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    Host-specific symbioses and the microbial prey of a pelagic tunicate (Pyrosoma atlanticum)

    1.Perissinotto, R., Mayzaud, P., Nichols, P. D. & Labat, J. P. Grazing by Pyrosoma atlanticum (Tunicata, Thaliacea) in the south Indian Ocean. Mar. Ecol. Prog. Ser. 330, 1–11 (2007).CAS 
    Article 

    Google Scholar 
    2.Drits, A. V., Arashkevich, E. G. & Semenova, T. N. Pyrosoma atlanticum (Tunicata, Thaliacea): grazing impact on phytoplankton standing stock and role in organic carbon flux. J. Plankton Res. 14, 799–809 (1992).Article 

    Google Scholar 
    3.Henschke, N. et al. Large vertical migrations of Pyrosoma atlanticum play an important role in active carbon transport. J. Geophys. Res. Biogeosci. 124, 1056–1070 (2019).Article 

    Google Scholar 
    4.Schram, J. B., Sorensen, H. L., Brodeur, R. D., Galloway, A. W. E. & Sutherland, K. R. Abundance, distribution, and feeding ecology of Pyrosoma atlanticum in the Northern California Current. Mar. Ecol. Prog. Ser. 651, 97–110 (2020).5.O’Loughlin, J. H. et al. Implications of Pyrosoma atlanticum range expansion on phytoplankton standing stocks in the Northern California Current. Prog. Oceanogr. 188, 102424 (2020).6.Hobson, E. S. & Chess, J. Trophic relations of the blue rockfish, Sebastes mystinus, in a coastal upwelling system off northern California. in Fishery Bulletin, Vol. 86, 715–743 (National Marine Fisheries Service, 1988).7.Bulman, C. M., He, X. & Koslow, J. A. Trophic ecology of the mid-slope demersal fish community off Southern Tasmania, Australia. Mar. Freshw. Res. 53, 59–72 (2002).Article 

    Google Scholar 
    8.Harbison, G. R. The parasites and predators of Thaliacea. in The Biology of Pelagic Tunicates (Oxford University Press, 1998).9.James, G. D. & Stahl, J. -C. Diet of southern Buller’s albatross (Diomedea bulleri bulleri) and the importance of fishery discards during chick rearing. N. Z. J. Mar. Freshw. Res. 34, 435–454 (2000).Article 

    Google Scholar 
    10.Hedd, A. & Gales, R. The diet of shy albatrosses (Thalassarche cauta) at Albatross Island, Tasmania. J. Zool. 253, 69–90 (2001).Article 

    Google Scholar 
    11.Childerhouse, S., Dix, B. & Gales, N. Diet of New Zealand sea lions (Phocarctos hookeri) at the Auckland Islands. Wildl. Res. 28, 291–298 (2001).Article 

    Google Scholar 
    12.Lindley, J. A., Hernández, F., Scatllar, J. & Docoito, J. Funchalia sp. (Crustacea: Penaeidae) associated with Pyrosoma atlanticum (Thaliacea: Pyrosomidae) off the Canary Islands. J. Mar. Biol. Assoc. UK 81, 173–174 (2001).Article 

    Google Scholar 
    13.Lebrato, M. & Jones, D. O. B. Mass deposition event of Pyrosoma atlanticum carcasses off Ivory Coast (West Africa). Limnol. Oceanogr. 54, 1197–1209 (2009).CAS 
    Article 

    Google Scholar 
    14.Archer, S. K. et al. Pyrosome consumption by benthic organisms during blooms in the northeast Pacific and Gulf of Mexico. Ecology 99, 981–984 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    15.McFall-Ngai, M. et al. Animals in a bacterial world, a new imperative for the life sciences. Proc. Natl Acad. Sci. 110, 3229–3236 (2013).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    16.Sherr E. & Sherr B. Understanding roles of microbes in marine pelagic food webs: a brief history. in Microbial Ecology of the Oceans 27–44 (John Wiley & Sons Ltd, 2008).17.Falkowski, P. G., Fenchel, T. & Delong, E. F. The microbial engines that drive earth’s biogeochemical cycles. Science 320, 1034–1039 (2008).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    18.Décima, M., Stukel, M. R., López-López, L. & Landry, M. R. The unique ecological role of pyrosomes in the Eastern Tropical Pacific. Limnol. Oceanogr. 64, 728–743 (2019).Article 

    Google Scholar 
    19.Gauns, M., Mochemadkar, S., Pratihary, A., Roy, R. & Naqvi, S. W. A. Biogeochemistry and ecology of Pyrosoma spinosum from the Central Arabian Sea. Zool. Stud. 54, 3 (2015).Article 
    CAS 

    Google Scholar 
    20.Bowlby, M. R., Widder, E. A. & Case, J. F. Patterns of stimulated bioluminescence in two pyrosomes (Tunicata: Pyrosomatidae). Biol. Bull. 179, 340–350 (1990).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    21.Haddock, S. H. D., Moline, M. A. & Case, J. F. Bioluminescence in the sea. Annu. Rev. Mar. Sci. 2, 443–493 (2010).Article 

    Google Scholar 
    22.Swift, E., Biggley, W. H. & Napora, T. A. The bioluminescence emission spectra of Pyrosoma atlanticum, P. spinosum (Tunicata), Euphausia tenera (Crustacea) and Gonostoma sp. (Pisces). J. Mar. Biol. Assoc. UK 57, 817–823 (1977).23.Martínez‐García, M. et al. Ammonia-oxidizing Crenarchaeota and nitrification inside the tissue of a colonial ascidian. Environ. Microbiol. 10, 2991–3001 (2008).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    24.Donia, M. S. et al. Complex microbiome underlying secondary and primary metabolism in the tunicate-Prochloron symbiosis. Proc. Natl Acad. Sci. 108, E1423–E1432 (2011).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    25.Kwan, J. C. et al. Host control of symbiont natural product chemistry in cryptic populations of the tunicate Lissoclinum patella. PLoS ONE 9, e95850 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    26.Purcell, J. E. & Arai, M. N. Interactions of pelagic cnidarians and ctenophores with fish: a review. Hydrobiologia. 451, 27–44 (2001).Article 

    Google Scholar 
    27.Delannoy, C. M. J., Houghton, J. D. R., Fleming, N. E. C. & Ferguson, H. W. Mauve stingers (Pelagia noctiluca) as carriers of the bacterial fish pathogen Tenacibaculum maritimum. Aquaculture. 311, 255–257 (2011).Article 

    Google Scholar 
    28.Lee, M. D., Kling, J. D., Araya, R. & Ceh, J. Jellyfish life stages shape associated microbial communities, while a core microbiome is maintained across all. Front. Microbiol. 9, 1534 (2018).29.Troussellier, M., Escalas, A., Bouvier, T. & Mouillot, D. Sustaining rare marine microorganisms: macroorganisms as repositories and dispersal agents of microbial diversity. Front. Microbiol. 8 (2017).30.Brodeur, R. et al. An unusual gelatinous plankton event in the NE Pacific: the Great Pyrosome Bloom of 2017. PICES Press; Sidney Vol. 26, 22–27 (Winter, 2018).31.Sutherland, K. R., Sorensen, H. L., Blondheim, O. N., Brodeur, R. D. & Galloway, A. W. E. Range expansion of tropical pyrosomes in the northeast Pacific Ocean. Ecology 99, 2397–2399 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    32.Miller, R. R. et al. Distribution of pelagic Thaliaceans, Thetys vagina and Pyrosoma Atlanticum, during a period of mass occurrence within the California current. CalCOFI Rep. 60, (2019).33.Guigand, C. M., Cowen, R. K., Llopiz, J. K. & Richardson, D. E. A coupled asymmetrical multiple opening closing net with environmental sampling system. Mar. Technol. Soc. J. 39, 22–24 (2005).34.Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    35.Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    36.Cole, J. R. et al. Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 42, D633–D642 (2014).CAS 
    Article 

    Google Scholar 
    37.Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).CAS 
    PubMed 
    PubMed Central 
    Article 

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

    Google Scholar 
    39.Johnson, M. et al. NCBI BLAST: a better web interface. Nucleic Acids Res. 36, W5–W9 (2008).CAS 
    PubMed 
    PubMed Central 
    Article 

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

    Google Scholar 
    41.Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Aust. J. Ecol. 18, 117–143 (1993).Article 

    Google Scholar 
    42.Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    43.McMurdie, P. J. & Holmes, S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput. Biol. 10, e1003531 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    44.Duperron, S. Microbial Symbioses 168 p. (Elsevier, 2016).45.Schmitt, S. et al. Assessing the complex sponge microbiota: core, variable and species-specific bacterial communities in marine sponges. ISME J. 6, 564–576 (2012).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    46.Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: and this is not optional. Front. Microbiol. 8 (2017).47.Urbanczyk, H., Ast, J. C., Higgins, M. J., Carson, J. & Dunlap, P. V. Reclassification of Vibrio fischeri, Vibrio logei, Vibrio salmonicida and Vibrio wodanis as Aliivibrio fischeri gen. nov., comb. nov., Aliivibrio logei comb. nov., Aliivibrio salmonicida comb. nov. and Aliivibrio wodanis comb. nov. Int. J. Syst. Evol. Microbiol. 57, 2823–2829 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    48.Stecher, G., Tamura, K. & Kumar, S. Molecular Evolutionary Genetics Analysis (MEGA) for macOS. Mol. Biol. Evol. 37, 1237–1239 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    49.Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 47, W256–W259 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    50.Booth, B. C. Marine phytoplankton. A guide to naked flagellates and coccolithophorids (C. R. Tomas [ed.]). Limnol. Oceanogr. 39, 982–983 (1994).Article 

    Google Scholar 
    51.Halse, G. R. & Syvertsen, E. E. Chapter 2—marine diatoms. in Identifying Marine Diatoms and Dinoflagellates (ed. Tomas C. R.) 5–385 (Academic Press, 1996).52.Steidinger, K. A. & Tangen, K. Chapter 3—dinoflagellates. in Identifying Marine Diatoms and Dinoflagellates (ed. Tomas C. R.) 387–584 (Academic Press, 1996).53.Daniels, C. & Breitbart, M. Bacterial communities associated with the ctenophores Mnemiopsis leidyi and Beroe ovata. FEMS Microbiol. Ecol. 82, 90–101 (2012).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    54.Kramar, M. K., Tinta, T., Lučić, D., Malej, A. & Turk, V. Bacteria associated with moon jellyfish during bloom and post-bloom periods in the Gulf of Trieste (northern Adriatic). PLoS ONE 14, e0198056 (2019).Article 
    CAS 

    Google Scholar 
    55.Hernandez-Agreda, A., Leggat, W., Bongaerts, P., Herrera, C. & Ainsworth, T. D. Rethinking the coral microbiome: simplicity exists within a diverse microbial biosphere. mBio 9, e00812–18 (2018).56.Webster, N. S. & Bourne, D. Bacterial community structure associated with the Antarctic soft coral, Alcyonium antarcticum. FEMS Microbiol. Ecol. 59, 81–94 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    57.Rodrigues, C. F., Hilário, A., Cunha, M. R., Weightman, A. J. & Webster, G. Microbial diversity in Frenulata (Siboglinidae, Polychaeta) species from mud volcanoes in the Gulf of Cadiz (NE Atlantic). Antonie Van Leeuwenhoek 100, 83–98 (2011).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    58.McCann, J., Stabb, E. V., Millikan, D. S. & Ruby, E. G. Population dynamics of Vibrio fischeri during Infection of Euprymna scolopes. Appl. Environ. Microbiol. 69, 5928–5934 (2003).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    59.Hammann, S., Moss, A. & Zimmer, M. Sterile surfaces of Mnemiopsis leidyi; (Ctenophora) in bacterial suspension—a key to invasion success? Open J. Mar. Sci. 05, 237–246 (2015).Article 

    Google Scholar 
    60.Hammer, T. J., Sanders, J. G. & Fierer, N. Not all animals need a microbiome. FEMS Microbiol. Lett. 366, fnz117 https://doi.org/10.1093/femsle/fnz117 (2019).61.Nedashkovskaya, O. I., Kukhlevskiy, A. D., Zhukova, N. V. & Kim, S. B. Amylibacter ulvae sp. nov., a new alphaproteobacterium isolated from the Pacific green alga Ulva fenestrata. Arch. Microbiol. 198, 251–256 (2016).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    62.Burke, C., Thomas, T., Lewis, M., Steinberg, P. & Kjelleberg, S. Composition, uniqueness and variability of the epiphytic bacterial community of the green alga Ulva australis. ISME J. 5, 590–600 (2011).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    63.Catão, E. C. P. et al. Shear stress as a major driver of marine biofilm communities in the NW Mediterranean Sea. Front. Microbiol. 10 (2019).64.Chafee, M. et al. Recurrent patterns of microdiversity in a temperate coastal marine environment. ISME J. 12, 237–252 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    65.Bondoso, J. et al. Roseimaritima ulvae gen. nov., sp. nov. and Rubripirellula obstinata gen. nov., sp. nov. two novel planctomycetes isolated from the epiphytic community of macroalgae. Syst. Appl. Microbiol. 38, 8–15 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    66.Zhu, P., Li, Q. & Wang, G. Unique microbial signatures of the Alien Hawaiian marine sponge Suberites zeteki. Microb. Ecol. 55, 406–414 (2008).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    67.Pimentel-Elardo, S., Wehrl, M., Friedrich, A. B., Jensen, P. R. & Hentschel, U. Isolation of planctomycetes from Aplysina sponges. Aquat. Microb. Ecol. 33, 239–245 (2003).Article 

    Google Scholar 
    68.da Silva Oliveira, F. A. et al. Microbial epibionts of the colonial ascidians Didemnum galacteum and Cystodytes sp. Symbiosis 59, 57–63 (2013).Article 

    Google Scholar 
    69.Yakimov, M. M. et al. Phylogenetic survey of metabolically active microbial communities associated with the deep-sea coral Lophelia pertusa from the Apulian plateau, Central Mediterranean Sea. Deep Sea Res. A Oceanogr. Res. Pap. 53, 62–75 (2006).Article 

    Google Scholar 
    70.Duque-Alarcón, A., Santiago-Vázquez, L. Z. & Kerr, R. G. A microbial community analysis of the octocoral Eunicea fusca. Electron. J. Biotechnol. 15, 15–15 (2012).
    Google Scholar 
    71.Wiegand, S., Jogler, M. & Jogler, C. On the maverick Planctomycetes. FEMS Microbiol. Rev. 42, 739–760 (2018).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    72.Lage, O. M. & Bondoso, J. Planctomycetes and macroalgae, a striking association. Front. Microbiol. 5 (2014).73.Ward, A. C. & Bora, N. Diversity and biogeography of marine Actinobacteria. Curr. Opin. Microbiol. 9, 279–286 (2006).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    74.Hahn, M. W. Description of seven candidate species affiliated with the phylum Actinobacteria, representing planktonic freshwater bacteria. Int. J. Syst. Evol. Microbiol. 59, 112–117 (2009).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    75.Gandhimathi, R. et al. Antimicrobial potential of sponge associated marine actinomycetes. J. Mycol. Méd. 18, 16–22 (2008).Article 

    Google Scholar 
    76.Abdelmohsen, U. R., Bayer, K. & Hentschel, U. Diversity, abundance and natural products of marine sponge-associated actinomycetes. Nat. Prod. Rep. 31, 381–399 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    77.Wu, Z. et al. A new tetrodotoxin-producing actinomycete, Nocardiopsis dassonvillei, isolated from the ovaries of puffer fish Fugu rubripes. Toxicon. 45, 851–859 (2005).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    78.Reichenbach, H. The ecology of the myxobacteria. Environ. Microbiol. 1, 15–21 (1999).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    79.Marshall, R. C. & Whitworth, D. E. Is “Wolf-Pack” predation by antimicrobial bacteria cooperative? Cell behaviour and predatory mechanisms indicate profound selfishness, even when working alongside Kin. BioEssays 41, 1800247 (2019).Article 

    Google Scholar 
    80.Welsh, R. M. et al. Bacterial predation in a marine host-associated microbiome. ISME J. 10, 1540–1544 (2016).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    81.Wang, Z., Kadouri, D. E. & Wu, M. Genomic insights into an obligate epibiotic bacterial predator: Micavibrio aeruginosavorus ARL-13. BMC Genomics 12, 453 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    82.Garcia, G. D. et al. Metagenomic analysis of healthy and white plague-affected Mussismilia braziliensis corals. Microb. Ecol. 65, 1076–1086 (2013).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    83.Rosales, S. M. et al. Microbiome differences in disease-resistant vs. susceptible Acropora corals subjected to disease challenge assays. Sci. Rep. 9, 18279 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    84.Evans, A. G. L. et al. Predatory activity of Myxococcus xanthus outer-membrane vesicles and properties of their hydrolase cargo. Microbiology 158, 2742–2752 (2012).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    85.Sudo, S. & Dworkin, M. Bacteriolytic enzymes produced by Myxococcus xanthus. J. Bacteriol. 110, 236–245 (1972).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    86.Tessler, M. et al. A putative chordate luciferase from a cosmopolitan tunicate indicates convergent bioluminescence evolution across phyla. Sci. Rep. 10, 17724 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    87.Berger, A. et al. Microscopic and Genetic Characterization of Bacterial Symbionts With Bioluminescent Potential in Pyrosoma Atlanticum. Frontiers in Marine Science. 8 https://doi.org/10.3389/fmars.2021.606818 (2021).88.Leisman, G., Cohn, D. H. & Nealson, K. H. Bacterial origin of luminescence in marine animals. Science 208, 1271–1273 (1980).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    89.Mackie, G. O. & Bone, Q. Luminescence and associated effector activity in Pyrosoma (Tunicata: Pyrosomida). Proc. R. Soc. Lond. B Biol. Sci. 202, 483–495 (1978).Article 

    Google Scholar 
    90.Nyholm, S. V. & McFall-Ngai, M. The winnowing: establishing the squid–vibrio symbiosis. Nat. Rev. Microbiol. 2, 632–642 (2004).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    91.Takemura, A. F., Chien, D. M. & Polz M. F. Associations and dynamics of Vibrionaceae in the environment, from the genus to the population level. Front. Microbiol. 5 (2014).92.Barnes, E. M., Carter, E. L. & Lewis, J. D. Predicting microbiome function across space is confounded by strain-level differences and functional redundancy across taxa. Front. Microbiol. 11 (2020).93.Tian, L. et al. Deciphering functional redundancy in the human microbiome. bioRxiv 176313 https://doi.org/10.1101/176313 (2017).94.Kaeding, A. J. et al. Phylogenetic diversity and cosymbiosis in the bioluminescent symbioses of “Photobacterium mandapamensis”. Appl. Environ. Microbiol. 73, 3173–3182 (2007).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    95.Baker, L. J. et al. Diverse deep-sea anglerfishes share a genetically reduced luminous symbiont that is acquired from the environment. eLife 8 e47606 (2019).96.Godeaux, J. E. A., Bone, Q. & Braconnot, J. C. Anatomy of Thaliacea. in The Biology of Pelagic Tunicates (Oxford University Press, 1998).97.Alldredge, A. L. & Madin, L. P. Pelagic tunicates: unique herbivores in the marine plankton. BioScience. 32, 655–663 (1982).Article 

    Google Scholar 
    98.Bone, Q., Carre, C. & Ryan, K. P. The endostyle and the feeding filter in salps (Tunicata). J. Mar. Biol. Assoc. UK 80, 523–534 (2000).Article 

    Google Scholar 
    99.Sutherland, K. R., Madin, L. P. & Stocker, R. Filtration of submicrometer particles by pelagic tunicates. Proc. Natl Acad. Sci. 107, 15129–15134 (2010).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    100.Dadon-Pilosof, A. et al. Surface properties of SAR11 bacteria facilitate grazing avoidance. Nat. Microbiol. 2, 1608–1615 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    101.Larson, R. J. Daily ration and predation by medusae and ctenophores in Saanich Inlet, B.C., Canada. Neth. J. Sea Res. 21, 35–44 (1987).Article 

    Google Scholar 
    102.Suchman, C. L., Daly, E. A., Keister, J. E., Peterson, W. T. & Brodeur, R. D. Feeding patterns and predation potential of scyphomedusae in a highly productive upwelling region. Mar. Ecol. Prog. Ser. 358, 161–172 (2008).Article 

    Google Scholar 
    103.Bennke, C. M. et al. The distribution of phytoplankton in the Baltic Sea assessed by a prokaryotic 16S rRNA gene primer system. J. Plankton Res. 40, 244–254 (2018).CAS 
    Article 

    Google Scholar 
    104.Green, B. R. Chloroplast genomes of photosynthetic eukaryotes. Plant J. 66, 34–44 (2011).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    105.Luo, J. Y. et al. Gelatinous zooplankton-mediated carbon flows in the global oceans: a data-driven modeling study. Glob. Biogeochem. Cycles. 34, e2020GB006704 (2020).106.Dadon‐Pilosof, A., Lombard, F., Genin, A., Sutherland, K. R. & Yahel, G. Prey taxonomy rather than size determines salp diets. Limnol. Oceanogr. 64, 1996–2010 (2019).Article 

    Google Scholar 
    107.Brand, A., Liz, A., Micah, A., Marjorie, H. & Jo, S. Beyond Authorship: Attribution, Contribution, Collaboration, and Credit. Learned Publishing. 28, 151–155 (2015).Article 

    Google Scholar  More

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    Landscape structure affects the sunflower visiting frequency of insect pollinators

    1.Stanley, D. & Stout, J. Pollinator sharing between mass-flowering oilseed rape and co-flowering wild plants: implications for wild plant pollination. Plant Ecol. 215, 315–325. https://doi.org/10.1007/s11258-014-0301-7 (2014).Article 

    Google Scholar 
    2.Kovacs-Hostyanszki, A. et al. Contrasting effects of mass-flowering crops on bee pollination of hedge plants at different spatial and temporal scales. Ecol. Appl. 23, 1938–1946. https://doi.org/10.1890/12-2012.1 (2013).Article 
    PubMed 

    Google Scholar 
    3.Holzschuh, A. et al. Mass-flowering crops dilute pollinator abundance in agricultural landscapes across Europe. Ecol. Lett. 19, 1228–1236. https://doi.org/10.1111/ele.12657 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    4.Potts, S. G. et al. Global pollinator declines: trends, impacts and drivers. Trends Ecol. Evol. 25, 345–353. https://doi.org/10.1016/j.tree.2010.01.007 (2010).Article 
    PubMed 

    Google Scholar 
    5.Kremen, C., Williams, N. M. & Thorp, R. W. Crop pollination from native bees at risk from agricultural intensification. Proc Natl Acad Sci U S A 99, 16812–16816. https://doi.org/10.1073/pnas.262413599 (2002).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    6.Ollerton, J., Erenler, H., Edwards, M. & Crockett, R. Pollinator declines: extinctions of aculeate pollinators in Britain and the role of large-scale agricultural changes. Science 346, 1360–1362. https://doi.org/10.1126/science.1257259 (2014).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    7.Kovacs-Hostyanszki, A. et al. Ecological intensification to mitigate impacts of conventional intensive land use on pollinators and pollination. Ecol. Lett. 20, 673–689. https://doi.org/10.1111/ele.12762 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    8.Tscharntke, T., Klein, A. M., Kruess, A., Steffan-Dewenter, I. & Thies, C. Landscape perspectives on agricultural intensification and biodiversity: ecosystem service management. Ecol. Lett. 8, 857–874. https://doi.org/10.1111/j.1461-0248.2005.00782.x (2005).Article 

    Google Scholar 
    9.Holland, J. M. et al. Semi-natural habitats support biological control, pollination and soil conservation in Europe: a review. Agron. Sustain. Dev. https://doi.org/10.1007/s13593-017-0434-x (2017).Article 

    Google Scholar 
    10.Garibaldi, L. A. et al. Stability of pollination services decreases with isolation from natural areas despite honey bee visits. Ecol. Lett. 14, 1062–1072. https://doi.org/10.1111/j.1461-0248.2011.01669.x (2011).Article 
    PubMed 

    Google Scholar 
    11.Bartomeus, I. et al. Contribution of insect pollinators to crop yield and quality varies with agricultural intensification. PeerJ 2, e328. https://doi.org/10.7717/peerj.328 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    12.Holzschuh, A., Dudenhoffer, J. H. & Tscharntke, T. Landscapes with wild bee habitats enhance pollination, fruit set and yield of sweet cherry. Biol. Conserv. 153, 101–107. https://doi.org/10.1016/j.biocon.2012.04.032 (2012).Article 

    Google Scholar 
    13.Marini, L. et al. Crop management modifies the benefits of insect pollination in oilseed rape. Agric. Ecosyst. Environ. 207, 61–66. https://doi.org/10.1016/j.agee.2015.03.027 (2015).Article 

    Google Scholar 
    14.Persson, A. S. & Smith, H. G. Seasonal persistence of bumblebee populations is affected by landscape context. Agric. Ecosyst. Environ. 165, 201–209. https://doi.org/10.1016/j.agee.2012.12.008 (2013).Article 

    Google Scholar 
    15.Rundlof, M., Persson, A. S., Smith, H. G. & Bommarco, R. Late-season mass-flowering red clover increases bumble bee queen and male densities. Biol. Conserv. 172, 138–145. https://doi.org/10.1016/j.biocon.2014.02.027 (2014).Article 

    Google Scholar 
    16.Westphal, C., Steffan-Dewenter, I. & Tscharntke, T. Mass flowering oilseed rape improves early colony growth but not sexual reproduction of bumblebees. J. Appl. Ecol. 46, 187–193. https://doi.org/10.1111/j.1365-2664.2008.01580.x (2009).Article 

    Google Scholar 
    17.Williams, N. M., Regetz, J. & Kremen, C. Landscape-scale resources promote colony growth but not reproductive performance of bumble bees. Ecology 93, 1049–1058. https://doi.org/10.1890/11-1006.1 (2012).Article 
    PubMed 

    Google Scholar 
    18.Steffan-Dewenter, I., Munzenberg, U., Burger, C., Thies, C. & Tscharntke, T. Scale-dependent effects of landscape context on three pollinator guilds. Ecology 83, 1421–1432. https://doi.org/10.2307/3071954 (2002).Article 

    Google Scholar 
    19.Steffan-Dewenter, I., Münzenberg, U. & Tscharntke, T. Pollination, seed set and seed predation on a landscape scale. Proc. Natl. Acad. Sci. USA 268, 1685–1690. https://doi.org/10.1098/rspb.2001.1737 (2001).CAS 
    Article 

    Google Scholar 
    20.Bartual, A. et al. The potential of different semi-natural habitats to sustain pollinators and natural enemies in European agricultural landscapes. Agric. Ecosyst. Environ. 279, 43–52. https://doi.org/10.1016/j.agee.2019.04.009 (2019).Article 

    Google Scholar 
    21.Ewers, R. M. & Didham, R. K. Confounding factors in the detection of species responses to habitat fragmentation. Biol. Rev. Camb. Philos. Soc. 81, 117–142. https://doi.org/10.1017/s1464793105006949 (2006).Article 
    PubMed 

    Google Scholar 
    22.Blaauw, B. R. & Isaacs, R. Larger patches of diverse floral resources increase insect pollinator density, diversity, and their pollination of native wild flowers. Basic Appl. Ecol. 15, 701–711. https://doi.org/10.1016/j.baae.2014.10.001 (2014).Article 

    Google Scholar 
    23.Martin, E. A. et al. The interplay of landscape composition and configuration: new pathways to manage functional biodiversity and agroecosystem services across Europe. Ecol. Lett. 22, 1083–1094. https://doi.org/10.1111/ele.13265 (2019).Article 
    PubMed 

    Google Scholar 
    24.Bihaly, Á., Dóra, V., Lajos, K. & Sárospataki, M. Effect of semi-natural habitat patches on the pollinator assemblages of sunflower in an intensive agricultural landscape. Tájökológiai Lapok 16, 45–52 (2018).
    Google Scholar 
    25.Foldesi, R. et al. Relationships between wild bees, hoverflies and pollination success in apple orchards with different landscape contexts. Agric. For. Entomol. 18, 68–75. https://doi.org/10.1111/afe.12135 (2016).Article 

    Google Scholar 
    26.Sárospataki, M. et al. The role of local and landscape level factors in determining bumblebee abundance and richness. Acta Zool. Acad. Sci. Hung. 62, 387–407. https://doi.org/10.17109/AZH.62.4.387.2016 (2016).Article 

    Google Scholar 
    27.Schellhorn, N. A., Gagic, V. & Bommarco, R. Time will tell: resource continuity bolsters ecosystem services. Trends Ecol. Evol. 30, 524–530. https://doi.org/10.1016/j.tree.2015.06.007 (2015).Article 
    PubMed 

    Google Scholar 
    28.Tscharntke, T. et al. Landscape moderation of biodiversity patterns and processes: eight hypotheses. Biol. Rev. Camb. Philos. Soc. 87, 661–685. https://doi.org/10.1111/j.1469-185X.2011.00216.x (2012).Article 
    PubMed 

    Google Scholar 
    29.Stephens, A. E. A. & Myers, J. H. Resource concentration by insects and implications for plant populations. J. Ecol. 100, 923–931. https://doi.org/10.1111/j.1365-2745.2012.01971.x (2012).Article 

    Google Scholar 
    30.Tscheulin, T., Neokosmidis, L., Petanidou, T. & Settele, J. Influence of landscape context on the abundance and diversity of bees in Mediterranean olive groves. Bull. Entomol. Res. 101, 557–564. https://doi.org/10.1017/S0007485311000149 (2011).CAS 
    Article 
    PubMed 

    Google Scholar 
    31.Kennedy, C. M. et al. A global quantitative synthesis of local and landscape effects on wild bee pollinators in agroecosystems. Ecol. Lett. 16, 584–599. https://doi.org/10.1111/ele.12082 (2013).Article 
    PubMed 

    Google Scholar 
    32.Eurostat. Archive: Main annual crop statistics, https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Main_annual_crop_statistics&oldid=389868#Oilseeds (2018).33.KSH. STADAT tables – Agriculture. http://www.ksh.hu/docs/hun/xstadat/xstadat_eves/i_omn007b.html. (KSH, 2019).34.Hevia, V. et al. Bee diversity and abundance in a livestock drove road and its impact on pollination and seed set in adjacent sunflower fields. Agric. Ecosyst. Environ. 232, 336–344. https://doi.org/10.1016/j.agee.2016.08.021 (2016).Article 

    Google Scholar 
    35.Silva, C. et al. Bee pollination highly improves oil quality in sunflower. Sociobiology 65, 583–590. https://doi.org/10.13102/sociobiology.v65i4.3367 (2018).Article 

    Google Scholar 
    36.Terzić, S., Miklič, V. & Čanak, P. Review of 40 years of research carried out in Serbia on sunflower pollination. OCL 24, D608 (2017).Article 

    Google Scholar 
    37.Perrot, T. et al. Experimental quantification of insect pollination on sunflower yield, reconciling plant and field scale estimates. Basic Appl. Ecol. 34, 75–84. https://doi.org/10.1016/j.baae.2018.09.005 (2019).Article 

    Google Scholar 
    38.Martin, C. S. & Farina, W. M. Honeybee floral constancy and pollination efficiency in sunflower (Helianthus annuus) crops for hybrid seed production. Apidologie 47, 161–170 (2016).Article 

    Google Scholar 
    39.DeGrandi-Hoffman, G. & Watkins, J. C. The foraging activity of honey bees Apis mellifera and non—Apis bees on hybrid sunflowers (Helianthus annuus) and its influence on cross—pollination and seed set. J. Apic. Res. 39, 37–45. https://doi.org/10.1080/00218839.2000.11101019 (2000).Article 

    Google Scholar 
    40.Cerrutti, N. & Pontet, C. Differential attractiveness of sunflower cultivars to the honeybee Apis mellifera L. OCL 23, D204 (2016).Article 

    Google Scholar 
    41.Chambó, E. D., Garcia, R. C., Oliveira, N. T. E. D. & Duarte-Júnior, J. B. Honey bee visitation to sunflower: effects on pollination and plant genotype. Sci. Agric. 68, 647–651 (2011).Article 

    Google Scholar 
    42.Oz, M., Karasu, A., Cakmak, I., Goksoy, A. T. & Turan, Z. M. Effects of honeybee (Apis mellifera) pollination on seed set in hybrid sunflower (Helianthus annuus L.). Afr. J. Biotechnol. 8 (2009).43.Puškadija, Z. et al. Influence of weather conditions on honey bee visits (Apis mellifera carnica) during sunflower (Helianthus annuus L.) blooming period. Poljoprivreda 13, 230–233 (2007).
    Google Scholar 
    44.Greenleaf, S. S. & Kremen, C. Wild bees enhance honey bees’ pollination of hybrid sunflower. Proc. Natl. Acad. Sci. USA 103, 13890–13895 (2006).ADS 
    CAS 
    Article 

    Google Scholar 
    45.Nderitu, J., Nyamasyo, G., Kasina, M. & Oronje, M. Diversity of sunflower pollinators and their effect on seed yield in Makueni District, Eastern Kenya. Span. J. Agric. Res. 6, 271–278 (2008).Article 

    Google Scholar 
    46.Carvalheiro, L. G. et al. Natural and within-farmland biodiversity enhances crop productivity. Ecol. Lett. 14, 251–259. https://doi.org/10.1111/j.1461-0248.2010.01579.x (2011).Article 
    PubMed 

    Google Scholar 
    47.Sardiñas, H. S. & Kremen, C. Pollination services from field-scale agricultural diversification may be context-dependent. Agric. Ecosyst. Environ. 207, 17–25 (2015).Article 

    Google Scholar 
    48.Riedinger, V., Renner, M., Rundlof, M., Steffan-Dewenter, I. & Holzschuh, A. Early mass-flowering crops mitigate pollinator dilution in late-flowering crops. Landscape Ecol. 29, 425–435. https://doi.org/10.1007/s10980-013-9973-y (2014).Article 

    Google Scholar 
    49.Bennett, A. B. & Isaacs, R. Landscape composition influences pollinators and pollination services in perennial biofuel plantings. Agric. Ecosyst. Environ. 193, 1–8. https://doi.org/10.1016/j.agee.2014.04.016 (2014).Article 

    Google Scholar 
    50.Lowenstein, D. M., Huseth, A. S. & Groves, R. L. Response of wild bees (Hymenoptera: Apoidea: Anthophila) to surrounding land cover in Wisconsin pickling cucumber. Environ. Entomol. 41, 532–540. https://doi.org/10.1603/EN11241 (2012).CAS 
    Article 
    PubMed 

    Google Scholar 
    51.Pfister, S. C. et al. Dominance of cropland reduces the pollen deposition from bumble bees. Sci. Rep. 8, 13873. https://doi.org/10.1038/s41598-018-31826-3 (2018).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    52.Gathmann, A. & Tscharntke, T. Foraging ranges of solitary bees. J. Anim. Ecol. 71, 757–764. https://doi.org/10.1046/j.1365-2656.2002.00641.x (2002).Article 

    Google Scholar 
    53.Greenleaf, S. S., Williams, N. M., Winfree, R. & Kremen, C. Bee foraging ranges and their relationship to body size. Oecologia 153, 589–596. https://doi.org/10.1007/s00442-007-0752-9 (2007).ADS 
    Article 
    PubMed 

    Google Scholar 
    54.Lihoreau, M., Chittka, L., Le Comber, S. C. & Raine, N. E. Bees do not use nearest-neighbour rules for optimization of multi-location routes. Biol. Lett. 8, 13–16. https://doi.org/10.1098/rsbl.2011.0661 (2012).Article 
    PubMed 

    Google Scholar 
    55.Berger-Tal, O. & Bar-David, S. Recursive movement patterns: review and synthesis across species. Ecosphere 6, 149. https://doi.org/10.1890/es15-00106.1 (2015).Article 

    Google Scholar 
    56.Wesserling, J. Habitatwahl und Ausbreitungsverhalten von Stechimmen (Hymenoptera: Aculeata) in Sandgebieten unterschiedlicher Sukzessionsstadien, University of Karlsruhe, (1996).57.Hagler, J. R., Mueller, S., Teuber, L. R., Machtley, S. A. & Van Deynze, A. Foraging range of honey bees, Apis mellifera, in alfalfa seed production fields. J. Insect Sci. 11, 144 (2011).PubMed 
    PubMed Central 

    Google Scholar 
    58.Couvillon, M. J. et al. Honey bee foraging distance depends on month and forage type. Apidologie 46, 61–70. https://doi.org/10.1007/s13592-014-0302-5 (2015).Article 

    Google Scholar 
    59.Beekman, M. & Ratnieks, F. L. W. Long-range foraging by the honey-bee, Apis mellifera L.. Funct. Ecol. 14, 490–496. https://doi.org/10.1046/j.1365-2435.2000.00443.x (2000).Article 

    Google Scholar 
    60.Gary, N. E., Witherell, P. C. & Lorenzen, K. Effect of age on honey bee foraging distance and pollen collection. Environ. Entomol. 10, 950–952 (1981).Article 

    Google Scholar 
    61.Walther-Hellwig, K. & Frankl, R. Foraging habitats and foraging distances of bumblebees, Bombus spp. (Hym., Apidae), in an agricultural landscape. J. Appl. Entomol. 124, 299–306. https://doi.org/10.1046/j.1439-0418.2000.00484.x (2000).Article 

    Google Scholar 
    62.Dramstad, W. E. Do bumblebees (Hymenoptera: Apidae) really forage close to their nests?. J. Insect Behav. 9, 163–182. https://doi.org/10.1007/bf02213863 (1996).Article 

    Google Scholar 
    63.Knight, M. E. et al. An interspecific comparison of foraging range and nest density of four bumblebee (Bombus) species. Mol. Ecol. 14, 1811–1820 (2005).CAS 
    Article 

    Google Scholar 
    64.Wolf, S. & Moritz, R. F. Foraging distance in Bombus terrestris L. (Hymenoptera: Apidae). Apidologie 39, 419–427 (2008).Article 

    Google Scholar 
    65.Osborne, J. L. et al. Bumblebee flight distances in relation to the forage landscape. J. Anim. Ecol. 77, 406–415 (2008).Article 

    Google Scholar 
    66.Zurbuchen, A. et al. Maximum foraging ranges in solitary bees: only few individuals have the capability to cover long foraging distances. Biol. Conserv. 143, 669–676 (2010).Article 

    Google Scholar 
    67.Hopfenmuller, S., Steffan-Dewenter, I. & Holzschuh, A. Trait-specific responses of wild bee communities to landscape composition, configuration and local factors. PLoS ONE 9, e104439. https://doi.org/10.1371/journal.pone.0104439 (2014).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    68.Hung, K. J., Kingston, J. M., Albrecht, M., Holway, D. A. & Kohn, J. R. The worldwide importance of honey bees as pollinators in natural habitats. Proc R Soc Biol Sci Ser B 285, 20172140. https://doi.org/10.1098/rspb.2017.2140 (2018).Article 

    Google Scholar 
    69.Requier, F. et al. Honey bee diet in intensive farmland habitats reveals an unexpectedly high flower richness and a major role of weeds. Ecol. Appl. 25, 881–890. https://doi.org/10.1890/14-1011.1 (2015).Article 
    PubMed 

    Google Scholar 
    70.Bonoan, R. E., Gonzalez, J. & Starks, P. T. The perils of forcing a generalist to be a specialist: lack of dietary essential amino acids impacts honey bee pollen foraging and colony growth. J. Apic. Res. 59, 95–103. https://doi.org/10.1080/00218839.2019.1656702 (2020).Article 

    Google Scholar 
    71.Di Pasquale, G. et al. Influence of pollen nutrition on honey bee health: Do pollen quality and diversity matter?. PLoS ONE 8, e72016. https://doi.org/10.1371/journal.pone.0072016 (2013).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    72.Di Pasquale, G. et al. Variations in the availability of pollen resources affect honey bee health. PLoS ONE 11, e0162818. https://doi.org/10.1371/journal.pone.0162818 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    73.Alaux, C., Ducloz, F., Crauser, D. & Le Conte, Y. Diet effects on honeybee immunocompetence. Biol. Lett. 6, 562–565. https://doi.org/10.1098/rsbl.2009.0986 (2010).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    74.Colwell, M. J., Williams, G. R., Evans, R. C. & Shutler, D. Honey bee-collected pollen in agro-ecosystems reveals diet diversity, diet quality, and pesticide exposure. Ecol. Evol. 7, 7243–7253. https://doi.org/10.1002/ece3.3178 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    75.Zhang, G., St. Clair, A. L., Dolezal, A., Toth, A. L. & O’Neal, M. Honey Bee (Hymenoptera: Apidea) pollen forage in a highly cultivated agroecosystem: limited diet diversity and its relationship to virus resistance. J. Econ. Entomol. 113, 1062–1072 (2020).76.QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation. http://qgis.osgeo.org. (2009).77.FÖMI. MePAR, the Hungarian Agricultural Land Parcel Identification System, accessed 22 November 2019 http://www.mepar.hu/ (2016).78.McGarigal, K., Cushman, S. & Ene, E. Spatial Pattern Analysis Program for Categorical and Continuous Maps. available from http://www.umass.edu/landeco/research/fragstats/fragstats.html. (University of Massachusetts, 2012).79.McGarigal, K. FRAGSTATS help. Documentation for FRAGSTATS, 4. (2014).80.McGarigal, K. (2017). Landscape metrics for categorical map patterns. Lecture Notes. Available online: accessed 28 Feb 2021 http://www.umass.edu/landeco/teaching/landscape_ecology/schedule/chapter9_metrics.pdf.81.R Core Team. R: A Language and Environment for Statistical Computing. version 3.6.0. https://www.R-project.org. (R Foundation for Statistical Computing, 2020).82.Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528. https://doi.org/10.1093/bioinformatics/bty633 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    83.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models Usinglme4. Journal of Statistical Software 67, https://doi.org/10.18637/jss.v067.i01 (2015).84.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).
    Google Scholar 
    85.DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. v. 0.3.3.0. (2020).86.Fox, J. & Weisberg, S. An R Companion to Applied Regression, Third edition. Sage, Thousand Oaks CA. https://socialsciences.mcmaster.ca/jfox/Books/Companion/. (2019). More

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    Legacies of Indigenous land use shaped past wildfire regimes in the Basin-Plateau Region, USA

    1.Marlon, J. R. et al. Climate and human influences on global biomass burning over the past two millennia. Nat. Geosci. 1, 697–702 (2008).CAS 
    Article 

    Google Scholar 
    2.Pausas, J. G. & Keeley, J. E. A burning story: the role of fire in the history of life. BioScience 59, 593–601 (2009).Article 

    Google Scholar 
    3.Dennison, P. E., Brewer, S. C., Arnold, J. D. & Mortiz, M. A. Large wildfire trends in the western United States, 1984–2011. Geophys. Res. Lett. 41, 2928–2933 (2014).Article 

    Google Scholar 
    4.Abatzoglou, J. T. & Williams, A. P. Impact of anthropogenic climate change on wildfire across western US forests. Proc. Natl Acad. Sci. USA 113, 11770–11775 (2016).CAS 
    Article 

    Google Scholar 
    5.Westerling, A. L., Hidalgo, H. G., Cayan, D. R. & Swetnam, T. W. Warming and earlier spring increase western U.S. forest wildfire activity. Science 313, 940–943 (2006).CAS 
    Article 

    Google Scholar 
    6.Westerling, A. L. R. Increasing western US forest wildfire activity: Sensitivity to changes in the timing of spring. Phil. Trans. R. Soc. B Biol. Sci. 371, 1–10 (2016).
    Google Scholar 
    7.Schwartz, M. W. et al. Increasing elevation of fire in the Sierra Nevada and implications for forest change. Ecosphere 6, 1–10 (2015).Article 

    Google Scholar 
    8.Trujillo, E., Molotch, N. P., Goulden, M. L., Kelly, A. E. & Bales, R. C. Elevation-dependent influence of snow accumulation on forest greening. Nat. Geosci. 5, 705–709 (2012).CAS 
    Article 

    Google Scholar 
    9.Trouet, V., Taylor, A. H., Wahl, E. R., Skinner, C. N. & Stephens, S. L. Fire-climate interactions in the American West since 1400 CE. Geophys. Res. Lett. 37, 1–5 (2010).Article 

    Google Scholar 
    10.Kitchen, S. G. Climate and human influences on historical fire regimes (AD 1400–1900) in the eastern Great Basin (USA). Holocene 26, 397–407 (2016).Article 

    Google Scholar 
    11.Klimaszewski-Patterson, A., Weisberg, P. J., Mensing, S. A. & Scheller, R. M. Using paleolandscape modeling to investigate the impact of native American–set fires on pre-Columbian forests in the Southern Sierra Nevada, California, USA. Ann. Am. Assoc. Geographers 108, 1635–1654 (2018).
    Google Scholar 
    12.Taylor, A. H., Trouet, V., Skinner, C. N. & Stephens, S. Socioecological transitions trigger fire regime shifts and modulate fire-climate interactions in the Sierra Nevada, USA, 1600-2015 CE. Proc. Natl Acad. Sci. USA 113, 13684–13689 (2016).CAS 
    Article 

    Google Scholar 
    13.Ryan, K. C., Knapp, E. E. & Varner, J. M. Prescribed fire in North American forests and woodlands: history, current practice, and challenges. Front. Ecol. Environ. 11, e15–e24 (2013).14.Herring, E. M., Anderson, R. S. & San Miguel, G. L. Fire, vegetation, and Ancestral Puebloans: a sediment record from Prater Canyon in Mesa Verde National Park, Colorado, USA. Holocene 24, 853–863 (2014).Article 

    Google Scholar 
    15.Liebmann, M. J. et al. Native American depopulation, reforestation, and fire regimes in the Southwest United States, 1492-1900 CE. Proc. Natl Acad. Sci. USA 113, E696–E704 (2016).CAS 
    Article 

    Google Scholar 
    16.Swetnam, T. W. et al. Multiscale perspectives of fire, climate and humans in Western North America and the Jemez Mountains, USA. Phil. Trans. R. Soc. B Biol. Sci. 371, (2016).17.Levis, C. et al. Persistent effects of pre-Columbian plant domestication on Amazonian forest composition. Science 358, 925–931 (2017).Article 
    CAS 

    Google Scholar 
    18.Maezumi, S. Y. et al. The legacy of 4,500 years of polyculture agroforestry in the eastern Amazon. Nat. Plants 4, 540–547 (2018).Article 

    Google Scholar 
    19.Vale, T. R. The Pre-European landscape of the United States: Pristine or Humanized? in Fire, Native Peoples, and the Natural Landscape 1–39 (Island Press, 2002).20.Lightfoot, K. G. & Lopez, V. The study of indigenous management practices in California: an introduction. California Archaeol. 5, 209–219 (2013).Article 

    Google Scholar 
    21.Oswald, W. W. et al. Conservation implications of limited Native American impacts in pre-contact New England. Nat. Sustain. 3, 241–246 (2020).Article 

    Google Scholar 
    22.Vachula, R. S., Russell, J. M. & Huang, Y. Climate exceeded human management as the dominant control of fire at the regional scale in California’s Sierra Nevada. Environ. Res. Lett. 14, 104011 (2019).CAS 
    Article 

    Google Scholar 
    23.Baker, W. L. Indians and Fire in the Rocky Mountains: The Wilderness Hypothesis Renewed. in Fire, Native Peoples, and the Natural Landscape 41–76 (2002).24.Kimmerer, R. W. & Lake, F. K. Maintaining the Mosaic: the role of indigenous burning in land management. J. Forestry 99, 36–41 (2001).
    Google Scholar 
    25.Power, M. J. et al. Human fire legacies on ecological landscapes. Front. Earth Sci. 6, 1–6 (2018).Article 

    Google Scholar 
    26.Keeley, J. E. Native American impacts on fire regimes of the California coastal ranges. J. Biogeogr. 29, 303–320 (2002).Article 

    Google Scholar 
    27.Lightfoot, K. G., Parrish, O., Panich, L. M. & Schneider, T. D. California Indians and Their Environment: An Introduction (Univ. California Press, 2009).28.Ryan, K. C., Jones, A. T., Koerner, C. L. & Lee, K. M. Wildland Fire in Ecosystems: Effects of Fire on Cultural Resources and Archaeology. Vol. 3., 224. Rocky Mountain Research Station General Technical Report RMRS-GTR-42 (US Department of Agriculture, Forest Service, 2012).29.Roos, C. I., Zedeño, M. N., Hollenback, K. L. & Erlick, M. M. H. Indigenous impacts on North American Great Plains fire regimes of the past millennium. Proc. Natl Acad. Sci. USA 115, 8143–8148 (2018).CAS 
    Article 

    Google Scholar 
    30.Thomas, D. H. The 1981 Alta Toquima Village project: A Preliminary Report. Desert Research Institute Social Sciences and Humanities Publications Technical Report 27, 1–202 (Desert Research Institute Social Sciences and Humanities Publications, 1982).31.Benedict, J. B. Footprints in the snow: high-altitude cultural ecology of the Colorado Front Range, USA. Arctic Alpine Res. 24, 1–16 (1992).Article 

    Google Scholar 
    32.Stevens, N. E. Changes in prehistoric land use in the Alpine Sierra Nevada: a regional exploration using temperature-adjusted obsidian hydration rates. J. California Great Basin Anthropol. 25, 187–205 (2005).
    Google Scholar 
    33.Klimaszewski-Patterson, A. & Mensing, S. Paleoecological and paleolandscape modeling support for pre-Columbian burning by Native Americans in the Golden Trout Wilderness Area, California, USA. Landscape Ecol. https://doi.org/10.1007/s10980-020-01081-x (2020).34.Swetnam, T. W., Allen, C. D. & Betancourt, J. L. Applied historical ecology: using the past to manage for the future. Ecol. Appl. 9, 1189–1206 (1999).Article 

    Google Scholar 
    35.Roos, C. I., Williamson, G. J. & Bowman, D. M. Is anthropogenic pyrodiversity invisible in paleofire records? Fire 2, 42 (2019).Article 

    Google Scholar 
    36.Marlon, J. R. et al. Global biomass burning: a synthesis and review of Holocene paleofire records and their controls. Quat. Sci. Rev. 65, 5–25 (2013).Article 

    Google Scholar 
    37.Bowman, D. M. et al. The human dimension of fire regimes on Earth. J. Biogeogr. 38, 2223–2236 (2011).Article 

    Google Scholar 
    38.Adolf, C. et al. The sedimentary and remote-sensing reflection of biomass burning in Europe. Global Ecol. Biogeogr. 27, 199–212 (2018).Article 

    Google Scholar 
    39.Vachula, R. S. A meta-analytical approach to understanding the charcoal source area problem. Palaeogeogr. Palaeoclimatol. Palaeoecol. 562, 110111 https://doi.org/10.1016/j.palaeo.2020.110111 (2021).40.Munoz, S. E., Gajewski, K. & Peros, M. C. Synchronous environmental and cultural change in the prehistory of the northeastern United States. Proc. Natl Acad. Sci. USA 107, 22008–22013 (2010).CAS 
    Article 

    Google Scholar 
    41.Peros, M. C., Munoz, S. E., Gajewski, K. & Viau, A. E. Prehistoric demography of North America inferred from radiocarbon data. J. Archaeol. Sci. 37, 656–664 (2010).Article 

    Google Scholar 
    42.Brown, P. M., Heyerdahl, E. K., Kitchen, S. G. & Weber, M. H. Climate effects on historical fires (1630-1900) in Utah. Int. J. Wildland Fire 17, 28–39 (2008).Article 

    Google Scholar 
    43.Li, J. et al. Interdecadal modulation of El Niño amplitude during the past millennium. Nat. Clim. Change 1, 114–118 (2011).CAS 
    Article 

    Google Scholar 
    44.Gedalof, Z. & Peterson, D. L. & Mantua, N. J. Atmospheric, climatic, and ecological controls on extreme wildfire years in the Northwestern United States. Ecol. Appl. 15, 154–174 (2005).45.Morgan, P., Hardy, C. C., Swetnam, T. W., Rollins, M. G. & Long, D. G. Mapping fire regimes across time and space: Understanding coarse and fine-scale fire patterns. Int. J. Wildland Fire 10, 329–342 (2001).Article 

    Google Scholar 
    46.Marchetti, D. W., Harris, M. S., Bailey, C. M., Cerling, T. E. & Bergman, S. Timing of glaciation and last glacial maximum paleoclimate estimates from the Fish Lake Plateau, Utah. Quat. Res. 75, 183–195 (2011).CAS 
    Article 

    Google Scholar 
    47.Kemperman, J. A. & Barnes, B. V. Clone size in American aspens. Can. J. Botany 54, 2603–2607 (1976).Article 

    Google Scholar 
    48.Mitton, J. B. & Grant, M. C. Genetic variation and the natural history of quaking Aspen. BioScience 46, 25–31 (1996).Article 

    Google Scholar 
    49.Wood, S. N. Generalized Additive Models: an Introduction with R (Chapman and Hall, 2006).50.Hastie, T. & Tibshirani, R. Generalized additive models. Stat. Sci. 1, 297–318 (1992).
    Google Scholar 
    51.Madsen, D. B. & Simms, S. R. The Fremont complex: a behavioral perspective. J. World Prehistory 12, 255–336 (1998).Article 

    Google Scholar 
    52.Massimino, J. & Metcalfe, D. New form for the formative. Utah Archaeol. 12, 1–16 (1999).
    Google Scholar 
    53.Coltrain, J. B. & Leavitt, S. W. Climate and diet in Fremont prehistory: economic variability and abandonment of maize agriculture in the Great Salt Lake Basin. Am. Antiquity 67, 453–485 (2002).Article 

    Google Scholar 
    54.Magargal, K. E., Parker, A. K., Vernon, K. B., Rath, W. & Codding, B. F. The ecology of population dispersal: modeling alternative basin-plateau foraging strategies to explain the Numic expansion. Am. J. Hum. Biol. 29, 1–14 (2017).
    Google Scholar 
    55.Thomson, M. J., Balkovič, J., Krisztin, T. & MacDonald, G. M. Simulated impact of paleoclimate change on Fremont Native American maize farming in Utah, 850–1449 CE, using crop and climate models. Quat. Int. 507, 95–107 (2019).Article 

    Google Scholar 
    56.Finley, J. B., Robinson, E., Derose, R. J. & Hora, E. Multidecadal climate variability and the florescence of Fremont societies in Eastern Utah. American Antiquity 85, 93–112 (2020).Article 

    Google Scholar 
    57.Janetski, J. C. Archaeology and Native American history at Fish Lake, Central Utah. vol. 16 (Museum of Peoples and Cultures, Brigham Young University, 2010).58.Fowler, C. S. in Handbook of North American Indians (eds. Sturtevant, W. C. & D’Azevedo, W. L.) vol. 11, 64–97 (Smithsonian Institution, 1986).59.Sullivan, A. P. & Mink, P. B. Theoretical and socioecological consequences of fire foodways. Am. Antiquity 83, 619–638 (2018).Article 

    Google Scholar 
    60.Mann, M. E. et al. Global signatures and dynamical origins of the Little Ice Age and Medieval Climate Anomaly. Science 326, 1256–1260 (2009).CAS 
    Article 

    Google Scholar 
    61.Woodhouse, C. A., Meko, D. M., MacDonald, G. M., Stahle, D. W. & Cook, E. R. A 1,200-year perspective of 21st century drought in southwestern North America. Proc. Natl Acad. Sci. USA 107, 21283–21288 (2010).CAS 
    Article 

    Google Scholar 
    62.Meko, D. M. et al. Medieval drought in the upper Colorado River Basin. Geophys. Res. Lett. 34, 1–5 (2007).Article 

    Google Scholar 
    63.Salzer, M. W. & Kipfmueller, K. F. Reconstructed temperature and precipitation on a millennial timescale from tree-rings in the southern Colorado Plateau, U.S.A. Clim. Change 70, 465–487 (2005).CAS 
    Article 

    Google Scholar 
    64.Knight, T. A., Meko, D. M. & Baisan, C. H. A bimillennial-length tree-ring reconstruction of precipitation for the Tavaputs Plateau, Northeastern Utah. Quat. Res. 73, 107–117 (2010).Article 

    Google Scholar 
    65.Margolis, E. Q. & Swetnam, T. W. Historical fire-climate relationships of upper elevation fire regimes in the south-western United States. Int. J. Wildland Fire 22, 588–598 (2013).Article 

    Google Scholar 
    66.Calder, W. J., Parker, D., Stopka, C. J., Jiménez-Moreno, G. & Shuman, B. N. Medieval warming initiated exceptionally large wildfire outbreaks in the Rocky Mountains. Proc. Natl Acad. Sci. USA 112, 13261–13266 (2015).CAS 
    Article 

    Google Scholar 
    67.Bliege, R. B., Codding, B. F., Kauhanen, P. G. & Bird, D. W. Aboriginal hunting buffers climate-driven fire-size variability in Australia’s spinifex grasslands. Proc. Natl Acad. Sci. USA 109, 10287–10292 (2012).Article 

    Google Scholar 
    68.Parisien, M. A. et al. The spatially varying influence of humans on fire probability in North America. Environ. Res. Lett. 11, 075005 (2016).Article 

    Google Scholar 
    69.Codding, B. F. et al. Socioecological dynamics structuring the spread of farming in the North American Basin-Plateau Region. Environ. Archaeol. (in review).70.Robinson, E., Nicholson, C. & Kelly, R. L. The importance of spatial data to open-access national archaeological databases and the development of paleodemography research. Adv. Archaeol. Pract. 7, 395–408 (2019).Article 

    Google Scholar 
    71.Marlon, J. R. et al. Long-term perspective on wildfires in the western USA. Proc. Natl Acad. Sci. USA 109, 535–543 (2012).Article 

    Google Scholar 
    72.Kent McAdoo, J., Schultz, B. W. & Swanson, S. R. Aboriginal precedent for active management of sagebrush-perennial grass communities in the Great Basin. Rangeland Ecol. Manag. 66, 241–253 (2013).Article 

    Google Scholar 
    73.Heyerdahl, E. K., Brown, P. M., Kitchen, S. G. & Weber, M. H. Multicentury Fire and Forest Histories at 19 sites in Utah and Eastern Nevada. Rocky Mountain Research Station General Technical Report RMRS-GTR-261WWW, 192 (US Department of Agriculture, Forest Service, 2011).74.Charles, K. Long-term Vegetation Change on Utah’s Fishlake National Forest: A Study in Repeat Photography (Utah State Univ., 2003).75.USDA Forest Service. Fishlake National Forest (N.F.), Salina Planning Unit: Environmental Impact Statement. 1–125 (USDA Forest Service, 1976).76.Morris, J. L., Brunelle, A., Munson, A. S., Spencer, J. & Power, M. J. Holocene vegetation and fire reconstructions from the Aquarius Plateau, Utah, USA. Quat. Int. 310, 111–123 (2013).Article 

    Google Scholar 
    77.MTBS Data Access: Fire Level Geospatial Data. (2020, November – last revised). MTBS Project (USDA Forest Service/U.S. Geological Survey). Available online: http://mtbs.gov/direct-download [2020, December 15].78.Kitzberger, T., Falk, D. A., Westerling, A. L. & Swetnam, T. W. Direct and indirect climate controls predict heterogeneous early-mid 21st century wildfire burned area across western and boreal North America. PLoS ONE 12, e0188486 (2017).Article 
    CAS 

    Google Scholar 
    79.Dean, W. E. Jr. Determination of carbonate and organic matter in calcareous sediments and sedimentary rocks by loss on ignition: comparision with other methods. J. Sediment. Petrol. 44, 242–248 (1974).CAS 

    Google Scholar 
    80.Reimer, P. J. et al. Intcal13 and Marine13 radiocarbon age calibration curves 0–50,000 years cal Bp. Radiocarbon 55, 1869–1887 (2013).CAS 
    Article 

    Google Scholar 
    81.Blaauw, M. & Christen, J. A. Flexible paleoclimate age-depth models using an autoregressive gamma process. Bayesian Anal. 6, 457–474 (2011).Article 

    Google Scholar 
    82.Higuera, P. E., Brubaker, L. B., Anderson, P. M., Hu, F. S. & Brown, T. A. Vegetation mediated the impacts of postglacial climate change on fire regimes in the south-central Brooks Range, Alaska. Ecol. Monographs 79, 201–219 (2009).Article 

    Google Scholar 
    83.Crema, E. R., Bevan, A. & Shennan, S. Spatio-temporal approaches to archaeological radiocarbon dates. J. Archaeol. Sci. 87, 1–9 (2017).CAS 
    Article 

    Google Scholar 
    84.Kelly, R. L., Surovell, T. A., Shuman, B. N. & Smith, G. M. A continuous climatic impact on Holocene human population in the Rocky Mountains. Proc. Natl Ac. Sci. USA 110, 443–447 (2013).CAS 
    Article 

    Google Scholar 
    85.Shennan, S. et al. Regional population collapse followed initial agriculture booms in mid-Holocene Europe. Nat Commun. 4, 31–34 (2013).Article 
    CAS 

    Google Scholar 
    86.Bevan, A. & Crema, E. rcarbon v1. 2.0: Methods for calibrating and analysing radiocarbon dates, https://cran.r-project.org/web/packages/rcarbon/index.html (2018).87.Contreras, D. A. & Meadows, J. Summed radiocarbon calibrations as a population proxy: A critical evaluation using a realistic simulation approach. J. Archaeol. Sci. 52, 591–608 (2014).Article 

    Google Scholar 
    88.Wood, S. N. Package ‘mgvc,’ https://cran.r-project.org/web/packages/mgcv/mgcv.pdf (2017). More

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    Nitrogen isotope effects can be used to diagnose N transformations in wastewater anammox systems

    Variation in the N isotope effect imparted by ammonium oxidationWhile the ammonium isotope effect, 15ε(NH4+), varies by over 13‰ across all experiments, it exhibits a narrower range for a specific experimental setting with distinct cultivation conditions (mainstream, enrichment, sidestream), it exhibits a broader range across all experiments (Fig. 2). There are a number of possible physiological and experimental conditions that differ among experiments, including reaction rate, temperature, and initial concentration of substrates. As shown in Fig. 6, there is a systematic decrease in 15ε(NH4+) at decreasing initial ammonium concentration, while at the highest ammonium concentrations, the value of 15ε(NH4+) appears to plateau at a value near 32‰, close to the maximum value (32.7 ± 0.7‰) observed by Kobayashi and coworkers in chemostat experiments with enriched cultures19. Typically, the isotope effect imparted into a substrate pool by a kinetic process is set at the first irreversible step; any isotope effects that occur before this point can be expressed, while any that occur after it are concealed37,38. Substrate supply limitations can decrease the reversibility of a given step, and thereby let it modulate the net isotope effect of a multi-step process. This behavior has been proposed to play important roles in controlling the isotopic signatures of microbial sulfate39,40 and nitrate41,42 reduction.Figure 615ε(NH4+) for each individual experiment, compared to the concentration of ammonium at the start of that experiment. Mainstream experiments are plotted in yellow circles, sidestream experiments in blue squares, and enrichment experiments in grey diamonds.Full size imageIn the case of anammox bacteria, this pattern suggests that the relative kinetics of ammonium uptake and oxidation control the observed value of 15ε(NH4+). The typical path of an ammonium molecule through an anammox cell requires crossing several cell membranes to the eventual site of reaction, inside the anammoxosome43,44. When ammonium concentrations are relatively high and ammonium oxidation is not uptake-limited, 15ε(NH4+) is set at the hydrazine synthase (Hzs) enzyme, at which ammonium binds and is subsequently oxidized to hydrazine43. The maximum observed value of 15ε(NH4+) would then be the expression of all isotope effects up to, and including, this bond-breaking step. But if, at relatively low ammonium concentrations in the external medium, the rate of ammonium oxidation is limited by its uptake, then active transport or passive diffusion of ammonium will become the first irreversible step and thereby set the observed value of 15ε(NH4+). Indeed, for assimilatory uptake by a marine bacterium, 15ε(NH4+) has been shown to depend on the external ammonium concentration, varying from 3.8 to 26.5‰ across an ammonium concentration range of 0.3 to 316 mg-N/L, as the first irreversible step changes from active transport at low NH4+ concentrations to diffusion at intermediate concentrations and the enzymatic reaction associated with assimilation at high concentrations18.In the process of NH4+ uptake by anammox bacteria, a number of steps could imaginably impact the isotope effect imparted on the ammonium pool. Prior to oxidation within the anammoxosome, ammonium must cross three membranes to reach the Hzs enzyme44. This is distinct from many other respiratory processes in the N cycle, including aerobic ammonia oxidation, where ammonia is thought to be oxidized in the periplasm45,46. Metagenomic characterization of anammox bacteria in the mainstream system used in these experiments reveals the presence of genes for amt ammonium transporters in these species23, while past studies of Ca. Kuenenia stuttgartiensis shows that anammox bacteria feature a number of genes homologous to those that express AmtB ammonium transporters in other bacteria47,48. These transporters could function to transport ammonium across two membranes, first into the cytoplasm and then into the anammoxosome49. In addition, passive, diffusive influx of ammonium into the cell could play a role, especially at relatively high external ammonium concentrations. Therefore, it is easily imaginable that under different physiological conditions, the observed value of 15ε(NH4+) could reflect (1) free motion of ammonium into the anammoxosome and the full expression of the isotope effect associated with NH4+ oxidation, (2) irreversibility in either of two active transport steps, or (3) irreversibility of diffusive transport of ammonium into the periplasm.We do not know the N isotope effects for ammonium diffusion into the cell and ammonium transport to the anammoxosome. But in analogy to considerations made for the diffusion and active transport of nitrate and active transport into the cells of denitrifying bacteria41, we conclude that it is reasonable to expect the N isotope effects for both passive ammonium diffusion and active transport to be much smaller that the enzyme-level N isotope effect associated with the actual ammonium oxidation. Therefore, when external ammonium is relatively low, and NH4+ transport becomes the rate-limiting step in anaerobic ammonium oxidation, the overall N isotope effect will approach that associated with NH4+ uptake or transport, and will likely be lower than under NH4+-replete conditions, where the full expression of the isotope effect associated with NH4+ oxidation may be expressed. This also supports the observation (Fig. 6) of decreasing 15ε(NH4+) under decreasing ammonium availability.Importantly, in a different microbial setting, e.g., in an oceanic environment, which has anammox bacteria with different (e.g., higher) affinities for ammonium uptake and oxidation, we predict that the same endmember values of 15ε(NH4+) that are seen in these experiments will be observed, but with the relationship between them unfolding at different (e.g., lower) values of ammonium concentration, cell densities, and in turn different cell-specific anammox rates. It is also important to note that the cell-specific anammox rate, not the bulk reaction rate, is the essential parameter for understanding the balance between the different processes at work. Unfortunately, because of the biofilm-dwelling nature of the anammox communities in this study, it is challenging to estimate accurately the number of anammox cells present, and so we were not able to determine the cell-specific anammox rate in our experimental setup. Yet, even at substrate concentrations that are much higher than those typically found in the natural environment, NH4+ uptake can be limiting if the bacterial cell density is high, as is the case in this study.For understanding the role that the balance between ammonium uptake and oxidation may play in controlling 15ε(NH4+), it is useful to compare anammox bacteria to aerobic ammonia oxidizing bacteria (AOB) and archaea (AOA). It is notable that the range of 15ε(NH4+) is similar for anammox bacteria and aerobic ammonia oxidizers; the AOB and AOA express values of 15ε(NH4+) between 14 and 42‰14–17. AOB and AOA perform catabolic ammonia oxidation using the ammonia monooxygenase enzyme. In AOB, this enzyme is located in the periplasm45,46, not in an internal cell structure like the anammoxosome, and so it is unlikely that active transport controls observed isotope effects. Instead, it has been proposed that variations in 15ε(NH4+) for the AOB are related to sequence variations in ammonia monooxygenase16. But recent results from Kobayashi and coworkers suggest that 15ε(NH4+) for anammox bacteria is species-independent; for the three different species tested under similar experimental conditions, the N isotope effects were consistent19. In our experiments, variations in the 15ε(NH4+) values were observed in mainstream and enrichment experiments, where the anammox bacteria population is expected to be similar, which also argues against species dependence. Indeed, the Hzs enzyme seems well conserved across anammox clades50, and, therefore, the ammonium N isotope effect variation observed here cannot be attributed to sequence variations, and is more likely due to the changing experimental conditions (NH4+ concentrations), as discussed above.Irrespective of the explanations for the observed N-isotope effect variability for both ammonium oxidation modes, the overlap in the ranges of values for 15ε(NH4+) for anammox and aerobic ammonia oxidation suggests that in a system that might be either aerobic or anaerobic, the mechanism of ammonium oxidation cannot necessarily be identified based on the ammonium N isotope signature. That is, an enrichment in 15 N associated with ammonium consumption cannot be attributed to ammonia oxidizing bacteria or anammox bacteria based on this measurement alone. Further work to compare the responses of both aerobic ammonia oxidizers and anammox bacteria to changing concentrations and cell-specific reaction rates would be helpful for identifying the overall environmental controls on 15ε(NH4+) under both oxic and anoxic conditions.
    15ε(NO2
    –) reflects a mixture of processesThe parameter 15ε(NO2–) reflects the weighted sum of the isotope effects for the consumption of nitrite by reduction to N2 and by oxidation to nitrate, and so its value depends on these two processes, as well as upon the stoichiometric ratio between them. For considering the physiology of anammox and its role in a biogeochemical N cycling network, it is of limited use, but in a system where the δ15N of nitrite can be readily measured it is valuable to know how to interpret it. It exhibits relatively little variation across the experimental conditions described here, and is also consistent with the result reported by Brunner and coworkers for Ca. K. stuttgartiensis enrichment cultures (Fig. 3)10, but our results differ from 15ε(NO2–) values for other anammox species by Kobayashi and coworkers19. The principal cause of the constancy of 15ε(NO2–) in these experiments is likely the stability of 15ε(NO2––N2) across all experiments, and is discussed in the sections that follow.The N isotope effect associated with the reduction of nitrite by anammox, 15ε(NO2
    ––N2), reflects the microbial communityThe N isotope effect associated with the reduction of nitrite to N2 in anammox, 15ε(NO2––N2), is consistent across all three experimental settings (Fig. 5), which is notable when compared to the broad range in 15ε(NO2––N2) observed in previous pure culture experiments with members of the genera Ca. Kuenenia, Ca. Scalindua, Ca. Jettenia, and Ca. Brocadia10,19, as well as in anammox incubation experiments20. This consistancy is also striking in light of the variation of 15ε(NH4+) observed in this study, and suggests that variations in substrate concentrations, reaction rates, or other physiological conditions are not strong controls on 15ε(NO2––N2). Instead, the identity of the anammox bacteria, and in turn its biochemical processing of nitrite, appears to exert control over this isotope effect. In the mainstream system used for these experiments, it has been shown that species in the genus Ca. Brocadia are the principal members of the anammox community present, but that Ca. Kuenenia and Ca. Jettenia are also represented (Table S1)23. Indeed, using the metagenomic characterization of the mainstream system reported by Niederdorfer and coworkers23, as well as the observed values of 15ε(NO2––N2) from previous studies10,19, we calculate an expected value of 15ε(NO2––N2) for the mainstream system of 7.5‰ ± 5.5‰ (1 s.d.). This result is close to, but distinct from, the observed value, and leads to the conclusion that 15ε(NO2––N2) in these systems is the result of a stable mixture of different anammox species, but that the contribution of different species to anammox activity under a specific set of experimental conditions may not directly reflect their cellular abundance. Likewise, although we do not yet know the microbial community composition of the material used in the sidestream experiment, we predict, based on its consistent value for 15ε(NO2––N2), that it is similar to that seen in the mainstream and enrichment settings, and we speculate that this microbial community has been stable over the course of the ~ 5 years between 2014 and 2019.The distinct values of 15ε(NO2––N2) observed for different species can be connected to key variations in the anammox metabolism. Although the canonical anammox mechanism includes the reduction of nitrite to NO by a nitrite reductase enzyme51, genomes of anammox bacteria of the Genus Ca. Brocadia52,53, including 5 of 6 metagenome-assembled genomes for bacteria in the mainstream system used in this study23, typically lack any canonical nitrite reductase in their genomes. Instead, it has been proposed that Ca. Brocadia do not produce NO and instead have hydroxylamine as the intermediate between nitrite and hydrazine52. This hypothesis is supported further by the nature of Hzs, which has two catalytic centers, one of which reduces NO to hydroxylamine, while the second conproportionates hydroxylamine and ammonia to generate hydrazine54; it is possible that Ca. Brocadia can bypass NO entirely and deliver hydroxylamine directly to Hzs.In contrast, Ca. Kuenenia, Ca. Scalindua, and Ca. Jettenia all include a canonical nitrite reductase in their genomes. Indeed, Kobayashi and coworkers19 observed that the offset in 15ε(NO2––N2) between measured values for Ca. Kuenenia and Ca. Scalindua, which have the iron-bearing nitrite reductase NirS, and Ca. Jettenia, which has the copper-bearing nitrite reductase NirK, corresponds to that observed for NirK and NirS in bacterial denitrifiers55. This interpretation is complicated by the observation that the genes for these canonical nitrite reductases are often not expressed49,56 or translated57 under environmental conditions. Nevertheless, the differences in N isotopic discrimination of nitrite among anammox clades appear to correspond to fundamental differences in the conversion of nitrite, but the molecular mechanisms of these steps remain poorly understood.Inverse isotope effect imparted in 15ε(NO2
    ––NO3
    –) by nitrite oxidationA pronounced inverse isotope effect, in which nitrate becomes enriched in 15 N relative to nitrite from which it is produced, was observed in all experimental settings. Such an inverse isotope effect appears to be a signature feature of microbial nitrite oxidation to nitrate, both under oxic and anoxic (i.e., anammox) conditions58,59 In culture-based experiments with nitrite oxidizing bacteria (NOB), 15ε(NO2––NO3–) has been found to vary between − 7.8‰ and − 23.6‰59,60, while anammox bacteria have been shown to express 15ε(NO2––NO3–) values in pure or highly-enriched cultures between − 30 and − 45‰10,19, with values as low as − 78‰ in a wastewater incubation experiment20. In our experiments, we found N isotope effects that cover nearly this whole range (Fig. 4). In both anammox and the NOB, nitrite oxidation is thought to be performed by the enzyme nitrate:nitrite oxidoreductase (Nxr)25,43,59, which is also closely related to bacterial membrane-bound and periplasmic nitrate reductases61,62. The structural details of the Nxr enzyme family are not yet well explored, especially in light of its diverse metabolic roles63, and so it remains unclear what metabolic or microbial processes are responsible for the observed and reported variation in 15ε(NO2––NO3–). At least for anammox bacteria, the inverse kinetic N isotope effect associated with the enzymatic oxidation of nitrite to nitrate may be superposed in part by a relatively large equilibrium N isotope effect between nitrite and nitrate10, perhaps promoted by the reversibility of the enzymatic nitrite oxidation reaction64. It is notable that the most negative end of the observed range for 15ε(NO2––NO3–) in this study approaches the theoretical limit for the isotope effect set by the N isotope equilibrium between nitrite and nitrate, which at 20 °C is – 54.6‰59, and which the NOB have not been observed to approach. This suggests that under the metabolic conditions of anammox, the Nxr enzyme is more likely to catalyze reversible reactions, and so the corresponding N isotope effect is closer to the equilibrium limit, than in aerobic nitrite oxidation. However, the great range observed in 15ε(NO2––NO3–) for anammox makes it difficult to predict a priori how much fractionation anammox will impart on a nitrate pool.On the other hand, observations in this study and elsewhere10,19 (Fig. 4) of values of 15ε(NO2––NO3–) falling near − 30‰ for nitrate generated by anammox match a prediction from water column measurements of N isotope ratios in nitrate and nitrite in the Peru oxygen deficient zone (ODZ)65. The large and variable magnitude of this inverse isotope effect means that even though only ~ 25% of the nitrite oxidized by anammox is converted to nitrate, it can have an outsize effect on nitrate and nitrite pools that can be mistaken for either nitrite oxidation by NOB or nitrite generation by denitrification.Implications for N isotope measurements in natural and engineered environmentsTaken together, the results measured in this study suggest both potential and pitfalls for the application of N isotope measurements to disentangle the systematics of microbial N cycling processes. By its interaction with the nitrate, nitrite, ammonium, and N2 pools, anammox already complicates analysis of the N cycle in any setting where it acts; not only does it impact the stable isotope pools of these molecules, but also its effects on each of these pools can vary greatly depending on physiological or metabolic variables or the identity of the dominant anammox bacteria species. In the context of a wastewater treatment process, measurements of δ15N alone may not be able to directly diagnose what processes are occurring, but when coupled to rate and stoichiometry measurements, may provide insights into the efficiency or limitations of those processes.The removal of 15 N-depleted N by anammox may partly explain the heavy N isotope values ( > 15‰) for nitrate in ODZs that have been formerly attributed to denitrification alone. It remains uncertain, however, what the exact expression of the range of N isotope effects reported here is under natural and variable substrate concentrations. First, as shown here for ammonium, concentration levels will have an effect on the relative kinetics of uptake and oxidation, and in turn on the cell-specific N isotope effect. Second, ammonium concentrations are generally at or below detection in the interior of OMZs, indicating that ammonium supplied by degradation of organic matter may be quantitatively oxidized to N2 by anammox. Under these conditions, the N isotope effect associated with the conversion of ammonium to N2 will be suppressed. Previously published estimates of the overall N isotope effect of dissolved inorganic N (DIN) elimination to N2 in ODZs based on comparing nitrate δ15N values to observed water-column nitrate deficits has inherently included any potential non-fractionating loss of ammonium, and has thus implicitly represented a community N loss isotope effect that depends on the balance between anammox and canonical denitrification. The overall expression on DIN lost by the combined processes of denitrification and anammox in sediments, however, may be completely different. In contrast to nitrate and nitrite66, ammonium is usually not limiting in sediments and its fractional loss to overlying waters allows the N isotope effect of ammonium oxidation to N2 by benthic anammox to be expressed.In both natural and engineered systems where anammox is known to be occurring, measurements of 15ε(NH4+) may be able to diagnose substrate limitations or other physiological limitations. And in the case where ammonium is observed to be consumed by an unknown pathway, it can be expected that isotope effects will fall into a similar range for both aerobic and anaerobic ammonium oxidation; on the other hand, 15ε(NH4+) is of little use for distinguishing ammonium consumption by AOB and anammox. We have also found that despite the great possibility for variation in 15ε(NO2––N2) amongst different anammox species, values of 15ε(NO2––N2) remain relatively stable in a given system that has a stable microbial community, and so this parameter has some potential to be used in monitoring such microbial community stability. Further work is needed to explore how metabolic variation amongst anammox species is related to variations in this parameter, but it appears that subtle changes in the mix of anammox species present, which have no observed effect on anammox rates or stoichiometry can lead to major changes in 15ε(NO2––N2). Finally, this study expands the range of 15ε(NO2––NO3–) for anammox bacteria. On one hand, this result lends further support to the observation that through a strong manifestation of the inverse isotope effect associated with nitrite oxidation, anammox can produce nitrate strongly enriched in 15 N, thereby complicating N mass balances based on tracking the nitrate pool. But we also find that anammox can have 15ε(NO2––NO3–) values much closer to 0‰, falling in the same range as for NOB bacteria, so the contribution of anammox to the δ15N composition of the nitrate pool can in fact vary greatly. Finally, we find that there are no systematic relationships among 15ε(NH4+), 15ε(NO2––N2), 15ε(NO2––NO3–), which is consistent with the conclusion that each of these parameters is controlled at a distinct point in the anammox metabolism. More

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    Evaluating sediment and water sampling methods for the estimation of deep-sea biodiversity using environmental DNA

    High-throughput sequencing resultsA total of 26 million COI reads, 19 million raw 18S V1-V2 reads,, 14 million 18S V4 reads, and 17 million 16S V4–V5 reads were obtained from three Illumina HiSeq runs of amplicon libraries built from pooled triplicate PCRs of 22 environmental samples, 2 extraction blanks, and 4–6 PCR blanks (Supplementary Table S4 online). The in situ pump yielded less raw reads for COI and 16S (Supplementary Fig. S1 online, F = 4.02–14.4, p = 0.0003–0.03), while more raw reads were recovered from both water sampling methods with 18S V4 (F = 6.5, p = 0.007). Water samples generally yielded fewer raw clusters (F = 5.1–35.1, p = 3.2 × 10−6–0.02), except for 18S V4 where numbers were comparable across sample types (Supplementary Fig. S1 online).Bioinformatic processing (quality filtering, error correction, chimera removal, and clustering for metazoans) reduced read numbers to 20 million for COI, 12 million for 18S V1–V2, 11 million for 18S V4, and 10 million for 16S V4–V5, resulting in 10,351 and 17,608 raw OTUs for COI and 18S V1–V2 respectively; 35,538 raw 18S V4 ASVs, and 62,646 raw 16S ASVs (Supplementary Table S4 online). For eukaryote markers, 17–55% of the raw reads remained in PCR blanks after bioinformatic processing, while 50–75% remained in extraction blanks and 52–87% in true samples. In contrast, with 16S, these values were at 87% for PCR blanks, 67% for extraction blanks, and 29–73% for true samples. Thus, negative control samples accounted for 7–13% of bioinformatically processed reads with eukaryotes, compared to 27% with prokaryotes. The vast majority of 16S reads generated by negative controls belonged to a common contaminant of Phusion polymerase kits, which is well amplified in low concentration samples such as negative controls. These reads however accounted for  20 µm size class, and the sampling box targeting both the 2–20 µm and the 0.2–2 µm size classes, detected different community assemblages. For protists, the in situ pump detected higher proportions of ASVs for Bacillariophyta, Ciliophora, Labyrinthulea, or Phaeodarea, while the sampling box detected more cryptophytes, haptophytes, MAST, and telonemians (Fig. 3 18S V4). For prokaryotes, the sampling box detected more diversity in the Alphaproteobacteria, Chloroflexi, or Marinimicrobia (Fig. 3 16S V4–V5). More

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    Global climate and nutrient controls of photosynthetic capacity

    1.De Kauwe, M. G. et al. Do land surface models need to include differential plant species responses to drought? Examining model predictions across a mesic-xeric gradient in Europe. Biogeosciences 12, 7503–7518 (2015).Article 

    Google Scholar 
    2.Smith, N. G. & Keenan, T. F. Mechanisms underlying leaf photosynthetic acclimation to warming and elevated CO2 as inferred from least‐cost optimality theory. Global Change Biol. 26, 5202–5216 (2020).Article 

    Google Scholar 
    3.Farquhar, G. D., von Caemmerer, S. & Berry, J. A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90 (1980).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    4.Wullschleger, S. D. Biochemical limitations to carbon assimilation in C3 plants—a retrospective analysis of the A/Ci curves from 109 Species. J. Exp. Bot. 44, 907–920 (1993).CAS 
    Article 

    Google Scholar 
    5.Lloyd, J., Bloomfield, K., Domingues, T. F. & Farquhar, G. D. Photosynthetically relevant foliar traits correlating better on a mass vs an area basis: of ecophysiological relevance or just a case of mathematical imperatives and statistical quicksand? New Phytol. 199, 311–321 (2013).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    6.De Kauwe, M. G. et al. A test of the ‘one-point method’ for estimating maximum carboxylation capacity from field-measured, light-saturated photosynthesis. New Phytol. 210, 1130–1144 (2015).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    7.Ferreira Domingues, T. et al. Biome-specific effects of nitrogen and phosphorus on the photosynthetic characteristics of trees at a forest-savanna boundary in Cameroon. Oecologia 178, 659–672 (2015).PubMed Central 
    Article 

    Google Scholar 
    8.Domingues, T. F. et al. Co-limitation of photosynthetic capacity by nitrogen and phosphorus in West Africa woodlands. Plant, Cell Environ. 33, 959–980 (2010).CAS 
    Article 

    Google Scholar 
    9.Walker, A. P. et al. The relationship of leaf photosynthetic traits -VcmaxandJmax- to leaf nitrogen, leaf phosphorus, and specific leaf area: a meta-analysis and modeling study. Ecol. Evol. 4, 3218–3235 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    10.Wang, H. et al. Towards a universal model for carbon dioxide uptake by plants. Nat. Plants 3, 734–741 (2017).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    11.Smith, N. G. et al. Global photosynthetic capacity is optimized to the environment. Ecol. Lett. 22, 506–517 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    12.Prentice, I. C., Dong, N., Gleason, S. M., Maire, V. & Wright, I. J. Balancing the costs of carbon gain and water transport: testing a new theoretical framework for plant functional ecology. Ecol. Lett. 17, 82–91 (2014).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    13.Givnish, T. J. On the Economy of Plant Form and Function: Proceedings of the Sixth Maria Moors Cabot Symposium, Vol. 6 (Cambridge University Press, 1986).14.Maire, V. et al. Global effects of soil and climate on leaf photosynthetic traits and rates. Glob. Ecol. Biogeogr. 24, 706–717 (2015).Article 

    Google Scholar 
    15.Franklin, O. et al. Organizing principles for vegetation dynamics. Nat. Plants 6, 444–453 (2020).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    16.Ali, A. A. et al. A global scale mechanistic model of photosynthetic capacity (LUNA V1.0). Geosci. Model Dev. 9, 587–606 (2016).Article 

    Google Scholar 
    17.Dewar, R. et al. New insights into the covariation of stomatal, mesophyll and hydraulic conductances from optimization models incorporating nonstomatal limitations to photosynthesis. New Phytol. 217, 571–585 (2017).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    18.Caldararu, S., Thum, T., Yu, L. & Zaehle, S. Whole-plant optimality predicts changes in leaf nitrogen under variable CO 2 and nutrient availability. New Phytol. 225, 2331–2346 (2019).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    19.Wright, I. J. et al. The worldwide leaf economics spectrum. Nature 428, 821–827 (2004).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    20.Wang, H. et al. The China Plant Trait Database: toward a comprehensive regional compilation of functional traits for land plants. Ecology 99, 500–500 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    21.Wang, H. et al. Photosynthetic responses to altitude: an explanation based on optimality principles. New Phytol. 213, 976–982 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    22.Lavergne, A. et al. Historical changes in the stomatal limitation of photosynthesis: empirical support for an optimality principle. New Phytol. 225, 2484–2497 (2019).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    23.Maire, V. et al. The coordination of leaf photosynthesis links C and N fluxes in C3 plant species. PLoS ONE 7, e38345 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    24.Fürstenau Togashi, H. et al. Thermal acclimation of leaf photosynthetic traits in an evergreen woodland, consistent with the coordination hypothesis. Biogeosciences 15, 3461–3474 (2018).Article 
    CAS 

    Google Scholar 
    25.Kumarathunge, D. P. et al. Acclimation and adaptation components of the temperature dependence of plant photosynthesis at the global scale. New Phytol. 222, 768–784 (2019).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    26.Wright, I. J., Reich, P. B. & Westoby, M. Strategy shifts in leaf physiology, structure and nutrient content between species of high- and low-rainfall and high- and low-nutrient habitats. Funct. Ecol. 15, 423–434 (2001).Article 

    Google Scholar 
    27.Rogers, A. The use and misuse of V c,max in earth system models. Photosynth. Res. 119, 15–29 (2013).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    28.Dong, N. et al. Leaf nitrogen from first principles: field evidence for adaptive variation with climate. Biogeosciences 14, 481–495 (2017).CAS 
    Article 

    Google Scholar 
    29.Reich, P. B. & Schoettle, A. W. Role of phosphorus and nitrogen in photosynthetic and whole plant carbon gain and nutrient use efficiency in eastern white pine. Oecologia 77, 25–33 (1988).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    30.Raaimakers, D., Boot, R. G. A., Dijkstra, P. & Pot, S. Photosynthetic rates in relation to leaf phosphorus content in pioneer versus climax tropical rainforest trees. Oecologia 102, 120–125 (1995).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    31.Goll, D. S. et al. Nutrient limitation reduces land carbon uptake in simulations with a model of combined carbon, nitrogen and phosphorus cycling. Biogeosciences 9, 3547–3569 (2012).CAS 
    Article 

    Google Scholar 
    32.Reich, P. B., Oleksyn, J. & Wright, I. J. Leaf phosphorus influences the photosynthesis–nitrogen relation: a cross-biome analysis of 314 species. Oecologia 160, 207–212 (2009).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    33.Evans, J. R. Photosynthesis and nitrogen relationships in leaves of C3 plants. Oecologia 78, 9–19 (1989).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    34.Reich, P. B., Walters, M. B., Ellsworth, D. S. & Uhl, C. Photosynthesis-nitrogen relations in Amazonian tree species. Oecologia 97, 62–72 (1994).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    35.Kattge, J., Knorr, W., Raddatz, T. & Wirth, C. Quantifying photosynthetic capacity and its relationship to leaf nitrogen content for global-scale terrestrial biosphere models. Global Change Biol. 15, 976–991 (2009).Article 

    Google Scholar 
    36.Evans, J. R. & Clarke, V. C. The nitrogen cost of photosynthesis. J. Exp. Bot. 70, 7–15 (2018).Article 
    CAS 

    Google Scholar 
    37.Marschner, H. in Mineral Nutrition of Higher Plants, 405–435 (Elsevier, 1995).38.Niinemets, Ü., Wright, I. J. & Evans, J. R. Leaf mesophyll diffusion conductance in 35 Australian sclerophylls covering a broad range of foliage structural and physiological variation. J. Exp. Bot. 60, 2433–2449 (2009).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    39.Malhi, Y. et al. The variation of productivity and its allocation along a tropical elevation gradient: a whole carbon budget perspective. New Phytol. 214, 1019–1032 (2016).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    40.Wang, H. et al. Acclimation of leaf respiration consistent with optimal photosynthetic capacity. Global Change Biol. 26, 2573–2583 (2020).Article 

    Google Scholar 
    41.Peng, Y., Bloomfield, K. J. & Prentice, I. C. A theory of plant function helps to explain leaf-trait and productivity responses to elevation. New Phytol. 226, 1274–1284, (2020).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    42.Gvozdevaite, A. et al. Leaf-level photosynthetic capacity dynamics in relation to soil and foliar nutrients along forest–savanna boundaries in Ghana and Brazil. Tree Physiol. 38, 1912–1925 (2018).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    43.Terrer, C. et al. Nitrogen and phosphorus constrain the CO 2 fertilization of global plant biomass. Nat. Clim. Change 9, 684–689 (2019).CAS 
    Article 

    Google Scholar 
    44.Meir, P. et al. in Advances in Photosynthesis and Respiration, 89–105 (Springer International Publishing, 2017).45.Luo, X. & Keenan, T. F. Global evidence for the acclimation of ecosystem photosynthesis to light. Nat. Ecol. Evol. 4, 1351–1357 (2020).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    46.Stocker, B. D. et al. P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production. Geosci. Model Dev. 13, 1545–1581 (2020).Article 

    Google Scholar 
    47.Lavergne, A., Sandoval, D., Hare, V. J., Graven, H. & Prentice, I. C. Impacts of soil water stress on the acclimated stomatal limitation of photosynthesis: insights from stable carbon isotope data. Global Change Biol. 26, 7158–7172 (2020).48.Zhou, S., Duursma, R. A., Medlyn, B. E., Kelly, J. W. G. & Prentice, I. C. How should we model plant responses to drought? An analysis of stomatal and non-stomatal responses to water stress. Agric. For. Meteorol. 182-183, 204–214 (2013).Article 

    Google Scholar 
    49.Zhou, S. et al. Short-term water stress impacts on stomatal, mesophyll and biochemical limitations to photosynthesis differ consistently among tree species from contrasting climates. Tree Physiol. 34, 1035–1046 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    50.Katul, G., Manzoni, S., Palmroth, S. & Oren, R. A stomatal optimization theory to describe the effects of atmospheric CO2 on leaf photosynthesis and transpiration. Ann. Bot. 105, 431–442 (2009).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    51.Manzoni, S. et al. Optimizing stomatal conductance for maximum carbon gain under water stress: a meta-analysis across plant functional types and climates. Funct. Ecol. 25, 456–467 (2011).Article 

    Google Scholar 
    52.Medlyn, B. E. et al. Reconciling the optimal and empirical approaches to modelling stomatal conductance. Global Change Biol. 17, 2134–2144 (2011).Article 

    Google Scholar 
    53.Crous, K. Y. et al. Photosynthesis of temperate Eucalyptus globulus trees outside their native range has limited adjustment to elevated CO2 and climate warming. Global Change Biol. 19, 3790–3807 (2013).Article 

    Google Scholar 
    54.Zhou, S.-X., Medlyn, B. E. & Prentice, I. C. Long-term water stress leads to acclimation of drought sensitivity of photosynthetic capacity in xeric but not riparian Eucalyptus species. Ann. Bot. 117, 133–144 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    55.Smith, N. G. & Dukes, J. S. Short-term acclimation to warmer temperatures accelerates leaf carbon exchange processes across plant types. Global Change Biol. 23, 4840–4853 (2017).Article 

    Google Scholar 
    56.Katul, G., Leuning, R. & Oren, R. Relationship between plant hydraulic and biochemical properties derived from a steady‐state coupled water and carbon transport model. Plant, Cell Environ. 26, 339–350 (2003).CAS 
    Article 

    Google Scholar 
    57.Stocker, B. D. et al. Quantifying soil moisture impacts on light use efficiency across biomes. New Phytol. 218, 1430–1449 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    58.Kattge, J. & Knorr, W. Temperature acclimation in a biochemical model of photosynthesis: a reanalysis of data from 36 species. Plant, Cell Environ. 30, 1176–1190 (2007).CAS 
    Article 

    Google Scholar 
    59.van der Plas, F. et al. Plant traits alone are poor predictors of ecosystem properties and long-term ecosystem functioning. Nat. Ecol. Evol. 4, 1602–1611 (2020).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    60.Quesada, M. et al. Succession and management of tropical dry forests in the Americas: review and new perspectives. For. Ecol. Manag. 258, 1014–1024 (2009).Article 

    Google Scholar 
    61.Phillips, O. L. et al. Drought–mortality relationships for tropical forests. New Phytol. 187, 631–646 (2010).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    62.Laliberté, E., Lambers, H., Burgess, T. I. & Wright, S. J. Phosphorus limitation, soil-borne pathogens and the coexistence of plant species in hyperdiverse forests and shrublands. New Phytol. 206, 507–521 (2014).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    63.Conroy, J. P., Smillie, R. M., Küppers, M., Bevege, D. I. & Barlow, E. W. Chlorophyll a fluorescence and photosynthetic and growth responses of pinus radiata to phosphorus deficiency, drought stress, and high CO2. Plant Physiol. 81, 423–429 (1986).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    64.Loustau, D., Brahim, M. B., Gaudillere, J. P. & Dreyer, E. Photosynthetic responses to phosphorus nutrition in two-year-old maritime pine seedlings. Tree Physiol. 19, 707–715 (1999).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    65.Warren, C. R. & Adams, M. A. Phosphorus affects growth and partitioning of nitrogen to Rubisco in Pinus pinaster. Tree Physiol. 22, 11–19 (2002).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    66.Bloomfield, K. J., Farquhar, G. D. & Lloyd, J. Photosynthesis–nitrogen relationships in tropical forest tree species as affected by soil phosphorus availability: a controlled environment study. Funct. Plant Biol. 41, 820–832 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    67.Crous, K. Y., Ósvaldsson, A. & Ellsworth, D. S. Is phosphorus limiting in a mature Eucalyptus woodland? Phosphorus fertilisation stimulates stem growth. Plant Soil 391, 293–305 (2015).CAS 
    Article 

    Google Scholar 
    68.Sivak, M. N. & Walker, D. A. Photosynthesis in vivo can be limited by phosphate supplY. New Phytol. 102, 499–512 (1986).CAS 
    Article 

    Google Scholar 
    69.Kiirats, O., Cruz, J. A., Edwards, G. E. & Kramer, D. M. Feedback limitation of photosynthesis at high CO2 acts by modulating the activity of the chloroplast ATP synthase. Funct. Plant Biol. 36, 893–901 (2009).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    70.Ellsworth, D. S., Crous, K. Y., Lambers, H. & Cooke, J. Phosphorus recycling in photorespiration maintains high photosynthetic capacity in woody species. Plant, Cell Environ. 38, 1142–1156 (2015).CAS 
    Article 

    Google Scholar 
    71.Zhang, S. & Dang, Q. L. Effects of carbon dioxide concentration and nutrition on photosynthetic functions of white birch seedlings. Tree Physiol. 26, 1457–1467 (2006).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    72.Lambers, H. et al. Proteaceae from severely phosphorus-impoverished soils extensively replace phospholipids with galactolipids and sulfolipids during leaf development to achieve a high photosynthetic phosphorus-use-efficiency. New Phytol. 196, 1098–1108 (2012).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    73.Meir, P., Levy, P. E., Grace, J. & Jarvis, P. G. Photosynthetic parameters from two contrasting woody vegetation types in West Africa. Plant Ecol. 192, 277–287 (2007).Article 

    Google Scholar 
    74.Kull, O. Acclimation of photosynthesis in canopies: models and limitations. Oecologia 133, 267–279 (2002).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    75.Field, C. & Mooney, H. in On the Economy of Plant Form and Function: Proceedings of the Sixth Maria Moors Cabot Symposium, Evolutionary Constraints on Primary Productivity, Adaptive Patterns of Energy Capture in Plants, Harvard Forest, August 1983 (Cambridge University Press, 1986).76.Niinemets, Ü. Research review. Components of leaf dry mass per area – thickness and density – alter leaf photosynthetic capacity in reverse directions in woody plants. New Phytol. 144, 35–47 (1999).Article 

    Google Scholar 
    77.Lloyd, J. et al. Optimisation of photosynthetic carbon gain and within-canopy gradients of associated foliar traits for Amazon forest trees. Biogeosciences 7, 1833–1859 (2010).CAS 
    Article 

    Google Scholar 
    78.Anten, N. P. R. Optimal photosynthetic characteristics of individual plants in vegetation stands and implications for species coexistence. Ann. Bot. 95, 495–506 (2004).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    79.Alton, P. B. & North, P. Interpreting shallow, vertical nitrogen profiles in tree crowns: a three-dimensional, radiative-transfer simulation accounting for diffuse sunlight. Agric. For. Meteorol. 145, 110–124 (2007).Article 

    Google Scholar 
    80.Rogers, A. et al. A roadmap for improving the representation of photosynthesis in Earth system models. New Phytol. 213, 22–42 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    81.Tosens, T. & Laanisto, L. Mesophyll conductance and accurate photosynthetic carbon gain calculations. J. Exp. Bot. 69, 5315–5318 (2018).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    82.Niinemets, Ü., Díaz-Espejo, A., Flexas, J., Galmés, J. & Warren, C. R. Importance of mesophyll diffusion conductance in estimation of plant photosynthesis in the field. J. Exp. Bot. 60, 2271–2282 (2009).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    83.Farquhar, G. D., O’Leary, M. H. & Berry, J. A. On the relationship between carbon isotope discrimination and the intercellular carbon dioxide concentration in leaves. Funct. Plant Biol. 9, 121–137 (1982).CAS 
    Article 

    Google Scholar 
    84.Bernacchi, C. J., Singsaas, E. L., Pimentel, C., Portis, A. R. Jr & Long, S. P. Improved temperature response functions for models of Rubisco-limited photosynthesis. Plant, Cell Environ. 24, 253–259 (2001).CAS 
    Article 

    Google Scholar 
    85.Bernacchi, C. J., Pimentel, C. & Long, S. P. In vivo temperature response functions of parameters required to model RuBP-limited photosynthesis. Plant, Cell Environ. 26, 1419–1430 (2003).CAS 
    Article 

    Google Scholar 
    86.Scafaro, A. P. et al. Strong thermal acclimation of photosynthesis in tropical and temperate wet-forest tree species: the importance of altered Rubisco content. Global Change Biol. 23, 2783–2800 (2017).Article 

    Google Scholar 
    87.Warton, D. I., Wright, I. J., Falster, D. S. & Westoby, M. Bivariate line-fitting methods for allometry. Biol. Rev. 81, 259–291 (2006).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    88.Team, R. C. R.: a Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).89.Atkin, O. K. et al. Global variability in leaf respiration in relation to climate, plant functional types and leaf traits. New Phytol. 206, 614–636 (2015).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    90.Bahar, N. H. A. et al. Leaf-level photosynthetic capacity in lowland Amazonian and high-elevation Andean tropical moist forests of Peru. New Phytol. 214, 1002–1018 (2016).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    91.Bloomfield, K. J. et al. The validity of optimal leaf traits modelled on environmental conditions. New Phytol. 221, 1409–1423 (2018).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    92.Cernusak, L. A., Hutley, L. B., Beringer, J., Holtum, J. A. M. & Turner, B. L. Photosynthetic physiology of eucalypts along a sub-continental rainfall gradient in northern Australia. Agric. For. Meteorol. 151, 1462–1470 (2011).Article 

    Google Scholar 
    93.Xu, H. Y., et al. Predictability of leaf traits with climate and elevation: a case study in Gongga Mountain, China. Tree Physiol. https://doi.org/10.1093/treephys/tpab003 (2021).94.Walker, A. P., et al. A Global Data Set of Leaf Photosynthetic Rates, Leaf N and P, and Specific Leaf Area (Oak Ridge National Laboratory Distributed Active Archive Center, 2014). https://doi.org/10.3334/ORNLDAAC/1224.95.Kattge, J. et al. TRY–a global database of plant traits. Global Change Biol. 17, 2905–2935 (2011).Article 

    Google Scholar 
    96.Collatz, G. J., Ribas-Carbo, M. & Berry, J. A. Coupled photosynthesis-stomatal conductance model for leaves of C4 plants. Funct. Plant Biol. 19, 519 (1992).Article 

    Google Scholar 
    97.Rogers, A., Serbin, S. P., Ely, K. S., Sloan, V. L. & Wullschleger, S. D. Terrestrial biosphere models underestimate photosynthetic capacity and CO2 assimilation in the Arctic. New Phytol. 216, 1090–1103 (2017).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    98.Burnett, A. C., Davidson, K. J., Serbin, S. P. & Rogers, A. The “one‐point method” for estimating maximum carboxylation capacity of photosynthesis: a cautionary tale. Plant, Cell Environ. 42, 2472–2481 (2019).CAS 
    Article 

    Google Scholar 
    99.Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations–the CRU TS3.10 Dataset. Int. J. Climatol. 34, 623–642 (2013).Article 

    Google Scholar 
    100.Jones, H. G. Plants and Microclimate (Cambridge University Press, 2009).101.Weedon, G. P. et al. The WFDEI meteorological forcing data set: WATCH forcing data methodology applied to ERA-interim reanalysis data. Water Resour. Res. 50, 7505–7514 (2014).Article 

    Google Scholar 
    102.Davis, T. W. et al. Simple process-led algorithms for simulating habitats (SPLASH v.1.0): robust indices of radiation, evapotranspiration and plant-available moisture. Geosci. Model Dev. 10, 689–708 (2017).Article 

    Google Scholar 
    103.Berberan-Santos, M. N., Bodunov, E. N. & Pogliani, L. On the barometric formula. Am. J. Phys. 65, 404–412 (1997).Article 

    Google Scholar 
    104.Peng, Y., et al. Dataset of Global Climate and Nutrient Controls of Photosynthetic Capacity (Zenodo, 2021). https://doi.org/10.5281/zenodo.4568148. More

  • in

    Corrosion and transformation of solution combustion synthesized Co, Ni and CoNi nanoparticles in synthetic freshwater with and without natural organic matter

    1.Inshakova, E. & Inshakova, A. Nanomaterials and nanotechnology: prospects for technological re-equipment in the power engineering industry. IOP Conference Series: Materials Science and Engineering 709, 033020. https://doi.org/10.1088/1757-899x/709/3/033020 (2020).CAS 
    Article 

    Google Scholar 
    2.Grassian, V. H. When size reallymatters: size-dependent properties and surface chemistry of metal and metal oxide nanoparticles in gas and liquid phase environments. J. Phys. Chem. C 112, 18303–18313. https://doi.org/10.1021/jp806073t (2008).CAS 
    Article 

    Google Scholar 
    3.Jayathilaka, W. et al. Significance of nanomaterials in wearables: a review on wearable actuators and sensors. Adv. Mater. 31, e1805921. https://doi.org/10.1002/adma.201805921 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    4.Pokhrel, S. & Mädler, L. Flame made particles for sensors, catalysis and energy storage applications. Energy Fuels https://doi.org/10.1021/acs.energyfuels.0c02220 (2020).Article 
    PubMed 

    Google Scholar 
    5.Anthony, L. S., Perumal, V., Mohamed, N. M., Saheed, M. S. M. & Gopinath, S. C. B. in Nanomaterials for Healthcare, Energy and Environment Advanced Structured Materials Ch. Chapter 3, 51–69 (2019).
    Google Scholar 
    6.Sharma, N., Ojha, H., Bharadwaj, A., Pathak, D. P. & Sharma, R. K. Preparation and catalytic applications of nanomaterials: a review. RSC Adv. 5, 53381–53403. https://doi.org/10.1039/c5ra06778b (2015).CAS 
    Article 
    ADS 

    Google Scholar 
    7.Xin, Y. et al. High-entropy alloys as a platform for catalysis: progress, challenges, and opportunities. ACS Catal. 10, 11280–11306. https://doi.org/10.1021/acscatal.0c03617 (2020).CAS 
    Article 

    Google Scholar 
    8.Wu, W. Inorganic nanomaterials for printed electronics: a review. Nanoscale 9, 7342–7372. https://doi.org/10.1039/c7nr01604b (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    9.Abdalla, A. M. et al. Nanomaterials for solid oxide fuel cells: a review. Renew. Sustain. Energy Rev. 82, 353–368. https://doi.org/10.1016/j.rser.2017.09.046 (2018).CAS 
    Article 

    Google Scholar 
    10.Choudhary, N. et al. Asymmetric supercapacitor electrodes and devices. Adv. Mater. https://doi.org/10.1002/adma.201605336 (2017).Article 
    PubMed 

    Google Scholar 
    11.Yu, Z., Tetard, L., Zhai, L. & Thomas, J. Supercapacitor electrode materials: nanostructures from 0 to 3 dimensions. Energy Environ. Sci. 8, 702–730. https://doi.org/10.1039/c4ee03229b (2015).CAS 
    Article 

    Google Scholar 
    12.Das, S., Sen, B. & Debnath, N. Recent trends in nanomaterials applications in environmental monitoring and remediation. Environ. Sci. Pollut. Res. Int. 22, 18333–18344. https://doi.org/10.1007/s11356-015-5491-6 (2015).Article 
    PubMed 

    Google Scholar 
    13.Santhosh, C. et al. Role of nanomaterials in water treatment applications: a review. Chem. Eng. J. 306, 1116–1137. https://doi.org/10.1016/j.cej.2016.08.053 (2016).CAS 
    Article 

    Google Scholar 
    14.Riley, M. K. & Vermerris, W. Recent advances in nanomaterials for gene delivery-a review. Nanomater. (Basel) https://doi.org/10.3390/nano7050094 (2017).Article 

    Google Scholar 
    15.Dasari Shareena, T. P., McShan, D., Dasmahapatra, A. K. & Tchounwou, P. B. A review on graphene-based nanomaterials in biomedical applications and risks in environment and health. Nanomicro Lett. https://doi.org/10.1007/s40820-018-0206-4 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    16.Jeyaraj, M., Gurunathan, S., Qasim, M., Kang, M. H. & Kim, J. H. A comprehensive review on the synthesis, characterization, and biomedical application of platinum nanoparticles. Nanomater. (Basel) https://doi.org/10.3390/nano9121719 (2019).Article 

    Google Scholar 
    17.Abazari, S., Shamsipur, A., Bakhsheshi-Rad, H. R., Ramakrishna, S. & Berto, F. Graphene family nanomaterial reinforced magnesium-based matrix composites for biomedical application: a comprehensive review. Metals https://doi.org/10.3390/met10081002 (2020).Article 

    Google Scholar 
    18.Siddique, S. & Chow, J. C. L. Application of nanomaterials in biomedical imaging and cancer therapy. Nanomater. (Basel) https://doi.org/10.3390/nano10091700 (2020).Article 

    Google Scholar 
    19.Mayakrishnan, G., Elayappan, V., Kim, I. S. & Chung, I. M. Sea-island-like morphology of cuni bimetallic nanoparticles uniformly anchored on single layer graphene oxide as a highly efficient and noble-metal-free catalyst for cyanation of aryl halides. Sci. Rep. 10, 677. https://doi.org/10.1038/s41598-020-57483-z (2020).CAS 
    Article 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    20.Sheikh-Mohseni, M. A., Hassanzadeh, V. & Habibi, B. Reduced graphene oxide supported bimetallic Ni–Co nanoparticles composite as an electrocatalyst for oxidation of methanol. Solid State Sci. https://doi.org/10.1016/j.solidstatesciences.2019.106022 (2019).Article 

    Google Scholar 
    21.Khort, A., Romanovski, V., Leybo, D. & Moskovskikh, D. CO oxidation and organic dyes degradation over graphene–Cu and graphene–CuNi catalysts obtained by solution combustion synthesis. Sci. Rep. https://doi.org/10.1038/s41598-020-72872-0 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    22.Wang, D. et al. Nickel-cobalt layered double hydroxide nanosheets with reduced graphene oxide grown on carbon cloth for symmetric supercapacitor. Appl. Surf. Sci. 483, 593–600. https://doi.org/10.1016/j.apsusc.2019.03.345 (2019).CAS 
    Article 
    ADS 

    Google Scholar 
    23.Khort, A. et al. Graphene@metal nanocomposites by solution combustion synthesis. Inorg. Chem. https://doi.org/10.1021/acs.inorgchem.0c00673 (2020).Article 
    PubMed 

    Google Scholar 
    24.Xu, L. et al. The crucial role of environmental coronas in determining the biological effects of engineered nanomaterials. Small https://doi.org/10.1002/smll.202003691 (2020).Article 
    PubMed 

    Google Scholar 
    25.Wang, X., Odnevall Wallinder, I. & Hedberg, Y. Bioaccessibility of nickel and cobalt released from occupationally relevant alloy and metal powders at simulated human exposure scenarios. Ann. Work Expo. Health https://doi.org/10.1093/annweh/wxaa042 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    26.Atapour, M., Wang, X., Färnlund, K., Odnevall Wallinder, I. & Hedberg, Y. Corrosion and metal release investigations of selective laser melted 316L stainless steel in a synthetic physiological fluid containing proteins and in diluted hydrochloric acid. Electrochim. Acta 354, 136748. https://doi.org/10.1016/j.electacta.2020.136748 (2020).CAS 
    Article 

    Google Scholar 
    27.Mei, N., Hedberg, J., Odnevall Wallinder, I. & Blomberg, E. Influence of biocorona formation on the transformation and dissolution of cobalt nanoparticles under physiological conditions. ACS Omega 4, 21778–21791. https://doi.org/10.1021/acsomega.9b02641 (2019).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    28.Ekvall, M. T., Hedberg, J., Odnevall Wallinder, I., Hansson, L. A. & Cedervall, T. Long-term effects of tungsten carbide (WC) nanoparticles in pelagic and benthic aquatic ecosystems. Nanotoxicology 12, 79–89. https://doi.org/10.1080/17435390.2017.1421274 (2018).CAS 
    Article 
    PubMed 

    Google Scholar 
    29.Hedberg, J., Ekvall, M. T., Hansson, L.-A., Cedervall, T. & Odnevall Wallinder, I. Tungsten carbide nanoparticles in simulated surface water with natural organic matter: dissolution, agglomeration, sedimentation and interaction with Daphnia magna. Environ. Sci. Nano 4, 886–894. https://doi.org/10.1039/c6en00645k (2017).CAS 
    Article 

    Google Scholar 
    30.Hedberg, J., Blomberg, E. & Odnevall Wallinder, I. In the search for nanospecific effects of dissolution of metallic nanoparticles at freshwater-like conditions: a critical review. Environ. Sci. Technol. 53, 4030–4044. https://doi.org/10.1021/acs.est.8b05012 (2019).CAS 
    Article 
    PubMed 
    ADS 

    Google Scholar 
    31.Cappellini, F. et al. Mechanistic insight into reactivity and (geno)toxicity of well-characterized nanoparticles of cobalt metal and oxides. Nanotoxicology 12, 602–620. https://doi.org/10.1080/17435390.2018.1470694 (2018).CAS 
    Article 
    PubMed 

    Google Scholar 
    32.Varma, A., Mukasyan, A. S., Rogachev, A. S. & Manukyan, K. V. Solution combustion synthesis of nanoscale materials. Chem Rev 116, 14493–14586. https://doi.org/10.1021/acs.chemrev.6b00279 (2016).CAS 
    Article 
    PubMed 

    Google Scholar 
    33.Khort, A., Podbolotov, K., Serrano-García, R. & Gunko, Y. One-step solution combustion synthesis of pure Ni nanopowders with enhanced coercivity: the fuel effect. J. Solid State Chem. https://doi.org/10.1016/j.jssc.2017.05.043 (2017).Article 

    Google Scholar 
    34.Podbolotov, K. B. et al. Solution combustion synthesis of copper nanopowders: the fuel effect. Combust. Sci. Technol. 189, 1878–1890. https://doi.org/10.1080/00102202.2017.1334646 (2017).CAS 
    Article 

    Google Scholar 
    35.Khort, A., Podbolotov, K., Serrano-Garcia, R. & Gun’ko, Y. One-step solution combustion synthesis of cobalt nanopowder in air atmosphere: the fuel effect. Inorg. Chem. 57, 1464–1473. https://doi.org/10.1021/acs.inorgchem.7b02848 (2018).CAS 
    Article 
    PubMed 

    Google Scholar 
    36.Yermekova, Z., Roslyakov, S. I., Kovalev, D. Y., Danghyan, V. & Mukasyan, A. S. One-step synthesis of pure γ-FeNi alloy by reactive sol–gel combustion route: mechanism and properties. J. Sol-Gel Sci. Technol. https://doi.org/10.1007/s10971-020-05252-9 (2020).Article 

    Google Scholar 
    37.Khort, A. A. & Podbolotov, K. B. Preparation of BaTiO3 nanopowders by the solution combustion method. Ceram. Int. 42, 15343–15348. https://doi.org/10.1016/j.ceramint.2016.06.178 (2016).CAS 
    Article 

    Google Scholar 
    38.Xiang, H.-Z., Xie, H.-X., Mao, A., Jia, Y.-G. & Si, T.-Z. Facile preparation of single phase high-entropy oxide nanocrystalline powders by solution combustion synthesis. Int. J. Mater. Res. https://doi.org/10.3139/146.111874 (2020).Article 

    Google Scholar 
    39.Mukasyan, A. S., Rogachev, A. S. & Aruna, S. T. Combustion synthesis in nanostructured reactive systems. Adv. Powder Technol. 26, 954–976. https://doi.org/10.1016/j.apt.2015.03.013 (2015).CAS 
    Article 

    Google Scholar 
    40.Pradhan, S. et al. Influence of humic acid and dihydroxy benzoic acid on the agglomeration, adsorption, sedimentation and dissolution of copper, manganese, aluminum and silica nanoparticles—a tentative exposure scenario. PLoS ONE 13, e0192553. https://doi.org/10.1371/journal.pone.0192553 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    41.Pradhan, S., Hedberg, J., Blomberg, E., Wold, S. & Odnevall Wallinder, I. Effect of sonication on particle dispersion, administered dose and metal release of non-functionalized, non-inert metal nanoparticles. J. Nanopart. Res. 18, 285. https://doi.org/10.1007/s11051-016-3597-5 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    42.Malloy, A. & Carr, B. NanoParticle tracking analysis—the haloTM system. Part. Part. Syst. Charact. 23, 197–204. https://doi.org/10.1002/ppsc.200601031 (2006).Article 

    Google Scholar 
    43.Patil, K. C., Hegde, M. S., Rattan, T. & Aruna, S. T. Chemistry of Nanocrystalline Oxide Materials. Combustion Synthesis, Properties and Applications (World Scientific Publishing Co. Pte. Ltd., 2008).44.Sdobnyakov, N. et al. Solution combustion synthesis and Monte Carlo simulation of the formation of CuNi integrated nanoparticles. Comput. Mater. Sci. 184, 109936. https://doi.org/10.1016/j.commatsci.2020.109936 (2020).CAS 
    Article 

    Google Scholar 
    45.Niu, B. et al. Sol-gel autocombustion synthesis of nanocrystalline high-entropy alloys. Sci. Rep. 7, 3421. https://doi.org/10.1038/s41598-017-03644-6 (2017).CAS 
    Article 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    46.Cheng, M. et al. Core@shell CoO@Co 3 O 4 nanocrystals assembling mesoporous microspheres for high performance asymmetric supercapacitors. Chem. Eng. J. 327, 100–108. https://doi.org/10.1016/j.cej.2017.06.042 (2017).CAS 
    Article 

    Google Scholar 
    47.Biesinger, M. C. et al. Resolving surface chemical states in XPS analysis of first row transition metals, oxides and hydroxides: Cr, Mn, Fe, Co and Ni. Appl. Surf. Sci. 257, 2717–2730. https://doi.org/10.1016/j.apsusc.2010.10.051 (2011).CAS 
    Article 
    ADS 

    Google Scholar 
    48.Dubey, P., Kaurav, N., Devan, R. S., Okram, G. S. & Kuo, Y. K. The effect of stoichiometry on the structural, thermal and electronic properties of thermally decomposed nickel oxide. RSC Adv. 8, 5882–5890. https://doi.org/10.1039/c8ra00157j (2018).CAS 
    Article 
    ADS 

    Google Scholar 
    49.Preda, I. et al. Surface contributions to the XPS spectra of nanostructured NiO deposited on HOPG. Surf. Sci. 606, 1426–1430. https://doi.org/10.1016/j.susc.2012.05.005 (2012).CAS 
    Article 
    ADS 

    Google Scholar 
    50.Lynch, I., Dawson, K. A., Lead, J. R. & Valsami-Jones, E. In Nanoscience and the Environment Vol. 7 (eds Jamie R. Lead & Eugenia Valsami-Jones) Ch. 4, 127–156 (Elsiver, 2014).51.Lefevre, G. In situ Fourier-transform infrared spectroscopy studies of inorganic ions adsorption on metal oxides and hydroxides. Adv. Colloid Interface Sci. 107, 109–123. https://doi.org/10.1016/j.cis.2003.11.002 (2004).CAS 
    Article 
    PubMed 

    Google Scholar 
    52.Hay, M. B. & Myneni, S. C. B. Structural environments of carboxyl groups in natural organic molecules from terrestrial systems. Part 1: Infrared spectroscopy. Geochim. Cosmochim. Acta 71, 3518–3532. https://doi.org/10.1016/j.gca.2007.03.038 (2007).CAS 
    Article 
    ADS 

    Google Scholar 
    53.Mudunkotuwa, I. A. & Grassian, V. H. Biological and environmental media control oxide nanoparticle surface composition: the roles of biological components (proteins and amino acids), inorganic oxyanions and humic acid. Environ. Sci. Nano 2, 429–439. https://doi.org/10.1039/c4en00215f (2015).CAS 
    Article 

    Google Scholar 
    54.Li, H. et al. The gas-phase formation of tin dioxide nanoparticles in single droplet combustion and flame spray pyrolysis. Combust. Flame 215, 389–400. https://doi.org/10.1016/j.combustflame.2020.02.004 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    55.Xu, C. et al. One-step solution combustion synthesis of CuO/Cu2O/C anode for long cycle life Li-ion batteries. Carbon 142, 51–59. https://doi.org/10.1016/j.carbon.2018.10.016 (2019).CAS 
    Article 

    Google Scholar 
    56.Trusov, G. V. et al. Spray solution combustion synthesis of metallic hollow microspheres. J. Phys. Chem. C 120, 7165–7171. https://doi.org/10.1021/acs.jpcc.6b00788 (2016).CAS 
    Article 

    Google Scholar 
    57.Hedberg, Y. S. & Odnevall Wallinder, I. Metal release from stainless steel in biological environments: a review. Biointerphases 11, 018901. https://doi.org/10.1116/1.4934628 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    58.Dale, A. L., Lowry, G. V. & Casman, E. A. Accurate and fast numerical algorithms for tracking particle size distributions during nanoparticle aggregation and dissolution. Environ. Sci. Nano 4, 89–104. https://doi.org/10.1039/c6en00330c (2017).CAS 
    Article 

    Google Scholar 
    59.He, D., Bligh, M. W. & Waite, T. D. Effects of aggregate structure on the dissolution kinetics of citrate-stabilized silver nanoparticles. Environ. Sci. Technol. 47, 9148–9156. https://doi.org/10.1021/es400391a (2013).CAS 
    Article 
    PubMed 
    ADS 

    Google Scholar 
    60.Korshin, G. V., Perry, S. A. L. & Ferguson, J. F. Influence of NOM on copper corrosion. J. Am. Water Works Assoc. 88, 36–47. https://doi.org/10.1002/j.1551-8833.1996.tb06583.x (1996).CAS 
    Article 

    Google Scholar 
    61.Sarker, P. et al. High-entropy high-hardness metal carbides discovered by entropy descriptors. Nat. Commun. 9, 4980. https://doi.org/10.1038/s41467-018-07160-7 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    62.Pei, Z., Yin, J., Hawk, J. A., Alman, D. E. & Gao, M. C. Machine-learning informed prediction of high-entropy solid solution formation: beyond the Hume-Rothery rules. npj Comput. Mater. https://doi.org/10.1038/s41524-020-0308-7 (2020).Article 

    Google Scholar 
    63.Balasubramanian, K., Khare, S. V. & Gall, D. Valence electron concentration as an indicator for mechanical properties in rocksalt structure nitrides, carbides and carbonitrides. Acta Mater. 152, 175–185. https://doi.org/10.1016/j.actamat.2018.04.033 (2018).CAS 
    Article 
    ADS 

    Google Scholar 
    64.Moskovskikh, D. et al. Extremely hard and tough high entropy nitride ceramics. Sci. Rep. 10, 19874. https://doi.org/10.1038/s41598-020-76945-y (2020).CAS 
    Article 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    65.Sangiovanni, D. G., Hultman, L. & Chirita, V. Supertoughening in B1 transition metal nitride alloys by increased valence electron concentration. Acta Mater. 59, 2121–2134. https://doi.org/10.1016/j.actamat.2010.12.013 (2011).CAS 
    Article 
    ADS 

    Google Scholar  More

  • in

    Trees outside forests are an underestimated resource in a country with low forest cover

    1.Turner, W. R., Nakamura, T. & Dinetti, M. Global urbanization and the separation of humans from nature. Bioscience 54, 585–590 (2004).Article 

    Google Scholar 
    2.Schnell, S., Kleinn, C. & Ståhl, G. Monitoring trees outside forests: a review. Environ. Monitor. Assess. 187, 600 (2015).Article 

    Google Scholar 
    3.Ahmed, P. Trees outside forests (TOF): a case study of wood production and consumption in Haryana. Int. For. Rev. 10, 165–172 (2008).
    Google Scholar 
    4.Krishnankutty, C. N., Thampi, K. B. & Chundamannil, M. Trees outside forests (TOF): a case study of the wood productionconsumption situation in Kerala. Int. For. Rev. 10, 156–164 (2008).
    Google Scholar 
    5.Smeets, E. M. W. & Faaij, A. P. C. Bioenergy potentials from forestry in 2050: an assessment of the drivers that determine the potentials. Clim. Change 81, 353–390 (2007).CAS 
    Article 
    ADS 

    Google Scholar 
    6.Schnell, S., Altrell, D., Ståhl, G. & Kleinn, C. The contribution of trees outside forests to national tree biomass and carbon stocks-a comparative study across three continents. Environ. Monitor. Assess. 187, 4197 (2015).Article 

    Google Scholar 
    7.Zomer, R. J. et al. Trees on farms: an update and reanalysis of agroforestry’s global extent and socio-ecological characteristics. World Agroforestry Center Working Paper 179 (2014).8.Ghosh, M. & Sinha, B. Policy analysis for realizing the potential of timber production from trees outside forests (TOF) in India. Int. For. Rev. 20, 89–103 (2018).
    Google Scholar 
    9.Pain-Orcet, M. & Bellefontaine, R. Trees outside the forest: a new perspective on the management of forest resources in the tropics. Beyond tropical deforestation: from tropical deforestation to forest cover dynamics and forest development, 423–430 (2004)10.Bellefontaine, R., Petit, S., Pain Orcet, M., Deleporte, P. & Bertault, J.G. Trees outside forests: towards better awareness. Food and Agriculture Organization, 216 (Rome, 2002)11.Kleinn, C. On large-area inventory and assessment of trees outside forests. UNASYLVA-FAO- 3–10 (2000).12.FAO. Global forest resources assessment 2005: Progress towards sustainable forest management. Food and Agriculture Organization of the United Nations (2006).13.Eggleston, S., Buendia, L., Miwa, K., Ngara, T. & Tanabe, K. 2006 IPCC guidelines for national greenhouse gas inventories Vol. 5 (Institute for Global Environmental Strategies Hayama, Japan, 2006).14.FAO. World Urbanization Prospects the Revision 2012 (Technical Report, 2011).15.Tewari, V. P., Sukumar, R., Kumar, R. & Gadow, K. Forest observational studies in India: past developments and considerations for the future. For. Ecol. Manag. 316, 32–46 (2014).Article 

    Google Scholar 
    16.Nath, T. K. & Inoue, M. Impacts of participatory forestry on livelihoods of ethnic people: experience from Bangladesh. Soc. Nat. Resour. 23, 1093–1107 (2010).Article 

    Google Scholar 
    17.Islam, S.S. Stratified Two-Stage Sampling (Self-Weighted) for assessment of village forest resources. J. Trop. For. Sci., 9–16 (2004)18.Zashimuddin, M. Community forestry for poverty reduction in Bangladesh. For. Poverty Reduct. Commun. For. Make Money, 81–94 (2004).19.FAO. Global Forest Resources Assessment 2015. Technical Report, Rome (2015).20.Muhammed, N., Koike, M. & Haque, F. Forest policy and sustainable forest management in Bangladesh: an analysis from national and international perspectives. New For. 36, 201–216 (2008).Article 

    Google Scholar 
    21.Manning, A. D., Fischer, J. & Lindenmayer, D. B. Scattered trees are keystone structures-implications for conservation. Biol. Conserv. 132, 311–321 (2006).Article 

    Google Scholar 
    22.Potapov, P. et al. Comprehensive monitoring of Bangladesh tree cover inside and outside of forests, 2000–2014. Environ. Res. Lett. 12, 104015 (2017).Article 
    ADS 

    Google Scholar 
    23.Schumacher, J. & Nord-Larsen, T. Wall-to-wall tree type classification using airborne lidar data and CIR images. Int. J. Remote Sens. 35, 3057–3073 (2014).Article 
    ADS 

    Google Scholar 
    24.Ouma, Y. O. & Tateishi, R. Urban-trees extraction from Quickbird imagery using multiscale spectex-filtering and non-parametric classification. ISPRS J. Photogramm. Remote Sens. 63, 333–351 (2008).Article 
    ADS 

    Google Scholar 
    25.Levin, N. et al. Mapping forest patches and scattered trees from SPOT images and testing their ecological importance for woodland birds in a fragmented agricultural landscape. Int. J. Remote Sens. 30, 3147–3169 (2009).Article 
    ADS 

    Google Scholar 
    26.Sandberg, G., Ulander, L. M. H., Wallerman, J. & Fransson, J. E. S. Measurements of forest biomass change using P-band synthetic aperture radar backscatter. IEEE Trans. Geosci. Remote Sens. 52, 6047–6061 (2014).Article 
    ADS 

    Google Scholar 
    27.Mitchard, E. T. A. et al. Mapping tropical forest biomass with radar and spaceborne LiDAR in Lopé National Park, Gabon: overcoming problems of high biomass and persistent cloud. Biogeosciences 9, 179–191 (2012).Article 
    ADS 

    Google Scholar 
    28.Minh, D. H. T. et al. Relating P-band synthetic aperture radar tomography to tropical forest biomass. IEEE Trans. Geosci. Remote Sens. 52, 967–979 (2013).Article 

    Google Scholar 
    29.Stovall, A. E. L., Shugart, H. & Yang, X. Tree height explains mortality risk during an intense drought. Nat. Commun. 10, 1–6 (2019).Article 

    Google Scholar 
    30.Swatantran, A., Tang, H., Barrett, T., DeCola, P. & Dubayah, R. Rapid, high-resolution forest structure and terrain mapping over large areas using single photon lidar. Sci. Rep. 6, 28277 (2016).CAS 
    Article 
    ADS 

    Google Scholar 
    31.Stovall, A. E. L. & Shugart, H. H. Improved biomass calibration and validation with terrestrial LiDAR: implications for future LiDAR and SAR missions. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 11, 3527–3537 (2018).Article 
    ADS 

    Google Scholar 
    32.Hansen, M.C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).CAS 
    Article 
    ADS 

    Google Scholar 
    33.Martone, M. et al. The global forest/non-forest map from TanDEM-X interferometric SAR data. Remote Sens. Environ. 205, 352–373 (2018).Article 
    ADS 

    Google Scholar 
    34.Lang, N., Schindler, K. & Wegner, J. D. Country-wide high-resolution vegetation height mapping with Sentinel-2. Remote Sens. Environ. 233, 111347 (2019).Article 
    ADS 

    Google Scholar 
    35.UNFAO. The State of World fisheries and Aquaculture 2014, vol. 24 (2014).36.Reddy, C. S., Pasha, S. V., Jha, C. S., Diwakar, P. G. & Dadhwal, V. K. Development of national database on long-term deforestation (1930–2014) in Bangladesh. Glob. Planet. Change 139, 173–182 (2016).Article 
    ADS 

    Google Scholar 
    37.Long, A. J. & Nair, P. K. R. Trees outside forests: agro-, community, and urban forestry. In Planted Forests: Contributions to the Quest for Sustainable Societies, 145–174 (Springer, 1999).38.Muhammed, N., Masum, M. F. H., Hossain, M. M., Chakma, S. & Oesten, G. Economic dependence of rural people on homestead forestry in Mymensingh, Bangladesh. J. For. Res. 24, 591–597 (2013).Article 

    Google Scholar 
    39.Motiur, R. M., Furukawa, Y., Kawata, I., Rahman, M. M. & Alam, M. Role of homestead forests in household economy and factors affecting forest production: a case study in southwest Bangladesh. J. For. Res. 11, 89–97 (2006).Article 

    Google Scholar 
    40.Salam, M. A., Noguchi, T. & Koike, M. Understanding why farmers plant trees in the homestead agroforestry in Bangladesh. Agrofor. Syst. 50, 77–93 (2000).Article 

    Google Scholar 
    41.Rossi, J.-P. & Rousselet, J. The spatial distribution of trees outside forests in a large open-field region and its potential impact on habitat connectivity for forest insects. Türkiye Ormancılık Dergisi 17, 62–64 (2016).Article 

    Google Scholar 
    42.Kabir, M. E. & Webb, E. L. Can homegardens conserve biodiversity in Bangladesh?. Biotropica 40, 95–103 (2008).
    Google Scholar 
    43.Gibbons, P. et al. The future of scattered trees in agricultural landscapes. Conserv. Biol. 22, 1309–1319 (2008).CAS 
    Article 

    Google Scholar 
    44.World Bank. No Title (2018).45.Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on eartha new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. BioScience 51, 933–938 (2001).Article 

    Google Scholar 
    46.Shimada, M. et al. New global forest/non-forest maps from ALOS PALSAR data (2007–2010). Remote Sens. Environ. 155, 13–31. https://doi.org/10.1016/j.rse.2014.04.014 (2014).Article 
    ADS 

    Google Scholar 
    47.GDAL/OGR contributors (2019). GDAL/OGR Geospatial Data Abstraction software Library. Open Source Geospatial Foundation. https://gdal.org.48.Montesano, P. M., Sun, G., Dubayah, R. & Ranson, K. J. The uncertainty of plot-scale forest height estimates from complementary spaceborne observations in the taiga-tundra ecotone. Remote Sens. 6, 10070–10088 (2014).Article 
    ADS 

    Google Scholar 
    49.Montesano, P. M., Sun, G., Dubayah, R. O. & Ranson, K. J. Spaceborne potential for examining taiga-tundra ecotone form and vulnerability. Biogeosciences 13, 3847–3861 (2016).Article 
    ADS 

    Google Scholar 
    50.Montesano, P. M. et al. The use of sun elevation angle for stereogrammetric boreal forest height in open canopies. Remote Sens. Environ. 196, 76–88 (2017).Article 
    ADS 

    Google Scholar 
    51.Olofsson, P. et al. Good practices for estimating area and assessing accuracy of land change. Remote Sens. Environ. 148, 42–57 (2014).Article 
    ADS 

    Google Scholar  More