretsky RS, Orphan VJ. Deep-sea archaea fix and share nitrogen in methane-consuming microbial consortia. Science. 2009;326:422–6.
    CAS  PubMed  Article  Google Scholar 

    10.
    Dekas AE, Chadwick GL, Bowles MW, Joye SB, Orphan VJ. Spatial distribution of nitrogen fixation in methane seep sediment and the role of the ANME archaea. Environ Microbiol. 2014;16:3012–29.
    CAS  PubMed  Article  Google Scholar 

    11.
    Orphan VJ, Turk KA, Green AM, House CH. Patterns of 15N assimilation and growth of methanotrophic ANME-2 archaea and sulfate-reducing bacteria within structured syntrophic consortia revealed by FISH-SIMS. Environ Microbiol. 2009;11:1777–91.
    CAS  PubMed  Article  Google Scholar 

    12.
    Evans PN, Boyd JA, Leu AO, Woodcroft BJ, Parks DH, Hugenholtz P, et al. An evolving view of methane metabolism in the Archaea. Nat Rev Microbiol. 2019;17:219–32.
    CAS  PubMed  Article  Google Scholar 

    13.
    Krukenberg V, Riedel D, Gruber‐Vodicka HR, Buttigieg PL, Tegetmeyer HE, Boetius A, et al. Gene expression and ultrastructure of meso- and thermophilic methanotrophic consortia. Environ Microbiol. 2018;20:1651–66.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    14.
    Skennerton CT, Chourey K, Iyer R, Hettich RL, Tyson GW, Orphan VJ. Methane-fueled syntrophy through extracellular electron transfer: uncovering the genomic traits conserved within diverse bacterial partners of anaerobic methanotrophic archaea. mBio. 2017;8:e00530–17.
    PubMed  PubMed Central  Google Scholar 

    15.
    Schreiber L, Holler T, Knittel K, Meyerdierks A, Amann R. Identification of the dominant sulfate-reducing bacterial partner of anaerobic methanotrophs of the ANME-2 clade. Environ Microbiol. 2010;12:2327–40.
    CAS  PubMed  Google Scholar 

    16.
    Green-Saxena A, Dekas AE, Dalleska NF, Orphan VJ. Nitrate-based niche differentiation by distinct sulfate-reducing bacteria involved in the anaerobic oxidation of methane. ISME J. 2014;8:150–63.
    CAS  PubMed  Article  Google Scholar 

    17.
    Hinrichs K-U, Hayes JM, Sylva SP, Brewer PG, DeLong EF. Methane-consuming archaebacteria in marine sediments. Nature. 1999;398:802.
    CAS  PubMed  Article  Google Scholar 

    18.
    Hallam SJ, Girguis PR, Preston CM, Richardson PM, DeLong EF. Identification of methyl coenzyme M Reductase A (mcrA) genes associated with methane-oxidizing archaea. Appl Environ Microbiol. 2003;69:5483–91.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    19.
    Michaelis W, Seifert R, Nauhaus K, Treude T, Thiel V, Blumenberg M, et al. Microbial reefs in the black sea fueled by anaerobic oxidation of methane. Science. 2002;297:1013–5.
    CAS  PubMed  Article  Google Scholar 

    20.
    Knittel K, Lösekann T, Boetius A, Kort R, Amann R. Diversity and distribution of methanotrophic archaea at cold seeps. Appl Environ Microbiol. 2005;71:467–79.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    21.
    Orphan VJ, Hinrichs K-U, Ussler W, Paull CK, Taylor LT, Sylva SP, et al. Comparative analysis of methane-oxidizing archaea and sulfate-reducing bacteria in anoxic marine sediments. Appl Environ Microbiol. 2001;67:1922–34.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    22.
    Orphan VJ, House CH, Hinrichs K-U, McKeegan KD, DeLong EF. Multiple archaeal groups mediate methane oxidation in anoxic cold seep sediments. Proc Natl Acad Sci. 2002;99:7663–8.
    CAS  PubMed  Article  Google Scholar 

    23.
    Raghoebarsing AA, Pol A, Pas-Schoonen KT, van de, Smolders AJP, Ettwig KF, Rijpstra WIC, et al. A microbial consortium couples anaerobic methane oxidation to denitrification. Nature. 2006;440:918.
    CAS  PubMed  Article  Google Scholar 

    24.
    Haroon MF, Hu S, Shi Y, Imelfort M, Keller J, Hugenholtz P, et al. Anaerobic oxidation of methane coupled to nitrate reduction in a novel archaeal lineage. Nature. 2013;500:567–70.
    CAS  PubMed  Article  Google Scholar 

    25.
    Niemann H, Lösekann T, Beer D, de, Elvert M, Nadalig T, Knittel K, et al. Novel microbial communities of the Haakon Mosby mud volcano and their role as a methane sink. Nature. 2006;443:854.
    CAS  PubMed  Article  Google Scholar 

    26.
    Lösekann T, Knittel K, Nadalig T, Fuchs B, Niemann H, Boetius A, et al. Diversity and abundance of aerobic and anaerobic methane oxidizers at the Haakon Mosby Mud Volcano, Barents Sea. Appl Environ Microbiol. 2007;73:3348–62.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    27.
    Manz W, Eisenbrecher M, Neu TR, Szewzyk U. Abundance and spatial organization of gram-negative sulfate-reducing bacteria in activated sludge investigated by in situ probing with specific 16S rRNA targeted oligonucleotides. FEMS Microbiol Ecol. 1998;25:43–61.
    CAS  Article  Google Scholar 

    28.
    Nauhaus K, Albrecht M, Elvert M, Boetius A, Widdel F. In vitro cell growth of marine archaeal-bacterial consortia during anaerobic oxidation of methane with sulfate. Environ Microbiol. 2007;9:187–96.
    CAS  PubMed  Article  Google Scholar 

    29.
    Pernthaler A, Dekas AE, Brown CT, Goffredi SK, Embaye T, Orphan VJ. Diverse syntrophic partnerships from deep-sea methane vents revealed by direct cell capture and metagenomics. Proc Natl Acad Sci USA. 2008;105:7052–7.
    CAS  PubMed  Article  Google Scholar 

    30.
    Vigneron A, Cruaud P, Pignet P, Caprais J-C, Cambon-Bonavita M-A, Godfroy A, et al. Archaeal and anaerobic methane oxidizer communities in the Sonora Margin cold seeps, Guaymas Basin (Gulf of California). ISME J. 2013;7:1595–608.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    31.
    McGlynn SE, Chadwick GL, O’Neill A, Mackey M, Thor A, Deerinck TJ, et al. Subgroup characteristics of marine methane-oxidizing ANME-2 archaea and their syntrophic partners as revealed by integrated multimodal analytical microscopy. Appl Environ Microbiol. 2018;84:e00399–18.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    32.
    Treude T, Krüger M, Boetius A, Jørgensen BB. Environmental control on anaerobic oxidation of methane in the gassy sediments of Eckernförde Bay (German Baltic). Limnol Oceanogr. 2005;50:1771–86.
    CAS  Article  Google Scholar 

    33.
    Girguis PR, Orphan VJ, Hallam SJ, DeLong EF. Growth and methane oxidation rates of anaerobic methanotrophic archaea in a continuous-flow bioreactor. Appl Environ Microbiol. 2003;69:5472–82.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    34.
    Kleindienst S, Ramette A, Amann R, Knittel K. Distribution and in situ abundance of sulfate-reducing bacteria in diverse marine hydrocarbon seep sediments. Environ Microbiol. 2012;14:2689–710.
    CAS  PubMed  Article  Google Scholar 

    35.
    Holler T, Widdel F, Knittel K, Amann R, Kellermann MY, Hinrichs K-U, et al. Thermophilic anaerobic oxidation of methane by marine microbial consortia. ISME J. 2011;5:1946–56.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    36.
    Loy A, Lehner A, Lee N, Adamczyk J, Meier H, Ernst J, et al. Oligonucleotide Microarray for 16S rRNA Gene-Based Detection of All Recognized Lineages of Sulfate-Reducing Prokaryotes in the Environment. Appl Environ Microbiol. 2002;68:5064–81.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    37.
    Trembath-Reichert E, Case DH, Orphan VJ. Characterization of microbial associations with methanotrophic archaea and sulfate-reducing bacteria through statistical comparison of nested Magneto-FISH enrichments. PeerJ. 2016;4:e1913.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    38.
    Trembath-Reichert E, Green-Saxena A, Orphan VJ. Chapter Two—whole cell immunomagnetic enrichment of environmental microbial consortia using rRNA-targeted magneto-FISH. In: DeLong EF (eds). Methods in Enzymology. (Academic Press, San Diego, 2013) pp 21–44.

    39.
    Hatzenpichler R, Connon SA, Goudeau D, Malmstrom RR, Woyke T, Orphan VJ. Visualizing in situ translational activity for identifying and sorting slow-growing archaeal−bacterial consortia. Proc Natl Acad Sci. 2016;113:E4069–78.
    CAS  PubMed  Article  Google Scholar 

    40.
    Degnan PH, Ochman H. Illumina-based analysis of microbial community diversity. ISME J. 2012;6:183–94.
    CAS  PubMed  Article  Google Scholar 

    41.
    Friedman J, Alm EJ. Inferring correlation networks from genomic survey data. PLOS Comput Biol. 2012;8:e1002687.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    42.
    Kurtz ZD, Müller CL, Miraldi ER, Littman DR, Blaser MJ, Bonneau RA. Sparse and compositionally robust inference of microbial ecological networks. PLOS Comput Biol. 2015;11:e1004226.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    43.
    Schwager E, Mallick H, Ventz S, Huttenhower C. A Bayesian method for detecting pairwise associations in compositional data. PLOS Comput Biol. 2017;13:e1005852.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    44.
    Lima-Mendez G, Faust K, Henry N, Decelle J, Colin S, Carcillo F, et al. Determinants of community structure in the global plankton interactome. Science. 2015;348:1–9.
    Article  CAS  Google Scholar 

    45.
    Bohrmann G, Heeschen K, Jung C, Weinrebe W, Baranov B, Cailleau B, et al. Widespread fluid expulsion along the seafloor of the Costa Rica convergent margin. Terra Nova. 2002;14:69–79.
    Article  Google Scholar 

    46.
    Mau S, Sahling H, Rehder G, Suess E, Linke P, Soeding E. Estimates of methane output from mud extrusions at the erosive convergent margin off Costa Rica. Mar Geol. 2006;225:129–44.
    CAS  Article  Google Scholar 

    47.
    Sahling H, Masson DG, Ranero CR, Hühnerbach V, Weinrebe W, Klaucke I, et al. Fluid seepage at the continental margin offshore Costa Rica and southern Nicaragua. Geochem Geophys Geosyst. 2008;9:1–22.
    Article  Google Scholar 

    48.
    Glass JB, Yu H, Steele JA, Dawson KS, Sun S, Chourey K, et al. Geochemical, metagenomic and metaproteomic insights into trace metal utilization by methane-oxidizing microbial consortia in sulphidic marine sediments. Environ Microbiol. 2014;16:1592–611.
    CAS  PubMed  Article  Google Scholar 

    49.
    Case DH, Pasulka AL, Marlow JJ, Grupe BM, Levin LA, Orphan VJ. Methane seep carbonates host distinct, diverse, and dynamic microbial assemblages. mBio. 2015;6:1–12.
    CAS  Article  Google Scholar 

    50.
    Parada AE, Needham DM, Fuhrman JA. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol. 2016;18:1403–14.
    CAS  PubMed  Article  Google Scholar 

    51.
    Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    52.
    Mason OU, Case DH, Naehr TH, Lee RW, Thomas RB, Bailey JV, et al. Comparison of archaeal and bacterial diversity in methane seep carbonate nodules and host sediments, Eel River Basin and Hydrate Ridge, USA. Micro Ecol. 2015;70:766–84.
    CAS  Article  Google Scholar 

    53.
    Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.
    CAS  Article  Google Scholar 

    54.
    Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.
    CAS  PubMed  Article  Google Scholar 

    55.
    Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    56.
    Towns J, Cockerill T, Dahan M, Foster I, Gaither K, Grimshaw A, et al. XSEDE: accelerating scientific discovery. Comput Sci Eng. 2014;16:62–74.
    Article  CAS  Google Scholar 

    57.
    Miller MA, Pfeiffer W, Schwartz T. Creating the CIPRES Science Gateway for inference of large phylogenetic trees. In: Proceedings of the 2010 Gateway Computing Environments Workshop (GCE). (San Diego Supercomputing Center, San Diego, 2010) pp 1–8.

    58.
    Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    59.
    Eren AM, Esen ÖC, Quince C, Vineis JH, Morrison HG, Sogin ML, et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ. 2015;3:e1319.
    PubMed  PubMed Central  Article  Google Scholar 

    60.
    Campbell BJ, Yu L, Heidelberg JF, Kirchman DL. Activity of abundant and rare bacteria in a coastal ocean. Proc Natl Acad Sci. 2011;108:12776–81.
    CAS  PubMed  Article  Google Scholar 

    61.
    Letunic I, Bork P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 2019;47:W256–9.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    62.
    Ludwig W, Strunk O, Westram R, Richter L, Meier H, Yadhukumar, et al. ARB: a software environment for sequence data. Nucleic Acids Res. 2004;32:1363–71.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    63.
    Daims H, Stoecker K, Wagner M, Stoecker K, Wagner M. Fluorescence in situ hybridization for the detection of prokaryotes. Mol Microbial Ecol. https://www.taylorfrancis.com/. Accessed 15 Jul 2019.

    64.
    Glöckner FO, Fuchs BM, Amann R. Bacterioplankton compositions of lakes and oceans: a first comparison based on fluorescence in situ hybridization. Appl Environ Microbiol. 1999;65:3721–6.
    PubMed  PubMed Central  Article  Google Scholar 

    65.
    Dirks RM, Pierce NA. Triggered amplification by hybridization chain reaction. Proc Natl Acad Sci. 2004;101:15275–8.
    CAS  PubMed  Article  Google Scholar 

    66.
    Choi HMT, Beck VA, Pierce NA. Next-generation in situ hybridization chain reaction: higher gain, lower cost, greater durability. ACS Nano. 2014;8:4284–94.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    67.
    Yamaguchi T, Kawakami S, Hatamoto M, Imachi H, Takahashi M, Araki N, et al. In situ DNA-hybridization chain reaction (HCR): a facilitated in situ HCR system for the detection of environmental microorganisms. Environ Microbiol. 2015;17:2532–41.
    CAS  PubMed  Article  Google Scholar 

    68.
    Choi HMT, Schwarzkopf M, Fornace ME, Acharya A, Artavanis G, Stegmaier J, et al. Third-generation in situ hybridization chain reaction: multiplexed, quantitative, sensitive, versatile, robust. Development. 2018;145:1–10.
    Article  CAS  Google Scholar 

    69.
    Bolte S, Cordelières FP. A guided tour into subcellular colocalization analysis in light microscopy. J Microsc. 2006;224:213–32.
    CAS  PubMed  Article  Google Scholar 

    70.
    Dabundo R, Lehmann MF, Treibergs L, Tobias CR, Altabet MA, Moisander PA, Granger J. The contamination of commercial 15N2 gas stocks with 15N-labeled nitrate and ammonium and consequences for nitrogen fixation measurements. PLoS ONE. 2014;9:e110335.
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    71.
    Cline JD. Spectrophotometric determination of hydrogen sulfide in natural waters1. Limnol Oceanogr. 1969;14:454–8.
    CAS  Article  Google Scholar 

    72.
    Dekas AE, Orphan VJ. Chapter Twelve—identification of diazotrophic microorganisms in marine sediment via fluorescence in situ hybridization coupled to nanoscale secondary ion mass spectrometry (FISH-NanoSIMS). In: Klotz MG, editor. Methods in enzymology. Academic Press; 2011. p 281–305.

    73.
    Polerecky L, Adam B, Milucka J, Musat N, Vagner T, Kuypers MMM. Look@NanoSIMS-a tool for the analysis of nanoSIMS data in environmental microbiology. Environ Microbiol. 2012;14:1009–23.
    CAS  PubMed  Article  Google Scholar 

    74.
    Berry D, Widder S. Deciphering microbial interactions and detecting keystone species with co-occurrence networks. Front Microbiol. 2014;5:1–14.
    Article  Google Scholar 

    75.
    David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE, Wolfe BE, et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature. 2014;505:559–63.
    CAS  Article  Google Scholar 

    76.
    Leone V, Gibbons SM, Martinez K, Hutchison AL, Huang EY, Cham CM, et al. Effects of diurnal variation of gut microbes and high-fat feeding on host circadian clock function and metabolism. Cell Host Microbe. 2015;17:681–9.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    77.
    Ruff SE, Biddle JF, Teske AP, Knittel K, Boetius A, Ramette A. Global dispersion and local diversification of the methane seep microbiome. Proc Natl Acad Sci. 2015;112:4015–20.
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    78.
    Fruchterman TMJ, Reingold EM. Graph drawing by force-directed placement. Softw Pr Exp. 1991;21:1129–64.
    Article  Google Scholar 

    79.
    Moody J, White DR. Structural cohesion and embeddedness: a hierarchical concept of social groups. Am Socio Rev. 2003;68:103–27.
    Article  Google Scholar 

    80.
    Gu Z, Gu L, Eils R, Schlesner M, Brors B. Circlize implements and enhances circular visualization in R. Bioinformatics. 2014;30:2811–2.
    CAS  PubMed  Article  Google Scholar 

    81.
    Nikolakakis K, Lehnert E, McFall-Ngai MJ, Ruby EG. Use of hybridization chain reaction-fluorescent in situ hybridization to track gene expression by both partners during initiation of symbiosis. Appl Environ Microbiol. 2015;81:4728–35.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    82.
    DePas WH, Starwalt-Lee R, Sambeek LV, Kumar SR, Gradinaru V, Newman DK. Exposing the three-dimensional biogeography and metabolic states of pathogens in cystic fibrosis sputum via hydrogel embedding, clearing, and rRNA Labeling. mBio. 2016;7:1–11.
    Article  Google Scholar 

    83.
    Imachi H, Nobu MK, Nakahara N, Morono Y, Ogawara M, Takaki Y, et al. Isolation of an archaeon at the prokaryote–eukaryote interface. Nature. 2020;577:519–25.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    84.
    Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ. Microbiome datasets are compositional: and this is not optional. Front Microbiol. 2017;8:1–6.
    Article  Google Scholar 

    85.
    Sampayo EM, Ridgway T, Bongaerts P, Hoegh-Guldberg O. Bleaching susceptibility and mortality of corals are determined by fine-scale differences in symbiont type. Proc Natl Acad Sci. 2008;105:10444–9.
    CAS  PubMed  Article  Google Scholar 

    86.
    Parkinson JE, Baumgarten S, Michell CT, Baums IB, LaJeunesse TC, Voolstra CR. Gene expression variation resolves species and individual strains among coral-associated dinoflagellates within the genus symbiodinium. Genome Biol Evol. 2016;8:665–80.
    PubMed  PubMed Central  Article  Google Scholar 

    87.
    Barshis DJ, Ladner JT, Oliver TA, Palumbi SR. Lineage-specific transcriptional profiles of Symbiodinium spp. unaltered by heat stress in a coral host. Mol Biol Evol. 2014;31:1343–52.
    CAS  PubMed  Article  Google Scholar 

    88.
    Kapili BJ, Barnett SE, Buckley DH, Dekas AE. Evidence for phylogenetically and catabolically diverse active diazotrophs in deep-sea sediment. ISME J. 2020;14:971–83.
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    89.
    Klawonn I, Eichner MJ, Wilson ST, Moradi N, Thamdrup B, Kümmel S, et al. Distinct nitrogen cycling and steep chemical gradients in Trichodesmium colonies. ISME J. 2020;14:399–412.
    CAS  PubMed  Article  Google Scholar 

    90.
    Petroff AP, Wu T-D, Liang B, Mui J, Guerquin-Kern J-L, Vali H, et al. Reaction–diffusion model of nutrient uptake in a biofilm: Theory and experiment. J Theor Biol. 2011;289:90–5.
    CAS  PubMed  Article  Google Scholar 

    91.
    Dekas AE, Fike DA, Chadwick GL, Green‐Saxena A, Fortney J, Connon SA, et al. Widespread nitrogen fixation in sediments from diverse deep-sea sites of elevated carbon loading. Environ Microbiol. 2018;20:4281–96.
    CAS  PubMed  Article  Google Scholar 

    92.
    Knapp AN. The sensitivity of marine N2 fixation to dissolved inorganic nitrogen. Front Microbiol. 2012;3:1–14.
    Google Scholar 

    93.
    Bertics VJ, Löscher CR, Salonen I, Dale AW, Gier J, Schmitz RA, et al. Occurrence of benthic microbial nitrogen fixation coupled to sulfate reduction in the seasonally hypoxic Eckernförde Bay, Baltic Sea. Biogeosciences. 2013;10:1243–58.
    CAS  Article  Google Scholar 

    94.
    Gier J, Sommer S, Löscher CR, Dale AW, Schmitz RA, Treude T. Nitrogen fixation in sediments along a depth transect through the Peruvian oxygen minimum zone. Biogeosciences. 2016;13:4065–80.
    CAS  Article  Google Scholar 

    95.
    Ackermann M. A functional perspective on phenotypic heterogeneity in microorganisms. Nat Rev Microbiol. 2015;13:497–508.
    CAS  PubMed  Article  Google Scholar 

    96.
    Schreiber F, Littmann S, Lavik G, Escrig S, Meibom A, Kuypers MMM, et al. Phenotypic heterogeneity driven by nutrient limitation promotes growth in fluctuating environments. Nat Microbiol. 2016;1:1–7.
    Article  CAS  Google Scholar 

    97.
    Masuda T, Inomura K, Takahata N, Shiozaki T, Yuji S. Heterogeneous nitrogen fixation rates confer energetic advantage and expanded ecological niche of unicellular diazotroph populations. Commun Biol. 2020;3:1–12.
    Article  CAS  Google Scholar 

    98.
    Raymond J, Siefert JL, Staples CR, Blankenship RE. The natural history of nitrogen fixation. Mol Biol Evol. 2004;21:541–54.
    CAS  PubMed  Article  Google Scholar  More

  • in

    Coexisting with sharks: a novel, socially acceptable and non-lethal shark mitigation approach

    1.
    Thirgood, S., Woodroffe, R. & Rabinowitz, A. The impact of human–wildlife conflict on human lives and livelihoods. In People and Wildlife, Conflict or Co-existence? Conservation Biology (eds Rabinowitz, A. et al.) 13–26 (Cambridge University Press, Cambridge, 2005).
    Google Scholar 
    2.
    Nyhus, P. J. Human-wildlife conflict and coexistence. Annu. Rev. Environ. Resour. 41, 143–171. https://doi.org/10.1146/annurev-environ-110615-085634 (2016).
    Article  Google Scholar 

    3.
    Curtis, T. et al. Responding to the risk of white shark attack: updated statistics, prevention, control methods, and recommendations. In Global Perspectives on the Biology and Life History of the White SharkEdition: First edition, pp 477–509 (ed. Domeier, M. L.) (CRC Press Taylor and Francis, Boca Raton, FL, 2012).
    Google Scholar 

    4.
    Sillero-Zubiri, C. et al. (eds) Canids: Foxes, Wolves, Jackals, and Dogs: Status Survey and Conservation Action Plan 430 (Gland, Cambridge, 2004).
    Google Scholar 

    5.
    Soulé, M. The, “New Conservation”. Conserv. Biol. 27, 895–897. https://doi.org/10.1111/cobi.12147 (2013).
    Article  PubMed  Google Scholar 

    6.
    Gibbs, L. & Warren, A. Transforming shark hazard policy: learning from ocean-users and shark encounter in Western Australia. Mar. Policy 58, 116–124. https://doi.org/10.1016/j.marpol.2015.04.014 (2015).
    Article  Google Scholar 

    7.
    McCagh, C., Sneddon, J. & Blache, D. Killing sharks: the media’s role in public and political response to fatal human–shark interactions. Mar. Policy 62, 271–278. https://doi.org/10.1016/j.marpol.2015.09.016 (2015).
    Article  Google Scholar 

    8.
    McPhee, D. Unprovoked shark bites: are they becoming more prevalent?. Coast. Manag. 42, 478–492 (2014).
    Article  Google Scholar 

    9.
    Chapman, B. K. & McPhee, D. Global shark attack hotspots: identifying underlying factors behind increased unprovoked shark bite incidence. Ocean Coast. Manag. 133, 72–84. https://doi.org/10.1016/j.ocecoaman.2016.09.010 (2016).
    Article  Google Scholar 

    10.
    Lagabrielle, E. et al. Environmental and anthropogenic factors affecting the increasing occurrence of shark-human interactions around a fast-developing Indian Ocean island. Sci. Rep. 8, 3676. https://doi.org/10.1038/s41598-018-21553-0 (2018).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    11.
    Stevens, J. D., Bonfil, R., Dulvy, N. K. & Walker, P. A. The effects of fishing on sharks, rays, and chimaeras (chondrichthyans), and the implications for marine ecosystems. ICES J. Mar. Sci. 57, 476–494. https://doi.org/10.1006/jmsc.2000.0724 (2000).
    Article  Google Scholar 

    12.
    Roff, G., Brown, C. J., Priest, M. A. & Mumby, P. J. Decline of coastal apex shark populations over the past half century. Commun. Biol. 1, 223. https://doi.org/10.1038/s42003-018-0233-1 (2018).
    Article  PubMed  PubMed Central  Google Scholar 

    13.
    Gibbs, L. et al. Effects and effectiveness of lethal shark hazard management: the Shark Meshing (Bather Protection) Program, NSW, Australia. People Nat. 2, 189–203. https://doi.org/10.1002/pan3.10063 (2020).
    Article  Google Scholar 

    14.
    Berkes, F., Folke, C. & Colding, J. Linking Social and Ecological Systems: Management Practices and Social Mechanisms for Building Resilience (Cambridge University Press, Cambridge, 1998).
    Google Scholar 

    15.
    Green, M., Ganassin, C. & Reid, D. D. Report into the NSW Shark Meshing (Bather Protection) Program: Incorporating a Review of the Existing Program and Environmental Assessment/NSW Dept of Primary Industries (Department of Primary Industries DPI Fisheries Conservation and Aquaculture Branch, Orange, NSW, 2009).
    Google Scholar 

    16.
    Cliff, G. & Dudley, S. F. J. Reducing the environmental impact of shark-control programs: a case study from KwaZulu-Natal, South Africa. Mar. Freshw. Res. 62, 700–709. https://doi.org/10.1071/MF10182 (2011).
    CAS  Article  Google Scholar 

    17.
    Holland, K. N., Wetherbee, B. M., Lowe, C. G. & Meyer, C. G. Movements of tiger sharks (Galeocerdo cuvier) in coastal Hawaiian waters. Mar. Biol. 134, 665–673. https://doi.org/10.1007/s002270050582 (1999).
    Article  Google Scholar 

    18.
    Wetherbee, B., Lowe, C. & Crow, G. A review of shark control in Hawaii with recommendations for future research. Pac. Sci. 48, 95–115 (1994).
    Google Scholar 

    19.
    Neff, C. L. & Yang, J. Y. H. Shark bites and public attitudes: policy implications from the first before and after shark bite survey. Mar. Policy 38, 545–547. https://doi.org/10.1016/j.marpol.2012.06.017 (2013).
    Article  Google Scholar 

    20.
    McPhee, D. P. Likely Effectiveness of Netting or Other Capture Programs as a Shark Hazard Mitigation Strategy Under Western Australian Conditions (Western Australia Department of Fisheries, Perth, 2012).
    Google Scholar 

    21.
    Lemahieu, A. et al. Human-shark interactions: The case study of Reunion island in the south-west Indian Ocean. Ocean Coast. Manag. 136, 73–82. https://doi.org/10.1016/j.ocecoaman.2016.11.020 (2017).
    Article  Google Scholar 

    22.
    Simmons, P. & Mehmet, M. I. Shark management strategy policy considerations: Community preferences, reasoning and speculations. Mar. Policy 96, 111–119. https://doi.org/10.1016/j.marpol.2018.08.010 (2018).
    Article  Google Scholar 

    23.
    Robbins, W. D., Peddemors, V. M., Kennelly, S. J. & Ives, M. C. Experimental evaluation of shark detection rates by aerial observers. PLoS ONE 9, e83456. https://doi.org/10.1371/journal.pone.0083456 (2014).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    24.
    Kock, A. A. et al. Shark spotters: a pioneering shark safety program in Cape Town, South Africa. In Global Perspectives on the Biology and Life History of the Great White Shark (ed. Domeier, M.) 447–466 (CRC Press, Boca Raton, FL, 2012).
    Google Scholar 

    25.
    Engelbrecht, T., Kock, A., Waries, S. & O’Riain, M. J. Shark spotters: successfully reducing spatial overlap between white sharks (Carcharodon carcharias) and recreational water users in False Bay, South Africa. PLoS ONE 12, e0185335. https://doi.org/10.1371/journal.pone.0185335 (2017).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    26.
    Stokes, D. et al. Beach-user perceptions and attitudes towards drone surveillance as a shark-bite mitigation tool. Mar. Policy 120, 104127. https://doi.org/10.1016/j.marpol.2020.104127 (2020).
    Article  Google Scholar 

    27.
    Colefax, A. P., Butcher, P. A. & Kelaher, B. P. The potential for unmanned aerial vehicles (UAVs) to conduct marine fauna surveys in place of manned aircraft. ICES J. Mar. Sci. 75, 1–8. https://doi.org/10.1093/icesjms/fsx100 (2018).
    Article  Google Scholar 

    28.
    Carter, N. H. & Linnell, J. D. C. Co-adaptation is key to coexisting with large carnivores. Trends Ecol. Evol. 31, 575–578. https://doi.org/10.1016/j.tree.2016.05.006 (2016).
    Article  PubMed  Google Scholar 

    29.
    Althoff, W. F. Sky Ships: A History of the Airship in the United States Navy. Vol. 25th anniversary edition (The Naval Institute Press, Annapolis, 2016).
    Google Scholar 

    30.
    Hain, J. H. W. Lighter-than-air platforms (blimps and aerostats) for oceanographic and atmospheric research and monitoring in OCEANS 2000 MTS/IEEE Conference and Exhibition.1933–1936.

    31.
    Hodgson, A. BLIMP-CAM: aerial video observations of marine mammals. Mar. Technol. Soc. J. 41, 39–43 (2007).
    Article  Google Scholar 

    32.
    Nosal, A. P. et al. Demography and movement patterns of leopard sharks (Triakis semifasciata) aggregating near the head of a submarine canyon along the open coast of southern California, USA. Environ. Biol. Fish. 96, 865–878. https://doi.org/10.1007/s10641-012-0083-5 (2012).
    Article  Google Scholar 

    33.
    Adams, K., Broad, A., Ruiz-García, D. & Davis, A. R. Continuous wildlife monitoring using blimps as an aerial platform: a case study observing marine megafauna. Austral. Zool. 40(3), 407–415. https://doi.org/10.7882/AZ.2020.004 (2020).
    Article  Google Scholar 

    34.
    Sandbrook, C. The social implications of using drones for biodiversity conservation. Ambio 44, 636–647. https://doi.org/10.1007/s13280-015-0714-0 (2015).
    Article  PubMed  PubMed Central  Google Scholar 

    35.
    Fox, S. J. The rise of the drones: framework and governance—why risk it!. J. Air Law Commerce 82, 683 (2017).
    Google Scholar 

    36.
    Linchant, J., Lisein, J., Semeki, J., Lejeune, P. & Vermeulen, C. Are unmanned aircraft systems (UASs) the future of wildlife monitoring? A review of accomplishments and challenges. Mammal Rev. 45, 239–252. https://doi.org/10.1111/mam.12046 (2015).
    Article  Google Scholar 

    37.
    Gururatsakul, S., Gibbins, D., Kearney, D. & Lee, I. Shark detection using optical image data from a mobile aerial platform in 2010 25th International Conference of Image and Vision Computing New Zealand. 1–8.

    38.
    Gorkin, R. et al. Sharkeye: real-time autonomous personal shark alerting via aerial surveillance. Drones https://doi.org/10.3390/drones4020018 (2020).
    Article  Google Scholar 

    39.
    Kammler, M. & Schernewski, G. Spatial and temporal analysis of beach tourism using webcam and aerial photographs. Coastline Rep. 2, 121–128 (2004).
    Google Scholar 

    40.
    Moreno, A., Amelung, B. & Santamarta, L. Linking beach recreation to weather conditions: a case study in Zandvoort, Netherlands. Tour. Mar. Environ. 5(2–3), 111–119 (2008).
    Article  Google Scholar 

    41.
    Ryan, L. A., Meeuwig, J. J., Hemmi, J. M., Collin, S. P. & Hart, N. S. It is not just size that matters: shark cruising speeds are species-specific. Mar. Biol. 162, 1307–1318. https://doi.org/10.1007/s00227-015-2670-4 (2015).
    Article  Google Scholar 

    42.
    Butcher, P. et al. Beach safety: can drones provide a platform for sighting sharks?. Wildl. Res. 46, 701–712 (2019).
    Article  Google Scholar 

    43.
    Robbins, W. D., Peddemors, V. M. & Kennelly, S. J. Assessment of shark sighting rates by aerial beach patrols Vol. 38 (NSW Department of Primary Industries Cronulla, NSW Australia, 2012).
    Google Scholar 

    44.
    Westgate, A. J., Koopman, H. N., Siders, Z. A., Wong, S. N. P. & Ronconi, R. A. Population density and abundance of basking sharks Cetorhinus maximus in the lower Bay of Fundy, Canada. Endanger. Species Res. 23, 177–185. https://doi.org/10.3354/esr00567 (2014).
    Article  Google Scholar 

    45.
    Kelaher, B. P., Peddemors, V. M., Hoade, B., Colefax, A. P. & Butcher, P. A. Comparison of sampling precision for nearshore marine wildlife using unmanned and manned aerial surveys. J. Unmanned Veh. Syst. https://doi.org/10.1139/juvs-2018-0023 (2020).
    Article  Google Scholar 

    46.
    Colefax, A. P., Butcher, P. A., Pagendam, D. E. & Kelaher, B. P. Reliability of marine faunal detections in drone-based monitoring. Ocean Coast. Manag. 174, 108–115. https://doi.org/10.1016/j.ocecoaman.2019.03.008 (2019).
    Article  Google Scholar 

    47.
    Pepin-Neff, C. In Sharks: Conservation, Governance and Management (eds Techera, E. J. & Klein, N.) 107–131 (Routledge, Oxon, 2014).
    Google Scholar 

    48.
    Crossley, R., Collins, C. M., Sutton, S. G. & Huveneers, C. Public perception and understanding of shark attack mitigation measures in Australia. Human Dimens. Wildl. 19, 154–165. https://doi.org/10.1080/10871209.2014.844289 (2014).
    Article  Google Scholar 

    49.
    Gray, G. M. E. & Gray, C. A. Beach-user attitudes to shark bite mitigation strategies on coastal beaches; Sydney, Australia. Human Dimens. Wildl. 22, 282–290. https://doi.org/10.1080/10871209.2017.1295491 (2017).
    Article  Google Scholar 

    50.
    Huveneers, C. et al. Effectiveness of five personal shark-bite deterrents for surfers. PeerJ 6, e5554. https://doi.org/10.7717/peerj.5554 (2018).
    Article  PubMed  PubMed Central  Google Scholar 

    51.
    Anonymous. Dorsal, https://www.dorsalwatch.com/ (2018).

    52.
    Anonymous. SharkSmart, https://www.sharksmart.nsw.gov.au/ (2018).

    53.
    Anonymous. SharkSmart, https://www.sharksmart.com.au/ (2018).

    54.
    Anonymous. SharkMate, https://digitallivinglab.uow.edu.au/portfolio/sharkmate-app/ (2018).

    55.
    Anonymous. Sharks Spotters, https://sharkspotters.org.za/ (2018).

    56.
    Fretwell, P. T., Staniland, I. J. & Forcada, J. Whales from space: counting southern right whales by satellite. PLoS ONE 9, e88655. https://doi.org/10.1371/journal.pone.0088655 (2014).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    57.
    Hodgson, A., Kelly, N. & Peel, D. Unmanned aerial vehicles (UAVs) for surveying marine fauna: a dugong case study. PLoS ONE 8, e79556. https://doi.org/10.1371/journal.pone.0079556 (2013).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    58.
    Joyce, K. E., Duce, S., Leahy, S. M., Leon, J. & Maier, S. W. Principles and practice of acquiring drone-based image data in marine environments. Mar. Freshw. Res. https://doi.org/10.1071/mf17380 (2019).
    Article  Google Scholar 

    59.
    Kiszka, J. J. & Heithaus, M. R. Using aerial surveys to investigate the distribution, abundance, and behavior of sharks and rays. In Shark Research: Emerging Technologies and Applications for the Field and Laboratory (eds Carrier, J. C. et al.) (CRC Press, Boca Raton, FL, 2018).
    Google Scholar 

    60.
    R Development Core Team. R: A Language and Environment for Statistical Computing,https://www.R-project.org (2008).

    61.
    Bates, D., Maechler, M. & Bolker, B. lme4: Linear Mixed-Effects Models Using S4 Classes, https://cran.r-project.org/web/packages/lme4/index.html (2012).

    62.
    Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biometric. J. 50(3), 346–363 (2008).
    MathSciNet  Article  Google Scholar 

    63.
    Hothorn, T. Bretz, F., Westfall, P., Heiberger, R. M., Schuetzenmeister, A., Scheibe, S. & Hothorn, M. T. multcomp: Simultaneous Inference in General Parametric Models, https://cran.stat.sfu.ca/web/packages/multcomp/multcomp.pdf (2016).

    64.
    Stanislaw, H. & Todorov, N. Calculation of signal detection theory measures. Behav. Res. Methods Instrum. Comput. 31, 137–149. https://doi.org/10.3758/BF03207704 (1999).
    CAS  Article  PubMed  Google Scholar 

    65.
    Macmillan, N. & Kaplan, H. L. Detection theory analysis of group data. Estimating sensitivity from average hit and false-alarm rates. Psychol. Bull. 98(1), 185 (1985).
    CAS  Article  Google Scholar  More

  • in

    Georgina Mace (1953–2020)

    OBITUARY
    15 October 2020

    Pioneer of biodiversity accounting who overhauled the Red List of threatened species.

    Nathalie Pettorelli

    Nathalie Pettorelli, a senior research fellow, started at the Institute of Zoology, London under Georgina’s directorship; they co-supervised a PhD student at Imperial College London.
    Contact

    Search for this author in:

    Credit: Jussi Puikkonen/KNAW

    Georgina Mace shaped two cornerstones of modern ecology and conservation. One was the global inventory of species threatened with extinction, the International Union for Conservation of Nature (IUCN) Red List. The second was the United Nations Millennium Ecosystem Assessment. One of the sharpest minds of her generation, she strove to document and stem biodiversity loss with analytical rigour and multidisciplinary approaches. She died on 19 September, aged 67.
    Throughout her career, Mace developed tools for evidence-based policymaking. Before her, the Red List was based on nominations from experts rather than data, undermining confidence in its accuracy. She devised criteria to standardize assessments. The Red List is now the most used and trusted source for assessing trends in global biodiversity.
    Mace was born in London in 1953. She studied zoology at the University of Liverpool, UK, before doing a PhD in the 1970s at the University of Sussex in Brighton, UK, where John Maynard Smith was pioneering mathematical approaches to evolutionary ecology. As a postdoc at the Smithsonian Institution in Washington DC, she studied the impacts of inbreeding on captive animals.
    In 1983, she joined the Institute of Zoology, the research arm of the Zoological Society of London, where she remained for 23 years, latterly as director from 2000 to 2006. There, Mace continued to work on the genetic management of zoological collections and small populations. Her findings informed the conservation status of several species, including the western lowland gorilla (Gorilla gorilla gorilla), and highlighted the value of reproductive technology in managing captive populations of endangered species, such as the Arabian oryx (Oryx leucoryx) and Przewalski’s horse (Equus przewalskii). She became increasingly interested in population viability, extinction risk and setting conservation priorities.
    In 1991, this led her, together with US population biologist Russell Lande, to question the IUCN categories of threats and the associated nomination process as being largely subjective. They suggested three categories: critical, endangered and vulnerable. These they defined in terms of the probability of a species becoming extinct within a specific period, such as five years or two generations. They drew up standardized criteria based on population-biology theory. These included, for example, total effective population size, the population trend over the past five years and observed or projected habitat loss. Mace later introduced, among other things, categories for species that are not currently under threat. This work ultimately defined the categories that the IUCN uses now.
    In 2006, Mace became director of the NERC Centre for Population Biology at Imperial College London. There, she worked on the definition of biodiversity targets and assessing species’ vulnerability to climate change. She also studied the link between biodiversity and ecosystem services — the benefits that humans draw from nature, such as carbon sequestration, medicines or waste decomposition.
    From 2012, as founding director of the Centre for Biodiversity and Environment Research at University College London, she developed an interest in natural-capital accounting, the process of calculating the total stocks and flows of natural resources and services in an ecosystem or region. Her blending of economics and ecological theory to define a risk register for natural capital helped to provide an effective focus for monitoring and data gathering. It also contributed to a common understanding of priorities across fields.
    Mace bridged the gaps between genetics, population ecology and macroecology, sub-disciplines in which she regularly supervised students, networked and published. She also demonstrated the importance of conservationists engaging with researchers in other disciplines, such as climate science, economics and social science. She excelled in building consensus, a key step towards evidence-based policy.
    Mace was coordinating lead author for biodiversity on the Millennium Ecosystem Assessment, launched in 2001, which demonstrated that rapidly growing demand for food, fresh water, timber, fibre and fuel resulted in a large and largely irreversible loss in biodiversity. She supported the development of assessments for the biodiversity target of the UN Convention on Biological Diversity in 2010 and, most recently, acted as review editor for the Global Assessment of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. She held similarly pivotal roles at the national level, on UK climate and environmental assessments.
    She broke several glass ceilings. Mace was the first president of the international Society for Conservation Biology from outside North America, and the first female president of the British Ecological Society. Her many awards and honours included a fellowship of the Royal Society and, in 2016, she was made a dame.
    Georgina was a role model: firm but fair, collaborative, reliable, unassuming, approachable — the kind of critical friend we all need. She supported the career progression of numerous ecologists and influenced many more. She’d nominate you for a post even when you didn’t think she had noticed your work; she’d make a witty remark in the middle of a heated discussion. Few knew that she had cancer. Never one to make a fuss about herself, nine days before she died, she published a paper on habitat conversion and biodiversity loss (D. Leclère et al. Nature 585, 551–556; 2020). Her death leaves a void: she will be sorely missed.

    Nature 586, 495 (2020)

    Latest on:

    Biodiversity

    An essential round-up of science news, opinion and analysis, delivered to your inbox every weekday.

    Related Articles More

  • in

    Achieving fast start-up of anammox process by promoting the growth of anammox bacteria with FeS addition

    Effects of FeS on nitrogen removal
    The start-up period could be divided into two phases based on the operating strategy of the reactor, as illustrated in Table 1. The first phase was characterized by high HRT and low substrate concentration (days 0–18), in which the HRT was 48 h and the concentrations of influent NH4+-N and NO2−-N were 50 and 60 mg L−1, respectively. The second phase was characterized by low HRT and high substrate concentration (days 24–68), in which the HRT was 36 h and the theoretical concentrations of influent NH4+-N and NO2−-N were 100 and 120 mg L−1, respectively.
    Table 1 Operational conditions of R1 and R2 under different phases.
    Full size table

    The effluent ammonium concentration was significantly higher than that of influent at the beginning of the reactor operation shown in Fig. 1a. On the first day, the effluent NH4+-N concentration of R1 and R2 reached 106.0 and 80.6 mg L−1, respectively, nearly twice as high as the influent NH4+-N concentration. This is mainly due to the fact that some microorganisms were unable to adapt to the new environmental conditions, inducing cellular lysis21. At the same time, effluent NO2−-N concentration of R1 and R2 on the fourth day were 18.4 and 17.3 mg L−1, respectively, with the removal efficiency of more than 70% (Fig. 1b); and NO3−-N accumulated in the effluent. The high-throughput results showed that Nitrospirae, which contained massive nitrite-oxidizing bacteria (NOB), accounted for a higher proportion in the inoculation sludge (Supplementary Fig. 1)22. qPCR results also indicated that NOB abundance was higher in the inoculation sludge as shown in the section “Effect of FeS on functional bacteria abundance”. Therefore, the removal of NO2−-N in the beginning might be attributed to the role of nitrification. Denitrification also might promote the decrease of NO2−-N through using the organic matter which was released by decay of biomass23. From day 7 to day 10, effluent NH4+-N of R1 and R2 decreased rapidly from 38.1 and 49.4 mg L−1 to 6.8 and 6.8 mg L−1, respectively, however the removal rate of NO2−-N did not change much. From day 1 to day 18, the accumulation of NO3−-N in R1 and R2 gradually decreased from 10 mg L−1 to 0 mg L−1. These phenomena indicated that NOB was gradually eliminated in the low-oxygen environment and the activity of anammox bacteria was increasing. In addition, microbial metabolism and decay of biomass will release organic carbon, which can be used as carbon sources by denitrifying bacteria23. From day 4 to day 18, the total nitrogen removal efficiency (TNRE) of R1 and R2 increased from 30.4% and 22.2% to 96.0% and 98.3%, respectively. On day 18, the values of removed NO2−-N/NH4+-N and produced NO3−-N/removed NH4+-N were 1.14 and 0 in R1 while these were 1.17 and 0 in R2, which was the result of the combined action of nitrifying bacteria, denitrifying bacteria and anammox bacteria.
    Fig. 1: Nitrogen removal performances of R1 and R2.

    a Influent and effluent NH4+-N concentration; b Influent and effluent NO2−-N concentration; c Nitrogen loading rate (NLR), nitrogen removal rate (NRR), and total nitrogen removal efficiency (TNRE).

    Full size image

    On the 21st day, when influent NH4+-N and NO2−-N concentrations increased to 100.3 and 138.1 mg L−1, effluent NH4+-N and NO2−-N concentrations of R1 increased to 6.5 and 24.2 mg L−1, respectively; while those of R2 increased to 2.6 and 19.9 mg L−1. On the 24th day, when HRT decreased from 48 h to 36 h, effluent NH4+-N and NO2−-N continued to increase. At this time, the abundance of anammox bacteria in the reactors was relatively low and had not played a dominant role. Meanwhile, the cell lysis phase was over and denitrifying bacteria activity began to decrease with the continuous consumption of organic substance23. Therefore, the NH4+-N and NO2−-N removal efficiencies fluctuated widely when the nitrogen loading rate (NLR) increased. Moreover, the higher removal rate of NH4+-N and NO2−-N in R2 can be attributed to the promotion effect of FeS on anammox growth. On the 27th day, effluent NO2−-N concentration of R1 and R2 reached the highest values (81.8 mg L−1, 71.1 mg L−1); the TNRE was the lowest, which were 52.8% and 61.0%, respectively. After this point, the NH4+-N and NO2−-N removal efficiencies of both R1 and R2 gradually increased and there were significant differences in total nitrogen removal capability between the two reactors. As shown in Fig. 1c, the TNRE of R2 on the 30th day increased to 73.3%; R1 achieved a TNRE of over 70% 12 days later, while the TNRE of R2 reached over 80% at this time. On the 45th day, the accumulation of nitrate appeared again in the effluent of the two reactors, meaning anammox was predominant. On the 51st day, the NH4+-N and NO2−-N removal in R2 reached more than 85% simultaneously, and the values of removed NO2−-N/NH4+-N and produced NO3−-N/removed NH4+-N were 1.12 and 0.17, respectively, closing to the theoretical stoichiometric ratio of anammox reaction, which marks that anammox reactor was started up successfully21. Based on Eq. (1) and the experimental data on day 51, an assumed transformation model was constructed to reflect the pathways of the nitrogen conversions in the system as shown in Supplementary Fig. 2. Due to the lack of oxygen and organic matter and the inhibition of denitrification by FeS, anammox played a dominant role. The same phenomenon occurred in R1 on day 56. Bi et al. studied the effect of Fe(II) concentration on the start-up of anammox process with a HRT of 12 h and found that the start-up time of anammox process could be shortened from 70 to 58 days when the concentration of Fe(II) ranged from 1.68 to 3.36 mg L−121. Because the concentration of Fe(II) was relatively lower than previous study, the influence was relatively less but this method is more convenient. The heme c content at day 50 in R2 was higher than that in R1 as shown in the section “Fe2+ release and Heme c content”, demonstrating that the activity of anammox bacteria in R2 was higher than that in R1. In summary, FeS effectively shortened the start-up time and improved the nitrogen removal performance.
    On the 71st day, when influent NH4+-N and NO2−-N concentrations increased to 150 mg L−1 and 180 mg L−1, respectively, the NH4+-N and NO2−-N removal rates in the two reactors decreased. On the 75th day, effluent NH4+-N concentrations of R1 and R2 increased to 37.1 and 35.3 mg L−1, meantime effluent NO2−-N concentration increased to 93.3 and 84.8 mg L−1. Although the nitrogen removal rate of the two reactors decreased obviously after the NLR was increased, it quickly recovered to the original level. As shown in Fig. 1a, b, on day 81, effluent NH4+-N in R1 and R2 decreased to 11.1 and 7.1 mg L−1 and effluent NO2−-N concentrations decreased to 16.5 and 6.2 mg L−1. The TNRE increased to about 90%. This indicated that the reactors have a certain capacity in resistance to weak shock loading due to the enrichment of anammox bacteria. And, when influent NH4+-N and NO2−-N were further increased, effluent NH4+-N and NO2−-N concentrations of R2 were significantly lower than these of R1. Meantime, the responses caused by the unit intensity of shock (R) of R2 was substantially lower than these of R1 as shown in Supplementary Table 1, indicating that R2 had more resistance to shock loading rate. The same trend was observed when HRT were further shortened to 36 h and 12 h, suggested that the stability of anammox reactors can be improved with the addition of FeS.
    During the start-up period, the NO3−-N concentration in R2 was substantially higher than that in R1 as shown in Supplementary Fig. 3, which might be attributed to the inhibition of denitrification process in R2 by FeS24,25. However, in the stabilization period, the NO3−-N concentration in R2 was substantially lower than that in R1. This was due to the lack of organic matter in R1 which inactivated denitrifying bacteria. Meantime, the presence of FeS in R2 might promote sulfur autotrophic denitrification and DNRA to reduce nitrate. The KEGG function prediction result as shown in the section “Effect of FeS on microbial community” verified this inference.
    FeS structure change
    The appearance of FeS with dark brown color, particle size between 1 and 5 mm and compact texture before being added to the reactor was observed (Supplementary Fig. 4). After 180 days of reactor operation, the FeS materials remaining in R2 were found to be covered with a layer of sludge. And the appearance displayed clear differences: most of the color changed from dark brown to khaki and the texture was sparse, which may be caused by the oxidation of FeS. Moreover, the red anammox granule sludge as shown in Supplementary Fig. 4 was observed in R2. Touching these red anammox granule sludge felt that the interior was relatively hard, which was made of FeS particles. FeS may promote the formation of anammox granular sludge.
    To further understand the structure change, the morphology of FeS before and after reaction were observed by SEM at different magnifications. As shown in Fig. 2c, d, there were many honeycomb style holes on the surface and inside of the FeS particles after the reaction. The voids formed on the surface may facilitate the attachment of microorganisms, which acted like microbial carriers. Therefore, anammox granular sludge containing FeS as inert cores formed in R2. In addition, Fe2+/Fe3+ produced by oxidation and ionization of FeS could weaken the electrostatic repulsion among negatively charged anammox cells and promote the aggregation of anammox bacteria into zoogloea by the effect of salt bridge26. Thus, the addition of FeS could promote the formation of anammox granular sludge, then improve the stability of the reactor. Figure 2e, f showed that many plate-shaped secondary minerals were produced after the reaction of FeS. In the presence of dissolved oxygen (DO), O2 can diffuse into the FeS surface and oxidize Fe2+ to Fe3+ (Eq. (5))6. The formation of these secondary minerals may hinder the release of iron ions from FeS27.

    $${mathrm{FeS}} + {mathrm{2}}{mathrm{.25}}{mathrm{O}}_2 + {mathrm{2}}{mathrm{.5}},{mathrm{H}}_2{mathrm{O}} to {mathrm{Fe}}({mathrm{OH}})_3 + {mathrm{S}}{mathrm{O}}_4^{{mathrm{2}} – } + {mathrm{2}}{mathrm{H}}^ +$$
    (5)

    Fig. 2: SEM of FeS.

    Before (a, b) and after (c–f) reaction.

    Full size image

    Effect of FeS on functional bacteria abundance
    The abundance of anammox bacteria in the two reactors were monitored during the period of their operation. As shown in Fig. 3a, the copy numbers of anammox 16S rRNA gene in the inoculation sludge was 3.31 × 106 copies per ng DNA. After 150 days of cultivation, the copy numbers of anammox 16S rRNA gene in R1 and R2 (1.21 × 107, 1.42 × 107copies per ng DNA) were significantly higher than that in the inoculation sludge. The data demonstrate that although the content of anammox in the inoculation sludge was low, anammox bacteria can be rapidly enriched and the reactor could be properly started-up as long as the cultural conditions for anammox bacteria growth were suitable. The anammox 16S rRNA gene copy numbers of R1 and R2 were 5.68 × 106 and 7.04 × 106 copies per ng DNA on day 70, respectively. Compared with R1, the abundance of anammox bacteria in R2 was increased by 29%. The contrast in cell quantities between R1 and R2 indicated that the addition of FeS with this concentration promoted the growth of anammox bacteria. Combined with the water quality results, the faster growth rate of anammox bacteria in R2 was responsible for the higher removal efficiencies of NH4+-N and NO2−-N and shorter start-up time of reactor.
    Fig. 3: The qPCR results of sludge samples.

    a Anammox 16S rRNA gene copy number in different period; b other functional genes copy number on day 70. Data indicate average, and error bars represent standard deviation of the results from three independent sampling, each tested in triplicate.

    Full size image

    In addition to anammox, the contents of ammonia-oxidizing bacteria (AOB), NOB and denitrifying bacteria also affect the start-up time and nitrogen removal capacity of anammox reactor. Compared with the inoculation sludge, the expression levels of amoA (NH4+ → NO2−) and nirS (NO3− → NO2−) genes in both R1 and R2 were increased, while the expression levels of Nitrospira spp. 16S rRNA genes (NO2− → NO3−) and nirK (NO3− → NO2−) genes were decreased (Fig. 3b). The expression levels of Nitrospira spp. 16S rRNA genes could reflect the content of NOB in anammox reactor28. As anammox was cultured in an anaerobic environment, which was not conducive to the growth of NOB, the content of NOB was gradually decreased with the increase of culture time. And the expression level of Nitrospira spp. 16S rRNA genes in the inoculated sludge was 2.14 × 106 copies per ng DNA, which was consistent with the higher nitrite removal efficiency initially. On day 70, the expression levels of amoA gene in R1 and R2 were 1.34 × 104 and 2.07 × 103 copies per ng DNA, while anammox 16S rRNA gene expression level was 5.68 × 106 and 7.04 × 106 copies per ng DNA. It was clear that the content of anammox was two or three orders of magnitude higher than AOB. The qPCR results also demonstrated that the anammox bacteria were dominant after 70 days of operation, at which time the removal of ammonium nitrogen was mainly from anammox. In addition, the expression level of amoA gene in R2 was much lower than that of R1, and the NOB content of both reactors was higher than AOB content on day 70 (Fig. 3b). FeS could react with dissolved oxygen (DO) in the reactor, leading to an inhibitory effect on the growth of AOB6. But Nitrospira-like NOB has higher hypoxia tolerance ability than AOB. Liu et al. reported that when the reactor was operated under low oxygen conditions (0.16 mg DO L−1) for a long time, some of Nitrospira-like NOB can adapt to anaerobic environment and maintain activity29. Both nirS and nirK are functional genes of denitrifying bacteria. The expression level of nirS gene in R2 (2.05 × 106 copies per ng DNA) was higher than that of R1 (1.11 × 106 copies per ng DNA), while the expression of nirK gene in R2 (3.27 × 106 copies per ng DNA) was slightly lower than that of R1 (3.65 × 106 copies per ng DNA). According to previous reports, the nirK gene encodes copper-containing nitrite reductase and the nirS gene encodes heme-containing cd1 nitrite reductase which contains heme d as its catalytic center30. And iron ions are involved in the synthesis of various types of heme. It is reasonable to speculate that the synthesis of cd1 nitrite reductase in microorganisms was promoted after adding FeS into the reactor.
    Fe2+ release and Heme c content
    The effluent Fe2+ and intracellular heme c concentrations were determined and illustrated in Fig. 4. Initially, the Fe2+ content in the effluent of R1 and R2 was similar because FeS particles with compact texture had a smaller specific surface area (Fig. 2a, b) and released less iron ions (Fig. 4a). After the reactor was operated for a period, the effluent Fe2+ concentration of R2 was significantly higher than that of R1. On the 30th day, the effluent Fe2+ concentration of R1 and R2 were 0.132 and 1.762 mg L−1, respectively. The results on days 45 and 60 also showed that there was a significant difference in effluent Fe2+ concentration between R1 and R2. During this period, massive holes were corroded on the surface and inside of FeS particles as shown in Fig. 2, the specific surface area of FeS increased and the activity of FeS was higher, contributing to more release of iron ions. On day 70, the content of heme c in R1 and R2 was 7.2 and 11.8 μmol per g-protein, respectively (Fig. 4b). It has been reported that Fe2+ was involved in the formation of heme c, which was the active center of many enzyme proteins31. In many biochemical reactions, the transformation of substrate and intermediate is accomplished by the catalysis and electron transfer of c-type heme protein32,33. Anammox cells contain a large amount of multi-heme cytochromes, for example one subunit of hydroxylamine oxidoreductase enzyme (HAO) binds 8 heme c34. In this experiment, the positive correlation between Fe2+ and heme c confirmed that the concentration of Fe2+ in the reactor could be increased with the addition of FeS, then promoting the synthesis of heme c. On the 75th and 90th days, the Fe2+ content in the effluent of both reactors became lower, probably because the abundance of anammox bacteria increased gradually, corresponding to an increased consumption of iron ions. At the same time, the results showed that the content of Fe2+ in R2 effluent did not differ much from that in R1 effluent. On one hand, as the reaction progress, secondary minerals and biofilm were formed on the surface of FeS (Fig. 2), which led to a decrease in FeS activity. On the other hand, the abundance of anammox bacteria in R2 was higher than that in R1 (Fig. 3), thus more iron ions would be consumed.
    Fig. 4: Effluent Fe2+ concentration and the content of Heme c.

    a effluent Fe2+ concentration; b the content of Heme c. Data indicate average, and error bars represent standard deviation of the results from three independent sampling, each tested in triplicate.

    Full size image

    Effect of FeS on microbial community
    Through clustering analysis of OTU, the number of OTUs shared among samples and the number of OTUs unique to each sample can be intuitively observed. The number of OTUs shared by the R1 and R2 samples was 816, which accounted for 71.8% and 69.9% of the total OTUs, respectively; the number of OUT unique to R1 was 321 and that for R2 was 352 (Supplementary Fig. 5). The addition of FeS led to different species composition of the two communities. The shared OTUs number of R1 and R2 samples with inoculated sludge was 168, accounting for 14.8% and 14.4% of the total OTUs of R1 and R2 samples, respectively. Obviously, after domestication, the R1 and R2 samples were less similar to the inoculated sludge.
    The ACE, Chao1, Simpson and Shannon listed in Table 2 are the alpha diversity indexes that reflect the richness and diversity of the community. The ACE and Chao 1 indexes are mainly used to indicate the richness of the community. In general, the larger the two index values are, the higher the richness of the community is. Comparing the ACE and Chao1 index values of R1 and R2 samples, the richness of R2 community was higher than that of R1. The Simpson and Shannon indexes account for the richness and evenness of the community. The larger the two index values are, the higher the diversity of the community is. As shown in Table 2, the two index values of R2 samples were higher than these of R1, so the diversity of R2 community was higher. In summary, the community of R2 sample had higher richness and diversity. During the cultivation and acclimation process, some species in the seed sludge couldn’t adapt to the new environmental conditions and were gradually washed out from the system. The addition of FeS reduced the tendency of some species to disappear under its role in facilitating the formation of granular sludge.
    Table 2 The OTU numbers and bacterial diversity indices of sludge samples.
    Full size table

    It can be seen from the results of microbial diversity analysis that the addition of FeS had a certain influence on the number of species of R1 and R2. The differences in microbial community composition at different classification levels with or without the presence of FeS were shown in Fig. 5.
    Fig. 5: The microbial community of sludge samples at different levels on day 180.

    a Phylum level; b top 9 abundant genera at genus level; c the microbial community of Brocadiaceae.

    Full size image

    The microbial community composition of R1 and R2 was similar at phylum classification level (Fig. 5a). The dominant phylum in two reactors was Protobacteria, accounting for 40.1% and 29.6%, respectively, followed by Chloroflexi (12.5% and 14.1%). Other reports also showed there were higher contents of Protobacteria and Chloroflexi in anammox reactor35,36. The relative abundance of Planctomycetes which anammox belonged to in R1 and R2 was 9.1% and 9.9%, respectively. The values were not very high, mainly due to the small proportion of Planctomycetes in the inoculated sludge (Supplementary Fig. 1) and the slower growth rate of the anammox bacteria. The proportion of Acidobacteria in R1 and R2 showed obvious difference, with relative abundances of 7.0% and 11.9%, respectively. Several publications demonstrated that some microorganisms belonged to Acidobacteria have the ability to dissimilate iron reduction with various simple organic acids such as acetate as alternative electron donors under anaerobic conditions37,38,39. In addition, the relative abundance of Nitrospirae which Nitrospira belonged to in R1 and R2 was extremely low compared with the inoculated sludge, which was reduced from 16.58% to 0.45% and 0.15%, respectively (Supplementary Fig. 1). This result was consistent with the water quality.
    Figure 5b showed the genus of the top 9 abundance in the microbial community of R1 and R2. The most abundant genus in R1 was Halomonas, accounting for 9.7%. Most parts of the microbes belonged to Halomonas were denitrifying bacteria, which could reduce NO3−-N to NO2−-N40. Denitratisoma with a high relative abundance (7.3%) in R1 is also one type of denitrifying bacteria41. The proportions of Halomonas and Denitratisoma in R2 was 6.5% and 4.3%, respectively, significantly lower than these in R1. The relative abundance of Thiobacillus, which was the major autotrophic denitrifier detected in most sulfur-based autotrophic denitrification reactors, increased from 0.012% in R1 to 0.041% in R2 with the addition of FeS42,43. The most abundant genus in R2 was Clone_Anammox_20, accounting for 9.0%. Clone_Anammox_20 and Clone_Anammox_2 are a class of microorganisms with anammox function. The most abundant anammox genus obtained in both reactors was “Ca. Kuenenia” and the proportion was relatively close. In order to further explore the effect of FeS on the distribution of anammox bacteria, the composition of R1 and R2 samples on Brocadiaceae classification level was analyzed. The Brocadiaceae family in R1 consisted of three anammox genus, “Ca. Kuenenia”, “Ca. Brocadia” and “Ca. Jettenia”, accounting for 99%, 0.9%, and 0.1%, while the Brocadiaceae family in R2 consisted of two anammox genus, “Ca. Kuenenia” and “Ca. Brocadia”, accounting for 98% and 2%, respectively (Fig. 5c). The dominant anammox bacteria in R1 and R2 was “Ca. Kuenenia”, and the proportion of “Ca. Brocadia” in R2 was higher than in R1. Other works have found that some of the anammox bacteria under the genus “Ca. Kuenenia” and “Ca. Brocadia” could oxidize Fe2+ with NO3−-N while anammox process occurred44. Thus, FeS might affect the distribution of species and relative abundance of anammox genus but did not change the dominant status of the anammox bacteria in the community.
    To further explore the influence mechanism of FeS on nitrogen transportation, PICRUSTs was used in this experiment to predict the contents of enzymes related to NO2−-N conversion based on KEGG database. As shown in Fig. 6a, nitrite can be reduced to nitrogen (NO2−-N→N2) through denitrification and ammonia nitrogen (NO2−-N→NH4+-N) through dissimilatory nitrate reduction to ammonium (DNRA), in addition to being removed by anammox. The nitrite reductase (ammonia-forming) content of R2 was significantly higher than that of R1, while nitrite reductase (NO-forming) and nitric oxide reductase content of R2 was lower than that of R1. It had been reported that some DNRA bacteria can conduct DNRA process with sulfide (S2−) or elemental sulfur (S0) as electron donors45. And sulfide had an inhibitory effect on nitrous oxide reductase and nitric oxide reductase, which can inhibit the denitrification reaction, have an inhibitory effect on nitrite reductase (NO-forming) due to the accumulation of NO and promote the nitrite reduction reaction by the DNRA process24,25,46. In addition, heme was involved in the formation of nitrite reductase (ammonia-forming)47. Robertson et al. found that the addition of Fe2+ improved DNRA and inhibited denitrification48,49. It is postulated that the iron ions and sulfur ions released by FeS encouraged the occurrence of DNRA process and somehow decreased the denitrification reaction. Therefore, the removal rates of NO2−-N in the two reactors were significantly different, and the removal rates of NH4+-N were similar. This may also account for the relatively low abundance of denitrifying bacteria in R2. Moreover, Fig. 6b showed the predicted metabolism function of the two reactors’ communities, and the results indicated that the metabolic function of R2 was slightly higher than that of R1. It can be seen that the addition of FeS to the anammox reactor can increase microbial metabolism.
    Fig. 6: Prediction of community functions based on KEGG.

    a Nitrogen invertase content; b metabolism functions.

    Full size image

    Engineering significance
    As a new type of environmentally-friendly biological nitrogen removal process, the anammox process has been a research hotspot, but it still encounters some issues to limit its wider application. Anammox bacteria are slow-growing microorganisms, and are sensitive to environmental conditions, such as salinity and organic carbon50. Another challenge of the anammox process system is the maintenance of effluent quality since about 10% nitrate would be produced in the anammox reaction, failing to meet discharge standards51.
    In this study, the start-up time of the anammox reactor was shortened and the nitrogen removal rate was significantly increased with the addition of FeS. There were mainly two reasons: On one hand, FeS promoted the formation of anammox granular sludge and increased the abundance of anammox bacteria; on the other hand, FeS stimulated the synthesis of the heme c, which participated in the synthesis of a variety of enzymes. In addition, FeS can promote the DNRA process by inhibiting denitrification. Microbial oxidation of FeS, which links to the efficiency of denitrification, DNRA and anammox, plays an important role in the N cycle and S cycle15. According to previous report, FeS could function as an alternative electron donor for sulfur-dependent autotrophic denitrification52. Nitrate reduction was achieved by using pyrrhotite as the biofilter medium and autotrophic denitrifiers as seed in lab17. And DNRA process could occur due to HS− release18. This study found that FeS could promote the start-up of anammox process and promote the nitrite reduction reaction by the DNRA process through inhibiting denitrification. Therefore, it is possible to couple anammox with sulfur-autotrophic DNRA or sulfur-autotrophic denitrification in full-scale application by adding FeS to improve the total nitrogen removal efficiency. More

  • in

    The constraint of ignoring the subtidal water climatology in evaluating the changes of coralligenous reefs due to heating events

    1.
    Walther, G. R. Community and ecosystem responses to recent climate change. Philos Trans R Soc B Biol Sci 365, 2019–2024 (2010).
    Article  Google Scholar 
    2.
    Poloczanska, E. S. et al. Global imprint of climate change on marine life. Nat. Clim. Change. 3, 919–925 (2013).
    ADS  Article  Google Scholar 

    3.
    Hoegh-Guldberg, O. & Poloczanska, E. S. The effect of climate change across ocean regions. Front. Mar. Sci. 4, 361 (2017).
    Article  Google Scholar 

    4.
    Bruno, J. F. et al. Climate change threatens the world’s marine protected areas. Nat. Clim. Change. 8, 499–503 (2018).
    ADS  Article  Google Scholar 

    5.
    Hoegh-Guldberg, O. & Bruno, J. F. The impact of climate change on the world’s marine ecosystems. Science 328, 1523–1528 (2010).
    ADS  CAS  Article  Google Scholar 

    6.
    Smale, D. A., Taylor, J. D., Coombs, S. H., Moore, G. & Cunliffe, M. Community responses to seawater warming are conserved across diverse biological groupings and taxonomic resolutions. Proc. R. Soc. B Biol. Sci. 284, 20170534 (2017).
    Article  Google Scholar 

    7.
    Gauzens, B., Rall, B. C., Mendonça, V., Vinagre, C. & Brose, U. Biodiversity of intertidal food webs in response to warming across latitudes. Nat. Clim. Change. 10, 264–269 (2020).
    ADS  Article  Google Scholar 

    8.
    Sahney, S. & Benton, M. J. Recovery from the most profound mass extinction of all time. Proc. R. Soc. B Biol. Sci. 275, 759–765 (2008).
    Article  Google Scholar 

    9.
    Urban, M. C. Accelereting extinction risk from climate change. Science 348, 571–573 (2012).
    ADS  Article  CAS  Google Scholar 

    10.
    Wiens, J. J. Climate-related local extinctions are already widespread among plant and animal species. PLoS Biol. 14, e2001104 (2016).
    Article  CAS  PubMed  PubMed Central  Google Scholar 

    11.
    Pinsky, M. L., Eikeset, A. M., McCauley, D. J., Payne, J. L. & Sunday, J. M. Greater vulnerability to warming of marine versus terrestrial ectotherms. Nature 569, 108–111 (2019).
    ADS  CAS  Article  PubMed  Google Scholar 

    12.
    Smale, D. A. & Wernberg, T. Extreme climatic event drives range contraction of a habitat-forming species. Proc. R. Soc. B Biol. Sci. 280, 20122829 (2013).
    Article  Google Scholar 

    13.
    Wernberg, T. et al. An extreme climatic event alters marine ecosystem structure in a global biodiversity hotspot. Nat. Clim. Change. 3, 78–82 (2013).
    ADS  Article  Google Scholar 

    14.
    Wernberg, T. et al. Climate-driven regime shift of a temperate marine ecosystem. Science 353, 169–172 (2016).
    ADS  CAS  Article  PubMed  Google Scholar 

    15.
    Hobday, A. J. et al. A hierarchical approach to defining marine heatwaves. Prog. Oceanogr. 141, 227–238 (2016).
    ADS  Article  Google Scholar 

    16.
    Oliver, E. C. J. et al. Longer and more frequent marine heatwaves over the past century. Nat. Commun. 9, 1–12 (2018).
    CAS  Article  Google Scholar 

    17.
    Oliver, E. C. J. et al. Projected marine heatwaves in the 21st century and the potential for ecological impact. Front. Mar. Sci. 6, 1–12 (2019).
    Article  Google Scholar 

    18.
    Smale, D. A. et al. Marine heatwaves threaten global biodiversity and the provision of ecosystem services. Nat. Clim. Change. 9, 306–312 (2019).
    ADS  Article  Google Scholar 

    19.
    Eakin, C. M. et al. Caribbean corals in crisis: record thermal stress, bleaching, and mortality in 2005. PLoS ONE 5, e13969 (2010).
    ADS  Article  CAS  PubMed  PubMed Central  Google Scholar 

    20.
    Bruno, J. F. & Valdivia, A. Coral reef degradation is not correlated with local human population density. Sci. Rep. 6, 29778 (2016).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    21.
    Marbà, N. & Duarte, C. M. Mediterranean warming triggers seagrass (Posidonia oceanica) shoot mortality. Glob. Chang. Biol. 16, 2366–2375 (2010).
    ADS  Article  Google Scholar 

    22.
    Thomson, J. A. et al. Extreme temperatures, foundation species, and abrupt ecosystem change: An example from an iconic seagrass ecosystem. Glob. Chang. Biol. 21, 1463–1474 (2015).
    ADS  Article  PubMed  Google Scholar 

    23.
    Hyndes, G. A. et al. Accelerating tropicalization and the transformation of temperate seagrass meadows. Bioscience 66, 938–945 (2016).
    Article  PubMed  PubMed Central  Google Scholar 

    24.
    Babcock, R. C. et al. Severe continental-scale impacts of climate change are happening now: Extreme climate events impact marine habitat forming communities along 45% of Australia’s coast. Front. Mar. Sci. 6, 411 (2019).
    Article  Google Scholar 

    25.
    Rogers-Bennett, L. & Catton, C. A. Marine heat wave and multiple stressors tip bull kelp forest to sea urchin barrens. Sci. Rep. 9, 15050 (2019).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    26.
    E.C., MSFD 2008/56/EC of the European Parliament and of the Council, 17 June 2008, establishing a framework for Community action in the field of marine environmental policy (Marine Strategy Framework Directive). Off. J. Eur. Comm. 25/6/2008, L164/19, 22 (2008).

    27.
    Martin, C. S. et al. Coralligenous and maërl habitats: Predictive modelling to identify their spatial distributions across the Mediterranean sea. Sci. Rep. 4, 5073 (2015).
    Article  CAS  Google Scholar 

    28.
    Ballesteros, E., Avançats, E. & Csic, D. B. Mediterranean coralligenous assemblages: A synthesis of present knowledge. Oceanogr. Mar. Biol. 44, 123–195 (2006).
    Article  Google Scholar 

    29.
    Kružić, P. Bioconstructions in the Mediterranean: present and futture in The Mediterranean sea: its history and present challenges (ed. Goffredo, S. & Dubinsky, Z) 435–447 (2014).

    30.
    E.C., Council Directive 92/43/EEC (Habitat Directive) of 21 May 1992 on the conservation of natural habitats and of wild fauna and flora. Off. J. Eur. Comm. 22/7/1992, L206, 7 (1992).

    31.
    Martin, S. & Gattuso, J. P. Response of Mediterranean coralline algae to ocean acidification and elevated temperature. Glob. Chang. Biol. 15, 2089–2100 (2009).
    ADS  Article  Google Scholar 

    32.
    Boudouresque, C. F. et al.Where seaweed forests meet animal forests: The examples of macroalgae in coral reefs and the Mediterranean coralligenous ecosystem marine animal forests in Marine Animal Forests. Springer, Berlin, pp 1–28 (2016).

    33.
    Coma, R., Pola, E., Ribes, M. & Zabala, M. Long-term assessment of temperate octocoral mortality patterns, protected vs. unprotected areas. Ecol. Appl. 14, 1466–1478 (2004).
    Article  Google Scholar 

    34.
    Salomidi, M. et al. Assessment of goods and services, vulnerability, and conservation status of European seabed biotopes: a stepping stone towards ecosystem-based marine spatial management. Mediterr. Mar. Sci. 13, 49–88 (2012).
    Article  Google Scholar 

    35.
    Piazzi, L., La Manna, G., Cecchi, E., Serena, F. & Ceccherelli, G. Protection changes the relevancy of scales of variability in coralligenous assemblages. Estuar. Coast. Shelf Sci. 175, 62–69 (2016).
    ADS  Article  Google Scholar 

    36.
    Cerrano, C. et al. A catastrophic mass-mortality episode of gorgonians and other organisms in the Ligurian Sea (North-western Mediterranean), summer 1999. Ecol. Lett. 3, 284–293 (2000).
    Article  Google Scholar 

    37.
    Garrabou, J. et al. Mass mortality in Northwestern Mediterranean rocky benthic communities: Effects of the 2003 heat wave. Glob. Chang. Biol. 15, 1090–1103 (2009).
    ADS  Article  Google Scholar 

    38.
    Gatti, G. et al. Ecological change, sliding baselines and the importance of historical data: Lessons from combing observational and quantitative data on a temperate reef over 70 years. PLoS ONE 10, e0118581 (2015).
    Article  CAS  PubMed  PubMed Central  Google Scholar 

    39.
    Coma, R. et al. Consequences of a mass mortality in populations of Eunicella singularis (Cnidaria: Octocorallia) in Menorca (NW Mediterranean). Mar. Ecol. Prog. Ser. 327, 51–60 (2006).
    ADS  Article  Google Scholar 

    40.
    Huete-Stauffer, C. et al. Paramuricea clavata (Anthozoa, Octocorallia) loss in the Marine Protected Area of Tavolara (Sardinia, Italy) due to a mass mortality event. Mar. Ecol. 32, 107–116 (2011).
    ADS  Article  Google Scholar 

    41.
    Martin, Y., Bonnefont, J. L. & Chancerelle, L. Gorgonians mass mortality during the 1999 late summer in French Mediterranean coastal waters: the bacterial hypothesis. Water Res. 36, 779–782 (2001).
    Article  Google Scholar 

    42.
    Crisci, C., Bensoussan, N., Romano, J. C. & Garrabou, J. Temperature anomalies and mortality events in marine communities: Insights on factors behind differential mortality impacts in the NW Mediterranean. PLoS ONE 6, e23814 (2011).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    43.
    Torrents, O., Tambutté, E., Caminiti, N. & Garrabou, J. Upper thermal thresholds of shallow vs deep populations of the precious Mediterranean red coral Corallium rubrum (L.): Assessing the potential effects of warming in the NW Mediterranean. J. Exp. Mar. Biol. Ecol. 357, 7–19 (2008).
    Article  Google Scholar 

    44.
    Pagès-Escolà, M. et al. Divergent responses to warming of two common co-occurring Mediterranean bryozoans. Sci. Rep. 8, 17455 (2018).
    ADS  Article  CAS  PubMed  PubMed Central  Google Scholar 

    45.
    Gómez-Gras, D. et al. Response diversity in Mediterranean coralligenous assemblages facing climate change: Insights from a multispecific thermotolerance experiment. Ecol. Evol. 9, 4168–4180 (2019).
    Article  PubMed  PubMed Central  Google Scholar 

    46.
    Galli, G., Solidoro, C. & Lovato, T. Marine heat waves hazard 3D maps and the risk for low motility organisms in a warming Mediterranean Sea. Front. Mar. Sci. 4, 136 (2017).
    Article  Google Scholar 

    47.
    Pansch, C. et al. Heat waves and their significance for a temperate benthic community: A near-natural experimental approach. Glob. Chang. Biol. 24, 4357–4367 (2018).
    ADS  Article  PubMed  Google Scholar 

    48.
    Hobday, A. J. et al. Categorizing and naming Marine Heatwaves. Oceanography 31, 162–173 (2018).
    Article  Google Scholar 

    49.
    Roberts, S. D., Van Ruth, P. D., Wilkinson, C., Bastianello, S. S. & Bansemer, M. S. Marine heatwave, harmful algae blooms and an extensive fish kill event during 2013 in South Australia. Front. Mar. Sci. 6, 610 (2019).
    Article  Google Scholar 

    50.
    Smale, D. A. & Wernberg, T. Satellite-derived SST data as a proxy for water temperature in nearshore benthic ecology. Mar. Ecol. Prog. Ser. 387, 27–37 (2009).
    ADS  Article  Google Scholar 

    51.
    Bensoussan, N., Romano, J. C., Harmelin, J. G. & Garrabou, J. High resolution characterization of northwest Mediterranean coastal waters thermal regimes: To better understand responses of benthic communities to climate change. Estuar. Coast. Shelf Sci. 87, 431–441 (2010).
    ADS  Article  Google Scholar 

    52.
    Bruno, J. F., Carr, L. A. & O’Connor, M. I. Exploring the role of temperature in the ocean through metabolic scaling. Ecology 96, 3126–3140 (2015).
    Article  PubMed  Google Scholar 

    53.
    Silbiger, N. J., Goodbody-Gringley, G., Bruno, J. F. & Putnam, H. M. Comparative thermal performance of the reef-building coral Orbicella franksi at its latitudinal range limits. Mar. Biol. 166, 126 (2019).
    Article  Google Scholar 

    54.
    Linares, C., Cebrian, E., Kipson, S. & Garrabou, J. Does thermal history influence the tolerance of temperate gorgonians to future warming?. Mar. Environ. Res. 89, 45–52 (2013).
    CAS  Article  PubMed  Google Scholar 

    55.
    Piazzi, L. et al. What’s in an index? Comparing the ecological information provided by two indices to assess the status of coralligenous reefs in the NW Mediterranean Sea. Aquat. Conserv. Mar. Freshw. Ecosyst. 27, 1091–1100 (2017).
    Article  Google Scholar 

    56.
    Ceccherelli G., et al. Vertical gradient and spatial variability of Coralligenous reefs in Sardinia: the interactive effect of depth and location. S.It.E. (Italian Society of Ecology) conference (Ferrara, Italy 10–12 September 2019) https://www.ecologia.it/wp-content/uploads/2019/09/AbstractBook-SItE-Ferrara-2019.pdf, 124 (2019).

    57.
    Holbrook, N. J. et al. A global assessment of marine heatwaves and their drivers. Nat. Commun. 10, 2624 (2019).
    ADS  Article  CAS  PubMed  PubMed Central  Google Scholar 

    58.
    Smit, A. J. et al. A coastal seawater temperature dataset for biogeographical studies: Large biases between in situ and remotely-sensed data sets around the coast of South Africa. PLoS ONE 8, e81944 (2013).
    ADS  Article  CAS  PubMed  PubMed Central  Google Scholar 

    59.
    Brewin, R. J. W. et al. Evaluating operational AVHRR sea surface temperature data at the coastline using benthic temperature loggers. Remote Sens. 10, 925 (2018).
    ADS  Article  Google Scholar 

    60.
    Coma, R. et al. Global warming-enhanced stratification and mass mortality events in the Mediterranean. Proc. Natl. Acad. Sci. 106, 6176–6181 (2009).
    ADS  CAS  Article  PubMed  Google Scholar 

    61.
    Verdura, J. et al. Biodiversity loss in a Mediterranean ecosystem due to an extreme warming event unveils the role of an engineering gorgonian species. Sci. Rep. 9, 5911 (2019).
    ADS  Article  CAS  PubMed  PubMed Central  Google Scholar 

    62.
    Kendrick, G. A. et al. A systematic review of how multiple stressors from an extreme event drove ecosystem-wide loss of resilience in an iconic seagrass community. Front. Mar. Sci. 6, 455 (2019).
    Article  Google Scholar 

    63.
    Kim, J. B., Park, J. I., Jung, C. S., Lee, P. Y. & Lee, K. S. Distributional range extension of the seagrass Halophila nipponica into coastal waters off the Korean peninsula. Aquat. Bot. 90, 269–272 (2009).
    Article  Google Scholar 

    64.
    Johnson, C. R. et al. Climate change cascades: shifts in oceanography, species’ ranges and subtidal marine community dynamics in eastern Tasmania. J. Exp. Mar. Bio. Ecol. 400, 17–32 (2011).
    Article  Google Scholar 

    65.
    Saha, M. et al. Response of foundation macrophytes to near-natural simulated marine heatwaves. Glob. Chang. Biol. 26, 417–430 (2020).
    ADS  Article  PubMed  Google Scholar 

    66.
    Garrabou, J. et al. Collaborative database to track mass mortality events in the Mediterranean Sea. Front. Mar. Sci. 6, 707 (2019).
    Article  Google Scholar 

    67.
    Hartley, S. & Kunin, W. E. Scale Dependency of rarity, extinction risk, and conservation priority. Conserv. Biol. 17, 1559–1570 (2003).
    Article  Google Scholar 

    68.
    Bavestrello, G. et al. Mass mortality of Paramuricea clavata (Anthozoa, Cnidaria) on Portofino Promontory cliffs, Ligurian Sea. Mediterranean Sea. Mar. Life 4, 15–19 (1994).
    Google Scholar 

    69.
    Ponti, M. et al. Ecological shifts in mediterranean coralligenous assemblages related to gorgonian forest loss. PLoS ONE 9, e102782 (2014).
    ADS  Article  CAS  PubMed  PubMed Central  Google Scholar 

    70.
    Lombardi, C., Cocito, S., Occhipinti-Ambrogi, A. & Hiscock, K. The influence of seawater temperature on zooid size and growth rate in Pentapora fascialis (Bryozoa: Cheilostomata). Mar. Biol. 149, 1103–1109 (2006).
    Article  Google Scholar 

    71.
    Novosel, M., Požar-Domac, A. & Pasarić, M. Diversity and distribution of the bryozoa along underwater cliffs in the Adriatic sea with special reference to thermal regime. Mar. Ecol. 25, 155–170 (2004).
    ADS  Article  Google Scholar 

    72.
    Rindi, F. et al. Coralline algae in a changing Mediterranean Sea: how can we predict their future, if we do not know their present?. Front. Mar. Sci. 6, 2 (2019).
    Article  Google Scholar 

    73.
    Crisci, C. et al. Regional and local environmental conditions do not shape the response to warming of a marine habitat-forming species. Sci. Rep. 7, 5069 (2017).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    74.
    Piazzi, L. et al. STAR: An integrated and standardized procedure to evaluate the ecological status of coralligenous reefs. Aquat. Conserv. Mar. Freshw. Ecosyst. 29, 189–201 (2019).
    Article  Google Scholar 

    75.
    Piazzi, L. et al. Integration of ESCA index through the use of sessile invertebrates. Sci. Mar. 81, 283–290 (2017).
    Article  Google Scholar 

    76.
    Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. Mixed effects models and extensions in ecology with R (Springer, Berlin, 2009).
    Google Scholar 

    77.
    Hastie, T. & Tibshirani, R. Generalized additive models (Taylor and Francis Ltd, New York, 1990).
    Google Scholar  More

  • in

    The immune response of bats differs between pre-migration and migration seasons

    1.
    Lochmiller, R. L. & Deerenberg, C. Trade-offs in evolutionary immunology: Just what is the cost of immunity?. Oikos 88(1), 87–98 (2000).
    Article  Google Scholar 
    2.
    Martin, L. B., Scheuerlein, A. & Wikelski, M. Immune activity elevates energy expenditure of house sparrows: A link between direct and indirect costs?. Proc. R. Soc. Lond. B 270(1511), 153–158 (2003).
    Article  Google Scholar 

    3.
    Klasing, J.C. The costs of immunity. Acta Zool. Sin. 50, 961–969 (2004).

    4.
    Hasselquist, D. & Nilsson, J. Å. Physiological mechanisms mediating costs of immune responses: What can we learn from studies of birds?. Anim. Behav. 83(6), 1303–1312 (2012).
    Article  Google Scholar 

    5.
    Demas, G. E., Chefer, V., Talan, M. I. & Nelson, R. J. Metabolic costs of mounting an antigen-stimulated immune response in adult and aged C57BL/6J mice. Am. J. Physiol 273, R1631–R1637 (1997).
    CAS  PubMed  PubMed Central  Google Scholar 

    6.
    Otálora-Ardila, A., Herrera, M. L. G., Flores-Martinez, J. J. & Welch, K. C. Jr. Metabolic cost of the activation of immune response in the fish-eating myotis (Myotis vivesi): The effects of inflammation and the acute phase response. PLoS ONE 11, e0164938 (2016).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    7.
    Costantini, D. & Møller, A. P. Does immune response cause oxidative stress in birds? A meta-analysis. Comp. Biochem. Physiol. A 153, 339–344 (2009).
    Article  CAS  Google Scholar 

    8.
    Canale, C. I. & Henry, P. Y. Energetic costs of the immune response and torpor use in a primate. Funct. Ecol. 25, 557–565 (2011).
    Article  Google Scholar 

    9.
    Wikelski, M. et al. Costs of migration in free-flying songbirds. Nature 423(6941), 704–704 (2003).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    10.
    Jenni-Eiermann, S., Jenni, L., Smith, S. & Costantini, D. Oxidative stress in endurance flight: An unconsidered factor in bird migration. PLoS ONE 9, e97650 (2014).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    11.
    Costantini, D., Lindecke, O., Petersons, G. & Voigt, C. C. Migratory flight imposes oxidative stress in bats. Curr. Zool. 65, 147–153 (2019).
    PubMed  Article  Google Scholar 

    12.
    Troxell, S. A., Holderied, M. W., Pētersons, G. & Voigt, C. C. Nathusius’ bats optimize long-distance migration by flying at maximum range speed. J. Exp. Biol. 222, jeb176396 (2019).

    13.
    Dierschke, V., Mendel, B. & Schmaljohann, H. Differential timing of spring migration in northern wheatears Oenanthe oenanthe: Hurried males or weak females?. Behav. Ecol. Sociobiol. 57, 470–480 (2005).
    Article  Google Scholar 

    14.
    Hasselquist, D. Comparative immunoecology in birds: Hypotheses and tests. J. Ornith. 148(2), 571–582 (2007).
    Article  Google Scholar 

    15.
    Buehler, D. M. & Piersma, T. Travelling on a budget: predictions and ecological evidence for bottlenecks in the annual cycle of long-distance migrants. Philos. Trans. R. Soc. B 363(1490), 247–266 (2007).
    Article  Google Scholar 

    16.
    Svensson, E., Råberg, L., Koch, C. & Hasselquist, D. Energetic stress, immunosuppression and the costs of an antibody response. Funct. Ecol. 12(6), 912–919 (1998).
    Article  Google Scholar 

    17.
    Owen, J. C. & Moore, F. R. Seasonal differences in immunological condition of three species of thrushes. Condor 108(2), 389–398 (2006).
    Article  Google Scholar 

    18.
    Altizer, S. et al. Animal migration and infectious disease risk. Science 331(6015), 296–302 (2011).
    ADS  CAS  PubMed  Article  Google Scholar 

    19.
    Eikenaar, C., Isaksson, C. & Hegemann, A. A hidden cost of migration? Innate immune function versus antioxidant defense. Ecol. Evol. 8(5), 2721–2728 (2018).
    PubMed  PubMed Central  Article  Google Scholar 

    20.
    Weber, T. P. & Stilianakis, N. I. Ecologic immunology of avian influenza (H5N1) in migratory birds. Emerg. Infect. Dis. 13(8), 1139 (2007).
    PubMed  PubMed Central  Article  Google Scholar 

    21.
    Owen, J. C. & Moore, F. R. Relationship between energetic condition and indicators of immune function in thrushes during spring migration. Can. J. Zool. 7, 638–647 (2008).
    Article  CAS  Google Scholar 

    22.
    Tobler, M., Ballen, C., Healey, M., Wilson, M. & Olsson, M. Oxidant trade-offs in immunity: An experimental test in a lizard. PLoS ONE 10(5), e0126155 (2015).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    23.
    Wang, D., Malo, D. & Hekimi, S. Elevated mitochondrial reactive oxygen species generation affects the immune response via hypoxia-inducible factor-1 in long-lived Mclk1+/− mouse mutants. J. Immunol. 184, 582–590 (2009).
    PubMed  Article  CAS  Google Scholar 

    24.
    Case, A. J. et al. Elevated mitochondrial superoxide disrupts normal T cell development, impairing adaptive immune responses to an influenza challenge. Free Radic. Biol. Med. 50, 448–458 (2011).
    CAS  PubMed  Article  Google Scholar 

    25.
    Møller, A. P. & Erritzøe, J. Host immune defence and migration in birds. Evol. Ecol. 12(8), 945–953 (1998).
    Article  Google Scholar 

    26.
    Popa-Lisseanu, A. G. & Voigt, C. C. Bats on the move. J. Mammal. 90(6), 1283–1289 (2009).
    Article  Google Scholar 

    27.
    Krauel, J.J., & McCracken, G. F. Recent advances in bat migration research. in Bat Evolution, Ecology, and Conservation 293–313. (Springer, New York, 2013).

    28.
    Steffens, R., Zöphel, U. & Brockmann, D. 40th Anniversary Bat Marking Centre Dresden—Evaluation of Methods and Overview of Results. (Sächsisches Landesamt für Umwelt und Geologie, Dresden, 2004).

    29.
    Roberts, B. J., Catterall, C. P., Eby, P. & Kanowski, J. Long-distance and frequent movements of the flying-fox Pteropus poliocephalus: Implications for management. PLoS ONE 7(8), e42532 (2012).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    30.
    Speakman, J. R., Thomas, D. W., Kunz, T. H., & Fenton, M. B. Physiological ecology and energetics of bats. in Bat Ecology (eds. Kunz, T.H. & Fenton M.B.), 430–490 (Chicago University Press, Chicago, 2003).

    31.
    Voigt, C. C., Borrisov, I. M. & Voigt-Heucke, S. L. Terrestrial locomotion imposes high metabolic requirements on bats. J. Exp. Biol. 215(24), 4340–4344 (2012).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    32.
    McGuire, L. P., Jonasson, K. A., & Guglielmo, C.G. Bats on a budget: torpor-assisted migration saves time and energy. PLoS ONE9(12) (2014).

    33.
    Brunet-Rossinni, A. K. Reduced free-radical production and extreme longevity in the little brown bat (Myotis lucifugus) versus two non-flying mammals. Mech. Ageing Dev. 125, 11–20 (2004).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    34.
    Filho, D. W., Althoff, S. L., Dafré, A. L. & Boveris, A. Antioxidant defenses, longevity and ecophysiology of South American bats. Comp. Biochem. Physiol. Part C 146, 214–220 (2007).
    Google Scholar 

    35.
    Salmon, A. B. et al. The long lifespan of two bat species is correlated with resistance to protein oxidation and enhanced protein homeostasis. FASEB J 23, 2317–2326 (2009).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    36.
    Zhang, G. et al. Comparative analysis of bat genomes provides insight into the evolution of flight and immunity. Science 339, 456–460 (2013).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    37.
    Schneeberger, K., Czirják, G. Á. & Voigt, C. C. Frugivory is associated with low measures of plasma oxidative stress and high antioxidant concentration in free-ranging bats. Naturwissenschaften 101(4), 285–290 (2014).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    38.
    Schneeberger, K., Czirják, G. Á. & Voigt, C. C. Inflammatory challenge increases measures of oxidative stress in a free-ranging, long-lived mammal. J. Exp. Biol. 216, 4514–4519 (2013).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    39.
    Costantini D, Czirják, G. Á., Bustamante, P., Bumrungsri, S., & Voigt, C.C. Impact of land use on an insectivorous tropical bat: the importance of mercury, physio-immunology and trophic position. Sci. Total Environ.671, 1077–1085 (2019).

    40.
    Wibbelt, G., Moore, M. S., Schountz, T. & Voigt, C. C. Emerging diseases in Chiroptera: Why bats?. Biol. Lett. 6, 438–440 (2010).
    PubMed  PubMed Central  Article  Google Scholar 

    41.
    Luis, A. D. et al. A comparison of bats and rodents as reservoirs of zoonotic viruses: Are bats special?. Proc. R. Soc. B 280(1756), 20122753 (2013).
    PubMed  Article  PubMed Central  Google Scholar 

    42.
    Olival, K. J. et al. Host and viral traits predict zoonotic spillover from mammals. Nature 546(7660), 646–650 (2017).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    43.
    Drexler, J. F. et al. Bats host major mammalian paramyxoviruses. Nat. Commun. 3, 796 (2012).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    44.
    Hayman, D. T. S. et al. Ecology of zoonotic infectious diseases in bats: Current knowledge and future directions. Zoonoses Public Health 60(1), 2–21 (2013).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    45.
    Pētersons, G. Seasonal migrations of northeastern populations of Pipistrellus nathusii. Myotis 41–42, 29–56 (2004).
    Google Scholar 

    46.
    Lee, K. A. Linking immune defenses and life history at the levels of the individual and the species. Integr. Comp. Biol. 46(6), 1000–1015 (2006).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    47.
    Fritze, M., et al. Immune response of hibernating European bats to a fungal challenge. Biol. Open8, bio046078 (2019).

    48.
    Stockmaier, S., Dechmann, D. K., Page, R. A. & O’Mara, M. T. No fever and leukocytosis in response to a lipopolysaccharide challenge in an insectivorous bat. Biol. Lett. 11, 4–7 (2015).
    Article  CAS  Google Scholar 

    49.
    Weise, P., Czirják, G. Á., Lindecke, O., Bumrungsri, S. & Voigt, C. C. Simulated bacterial infection disrupts the circadian fluctuation of immune cells in wrinkle-lipped bats (Chaereophon plicatus). PeerJ 5, e3570 (2017).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    50.
    Hegemann, A. et al. Immune function and blood parasite infections impact stopover ecology in passerine birds. Oecologia 188(4), 1011–1024 (2018).
    ADS  PubMed  PubMed Central  Article  Google Scholar 

    51.
    Eikenaar, C. & Hegemann, A. Migratory common blackbirds have lower innate immune function during autumn migration than resident conspecifics. Biol. Lett. 12, 20160078 (2016).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    52.
    Owen, J. C. & Moore, F. R. Swainson’s thrushes in migratory disposition exhibit reduced immune function. J. Ethol. 26(3), 383–388 (2008).
    Article  Google Scholar 

    53.
    Sikes, R. S. & Gannon, W. L. Guidelines of the American Society of Mammalogists for the use of wild mammals in research. J. Mammal. 92, 235–253 (2011).
    Article  Google Scholar 

    54.
    Kozak, W.I.E.S., Conn, C.A. & Kluger, M. J.Lipopolysaccharide induces fever and depresses locomotor activity in unrestrained mice. Am. J. Physiol. Reg. Integr. Comp. Physiol.266(1), R125–R135 (1994).

    55.
    Schneeberger, K., Czirják, G. Á. & Voigt, C. C. Measures of the constitutive immune system are linked to diet and roosting habits of neotropical bats. PLoS ONE 8(1), e54023 (2013).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    56.
    Cray, C., Zaias, J. & Altman, N. H. Acute phase response in animals: A review. Comp. Med. 59, 517–526 (2009).
    CAS  PubMed  PubMed Central  Google Scholar 

    57.
    Field, K. A. et al. The white-nose syndrome transcriptome: Activation of anti-fungal host responses in wing tissue of hibernating little brown myotis. PLoS Pathog. 11(10), e1005168 (2015).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    58.
    Costantini, D., Dell’Ariccia, G. & Lipp, H.-P. Long flights and age affect oxidative status of homing pigeons (Columba livia). J. Exp. Biol. 211, 377–381 (2008).
    CAS  PubMed  Article  Google Scholar 

    59.
    Kuznetsova, A., Brockhoff, P. B. & Bojesen Christensen, R. H. Package ‘lmerTest’. CRAN. https://cran.r-project.org/web/packages/lmerTest/lmerTest.pdf (2019).

    60.
    Zeileis, A., Cribari-Neto, F., Gruen, B., Kosmidis, I., Simas, A. B., & Rocha, A. V. Package ‘betareg’. CRAN, https://cran.r-project.org/web/packages/betareg/betareg.pdf (2020). More