Philippa Kaur

1.
Mukherjee, S. et al. Genomes OnLine database (GOLD) v.7: Updates and new features. Nucleic Acids Res.47, D649–D659 (2019).
CAS  PubMed  Google Scholar 
2.
Agarwala, R. et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res.46, D8–D13 (2018).
Google Scholar 

3.
Chen, I. M. A. et al. IMG/M v.5.0: An integrated data management and comparative analysis system for microbial genomes and microbiomes. Nucleic Acids Res.47, D666–D677 (2019).
CAS  PubMed  Google Scholar 

4.
Blakemore, R. P. Magnetotactic Bacteria. Science190, 377–379 (1975).
ADS  CAS  PubMed  Google Scholar 

5.
Benoit, M. R. et al. Visualizing implanted tumors in mice with magnetic resonance imaging using magnetotactic bacteria. Clin Cancer Res.15, 5170–5177 (2009).
CAS  PubMed  PubMed Central  Google Scholar 

6.
Alphandéry, E., Chebbi, I., Guyot, F. & Durand-Dubief, M. Use of bacterial magnetosomes in the magnetic hyperthermia treatment of tumours: A review. Int. J. Hyperth.29, 801–809 (2013).
Google Scholar 

7.
Chang, S. & Kirschvink, J. L. Magnetofossils, the magnetization of sediments, and the evolution of magnetite biomineralization. Annu. Rev. Earth Planet. Sci17, 169–95 (1989).
ADS  CAS  Google Scholar 

8.
Kodama, K. P., Moeller, R. E., Bazylinski, D. A., Kopp, R. E. & Chen, A. P. The mineral magnetic record of magnetofossils in recent lake sediments of Lake Ely, PA. Glob. Planet. Change110, 350–363 (2013).
ADS  Google Scholar 

9.
Kopp, R. E. & Kirschvink, J. L. The identification and biogeochemical interpretation of fossil magnetotactic bacteria. Earth-Science Rev.86, 42–61 (2008).
ADS  Google Scholar 

10.
Mckay, C. P., Friedmann, E. I., Frankel, R. B. & Bazylinski, D. A. Magnetotactic bacteria on Earth and on Mars. Astrobiology3, 263–271 (2003).
ADS  CAS  PubMed  Google Scholar 

11.
Uebe, R. & Schüler, D. Magnetosome biogenesis in magnetotactic bacteria. Nature Reviews Microbiology14, 621–637 (2016).
CAS  PubMed  Google Scholar 

12.
Lin, W., Pan, Y. & Bazylinsky, D. A. Diversity and ecology of and biomineralization by magnetotactic bacteria. Environ. Microbiol. Rep.9, 345–356 (2017).
CAS  PubMed  Google Scholar 

13.
Lin, W. et al. Genomic insights into the uncultured genus ‘Candidatus Magnetobacterium’ in the phylum Nitrospirae. ISME J.8, 2463–2477 (2014).
CAS  PubMed  PubMed Central  Google Scholar 

14.
Lin, W. & Pan, Y. A putative greigite-type magnetosome gene cluster from the candidate phylum Latescibacteria. Environ. Microbiol. Rep.7, 237–242 (2015).
CAS  PubMed  Google Scholar 

15.
Lin, W. et al. Genomic expansion of magnetotactic bacteria reveals an early common origin of magnetotaxis with lineage-specific evolution. ISME J. 201812, 1508–1519 (2018).
CAS  Google Scholar 

16.
Ji, B. et al. Comparative genomic analysis provides insights into the evolution and niche adaptation of marine Magnetospira sp. QH-2 strain. Environ. Microbiol.16, 525–544 (2014).
CAS  PubMed  Google Scholar 

17.
Koziaeva, V. V. et al. Magnetospirillum kuznetsovii sp. nov., a novel magnetotactic bacterium isolated from a lake in the Moscow region. Int. J. Syst. Evol. Microbiol.69, 1953–1959 (2019).
CAS  Google Scholar 

18.
Matsunaga, T. et al. Complete genome sequence of the facultative anaerobic magnetotactic bacterium Magnetospirillum sp. strain AMB-1. DNA Res.12, 157–166 (2005).
CAS  PubMed  Google Scholar 

19.
Smalley, M. D., Marinov, G. K., Bertani, L. E. & DeSalvo, G. Genome sequence of Magnetospirillum magnetotacticum strain MS-1. Genome Announc.3, e00233–15 (2015).
PubMed  PubMed Central  Google Scholar 

20.
Koziaeva, V. V. et al. Draft Genome sequences of two magnetotactic bacteria, Magnetospirillum moscoviense BB-1 and Magnetospirillum marisnigri SP-1. Genome Announc.4, e00814–16 (2016).
PubMed  PubMed Central  Google Scholar 

21.
Ke, L., Liu, P., Liu, S. & Gao, M. Complete genome sequence of Magnetospirillum sp. ME-1, a novel magnetotactic bacterium isolated from East Lake, Wuhan, China. Genome Announc.5, e00485–17 (2017).
PubMed  PubMed Central  Google Scholar 

22.
Wang, Y. et al. Complete genome sequence of Magnetospirillum sp. Strain XM-1, isolated from the Xi’an City Moat. China. Genome Announc.4, e01171–16 (2016).
PubMed  Google Scholar 

23.
Grouzdev, D. S. et al. Draft genome sequence of Magnetospirillum sp. Strain SO-1, a freshwater magnetotactic bacterium isolated from the Ol’khovka River, Russia. Genome Announc.2, e00235–14 (2014).
PubMed  PubMed Central  Google Scholar 

24.
Ullrich, S., Kube, M., Schübbe, S., Reinhardt, R. & Schüler, D. A hypervariable 130-kilobase genomic region of Magnetospirillum gryphiswaldense comprises a magnetosome island which undergoes frequent rearrangements during stationary growth. J. Bacteriol.187, 7176–7184 (2005).
CAS  PubMed  PubMed Central  Google Scholar 

25.
Trubitsyn, D. et al. Draft genome sequence of Magnetovibrio blakemorei strain MV-1, a marine vibrioid magnetotactic bacterium. Genome Announc.4, e01330–16 (2016).
PubMed  PubMed Central  Google Scholar 

26.
Jogler, C. et al. Comparative analysis of magnetosome gene clusters in magnetotactic bacteria provides further evidence for horizontal gene transfer. Environ. Microbiol.11, 1267–1277 (2009).
CAS  PubMed  Google Scholar 

27.
Monteil, C. L. et al. Genomic study of a novel magnetotactic Alphaproteobacteria uncovers the multiple ancestry of magnetotaxis. Environ. Microbiol.20, 4415–4430 (2018).
CAS  PubMed  Google Scholar 

28.
Schübbe, S. et al. Complete genome sequence of the chemolithoautotrophic marine magnetotactic coccus strain MC-1. Appl. Environ. Microbiol.75, 4835–4852 (2009).
PubMed  PubMed Central  Google Scholar 

29.
Ji, B. et al. The chimeric nature of the genomes of marine magnetotactic coccoid-ovoid bacteria defines a novel group of Proteobacteria. Environ. Microbiol.19, 1103–1119 (2017).
CAS  PubMed  Google Scholar 

30.
Morillo, V. et al. Isolation, cultivation and genomic analysis of magnetosome biomineralization genes of a new genus of South-seeking magnetotactic cocci within the Alphaproteobacteria. Front. Microbiol.5, 72 (2014).
PubMed  PubMed Central  Google Scholar 

31.
Koziaeva, V. et al. Genome-based metabolic reconstruction of a novel uncultivated freshwater magnetotactic coccus “Ca. Magnetaquicoccus inordinatus” UR-1, and proposal of a candidate family “Ca. Magnetaquicoccaceae”. Front. Microbiol.10, 2290 (2019).
PubMed  PubMed Central  Google Scholar 

32.
Abreu, F. et al. Deciphering unusual uncultured magnetotactic multicellular prokaryotes through genomics. ISME J.8, 1055–1068 (2014).
CAS  PubMed  Google Scholar 

33.
Kolinko, S., Richter, M., Glöckner, F. O., Brachmann, A. & Schüler, D. Single-cell genomics reveals potential for magnetite and greigite biomineralization in an uncultivated multicellular magnetotactic prokaryote. Environ. Microbiol. Rep.6, 524–531 (2014).
CAS  PubMed  Google Scholar 

34.
Lefèvre, C. T. et al. Comparative genomic analysis of magnetotactic bacteria from the Deltaproteobacteria provides new insights into magnetite and greigite magnetosome genes required for magnetotaxis. Environ. Microbiol.15, 2712–2735 (2013).
PubMed  Google Scholar 

35.
Nakazawa, H. et al. Whole genome sequence of Desulfovibrio magneticus strain RS-1 revealed common gene clusters in magnetotactic bacteria. Genome Res.19, 1801–1808 (2009).
CAS  PubMed  PubMed Central  Google Scholar 

36.
Lefèvre, C. T. et al. Novel magnetite-producing magnetotactic bacteria belonging to the Gammaproteobacteria. ISME J.6, 440–450 (2012).
PubMed  Google Scholar 

37.
Baker, B. J., Lazar, C. S., Teske, A. P. & Dick, G. J. Genomic resolution of linkages in carbon, nitrogen, and sulfur cycling among widespread estuary sediment bacteria. Microbiome3 (2015).

38.
Jogler, C. et al. Cultivation-independent characterization of ‘Candidatus Magnetobacterium bavaricum’ via ultrastructural, geochemical, ecological and metagenomic methods. Environ. Microbiol.12, 2466–2478 (2010).
CAS  PubMed  Google Scholar 

39.
Kolinko, S., Richter, M., Glöckner, F. O., Brachmann, A. & Schüler, D. Single-cell genomics of uncultivated deep-branching magnetotactic bacteria reveals a conserved set of magnetosome genes. Environ. Microbiol.18, 21–37 (2016).
CAS  PubMed  Google Scholar 

40.
Lin, W. et al. Origin of microbial biomineralization and magnetotaxis during the Archean. Proc. Natl. Acad. Sci.114, 2171–2176 (2017).
ADS  CAS  PubMed  Google Scholar 

41.
Koziaeva, V. V. et al. Biodiversity of magnetotactic bacteria in the freshwater lake Beloe Bordukovskoe, Russia. Microbiology89, 348–358, https://doi.org/10.1134/S002626172003008X (2020).

42.
Wrighton, K. C. et al. Fermentation, hydrogen, and sulfur metabolism in multiple uncultivated bacterial phyla. Science (80−).337, 1661–1665 (2012).
ADS  CAS  Google Scholar 

43.
Kolinko, S. et al. Single-cell analysis reveals a novel uncultivated magnetotactic bacterium within the candidate division OP3. Environ. Microbiol.14, 1709–1721 (2012).
CAS  PubMed  Google Scholar 

44.
BioSample of Candidatus Hydrogenedentes bacterium MAG_17971_hgd_130. NCBI BioSample https://identifiers.org/ncbi/biosample:SAMN14911668 (2020).

45.
Thrash, C. J. et al. Metagenomic assembly and prokaryotic metagenome-assembled genome sequences from the northern Gulf of Mexico “Dead Zone”. Microbiol. Resour. Announc.7, 4–6 (2018).
Google Scholar 

46.
Watson, S. W. & Waterbury, J. B. Characteristics of two marine nitrite oxidizing bacteria, Nitrospina gracilis nov. gen. nov. sp. and Nitrococcus mobilis nov. gen. nov. sp. Arch. Microbiol.77, 203–230 (1971).
Google Scholar 

47.
Tian, R. M. et al. The deep-sea glass sponge Lophophysema eversa harbours potential symbionts responsible for the nutrient conversions of carbon, nitrogen and sulfur. Environ. Microbiol.18, 2481–2494 (2016).
CAS  PubMed  Google Scholar 

48.
BioSample of Deltaproteobacteria bacterium MAG_22309_dsfv_022. NCBI BioSample https://identifiers.org/ncbi/biosample:SAMN14911677 (2020).

49.
Didonato, R. J. et al. Genome sequence of the deltaproteobacterial strain NaphS2 and analysis of differential gene expression during anaerobic growth on naphthalene. PLos One5, e14072 (2010).
ADS  PubMed  PubMed Central  Google Scholar 

50.
Parks, D. H. et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat. Microbiol.2, 1533–1542 (2017).
CAS  PubMed  Google Scholar 

51.
Tully, B. J., Graham, E. D. & Heidelberg, J. F. The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans. Sci. Data5, 1–8 (2018).
Google Scholar 

52.
Sizova, M. V., Panikov, N. S., Spiridonova, E. M., Slobodova, N. V. & Tourova, T. P. Novel facultative anaerobic acidotolerant Telmatospirillum siberiense gen. nov. sp. nov. isolated from mesotrophic fen. Syst. Appl. Microbiol.30, 213–220 (2007).
CAS  PubMed  Google Scholar 

53.
Bazylinski, D. A. et al. Magnetococcus marinus gen. nov., sp. nov., a marine, magnetotactic bacterium that represents a novel lineage (Magnetococcaceae fam. nov., Magnetococcales ord. nov.) at the base of the Alphaproteobacteria. Int. J. Syst. Evol. Microbiol.63, 801–808 (2013).
CAS  PubMed  Google Scholar 

54.
Lebedeva, E. V. et al. Isolation and characterization of a moderately thermophilic nitrite-oxidizing bacterium from a geothermal spring. FEMS Microbiol. Ecol.75, 195–204 (2011).
CAS  PubMed  Google Scholar 

55.
Lefèvre, C. T. et al. Moderately thermophilic magnetotactic bacteria from hot springs in Nevada. Appl. Environ. Microbiol.76, 3740–3743 (2010).
PubMed  PubMed Central  Google Scholar 

56.
Lefèvre, C. T. et al. Comparative genomic analysis of magnetotactic bacteria from the Deltaproteobacteria provides new insights into magnetite and greigite magnetosome genes required for magnetotaxis. Syst. Appl. Microbiol.40, 280–289 (2017).
PubMed  Google Scholar 

57.
Mikaelyan, A. et al. High-resolution phylogenetic analysis of Endomicrobia reveals multiple acquisitions of endosymbiotic lineages by termite gut flagellates. Environ. Microbiol. Rep.9, 477–483 (2017).
CAS  PubMed  Google Scholar 

58.
Izawa, K. et al. Discovery of ectosymbiotic Endomicrobium lineages associated with protists in the gut of stolotermitid termites. Environ. Microbiol. Rep.9, 411–418 (2017).
CAS  PubMed  Google Scholar 

59.
Ohkuma, M. et al. The candidate phylum ‘Termite Group 1’ of bacteria: Phylogenetic diversity, distribution, and endosymbiont members of various gut flagellated protists. FEMS Microbiol. Ecol.60, 467–476 (2007).
CAS  PubMed  Google Scholar 

60.
Dufour, S. C. et al. Magnetosome-containing bacteria living as symbionts of bivalves. ISME J.8, 2453–2462 (2014).
CAS  PubMed  PubMed Central  Google Scholar 

61.
Monteil, C. L. et al. Ectosymbiotic bacteria at the origin of magnetoreception in a marine protist. Nat. Microbiol.4, 1088–1095 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

62.
Rinke, C. et al. Insights into the phylogeny and coding potential of microbial dark matter. Nature499, 431–437 (2013).
ADS  CAS  PubMed  Google Scholar 

63.
Probst, A. J. et al. Genomic resolution of a cold subsurface aquifer community provides metabolic insights for novel microbes adapted to high CO2 concentrations. Environ. Microbiol.19, 459–474 (2016).
PubMed  Google Scholar 

64.
Tully, B. J., Wheat, C. G., Glazer, B. T. & Huber, J. A. A dynamic microbial community with high functional redundancy inhabits the cold, oxic subseafloor aquifer. ISME J.12, 1–16 (2018).
CAS  PubMed  Google Scholar 

65.
Lücker, S., Nowka, B., Rattei, T., Spieck, E. & Daims, H. The genome of Nitrospina gracilis illuminates the metabolism and evolution of the major marine nitrite oxidizer. Front. Microbiol.4, 27 (2013).
PubMed  PubMed Central  Google Scholar 

66.
Mendler, K. et al. Annotree: Visualization and exploration of a functionally annotated microbial tree of life. Nucleic Acids Res.47, 4442–4448 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

67.
Laczny, C. C. et al. BusyBee Web: Metagenomic data analysis by bootstrapped supervised binning and annotation. Nucleic Acids Res.45, W171–W179 (2017).
CAS  PubMed  PubMed Central  Google Scholar 

68.
Wu, Y. W., Simmons, B. A. & Singer, S. W. MaxBin 2.0: An automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics32, 605–607 (2016).
CAS  Google Scholar 

69.
Lin, H. H. & Liao, Y. C. Accurate binning of metagenomic contigs via automated clustering sequences using information of genomic signatures and marker genes. Sci. Rep.6, 12–19 (2016).
Google Scholar 

70.
Sieber, C. M. K. et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat. Microbiol.3, 836–843 (2018).
CAS  PubMed  PubMed Central  Google Scholar 

71.
Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: Assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res.25, 1043–1055 (2015).
CAS  PubMed  PubMed Central  Google Scholar 

72.
Gurevich, A., Saveliev, V., Vyahhi, N. & Tesler, G. QUAST: Quality assessment tool for genome assemblies. Bioinformatics29, 1072–1075 (2013).
CAS  PubMed  PubMed Central  Google Scholar 

73.
Jain, C., Rodriguez-R, L. M., Phillippy, A. M., Konstantinidis, K. T. & Aluru, S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat. Commun.9, 5114 (2018).
ADS  PubMed  PubMed Central  Google Scholar 

74.
Murali, A., Bhargava, A. & Wright, E. S. IDTAXA: A novel approach for accurate taxonomic classification of microbiome sequences. Microbiome6, 140 (2018).
PubMed  PubMed Central  Google Scholar 

75.
Chaumeil, P., Mussig, A. J., Parks, D. H. & Hugenholtz, P. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 1–3, https://doi.org/10.1093/bioinformatics/btz848 (2019).

76.
Parks, D. H. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat. Biotechnol.36, 996 (2018).
CAS  PubMed  Google Scholar 

77.
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol.30, 772–80 (2013).
CAS  PubMed  PubMed Central  Google Scholar 

78.
Castresana, J. Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Mol. Biol. Evol.17, 540–552 (2000).
CAS  PubMed  Google Scholar 

79.
Nguyen, L. T., Schmidt, H. A., Von Haeseler, A. & Minh, B. Q. IQ-TREE: A fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol.32, 268–274 (2015).
CAS  PubMed  Google Scholar 

80.
Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., Haeseler, A. V. & Jermiin, L. S. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat. Methods14, 587–589 (2017).
CAS  PubMed  PubMed Central  Google Scholar 

81.
Hoang, D. T., Chernomor, O., Von Haeseler, A., Minh, B. Q. & Vinh, L. S. UFBoot2: Improving the ultrafast bootstrap approximation. Mol. Biol. Evol.35, 518–522 (2018).
CAS  PubMed  Google Scholar 

82.
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  Google Scholar 

83.
ASM268676v1 assembly for Magnetovibrio sp. NCBI Assembly https://identifiers.org/ncbi/insdc.gca:GCA_002686765.1 (2013).

84.
ASM240148v1assembly for Elusimicrobia bacterium NORP122. NCBI Assembly https://identifiers.org/ncbi/insdc.gca:GCA_002401485.1 (2017).

85.
BioSample of Candidatus Hydrogenedentes bacterium MAG_17963_hgd_111. NCBI BioSample https://identifiers.org/ncbi/biosample:SAMN14911667 (2020).

86.
BioSample of Deltaproteobacteria bacterium MAG_00134_naph_006. NCBI BioSample https://identifiers.org/ncbi/biosample:SAMN14911648 (2020).

87.
BioSample of Deltaproteobacteria bacterium MAG_00241_naph_010. NCBI BioSample https://identifiers.org/ncbi/biosample:SAMN14911655 (2020).

88.
BioSample of Deltaproteobacteria bacterium MAG_00792_naph_016. NCBI BioSample https://identifiers.org/ncbi/biosample:SAMN14911656 (2020).

89.
BioSample of Deltaproteobacteria bacterium MAG_09788_naph_37. NCBI BioSample https://identifiers.org/ncbi/biosample:SAMN14911662 (2020).

90.
BioSample of Deltaproteobacteria bacterium MAG_15370_dsfb_81. NCBI BioSample https://identifiers.org/ncbi/biosample:SAMN14911665 (2020).

91.
BioSample of Deltaproteobacteria bacterium MAG_17929_sntb_26. NCBI BioSample https://identifiers.org/ncbi/biosample:SAMN14911666 (2020).

92.
BioSample of Deltaproteobacteria bacterium MAG_17996_sntb_20. NCBI BioSample https://identifiers.org/ncbi/biosample:SAMN14911670 (2020).

93.
BioSample of Deltaproteobacteria bacterium MAG_22204_dsfv_001. NCBI BioSample https://identifiers.org/ncbi/biosample:SAMN14911675 (2020).

94.
BioSample of Gammaproteobacteria bacterium MAG_00150_gam_010. NCBI BioSample https://identifiers.org/ncbi/biosample:SAMN14911649 (2020).

95.
BioSample of Gammaproteobacteria bacterium MAG_00160_gam_009. NCBI BioSample https://identifiers.org/ncbi/biosample:SAMN14911650 (2020).

96.
BioSample of Gammaproteobacteria bacterium MAG_00172_gam_018. NCBI BioSample https://identifiers.org/ncbi/biosample:SAMN14911651 (2020).

97.
BioSample of Gammaproteobacteria bacterium MAG_00188_gam_006. NCBI BioSample https://identifiers.org/ncbi/biosample:SAMN14911652 (2020).

98.
BioSample of Gammaproteobacteria bacterium MAG_00212_gam_1. NCBI BioSample https://identifiers.org/ncbi/biosample:SAMN14911653 (2020).

99.
BioSample of Gammaproteobacteria bacterium MAG_00215_gam_020. NCBI BioSample https://identifiers.org/ncbi/biosample:SAMN14911654 (2020).

100.
BioSample of Magnetococcales bacterium MAG_21055_mgc_1. NCBI BioSample https://identifiers.org/ncbi/biosample:SAMN14911672 (2020).

101.
BioSample of Nitrospinae bacterium MAG_09705_ntspn_70. NCBI BioSample https://identifiers.org/ncbi/biosample:SAMN14911661 (2020).

102.
BioSample of Nitrospirae bacterium MAG_10313_ntr_31. NCBI BioSample https://identifiers.org/ncbi/biosample:SAMN14911663 (2020).

103.
BioSample of Desulfuromonadales bacterium MAG_21601_9_030. NCBI BioSample https://identifiers.org/ncbi/biosample:SAMN14911674 (2020).

104.
BioSample of Desulfuromonadales bacterium MAG_13126_9_058. NCBI BioSample https://identifiers.org/ncbi/biosample:SAMN14911678 (2020).

105.
BioSample of Desulfuromonadales bacterium MAG_21600_9_004. NCBI BioSample https://identifiers.org/ncbi/biosample:SAMN14911673 (2020).

106.
BioSample of Planctomycetes bacterium MAG_11118_pl_115. NCBI BioSample https://identifiers.org/ncbi/biosample:SAMN14911664 (2020).

107.
BioSample of Planctomycetes bacterium MAG_17991_pl_60. NCBI BioSample https://identifiers.org/ncbi/biosample:SAMN14911669 (2020).

108.
BioSample of Planctomycetes bacterium MAG_18080_pl_157. NCBI BioSample https://identifiers.org/ncbi/biosample:SAMN14911671 (2020).

109.
BioSample of Rhodospirillaceae bacterium MAG_04806_tlms_2. NCBI BioSample https://identifiers.org/ncbi/biosample:SAMN14911657 (2020).

110.
BioSample of Rhodospirillaceae bacterium MAG_05422_2-02_14. NCBI BioSample https://identifiers.org/ncbi/biosample:SAMN14911658 (2020).

111.
BioSample of Rhodospirillaceae bacterium MAG_05596_2-02_51. NCBI BioSample https://identifiers.org/ncbi/biosample:SAMN14911659 (2020).

112.
BioSample of Rhodospirillaceae bacterium MAG_06104_tlms_034. NCBI BioSample https://identifiers.org/ncbi/biosample:SAMN14911660 (2020).

113.
BioSample of Rhodospirillaceae bacterium MAG_22225_2-02_112. NCBI BioSample https://identifiers.org/ncbi/biosample:SAMN14911676 (2020).

114.
Assembly for unclassified Nitrospina Bin 25. IMG https://identifiers.org/img.taxon:2651870060 (2016).

115.
Assembly for Planctomycetes bacterium SCGC JGI090-P21. IMG Assembly https://identifiers.org/img.taxon:2264265205 (2015).

116.
Assembly for Omnitrophica bacterium SCGC_AG-290-C17. IMG Assembly https://identifiers.org/img.taxon:3300015153 (2017).

117.
Assembly for uncultured microorganism SbSrfc.SA12.01.D19. IMG Assembly https://identifiers.org/img.taxon:3300022116 (2017).

118.
Uzun, M., Alekseeva, L., Krutkina, M., Koziaeva, V. & Grouzdev, D. Analysis: unravelling the diversity of magnetotactic bacteria through analysis of open genomic databases. fighsare https://doi.org/10.6084/m9.figshare.c.4883706 (2020).

119.
Espínola, F. et al. Metagenomic Analysis of Subtidal Sediments from Polar and Subpolar Coastal Environments Highlights the Relevance of Anaerobic Hydrocarbon Degradation Processes. Microb. Ecol.75, 123–139 (2018).
PubMed  Google Scholar 

120.
Wu, X. et al. Microbial metagenomes from three aquifers in the Fennoscandian shield terrestrial deep biosphere reveal metabolic partitioning among populations. ISME J.10, 1192–1203 (2016).
CAS  PubMed  Google Scholar  More

1.
Poulter, B. et al. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature509, 600–603 (2014).
CAS  PubMed  Google Scholar 
2.
Ahlstrom, A. et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science348, 895–899 (2015).
PubMed  Google Scholar 

3.
Legates, D. R. & Willmott, C. J. Mean seasonal and spatial variability in gauge-corrected, global precipitation. Int. J. Clim.10, 111–127 (1990).
Google Scholar 

4.
Rotenberg, E. & Yakir, D. Contribution of semi-arid forests to the climate system. Science327, 451–454 (2010).
CAS  PubMed  Google Scholar 

5.
Huxman, T. E. et al. Convergence across biomes to a common rain-use efficiency. Nature429, 651–654 (2004).
CAS  PubMed  Google Scholar 

6.
Lal, R. Carbon sequestration in dryland ecosystems. Environ. Manag.33, 528–544 (2004).
Google Scholar 

7.
Arzai, A. H. & Aliyu, B. S. Fruit tree and vine sprayer calibration based on canopy size and length of row: unit canopy row method. Bayero J. Pure Appl. Sci.3, 260–263 (2010).
Google Scholar 

8.
Hartmann, H. Will a 385 million year-struggle for light become a struggle for water and for carbon?–How trees may cope with more frequent climate change-type drought events. Glob. Change Biol.17, 642–655 (2011).
Google Scholar 

9.
Sitch, S. et al. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Glob. Change Bio.9, 161–185 (2003).
Google Scholar 

10.
Zaehle, S., Sitch, S., Smith, B. & Hatterman, F. Effects of parameter uncertainties on the modeling of terrestrial biosphere dynamics. Global Biogeochem. Cy. 19, GB3020 (2005).

11.
Collins, W. D. et al. The Community Climate System Model Version 3 (CCSM3). J. Clim.19, 2122–2143 (2006).
Google Scholar 

12.
Levis, S., Bonan, G., Vertenstein, M. & Oleson, K. The Community Land Model’s Dynamic Global Vegetation Model (CLM-DGVM): technical description and user’s guide. NCAR Tech. Note459, 1–50 (2004).
Google Scholar 

13.
Zaehle, S. & Friend, A. D. Carbon and nitrogen cycle dynamics in the O-CN land surface model, I: model description, site-scale evaluation and sensitivity to parameter estimates. Global Biogeochem. Cy.24, GB1005 (2010).

14.
Krinner, G. et al. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Glob. Biogeochem. Cy.19, GB1015 (2005).
Google Scholar 

15.
Haverd, V. et al. A new version of the CABLE land surface model (Subversion revision r4601) incorporating land use and land cover change, woody vegetation demography, and a novel optimisation-based approach to plant coordination of photosynthesis. Geosci. Model. Dev.11, 2995–3026 (2018).
CAS  Google Scholar 

16.
Friend, A. D., Stevens, A. K., Knox, R. G. & Cannell, M. G. R. A process-based, terrestrial biosphere model of ecosystem dynamics (Hybrid v3.0). Ecol. Model.95, 0–287 (1997).
CAS  Google Scholar 

17.
Levy, P. E., Cannell, M. G. R. & Friend, A. D. Modelling the impact of future changes in climate, CO2 concentration and land use on natural ecosystems and the terrestrial carbon sink. Glob. Environ. Change14, 0–30 (2004).
CAS  Google Scholar 

18.
Zeng, X., Li, F. & Song, X. Development of the IAP dynamic global vegetation model. Adv. Atmos. Sci.31, 505–514 (2014).
Google Scholar 

19.
Smith, B. et al. Implications of incorporating N cycling and N limitations on primary production in an individual-based dynamic vegetation model. Biogeosciences11, 2027–2054 (2014).
Google Scholar 

20.
Sato, H., Itoh, A. & Kohyama, T. SEIB-DGVM: a new dynamic global vegetation model using a spatially explicit individual-based approach. Ecol. Model.200, 279–307 (2007).
Google Scholar 

21.
Zhou, R. et al. Estimation of DBH at forest stand level based on multi-parameters and generalized regression neural network. Forests10, 778–796 (2019).
Google Scholar 

22.
Franceschini, T. & Schneider, R. Influence of shade tolerance and development stage on the allometry of ten temperate tree species. Oecologia176, 739–749 (2014).
PubMed  Google Scholar 

23.
Tao, S., Guo, Q., Li, C., Wang, Z. & Fang, J. Global patterns and determinants of forest canopy height. Ecology12, 3265–3270 (2016).
Google Scholar 

24.
Zimmermann, M. H. Hydraulic architecture of some diffuse-porous trees. Can. J. Bot.-Rev. Canadienne De. Botanique56, 2286–2295 (1978).
Google Scholar 

25.
Koch, G. W., Sillett, S. C., Jennings, G. M. & Davis, S. D. The limits to tree height. letters to nature. Nature428, 851–854 (2004).
CAS  PubMed  Google Scholar 

26.
Sterck, F. J., Bongers, F. & Newbery, D. M. Tree architecture in a Bornean lowland rain forest: intraspecific and interspecific patterns. Plant Ecol.153, 279–292 (2001).
Google Scholar 

27.
Stark, S. C. et al. Amazon forest carbon dynamics predicted by profiles of canopy leaf area and light environment. Ecol. Lett.15, 1406–1414 (2012).
PubMed  Google Scholar 

28.
McDowell, N. et al. Mechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought? N. Phytol.178, 719–739 (2008).
Google Scholar 

29.
Risto, S., Christophe, G., Theodore, M. D. & Eero, N. Functional–structural plant models: a growing paradigm for plant studies. Ann. Bot.-Lond.114, 599–603 (2014).
Google Scholar 

30.
Kevin, L., Paul, T., Michael, W., Benoît, S. & Martin, F. LiDAR remote sensing of forest structure. Prog. Phys. Geog.27, 88–106 (2003).
Google Scholar 

31.
Watt, P. J. & Donoghue, D. N. M. Measuring forest structure with terrestrial laser scanning. Int. J. Remote Sens.26, 1437–1446 (2005).
Google Scholar 

32.
Fernández-Sarríaa, A., Velázquez-Martíb, B., Sajdakb, M., Martíneza, L. & Estornella, J. Residual biomass calculation from individual tree architecture using terrestrial laser scanner and ground-level measurements. Comput. Electron. Agr.93, 90–97 (2013).
Google Scholar 

33.
Bayer, D., Seifert, S. & Pretzsch, H. Structural crown properties of Norway spruce (Picea abies L. Karst.) and European beech (Fagus sylvatica L.) in mixed versus pure stands revealed by terrestrial laser scanning. Trees27, 1035–1047 (2013).
Google Scholar 

34.
Hackenberg, J., Wassenberg, M., Spiecker, H. & Sun, D. Non-destructive method for biomass prediction combining TLS derived tree volume and wood density. Forests6, 1274–1300 (2015).
Google Scholar 

35.
Metz, J. et al. Crown modeling by terrestrial laser scanning as an approach to assess the effect of aboveground intra- and interspecific competition on tree growth. For. Ecol. Manag.310, 275–288 (2013).
Google Scholar 

36.
Disney, M. Terrestrial LiDAR: a 3D revolution in how we look at trees. N. Phytol.222, 1736–1741 (2018).
Google Scholar 

37.
Zeide, B. Primary unit of the tree crown. Ecology74, 1598–1602 (1993).
Google Scholar 

38.
Tyree, M. T. & Sperry, J. S. Vulnerability of xylem to cavitation and embolism. Annu. Rev. Plant Physiol. Plant Mol. Biol.40, 19–38 (2003).
Google Scholar 

39.
Schepper, V. D., Dusschoten, D., Copini, P., Jahnke, S. & Steppe, K. MRI links stem water content to stem diameter variations in transpiring trees. J. Exp. Bot.63, 2645–2653 (2012).
PubMed  Google Scholar 

40.
Pivovaroff, A. L. et al. Multiple strategies for drought survival among woody plant species. Func. Ecol.30, 517–526 (2016).
Google Scholar 

41.
Walcroft, A. S. et al. Radiative transfer and carbon assimilation in relation to canopy architecture, foliage area distribution and clumping in a mature temperate rainforest canopy in New Zealand. Agr. For. Meteorol.135, 326–339 (2005).
Google Scholar 

42.
Smith, J. M. B. Scrubland. https://www.britannica.com/science/scrubland (2009).

43.
Archibald, S. & Bond, W. J. Growing tall vs growing wide: tree architecture and allometry of Acacia karroo in forest, savanna, and arid environments. Oikos102, 3–14 (2003).
Google Scholar 

44.
Erdős, L. et al. The edge of two worlds: a new review and synthesis on Eurasian forest-steppes. Appl. Veg. Sci.21, 345–362 (2018).
Google Scholar 

45.
Jackson, T. et al. An architectural understanding of natural sway frequencies in trees. J. R. Soc. Interface16, 20190116 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

46.
Anav, A. et al. Spatiotemporal patterns of terrestrial gross primary production: a review. Rev. Geophys.53, 785–818 (2015).
Google Scholar 

47.
Winkler, A. J., Myneni, R. B., Alexandrov, G. A. & Brovkin, V. Earth system models underestimate carbon fixation by plants in the high latitudes. Nat. Commun.10, 885–893 (2019).
PubMed  PubMed Central  Google Scholar 

48.
Parazoo, N. et al. Optimal estimates of global terrestrial gross primary production from satellite fluorescence and DGVMs. 5th Int. Workshop Remote Sens. Vegetation Fluorescence1, 1–12 (2014).
Google Scholar 

49.
Xu, X., Wang, Z., Rahbek, C., Sanders, N. J. & Fang, J. Geographical variation in the importance of water and energy for oak diversity. J. Biogeogr.43, 279–288 (2016).
Google Scholar 

50.
Fick, S. E. & Hijmans, R. J. (2017) Worldclim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2016).

51.
Fleck, S. et al. Terrestrial lidar measurements for analysing canopy structure in an old-growth forest. In Proc. ISPRS Workshop on Laser Scanning 2007 and SilviLaser 2007 (2007).

52.
Taubin, G. Estimation of planar curves, surfaces, and nonplanar space-curves defined by implicit equations with applications to edge and range image segmentation. IEEE T. Pattern Anal.13, 1115–1138 (1991).
Google Scholar 

53.
Ohashi, Y. Machine vision methods and articles of manufacture for determination of convex hull and convex hull angle. U.S. Patent No. 5,801,966. 1 Sep. (1998).

54.
Li, Y. et al. Derivation, validation, and sensitivity analysis of terrestrial laser scanning-based leaf area index. Can. J. Remote Sens.42, 719–729 (2016).
Google Scholar 

55.
Grossiord, C. et al. Effect of climate change on reference evapotrature, drives functional responses of trees in semi-arid ecosystems. J. Ecol.105, 163–175 (2017).
Google Scholar 

56.
Feldpausch, T. R. et al. Height-diameter allometry of tropical forest trees. Biogeosciences8, 1081–1106 (2011).
Google Scholar 

57.
Solargis Database. http://solargis.cn/imaps. Accessed 21 Nov 2018 (2018).

58.
Dai, A. & National Center for Atmospheric Research Staff (eds). Last modified 18 Jul 2017. “The Climate Data Guide: Palmer Drought Severity Index (PDSI).” https://climatedataguide.ucar.edu/climate-data/palmer-drought-severity-index-pdsi. Accessed 5 June 2018 (2017).

59.
Zomer, R. J. et al. Climate change mitigation: a spatial analysis of global land suitability for clean development mechanism afforestation and reforestation. Agr. Ecosyst. Environ.126, 67–80 (2008).
Google Scholar 

60.
Geographic Data Sharing Infrastructure. College of Urban and Environmental Science, Peking University. http://geodata.pku.edu.cn. Accessed 25 Feb 2020 (2020).

61.
Fang, J., Wang, Z. & Tang, Z. Atlas of Woody Plants in China. (China Higher Education Press, 2009). More

Department of Animal Ecology and Tropical Biology, University of Würzburg, Würzburg, Germany
Lea Heidrich, Soyeon Bae, Sebastian Seibold, Simon Thorn & Jörg Müller

CSIRO Land and Water, Winnellie, Northern Territory, Australia
Shaun Levick

Terrestrial Ecology Research Group, Technical University of Munich, Freising, Germany
Sebastian Seibold, Wolfgang Weisser & Inken Doerfler

Department of Geoinformatics, Munich University of Applied Sciences, München, Germany
Peter Krzystek & Alla Serebryanyk

Forest Inventory and Remote Sensing, Faculty of Forest Sciences and Forest Ecology, University of Göttingen, Göttingen, Germany
Paul Magdon

Faculty of Geography, Philipps-University Marburg, Marburg, Germany
Thomas Nauss & Stephan Wöllauer

Silviculture and Forest Ecology of the Temperate Zones, Faculty of Forest Sciences and Forest Ecology, University of Göttingen, Göttingen, Germany
Peter Schall & Christian Ammer

Bavarian Forest National Park, Grafenau, Germany
Claus Bässler, Marco Heurich & Jörg Müller

Institute for Ecology, Evolution and Diversity, Faculty of Biological Sciences, Goethe University Frankfurt, Frankfurt, Germany
Claus Bässler

Institute of Biology and Environmental Science, Vegetation Science & Nature Conservation, University of Oldenburg, Oldenburg, Germany
Inken Doerfler

Institute of Plant Sciences, University of Bern, Bern, Switzerland
Markus Fischer

Forest Entomology, Swiss Federal Research Institute WSL, Birmensdorf, Switzerland
Martin M. Gossner

Chair of Wildlife Ecology and Wildlife Management, University of Freiburg, Freiburg, Germany
Marco Heurich

Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
Torsten Hothorn

Evolutionary Ecology and Conservation Genomics, University Ulm, Ulm, Germany
Kirsten Jung

Biodiversity, Macroecology & Biogeography, University of Goettingen, Göttingen, Germany
Holger Kreft

Centre of Biodiversity and Sustainable Land Use, University of Goettingen, Göttingen, Germany
Holger Kreft

Max Planck Institute for Biogeochemistry, Jena, Germany
Ernst-Detlef Schulze

Ecological Networks, Technical University of Darmstadt, Darmstadt, Germany
Nadja Simons More

While it is widely known that certain environmental trade-offs may have to be made in order to reduce carbon emissions and combat climate change, one major site of renewable energy development — solar power facilities in deserts — may have unexpected consequences for vulnerable plants in an understudied ecosystem.

Credit: Bloomberg / Contributor / Bloomberg / Getty

Stephen Grodsky and Rebecca Hernandez at the University of California, Davis, studied 35 plant species in the Mojave Desert, including scrub plants, cacti and Mojave yucca, in the area around one of the world’s largest concentrated solar plants in Ivanpah, California. Native plants in the area provide services not only for the ecosystem but also for indigenous peoples in the area who rely on the plants for food and cultural purposes.

However, Grodsky and Hernandez found that solar plant development negatively affected the richness and evenness of the native scrub and perennial species; the treatment of the ground caused by installation of the solar infrastructure also destroyed biological soil crusts, which seems to allow invasive grasses to spread more widely than they would otherwise. As deserts are home to some of the most vulnerable species and the poorest peoples who rely on that ecosystem, these trade-offs must be further researched and mitigated.

Author information

Affiliations

Nature Plants
Ryan Scarrow

Authors
Ryan Scarrow

Corresponding author
Correspondence to Ryan Scarrow.

Rights and permissions

About this article

Cite this article
Scarrow, R. Solar plants versus desert plants. Nat. Plants (2020). https://doi.org/10.1038/s41477-020-00753-5
Download citation More

1.
Mèndez-Fernandez, P. et al. Foraging ecology of five toothed whale species in the Northwest Iberian Peninsula, inferred using carbon and nitrogen isotope ratios. J. Exp. Mar. Bio. Ecol. 413, 150–158 (2012).
Google Scholar 
2.
Browning, N. E., Cockcroft, V. G. & Worthy, G. A. J. Resource partitioning among South African delphinids. J. Exp. Mar. Bio. Ecol. 457, 15–21 (2014).
Google Scholar 

3.
Loizaga de Castro, R., Saporiti, F., Vales, D. G., Cardona, L. & Crespo, E. A. Using stable isotopes to assess whether two sympatric dolphin species share trophic resources. Mar. Mammal Sci. 33, 1235–1244 (2017).
Google Scholar 

4.
Franco-Trecu, V., Drago, M., Costa, P., Dimitriadis, C. & Passadore, C. Trophic relationships in apex predators in an estuary system: a multiple-method approximation. J. Exp. Mar. Bio. Ecol. 486, 230–236 (2017).
Google Scholar 

5.
Newsome, S. D., Clementz, M. T. & Koch, P. L. Using stable isotope biogeochemistry to study marine mammal ecology. Mar. Mammal Sci. 26, 509–572 (2010).
CAS  Google Scholar 

6.
DeNiro, M. J. & Epstein, S. Influence of diet on the distribution of carbon isotopes in animals. Geochim. Cosmochim. Acta 42, 495–506 (1978).
ADS  CAS  Google Scholar 

7.
DeNiro, M. J. & Epstein, S. Influence of diet on the distribution of nitrogen isotopes in animals. Geo-Marine Lett. 45, 341–351 (1981).
CAS  Google Scholar 

8.
Minagawa, M. & Wada, E. Stepwise enrichment of 15N along food chains: Further evidence and the relation between δ15N and animal age. Geochim. Cosmochim. Acta 48, 1135–1140 (1984).
ADS  CAS  Google Scholar 

9.
Post, D. M. Using stable isotopes to estimate trophic position: models, methods, and assumptions. Ecology 83, 703–718 (2002).
Google Scholar 

10.
McCutchan, J. H., Lewis, W. M., Kendall, C. & Mcgrath, C. C. Variation in trophic shift for stable isotope ratios of carbon, nitrogen, and sulfur. OIKOS 102, 378–390 (2003).
CAS  Google Scholar 

11.
Vanderklift, M. A. & Ponsard, S. Sources of variation in consumer-diet δ15N enrichment: A meta-analysis. Oecologia 136, 169–182, https://doi.org/10.1007/s00442-003-1270-z (2003).
ADS  PubMed  Google Scholar 

12.
Fry, B. Stable Isotope Ecology (Springer, Berlin, 2006).
Google Scholar 

13.
Bearhop, S. et al. Determining trophic niche width: a novel approach using stable isotope analysis. J. Anim. Ecol. 73, 1007–1012 (2004).
Google Scholar 

14.
Newsome, S. D., Martinez del Rio, C., Bearhop, S. & Phillips, D. L. A niche for isotopic ecology. Front Ecol Env. 5, 429–436 (2007).

15.
Hutchinson, G. E. Concluding remarks. Cold Spring Harb. Symp. Quant. Biol. 22, 415–427 (1957).
Google Scholar 

16.
Layman, C. A., Arrington, D. A., Montana, C. G. & Post, D. M. Can stable isotope ratio provide for community-wide measures of trophic structure?. Ecology 88, 42–48 (2007).
PubMed  Google Scholar 

17.
Ryan, C. et al. Stable isotope analysis of baleen reveals resource partitioning among sympatric rorquals and population structure in fin whales. Mar. Ecol. Prog. Ser. 479, 251–261 (2013).
ADS  CAS  Google Scholar 

18.
Staudinger, M. D., McAlarney, R. J., McLellan, W. A. & Ann Pabst, D. Foraging ecology and niche overlap in pygmy (Kogia breviceps) and dwarf (Kogia sima) sperm whales from waters of the U.S. mid-Atlantic coast. Mar. Mammal Sci. 30, 626–655 (2014).

19.
Gallagher, A. J., Shiffman, D. S., Byrnes, E. E., Hammerschlag-Peyer, C. M. & Hammerschlag, N. Patterns of resource use and isotopic niche overlap among three species of sharks occurring within a protected subtropical estuary. Aquat. Ecol. 51, 435–448 (2017).
CAS  Google Scholar 

20.
Graham, B. S., Koch, P. L., Newsome, S. D., Mcmahon, K. W. & Aurioles, D. Isoscapes. in (ed. J.B.West) 299–318 (Springer Science, London 2010). https://doi.org/10.1007/978-90-481-3354-3

21.
Jaeger, A., Lecomte, V. J., Weimerskirch, H., Richard, P. & Cherel, Y. Seabird satellite tracking validates the use of latitudinal isoscapes to depict predators ’ foraging areas in the Southern Ocean. RAPID Commun. MASS Spectrom. 24, 3456–3460 (2010).
ADS  CAS  PubMed  Google Scholar 

22.
McMahon, K. W., Hamady, L. L. & Thorrold, S. R. A review of ecogeochemistry approaches to estimating movements of marine animals. Limnol. Ocean. 58, 697–714 (2013).
CAS  Google Scholar 

23.
Brault, E. K., Koch, P. L., Mcmahon, K. W. & Broach, K. H. Carbon and nitrogen zooplankton isoscapes in West Antarctica reflect oceanographic transitions. Mar. Ecol. Prog. Ser. 593, 29–45 (2018).
ADS  CAS  Google Scholar 

24.
Troina, C. G. et al. Deep-sea research part I Zooplankton-based δ13C and δ15N isoscapes from the outer continental shelf and slope in the subtropical western South Atlantic. Deep. Res. Part I https://doi.org/10.1016/j.dsr.2020.103235 (2020).
Article  Google Scholar 

25.
Montoya, J. P. Abundance as indicators of forest nitrogen status and soil carbon dynamics stable isotopes in ecology and environmental science. In Stable Isotopes in Ecology and Environmental Science, 176–201 (ed. Lajtha, R. M. K.) (Blackwell Publishing, London, 2007). https://doi.org/10.1002/9780470691854.ch3.
Google Scholar 

26.
Sigman, D. M., Karsh, K. L. & Casciotti, K. L. Ocean Process Tracers: Nitrogen Isotopes in the Ocean. in Encyclopedia of Ocean Sciences (ed. Steele, J.H., Turekian, K.K., Thorpe, S. A.) 4138–4153 (Elsevier Ltd, 2009).

27.
Laws, E. A., Popp, B. N., Bidigare, J. R. R., Kennicutt, M. C. & Macko, S. A. Dependence of phytoplankton carbon isotopic composition on growth rate and [CO2]aq: Theoretical considerations and experimental results. Geochim. Cosmochim. Acta 59, 1131–1138 (1995).
ADS  CAS  Google Scholar 

28.
Trueman, C. N. & St John Glew, K. Chapter 6—Isotopic Tracking of Marine Animal Movement Tracking Animal Migration with Stable Isotopes (Elsevier Inc., London, 2019). https://doi.org/10.1016/B978-0-12-814723-8.00006-4.
Google Scholar 

29.
France, R. L. Carbon-13 enrichment in benthic compared to planktonic algae: foodweb implications. Mar. Ecol. Prog. Ser. 124, 307–312 (1995).
ADS  Google Scholar 

30.
Mayer, M., Joye, B. & Aller, C. Importance of suspended participates in riverine delivery of bioavailable nitrogen to coastal zones. Glob. Biochem. Cycles 12, 573–579 (1998).
ADS  CAS  Google Scholar 

31.
Druffel, E. R. M., Bauer, J. E. & Griffin, S. Input of particulate organic and dissolved inorganic carbon from the Amazon to the Atlantic Ocean. Geochem. Geophys. Geosyst 6, 1–7 (2005).
Google Scholar 

32.
Weber, S. C. et al. Amazon River influence on nitrogen fixation and export production in the western tropical North Atlantic. Limnol. Ocean. https://doi.org/10.1002/lno.10448 (2016).
Article  Google Scholar 

33.
Cai, D. L. & Edmond, J. M. Sources and transport of particulate organic carbon in the Amazon River and Estuary. Estuar. Coast. Shelf Sci. 26, 1–14 (1988).
ADS  CAS  Google Scholar 

34.
Weber, S. C. et al. Habitat delineation in highly variable marine environments. Front. Mar. Sci. 6, 1–16 (2019).
ADS  Google Scholar 

35.
De Menezes, M. P. M., Berger, U. & Mehlig, U. Mangrove vegetation in Amazonia: a review of studies from the coast of Pará and Maranhão States, north Brazil. Acta Amaz. 38, 403–420 (2008).
Google Scholar 

36.
Hayashi, S. N., Souza-filho, P. W. M., Nascimento, W. R. Jr. & Fernandes, M. E. B. The effect of anthropogenic drivers on spatial patterns of mangrove land use on the Amazon coast. PLoS ONE 14, 1–20 (2019).
Google Scholar 

37.
Barthem, R. B. Ocorrência, distribuição e biologia dos peixes da Baía de Marajó, Estuário Amazônico. Bol. do Mus. Para. Emílio Goeldi. Série Zool. 2, 49–69 (1985).

38.
Camargo, M. & Isaac, V. Os peixes estuarinos da região Norte do Brasil: Lista de espécie e consideração sobre sua distribuição geográfica. Bol. do Mus. Para. Emílio Goeldi, Ser. Zool. 17, 135 – 147 (2002).

39.
Siciliano, S. et al. Revisão do conhecimento sobre os mamíferos aquáticos da costa norte do Brasil. Arq. do Mus. Nac. 66, 381–401 (2008).
Google Scholar 

40.
Meirelles, A. C. O. et al. Cetacean strandings on the coast of Ceará, north-eastern Brazil (1992–2005). J. Mar. Biol. Assoc. United Kingdom 89, 1–8 (2009).
Google Scholar 

41.
Emin-Lima, R. et al. Os mamíferos aquáticos associados aos manguezais da costa norte brasileira. in Mamíferos de Restingas e Manguezais do Brasil 45–57 (Sociedade Brasileira de Mastozoologia, 2010).

42.
Siciliano, S. et al. New genetic data extend the range of river dolphins Inia in the Amazon Delta. Hydrobiologia 777. https://doi.org/10.1007/s10750-016-2794-7 (2016).

43.
Siciliano, S. et al. Confirmed sightings of the Antillean manatee (Trichechus manatus) on the coast of Ilha de Marajó, northern Brazilian coast. JMBA Glob. Mar. Environ. 34–35 (2007).

44.
Alves, M. D. et al. First abundance estimate of the Antillean manatee (Trichechus manatus) in Brazil by aerial survey. J. Mar. Biol. Assoc. UK https://doi.org/10.1017/S0025315415000855 (2015).
Article  Google Scholar 

45.
Monteiro-Neto, C. et al. Impact of fisheries on the tucuxi (Sotalia fluviatilis) and rough-toothed dolphin (Steno bredanensis) populations off Ceará state, northeastern Brazil. Aquat. Mamm. 26, 49–56 (2000).
Google Scholar 

46.
Barreto, A. S. et al. Plano de Ação nacional para Conservação dos Mamíferos Aquáticos – Pequenos Cetáceos. (2010).

47.
Moura, J. F., Hauser-Davis, R. A., Lemos, L., Emin-Lima, R. & Siciliano, S. Guiana Dolphins (Sotalia guianensis) as marine ecosystem sentinels: ecotoxicology and emerging diseases. in Reviews of Environmental Contamination and Toxicology, Vol. 228 (ed Whitacre, D. M.) 1–29, https://doi.org/10.1007/978-1-4419-6260-7 (2014).

48.
Costa, A. F. et al. Stranding survey as a framework to investigate rare cetacean records of the north and north-eastern Brazilian coasts. Zookeys https://doi.org/10.3897/zookeys.688.12636 (2017).
Article  PubMed  PubMed Central  Google Scholar 

49.
Domning, D. P. Distribution and Status of manatees Trichechus spp. near the mouth of the Amazon River, Brazil. Biol. Conserv. 19, 85–97 (1981).
Google Scholar 

50.
Siciliano, S. et al. Going back to my roots: Confirmed sightings of the Antillean manatee (Trichechus manatus) on the coast of Ilha de Marajó, northern Brazilian coast. JMBA Glob. Mar. Environ. 1, 34–35 (2007).
Google Scholar 

51.
Cunha, H. A., da Silva, V. M. F. & Solé-Cava, A. M. Molecular Ecology and Systematics of Sotalia Dolphins. in Biology, Evolution and Conservation of Rivers Dolphins within South America and Asia (eds. Ruiz-Garcia, M. & Shostell, J.) 261–283 (Nova Science Publishers, Inc., 2010).

52.
Hrbek, T. et al. A new species of river dolphin from Brazil or: how little do we know our biodiversity. PLoS ONE 9, https://doi.org/10.1371/journal.pone.0083623 (2014).

53.
Niño-Torres, C. A., Gallo-Reynoso, J. P., Galván-Magaña, F., Escobar-Briones, E. & Macko, S. A. Isotopic analysis of δ13C, δ15N, and δ34S ‘a feeding tale’ in teeth of the longbeaked common dolphin, Delphinus capensis. Mar. Mammal Sci. 22, 831–846, https://doi.org/10.1111/j.1748-7692.2006.00065.x (2006).
Google Scholar 

54.
Pinela, A. M., Borrell, A., Cardona, L. & Aguilar, A. Stable isotope analysis reveals habitat partitioning among marine mammals off the NW African coast and unique trophic niches for two globally threatened species. Mar. Ecol. Prog. Ser. 416, 295–306, https://doi.org/10.3354/meps08790 (2010).
ADS  CAS  Article  Google Scholar 

55.
Walker, J. L. & Macko, S. A. Dietary studies of marine mammals using stable carbon and nitrogen isotopic ratios of teeth. Mar. Mammal Sci. 15, 314–334 (1999).
Google Scholar 

56.
Riccialdelli, L., Newsome, S. D., Fogel, M. L. & Goodall, R. N. P. Isotopic assessment of prey and habitat preferences of a cetacean community in the southwestern South Atlantic Ocean. Mar. Ecol. Prog. Ser. 418, 235–248 (2010).
ADS  Google Scholar 

57.
Vighi, M. et al. Stable isotopes indicate population structuring in the Southwest Atlantic population of right whales (Eubalaena australis). PLoS ONE 9, https://doi.org/10.1371/journal.pone.0090489 (2014).

58.
Junk, W. J. et al. A classification of major naturally-occurring amazonian lowland wetlands. Wetlands 31, 623–640 (2011).
Google Scholar 

59.
Rosário, R. P., Borba, T. A. C., Santos, A. S. & Rollnic, M. Variability of Salinity in Pará River Estuary: 2D Analysis with Flexible Mesh Model. J. Coast. Res. 75, 128–132 (2016).
Google Scholar 

60.
Bezerra, M. O. & Rollnic, M. Physical oceanographic behavior at the Guamá/Acará-Moju and the Paracauari river mouths, Amazon Coast (Brazil). in Journal of Coastal Research 1448–1452 (Proceedings of The 11th International Coastal Synposium, 2011).

61.
Rosário, R. P. & Santos, A. S. Contribution to understanding the surface seawater intrusion in the Pará River estuary during low discharge. Proceedings of the 17th Physics of Estuaries and Coastal Seas (PECS) (2014).

62.
Nittrouer, C. a. & DeMaster, D. J. The Amazon shelf setting: tropical, energetic, and influenced by a large river. Cont. Shelf Res. 16, 553–573 (1996).

63.
Moreira, A. M. & Mavigner, D. S. Conhecendo história e geografia do Piauí. (Gráfica Ferraz, 2007).

64.
Szczygielski, A. et al. Evolution of the Parnaíba Delta (NE Brazil) during the late Holocene. Geo-Marine Lett. 35. 105–117, https://doi.org/10.1007/s00367-014-0395-x (2014).
ADS  Google Scholar 

65.
Foote, A. D. et al. Tracking niche variation over millennial timescales in sympatric killer whale lineages. Proc. Biol. Sci. 280, 2–9 (2013).
Google Scholar 

66.
Newsome, S. D., Koch, P. L., Etnier, M. A. & Aurioles-Gamboa, D. Using Carbon and Nitrogen Isotope Values To Investigate Maternal Strategies in Northeast Pacific Otariids. Mar. Mammal Sci. 22, 556–572 (2006).
Google Scholar 

67.
Jackson, A. L., Inger, R., Parnell, A. C. & Bearhop, S. Comparing isotopic niche widths among and within communities: SIBER – Stable Isotope Bayesian Ellipses in R. J. Anim. Ecol. 80, 595–602 (2011).
PubMed  Google Scholar 

68.
Bisi, T. L. et al. Trophic relationships and habitat preferences of Delphinids from the Southeastern Brazilian Coast determined by carbon and nitrogen stable isotope composition. PLoS ONE 8, e82205 (2013).
ADS  PubMed  PubMed Central  Google Scholar 

69.
Botta, S., Hohn, A. A., Macko, S. A. & Secchi, E. R. Isotopic variation in delphinids from the subtropical western South Atlantic. J. Mar. Biol. Assoc. United Kingdom https://doi.org/10.1017/S0025315411000610 (2012).
Article  Google Scholar 

70.
Medeiros, P. et al. Fate of the Amazon River dissolved organic matter in the tropical Atlantic Ocean. Global Biogeochem. Cycles 29, 677–690 (2015).
ADS  CAS  Google Scholar 

71.
Moura, R. L. et al. An extensive reef system at the Amazon River mouth. Sci. Adv. https://doi.org/10.1126/sciadv.1501252 (2016).
Article  PubMed  PubMed Central  Google Scholar 

72.
Loick-Wilde, N. et al. Nitrogen sources and net growth efficiency of zooplankton in three Amazon River plume food webs. Limnol. Oceanogr. 61, 460–481 (2016).
ADS  CAS  Google Scholar 

73.
Fernández, R. et al. Stable isotope analysis in two sympatric populations of bottlenose dolphins Tursiops truncatus: Evidence of resource partitioning?. Mar. Biol. 158, 1043–1055. https://doi.org/10.1007/s00227-011-1629-3 (2011).
Article  Google Scholar 

74.
Riccialdelli, L. & Goodall, N. Intra-specific trophic variation in false killer whales (Pseudorca crassidens) from the southwestern South Atlantic Ocean through stable isotopes analysis. Mamm. Biol. 80, 298–302 (2015).
Google Scholar 

75.
Haro, D., Riccialdelli, L., Blank, O., Matus, R. & Sabat, P. Estimating the isotopic niche of males and females of false killer whales (Pseudorca crassidens) from Magellan Strait. Chile. Mar. Mammal Sci. https://doi.org/10.1111/mms.12564 (2018).
Article  Google Scholar 

76.
Bastida, R., Rodríguez, D. & Secchi, E. Mamiferos Acuaticos de Sudamerica y Antartida. (Vázquez Manzini Editores, 2007).

77.
Cardoso, J., Francisco, A., de Souza, S. P. & Siciliano, S. Rough-toothed dolphins (Steno bredanensis) Along Southeastern Brazil: report of an anomalous pigmented juvenile and description of social and feeding behaviors. Aquat. Mamm. 45, 30–36 (2019).
Google Scholar 

78.
Johns, W. E. et al. Annual cycle and variability of the North Brazil current. J. Phys. Oceanogr. 28, 103–128 (1998).
ADS  Google Scholar 

79.
Gross, A., Kiszka, J., Canneyt, O. V., Richard, P. & Ridoux, V. A preliminary study of habitat and resource partitioning among co-occurring tropical dolphins around Mayotte, southwest Indian Ocean. Estuar. Coast. Shelf Sci. 84, 367–374 (2009).
ADS  CAS  Google Scholar 

80.
Trites, A. W. W. Marine mammal trophic levels and interactions. In Encyclopedia of Ocean Sciences (eds Steele, J. H. et al.) 1628–1633 (Academic Press, Berlin, 2001). https://doi.org/10.1029/2002EO000342.
Google Scholar 

81.
Ruiz-Cooley, R. I., Engelhaupt, D. T. & Ortega-Ortiz, J. G. Contrasting C and N isotope ratios from sperm whale skin and squid between the Gulf of Mexico and Gulf of California: Effect of habitat. Mar. Biol. 159, 151–164 (2012).
CAS  Google Scholar 

82.
Goes, J. I. et al. Influence of the Amazon River discharge on the biogeography of phytoplankton communities in the western tropical north Atlantic. Prog. Oceanogr. 120, 29–40 (2014).
ADS  Google Scholar 

83.
Montoya, J. P., Landrum, J. P. & Weber, S. C. Amazon River influence on nitrogen fixation in the western tropical North Atlantic. J. Mar. Res. 77, 191–213 (2019).
Google Scholar 

84.
Beltrán-Pedreros, S. & Pantoja, T. M. A. Feeding habits of Sotalia fluviatilis in the Amazonian Estuary. Acta Sci. – Biol. Sci. 28, 389–393 (2006).
Google Scholar 

85.
Giarrizzo, T., Schwamborn, R. & Saint-Paul, U. Utilization of carbon sources in a northern Brazilian mangrove ecosystem. Estuar. Coast. Shelf Sci. 95, 447–457. https://doi.org/10.1016/j.ecss.2011.10.018 (2011).
ADS  CAS  Article  Google Scholar 

86.
Crema, L. C., da Silva, V. M. F., Botta, S., Trumbore, S. & Piedade, M. T. F. Does water type influence diet composition in Amazonian manatee (Trichechus inunguis)? A case study comparing black and clearwater rivers. Hydrobiologia https://doi.org/10.1007/s10750-019-3900-4 (2019).
Article  Google Scholar 

87.
Lins, A. L. F. A., Gurgel, E. S. C., Bastos, M. N. C., Sousa, M. E. M. & Emin-Lima, R. Which aquatic plants of the intertidal zone do manatees of the Amazon estuary eat? Sirenews 11–12 (2014).

88.
Ciotti, L. L., Luna, F. O. & Secchi, E. Intra- and interindividual variation in D13C and D15N composition in the Antillean manatee Trichechus manatus manatus from northeastern Brazil. Mar. Mammal Sci. https://doi.org/10.1111/mms.12102 (2014).
Article  Google Scholar 

89.
Sousa, M. E. M., Martins, B. M. L. & Fernandes, M. E. B. Meeting the giants: the need for local ecological knowledge (LEK) as a tool for the participative management of manatees on Marajó Island, Brazilian Amazonian coast. Ocean Coast. Manag. 86, 53–60 (2013).
Google Scholar 

90.
Best, R. C. & Da Silva, V. M. F. Inia geoffrensis. Mamm. Species 1–8 (1993).

91.
Costa, A. F. et al. How far does it go along the coast? Distribution and first genetic analyses of the boto (Inia geoffrensis) along the coast of Pará, Amazon, Brazil. In 2013 SC65a Meeting of The International Whaling Commission 1–12 (IWC/Scientific Commitee, Jeju Island, South Korea, Small Cetaceans, Cambridge, 2013).

92.
Martin, A. R. R. & Da Silva, V. M. F. River dolphins and flooded forest: seasonal habitat use and sexual segregation of botos (Inia geoffrensis) in an extreme cetacean environment. J. Zool. Lond. 263, 295–305 (2004).

93.
Da Silva, V. M. F., Goulding, M. & Barthem, R. Golfinhos da Amazônia. (INPA, 2008).

94.
Di Beneditto, A. P. M. & Ramos, R. M. A. Biology of the marine tucuxi dolphin (Sotalia fluviatilis) in south-eastern Brazil. J. Mar. Biol. Assoc. United Kingdom 84, 1245–1250 (2004).
Google Scholar 

95.
Pansard, K. C. A., Gurgel, H. D. C. B., Andrade, L. C. D. A. & Yamamoto, M. E. Feeding ecology of the estuarine dolphin (Sotalia guianensis) on the coast of Rio Grande do Norte, Brazil. Mar. Mammal Sci. 27, 673–687 (2011).
Google Scholar 

96.
Smith, T. D. & Burrows, A. M. Mobility of the axial regions in a captive Amazon river dolphin (Inia geoffrensis). in Biology, Evolution, and Conservation of river dolphins within South America and Asia (eds. Ruiz-Garcia, M. & Shostell, J. M.) 71–81 (Nova Science Publishers, Inc., 2009).

97.
Ramos, R. M. A. et al. Morphology of the Guiana dolphin (Sotalia guianensis) off southeastern Brazil: growth and geographic variation. Lat. Am. J. Aquat. Mamm. 8, 137–149 (2010).
Google Scholar 

98.
Emin-Lima, R. Preenchendo Lacunas em Saúde de Ecossistemas: Estudo Morfológico e de Contaminantes nos Botos-cinza (Sotalia guianensis) da Costa Norte do Brasil. (Tese de Doutorado. Programa de Pós-Graduação em Saúde Pública e Meio Ambiente. Escola Nacional e Saúde Pública, FIOCRUZ, 147p., 2012).

99.
Arcoverde, D. L. et al. Evaluation of periotic–timpanic bone complex of Sotalia guianensis (Cetacea: Delphinidae) as tool in identification of geographic variations. J. Mar. Biol. Assoc. United Kingdom 94, 1127–1132. https://doi.org/10.1017/S0025315413001689 (2013).
Article  Google Scholar 

100.
Botta, S. Caracterização do uso do habitat e identificação de Unidades Populacionais de pequenos cetáceos do Atlântico Sul-Ocidental através de métodos químicos. (Tese de Doutorado. PÓS-GRADUAÇÃO EM OCEANOGRAFIA BIOLÓGICA. UNIVERSIDADE FEDERAL DO RIO GRANDE – FURG, 238p., 2011).

101.
Camargo, M. & Isaac, V. Os peixes estuarinos da região norte do Brasil: lista de espécies e considerações sobre sua distribuição geográfica. Bol. do Mus. Para. Emílio Goeldi, série Antropol. 17, 133–157 (2002).

102.
Vieira, J. O. Diferenças alimentares em populações de boto-cinza Sotalia guianensis (Van Benédén, 1864) (Cetacea, Delphinidae) nas costas Norte e Nordeste brasileira. (Dissertação de Mestrado. Programa de Pós-Graduação em Zoologia. Universidade Federal do Pará, 2014). More

1.
Canchaya C, Proux C, Fournous G, Bruttin A, Brussow H. Prophage genomics. Microbiol Mol Biol Rev. 2003;67:238–76.
CAS  PubMed  PubMed Central  Google Scholar 
2.
Lu MJ, Henning U. Superinfection exclusion by T-even-type coliphages. Trends Microbiol. 1994;2:137–9.
CAS  PubMed  Google Scholar 

3.
Susskind MA, Wright A, Botstein D. Superinfection exclusion by P22 prophage in lysogens of Salmonella typhimurium. IV. genetics and physiology of sieB exclusion. Virology. 1974;62:367–84.
CAS  PubMed  Google Scholar 

4.
Asadulghani M, Ogura Y, Ooka T, Itoh T, Sawaguchi A, Iguchi A, et al. The defective prophage pool of Escherichia coli O157: prophage-prophage interactions potentiate horizontal transfer of virulence determinants. PLoS Pathog. 2009;5:e1000408.
PubMed  PubMed Central  Google Scholar 

5.
Matos RC, Lapaque N, Rigottier-Gois L, Debarbieux L, Meylheuc T, Gonzalez-Zorn B, et al. Enterococcus faecalis prophage dynamics and contributions to pathogenic traits. PLoS Genet. 2013;9:e1003539.
CAS  PubMed  PubMed Central  Google Scholar 

6.
Touchon M, Bobay LM, Rocha EP. The chromosomal accommodation and domestication of mobile genetic elements. Curr Opin Microbiol. 2014a;22:22–9.
CAS  PubMed  Google Scholar 

7.
Nakayama K, Takashima K, Ishihara H, Shinomiya T, Kageyama M, Kanaya S, et al. The R-type pyocin of Pseudomonas aeruginosa is related to P2 phage, and the F-type is related to lambda phage. Mol Microbiol. 2000;38:213–31.
CAS  PubMed  Google Scholar 

8.
Winstanley C, Langille MG, Fothergill JL, Kukavica-Ibrulj I, Paradis-Bleau C, Sanschagrin F, et al. Newly introduced genomic prophage islands are critical determinants of in vivo competitiveness in the Liverpool Epidemic Strain of Pseudomonas aeruginosa. Genome Res. 2009;19:12–23.
CAS  PubMed  PubMed Central  Google Scholar 

9.
Bossi L, Fuentes JA, Mora G, Figueroa-Bossi N. Prophage contribution to bacterial population dynamics. J Bacteriol. 2003;185:6467–71.
CAS  PubMed  PubMed Central  Google Scholar 

10.
Fortier LC, Sekulovic O. Importance of prophages to evolution and virulence of bacterial pathogens. Virulence. 2013;4:354–65.
PubMed  PubMed Central  Google Scholar 

11.
Nanda AM, Thormann K, Frunzke J. Impact of spontaneous prophage induction on the fitness of bacterial populations and host-microbe interactions. J Bacteriol. 2015;197:410–9.
PubMed  PubMed Central  Google Scholar 

12.
Brown SP, Le Chat L, De Paepe M, Taddei F. Ecology of microbial invasions: amplification allows virus carriers to invade more rapidly when rare. Curr Biol. 2006;16:2048–52.
CAS  PubMed  Google Scholar 

13.
Sousa JAM, Rocha EPC. Environmental structure drives resistance to phages and antibiotics during phage therapy and to invading lysogens during colonisation. Sci Rep. 2019;9:3149.
PubMed  PubMed Central  Google Scholar 

14.
Roux S, Hallam SJ, Woyke T, Sullivan MB. Viral dark matter and virus-host interactions resolved from publicly available microbial genomes. Elife. 2015;4:e08490.
PubMed Central  Google Scholar 

15.
Touchon M, Bernheim A, Rocha EP. Genetic and life-history traits associated with the distribution of prophages in bacteria. ISME J. 2016;10:2744–54.
CAS  PubMed  PubMed Central  Google Scholar 

16.
Bobay LM, Rocha EP, Touchon M. The adaptation of temperate bacteriophages to their host genomes. Mol Biol Evol. 2013;30:737–51.
CAS  PubMed  Google Scholar 

17.
Wagner PL, Waldor MK. Bacteriophage control of bacterial virulence. Infect Immun. 2002;70:3985–93.
CAS  PubMed  PubMed Central  Google Scholar 

18.
Chen J, Quiles-Puchalt N, Chiang YN, Bacigalupe R, Fillol-Salom A, Chee MSJ, et al. Genome hypermobility by lateral transduction. Science. 2018;362:207–12.
CAS  PubMed  Google Scholar 

19.
Touchon M, Moura de Sousa JA, Rocha EP. Embracing the enemy: the diversification of microbial gene repertoires by phage-mediated horizontal gene transfer. Curr Opin Microbiol. 2017;38:66–73.
CAS  PubMed  Google Scholar 

20.
Haaber J, Leisner JJ, Cohn MT, Catalan-Moreno A, Nielsen JB, Westh H, et al. Bacterial viruses enable their host to acquire antibiotic resistance genes from neighbouring cells. Nat Commun. 2016;7:13333.
CAS  PubMed  PubMed Central  Google Scholar 

21.
Brisse S, Grimont F, Grimont PAD. The genus Klebsiella. The Prokaryotes. New York, USA: Springer; 2006. p. 159–96.
Google Scholar 

22.
Lee CR, Lee JH, Park KS, Jeon JH, Kim YB, Cha CJ, et al. Antimicrobial resistance of hypervirulent Klebsiella pneumoniae: epidemiology, hypervirulence-associated determinants, and resistance mechanisms. Front Cell Infect Microbiol. 2017;7:483.
PubMed  PubMed Central  Google Scholar 

23.
Navon-Venezia S, Kondratyeva K, Carattoli A. Klebsiella pneumoniae: a major worldwide source and shuttle for antibiotic resistance. FEMS Microbiol Rev. 2017;41:252–75.
CAS  PubMed  Google Scholar 

24.
Blin C, Passet V, Touchon M, Rocha EPC, Brisse S. Metabolic diversity of the emerging pathogenic lineages of Klebsiella pneumoniae. Environ Microbiol. 2017;19:1881–98.
CAS  PubMed  Google Scholar 

25.
Wyres KL, Wick RR, Judd LM, Froumine R, Tokolyi A, Gorrie CL, et al. Distinct evolutionary dynamics of horizontal gene transfer in drug resistant and virulent clones of Klebsiella pneumoniae. PLoS Genet. 2019;15:e1008114.
CAS  PubMed  PubMed Central  Google Scholar 

26.
Holt KE, Wertheim H, Zadoks RN, Baker S, Whitehouse CA, Dance D, et al. Genomic analysis of diversity, population structure, virulence, and antimicrobial resistance in Klebsiella pneumoniae, an urgent threat to public health. Proc Natl Acad Sci USA. 2015;112:E3574–81.
CAS  PubMed  Google Scholar 

27.
Mori M, Ohta M, Agata N, Kido N, Arakawa Y, Ito H, et al. Identification of species and capsular types of Klebsiella clinical isolates, with special reference to Klebsiella planticola. Microbiol Immunol. 1989;33:887–95.
CAS  PubMed  Google Scholar 

28.
Wyres KL, Wick RR, Gorrie C, Jenney A, Follador R, Thomson NR, et al. Identification of Klebsiella capsule synthesis loci from whole genome data. Micro Genom. 2016;2:e000102.
Google Scholar 

29.
Pan YJ, Lin TL, Chen CT, Chen YY, Hsieh PF, Hsu CR, et al. Genetic analysis of capsular polysaccharide synthesis gene clusters in 79 capsular types of Klebsiella spp. Sci Rep. 2015;5:15573.
CAS  PubMed  PubMed Central  Google Scholar 

30.
Favre-Bonte S, Licht TR, Forestier C, Krogfelt KA. Klebsiella pneumoniae capsule expression is necessary for colonization of large intestines of streptomycin-treated mice. Infect Immun. 1999;67:6152–6.
CAS  PubMed  PubMed Central  Google Scholar 

31.
Alvarez D, Merino S, Tomas JM, Benedi VJ, Alberti S. Capsular polysaccharide is a major complement resistance factor in lipopolysaccharide O side chain-deficient Klebsiella pneumoniae clinical isolates. Infect Immun. 2000;68:953–5.
CAS  PubMed  PubMed Central  Google Scholar 

32.
Campos MA, Vargas MA, Regueiro V, Llompart CM, Alberti S, Bengoechea JA. Capsule polysaccharide mediates bacterial resistance to antimicrobial peptides. Infect Immun. 2004;72:7107–14.
CAS  PubMed  PubMed Central  Google Scholar 

33.
Doorduijn DJ, Rooijakkers SHM, van Schaik W, Bardoel BW. Complement resistance mechanisms of Klebsiella pneumoniae. Immunobiology. 2016;221:1102–9.
CAS  PubMed  Google Scholar 

34.
Rendueles O, Garcia-Garcera M, Neron B, Touchon M, Rocha EPC. Abundance and co-occurrence of extracellular capsules increase environmental breadth: Implications for the emergence of pathogens. PLoS Pathog. 2017;13:e1006525.
PubMed  PubMed Central  Google Scholar 

35.
Rendueles O, de Sousa JAM, Bernheim A, Touchon M, Rocha EPC. Genetic exchanges are more frequent in bacteria encoding capsules. PLoS Genet. 2018;14:e1007862.
PubMed  PubMed Central  Google Scholar 

36.
Chewapreecha C, Harris SR, Croucher NJ, Turner C, Marttinen P, Cheng L, et al. Dense genomic sampling identifies highways of pneumococcal recombination. Nat Genet. 2014;46:305–9.
CAS  PubMed  PubMed Central  Google Scholar 

37.
Moller AG, Lindsay JA, Read TD. Determinants of phage host range in Staphylococcus species. Appl Environ Microbiol. 2019;85:e00209–19.
CAS  PubMed  PubMed Central  Google Scholar 

38.
Negus D, Burton J, Sweed A, Gryko R, Taylor PW. Poly-gamma-(D)-glutamic acid capsule interferes with lytic infection of Bacillus anthracis by B. anthracis-specific bacteriophages. Appl Environ Microbiol. 2013;79:714–7.
CAS  PubMed  PubMed Central  Google Scholar 

39.
Scholl D, Adhya S, Merril C. Escherichia coli K1’s capsule is a barrier to bacteriophage T7. Appl Environ Microbiol. 2005;71:4872–4.
CAS  PubMed  PubMed Central  Google Scholar 

40.
Niemann H, Frank N, Stirm S. Klebsiella serotype-13 capsular polysaccharide: primary structure and depolymerization by a bacteriophage-borne glycanase. Carbohydr Res. 1977a;59:165–77.
CAS  PubMed  Google Scholar 

41.
Niemann H, Kwiatkowski B, Westphal U, Stirm S. Klebsiella serotype 25 capsular polysaccharide: primary structure and depolymerization by a bacteriophage-borne glycanase. J Bacteriol. 1977b;130:366–74.
CAS  PubMed  PubMed Central  Google Scholar 

42.
Pan YJ, Lin TL, Chen CC, Tsai YT, Cheng YH, Chen YY, et al. Klebsiella phage PhiK64-1 encodes multiple depolymerases for multiple host capsular types. J Virol. 2017;91:e02457–02416.
CAS  PubMed  PubMed Central  Google Scholar 

43.
Bessler W, Freund-Molbert E, Knufermann H, Rduolph C, Thurow H, Stirm S. A bacteriophage-induced depolymerase active on Klebsiella K11 capsular polysaccharide. Virology. 1973;56:134–51.
CAS  PubMed  Google Scholar 

44.
Thurow H, Niemann H, Rudolph C, Stirm S. Host capsule depolymerase activity of bacteriophage particles active on Klebsiella K20 and K24 strains. Virology. 1974;58:306–9.
CAS  PubMed  Google Scholar 

45.
Latka A, Maciejewska B, Majkowska-Skrobek G, Briers Y, Drulis-Kawa Z. Bacteriophage-encoded virion-associated enzymes to overcome the carbohydrate barriers during the infection process. Appl Microbiol Biotechnol. 2017;101:3103–19.
CAS  PubMed  PubMed Central  Google Scholar 

46.
Scholl D, Rogers S, Adhya S, Merril CR. Bacteriophage K1-5 encodes two different tail fiber proteins, allowing it to infect and replicate on both K1 and K5 strains of Escherichia coli. J Virol. 2001;75:2509–15.
CAS  PubMed  PubMed Central  Google Scholar 

47.
Lin TL, Hsieh PF, Huang YT, Lee WC, Tsai YT, Su PA, et al. Isolation of a bacteriophage and its depolymerase specific for K1 capsule of Klebsiella pneumoniae: implication in typing and treatment. J Infect Dis. 2014;210:1734–44.
CAS  PubMed  Google Scholar 

48.
Pan YJ, Lin TL, Chen YY, Lai PH, Tsai YT, Hsu CR, et al. Identification of three podoviruses infecting Klebsiella encoding capsule depolymerases that digest specific capsular types. Micro Biotechnol. 2019;12:472–86.
CAS  Google Scholar 

49.
Bertozzi Silva J, Storms Z, Sauvageau D. Host receptors for bacteriophage adsorption. FEMS Microbiol Lett. 2016;363:fnw002.
PubMed  Google Scholar 

50.
Arndt D, Grant JR, Marcu A, Sajed T, Pon A, Liang Y, et al. PHASTER: a better, faster version of the PHAST phage search tool. Nucleic Acids Res. 2016;44:W16–21.
CAS  PubMed  PubMed Central  Google Scholar 

51.
Daubin V, Lerat E, Perriere G. The source of laterally transferred genes in bacterial genomes. Genome Biol. 2003;4:R57.
PubMed  PubMed Central  Google Scholar 

52.
Rocha EP, Danchin A. Base composition bias might result from competition for metabolic resources. Trends Genet. 2002;18:291–4.
CAS  PubMed  Google Scholar 

53.
Bobay LM, Touchon M, Rocha EP. Pervasive domestication of defective prophages by bacteria. Proc Natl Acad Sci USA. 2014;111:12127–32.
CAS  PubMed  Google Scholar 

54.
Casjens SR, Gilcrease EB, Huang WM, Bunny KL, Pedulla ML, Ford ME, et al. The pKO2 linear plasmid prophage of Klebsiella oxytoca. J Bacteriol. 2004;186:1818–32.
CAS  PubMed  PubMed Central  Google Scholar 

55.
Leclercq S, Cordaux R. Do phages efficiently shuttle transposable elements among prokaryotes? Evolution. 2011;65:3327–31.
CAS  PubMed  Google Scholar 

56.
Ghazaryan L, Tonoyan L, Ashhab AA, Soares MI, Gillor O. The role of stress in colicin regulation. Arch Microbiol. 2014;196:753–64.
CAS  PubMed  Google Scholar 

57.
Labrie SJ, Samson JE, Moineau S. Bacteriophage resistance mechanisms. Nat Rev Microbiol. 2010;8:317–27.
CAS  PubMed  Google Scholar 

58.
Samson JE, Magadan AH, Sabri M, Moineau S. Revenge of the phages: defeating bacterial defences. Nat Rev Microbiol. 2013;11:675–87.
CAS  PubMed  Google Scholar 

59.
Wyres KL, Gorrie C, Edwards DJ, Wertheim HF, Hsu LY, Van Kinh N, et al. Extensive capsule locus variation and large-scale genomic recombination within the Klebsiella pneumoniae clonal group 258. Genome Biol Evol. 2015;7:1267–79.
CAS  PubMed  PubMed Central  Google Scholar 

60.
Tan D, Zhang Y, Qin J, Le S, Gu J, Chen LK et al. A frameshift mutation in wcaJ associated with phage resistance in Klebsiella pneumoniae. Microorganisms. 2020;8:378.
CAS  PubMed Central  Google Scholar 

61.
Buffet A, Rocha EPC, Rendueles O. Selection for the bacterial capsule in the absence of biotic and abiotic aggressions depends on growth conditions. BioRxiv. 2020. https://doi.org/10.1101/2020.04.27.059774.

62.
Julianelle LA. Bacterial variation in cultures of Friedlander’s Bacillus. J Exp Med. 1928;47:889–902.
CAS  PubMed  PubMed Central  Google Scholar 

63.
Randall WA. Colony and antigenic variation in Klebsiella pneumoniae types A, B and C. J Bacteriol. 1939;38:461–77.
CAS  PubMed  PubMed Central  Google Scholar 

64.
Flores CO, Meyer JR, Valverde S, Farr L, Weitz JS. Statistical structure of host-phage interactions. Proc Natl Acad Sci USA. 2011;108:E288–97.
CAS  PubMed  Google Scholar 

65.
Mathieu A, Dion M, Deng L, Tremblay D, Moncaut E, Shah SA, et al. Virulent coliphages in 1-year-old children fecal samples are fewer, but more infectious than temperate coliphages. Nat Commun. 2020;11:385.
Google Scholar 

66.
Weitz JS, Poisot T, Meyer JR, Flores CO, Valverde S, Sullivan MB, et al. Phage-bacteria infection networks. Trends Microbiol. 2013;21:82–91.
CAS  PubMed  Google Scholar 

67.
Brueggemann AB, Harrold CL, Rezaei Javan R, van Tonder AJ, McDonnell AJ, Edwards BA. Pneumococcal prophages are diverse, but not without structure or history. Sci Rep. 2017;7:42976.
CAS  PubMed  PubMed Central  Google Scholar 

68.
Castillo D, Kauffman K, Hussain F, Kalatzis P, Rorbo N, Polz MF, et al. Widespread distribution of prophage-encoded virulence factors in marine Vibrio communities. Sci Rep. 2018;8:9973.
PubMed  PubMed Central  Google Scholar 

69.
Allen HK, Looft T, Bayles DO, Humphrey S, Levine UY, Alt D, et al. Antibiotics in feed induce prophages in swine fecal microbiomes. mBio. 2011;2:e00260–11.
PubMed  PubMed Central  Google Scholar 

70.
Otsuji N, Sekiguchi M, Iijima T, Takagi Y. Induction of phage formation in the lysogenic Escherichia coli K-12 by mitomycin C. Nature. 1959;184:1079–80.
CAS  PubMed  Google Scholar 

71.
Comeau AM, Tetart F, Trojet SN, Prere MF, Krisch HM. Phage-antibiotic synergy (PAS): beta-lactam and quinolone antibiotics stimulate virulent phage growth. PLoS One. 2007;2:e799.
PubMed  PubMed Central  Google Scholar 

72.
Kim M, Jo Y, Hwang YJ, Hong HW, Hong SS, Park K et al. Phage-antibiotic synergy via delayed lysis. Appl Environ Microbiol. 2018;84:e02085–18.
CAS  PubMed  PubMed Central  Google Scholar 

73.
De Paepe M, Tournier L, Moncaut E, Son O, Langella P, Petit MA. Carriage of lambda latent virus is costly for its bacterial host due to frequent reactivation in monoxenic mouse intestine. PLoS Genet. 2016;12:e1005861.
PubMed  PubMed Central  Google Scholar 

74.
Phanphak S, Georgiades P, Li R, King J, Roberts IS, Waigh TA. Super-resolution fluorescence microscopy study of the production of K1 capsules by Escherichia coli: evidence for the differential distribution of the capsule at the poles and the equator of the cell. Langmuir. 2019;35:5635–46.
CAS  PubMed  PubMed Central  Google Scholar 

75.
Krinos CM, Coyne MJ, Weinacht KG, Tzianabos AO, Kasper DL, Comstock LE. Extensive surface diversity of a commensal microorganism by multiple DNA inversions. Nature. 2001;414:555–8.
CAS  PubMed  Google Scholar 

76.
Tzeng YL, Thomas J, Stephens DS. Regulation of capsule in Neisseria meningitidis. Crit Rev Microbiol. 2016;42:759–72.
CAS  PubMed  Google Scholar 

77.
Bondy-Denomy J, Qian J, Westra ER, Buckling A, Guttman DS, Davidson AR, et al. Prophages mediate defense against phage infection through diverse mechanisms. ISME J. 2016;10:2854–66.
PubMed  PubMed Central  Google Scholar 

78.
Zhang YF, LeJeune JT. Transduction of bla(CMY-2), tet(A), and tet(B) from Salmonella enterica subspecies enterica serovar Heidelberg to S-Typhimurium. Vet Microbiol. 2008;129:418–25.
CAS  PubMed  Google Scholar 

79.
Brussow H, Kutter E. Phage ecology. Bacteriophages: biology and application. Boca Raton, Florida: CRC Press; 2005. p. 129–64.
Google Scholar 

80.
Calef E, Marchelli C, Guerrini F. The formation of superinfection-double lysogens of phage lambda in Escherichia coli K-12. Virology. 1965;27:1–10.
CAS  PubMed  Google Scholar 

81.
Scott JR, West BW, Laping JL. Superinfection immunity and prophage repression in phage P1. IV. The c1 repressor bypass function and the role of c4 repressor in immunity. Virology. 1978;85:587–600.
CAS  PubMed  Google Scholar 

82.
Bakk A, Metzler R. Nonspecific binding of the OR repressors CI and Cro of bacteriophage lambda. J Theor Biol. 2004;231:525–33.
CAS  PubMed  Google Scholar 

83.
Casjens S. Prophages and bacterial genomics: what have we learned so far? Mol Microbiol. 2003;49:277–300.
CAS  PubMed  Google Scholar 

84.
Hendrix RW, Smith MCM, Burns RN, Ford ME, Hatfull GF. Evolutionary relationships among diverse bacteriophages and prophages: All the world’s a phage. Proc Natl Acad Sci USA. 1999;96:2192–7.
CAS  PubMed  Google Scholar 

85.
Ramisetty BCM, Sudhakari PA. Bacterial ‘Grounded’ Prophages: hotspots for genetic renovation and innovation. Front Genet. 2019;10:65.
CAS  PubMed  PubMed Central  Google Scholar 

86.
Venturini C, Ben Zakour N, Bowring B, Morales S, Cole R, Kovach Z et al. K. pneumoniae ST258 genomic variability and bacteriophage susceptibility. bioRxiv. 2019. https://doi.org/10.1101/628339.

87.
Hsieh PF, Lin HH, Lin TL, Chen YY, Wang JT. Two T7-like bacteriophages, K5-2 and K5-4, each encodes two capsule depolymerases: isolation and functional characterization. Sci Rep. 2017;7:4624.
PubMed  PubMed Central  Google Scholar 

88.
Majkowska-Skrobek G, Latka A, Berisio R, Squeglia F, Maciejewska B, Briers Y, et al. Phage-borne depolymerases decrease Klebsiella pneumoniae resistance to innate defense mechanisms. Front Microbiol. 2018;9:00209–19.
Google Scholar 

89.
Paczosa MK, Mecsas J. Klebsiella pneumoniae: going on the offense with a strong defense. Microbiol Mol Biol Rev. 2016;80:629–61.
CAS  PubMed  PubMed Central  Google Scholar 

90.
Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.
CAS  PubMed  Google Scholar 

91.
Grazziotin AL, Koonin EV, Kristensen DM. Prokaryotic virus orthologous groups (pVOGs): a resource for comparative genomics and protein family annotation. Nucleic Acids Res. 2017;45:D491–D498.
CAS  PubMed  Google Scholar 

92.
Eddy SR. Accelerated profile HMM searches. PLoS Comput Biol. 2011;7:e1002195.
CAS  PubMed  PubMed Central  Google Scholar 

93.
Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinforma. 2009;10:421.
Google Scholar 

94.
Siguier P, Perochon J, Lestrade L, Mahillon J, Chandler M. ISfinder: the reference centre for bacterial insertion sequences. Nucleic Acids Res. 2006;34:D32–6.
CAS  PubMed  Google Scholar 

95.
Touchon M, Cury J, Yoon EJ, Krizova L, Cerqueira GC, Murphy C, et al. The genomic diversification of the whole Acinetobacter genus: origins, mechanisms, and consequences. Genome Biol Evol. 2014b;6:2866–82.
CAS  PubMed  PubMed Central  Google Scholar 

96.
Steinegger M, Soding J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat Biotechnol. 2017;35:1026–8.
CAS  PubMed  Google Scholar 

97.
van Heel AJ, de Jong A, Song C, Viel JH, Kok J, Kuipers OP. BAGEL4: a user-friendly web server to thoroughly mine RiPPs and bacteriocins. Nucleic Acids Res. 2018;46:W278–81.
PubMed  PubMed Central  Google Scholar 

98.
Pires DP, Oliveira H, Melo LD, Sillankorva S, Azeredo J. Bacteriophage-encoded depolymerases: their diversity and biotechnological applications. Appl Microbiol Biotechnol. 2016;100:2141–51.
CAS  PubMed  Google Scholar 

99.
Couvin D, Bernheim A, Toffano-Nioche C, Touchon M, Michalik J, Neron B, et al. CRISPRCasFinder, an update of CRISRFinder, includes a portable version, enhanced performance and integrates search for Cas proteins. Nucleic Acids Res. 2018;46:W246–51.
CAS  PubMed  PubMed Central  Google Scholar 

100.
Oliveira PH, Touchon M, Rocha EP. Regulation of genetic flux between bacteria by restriction-modification systems. Proc Natl Acad Sci USA. 2016;113:5658–63.
CAS  PubMed  Google Scholar 

101.
Mucke M, Kruger DH, Reuter M. Diversity of Type II restriction endonucleases that require two DNA recognition sites. Nucleic Acids Res. 2003;31:6079–84.
CAS  PubMed  PubMed Central  Google Scholar 

102.
Ferrieres L, Hemery G, Nham T, Guerout AM, Mazel D, Beloin C, et al. Silent mischief: bacteriophage Mu insertions contaminate products of Escherichia coli random mutagenesis performed using suicidal transposon delivery plasmids mobilized by broad-host-range RP4 conjugative machinery. J Bacteriol. 2010;192:6418–27.
CAS  PubMed  PubMed Central  Google Scholar 

103.
Guy L, Kultima JR, Andersson SG. genoPlotR: comparative gene and genome visualization in R. Bioinformatics. 2010;26:2334–5.
CAS  PubMed  PubMed Central  Google Scholar  More

Affiliations

International Cotton Advisory Committee, Washington D.C., WA, USA
K. R. Kranthi

Department of Anthropology, Washington University, St. Louis, MO, USA
Glenn Davis Stone

Authors
K. R. Kranthi

Glenn Davis Stone

Corresponding author
Correspondence to Glenn Davis Stone. More