Philippa Kaur

1.
Beijerinck, M. Oxydation des mangancarbonates durch Bakterien und Schimmelpilze. Folia Microbiol. (Delft) 2, 123–134 (1913).
Google Scholar 
2.
Nealson, K. H., Tebo, B. M. & Rosson, R. A. Occurrence and mechanisms of microbial oxidation of manganese. Adv. Appl. Microbiol. 33, 279–318 (1988).
CAS  Google Scholar 

3.
Tebo, B. M., Johnson, H. A., McCarthy, J. K. & Templeton, A. S. Geomicrobiology of manganese(II) oxidation. Trends Microbiol. 13, 421–428 (2005).
CAS  Google Scholar 

4.
Hansel, C. & Learman, D. R. in Ehrlich’s Geomicrobiology (eds Ehrlich, H. L. et al.) 401–452 (CRC, 2015).

5.
Myers, C. R. & Nealson, K. H. Bacterial manganese reduction and growth with manganese oxide as the sole electron acceptor. Science 240, 1319–1321 (1988).
ADS  CAS  Google Scholar 

6.
Lovley, D. R. & Phillips, E. J. Novel mode of microbial energy metabolism: organic carbon oxidation coupled to dissimilatory reduction of iron or manganese. Appl. Environ. Microbiol. 54, 1472–1480 (1988).
CAS  PubMed  PubMed Central  Google Scholar 

7.
Winogradsky, S. Über schwefelbakterien. Bot. Ztg 45, 489ff (1887).
Google Scholar 

8.
Kelly, D. P. & Wood, A. P. in The Prokaryotes: Prokaryotic Communities and Ecophysiology (eds Rosenberg, E. et al.) 275–287 (Springer, 2013).

9.
Daims, H. et al. Complete nitrification by Nitrospira bacteria. Nature 528, 504–509 (2015).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

10.
Könneke, M. et al. Isolation of an autotrophic ammonia-oxidizing marine archaeon. Nature 437, 543–546 (2005).
ADS  Google Scholar 

11.
Strous, M. et al. Missing lithotroph identified as new planctomycete. Nature 400, 446–449 (1999).
ADS  CAS  Google Scholar 

12.
van Kessel, M. A. H. J. et al. Complete nitrification by a single microorganism. Nature 528, 555–559 (2015).
ADS  PubMed  PubMed Central  Google Scholar 

13.
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. Mikrobiol. 77, 203–230 (1971).
Google Scholar 

14.
Lovley, D. R., Holmes, D. E. & Nevin, K. P. in Advances in Microbial Physiology (ed Poole, R. K.) 219–286 (Elsevier, 2004).

15.
Henkel, J. V. et al. A bacterial isolate from the Black Sea oxidizes sulfide with manganese(IV) oxide. Proc. Natl Acad. Sci. USA 116, 12153–12155 (2019).
CAS  Google Scholar 

16.
Ghiorse, W. C. & Ehrlich, H. L. Microbial biomineralization of iron and manganese. Catena Suppl. 21, 75–99 (1992).
Google Scholar 

17.
Ehrlich, H. L. & Salerno, J. C. Energy coupling in Mn2+ oxidation by a marine bacterium. Arch. Microbiol. 154, 12–17 (1990).
CAS  Google Scholar 

18.
Ehrlich, H. L. Manganese as an energy source for bacteria. Environ. Biogeochem. 2, 633–644 (1976).
CAS  Google Scholar 

19.
Dick, G. J. et al. Genomic insights into Mn(II) oxidation by the marine alphaproteobacterium Aurantimonas sp. strain SI85-9A1. Appl. Environ. Microbiol. 74, 2646–2658 (2008).
CAS  PubMed  PubMed Central  Google Scholar 

20.
Nealson, K. H. in The Prokaryotes (eds Dworkin, M. et al.) 222–231 (Springer, 2006).

21.
van Veen, W. L. Biological oxidation of manganese in soils. Antonie van Leeuwenhoek 39, 657–662 (1973).
Google Scholar 

22.
Morgan, J. J. Kinetics of reaction between O2 and Mn(II) species in aqueous solutions. Geochim. Cosmochim. Acta 69, 35–48 (2005).
ADS  CAS  Google Scholar 

23.
Kits, K. D. et al. Kinetic analysis of a complete nitrifier reveals an oligotrophic lifestyle. Nature 549, 269–272 (2017).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

24.
Flagan, S. F. & Leadbetter, J. R. Utilization of capsaicin and vanillylamine as growth substrates by Capsicum (hot pepper)-associated bacteria. Environ. Microbiol. 8, 560–565 (2006).
CAS  PubMed  PubMed Central  Google Scholar 

25.
Kanzler, B. E. M., Pfannes, K. R., Vogl, K. & Overmann, J. Molecular characterization of the nonphotosynthetic partner bacterium in the consortium “Chlorochromatium aggregatum”. Appl. Environ. Microbiol. 71, 7434–7441 (2005).
CAS  PubMed  PubMed Central  Google Scholar 

26.
Emerson, D. & Moyer, C. Isolation and characterization of novel iron-oxidizing bacteria that grow at circumneutral pH. Appl. Environ. Microbiol. 63, 4784–4792 (1997).
CAS  PubMed  PubMed Central  Google Scholar 

27.
Neidhardt, F. C. Escherichia coli and Salmonella: Cellular and Molecular Biology, vol. 1 (ASM, 1996).

28.
Kostanjšek, R., Pašić, L., Daims, H. & Sket, B. Structure and community composition of sprout-like bacterial aggregates in a dinaric karst subterranean stream. Microb. Ecol. 66, 5–18 (2013).
Google Scholar 

29.
Wrighton, K. C. et al. Fermentation, hydrogen, and sulfur metabolism in multiple uncultivated bacterial phyla. Science 337, 1661–1665 (2012).
ADS  CAS  Google Scholar 

30.
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  Google Scholar 

31.
Castelle, C. et al. A new iron-oxidizing/O2-reducing supercomplex spanning both inner and outer membranes, isolated from the extreme acidophile Acidithiobacillus ferrooxidans. J. Biol. Chem. 283, 25803–25811 (2008).
CAS  PubMed  PubMed Central  Google Scholar 

32.
Jeans, C. et al. Cytochrome 572 is a conspicuous membrane protein with iron oxidation activity purified directly from a natural acidophilic microbial community. ISME J. 2, 542–550 (2008).
CAS  Google Scholar 

33.
Croal, L. R., Jiao, Y. & Newman, D. K. The fox operon from Rhodobacter strain SW2 promotes phototrophic Fe(II) oxidation in Rhodobacter capsulatus SB1003. J. Bacteriol. 189, 1774–1782 (2007).
CAS  Google Scholar 

34.
Jiao, Y. & Newman, D. K. The pio operon is essential for phototrophic Fe(II) oxidation in Rhodopseudomonas palustris TIE-1. J. Bacteriol. 189, 1765–1773 (2007).
CAS  Google Scholar 

35.
He, S., Barco, R. A., Emerson, D. & Roden, E. E. Comparative genomic analysis of neutrophilic iron(II) oxidizer genomes for candidate genes in extracellular electron transfer. Front. Microbiol. 8, 1584 (2017).
PubMed  PubMed Central  Google Scholar 

36.
Richardson, D. J. et al. The ‘porin-cytochrome’ model for microbe-to-mineral electron transfer. Mol. Microbiol. 85, 201–212 (2012).
CAS  Google Scholar 

37.
Luther, G. W., III. Manganese(II) oxidation and Mn(IV) reduction in the environment—two one-electron transfer steps versus a single two-electron Step. Geomicrobiol. J. 22, 195–203 (2005).
CAS  Google Scholar 

38.
Lücker, S. et al. A Nitrospira metagenome illuminates the physiology and evolution of globally important nitrite-oxidizing bacteria. Proc. Natl Acad. Sci. USA 107, 13479–13484 (2010).
ADS  Google Scholar 

39.
Mundinger, A. B., Lawson, C. E., Jetten, M. S. M., Koch, H. & Lücker, S. Cultivation and transcriptional analysis of a canonical Nitrospira under stable growth conditions. Front. Microbiol. 10, 1325 (2019).
PubMed  PubMed Central  Google Scholar 

40.
Koch, H. et al. Growth of nitrite-oxidizing bacteria by aerobic hydrogen oxidation. Science 345, 1052–1054 (2014).
ADS  CAS  Google Scholar 

41.
Levicán, G., Ugalde, J. A., Ehrenfeld, N., Maass, A. & Parada, P. Comparative genomic analysis of carbon and nitrogen assimilation mechanisms in three indigenous bioleaching bacteria: predictions and validations. BMC Genomics 9, 581 (2008).
PubMed  PubMed Central  Google Scholar 

42.
Berg, I. A. Ecological aspects of the distribution of different autotrophic CO2 fixation pathways. Appl. Environ. Microbiol. 77, 1925–1936 (2011).
CAS  PubMed  PubMed Central  Google Scholar 

43.
Thauer, R. K., Jungermann, K. & Decker, K. Energy conservation in chemotrophic anaerobic bacteria. Bacteriol. Rev. 41, 100–180 (1977).
CAS  PubMed  PubMed Central  Google Scholar 

44.
Baradaran, R., Berrisford, J. M., Minhas, G. S. & Sazanov, L. A. Crystal structure of the entire respiratory complex I. Nature 494, 443–448 (2013).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

45.
Chadwick, G. L., Hemp, J., Fischer, W. W. & Orphan, V. J. Convergent evolution of unusual complex I homologs with increased proton pumping capacity: energetic and ecological implications. ISME J. 12, 2668–2680 (2018).
CAS  PubMed  PubMed Central  Google Scholar 

46.
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 

47.
Watson, S. W., Bock, E., Valois, F. W., Waterbury, J. B. & Schlosser, U. Nitrospira marina gen. nov. sp. nov.: a chemolithotrophic nitrite-oxidizing bacterium. Arch. Microbiol. 144, 1–7 (1986).
Google Scholar 

48.
Hippe, H. Leptospirillum gen. nov. (ex Markosyan 1972), nom. rev., including Leptospirillum ferrooxidans sp. nov. (ex Markosyan 1972), nom. rev. and Leptospirillum thermoferrooxidans sp. nov. (Golovacheva et al. 1992). Int. J. Syst. Evol. Microbiol. 50, 501–503 (2000).
Google Scholar 

49.
Henry, E. A. et al. Characterization of a new thermophilic sulfate-reducing bacterium Thermodesulfovibrio yellowstonii, gen. nov. and sp. nov.: its phylogenetic relationship to Thermodesulfobacterium commune and their origins deep within the bacterial domain. Arch. Microbiol. 161, 62–69 (1994).
CAS  Google Scholar 

50.
Lin, X., Kennedy, D., Fredrickson, J., Bjornstad, B. & Konopka, A. Vertical stratification of subsurface microbial community composition across geological formations at the Hanford site. Environ. Microbiol. 14, 414–425 (2012).
CAS  Google Scholar 

51.
Flagan, S., Ching, W.-K. & Leadbetter, J. R. Arthrobacter strain VAI-A utilizes acyl-homoserine lactone inactivation products and stimulates quorum signal biodegradation by Variovorax paradoxus. Appl. Environ. Microbiol. 69, 909–916 (2003).
CAS  PubMed  PubMed Central  Google Scholar 

52.
Leadbetter, J. R. & Greenberg, E. P. Metabolism of acyl-homoserine lactone quorum-sensing signals by Variovorax paradoxus. J. Bacteriol. 182, 6921–6926 (2000).
CAS  PubMed  PubMed Central  Google Scholar 

53.
Krumbein, W. E. & Altmann, H. J. A new method for the detection and enumeration of manganese oxidizing and reducing microorganisms. Helgol. Wiss. Meeresunters. 25, 347–356 (1973).
CAS  Google Scholar 

54.
Emerson, D. & Revsbech, N. P. Investigation of an iron-oxidizing microbial mat community located near Aarhus, Denmark: laboratory studies. Appl. Environ. Microbiol. 60, 4032–4038 (1994).
CAS  PubMed  PubMed Central  Google Scholar 

55.
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  Google Scholar 

56.
Illumina. 16S Metagenomic sequencing library preparation, https://support.illumina.com/downloads/16s_metagenomic_sequencing_library_preparation.html (2013).

57.
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).
CAS  PubMed  PubMed Central  Google Scholar 

58.
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).
CAS  Google Scholar 

59.
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).
CAS  Google Scholar 

60.
Lane, D. J. in Nucleic Acid Techniques in Bacterial Systematics (eds Stackebrandt, E. & Goodfellow, M.) 115–175 (John Wiley & Sons, 1991).

61.
Ludwig, W. et al. ARB: a software environment for sequence data. Nucleic Acids Res. 32, 1363–1371 (2004).
CAS  PubMed  PubMed Central  Google Scholar 

62.
Parks, D. H. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat. Biotechnol. 36, 996–1004 (2018).
CAS  Google Scholar 

63.
Schönmann, S. et al. 16S rRNA gene-based phylogenetic microarray for simultaneous identification of members of the genus Burkholderia. Environ. Microbiol. 11, 779–800 (2009).
Google Scholar 

64.
Greuter, D., Loy, A., Horn, M. & Rattei, T. probeBase—an online resource for rRNA-targeted oligonucleotide probes and primers: new features 2016. Nucleic Acids Res. 44, D586–D589 (2016).
CAS  Google Scholar 

65.
Amann, R. I. et al. Combination of 16S rRNA-targeted oligonucleotide probes with flow cytometry for analyzing mixed microbial populations. Appl. Environ. Microbiol. 56, 1919–1925 (1990).
CAS  PubMed  PubMed Central  Google Scholar 

66.
Stoecker, K., Dorninger, C., Daims, H. & Wagner, M. Double labeling of oligonucleotide probes for fluorescence in situ hybridization (DOPE-FISH) improves signal intensity and increases rRNA accessibility. Appl. Environ. Microbiol. 76, 922–926 (2010).
CAS  PubMed  PubMed Central  Google Scholar 

67.
Schramm, A., Fuchs, B. M., Nielsen, J. L., Tonolla, M. & Stahl, D. A. Fluorescence in situ hybridization of 16S rRNA gene clones (Clone-FISH) for probe validation and screening of clone libraries. Environ. Microbiol. 4, 713–720 (2002).
CAS  PubMed  PubMed Central  Google Scholar 

68.
Daims, H., Stoecker, K. & Wagner, M. in Molecular Microbial Ecology (eds Osborn, M. A. and Smith, C. J.) 208–228 (Taylor & Francis, 2004).

69.
Daims, H., Lücker, S. & Wagner, M. daime, a novel image analysis program for microbial ecology and biofilm research. Environ. Microbiol. 8, 200–213 (2006).
CAS  Google Scholar 

70.
Taylor, G. J. & Crowder, A. A. Use of the DCB technique for extraction of hydrous iron oxides from roots of wetland plants. Am. J. Bot. 70, 1254 (1983).
CAS  Google Scholar 

71.
Polerecky, L. et al. Look@NanoSIMS—a tool for the analysis of nanoSIMS data in environmental microbiology. Environ. Microbiol. 14, 1009–1023 (2012).
CAS  Google Scholar 

72.
Brewer, P. G. & Spencer, D. W. Colorimetric determination of manganse in anoxic waters. Limnol. Oceanogr. 16, 107–110 (1971).
ADS  CAS  Google Scholar 

73.
Oldham, V. E., Miller, M. T., Jensen, L. T. & Luther, G. W. Revisiting Mn and Fe removal in humic rich estuaries. Geochim. Cosmochim. Acta 209, 267–283 (2017).
ADS  CAS  Google Scholar 

74.
Suzuki, M. T., Taylor, L. T. & DeLong, E. F. Quantitative analysis of small-subunit rRNA genes in mixed microbial populations via 5′-nuclease assays. Appl. Environ. Microbiol. 66, 4605–4614 (2000).
CAS  PubMed  PubMed Central  Google Scholar 

75.
William, S., Feil, H. & Copeland, A. Bacterial genomic DNA isolation using CTAB, Department of Energy Joint Genome Institute, https://jgi.doe.gov/user-programs/pmo-overview/protocols-sample-preparation-information/ (2012).

76.
Arkin, A. P. et al. KBase: the United States Department of Energy systems biology knowledgebase. Nat. Biotechnol. 36, 566–569 (2018).
CAS  PubMed  PubMed Central  Google Scholar 

77.
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
CAS  PubMed  PubMed Central  Google Scholar 

78.
Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).
MathSciNet  CAS  PubMed  PubMed Central  Google Scholar 

79.
Karst, S. M., Kirkegaard, R. H. & Albertsen, M. mmgenome: a toolbox for reproducible genome extraction from metagenomes. Preprint at https://www.biorxiv.org/content/ 10.1101/059121v1.full (2016).

80.
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

81.
Chen, I. 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  Google Scholar 

82.
NCBI Resource Coordinators. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 46, D8–D13 (2018).
Google Scholar 

83.
Bagos, P. G., Liakopoulos, T. D., Spyropoulos, I. C. & Hamodrakas, S. J. PRED-TMBB: a web server for predicting the topology of β-barrel outer membrane proteins. Nucleic Acids Res. 32, W400–W404 (2004).
CAS  PubMed  PubMed Central  Google Scholar 

84.
Federhen, S. The NCBI taxonomy database. Nucleic Acids Res. 40, D136–D143 (2012).
CAS  Google Scholar 

85.
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 

86.
Pruesse, E., Peplies, J. & Glöckner, F. O. SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics 28, 1823–1829 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

87.
Ronquist, F. et al. MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542 (2012).
PubMed  PubMed Central  Google Scholar 

88.
Letunic, I. & Bork, P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 44, W242–W245 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

89.
Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).
CAS  PubMed  PubMed Central  Google Scholar 

90.
Sievers, F. et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol. Syst. Biol. 7, 539 (2011).
PubMed  PubMed Central  Google Scholar 

91.
Lever, M. A. et al. A modular method for the extraction of DNA and RNA, and the separation of DNA pools from diverse environmental sample types. Front. Microbiol. 6, 476 (2015).
PubMed  PubMed Central  Google Scholar 

92.
Kopylova, E., Noé, L. & Touzet, H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28, 3211–3217 (2012).
CAS  Google Scholar 

93.
Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).
CAS  PubMed  Google Scholar 

94.
Pimentel, H., Bray, N. L., Puente, S., Melsted, P. & Pachter, L. Differential analysis of RNA-seq incorporating quantification uncertainty. Nat. Methods 14, 687–690 (2017).
CAS  Google Scholar 

95.
van Waasbergen, L. G., Hildebrand, M. & Tebo, B. M. Identification and characterization of a gene cluster involved in manganese oxidation by spores of the marine Bacillus sp. strain SG-1. J. Bacteriol. 178, 3517–3530 (1996).
PubMed  PubMed Central  Google Scholar 

96.
Jung, W. K. & Schweisfurth, R. Manganese oxidation by an intracellular protein of a Pseudomonas species. Z. Allg. Mikrobiol. 19, 107–115 (1979).
CAS  Google Scholar 

97.
Esteve-Núñez, A., Rothermich, M., Sharma, M. & Lovley, D. Growth of Geobacter sulfurreducens under nutrient-limiting conditions in continuous culture. Environ. Microbiol. 7, 641–648 (2005).
Google Scholar 

98.
Neubauer, S. C., Emerson, D. & Megonigal, J. P. Life at the energetic edge: kinetics of circumneutral iron oxidation by lithotrophic iron-oxidizing bacteria isolated from the wetland-plant rhizosphere. Appl. Environ. Microbiol. 68, 3988–3995 (2002).
CAS  PubMed  PubMed Central  Google Scholar 

99.
Nowka, B., Daims, H. & Spieck, E. Comparison of oxidation kinetics of nitrite-oxidizing bacteria: nitrite availability as a key factor in niche differentiation. Appl. Environ. Microbiol. 81, 745–753 (2015).
PubMed  PubMed Central  Google Scholar 

100.
Ehrich, S., Behrens, D., Lebedeva, E., Ludwig, W. & Bock, E. A new obligately chemolithoautotrophic, nitrite-oxidizing bacterium, Nitrospira moscoviensis sp. nov. and its phylogenetic relationship. Arch. Microbiol. 164, 16–23 (1995).
CAS  Google Scholar 

101.
Kim, S. & Lee, S. B. Catalytic promiscuity in dihydroxy-acid dehydratase from the thermoacidophilic archaeon Sulfolobus solfataricus. J. Biochem. 139, 591–596 (2006).
CAS  Google Scholar 

102.
Safarian, S. et al. Structure of a bd oxidase indicates similar mechanisms for membrane-integrated oxygen reductases. Science 352, 583–586 (2016).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

103.
Lovley, D. R. & Phillips, E. J. P. Manganese inhibition of microbial iron reduction in anaerobic sediments. Geomicrobiol. J. 6, 145–155 (1988).
CAS  Google Scholar 

104.
Perez-Benito, J. F., Arias, C. & Amat, E. A kinetic study of the reduction of colloidal manganese dioxide by oxalic acid. J. Colloid Interface Sci. 177, 288–297 (1996).
ADS  CAS  Google Scholar  More

1.
Bellard, C., Cassey, P. & Blackburn, T. M. Alien species as a driver of recent extinctions. Biol. Lett. 12, 20150623 (2016).
PubMed  PubMed Central  Google Scholar 
2.
IPBES. Global Assessment Report on Biodiversity and Ecosystem Services. (2019).

3.
Courchamp, F., Chapuis, J. L. & Pascal, M. Mammal invaders on islands: Impact, control and control impact. Biol. Rev. Camb. Philos. Soc. 78, 347–383 (2003).
PubMed  Google Scholar 

4.
Simberloff, D. et al. Impacts of biological invasions: What’s what and the way forward. Trends Ecol. Evol. 28, 58–66 (2013).
PubMed  Google Scholar 

5.
Pimentel, D., Lach, L., Zuniga, R. & Morrison, D. Environmental and economic costs of nonindigenous species in the United States. Bioscience 50, 53–65 (2000).
Google Scholar 

6.
Kettunen, M. et al. Technical support to EU strategy on invasive species (IAS). Assessment of the impacts of IAS in Europe and the EU. (2009).

7.
Katsanevakis, S. European Alien Species Information Network (EASIN): Supporting European policies and scientific research. Manag. Biol. Invasions 6, 147–157 (2015).
Google Scholar 

8.
Hulme, P. E., Pysek, P., Nentwig, W. & Vilà, M. Will threat of biological invasions unite the European Union ?. Science (80-). 324, 40–41 (2009).
ADS  CAS  Google Scholar 

9.
UNEP. Convention on Biological Diversity. (1992).

10.
EU Council. Council Decision of 25 October 1993 concerning the conclusion of the Convention on Biological Diversity. 1–20 (1993).

11.
Carboneras, C. et al. A prioritised list of invasive alien species to assist the effective implementation of EU legislation. J. Appl. Ecol. 55, 539–547 (2018).
Google Scholar 

12.
Stohlgren, T. J. & Schnase, J. L. Risk analysis for biological hazards: What we need to know about invasive species. Risk Anal. 26, 163–173 (2006).
PubMed  Google Scholar 

13.
Soberón, J. M. Niche and area of distribution modeling: A population ecology perspective. Ecography (Cop.) 33, 159–167 (2010).
Google Scholar 

14.
Guisan, A. & Thuiller, W. Predicting species distribution: Offering more than simple habitat models. Ecol. Lett. 8, 993–1009 (2005).
Google Scholar 

15.
Gallien, L., Douzet, R., Pratte, S., Zimmermann, N. E. & Thuiller, W. Invasive species distribution models: How violating the equilibrium assumption can create new insights. Glob. Ecol. Biogeogr. 21, 1126–1136 (2012).
Google Scholar 

16.
Jeschke, J. M. & Strayer, D. L. Usefulness of bioclimatic models for studying climate change and invasive species. Ann. N. Y. Acad. Sci. 1134, 1–24 (2008).
ADS  PubMed  Google Scholar 

17.
Vilà, M. et al. How well do we understand the impacts of alien species on ecosystem services? A pan-European, cross-taxa assessment. Front. Ecol. Environ. 8, 135–144 (2010).
Google Scholar 

18.
DAISIE. Handbook of Alien Species in Europe. Invading Nature: Springer Series in Invasion Ecology. Invading Nature. Springer Series in Invasion Ecology Vol. 3 (Springer, New York, 2009).
Google Scholar 

19.
McCullogh, D. R., Takatsuki, S. & Kaji, K. Sika Deer. Biology and Management of Native and Introduced Populations (Springer, New York, 2009). https://doi.org/10.1017/CBO9781107415324.004.

20.
Reyns, N. et al. Cost-benefit analysis for invasive species control: The case of greater Canada goose Branta canadensis in Flanders (northern Belgium). PeerJ 6, e4283 (2018).
PubMed  PubMed Central  Google Scholar 

21.
Vourc’h, G., Marmet, J., Chassagne, M., Bord, S. & Chapuis, J.-L. Borrelia burgdorferi Sensu Lato in Siberian Chipmunks (Tamias sibiricus) introduced in suburban forests in France. Vector-Borne Zoonotic Dis. 7, 637–642 (2007).
PubMed  Google Scholar 

22.
Nentwig, W. Biological Invasions Vol. 193 (Springer Science & Business Media, New York, 2007).
Google Scholar 

23.
Miaud, C. et al. Invasive North American bullfrogs transmit lethal fungus Batrachochytrium dendrobatidis infections to native amphibian host species. Biol. Invasions 18, 2299–2308 (2016).
Google Scholar 

24.
GBIF.org. GBIF Home Page. (2019). https://www.gbif.org.

25.
Gallardo, B., Zieritz, A. & Aldridge, D. C. The importance of the human footprint in shaping the global distribution of terrestrial, freshwater and marine invaders. PLoS ONE 10, 1–17 (2015).
Google Scholar 

26.
Dawson, W. et al. Global hotspots and correlates of alien species richness across taxonomic groups. Nat. Ecol. Evol. 1, 1–7 (2017).
Google Scholar 

27.
Gallardo, B. et al. Protected areas offer refuge from invasive species spreading under climate change. Glob. Change Biol. 23, 5331–5343 (2017).
ADS  Google Scholar 

28.
Pitt, W. C. & Witmer, G. W. Invasive vertebrate species and the challenges of management. In Proceedings of the Vertebrate Pest Conference, Vol. 1779 (2014).

29.
Genovesi, P. Eradications of invasive alien species in Europe: A review. Biol. Invasions 1995, 127–133 (2005).
Google Scholar 

30.
Kark, S. et al. Cross-boundary collaboration: Key to the conservation puzzle. Curr. Opin. Environ. Sustain. 12, 12–24 (2015).
Google Scholar 

31.
Wittenberg, R. & Cock, M. J. W. Invasive Alien Species: A Toolkit of Best Prevention and Management Practices (CAB International, Wallingford, 2001).
Google Scholar 

32.
Yiming, L., Zhengjun, W. & Duncan, R. P. Why islands are easier to invade: Human influences on bullfrog invasion in the Zhoushan archipelago and neighboring mainland China. Oecologia 148, 129–136 (2006).
ADS  PubMed  Google Scholar 

33.
Kaji, K., Miyaki, M., Saitoh, T., Ono, S. & Kaneko, M. Spatial distribution of an expanding sika deer population on Hokkaido Island, Japan. Wildl. Soc. Bull. 28, 699–707 (2000).
Google Scholar 

34.
Nordstrom, M. et al. Effects of feral mink removal on seabirds, waders and passerines on small islands in the Baltic Sea. Biol. Conserv. 109, 359–368 (2003).
Google Scholar 

35.
Bellard, C. et al. Will climate change promote future invasions?. Glob. Change Biol. 19, 3740–3748 (2013).
ADS  Google Scholar 

36.
Hattab, T. et al. A unified framework to model the potential and realized distributions of invasive species within the invaded range. Divers. Distrib. 23, 806–819 (2017).
Google Scholar 

37.
Roy, H. E. et al. Developing a list of invasive alien species likely to threaten biodiversity and ecosystems in the European Union. Glob. Change Biol. 25, 1032–1048 (2019).
ADS  Google Scholar 

38.
Union, E. Regulation (EU) No 1143/2014 of the European Parliament and the Council of 22 October 2014 on the prevention and management of the introduction and spread of invasive alien species. Off. J. Eur. Union 317, 35–55 (2014).
Google Scholar 

39.
Rodríguez-Sanchez, F., Pérez-Luque, A. J., Bartomeus, I. & Varela, S. Ciencia reproducible: ¿qué, por qué, cómo?. Ecosistemas 25, 83–92 (2016).
Google Scholar 

40.
Katsanevakis, S. et al. Implementing the European policies for alien species—networking, science, and partnership in a complex environment. Manag. Biol. Invasions 4, 3–6 (2013).
Google Scholar 

41.
Rocchini, D. et al. Accounting for uncertainty when mapping species distributions: The need for maps of ignorance. Prog. Phys. Geogr. 35, 211–226 (2011).
Google Scholar 

42.
Jiménez-Valverde, A. et al. Use of niche models in invasive species risk assessments. Biol. Invasions 13, 2785–2797 (2011).
Google Scholar 

43.
Brook, B. W., Sodhi, N. S. & Bradshaw, C. J. A. Synergies among extinction drivers under global change. Trends Ecol. Evol. 23, 453–460 (2008).
PubMed  Google Scholar 

44.
Latham, A. D. M., Latham, M. C., Cieraad, E., Tompkins, D. M. & Warburton, B. Climate change turns up the heat on vertebrate pest control. Biol. Invasions 17, 2821–2829 (2015).
Google Scholar 

45.
Hurtt, G. C. et al. Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Clim. Change 109, 117–161 (2011).
ADS  Google Scholar 

46.
Union, E. EU Regulation No 1143/2014 on the prevention and management of the introduction and spread of invasive alien species. Off. J. Eur. Union 317, 35–55 (2014).
Google Scholar 

47.
Beaumont, L. J. et al. Different climatic envelopes among invasive populations may lead to underestimations of current and future biological invasions. Divers. Distrib. 15, 409–420 (2009).
Google Scholar 

48.
R Core Team. R: A language and environment for statistical computing. (2019).

49.
Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 1–20 (2017).
Google Scholar 

50.
Natural Earth. Rivers and lake centerlines. https://www.naturalearthdata.com/downloads/10m-physical-vectors/10m-rivers-lake-centerlines/. (2018).

51.
EEA. Corine Land Cover (CLC), Copernicus Land Monitoring Service. (2018).

52.
LP DAAC. Global 30 arc-second elevation data set GTOPO30. Land Process Distributed Active Archive Center (2004). https://edcdaac.usgs.gov/gtopo30/gtopo30.asp. Accessed 1st Sep 2017.

53.
Nelson, A. Estimated travel time to the nearest city of 50,000 or more people in year 2000. Global Environment Monitoring Unit-Joint Research Centre of the European Comission (2008). https://bioval.jrc.ec.europa.eu/products/gam/. Accessed 1st Oct 2017.

54.
Naimi, B. Package ‘ usdm ’: Uncertainty analysis for species distribution models. (2017).

55.
Chamberlain, S. et al. rgbif: Interface to the Global Biodiversity Information Facility API. R package version 1.3.0. https://cran.r-project.org/package=rgbif. (2019).

56.
Pysek, P. et al. Geographical and taxonomic biases in invasion ecology. Trends Ecol. Evol. 23, 237–244 (2008).
PubMed  Google Scholar 

57.
Scharn, R. et al. CoordinateCleaner: Standardized cleaning of occurrence records from biological collection databases. Methods Ecol. Evol. 2019, 1–8 (2019).
Google Scholar 

58.
Beck, J., Böller, M., Erhardt, A. & Schwanghart, W. Spatial bias in the GBIF database and its effect on modeling species’ geographic distributions. Ecol. Inform. 19, 10–15 (2014).
Google Scholar 

59.
Thuiller, A. W., Georges, D., Engler, R., Georges, M. D. & Thuiller, C. W. The biomod2 package: The updated object-oriented version of BIOMOD package. Manag. Biol. Invasions https://doi.org/10.1098/Rspb.2014.1776 (2016).
Article  Google Scholar 

60.
Araújo, M. B. & New, M. Ensemble forecasting of species distributions. Trends Ecol. Evol. 22, 42–47 (2007).
PubMed  PubMed Central  Google Scholar 

61.
Hirzel, A. H., Randin, C. & Guisan, A. Evaluating the ability of habitat suitability models to predict species presences. Ecol. Model. 9, 142–152 (2006).
Google Scholar 

62.
Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).
Google Scholar 

63.
Liu, C., White, M. & Newell, G. Selecting thresholds for the prediction of species occurrence with presence-only data. J. Biogeogr. 40, 778–789 (2013).
Google Scholar 

64.
Hijmans, R. J., Phillips, S., Leathwick, J. R. & Elith, J. Dismo package for R, version 1.1-4. Circles https://doi.org/10.1016/j.jhydrol.2011.07.022 (2017).
Article  Google Scholar 

65.
Ruete, A. Displaying bias in sampling effort of data accessed from biodiversity databases using ignorance maps. Biodivers. Data J. 3, e5361 (2015).
Google Scholar 

66.
QGIS Development Team. QGIS Geographic Information System. (2018).

67.
DAISIE. Handbook of Alien Species in Europe. Invading nature. Springer series in invasion ecology 3 (Springer, New York, 2009).
Google Scholar 

68.
CABI. Invasive Species Compendium. (2020). More

1.
Nilsson, M.-C. & Wardle, D. A. Understory vegetation as a forest ecosystem driver: Evidence from the northern Swedish boreal forest. Front. Ecol. Environ. 3, 421–428 (2005).
Google Scholar 
2.
Hart, S. A. & Chen, H. Y. Understory vegetation dynamics of North American boreal forests. Crit. Rev. Plant Sci. 25, 381–397 (2006).
Google Scholar 

3.
Gilliam, F. S. The ecological significance of the herbaceous layer in temperate forest ecosystems. Bioscience 57, 845–858 (2007).
Google Scholar 

4.
De Grandpré, L., Gagnon, D. & Bergeron, Y. Changes in the understory of Canadian southern boreal forest after fire. J. Veg. Sci. 4, 803–810 (1993).
Google Scholar 

5.
Mackenzie, D. D. & Naeth, M. A. The role of the forest soil propagule bank in assisted natural recovery after oil sands mining. Restor. Ecol. 18, 418–427 (2010).
Google Scholar 

6.
Jung, K., Duan, M., House, J. & Chang, S. X. Textural interfaces affected the distribution of roots, water, and nutrients in some reconstructed forest soils in the Athabasca oil sands region. Ecol. Eng. 64, 240–249 (2014).
Google Scholar 

7.
Forsch, K. B. C. Oil Sands Reclamation Using Woody Debris with LFH Mineral Soil Mix And Peat Mineral Soil Mix Cover Soils: Impacts on Select Soil and Vegetation Properties (Department of Renewable Resources, University of Alberta, Edmonton, 2014).
Google Scholar 

8.
MacKenzie, M. & Quideau, S. Laboratory-based nitrogen mineralization and biogeochemistry of two soils used in oil sands reclamation. Can. J. Soil Sci. 92, 131–142 (2012).
CAS  Google Scholar 

9.
Archibald, H. A. Early ecosystem genesis using LFH and peat cover soils in Athabasca Oil Sands reclamation. MSc Thesis, University of Alberta (2014).

10.
Errington, R. C. & Pinno, B. D. Early successional plant community dynamics on a reclaimed oil sands mine in comparison with natural boreal forest communities. Écoscience 22, 133–144 (2015).
Google Scholar 

11.
Pinno, B. & Hawkes, V. Temporal trends of ecosystem development on different site types in reclaimed boreal forests. Forests 6, 2109–2124 (2015).
Google Scholar 

12.
Chen, H. Y., Biswas, S. R., Sobey, T. M., Brassard, B. W. & Bartels, S. F. Reclamation strategies for mined forest soils and overstorey drive understorey vegetation. J. Appl. Ecol. 55, 926–936 (2018).
Google Scholar 

13.
Pinno, B. & Das Gupta, S. Coarse woody debris as a land reclamation amendment at an oil sands mining operation in Boreal Alberta, Canada. Sustainability 10, 1640 (2018).
Google Scholar 

14.
Clark, J. et al. Interpreting recruitment limitation in forests. Am. J. Bot. 86, 1–16 (1999).
CAS  PubMed  Google Scholar 

15.
Boyes, L. J., Gunton, R. M., Griffiths, M. E. & Lawes, M. J. Causes of arrested succession in coastal dune forest. Plant Ecol. 212, 21–32 (2011).
Google Scholar 

16.
Holl, K. D. Factors limiting tropical rain forest regeneration in abandoned pasture: Seed rain, seed germination, microclimate, and soil 1. Biotropica 31, 229–242 (1999).
Google Scholar 

17.
Pajunen, A., Virtanen, R. & Roininen, H. Browsing-mediated shrub canopy changes drive composition and species richness in forest-tundra ecosystems. Oikos 121, 1544–1552 (2012).
Google Scholar 

18.
Hettenbergerova, E., Hajek, M., Zelený, D., Jiroušková, J. & Mikulášková, E. Changes in species richness and species composition of vascular plants and bryophytes along a moisture gradient. Preslia 85, 369–388 (2013).
Google Scholar 

19.
Walker, L. R. & del Moral, R. Lessons from primary succession for restoration of severely damaged habitats. Appl. Veg. Sci. 12, 55–67 (2009).
Google Scholar 

20.
Lepš, J., Michálek, J., Rauch, O. & Uhlík, P. Early succession on plots with the upper soil horizon removed. J. Veg. Sci. 11, 259–264 (2000).
Google Scholar 

21.
Martineau, Y. & Saugier, B. A process-based model of old field succession linking ecosystem and community ecology. Ecol. Model. 204, 399–419 (2007).
Google Scholar 

22.
Naeth, M., Wilkinson, S., Mackenzie, D., Archibald, H. & Powter, C. Potential of LFH mineral soil mixes for reclamation of forested lands in Alberta. Oil Sands Research and Information Network, University of Alberta, School of Energy and the Environment, Edmonton, Alberta. OSRIN Report No. (TR-35, 2013).

23.
Pinno, B. D. & Errington, R. C. Maximizing natural trembling aspen seedling establishment on a reclaimed boreal oil sands site. Ecol. Restorat. 33, 43–50 (2015).
Google Scholar 

24.
Huston, M. Soil nutrients and tree species richness in Costa Rican forests. J. Biogeogr. 7, 147–157 (1980).
Google Scholar 

25.
Small, C. J. & McCarthy, B. C. Relationship of understory diversity to soil nitrogen, topographic variation, and stand age in an eastern oak forest, USA. For. Ecol. Manage. 217, 229–243 (2005).
Google Scholar 

26.
Merlin, M., Leishman, F., Errington, R. C., Pinno, B. D. & Landhäusser, S. M. Exploring drivers and dynamics of early boreal forest recovery of heavily disturbed mine sites: A case study from a reconstructed landscape. New Forests 50, 217–239 (2019).
Google Scholar 

27.
Grace, J. B., Anderson, T. M., Olff, H. & Scheiner, S. M. On the specification of structural equation models for ecological systems. Ecol. Monogr. 80, 67–87 (2010).
Google Scholar 

28.
Beckingham, J. D. & Archibald, J. H. Field Guide to Ecosites of Northern Alberta. Vol. 5 (Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, Edmonton, Alberta, 1996).

29.
Moss, E. H. & Packer, J. G. Flora of Alberta 2nd edn. (University of Toronto Press, Toronto, Ontario, Canada, 1983).

30.
Shipley, B. Cause and Correlation in Biology: A User’s Guide to Path Analysis, Structural Equations and Causal Inference with R (Cambridge University Press, Cambridge, 2016).
Google Scholar 

31.
McCune, B. & Mefford, M. J. PC-ORD ver. 6.21, multivariate analysis of ecological data. MjM Software, Gleneden Beach (2011).

32.
R Development Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria, 2015). http://www.R-project.org/

33.
Laughlin, D. C., Abella, S. R., Covington, W. W. & Grace, J. B. Species richness and soil properties in Pinus ponderosa forests: A structural equation modeling analysis. J. Veg. Sci. 18, 231–242 (2007).
Google Scholar 

34.
Reich, P. B., Frelich, L. E., Voldseth, R. A., Bakken, P. & Adair, E. C. Understorey diversity in southern boreal forests is regulated by productivity and its indirect impacts on resource availability and heterogeneity. J. Ecol. 100, 539–545 (2012).
Google Scholar 

35.
Kwak, J.-H., Chang, S. X., Naeth, M. A. & Schaaf, W. Coarse woody debris increases microbial community functional diversity but not enzyme activities in reclaimed oil sands soils. PLoS ONE 10, e0143857 (2015).
PubMed  PubMed Central  Google Scholar 

36.
Gough, L. & Grace, J. B. Herbivore effects on plant species density at varying productivity levels. Ecology 79, 1586–1594 (1998).
Google Scholar 

37.
Bedford, B. L., Walbridge, M. R. & Aldous, A. Patterns in nutrient availability and plant diversity of temperate North American wetlands. Ecology 80, 2151–2169 (1999).
Google Scholar 

38.
McIntosh, A. C., Macdonald, S. E. & Quideau, S. A. Understory plant community composition is associated with fine-scale above-and below-ground resource heterogeneity in mature lodgepole pine (Pinus contorta) Forests. PLoS ONE 11, e0151436 (2016).
PubMed  PubMed Central  Google Scholar 

39.
Elser, J. et al. Biological stoichiometry from genes to ecosystems. Ecol. Lett. 3, 540–550 (2000).
Google Scholar 

40.
Pugnaire, F. I. & Lázaro, R. Seed bank and understorey species composition in a semi-arid environment: the effect of shrub age and rainfall. Ann. Bot. 86, 807–813 (2000).
Google Scholar 

41.
Nyland, R. D. Silviculture: Concepts and Applications (Waveland Press, Long Grove, 2016).
Google Scholar 

42.
Das Gupta, S. & Pinno, B. D. Spatial patterns and competition in trees in early successional reclaimed and natural boreal forests. Acta Oecol. 92, 138–147 (2018).
ADS  Google Scholar 

43.
Reich, P. B. et al. Influence of logging, fire, and forest type on biodiversity and productivity in southern boreal forests. Ecology 82, 2731–2748 (2001).
Google Scholar 

44.
Zhang, Y., Chen, H. Y. & Taylor, A. R. Positive species diversity and above-ground biomass relationships are ubiquitous across forest strata despite interference from overstorey trees. Funct. Ecol. 31, 419–426 (2017).
Google Scholar 

45.
Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).
Google Scholar 

46.
Forrester, D. I. The spatial and temporal dynamics of species interactions in mixed-species forests: From pattern to process. For. Ecol. Manage. 312, 282–292 (2014).
Google Scholar 

47.
Hector, A., Bazeley-White, E., Loreau, M., Otway, S. & Schmid, B. Overyielding in grassland communities: Testing the sampling effect hypothesis with replicated biodiversity experiments. Ecol. Lett. 5, 502–511 (2002).
Google Scholar 

48.
Loreau, M. & Hector, A. Partitioning selection and complementarity in biodiversity experiments. Nature 412, 72–76 (2001).
ADS  CAS  PubMed  Google Scholar 

49.
Connell, J. H. & Slatyer, R. O. Mechanisms of succession in natural communities and their role in community stability and organization. Am. Nat. 111, 1119–1144 (1977).
Google Scholar 

50.
Callaway, R. M. & Walker, L. R. Competition and facilitation: A synthetic approach to interactions in plant communities. Ecology 78, 1958–1965 (1997).
Google Scholar 

51.
Petchey, O. L. & Gaston, K. J. Functional diversity: Back to basics and looking forward. Ecol. Lett. 9, 741–758 (2006).
PubMed  Google Scholar 

52.
Gómez-Aparicio, L. et al. Applying plant facilitation to forest restoration in Mediterranean ecosystems. Ecol. Appl. 14, 1128–1138 (2004).
Google Scholar 

53.
Wright, A. J., Wardle, D. A., Callaway, R. & Gaxiola, A. The overlooked role of facilitation in biodiversity experiments. Trends Ecol. Evol. 32, 383–390 (2017).
PubMed  Google Scholar 

54.
Michalet, R. et al. Do biotic interactions shape both sides of the humped-back model of species richness in plant communities?. Ecol. Lett. 9, 767–773 (2006).
PubMed  Google Scholar 

55.
Wang, J. et al. Facilitation drives the positive effects of plant richness on trace metal removal in a biodiversity experiment. PLoS ONE 9, e93733 (2014). https://doi.org/10.1371/journal.pone.0093733.
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

56.
Aschehoug, E. T. & Callaway, R. M. Diversity increases indirect interactions, attenuates the intensity of competition, and promotes coexistence. Am. Nat. 186, 452–459 (2015).
PubMed  Google Scholar 

57.
Lankau, R. A., Wheeler, E., Bennett, A. E. & Strauss, S. Y. Plant–soil feedbacks contribute to an intransitive competitive network that promotes both genetic and species diversity. J. Ecol. 99, 176–185 (2011).
Google Scholar 

58.
Ward, S. & Koch, J. Biomass and nutrient distribution in a 15.5 year old forest growing on a rehabilitated bauxite mine. Aust. J. Ecol. 21, 309–315 (1996).
Google Scholar 

59.
Scoles-Sciulla, S. J. & DeFalco, L. A. Seed reserves diluted during surface soil reclamation in eastern Mojave Desert. Arid Land Res. Manag. 23, 1–13 (2009).
Google Scholar 

60.
Smyth, C. Early succession patterns with a native species seed mix on amended and unamended coal mine spoil in the Rocky Mountains of southeastern British Columbia, Canada. Arctic Alpine Res. 29, 184–195 (1997).
ADS  Google Scholar 

61.
Navus Environmental Inc. LFH and Shallow Mineral Horizons as Inoculants on Reclaimed Areas to Improve Native Species Catch. Status Report Prepared for Syncrude Canada Ltd. Vol. 28 (Fort McMurray, Alberta, 2009).
Google Scholar 

62.
Anyia, A. Final draft report: Germination and identification of indigenous plant species in Albian Sands Energy Inc. stripped soil used for reclamation of mined site. Report prepared for Albian Sands Energy Inc., Fort McMurray, Alberta (2005).

63.
Nenzén, H. K. et al. Projected climate change effects on Alberta’s boreal forests imply future challenges for oil sands reclamation. Restor. Ecol. 28, 39–50 (2020).
Google Scholar 

64.
Rowland, S., Prescott, C., Grayston, S., Quideau, S. & Bradfield, G. Recreating a functioning forest soil in reclaimed oil sands in northern Alberta: An approach for measuring success in ecological restoration. J. Environ. Qual. 38, 1580–1590 (2009).
CAS  PubMed  Google Scholar 

65.
Sorenson, P., Quideau, S., MacKenzie, M., Landhäusser, S. & Oh, S. Forest floor development and biochemical properties in reconstructed boreal forest soils. Appl. Soil. Ecol. 49, 139–147 (2011).
Google Scholar 

66.
Quideau, S., Swallow, M., Prescott, C., Grayston, S. & Oh, S.-W. Comparing soil biogeochemical processes in novel and natural boreal forest ecosystems. Biogeosciences 10, 5651–5661 (2013).
ADS  Google Scholar 

67.
Eggelsmann, R. The thermal constant of different high-bogs and sandy soils. Proc. 4th Int. Peat Congr. 3, 371–382 (1973).
Google Scholar 

68.
Tallon, L. Spatial Variability of Thermal Properties in Reclamation Cover Systems (University of Saskatchewan, Saskatoon, 2014).
Google Scholar 

69.
Schoonmaker, A. et al. Alternative Native Boreal Seed and Plant Delivery Systems for Oil Sands Reclamation. Oil Sands Research and Information Network, University of Alberta, School of Energy and the Environment, Edmonton, Alberta. OSRIN Report No. TR-55, pp 61. https://hdl.handle.net/10402/era.40099. (2014). Accessed 19 Mar 2019.

70.
Pinno, B. D., Li, E. H., Khadka, B. & Schoonmaker, A. Germination and early growth of boreal understory plants on 3 reclamation soil types under simulated drought conditions. Native Plants J. 18, 92–105 (2017).
Google Scholar  More

Ethics statement
The work was carried under the ethical Permit Number VARELY/711/2016 issued by the regional environmental centre of Finland. Every possible effort was made to minimise disturbance.
Field system set-up
The study was performed in a farmland area in Southern Finland, near Lammi Biological Station (61° N, 25° E). We selected the lapwing as the study species, a common breeder in the study area. This ground nesting medium-sized farmland bird is listed as IUCN Near Threatened globally but Endangered within Europe17. The main threat to the species is agricultural intensification, and particularly the destruction of nests in arable land due to large scale mechanical operations, such as sowing17. In Finland, as in many other countries, a fraction of lapwing nests on arable land are located by volunteers, and their protection secured. However, locating these nests is highly time consuming and challenging, and most nests are left unprotected, and likely destroyed, every spring18.
We selected nine field parcels with a total of 22 lapwing nests in 2016, and three field parcels with nine nests in 2017. Study field parcels were intensively searched for nests by experienced observers during the week preceding the start of the recording work (see below), and individual nest locations were recorded with a hand-held GPS (model Garmin GPSmap 62 s, position accuracy ~ 3 m). Overall five of the field parcels were represented by ploughed fields (with irregular substrate owing to the upper soil being turned over) and seven by unploughed fields (direct sowing or winter cereals). Flight routes were constructed and saved in MapPilot 1.5.1 (Drones Made Easy, San Diego, USA) for areas around nests that covered approximately 0.5–1 ha (see Fig. 4). A drone cruised along the pre-programmed route, each flight lasting approximately 6–10 min (the time was constrained by the drone battery). Flights were done between 5-25.5.2016 and 16-18.5.2017 at altitudes of 15 or 25 m above ground level. Distance between transects was approximately 7 m for flights at 15 m of altitude and 12 m for flights at 25 m of altitude. We aimed to achieve a minimum of at least one flight over each field at each altitude (15 and 25 m) and each period of the day (07—11 a.m. and 14—20 p.m.). Overall, 54 flights were completed in 2016 and 19 in 2017. Thermal images were acquired by using a DJI Phantom 3 Advanced quadcopter with a FLIR Tau 2, 336 * 256 pixel, 9 mm lens thermal camera mounted on it. To ensure quality, thermal images were continuously saved during flights directly to a USB stick using a ThermalCapture unit (TeAx Technology GmbH, Wilnsdorf, Germany). Image acquisition occurred with the camera pointed in nadir position. Temperature, cloud cover, air humidity and wind speed data for each flight were accessed afterwards through Airdata.com service. In all circumstances, the incubating adult left the nest as we approached the edge of the field before the start of the drone flight.
Figure 4

Schematic representation of the key steps of this study, from extensive nest search at selected fields (A), to flying the drone carrying the thermal sensor along a pre-programmed route over the field (B), preparing the thermal images by extracting the coordinates of the box drawn around the nest (C), and finally applying a neural network deep learning algorithm to classify images as having or not having a nest (D). The satellite image in (A) and (B) is taken from Google maps (www.google.it/maps, map data: Google). Figure created in Microsoft Office PowerPoint 2016 (www.microsoft.com).

Full size image

Deep learning algorithm for nest identification
To automatically classify images as having or not having a nest, we trained a deep learning model (more details in extended methods in Supplementary information) to detect nests while minimising the occurrence of false positives (hereafter false presences) and false negatives (hereafter false absences). In doing so, we choose a convolutional neural network (CNN) approach19 and YOLOv3 training program20, as done in a similar study3.
We selected all images with nests in them (positive images, n = 1969) from each flight, and a larger number of randomly selected images without a nest (negative images, n = 3,469). We manually labelled all positive images using software ImageJ 1.8.021 with bounding boxes, which coordinates were then indexed by corresponding image names to be used by the YOLOv3. Next, we split the set of positive images into a training set and a test set by randomly selecting 70% of the flights (and all associated images) for training, and using the remaining 30% of flight images for testing. Selection was done at the flight rather than the image level to avoid that similar images from the same flight would be used in both training and testing, causing testing results to be positively biased.
Training was executed for 800 epochs, whereby an epoch is defined as the entire dataset passing forward and backward through the neural network (training program available at http://github.com/ultralytics/yolov3). After training was completed, a weights file was generated, which included the final value stored by each neuron, represented by floating point numbers in series. We then developed a program which read the configuration file of the YOLOv3 architecture and constructed the network according to the architecture specified in PyTorch implementation. Developing our own program was done to increase flexibility of expanding functionalities without interrupting unfamiliar code flow. The program read the trained weights file in series and stored the values to proper neurons, resulting in the construction of a deep learning model specifically for nest detection. This was then used to validate and test all the trained weight files to select the best model.
Through the testing, an output value was produced representing the confidence that an image included a nest. Values close to zero indicate that an image contained no nests with a high confidence, while values close to one that an image included a nest with a high confidence. In the cases where multiple spots appeared as possible nests, the overall confidence for that image was taken from the spot with the highest confidence values.
Performance of the neural network in discriminating images as having a nest or not was assessed by means of calculating four standard evaluation metrics for these types of systems: Precision, recall (propensity to avoid false positives and false negatives, respectively), accuracy (overall discrimination accuracy) and F1 score (the harmonic mean of precision and recall; more details in extended methods in Supplementary information). Performance was evaluated separately for the training and for the testing parts of the system.
Statistical analyses
We quantified the effect of environmental factors on the performance of the deep learning algorithm. We ran two separate analyses, one focused on factors affecting occurrence of false presences (i.e. the algorithm erroneously identifies an image without a nest as having a nest), and one on factors affecting occurrence of false absences (algorithm erroneously identifies an image with a nest as not having a nest). As the response of the former model we used the confidence as given by the algorithm for all the test images known to have no nest in them (values close to one indicate high probability of false presence). This model used data from images without a nest from all available flights (n = 3,469 images from 73 flights, as these were not used for training). The response for the false absence model was the confidence of the test images (n = 497 images with a nest from the 30% flights, n = 14 flights, not used for training) to having no nest in them (values close to one indicate high probability of false absence). Because the response variable is a proportion, we used beta regression with logit link. In each model we also included flight identity as a random factor to account for pseudo-replication stemming from multiple images per flight.
Models were run using the glmmTMB package22 in R version 3.6.123. Covariates considered are: air temperature (hereafter temperature), cloud cover, wind speed, air humidity (hereafter humidity), drone height (either 15 or 25 m above ground level) and substrate type (two classes: ploughed or un-ploughed). These variables are deemed potentially relevant in affecting the nest visibility in thermal images, and consequent performance of the deep learning algorithm. Air temperature, humidity, cloud cover and wind speed may reduce the thermal contrast between a nest (warm) and the remaining landscape, and create heterogeneous thermal landscape that hampers nest detection24. Moreover, ploughing alters the physical structure of the substrate, making it rougher and more heterogeneous, with many physical objects that may resemble a nest shape, thereby affecting nest detection accuracy. Drone height may affect detection accuracy due to the decreasing size and sharpness of a nest at higher altitudes.
Prior to analyses, we checked for outliers and ran variance inflation (VIF) analyses to assess the collinearity level between covariates. For the false presence model, humidity had a high VIF value of 3.9 being strongly correlated to temperature (R = − 0.7) and was excluded from following analyses. For the false absence model, humidity and wind speed had high VIF (28.8 and 2.3, both correlated with temperature, R = − 0.9 and 0.7, respectively), and were excluded from analyses. The remaining covariates had a VIF  More

Test-case species
Perca fluviatilis (Actinopterygii, Percidae) is a common widely spread Eurasian species living in freshwater and brackish habitats49. Its economic (high market value) and recreational (i.e. fishing) interests have led to the development of its RAS (i.e. recirculating aquaculture systems) farming since the 1990’s49. Perca fluviatilis is among the most interesting species for the development of inland aquaculture in Europe49. However, despite its economic potential, the production is still limited. This is mainly due to major bottlenecks, especially in first-life stages, such as low growth rate, low survival rate, and high cannibalism rate49. This highlights the potential interest of re-starting new domestication programmes. An intraspecific differentiation was already showed for this species in standardised conditions (e.g. growth19,50, behaviour16, development51).
Biological material collection and pre-experiment rearing conditions
Population sampling and rearing conditions were adapted from Toomey et al.16. Egg ribbons (one ribbon corresponding to one female) were obtained during the May 2018 and May 2019 spawning seasons from four lakes (Appendix S1): Valkea-Müstajärvi (VAL; 2018; Finland; 61° 13′ 08″ N, 25° 07′ 05″ E), Iso-Valkjärvi (ISO; 2018; Finland; 60° 57′ 21″ N, 26° 13′ 3″ E), Geneva (GEN; 2019; France; 46° 22′ 7.20″ N, 6° 27′ 14.73″ E), and Balaton (BAL ; 2019 ; Hungary ; 46° 54′ 23.375″ N, 18° 2′ 43.119″ E). We chose these populations since a phenotypic differentiation is known between the Finnish and Geneva populations16 while genetic specificities of central Europe populations have been already observed52,53. After transportation, 13–19 egg ribbons per lake were incubated at the experimental platform of aquaculture (Unit of Animal Research and Functionality of Animal Products, University of Lorraine, Vandœuvre-lès-Nancy, France) at 13 °C in incubators (110 × 64 × 186 cm; one population per incubator to avoid potential disease transmission between populations, one to two incubators per population) containing nine racks each (45 × 7 × 12 cm). Each incubator had its own temperature control and recirculated water system (flow rate of 4 m3 h−1). Water was UV-sterilized. Temperature (13.0 ± 0.4 °C) and oxygen rate (10.0 ± 0.5 mg L−1) were checked daily while pH (8.0 ± 0.2) was monitored three times a week (± standard error). Ammonium and nitrite concentrations (lower than 0.05 mg L−1) were measured three times a week until hatching. Light intensity was 400 lx at the water surface. Photoperiod was 12L:12D (12 h of light and 12 h of darkness).
Experimental rearing protocol
Two independent experimental phases were performed: phase I from hatching until the end of weaning (i.e. transition from live feed to inert pellets; 26 days post-hatching, dph) and phase II from 27 dph until the end of nursery, at 60 dph. The larval period was split in two phases in order to ensure availability of larvae across the whole larval period since there is a very high mortality rate during first-life stages. Because wild egg ribbons are not available the same time for all populations (i.e. asynchronous spawning seasons) and in order to prevent potential pathogen transmission, all populations were reared in independent structures. Since there are increasing concerns about the epizootic disease Perhabdovirus in Europe49, all populations were tested for the occurrence of this virus (Laboratoire Département d’Analyses du Jura, Poligny, France). All results were negative to the presence of the Perhabdovirus.
Regarding phase I, larvae from the different egg ribbons of each population were mixed after hatching and transferred to three green (RGB: 137, 172, 118) internal-wall 71 L cylindro-conical tanks (three replicates per population; RAS) at a density of 50 larvae L−1. Photoperiod was 12L:12D (simulation of dawn and dusk for 30 min) and light intensity was 400 lx at the water surface. Temperature was gradually increased during the first 2 weeks to 20 °C (1 °C day−1). Larvae were hand-fed with newly hatched Artemia nauplii (Sep-Art, INVE; seven times a day, every 1 h 30 from 8.30 am to 5.30 pm) from 3 days post-hatching until at 16 dph, which corresponds to the beginning of the weaning (i.e. transition from live feed to inert dry artificial diet) period. At 16 dph, Artemia ration was decreased by 25% every 3 days and dry feed ration (BioMar, 300 µm until 21 dph, then 500 µm) was increased by the same ratio. After 25 dph, larvae were fed with dry feed ad libitum (BioMar 500 µm, then 700 µm at 44 dph until 60 dph). At 26 dph, the larvae left in the cylindro-conical tanks were removed in order to start phase II.
Regarding phase II, after hatching, larvae left (i.e. not sampled for phase I) were held in 2 m3 tanks (RAS). The same conditions as phase I were used (temperature, light intensity, feeding, and photoperiod regimes). At 27 dph, these larvae were transferred to the three cylindro-conical tanks in order to start phase II at a density of 19 larvae L−1. Light intensity was 80 lx at water surface, all else remaining the same as phase I (except for density).
During the two phases, oxygen concentration (8.5 ± 2.3 mg L−1) and temperature (20.0 ± 0.6 °C) were checked daily in all tanks (± standard error). Ammonium and nitrite concentrations (means inferior to 0.05 mg L−1) and pH (7.7 ± 0.6 mg L−1) were monitored three times a week (± standard error). Tanks were cleaned daily after first feeding and dead individuals were removed every morning.
Larviculture performance assessment
A trait is considered in this study at the replicate level. Each trait value is obtained from the mean of individual values.
Survival and development traits
Survival rate is one of the key traits contributing to the success of larviculture production49,54. Because of fast decomposition of dead larvae, it was not possible to count dead larvae during the first 5 days post-hatching. Consequently, the daily count of dead larvae was only performed in phase II. Therefore, survival rate in phase I was calculated for each cylindro-conical tank thanks to the final count of larvae using the following formula: Nf × 100/(Ni − Ns), where Nf is the final number of fish counted at the end of phase I, Ni the initial number of fish, and Ns the number of fish sampled along the phase (i.e. for behaviour experiments, see below). For phase II, the Bergot survival rate55 was used since it takes into account the number of fish removed for sampling and the daily mortality. Two traits related to the development of individuals and essential for larviculture were considered49: swim bladder inflation rate and deformity rate. Swim bladder inflation rate was estimated at the end of each experimental phase (following the protocol used in Jacquemond56; 20 g L−1 of sea salt and 70 mg L−1 of MS-222): 100 × (SB + /Nf) with SB + the number of larvae with swim bladder and Nf the final number of larvae. Deformity rate was estimated in the final counting of each experimental phase using the following formula: 100 × (Nm/Nf) with Nm the number of deformed larvae (only visible column deformities) and Nf the final number of larvae counted. Finally, the volume of the yolk sac was also evaluated at 1 day post-hatching since it reflects the quantity of nutritional reserves available before exogenous feeding49. It is calculated using the following formula: π/6 × YSL×YSH2, where YSL is the length of the yolk sac and YSH the height of the yolk sac57.
Behavioural traits
The ability of a biological unit to be efficiently produced in intensive conditions (i.e. intensive farming is an increasing trend in the aquaculture development) also depends on (1) inter-individual relationships, (2) inter-individual distances, and (3) activity16. Indeed, tolerance to conspecifics in a restricted area is essential for production since it can impact individual welfare58. Highlighting populations which present a cohesive group structure would be advantageous. Nevertheless, living in group is not costless because it can trigger aggressive behaviours which can lead to uneven competition for food, mortalities, stress, or immune suppression16. Therefore, both inter-individual distances and inter-individual interactions need to be considered. Finally, activity is also important since it contributes to the total energetic budget16. Furthermore, less active individuals could contribute to decrease the occurrence of inter-individual contacts and subsequent potential aggressive interactions.
Regarding aggressive interaction quantification, aggressiveness was calculated for phase II using the formula : 100 × (Ni − (Nf + Nd + Ns) + Nc)/(Nf − Ns) with Ni the initial number of larvae, Nf the final number of larvae, Nd the sum of dead larvae counted daily, Ns the number of sampled larvae, and Nc the number of truncated or enucleated (enucleation being a specific indicator of aggressiveness in perch16) larvae among the dead ones (not possible in phase I since it was not possible to count dead larvae during the first 5 days post-hatching due to fast decomposition; in addition, no truncated or enucleated individuals were recorded for phase I).
Regarding the evaluation of inter-individual distances and activity, the detailed protocol is available in Toomey et al.16. Briefly, for each population, three replicates for each cylindro-conical tank were performed (nine replicates per population) over 2 days for phase I (25 and 26 dph) and phase II (44 and 45 dph). For each population, 90 individuals (n = 30 per cylindro-conical tank, 10 individuals per replicate) were sampled and transferred to three aquaria (58 L; light intensity of 80 lx light intensity, 20 °C). After one night of acclimatisation, populations were tested per groups of ten individuals in circular arenas (30 cm diameter, 1.5 cm of water depth, 10 lx) to study inter-individual distances and activity16. After 30 min acclimatisation, individuals were filmed for 30 min. After the experiment, individuals were euthanized (overdose of MS-222) and kept in formalin 4% for later length measurements. Larvae tested in the circular arenas from ISO, VAL, BAL, and GEN were respectively 14.05 ± 0.55 mm, 12.90 ± 0.62 mm, 10.62 ± 0.47 mm, and 11.81 ± 1.01 mm during phase I and 26.74 ± 1.67 mm, 26.28 ± 1.99 mm, 19.24 ± 1.22 mm, and 12.26 ± 0.45 mm during phase II (± standard error). Analyses were performed using the ImageJ software59. Images were extracted from videos at 5-min interval (six images per video). For each image, coordinates of individuals were noted using the middle point between the eyes. The mean of inter-individual distances was evaluated per replicate. It corresponds to the mean of distances between a focal fish and all the other fish of the group and it is an indicator of the group cohesion. Detailed calculation is available in Colchen et al.60. Activity was analysed in ImageJ. Every 5 min, one image per second was extracted for six consecutive seconds. For each image, coordinates of each individual were noted. This allowed calculating distance swam every second during the 5 s. The mean allowed obtaining for each individual the mean distance swam per second. From these values, we were able to calculate an average activity per replicate.
Growth traits
Growth traits are important in larviculture production, more particularly the length at hatching, specific growth rate, and growth heterogeneity49. To evaluate these traits, 30 larvae per population (i.e. ten larvae per cylindro-conical tank) were sampled the first and last days of each experimental phase, euthanized with an overdose of MS-222, and kept in formalin 4%. For phases I and II, larvae were measured using ImageJ (± 0.01 mm). For phase II, larvae were also individually weighted (± 0.1 mg; not possible in phase I due to the imprecision of measure at 1 day post-hatching). Since specific growth rate (SGR) is a trait of interest at the end of larviculture, it was calculated only in phase II using the following formula: SGR = 100 × (ln(Xf) − ln(Xi)) × ∆T−1 where Xi and Xf are respectively the average initial and final weight/length and ∆T the duration of phase II. Final growth heterogeneity was calculated for both phases in the following way: CVXf/CVXi in which CV is the coefficient of variation (100 × standard deviation/mean) and Xi and Xf the initial and final weight/length, respectively.
Statistical analyses
All statistical analyses were performed in R 3.0.361 to assess if there were statistical differences (p value  More

Monthly climatic variables
Our dataset is based on simulations of monthly mean temperature (°C), precipitation (mm month−1), cloudiness (%), relative humidity (%) and wind speed (m s−1) of the HadCM3 general circulation model6,12,13. At a spatial grid resolution of 3.75° × 2.5°, these data cover the last 120,000 years in 72 snapshots (2,000 year time steps between 120,000 BP and 22,000 BP; 1,000 year time steps between 22,000 BP and the pre-industrial modern era), each representing climatic conditions averaged across a 30-year post-spin-up period. We denote these data by

$$begin{array}{c}{T}_{{rm{HadCM3}}}(m,t),;{P}_{{rm{HadCM3}}}(m,t),;{C}_{{rm{HadCM3}}}(m,t),\ {H}_{{rm{HadCM3}}}(m,t),;{W}_{{rm{HadCM3}}}(m,t),end{array}$$
(1)

where (m=1,ldots ,12) represents a given month, and (tin {T}_{{rm{120}}{rm{k}}}) represents a given one of the 72 points in time for which simulations are available, denoted ({T}_{{rm{120}}{rm{k}}}).
We downscaled and bias-corrected these data in two stages (Fig. 1). Both are based on variations of the Delta Method14, under which a high-resolution, bias-corrected reconstruction of climate at some time t in the past is obtained by applying the difference between modern-era low-resolution simulated and high-resolution observed climate – the correction term – to the simulated climate at time t. The Delta Method has previously been used to downscale and bias-correct palaeoclimate simulations, e.g. for the widely used WorldClim database9. A recent evaluation of three methods commonly used for bias-correction and downscaling15 showed that the Delta Method reduces the difference between climate simulation data and empirical palaeoclimatic reconstructions overall more effectively than two alternative methods (statistical downscaling using Generalised Additive Models, and Quantile Mapping). We therefore used this approach for generating our dataset.
Fig. 1

Method of reconstructing high-resolution climate. Yellow boxes represent raw simulated and observed data, the dark blue box represents the final data. Maps, showing modern-era climate, correspond to the datasets represented by the bottom three boxes.

Full size image

Downscaling to ~1° resolution
A key limitation of the Delta Method is that it assumes the modern-era correction term to be representative of past correction terms15. This assumption is substantially relaxed in the Dynamic Delta Method used in the first stage of our approach to downscale the data in Eq. (1) to a ~1° resolution. This involves the use of a set of high-resolution climate simulations that were run for a smaller but climatically diverse subset of ({T}_{120k}). Simulations at this resolution are very computationally expensive, and therefore running substantially larger sets of simulations is not feasible; however, these selected data can be very effectively used to generate a suitable time-dependent correction term for each (tin {T}_{120k}). In this way, we are able to increase the resolution of the original climate simulations by a factor of ~9, while simultaneously allowing for temporal variability in the correction term. In the following, we detail the approach.
We used high-resolution simulations of the same variables as in Eq. (1) from the HadAM3H model13,16,17, available at a 1.25° × 0.83° resolution for the last 21,000 years in 9 snapshots (2,000 year time steps between 12,000 BP and 6,000 BP; 3,000 year time steps otherwise). We denote these by

$$begin{array}{c}{T}_{{rm{HadAM3H}}}(m,t),;{P}_{{rm{HadAM3H}}}(m,t),;{C}_{{rm{HadAM3H}}}(m,t),\ {H}_{{rm{HadAM3H}}}(m,t),;{W}_{{rm{HadAM3H}}}(m,t),end{array}$$

respectively, where (tin {T}_{21k}), represents a given one of the 9 points in time for which simulations are available, denoted ({T}_{{rm{21}}{rm{k}}}).
For each variable (Xin {T,P,C,H,W}), we considered the differences between the medium- and the high-resolution data at times (tin {T}_{21k}) for which both are available,

$$Delta {X}_{{rm{HadAM3H}}}^{{rm{HadCM3}}}(m,t),:={X}_{{rm{HadAM3H}}}(m,t)-{X}_{{rm{HadCM3}}}^{ boxplus }(m,t),$$

where the ( boxplus )-notation indicates that the coarser-resolution data was interpolated to the grid of the higher-resolution data. For this, we used an Akima cubic Hermite interpolant18, which (unlike a bilinear interpolant) is smooth but (unlike a bicubic interpolant) avoids potential overshoots. For each (tin {T}_{120k}) and each (tau in {T}_{21k}),

$${X}_{{rm{HadCM3}}}^{ boxplus }(m,t)+Delta {X}_{{rm{HadAM3H}}}^{{rm{HadCM3}}}(m,tau )$$
(2)

provides a 1.25° × 0.83° resolution downscaled version of the data ({X}_{{rm{HadCM3}}}(m,t)) in Eq. (1). The same is true, more generally, for any weighted linear combination of the Δ({X}_{{rm{HadAM3H}}}^{{rm{HadCM3}}}(m,tau )), which is the approach taken here, yielding

$$X{{prime} }_{ sim {1}^{circ }}(m,t),:={X}_{{rm{HadCM3}}}^{ boxplus }(m,t)+mathop{underbrace{sum _{tau in {T}_{21k}}w(t,tau )cdot Delta {X}_{{rm{HadAM3H}}}^{{rm{HadCM3}}}(m,tau )}}limits_{{rm{time-variable}},{rm{correction}},{rm{term}}},$$
(3)

where ({sum }_{tau in {T}_{21k}}mathop{overbrace{w(t,tau )}}limits^{ge 0}=1) for any given (tin {T}_{125k}). We discuss the choice of an additive approach for all climatic variables later on. Crucially, in contrast to the classical Delta Method – which, for all (tin {T}_{125k}), would correspond to (w(t,{rm{present}},{rm{day}})=1) and (w(t,tau )=0) otherwise (cf. Eq. (5)) –, the resolution correction term that is added to ({X}_{{rm{HadCM3}}}^{ boxplus }(m,t)) in Eq. (3) need not be constant over time. Instead, the high-resolution heterogeneities that are applied to the medium-resolution HadCM3 data are chosen from the broad range of patterns simulated for ({T}_{21k}). The strength of this approach lies in the fact that the last 21,000 years account for a substantial portion of the range of climatic conditions present during the whole Late Quaternary. Thus, by choosing the weights (w(t,tau )) for a given time (tin {T}_{125k}) appropriately, we can construct a ({T}_{21k})-data-based correction term corresponding to a climatic state that is, in a sense yet to be specified, close to the climatic state at time t. Here, we used atmospheric CO2 concentration, a key determinant of the global climatic state19, as the metric according to which the (w(t,tau )) are chosen; i.e. we assigned a higher weight to Δ({X}_{{rm{HadAM3H}}}^{{rm{HadCM3}}}(m,tau )) the closer the CO2 level at time (tau ) was to that at time t. Specifically, we used

$$w{prime} (t,tau )=frac{1}{{({{rm{CO}}}_{2}(t)-{{rm{CO}}}_{2}(tau ))}^{2}},$$

and rescaled these to (w(t,tau )=frac{w{prime} (t,tau )}{{sum }_{tau in {T}_{21k}}w{prime} (t,tau )}) (Supplementary Fig. 1). In the special case of (tin {T}_{21k}), we have (w(t,t)=1) and (w(t,tau )=0) for (tau ne t), for which Eq. (3) simplifies to

$$X{{prime} }_{ sim {1}^{circ }}(m,t)={X}_{{rm{HadAM3H}}}(m,t)quad {rm{for}},{rm{all}},tin {T}_{21k}.$$

Formally, the correction term in Eq. (3) corresponds to an inverse square distance interpolation of the Δ({X}_{{rm{HadAM3H}}}^{{rm{HadCM3}}}) with respect to CO220. We also note that, for our choice of (w(t,tau )), the correction term is a smooth function of t, as would be desired. In particular, this would not the case for the approach in Eq. (2) (unless (tau ) is the same for all (tin {T}_{125k})).
The additive approach in Eq. (3) does not by itself ensure that the derived precipitation, relative humidity, cloudiness and wind speed are non-negative and that relative humidity and cloudiness do not exceed 100% across all points in time and space. We therefore capped values at the appropriate bounds, and obtain

$$begin{array}{lll}{T}_{ sim {1}^{circ }}(m,t) & := & T{{prime} }_{ sim {1}^{circ }}(m,t),\ {P}_{ sim {1}^{circ }}(m,t) & := & {rm{max }}left(0,P{{prime} }_{ sim {1}^{circ }}(m,t)right),\ {C}_{ sim {1}^{circ }}(m,t) & := & {rm{min }}left(100 % ,{rm{max }}(0 % ,C{{prime} }_{ sim {1}^{circ }}(m,t))right),\ {H}_{ sim {1}^{circ }}(m,t) & := & {rm{min }}left(100 % ,{rm{max }}(0 % ,H{{prime} }_{ sim {1}^{circ }}(m,t))right),\ {W}_{ sim {1}^{circ }}(m,t) & := & {rm{max }}left(0,W{{prime} }_{ sim {1}^{circ }}(m,t)right).end{array}$$
(4)

Supplementary Fig. 2 shows that this step only affects a very small number of data points, whose values are otherwise very close to the relevant bound.
Bias-correction and downscaling to 0.5° resolution
In the second stage of our approach, we applied the classical Delta Method to the previously downscaled simulation data. Similar to the approach in Eq. (3), this is achieved by applying a correction term, which is now given by the difference between present-era high-resolution observational climate and coarser-resolution simulated climate, to past simulated climate. This further increases the resolution and removes remaining biases in the data in Eq. (4).
Since our present-era simulation data correspond to pre-industrial conditions (280 ppm atmospheric CO2 concentration)6,12,13, it would be desirable for the observational dataset used in this step to be approximately representative of these conditions as well, so that the correction term can be computed based on the simulated and observed climate of a similar underlying scenario. There is generally a trade-off between the quality of observation-based global climate datasets of recent decades, and the extent to which they reflect anthropogenic climate change (which, by design, is not captured in our simulated data) – both of which increase towards the present. Fortunately, however, significant advances in interpolation methods21,22,23 have produced high-quality gridded datasets of global climatic conditions reaching as far back as the early 20th century23. Thus, here we used 0.5° resolution observational data representing 1901–1930 averages (~300 ppm atmospheric CO2) of terrestrial monthly temperature, precipitation and cloudiness23. For relative humidity and wind speed, we used a global data representing 1961–1990 average (~330 ppm atmospheric CO2) monthly values24 due to a lack of earlier datasets. We denote the data by

$${T}_{{rm{obs}}}(m,0),;{P}_{{rm{obs}}}(m,0),;{C}_{{rm{obs}}}(m,0),;{H}_{{rm{obs}}}(m,0),;{W}_{{rm{obs}}}(m,0).$$

We extrapolated these maps to current non-land grid cells using an inverse distance weighting approach so as to be able to use the Delta Method at times of lower sea level. The resulting data were used to bias-correct and further downscale the ~1° data in Eq. (3) to a 0.5° grid resolution via

$$X{{prime} }_{0.{5}^{circ }}(m,t),:={X}_{ sim {1}^{circ }}^{ boxplus }(m,t)+mathop{underbrace{{X}_{{rm{obs}}}(m,0)-{X}_{ sim {1}^{circ }}^{ boxplus }(m,0)}}limits_{{rm{correction}},{rm{term}}},$$
(5)

where (Xin {T,P,C,H,W}). In particular, the data for the present are identical to the empirically observed climate,

$$X{{prime} }_{0.{5}^{circ }}(m,0)={X}_{{rm{obs}}}(m,0).$$

Finally, we again capped values at the appropriate bounds, and obtained

$$begin{array}{lll}{T}_{0.{5}^{circ }}(m,t) & := & T{{prime} }_{0.{5}^{circ }}(m,t),\ {P}_{0.{5}^{circ }}(m,t) & := & {rm{max }}left(0,P{{prime} }_{0.{5}^{circ }}(m,t)right),\ {C}_{0.{5}^{circ }}(m,t) & := & {rm{min }}left(100 % ,{rm{max }}(0 % ,C{{prime} }_{0.{5}^{circ }}(m,t))right),\ {H}_{0.{5}^{circ }}(m,t) & := & {rm{min }}left(100 % ,{rm{max }}(0 % ,H{{prime} }_{0.{5}^{circ }}(m,t))right),\ {W}_{0.{5}^{circ }}(m,t) & := & {rm{max }}left(0,W{{prime} }_{0.{5}^{circ }}(m,t)right).end{array}$$
(6a)

Similar as in the analogous step in the first stage of our approach (Eq. (4)), only a relatively small number of data points is affected by the capping; their values are reasonably close to the relevant bounds, and their frequency decreases sharply with increasing distance to the bounds (Supplementary Fig. 2).
In principle, capping values, where necessary, can be circumvented by suitably transforming the relevant variable first, then applying the additive Delta Method, and back-transforming the result. In the case of precipitation, for example, a log-transformation is sometimes used, which is mathematically equivalent to a multiplicative Delta Method, in which low-resolution past simulated data is multiplied by the relative difference between high- and low-resolution modern-era data14; thus, instead of Eq. (5), we would have ({P}_{0.{5}^{circ }}(m,t),:={P}_{ sim {1}^{circ }}^{ boxplus }(m,t)cdot frac{{P}_{{rm{obs}}}(m,0)}{{P}_{ sim {1}^{circ }}^{ boxplus }(m,0)}). However, whilst this approach ensures non-negative values, it has three important drawbacks. First, if present-era observed precipitation in a certain month and grid cell is zero, i.e. ({P}_{{rm{obs}}}(m,0)=0), then ({P}_{0.{5}^{circ }}(m,t)=0) at all points in time, t, irrespectively of the simulated climate change signal. Specifically, this makes it impossible for current extreme desert areas to be wetter at any point in the past. Second, if present-era simulated precipitation in a grid cell is very low (or indeed identical to zero), i.e. ({P}_{ sim {1}^{circ }}^{ boxplus }(m,0)approx 0), then ({P}_{0.{5}^{circ }}(m,t)) can increase beyond all bounds. Very arid locations are particularly prone to this effect, which can generate highly improbable precipitation patterns for the past. In our scenario of generating global maps for a total of 864 individual months, this lack of robustness of the multiplicative Delta Method would be difficult to handle. Third, the multiplicative Delta Method is not self-consistent: applying it to the sum of simulated monthly precipitation does not produce the same result as applying it to simulated monthly precipitation first and then taking the sum of these values. The natural equivalent of the log-transformation for precipitation is the logit-transformation for cloudiness and relative humidity, however, this approach suffers from the same drawbacks.
Minimum and maximum annual temperature
Diurnal temperature data are not included in the available HadCM3 and HadAM3H simulation outputs. We therefore used the following approach to estimate minimum and maximum annual temperatures. Based on the monthly HadCM3 and HadAM3H temperature data, we created maps of the mean temperature of the coldest and the warmest month. In the same way as described above, we used these data to reconstruct the mean temperature of the coldest and warmest month at a 1.25° × 0.83° resolution by means of the Dynamic Delta Method, yielding

$${T}_{ sim {1}^{circ }}^{{rm{coldest}},{rm{month}}}(t),{rm{and}},{T}_{ sim {1}^{circ }}^{{rm{warmest}},{rm{month}}}(t),$$

for (tin {T}_{120k}). We then used observation-based 0.5° resolution global datasets of modern-era (1901–1930 average) minimum and maximum annual temperature23, denoted

$${T}_{{rm{obs}}}^{{rm{min }}}(0),{rm{and}},{T}_{{rm{obs}}}^{{rm{max }}}(0),$$

to estimate past minimum and maximum annual temperature as

$$begin{array}{lll}{T}_{0.{5}^{circ }}^{{rm{min }}}(t) & := & {T}_{ sim {1}^{circ }}^{{rm{coldest}},{rm{month}}, boxplus }(t)+{T}_{{rm{obs}}}^{{rm{min }}}(0)-{T}_{ sim {1}^{circ }}^{{rm{coldest}},{rm{month}}, boxplus }(0),\ {T}_{0.{5}^{circ }}^{{rm{max }}}(t) & := & {T}_{ sim {1}^{circ }}^{{rm{warmest}},{rm{month}}, boxplus }(t)+{T}_{{rm{obs}}}^{{rm{max }}}(0)-{T}_{ sim {1}^{circ }}^{{rm{warmest}},{rm{month}}, boxplus }(0),end{array}$$
(6b)

respectively. This approach assumes that the difference between past and present mean temperature of the coldest (warmest) month is similar to the difference between the past and present temperature of the coldest (warmest) day. Instrumental data of the recent past suggest that this assumption is well justified across space (Supplementary Fig. 3).
Land configuration
We used a reconstruction of mean global sea level25 and a global elevation and bathymetry map26, interpolated to a 0.5° resolution grid, to create land configuration maps for the last 120,000 years. Maps of terrestrial climate through time were obtained by cropping the global data in Eq. (6a and b) to the appropriate land masks. Values in non-land grid cells were set to missing values, except in the case of below-sea-level inland grid cells, such as the Aral, Caspian and Dead sea.
Bioclimatic data, net primary productivity, leaf area index, biome
Based on our reconstructions of minimum and maximum annual temperature, and monthly temperature and precipitation, we derived 17 bioclimatic variables27 listed in Table 1. In addition, we used the Biome4 global vegetation model28 to compute net primary productivity, leaf area index and biome type at a 0.5° resolution for all (tin {T}_{120k}), using reconstructed minimum annual temperature, and monthly temperature, precipitation and cloudiness. Similar to a previous approach21, we converted cloudiness to the percent of possible sunshine (required by Biome4) by using a standard conversion table and applying an additional latitude- and month-specific correction. Since Biome4 estimates ice biomes only based on climatic conditions and not ice sheet data, it can underestimate the spatial extent of ice. We therefore changed simulated non-ice biomes to ice, and set net primary production and leaf area index to 0, in grid cells covered by ice sheets according to the ICE-6g dataset29 at the relevant points in time. Whilst our data represent potential natural biomes, and as such do not account for local anthropogenic land use, maps of actual land cover can readily be generated by superimposing our data with available reconstructions of global land use during the Holocene30. More

Distinctive temperature responses along a substrate gradient
Within the temperature range of ~14 to ~34 °C in our incubations, the observed AORs at the ambient substrate level (AORambient, see Methods) varied over 3 orders of magnitude, from 0.5 to ~4000 nM d−1, across a wide spectrum of ambient ammonium levels ranging from 14 nM to 96 μM (Fig. 1). Three different types of temperature response of AORambient patterns at estuarine, shelf, and sea basin stations were observed: (I) a positive response with a Topt of ≥34 °C (Fig. 1a, b); (II) a negative response, which has never been reported before, with a Topt of ≤14 °C (Fig. 1d–f); and (III) a dome-shaped response with a Topt of 20–29 °C (Fig. 1c, g–i).
The Type I pattern was observed at two of the three estuarine stations (JLR1 and JLR2, Fig. 1a, b) where ammonium concentrations were high (≥24 μM), and the AOR increased linearly as the temperature increased from 14 to 34 °C. In these cases, the Topt was equal to or higher than the maximum experimental temperature of 34 °C (Fig. 1a, b). The Type II pattern was observed at the shelf stations (N1, M1, and M2), where NH4+ concentrations ranged from 45 to 550 nM (Fig. 1d–f). In contrast to the Type I pattern, the Topt of the Type II pattern was equal to or lower than the minimum experimental temperature of 14 °C, showing a continuously decreasing AOR as temperature increased. The Type III pattern was observed at station JLR3 (outer estuary), N2 (shelf), N3 and J1 (basin), for which the Topt of the AOR varied from 20 to 29 °C, with rates decreasing toward both higher and lower temperatures (Fig. 1c, g–i). The NH4+ concentrations of the Type III stations ranged from 14 to 5000 nM. Nevertheless, the highest Topt values were observed at coastal sites with the highest ambient ammonium concentrations (Fig. 1).
Substrate regulates AOR and its thermal optimum temperature
For those stations with low ammonium concentrations, the AOR at in situ temperature increased when the substrate was enriched (AORenriched, additions of 2000 nM 15NH4+) (Fig. 1f, i). Meanwhile, the Topt of the AOR shifted significantly toward higher values (t test, p  Q10Vmax (Supplementary Fig. 2). This criterion was fulfilled in both J1 and JLR4 cases (Fig. 2c–f). We see the positive shift of Topt due to the ammonium addition (up to ~100 nM at J1 station and up to ~10 μM at JLR4) can be closely predicted (Fig. 3c, d; Supplementary Fig. 2). Overall, the DAMM model successfully predicts the entire thermal response curve, including rates and Topt, except when the manipulated temperatures exceed Topt-sat (Fig. 3; Supplementary Fig. 2). AOR drops significantly when temperature is greater than the Topt-sat, so heat-impaired biological enzyme activity27,28 might result in deviations from the relationship between Vmax (Km) values and temperature from the Arrhenius law.
Fig. 3: Validation plot for the rate predictions and observations.

a, b Scatter plot of the predicted rates via the Dual Arrhenius and Michaelis–Menten kinetics model (DAMM model) and the measured rates under different substrate concentrations and temperatures (below the optimum temperature in substrate-saturated conditions, Topt-sat). Linear regressions between the model predictions and the measurements are presented (two-sided t test was used to generate the p value (95% confidence) to measure the strength of correlation coefficient. p values are uncorrected). c, d The rate patterns (dots) against temperature under different substrate concentrations. Curves stand for the predicted rates derived from the DAMM model and the symbols represent the measured rates. The shades denote the uncertainty of model prediction. The dashed black vertical lines represent the Topt-sat. The measured rates in (a, c) are presented as mean values, instead of standard deviation the given bars indicate the variation range of two independent experiments. The measured rates in (b, d) and the predicted rates in (a–d) are expressed as the mean values ± SD (n = 10 in (a, c); n = 48 in (b, d); independent experiments).

Full size image

The substrate-dependent thermal optimum is attributable to the effect of temperature on biochemical kinetics and the structural stability of the enzymes. Increasing temperature promotes catalytic rate, thus, Vmax increases due to increasing kinetic energy of reactants and rates of collision, as well as higher structural flexibility of enzymes27,29. However, higher structural flexibility (lower stability) also results in active sites with a reduced ability for ligand recognition and binding, therefore, lower kinetic efficiency. Accordingly, one important physiological response of an organism to rising temperature will be a reduction in substrate affinity (Supplementary Table 2), and thus higher substrate demands (i.e. higher Km value)25,29,30,31. In other words, higher substrate levels help to compensate for enzyme structural stability losses and so promote growth rates at higher temperature. Note that some other microbes may respond differently to temperature, with Q10Km ≤ Q10Vmax for instance. This may lead to predictable yet unidirectional rate increases in response to warming (without substrate-regulated Topt) until the Topt-sat is reached, regardless of substrate changes.
Similarly, nutrient-dependent Topt has been reported for phytoplankton growth in pure cultures previously. For instance, Thomas et al.32 indicated that the Topt for growth of a marine diatom was a saturating function of major nutrient (nitrate and phosphate) concentration, and that the Topt could decrease by 3–6 °C at low concentrations relative to that at saturated nutrient levels. In addition to studies of pure cultures, field studies have also suggested that organisms may tolerate higher temperature stresses when nutrients are more abundant. For example, kelp (Laminaria saccarina) with high nitrogen reserves have more capacity for thermal adaptation33, while corals with symbionts limited by phosphate are more susceptible to heat-induced bleaching34. Although these examples are functionally and taxonomically distant from AOA and ammonia-oxidizing bacteria (AOB), strong similarities in substrate/nutrient regulation characteristics may imply a similar mechanism of enzymatic thermal responses between chemoautotrophs and photoautotrophs.
Nevertheless, the higher thermal optimum of AO in the estuarine system (e.g., JRL1, JLR2, and JLR3) than in the offshore environment (e.g., N3 and J1) can be explained by a substrate-regulated Topt. Note that field AOR represents explicitly the collective activity of the AO community composed of AOA and AOB, which may have distinctive thermal tolerances and affinities for substrate. Therefore, community structure very likely plays a role in modulating the thermal response patterns of community AOR in the field environments, in addition to substrate concentration.
Rate proportion and community thermal optimum
To further examine to what extent the community structure (proportions of AOA and AOB) might shape the thermal response patterns of community AOR observed in the field, we added allylthiourea (ATU) to inhibit the activity of AOB for rate discrimination (see Methods; Supplementary Discussions). Results showed that the inhibitory efficiency of AOR was gradually reduced with increasing offshore distance (Fig. 4). That is, from the estuary (JLR4, JLR1, JLR2, and JLR3) to the shelf (N1 and N2) and the sea basin (N3), the relative contribution of AOB to the community AOR dropped from as high as ~100% in the upper estuary down to ~70% in the shelf transition zone, and near 0% in the basin. Meanwhile, the AOA/AOB gene copies data (see Supplementary Methods) from estuary to sea basin (Fig. 4) also clearly show that the abundance of AOA relative to AOB increased exponentially with increasing offshore distance. A similar offshore pattern of community distribution was also observed in other regions, such as from the Pearl River estuary to the South China Sea35, and from the freshwater region of the Chesapeake Bay to the coastal and open ocean water column36. This pattern suggests that AOB strongly prefer substrate-replete niches, and vice versa for AOA20,37, agreeing well with our M–M experimental data that the substrate saturation condition for AOB-dominated water at JLR4 was several orders of magnitude higher than that for AOA-dominated water from J1 (Supplementary Table 2). The Km values of AOB in JLR4 varied from 7 to 55 μM in accordance with varying temperatures from 10 to 37 °C, while the Km values of AOA in J1 varied from 13 to 44 nM over a similar temperature range (Supplementary Table 2). Results were supportive of previous pure culture and field studies which showed the minimum ammonium demand for AOB is >1 μM and Km values range from 28 to 4000 μM38,39,40,41, while minimum ammonium demand and Km value for AOA are  Q10Vmax, which also results in a reduction in α during warming for both AOA and AOB. Yet, the relative reduction in AOA specific affinity as temperature increases is more significant (Fig. 5a), suggesting AOAs are more competitive in low temperature environments relative to AOBs, and so may not be favored in a warming ocean. On the other hand, the specific affinity of AOBs is insensitive to temperature change, suggesting their adaptation to nearshore environments with greater temperature fluctuation. The seaward gradient in temperature fluctuations and ammonium concentrations determine the nitrifier community, thus, thermal response pattern of community AOR observed in the field.
Fig. 5: Thermal response projections in near- and offshore regions.

a The thermal responses of specific affinity at the J1 and JLR4 stations. Data are expressed as the mean values ± SD (n = 10 in J1 station; n = 48 in JLR4 station; independent experiments). b Normalized warming-driven variations in ammonia oxidation rates. Rate changed (%) is relative to the ammonia oxidation rate (AOR) at in situ temperature. The mean increase (nearshore hollow dots) is denoted by the dashed line and the mean reduction (offshore, solid dots) is denoted by the solid black line. The shaded area represents the 4 °C increase in temperature mentioned in the IPCC study. Note that the stations where the surface salinities are lower than 32 are classified as nearshore station, and the others are classified as offshore stations. The data in (b) are presented as mean values, instead of standard deviation the given bars indicate the variation range of two independent experiments.

Full size image

To predict the trends of AOR in different geographic spaces in warming ocean, we compile the available marine AOR data to examine the AOR changes empirically. If we assume the biogeographic distribution of AO community remains unchanged and consider solely the warming effect on AOR relative to the onsite temperature, we found the thermal responses of AOR in nearshore and offshore are quite different (Fig. 5b). More specifically, the higher Topt of these AO communities in nearshore regimes allows ocean warming to promote coastal AOR when the temperature change increment is More

1.
Flemming, H.-C. et al. Biofilms: an emergent form of bacterial life. Nat. Rev. Microbiol. 14, 563. https://doi.org/10.1038/nrmicro.2016.94 (2016).
CAS  Article  PubMed  Google Scholar 
2.
Boddey, J. A., Flegg, C. P., Day, C. J., Beacham, I. R. & Peak, I. R. Temperature-regulated microcolony formation by Burkholderia pseudomallei requires pilA and enhances association with cultured human cells. Infect. Immunity 74, 5374–5381. https://doi.org/10.1128/iai.00569-06 (2006).
CAS  Article  Google Scholar 

3.
Lister, J. L. & Horswill, A. R. Staphylococcus aureus biofilms: recent developments in biofilm dispersal. Front. Cell. Infect. Microbiol. https://doi.org/10.3389/fcimb.2014.00178 (2014).
Article  PubMed  PubMed Central  Google Scholar 

4.
Whitchurch, C. B., Tolker-Nielsen, T., Ragas, P. C. & Mattick, J. S. Extracellular DNA required for bacterial biofilm formation. Science 295, 1487–1487. https://doi.org/10.1126/science.295.5559.1487 (2002).
CAS  Article  PubMed  Google Scholar 

5.
Foster, T. J., Geoghegan, J. A., Ganesh, V. K. & Höök, M. Adhesion, invasion and evasion: the many functions of the surface proteins of Staphylococcus aureus. Nat. Rev. Microbiol. 12, 49. https://doi.org/10.1038/nrmicro3161 (2013).
CAS  Article  Google Scholar 

6.
Mack, D. et al. The intercellular adhesin involved in biofilm accumulation of Staphylococcus epidermidis is a linear beta-1,6-linked glucosaminoglycan: purification and structural analysis. J. Bacteriol. 178, 175–183 (1996).
CAS  Article  Google Scholar 

7.
Limoli, D. H., Jones, C. J. & Wozniak, D. J. Bacterial extracellular polysaccharides in biofilm formation and function. Microbiol. Spectrum. https://doi.org/10.1128/microbiolspec.MB-0011-2014 (2015).
Article  Google Scholar 

8.
Gallaher, T. K., Wu, S., Webster, P. & Aguilera, R. Identification of biofilm proteins in non-typeable Haemophilus Influenzae. BMC Microbiol. 6, 65. https://doi.org/10.1186/1471-2180-6-65 (2006).
CAS  Article  PubMed  PubMed Central  Google Scholar 

9.
Hu, W. et al. DNA builds and strengthens the extracellular matrix in Myxococcus xanthus biofilms by interacting with exopolysaccharides. PLoS ONE 7, e51905. https://doi.org/10.1371/journal.pone.0051905 (2012).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

10.
Boles, B. R. & Horswill, A. R. Staphylococcal biofilm disassembly. Trends Microbiol. 19, 449–455. https://doi.org/10.1016/j.tim.2011.06.004 (2011).
CAS  Article  PubMed  PubMed Central  Google Scholar 

11.
Reen, F. J. et al. Bile signalling promotes chronic respiratory infections and antibiotic tolerance. Sci. Rep. 6, 29768 (2016).
ADS  CAS  Article  Google Scholar 

12.
Duanis-Assaf, D., Steinberg, D., Chai, Y. & Shemesh, M. The LuxS based quorum sensing governs lactose induced biofilm formation by Bacillus subtilis. Front. Microbiol. https://doi.org/10.3389/fmicb.2015.01517 (2016).
Article  PubMed  PubMed Central  Google Scholar 

13.
Le, K. Y. & Otto, M. Quorum-sensing regulation in staphylococci-an overview. Front. Microbiol. 6, 1174–1174. https://doi.org/10.3389/fmicb.2015.01174 (2015).
Article  PubMed  PubMed Central  Google Scholar 

14.
Qi, L. et al. Relationship between antibiotic resistance, biofilm formation, and biofilm-specific resistance in Acinetobacter baumannii. Front. Microbiol. 7, 483–483. https://doi.org/10.3389/fmicb.2016.00483 (2016).
Article  PubMed  PubMed Central  Google Scholar 

15.
O’Callaghan, A. & van Sinderen, D. Bifidobacteria and their role as members of the human gut microbiota. Front. Microbiol. 7, 925–925. https://doi.org/10.3389/fmicb.2016.00925 (2016).
Article  PubMed  PubMed Central  Google Scholar 

16.
Hill, C. et al. The International Scientific Association for Probiotics and Prebiotics consensus statement on the scope and appropriate use of the term probiotic. Nat. Rev. Gastroenterol. Hepatol. 11, 506. https://doi.org/10.1038/nrgastro.2014.66 (2014).
Article  PubMed  Google Scholar 

17.
Sánchez, B., Ruiz, L., Gueimonde, M., Ruas-Madiedo, P. & Margolles, A. Adaptation of bifidobacteria to the gastrointestinal tract and functional consequences. Pharmacol. Res. 69, 127–136. https://doi.org/10.1016/j.phrs.2012.11.004 (2013).
Article  PubMed  Google Scholar 

18.
Holm, R., Müllertz, A. & Mu, H. Bile salts and their importance for drug absorption. Int. J. Pharm. 453, 44–55. https://doi.org/10.1016/j.ijpharm.2013.04.003 (2013).
CAS  Article  PubMed  Google Scholar 

19.
Islam, K. B. M. S. et al. Bile acid is a host factor that regulates the composition of the cecal microbiota in rats. Gastroenterology 141, 1773–1781. https://doi.org/10.1053/j.gastro.2011.07.046 (2011).
CAS  Article  PubMed  Google Scholar 

20.
Begley, M., Gahan, C. G. & Hill, C. The interaction between bacteria and bile. FEMS Microbiol. Rev. 29, 625–651. https://doi.org/10.1016/j.femsre.2004.09.003 (2005).
CAS  Article  PubMed  Google Scholar 

21.
Ruiz, L., Margolles, A. & Sanchez, B. Bile resistance mechanisms in Lactobacillus and Bifidobacterium. Front. Microbiol. 4, 396. https://doi.org/10.3389/fmicb.2013.00396 (2013).
Article  PubMed  PubMed Central  Google Scholar 

22.
Price, C. E., Reid, S. J., Driessen, A. J. & Abratt, V. R. The Bifidobacterium longum NCIMB 702259T ctr gene codes for a novel cholate transporter. Appl. Environ. Microbiol. 72, 923–926. https://doi.org/10.1128/aem.72.1.923-926.2006 (2006).
CAS  Article  PubMed  PubMed Central  Google Scholar 

23.
Gueimonde, M., Garrigues, C., van Sinderen, D., de los Reyes-Gavilan, C. G. & Margolles, A. Bile-inducible efflux transporter from Bifidobacterium longum NCC2705, conferring bile resistance. Appl. Environ. Microbiol. 75, 3153–3160. https://doi.org/10.1128/aem.00172-09 (2009).
CAS  Article  PubMed  PubMed Central  Google Scholar 

24.
Ruiz, L., Zomer, A., O’Connell-Motherway, M., van Sinderen, D. & Margolles, A. Discovering novel bile protection systems in Bifidobacterium breve UCC2003 through functional genomics. Appl. Environ. Microbiol. 78, 1123–1131. https://doi.org/10.1128/aem.06060-11 (2012).
CAS  Article  PubMed  PubMed Central  Google Scholar 

25.
Ruiz, L., Sánchez, B., Ruas-Madiedo, P., De Los Reyes-Gavilán, C. G. & Margolles, A. Cell envelope changes in Bifidobacterium animalis ssp. lactis as a response to bile. FEMS Microbiol. Lett. 274, 316–322. https://doi.org/10.1111/j.1574-6968.2007.00854.x (2007).
CAS  Article  PubMed  Google Scholar 

26.
Gómez Zavaglia, A., Kociubinski, G., Pérez, P., Disalvo, E. & De Antoni, G. Effect of bile on the lipid composition and surface properties of bifidobacteria. J. Appl. Microbiol. 93, 794–799. https://doi.org/10.1046/j.1365-2672.2002.01747.x (2002).
Article  PubMed  Google Scholar 

27.
An, H. et al. Integrated transcriptomic and proteomic analysis of the bile stress response in a centenarian-originated probiotic Bifidobacterium longum BBMN68. Mol. Cell. Proteom. 13, 2558–2572. https://doi.org/10.1074/mcp.M114.039156 (2014).
CAS  Article  Google Scholar 

28.
Sanchez, B., de los Reyes-Gavilan, C. G. & Margolles, A. The F1F0-ATPase of Bifidobacterium animalis is involved in bile tolerance. Environ. Microbiol. 8, 1825–1833. https://doi.org/10.1111/j.1462-2920.2006.01067.x (2006).
CAS  Article  PubMed  Google Scholar 

29.
Sanchez, B., Noriega, L., Ruas-Madiedo, P., de los Reyes-Gavilan, C. G. & Margolles, A. Acquired resistance to bile increases fructose-6-phosphate phosphoketolase activity in Bifidobacterium. FEMS Microbiol. Lett. 235, 35–41. https://doi.org/10.1016/j.femsle.2004.04.009 (2004).
CAS  Article  PubMed  Google Scholar 

30.
Sanchez, B. et al. Proteomic analysis of global changes in protein expression during bile salt exposure of Bifidobacterium longum NCIMB 8809. J. Bacteriol. 187, 5799–5808. https://doi.org/10.1128/jb.187.16.5799-5808.2005 (2005).
CAS  Article  PubMed  PubMed Central  Google Scholar 

31.
Noriega, L., Gueimonde, M., Sanchez, B., Margolles, A. & de los Reyes-Gavilan, C. G. Effect of the adaptation to high bile salts concentrations on glycosidic activity, survival at low PH and cross-resistance to bile salts in Bifidobacterium. Int. J. Food Microbiol. 94, 79–86. https://doi.org/10.1016/j.ijfoodmicro.2004.01.003 (2004).
CAS  Article  PubMed  Google Scholar 

32.
Tanaka, H., Hashiba, H., Kok, J. & Mierau, I. Bile salt hydrolase of Bifidobacterium longum-biochemical and genetic characterization. Appl. Environ. Microbiol. 66, 2502–2512. https://doi.org/10.1128/aem.66.6.2502-2512.2000 (2000).
CAS  Article  PubMed  PubMed Central  Google Scholar 

33.
Noriega, L., Cuevas, I., Margolles, A. & de los Reyes-Gavilán, C. G. Deconjugation and bile salts hydrolase activity by Bifidobacterium strains with acquired resistance to bile. Int. Dairy J. 16, 850–855. https://doi.org/10.1016/j.idairyj.2005.09.008 (2006).
CAS  Article  Google Scholar 

34.
Ambalam, P., Kondepudi, K. K., Nilsson, I., Wadstrom, T. & Ljungh, A. Bile enhances cell surface hydrophobicity and biofilm formation of bifidobacteria. Appl. Biochem. Biotechnol. 172, 1970–1981. https://doi.org/10.1007/s12010-013-0596-1 (2014).
CAS  Article  PubMed  Google Scholar 

35.
Pumbwe, L. et al. Bile salts enhance bacterial co-aggregation, bacterial-intestinal epithelial cell adhesion, biofilm formation and antimicrobial resistance of Bacteroides fragilis. Microb. Pathog. 43, 78–87. https://doi.org/10.1016/j.micpath.2007.04.002 (2007).
CAS  Article  PubMed  Google Scholar 

36.
Lebeer, S., Verhoeven, T. L., Perea Velez, M., Vanderleyden, J. & De Keersmaecker, S. C. Impact of environmental and genetic factors on biofilm formation by the probiotic strain Lactobacillus rhamnosus GG. Appl. Environ. Microbiol. 73, 6768–6775. https://doi.org/10.1128/aem.01393-07 (2007).
CAS  Article  PubMed  PubMed Central  Google Scholar 

37.
Macfarlane, S. & Macfarlane, G. T. Composition and metabolic activities of bacterial biofilms colonizing food residues in the human gut. Appl. Environ. Microbiol. 72, 6204–6211. https://doi.org/10.1128/aem.00754-06 (2006).
CAS  Article  PubMed  PubMed Central  Google Scholar 

38.
Macfarlane, M. J. H. G. T. M. S. Bacterial growth and metabolism on surfaces in the large intestine. Microb. Ecol. Health Dis. 12, 64–72. https://doi.org/10.1080/089106000750060314 (2000).
Article  Google Scholar 

39.
Pereira, C. S., Thompson, J. A. & Xavier, K. B. AI-2-mediated signalling in bacteria. FEMS Microbiol. Rev. 37, 156–181. https://doi.org/10.1111/j.1574-6976.2012.00345.x (2013).
CAS  Article  PubMed  Google Scholar 

40.
Hammer, B. K. & Bassler, B. L. Quorum sensing controls biofilm formation in Vibrio cholerae. Mol. Microbiol. 50, 101–104. https://doi.org/10.1046/j.1365-2958.2003.03688.x (2003).
CAS  Article  PubMed  Google Scholar 

41.
Solano, C., Echeverz, M. & Lasa, I. Biofilm dispersion and quorum sensing. Curr. Opin. Microbiol. 18, 96–104. https://doi.org/10.1016/j.mib.2014.02.008 (2014).
CAS  Article  PubMed  Google Scholar 

42.
Sun, Z., He, X., Brancaccio, V. F., Yuan, J. & Riedel, C. U. Bifidobacteria exhibit LuxS-dependent autoinducer 2 activity and biofilm formation. PLoS ONE 9, e88260. https://doi.org/10.1371/journal.pone.0088260 (2014).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

43.
Christiaen, S. E. et al. Autoinducer-2 plays a crucial role in gut colonization and probiotic functionality of Bifidobacterium breve UCC2003. PLoS ONE 9, e98111. https://doi.org/10.1371/journal.pone.0098111 (2014).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

44.
Yuan, J. et al. A proteome reference map and proteomic analysis of Bifidobacterium longum NCC2705. Mol. Cell. Proteom. 5, 1105–1118. https://doi.org/10.1074/mcp.M500410-MCP200 (2006).
CAS  Article  Google Scholar 

45.
D’Urzo, N. et al. Acidic pH strongly enhances in vitro biofilm formation by a subset of hypervirulent ST-17 Streptococcus agalactiae strains. Appl. Environ. Microbiol. 80, 2176–2185. https://doi.org/10.1128/aem.03627-13 (2014).
Article  PubMed  PubMed Central  Google Scholar 

46.
O’Neill, E. et al. Association between methicillin susceptibility and biofilm regulation in Staphylococcus aureus isolates from device-related infections. J. Clin. Microbiol. 45, 1379–1388. https://doi.org/10.1128/jcm.02280-06 (2007).
CAS  Article  PubMed  PubMed Central  Google Scholar 

47.
Hung, D. T., Zhu, J., Sturtevant, D. & Mekalanos, J. J. Bile acids stimulate biofilm formation in Vibrio cholerae. Mol. Microbiol. 59, 193–201. https://doi.org/10.1111/j.1365-2958.2005.04846.x (2006).
CAS  Article  PubMed  Google Scholar 

48.
Maze, A., O’Connell-Motherway, M., Fitzgerald, G. F., Deutscher, J. & van Sinderen, D. Identification and characterization of a fructose phosphotransferase system in Bifidobacterium breve UCC2003. Appl. Environ. Microbiol. 73, 545–553. https://doi.org/10.1128/aem.01496-06 (2007).
CAS  Article  PubMed  Google Scholar 

49.
Lanigan, N., Bottacini, F., Casey, P. G., O’Connell Motherway, M. & van Sinderen, D. Genome-wide search for genes required for bifidobacterial growth under iron-limitation. Front. Microbiol. 8, 964. https://doi.org/10.3389/fmicb.2017.00964 (2017).
Article  PubMed  PubMed Central  Google Scholar 

50.
Ruiz, L., Motherway, M. O., Lanigan, N. & van Sinderen, D. Transposon mutagenesis in Bifidobacterium breve: construction and characterization of a Tn5 transposon mutant library for Bifidobacterium breve UCC2003. PLoS ONE 8, e64699. https://doi.org/10.1371/journal.pone.0064699 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

51.
Fanning, S. et al. Bifidobacterial surface-exopolysaccharide facilitates commensal-host interaction through immune modulation and pathogen protection. Proc. Natl. Acad. Sci. USA. 109, 2108–2113. https://doi.org/10.1073/pnas.1115621109 (2012).
ADS  Article  PubMed  Google Scholar 

52.
Alonso-Casajus, N. et al. Glycogen phosphorylase, the product of the glgP Gene, catalyzes glycogen breakdown by removing glucose units from the nonreducing ends in Escherichia coli. J. Bacteriol. 188, 5266–5272. https://doi.org/10.1128/jb.01566-05 (2006).
CAS  Article  PubMed  PubMed Central  Google Scholar 

53.
Nocek, B. P., Gillner, D. M., Fan, Y., Holz, R. C. & Joachimiak, A. Structural basis for catalysis by the mono- and dimetalated forms of the dapE-encoded N-succinyl-L, L-diaminopimelic acid desuccinylase. J. Mol. Biol. 397, 617–626. https://doi.org/10.1016/j.jmb.2010.01.062 (2010).
CAS  Article  PubMed  PubMed Central  Google Scholar 

54.
Ethapa, T. et al. Multiple factors modulate biofilm formation by the anaerobic pathogen Clostridium difficile. J. Bacteriol. 195, 545–555. https://doi.org/10.1128/jb.01980-12 (2013).
Article  PubMed  PubMed Central  Google Scholar 

55.
Donlan, R. M. Biofilms: microbial life on surfaces. Emerg. Infect. Dis. 8, 881–890. https://doi.org/10.3201/eid0809.020063 (2002).
Article  PubMed  PubMed Central  Google Scholar 

56.
Bottacini, F., Ventura, M., van Sinderen, D. & O’Connell Motherway, M. Diversity, ecology and intestinal function of bifidobacteria. Microbial Cell Fact. https://doi.org/10.1186/1475-2859-13-s1-s4 (2014).
Article  Google Scholar 

57.
Legrand-Defretin, V., Juste, C., Henry, R. & Corring, T. Ion-pair high-performance liquid chromatography of bile salt conjugates: Application to pig bile. Lipids 26, 578–583. https://doi.org/10.1007/bf02536421 (1991).
CAS  Article  PubMed  Google Scholar 

58.
Sanchez, B. et al. Adaptation and response of Bifidobacterium animalis subsp. lactis to bile: a proteomic and physiological approach. Appl. Environ. Microbiol. 73, 6757–6767. https://doi.org/10.1128/aem.00637-07 (2007).
CAS  Article  PubMed  PubMed Central  Google Scholar 

59.
Ruas-Madiedo, P., Hernandez-Barranco, A., Margolles, A. & de los Reyes-Gavilan, C. G. A bile salt-resistant derivative of Bifidobacterium animalis has an altered fermentation pattern when grown on glucose and maltose. Appl. Environ. Microbiol. 71, 6564–6570. https://doi.org/10.1128/aem.71.11.6564-6570.2005 (2005).
CAS  Article  PubMed  PubMed Central  Google Scholar 

60.
Ruiz, L. et al. The cell-envelope proteome of Bifidobacterium longum in an in vitro bile environment. Microbiology 155, 957–967. https://doi.org/10.1099/mic.0.024273-0 (2009).
CAS  Article  PubMed  Google Scholar 

61.
Wang, G. et al. Functional role of oppA encoding an oligopeptide-binding protein from Lactobacillus salivarius Ren in bile tolerance. J. Ind. Microbiol. Biotechnol. 42, 1167–1174. https://doi.org/10.1007/s10295-015-1634-5 (2015).
CAS  Article  PubMed  Google Scholar 

62.
Lebeer, S. et al. Impact of luxS and suppressor mutations on the gastrointestinal transit of Lactobacillus rhamnosus GG. Appl. Environ. Microbiol. 74, 4711–4718. https://doi.org/10.1128/aem.00133-08 (2008).
CAS  Article  PubMed  PubMed Central  Google Scholar 

63.
Wilson, C. M., Aggio, R. B., O’Toole, P. W., Villas-Boas, S. & Tannock, G. W. Transcriptional and metabolomic consequences of LuxS inactivation reveal a metabolic rather than quorum-sensing role for LuxS in Lactobacillus reuteri 100–23. J. Bacteriol. 194, 1743–1746. https://doi.org/10.1128/jb.06318-11 (2012).
Article  PubMed  PubMed Central  Google Scholar 

64.
Rezzonico, F. & Duffy, B. Lack of genomic evidence of AI-2 receptors suggests a non-quorum sensing role for luxS in most bacteria. BMC Microbiol. 8, 154. https://doi.org/10.1186/1471-2180-8-154 (2008).
CAS  Article  PubMed  PubMed Central  Google Scholar 

65.
Giddens, S. R. et al. Mutational activation of niche-specific genes provides insight into regulatory networks and bacterial function in a complex environment. Proc. Natl. Acad. Sci. USA 104, 18247. https://doi.org/10.1073/pnas.0706739104 (2007).
ADS  Article  PubMed  Google Scholar 

66.
Thompson, A. P. et al. Glycolysis and pyrimidine biosynthesis are required for replication of adherent–invasive Escherichia coli in macrophages. Microbiology 162, 954–965. https://doi.org/10.1099/mic.0.000289 (2016).
CAS  Article  PubMed  Google Scholar 

67.
Sambrook, J. & Russell, D. Molecular Cloning: A Laboratory Manual 2001 Cold Spring Harbor (Cold Spring Harbor Laboratory Press, New York, 2001).
Google Scholar 

68.
O’Riordan, K. & Fitzgerald, G. F. Molecular characterisation of a 575-kb cryptic plasmid from Bifidobacterium breve NCFB 2258 and determination of mode of replication. FEMS Microbiol. Lett. 174, 285–294. https://doi.org/10.1111/j.1574-6968.1999.tb13581.x (1999).
CAS  Article  PubMed  Google Scholar 

69.
Alessandri, G. et al. Ability of bifidobacteria to metabolize chitin-glucan and its impact on the gut microbiota. Sci. Rep. 9, 5755–5755. https://doi.org/10.1038/s41598-019-42257-z (2019).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

70.
Duranti, S. et al. Bifidobacterium bifidum and the infant gut microbiota: an intriguing case of microbe-host co-evolution. Environ. Microbiol. 21, 3683–3695. https://doi.org/10.1111/1462-2920.14705 (2019).
CAS  Article  PubMed  Google Scholar 

71.
Fredheim, E. G. et al. Biofilm formation by Staphylococcus haemolyticus. J Clin Microbiol 47, 1172–1180. https://doi.org/10.1128/jcm.01891-08 (2009).
CAS  Article  PubMed  PubMed Central  Google Scholar  More