Philippa Kaur

1.
Salmond, G. P. C. & Fineran, P. C. A century of the phage: past, present and future. Nat. Rev. Microbiol. 13, 777–786 (2015).
CAS  PubMed  Google Scholar 
2.
Al-Shayeb, B. et al. Clades of huge phage from across Earth’s ecosystems. Nature 578, 425–431 (2020).
CAS  PubMed  PubMed Central  Google Scholar 

3.
Mann, N. H., Cook, A., Millard, A., Bailey, S. & Clokie, M. Bacterial photosynthesis genes in a virus. Nature 424, 741–741 (2003).
CAS  PubMed  Google Scholar 

4.
Sharon, I. et al. Photosystem I gene cassettes are present in marine virus genomes. Nature 461, 258–262 (2009).
CAS  PubMed  PubMed Central  Google Scholar 

5.
Anantharaman, K. et al. Sulfur oxidation genes in diverse deep-sea viruses. Science 344, 757–760 (2014).
CAS  PubMed  Google Scholar 

6.
Roux, S. et al. Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses. Nature 537, 689–693 (2016).
CAS  PubMed  Google Scholar 

7.
Ahlgren, N. A., Fuchsman, C. A., Rocap, G. & Fuhrman, J. A. Discovery of several novel, widespread, and ecologically distinct marine Thaumarchaeota viruses that encode amoC nitrification genes. ISME J. 13, 618–631 (2019).
CAS  PubMed  Google Scholar 

8.
Cicerone, R. J. & Oremland, R. S. Biogeochemical aspects of atmospheric methane. Global Biogeochem. Cycles 2, 299–327 (1988).
CAS  Google Scholar 

9.
Dunfield, P. F. et al. Methane oxidation by an extremely acidophilic bacterium of the phylum Verrucomicrobia. Nature 450, 879–882 (2007).
CAS  PubMed  Google Scholar 

10.
Op den Camp, H. J. M. et al. Environmental, genomic and taxonomic perspectives on methanotrophic Verrucomicrobia. Environ. Microbiol. Rep. 1, 293–306 (2009).
CAS  PubMed  Google Scholar 

11.
Sirajuddin, S. & Rosenzweig, A. C. Enzymatic oxidation of methane. Biochemistry 54, 2283–2294 (2015).
CAS  PubMed  PubMed Central  Google Scholar 

12.
Lieberman, R. L. & Rosenzweig, A. C. Crystal structure of a membrane-bound metalloenzyme that catalyses the biological oxidation of methane. Nature 434, 177–182 (2005).
CAS  PubMed  Google Scholar 

13.
Semrau, J. D., DiSpirito, A. A. & Yoon, S. Methanotrophs and copper. FEMS Microbiol. Rev. 34, 496–531 (2010).
CAS  PubMed  Google Scholar 

14.
Lieberman, R. L. & Rosenzweig, A. C. Biological methane oxidation: regulation, biochemistry, and active site structure of particulate methane monooxygenase. Crit. Rev. Biochem. Mol. Biol. 39, 147–164 (2004).
CAS  PubMed  Google Scholar 

15.
Stolyar, S., Costello, A. M., Peeples, T. L. & Lidstrom, M. E. Role of multiple gene copies in particulate methane monooxygenase activity in the methane-oxidizing bacterium Methylococcus capsulatus Bath. Microbiology 145, 1235–1244 (1999).
CAS  PubMed  Google Scholar 

16.
Mayr, M. J., Zimmermann, M., Dey, J., Wehrli, B. & Bürgmann, H. Lake mixing regime selects methane-oxidation kinetics of the methanotroph assemblage. Biogeosciences https://doi.org/10.5194/bg-2019-482 (2020).

17.
Bastviken, D., Cole, J., Pace, M. & Tranvik, L. Methane emissions from lakes: dependence of lake characteristics, two regional assessments, and a global estimate. Global Biogeochem. Cycles 18, GB4009 (2004).
Google Scholar 

18.
Falz, K. Z. et al. Vertical distribution of methanogens in the anoxic sediment of Rotsee (Switzerland). Appl. Environ. Microbiol. 65, 2402–2408 (1999).
CAS  Google Scholar 

19.
Linz, A. M. et al. Freshwater carbon and nutrient cycles revealed through reconstructed population genomes. PeerJ 6, e6075 (2018).
PubMed  PubMed Central  Google Scholar 

20.
Arriaga, D. et al. The co-importance of physical mixing and biogeochemical consumption in controlling water cap oxygen levels in Base Mine Lake. Appl. Geochem. 111, 104442 (2019).
CAS  Google Scholar 

21.
Risacher, F. F. et al. The interplay of methane and ammonia as key oxygen consuming constituents in early stage development of Base Mine Lake, the first demonstration oil sands pit lake. Appl. Geochem. 93, 49–59 (2018).
CAS  Google Scholar 

22.
Mori, J. F. et al. Putative mixotrophic nitrifying–denitrifying Gammaproteobacteria implicated in nitrogen cycling within the ammonia/oxygen transition zone of an oil sands pit lake. Front. Microbiol. 10, 2435 (2019).
PubMed  PubMed Central  Google Scholar 

23.
Slater, G. F. et al. Methane fluxes and consumption in an oil sands tailings end pit lake. American Geophysical Union Fall Meeting 2017 abstr. B43B-2130 (2017).

24.
Hoefman, S. et al. Methyloparacoccus murrellii gen. nov., sp. nov., a methanotroph isolated from pond water. Int. J. Syst. Evol. Microbiol. 64, 2100–2107 (2014).
CAS  PubMed  Google Scholar 

25.
An, D. et al. Metagenomics of hydrocarbon resource environments indicates aerobic taxa and genes to be unexpectedly common. Environ. Sci. Technol. 47, 10708–10717 (2013).
CAS  PubMed  PubMed Central  Google Scholar 

26.
Saidi-Mehrabad, A. et al. Methanotrophic bacteria in oil sands tailings ponds of northern Alberta. ISME J. 7, 908–921 (2013).
CAS  PubMed  Google Scholar 

27.
Tan, B. et al. Comparative analysis of metagenomes from three methanogenic hydrocarbon-degrading enrichment cultures with 41 environmental samples. ISME J. 9, 2028–2045 (2015).
PubMed  PubMed Central  Google Scholar 

28.
Rochman, F. F. et al. Benzene and naphthalene degrading bacterial communities in an oil sands tailings pond. Front. Microbiol. 8, 1845 (2017).
PubMed  PubMed Central  Google Scholar 

29.
Liew, E. F., Tong, D., Coleman, N. V. & Holmes, A. J. Mutagenesis of the hydrocarbon monooxygenase indicates a metal centre in subunit-C, and not subunit-B, is essential for copper-containing membrane monooxygenase activity. Microbiology 160, 1267–1277 (2014).
CAS  PubMed  Google Scholar 

30.
Ross, M. O. et al. Particulate methane monooxygenase contains only mononuclear copper centers. Science 364, 566–570 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

31.
Mihara, T. et al. Linking virus genomes with host taxonomy. Viruses 8, 66 (2016).
PubMed  PubMed Central  Google Scholar 

32.
Nishimura, Y. et al. ViPTree: the viral proteomic tree server. Bioinformatics 33, 2379–2380 (2017).
CAS  PubMed  Google Scholar 

33.
Lindell, D., Jaffe, J. D., Johnson, Z. I., Church, G. M. & Chisholm, S. W. Photosynthesis genes in marine viruses yield proteins during host infection. Nature 438, 86–89 (2005).
CAS  PubMed  Google Scholar 

34.
Thompson, L. R. et al. Phage auxiliary metabolic genes and the redirection of cyanobacterial host carbon metabolism. Proc. Natl Acad. Sci. USA 108, E757–E764 (2011).
CAS  PubMed  Google Scholar 

35.
Sullivan, M. B. et al. Genomic analysis of oceanic cyanobacterial myoviruses compared with T4-like myoviruses from diverse hosts and environments. Environ. Microbiol. 12, 3035–3056 (2010).
CAS  PubMed  PubMed Central  Google Scholar 

36.
Tyutikov, F. M., Bespalova, I. A., Rebentish, B. A., Aleksandrushkina, N. N. & Krivisky, A. S. Bacteriophages of methanotrophic bacteria. J. Bacteriol. 144, 375–381 (1980).
CAS  PubMed  PubMed Central  Google Scholar 

37.
Tyutikov, F. M. et al. Bacteriophages of methanotrophs isolated from fish. Appl. Environ. Microbiol. 46, 917–924 (1983).
CAS  PubMed  PubMed Central  Google Scholar 

38.
Emerson, J. B. et al. Host-linked soil viral ecology along a permafrost thaw gradient. Nat. Microbiol. 3, 870–880 (2018).
CAS  PubMed  PubMed Central  Google Scholar 

39.
Paez-Espino, D. et al. Uncovering Earth’s virome. Nature 536, 425–430 (2016).
CAS  PubMed  Google Scholar 

40.
Chen, L.-X., Anantharaman, K., Shaiber, A., Eren, A. M. & Banfield, J. F. Accurate and complete genomes from metagenomes. Genome Res. 30, 315–333 (2020).
PubMed  PubMed Central  Google Scholar 

41.
Ro, S. Y. et al. Native top-down mass spectrometry provides insights into the copper centers of membrane-bound methane monooxygenase. Nat. Commun. 10, 2675 (2019).
PubMed  PubMed Central  Google Scholar 

42.
Ro, S. Y. et al. From micelles to bicelles: effect of the membrane on particulate methane monooxygenase activity. J. Biol. Chem. 293, 10457–10465 (2018).
PubMed  PubMed Central  Google Scholar 

43.
Lee, J. Y., Li, Z. & Miller, E. S. Vibrio phage KVP40 encodes a functional NAD+ salvage pathway. J. Bacteriol. 199, e00855-16 (2017).
PubMed  PubMed Central  Google Scholar 

44.
Stolyar, S., Franke, M. & Lidstrom, M. E. Expression of individual copies of Methylococcus capsulatus bath particulate methane monooxygenase genes. J. Bacteriol. 183, 1810–1812 (2001).
CAS  PubMed  PubMed Central  Google Scholar 

45.
Erikstad, H.-A., Jensen, S., Keen, T. J. & Birkeland, N.-K. Differential expression of particulate methane monooxygenase genes in the verrucomicrobial methanotroph ‘Methylacidiphilum kamchatkense’ Kam1. Extremophiles 16, 405–409 (2012).
CAS  PubMed  Google Scholar 

46.
Berube, P. M., Samudrala, R. & Stahl, D. A. Transcription of all amoC copies is associated with recovery of Nitrosomonas europaea from ammonia starvation. J. Bacteriol. 189, 3935–3944 (2007).
CAS  PubMed  PubMed Central  Google Scholar 

47.
Günthel, M. et al. Contribution of oxic methane production to surface methane emission in lakes and its global importance. Nat. Commun. 10, 5497 (2019).
PubMed  PubMed Central  Google Scholar 

48.
Bižić, M. et al. Aquatic and terrestrial cyanobacteria produce methane. Sci. Adv. 6, eaax5343 (2020).
PubMed  PubMed Central  Google Scholar 

49.
Whaley-Martin, K. et al. The potential role of Halothiobacillus spp. in sulfur oxidation and acid generation in circum-neutral mine tailings reservoirs. Front. Microbiol. 10, 297 (2019).
PubMed  PubMed Central  Google Scholar 

50.
Bendall, M. L. et al. Genome-wide selective sweeps and gene-specific sweeps in natural bacterial populations. ISME J. 10, 1589–1601 (2016).
PubMed  PubMed Central  Google Scholar 

51.
Bushnell, B. BBTools: a suite of fast, multithreaded bioinformatics tools designed for analysis of DNA and RNA sequence data (Joint Genome Institute, 2018); https://jgi.doe.gov/data-and-tools/bbtools

52.
Peng, Y., Leung, H. C. M., Yiu, S. M. & Chin, F. Y. L. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 28, 1420–1428 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

53.
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

54.
Kang, D. D., Froula, J., Egan, R. & Wang, Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 3, e1165 (2015).
PubMed  PubMed Central  Google Scholar 

55.
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 11, 119 (2010).
Google Scholar 

56.
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).
CAS  PubMed  PubMed Central  Google Scholar 

57.
Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y. & Morishima, K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45, D353–D361 (2017).
CAS  PubMed  PubMed Central  Google Scholar 

58.
Suzek, B. E., Huang, H., McGarvey, P., Mazumder, R. & Wu, C. H. UniRef: comprehensive and non-redundant UniProt reference clusters. Bioinformatics 23, 1282–1288 (2007).
CAS  PubMed  Google Scholar 

59.
Apweiler, R. et al. UniProt: the Universal Protein knowledgebase. Nucleic Acids Res. 32, D115–D119 (2004).
CAS  PubMed  PubMed Central  Google Scholar 

60.
Lowe, T. M. & Eddy, S. R. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 25, 955–964 (1997).
CAS  PubMed  PubMed Central  Google Scholar 

61.
Brown, C. T. et al. Unusual biology across a group comprising more than 15% of domain bacteria. Nature 523, 208–211 (2015).
CAS  PubMed  Google Scholar 

62.
Johnson, L. S., Eddy, S. R. & Portugaly, E. Hidden Markov model speed heuristic and iterative HMM search procedure. BMC Bioinform. 11, 431 (2010).
Google Scholar 

63.
Haft, D. H., Selengut, J. D. & White, O. The TIGRFAMs database of protein families. Nucleic Acids Res. 31, 371–373 (2003).
CAS  PubMed  PubMed Central  Google Scholar 

64.
Pruitt, K. D., Tatusova, T. & Maglott, D. R. NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 35, D61–D65 (2007).
CAS  PubMed  Google Scholar 

65.
Huang, Y., Niu, B., Gao, Y., Fu, L. & Li, W. CD-HIT suite: a web server for clustering and comparing biological sequences. Bioinformatics 26, 680–682 (2010).
CAS  PubMed  PubMed Central  Google Scholar 

66.
Bushnell, B. BBMap: a fast, accurate, splice-aware aligner. In 9th Annual Genomics of Energy & Environment Meeting (2014).

67.
Brown, C. T., Olm, M. R., Thomas, B. C. & Banfield, J. F. Measurement of bacterial replication rates in microbial communities. Nat. Biotechnol. 34, 1256–1263 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

68.
Chen, L.-X. et al. Candidate phyla radiation roizmanbacteria from hot springs have novel and unexpectedly abundant CRISPR-Cas systems. Front. Microbiol. 10, 928 (2019).
PubMed  PubMed Central  Google Scholar 

69.
Kearse, M. et al. Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649 (2012).
PubMed  PubMed Central  Google Scholar 

70.
Yoon, S.-H., Ha, S.-M., Lim, J., Kwon, S. & Chun, J. A large-scale evaluation of algorithms to calculate average nucleotide identity. Antonie Van Leeuwenhoek 110, 1281–1286 (2017).
CAS  PubMed  Google Scholar 

71.
Zhu, J. et al. Microbiology and potential applications of aerobic methane oxidation coupled to denitrification (AME-D) process: a review. Water Res. 90, 203–215 (2016).
CAS  PubMed  Google Scholar 

72.
Bland, C. et al. CRISPR recognition tool (CRT): a tool for automatic detection of clustered regularly interspaced palindromic repeats. BMC Bioinform. 8, 209 (2007).
Google Scholar 

73.
Méheust, R., Burstein, D., Castelle, C. J. & Banfield, J. F. The distinction of CPR bacteria from other bacteria based on protein family content. Nat. Commun. 10, 4173 (2019).
PubMed  PubMed Central  Google Scholar 

74.
Steinegger, M. & Söding, J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat. Biotechnol. 35, 1026–1028 (2017).
CAS  PubMed  Google Scholar 

75.
Söding, J., Biegert, A. & Lupas, A. N. The HHpred interactive server for protein homology detection and structure prediction. Nucleic Acids Res. 33, W244–W248 (2005).
PubMed  PubMed Central  Google Scholar 

76.
Remmert, M., Biegert, A., Hauser, A. & Söding, J. HHblits: lightning-fast iterative protein sequence searching by HMM–HMM alignment. Nat. Methods 9, 173–175 (2011).
PubMed  Google Scholar 

77.
Enright, A. J., Van Dongen, S. & Ouzounis, C. A. An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res. 30, 1575–1584 (2002).
CAS  PubMed  PubMed Central  Google Scholar 

78.
Hug, L. A. et al. A new view of the tree of life. Nat. Microbiol. 1, 16048 (2016).
CAS  PubMed  Google Scholar 

79.
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 

80.
Nguyen, L.-T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).
CAS  PubMed  Google Scholar 

81.
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  PubMed  Google Scholar 

82.
Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).
CAS  PubMed  PubMed Central  Google Scholar 

83.
Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).
PubMed  PubMed Central  Google Scholar 

84.
Olm, M. R. et al. InStrain enables population genomic analysis from metagenomic data and rigorous detection of identical microbial strains. Preprint at https://doi.org/10.1101/2020.01.22.915579 (2020). More

1.
Waller, N. L., Gynther, I. C., Freeman, A. B., Lavery, T. H. & Leung, L. K.-P. Wildlife Res. 44, 9–21 (2017).
Article  Google Scholar 
2.
Caswell, H. Am. Nat. 120, 317–339 (1982).
Article  Google Scholar 

3.
Pimm, S. L., Jones, H. & Diamond, J. M. Am. Nat. 132, 757–785 (1988).
Article  Google Scholar 

4.
Bennett, P. M. & Owens, I. P. F. Proc. R. Soc. Lond. B 264, 401–408 (1997).
Article  Google Scholar 

5.
Martin, T. E. & Mouton, J. C. Nat. Clim. Change https://doi.org/10.1038/s41558-020-0864-3 (2020).

6.
Gienapp, P., Teplitsky, C., Alho, J. S., Mills, J. A. & Merilä, J. Mol. Ecol. 17, 167–178 (2008).
CAS  Article  Google Scholar 

7.
Williams, G. C. Am. Nat. 100, 687–690 (1966).
Article  Google Scholar 

8.
Vázquez, D. P., Gianoli, E., Morris, W. F. & Bozinovic, F. Biol. Rev. 92, 22–42 (2017).
Article  Google Scholar 

9.
Morris, W. F. et al. Ecology 89, 19–25 (2008).
Article  Google Scholar 

10.
Chevin, L.-M., Lande, R. & Mace, G. M. PLoS Biol. 8, e1000357 (2010).
Article  Google Scholar 

11.
Radchuk, V. et al. Nat. Commun. 10, 1–14 (2019).
CAS  Article  Google Scholar 

12.
Diamond, S. E. & Martin, R. A. Ann. N. Y. Acad. Sci. 1469, 38–51 (2020).
Article  Google Scholar  More

1.
Sánchez-Arcilla, A., Jiménez, J. A., Valdemoro, H. I. & Gracia, V. Implications of climatic change on spanish mediterranean low-lying coasts: the ebro delta case. J. Coast. Res. 242, 306–316 (2008).
Google Scholar 
2.
Renaud, F. G., Le, T. T. H., Lindener, C., Guong, V. T. & Sebesvari, Z. Resilience and shifts in agro-ecosystems facing increasing sea-level rise and salinity intrusion in Ben Tre Province, Mekong Delta. Clim. Change 133, 69–84 (2015).
ADS  CAS  Google Scholar 

3.
White, E. & Kaplan, D. Restore or retreat? Saltwater intrusion and water management in coastal wetlands. Ecosyst. Health Sustain. 3, e01258 (2017).
Google Scholar 

4.
Field, C. B. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. Special Report of the Intergovernmental Panel on Climate Change/Edited by Christopher B. Field (Cambridge University Press, Cambridge, 2012).
Google Scholar 

5.
Mekong River Commission (MRC). Basin Development Plan scenarios assessment. Technical Note 8: Impacts of changes in salinity intrusion (2010).

6.
Nguyen, Y. T. B., Kamoshita, A., van Dinh, T. H., Matsuda, H. & Kurokura, H. Salinity intrusion and rice production in Red River Delta under changing climate conditions. Paddy Water Environ. 15, 37–48 (2017).
Google Scholar 

7.
Nhan, D. K., Phap, V. A., Phuc, T. H. & Trung, N. H. Rice Production Response and Technological Measures to Adapt to Salinity Intrusion in the Coastal Mekong Delta (CanTho University Press, Can Tho, 2012).
Google Scholar 

8.
Cabello, F. C. Heavy use of prophylactic antibiotics in aquaculture: a growing problem for human and animal health and for the environment. Environ. Microbiol. 8, 1137–1144 (2006).
CAS  PubMed  Google Scholar 

9.
Le, T. X. & Munekage, Y. Residues of selected antibiotics in water and mud from shrimp ponds in mangrove areas in Viet Nam. Mar. Pollut. Bull. 49, 922–929 (2004).
CAS  PubMed  Google Scholar 

10.
Jechalke, S., Heuer, H., Siemens, J., Amelung, W. & Smalla, K. Fate and effects of veterinary antibiotics in soil. Trends Microbiol. 22, 536–545 (2014).
CAS  PubMed  Google Scholar 

11.
Rosendahl, I. et al. Dissipation and sequestration of the veterinary antibiotic sulfadiazine and its metabolites under field conditions. Environ. Sci. Technol. 45, 5216–5222 (2011).
ADS  CAS  PubMed  Google Scholar 

12.
Dalkmann, P. et al. Accumulation of pharmaceuticals, Enterococcus, and resistance genes in soils irrigated with wastewater for zero to 100 years in central Mexico. PLoS ONE 7, e45397 (2012).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

13.
Kemper, N. Veterinary antibiotics in the aquatic and terrestrial environment. Ecol. Indic. 8, 1–13 (2008).
CAS  Google Scholar 

14.
Jeon, D. S. et al. Reactions and behavior relevant to chemical and physical properties of various veterinary antibiotics in soil. J. Fac. Agric. Kyushu Univ. 59, 391–397 (2014).
CAS  Google Scholar 

15.
Blackwell, P. A., Kay, P. & Boxall, A. B. A. The dissipation and transport of veterinary antibiotics in a sandy loam soil. Chemosphere 67, 292–299 (2007).
ADS  CAS  PubMed  Google Scholar 

16.
Liu, F. et al. Dissipation of sulfamethoxazole, trimethoprim and tylosin in a soil under aerobic and anoxic conditions. Environ. Chem. 7, 370 (2010).
ADS  CAS  Google Scholar 

17.
Carstens, K. L., Gross, A. D., Moorman, T. B. & Coats, J. R. Sorption and photodegradation processes govern distribution and fate of sulfamethazine in freshwater-sediment microcosms. Environ. Sci. Technol. 47, 10877–10883 (2013).
ADS  CAS  PubMed  Google Scholar 

18.
Fang, H., Han, Y., Yin, Y., Pan, X. & Yu, Y. Variations in dissipation rate, microbial function and antibiotic resistance due to repeated introductions of manure containing sulfadiazine and chlortetracycline to soil. Chemosphere 96, 51–56 (2014).
ADS  CAS  PubMed  Google Scholar 

19.
Kreuzig, R. & Höltge, S. Investigations on the fate of sulfadiazine in manured soil: laboratory experiments and test plot studies. Environ. Toxicol. Chem. 24, 771–776 (2005).
CAS  PubMed  Google Scholar 

20.
Accinelli, C., Koskinen, W. C., Becker, J. M. & Sadowsky, M. J. Environmental fate of two sulfonamide antimicrobial agents in soil. J. Agric. Food Chem. 55, 2677–2682 (2007).
CAS  PubMed  Google Scholar 

21.
Hoang, T. T. T., Tu, L. T. C., Le, N. P., Dao, Q. P. & Trinh, P. H. Fate of fluoroquinolone antibiotics in Vietnamese coastal wetland ecosystem. Wetl. Ecol. Manag. 20, 399–408 (2012).
CAS  Google Scholar 

22.
Adamek, E., Baran, W. & Sobczak, A. Assessment of the biodegradability of selected sulfa drugs in two polluted rivers in Poland: effects of seasonal variations, accidental contamination, turbidity and salinity. J. Hazard. Mater. 313, 147–158 (2016).
CAS  PubMed  Google Scholar 

23.
Radke, M., Lauwigi, C., Heinkele, G., Mürdter, T. E. & Letzel, M. Fate of the antibiotic sulfamethoxazole and its two major human metabolites in a water sediment test. Environ. Sci. Technol. 43, 3135–3141 (2009).
ADS  CAS  PubMed  Google Scholar 

24.
Nguyen, D. G. C. et al. Occurrence and dissipation of the antibiotics sulfamethoxazole, sulfadiazine, trimethoprim, and enrofloxacin in the Mekong Delta, Vietnam. PLoS ONE 10, e0131855 (2015).
Google Scholar 

25.
Zhang, R. et al. Antibiotics in the offshore waters of the Bohai Sea and the Yellow Sea in China: occurrence, distribution and ecological risks. Environ. Pollut. 174, 71–77 (2013).
CAS  PubMed  Google Scholar 

26.
Chen, H. et al. Antibiotics in typical marine aquaculture farms surrounding Hailing Island, South China: occurrence, bioaccumulation and human dietary exposure. Mar. Pollut. Bull. 90, 181–187 (2015).
CAS  PubMed  Google Scholar 

27.
Li, B. & Zhang, T. Biodegradation and adsorption of antibiotics in the activated sludge process. Environ. Sci. Technol. 44, 3468–3473 (2010).
ADS  CAS  PubMed  Google Scholar 

28.
Lai, H.-T., Wang, T.-S. & Chou, C.-C. Implication of light sources and microbial activities on degradation of sulfonamides in water and sediment from a marine shrimp pond. Bioresour. Technol. 102, 5017–5023 (2011).
CAS  PubMed  Google Scholar 

29.
Li, J. & Zhang, H. Factors influencing adsorption and desorption of trimethoprim on marine sediments: mechanisms and kinetics. Environ. Sci. Pollut. Res. 24, 21929–21937 (2017).
CAS  Google Scholar 

30.
Sarmah, A. K., Meyer, M. T. & Boxall, A. B. A. A global perspective on the use, sales, exposure pathways, occurrence, fate and effects of veterinary antibiotics (VAs) in the environment. Chemosphere 65, 725–759 (2006).
ADS  CAS  PubMed  Google Scholar 

31.
Managaki, S., Murata, A., Takada, H., Tuyen, B. C. & Chiem, N. H. Distribution of macrolides, sulfonamides, and trimethoprim in tropical waters: ubiquitous occurrence of veterinary antibiotics in the Mekong Delta. Environ. Sci. Technol. 41, 8004–8010 (2007).
ADS  CAS  PubMed  Google Scholar 

32.
Shimizu, A. et al. Ubiquitous occurrence of sulfonamides in tropical Asian waters. Sci. Total Environ. 452–453, 108–115 (2013).
ADS  PubMed  Google Scholar 

33.
Jahn, R. Guidelines for Soil Description 4th edn. (Food and Agriculture Organization of the United Nations, Rome, 2006).
Google Scholar 

34.
ISO 10694:1995. Soil Quality – Determination of Organic and Total Carbon After Dry Combustion (Elementary Analysis) (1995).

35.
Sonmez, S., Buyuktas, D., Okturen, F. & Citak, S. Assessment of different soil to water ratios (1:1, 1:2.5, 1:5) in soil salinity studies. Geoderma 144, 361–369 (2008).
ADS  CAS  Google Scholar 

36.
Klute, A. Methods of Soil Analysis. 2nd edn. (American Society of Agronomy, Madison, 1982–86).

37.
Food and Agriculture Organization of the United Nations. World reference base for soil resources 2014. International soil classification system for naming soils and creating legends for soil maps (FAO, Rome, 2014).

38.
OECD. Test No. 302B. Inherent Biodegradability: Zahn-Wellens/EVPA Test (OECD, 1992).

39.
Förster, M. et al. Sequestration of manure-applied sulfadiazine residues in soils. Environ. Sci. Technol. 43, 1824–1830 (2009).
ADS  PubMed  Google Scholar 

40.
Golet, E. M., Strehler, A., Alder, A. C. & Giger, W. Determination of fluoroquinolone antibacterial agents in sewage sludge and sludge-treated soil using accelerated solvent extraction followed by solid-phase extraction. Anal. Chem. 74, 5455–5462 (2002).
CAS  PubMed  Google Scholar 

41.
Göbel, A. et al. Extraction and determination of sulfonamides, macrolides, and trimethoprim in sewage sludge. J. Chromatogr. A 1085, 179–189 (2005).
PubMed  Google Scholar 

42.
Weihermüller, L., Neuser, A., Herbst, M. & Vereecken, H. Problems associated to kinetic fitting of incubation data. Soil Biol. Biochem. 120, 260–271 (2018).
Google Scholar 

43.
Srinivasan, P. & Sarmah, A. K. Dissipation of sulfamethoxazole in pasture soils as affected by soil and environmental factors. Sci. Total Environ. 479–480, 284–291 (2014).
ADS  PubMed  Google Scholar 

44.
Ingerslev, F. & Halling-Sørensen, B. Biodegradability properties of sulfonamides in activated sludge. Environ. Toxicol. Chem. 19, 2467–2473 (2000).
CAS  Google Scholar 

45.
Pan, M. & Chu, L. M. Adsorption and degradation of five selected antibiotics in agricultural soil. Sci. Total Environ. 545–546, 48–56 (2016).
ADS  PubMed  Google Scholar 

46.
Schauss, K. et al. Analysis, fate and effects of the antibiotic sulfadiazine in soil ecosystems. Trends Anal. Chem. 28, 612–618 (2009).
CAS  Google Scholar 

47.
Wu, Y., Williams, M., Smith, L., Chen, D. & Kookana, R. Dissipation of sulfamethoxazole and trimethoprim antibiotics from manure-amended soils. J. Environ. Sci. Health B 47, 240–249 (2012).
CAS  PubMed  Google Scholar 

48.
Yang, J.-F. et al. Degradation behavior of sulfadiazine in soils under different conditions. J. Environ. Sci. Health B 44, 241–248 (2009).
CAS  PubMed  Google Scholar 

49.
Beulke, S. & Brown, C. Evaluation of methods to derive pesticide degradation parameters for regulatory modelling. Biol. Fertil. Soils 33, 558–564 (2001).
CAS  Google Scholar 

50.
Laabs, V., Amelung, W., Pinto, A., Altstaedt, A. & Zech, W. Leaching and degradation of corn and soybean pesticides in an Oxisol of the Brazilian Cerrados. Chemosphere 41, 1441–1449 (2000).
ADS  CAS  PubMed  Google Scholar 

51.
Figueroa-Diva, R. A., Vasudevan, D. & MacKay, A. A. Trends in soil sorption coefficients within common antimicrobial families. Chemosphere 79, 786–793 (2010).
ADS  CAS  PubMed  Google Scholar 

52.
Singh, B. K., Walker, A., Morgan, J. A. W. & Wright, D. J. Effects of soil pH on the biodegradation of chlorpyrifos and isolation of a chlorpyrifos-degrading bacterium. Appl. Environ. Microbiol. 69, 5198–5206 (2003).
CAS  PubMed  PubMed Central  Google Scholar 

53.
Yun, E.-Y., Ro, H.-M., Lee, G.-T. & Choi, W.-J. Salinity effects on chlorpyrifos degradation and phosphorus fractionation in reclaimed coastal tideland soils. Geosci. J. 14, 371–378 (2010).
ADS  CAS  Google Scholar 

54.
Yan, N., Marschner, P., Cao, W., Zuo, C. & Qin, W. Influence of salinity and water content on soil microorganisms. Int. Soil Water Conserv. Res. 3, 316–323 (2015).
Google Scholar 

55.
Wichern, J., Wichern, F. & Joergensen, R. G. Impact of salinity on soil microbial communities and the decomposition of maize in acidic soils. Geoderma 137, 100–108 (2006).
ADS  CAS  Google Scholar 

56.
Brady, N. C. & Weil, R. R. The Nature and Properties of Soils 12th edn. (Prentice Hall, London, 1999).
Google Scholar 

57.
Wegst-Uhrich, S. R., Navarro, D. A., Zimmerman, L. & Aga, D. S. Assessing antibiotic sorption in soil: a literature review and new case studies on sulfonamides and macrolides. Chem. Cent. J. 8, 5 (2014).
PubMed  PubMed Central  Google Scholar  More

1.
De Roy, K., Marzorati, M., Van den Abbeele, P., Van de Wiele, T. & Boon, N. Synthetic microbial ecosystems: an exciting tool to understand and apply microbial communities. Environ. Microbiol. 16, 1472–1481 (2014).
PubMed  Google Scholar 
2.
Mee, M. T. & Wang, H. H. Engineering ecosystems and synthetic ecologies. Mol. Biosyst. 8, 2470 (2012).
PubMed  PubMed Central  CAS  Google Scholar 

3.
Hendrickx, L. et al. Microbial ecology of the closed artificial ecosystem MELiSSA (micro-ecological life support system alternative): reinventing and compartmentalizing the Earth’s food and oxygen regeneration system for long-haul space exploration missions. Res. Microbiol. 157, 77–86 (2006).
PubMed  Google Scholar 

4.
Chen, Y. Development and application of co-culture for ethanol production by co-fermentation of glucose and xylose: a systematic review. J. Ind. Microbiol. Biotechnol. 38, 581–597 (2011).
PubMed  CAS  Google Scholar 

5.
Spus, M. et al. Strain diversity and phage resistance in complex dairy starter cultures. J. Dairy Sci. 98, 5173–5182 (2015).
PubMed  CAS  Google Scholar 

6.
Ma, Q. et al. Integrated proteomic and metabolomic analysis of an artificial microbial community for two-step production of vitamin C. PLoS ONE 6, e26108 (2011).
ADS  PubMed  PubMed Central  CAS  Google Scholar 

7.
Petrof, E. O. et al. Stool substitute transplant therapy for the eradication of Clostridium difficile infection: ‘RePOOPulating’ the gut. Microbiome 1, 3 (2013).
PubMed  PubMed Central  Google Scholar 

8.
Bolhuis, H. & Stal, L. J. Analysis of bacterial and archaeal diversity in coastal microbial mats using massive parallel 16S rRNA gene tag sequencing. ISME J. 5, 1701–1712 (2011).
PubMed  PubMed Central  CAS  Google Scholar 

9.
van Gemerden, H. Microbial mats: a joint venture. Mar. Geol. 113, 3–25 (1993).
ADS  Google Scholar 

10.
Tolker-Nielsen, T. & Molin, S. Spatial organization of microbial biofilm communities. Microb. Ecol. 40, 75–84 (2000).
PubMed  CAS  Google Scholar 

11.
Bolhuis, H., Cretoiu, M. S. & Stal, L. J. Molecular ecology of microbial mats. FEMS Microbiol. Ecol. 90, 335–350 (2014).
PubMed  CAS  Google Scholar 

12.
Cohen, S. E. & Golden, S. S. Circadian rhythms in cyanobacteria. Microbiol. Mol. Biol. Rev. 79, 373–385 (2015).
PubMed  PubMed Central  CAS  Google Scholar 

13.
Nakajima, M. et al. Reconstitution of circadian oscillation of cyanobacterial KaiC phosphorylation in vitro. Science 308, 414–415 (2005).
ADS  PubMed  CAS  Google Scholar 

14.
Murayama, Y. et al. Low temperature nullifies the circadian clock in cyanobacteria through Hopf bifurcation. Proc. Natl. Acad. Sci. USA. 114, 5641–5646 (2017).
PubMed  CAS  Google Scholar 

15.
Johnson, C. H., Zhao, C., Xu, Y. & Mori, T. Timing the day: what makes bacterial clocks tick?. Nat. Rev. Microbiol. 15, 232–242 (2017).
PubMed  PubMed Central  CAS  Google Scholar 

16.
Woelfle, M. A., Ouyang, Y., Phanvijhitsiri, K. & Johnson, C. H. The adaptive value of circadian clocks: an experimental assessment in cyanobacteria. Curr. Biol. 14, 1481–1486 (2004).
PubMed  CAS  Google Scholar 

17.
Welkie, D. G. et al. A hard day’s night: cyanobacteria in diel cycles. Trends Microbiol. 27, 231–242 (2019).
PubMed  CAS  Google Scholar 

18.
Ivleva, N. B., Gao, T., LiWang, A. C. & Golden, S. S. Quinone sensing by the circadian input kinase of the cyanobacterial circadian clock. Proc. Natl. Acad. Sci. USA. 103, 17468–17473 (2006).
ADS  PubMed  CAS  Google Scholar 

19.
Gutu, A. & O’Shea, E. K. Two antagonistic clock-regulated histidine kinases time the activation of circadian gene expression. Mol. Cell 50, 288–294 (2013).
PubMed  PubMed Central  CAS  Google Scholar 

20.
Rust, M. J., Golden, S. S. & O’Shea, E. K. Light-driven changes in energy metabolism directly entrain the cyanobacterial circadian oscillator. Science 331, 220–223 (2011).
ADS  PubMed  PubMed Central  CAS  Google Scholar 

21.
Pattanayak, G. & Rust, M. J. The cyanobacterial clock and metabolism. Curr. Opin. Microbiol. 18, 90–95 (2014).
PubMed  PubMed Central  CAS  Google Scholar 

22.
Mackey, S. R., Golden, S. S. & Ditty, J. L. The itty-bitty time machine genetics of the cyanobacterial circadian clock. Adv. Genet. 74, 13–53 (2011).
PubMed  PubMed Central  CAS  Google Scholar 

23.
Resch, A., Rosenstein, R., Nerz, C. & Götz, F. Differential gene expression profiling of Staphylococcus aureus cultivated under biofilm and planktonic conditions. Appl. Environ. Microbiol. 71, 2663–2676 (2005).
PubMed  PubMed Central  CAS  Google Scholar 

24.
Moorthy, S. & Watnick, P. I. Identification of novel stage-specific genetic requirements through whole genome transcription profiling of Vibrio cholerae biofilm development. Mol. Microbiol. 57, 1623–1635 (2005).
PubMed  PubMed Central  CAS  Google Scholar 

25.
Edgar, R. S. et al. Peroxiredoxins are conserved markers of circadian rhythms. Nature 485, 459–464 (2012).
ADS  PubMed  PubMed Central  CAS  Google Scholar 

26.
Pinto, F., Pacheco, C. C., Ferreira, D., Moradas-Ferreira, P. & Tamagnini, P. Selection of suitable reference genes for RT-qPCR analyses in cyanobacteria. PLoS ONE 7, 1–9 (2012).
Google Scholar 

27.
Pfaffl, M. W., Tichopad, A., Prgomet, C. & Neuvians, T. P. Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: Bestkeeper–Excel-based tool using pair-wise correlations. Biotechnol. Lett. 26, 509–515 (2004).
PubMed  CAS  Google Scholar 

28.
Oksanen, J. et al. Package ‘vegan’ Title Community Ecology Package. Community Ecol. Packag. 2, (2019).

29.
Wu, G., Anafi, R. C., Hughes, M. E., Kornacker, K. & Hogenesch, J. B. MetaCycle: an integrated R package to evaluate periodicity in large scale data. Bioinformatics 32, 3351–3353 (2016).
PubMed  PubMed Central  CAS  Google Scholar 

30.
Hörnlein, C., Confurius-Guns, V., Stal, L. J. & Bolhuis, H. Daily rhythmicity in coastal microbial mats. npj Biofilms Microbiomes 4, 11 (2018).
PubMed  PubMed Central  Google Scholar 

31.
Kondo, T. et al. Circadian rhythms in prokaryotes: luciferase as a reporter of circadian gene expression in cyanobacteria. Proc. Natl. Acad. Sci. USA. 90, 5672–5676 (1993).
ADS  PubMed  CAS  Google Scholar 

32.
Tomita, J. No transcription-translation feedback in circadian rhythm of KaiC phosphorylation. Science 307, 251–254 (2005).
ADS  PubMed  CAS  Google Scholar 

33.
Whale, G. F. & Walsby, A. E. Motility of the cyanobacterium Microcoleus chthonoplastes in mud. Br. Phycol. J. 19, 117–123 (1984).
Google Scholar 

34.
Urmeneta, J., Navarrete, A., Huete, J. & Guerrero, R. Isolation and characterization of cyanobacteria from microbial mats of the Ebro Delta Spain. Curr. Microbiol. 46, 199–204 (2003).
PubMed  CAS  Google Scholar 

35.
Kothari, A., Vaughn, M. & Garcia-Pichel, F. Comparative genomic analyses of the cyanobacterium, Lyngbya aestuarii BL J, a powerful hydrogen producer. Front. Microbiol. 4, 363 (2013).
PubMed  PubMed Central  Google Scholar 

36.
Rath, J. & Adhikary, S. P. Response of the estuarine cyanobacterium Lyngbya aestuarii to UV-B radiation. J. Appl. Phycol. 19, 529–536 (2007).
CAS  Google Scholar 

37.
Holtzendorff, J. et al. Genome streamlining results in loss of robustness of the circadian clock in the marine cyanobacterium Prochlorococcus marinus PCC 9511. J. Biol. Rhythms 23, 187–199 (2008).
PubMed  CAS  Google Scholar 

38.
O’Neill, J. & Reddy, A. Circadian clocks in human red blood cells. Nature 469, 498–503 (2011).
ADS  PubMed  PubMed Central  Google Scholar 

39.
Shi, T., Ilikchyan, I., Rabouille, S. & Zehr, J. P. Genome-wide analysis of diel gene expression in the unicellular N 2-fixing cyanobacterium Crocosphaera watsonii WH 8501. ISME J. 4, 621–632 (2010).
PubMed  CAS  Google Scholar 

40.
Červený, J., Sinetova, M. A., Valledor, L., Sherman, L. A. & Nedbal, L. Ultradian metabolic rhythm in the diazotrophic cyanobacterium Cyanothece sp. ATCC 51142. Proc. Natl. Acad. Sci. U. S. A. 110, 13210–5 (2013).
ADS  PubMed  PubMed Central  Google Scholar 

41.
Johnson, C. H., Stewart, P. L. & Egli, M. The cyanobacterial circadian system: from biophysics to bioevolution. Annu. Rev. Biophys. 40, 143–167 (2011).
PubMed  PubMed Central  CAS  Google Scholar 

42.
Mitsui, A. et al. Strategy by which nitrogen-fixing unicellular cyanobacteria grow photoautotrophically. Nature 323, 720–722 (1986).
ADS  CAS  Google Scholar 

43.
Berman-Frank, I. et al. Segregation of nitrogen fixation and oxygenic photosynthesis in the marine cyanobacterium Trichodesmium. Science 294, 1534–1537 (2001).
ADS  PubMed  CAS  Google Scholar 

44.
Besharova, O., Suchanek, V. M., Hartmann, R., Drescher, K. & Sourjik, V. Diversification of gene expression during formation of static submerged biofilms by Escherichia coli. Front. Microbiol. 7, 1568 (2016).
PubMed  PubMed Central  Google Scholar 

45.
Nishiyama, Y. et al. Oxidative stress inhibits the repair of photodamage to the photosynthetic machinery. EMBO J. 20, 5587–5594 (2001).
PubMed  PubMed Central  CAS  Google Scholar 

46.
Nishiyama, Y., Allakhverdiev, S. I., Yamamoto, H., Hayashi, H. & Murata, N. Singlet oxygen inhibits the repair of photosystem II by suppressing the translation elongation of the D1 protein in Synechocystis sp. PCC 6803. Biochemistry 43, 11321–11330 (2004).
PubMed  CAS  Google Scholar 

47.
Los, D. A. & Zinchenko, V. V. in Lipids Photosynth. Essent. Regul. Funct. (eds. Wada, H. & Murata, N.) 329–348 (Springer Netherlands, 2010). doi:10.1007/978–90–481–2863–1_15

48.
Zehr, J. P., Crumbliss, L. L., Church, M. J., Omoregie, E. O. & Jenkins, B. D. Nitrogenase genes in PCR and RT-PCR reagents: implications for studies of diversity of functional genes. Biotechniques 35, 996–1005 (2003).
PubMed  CAS  Google Scholar 

49.
Stal, L. J., Bolhuis, H. & Cretoiu, M. S. Phototrophic marine benthic microbiomes: the ecophysiology of these biological entities. Environ. Microbiol. 21, 1529-1551/////// (2019).
PubMed  Google Scholar 

50.
Bolhuis, H., Severin, I., Confurius-Guns, V., Wollenzien, U. I. a & Stal, L. J. Horizontal transfer of the nitrogen fixation gene cluster in the cyanobacterium Microcoleus chthonoplastes. ISME J. 4, 121–30 (2010).

51.
Lee, S. H., Pulakat, L., Parker, K. C. & Gavini, N. Genetic analysis on the NifW by utilizing the yeast two-hybrid system revealed that the NifW of Azotobacter vinelandii interacts with the NifZ to form higher-order complexes. Biochem. Biophys. Res. Commun. 244, 498–504 (1998).
PubMed  CAS  Google Scholar 

52.
Severin, I. & Stal, L. J. NifH expression by five groups of phototrophs compared with nitrogenase activity in coastal microbial mats. FEMS Microbiol. Ecol. 73, 55–67 (2010).
PubMed  CAS  Google Scholar 

53.
Ottesen, E. a et al. Pattern and synchrony of gene expression among sympatric marine microbial populations. Proc. Natl. Acad. Sci. U. S. A. 110, E488–97 (2013).

54.
Stal, L. J., Gemerden, H. & Krumbein, W. E. Structure and development of a benthic marine microbial mat. FEMS Microbiol. Lett. 31, 111–125 (1985).
CAS  Google Scholar 

55.
Villbrandt, M. & Stal, L. J. The effect of sulfide on nitrogen fixation in heterocystous and non-heterocystous cyanobacterial mat communities. Algol. Stud. für Hydrobiol. Suppl. 83, 549–563 (1996).
Google Scholar 

56.
Paerl, H. W., Pinckney, J. L. & Steppe, T. F. Cyanobacterial-bacterial mat consortia: Examining the functional unit of microbial survival and growth in extreme environments. Environ. Microbiol. 2, 11–26 (2000).
PubMed  CAS  Google Scholar 

57.
Fourçans, A. et al. Characterization of functional bacterial groups in a hypersaline microbial mat community (Salins-de-Giraud, Camargue, France). FEMS Microbiol. Ecol. 51, 55–70 (2004).
PubMed  Google Scholar 

58.
Kearse, M. et al. Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649 (2012).
PubMed  PubMed Central  Google Scholar 

59.
Pabinger, S., Rödiger, S., Kriegner, A., Vierlinger, K. & Weinhäusel, A. A survey of tools for the analysis of quantitative PCR (qPCR) data. Biomol. Detect. Quantif. 1, 23–33 (2014).
PubMed  PubMed Central  Google Scholar  More

1.
Bastin, J. F. et al. The global tree restoration potential. Science 364, 76–79 (2019).
Google Scholar 
2.
Lewis, S. L., Wheeler, C. E., Mitchard, E. T. A. & Koch, A. Regenerate natural forests to store carbon. Nature 568, 25–28 (2019).
CAS  PubMed  Google Scholar 

3.
Chazdon, R. L. & Brancalion, P. Restoring forests as a means to many ends. Science 365, 24–25 (2019).
CAS  PubMed  Google Scholar 

4.
Busch, J. et al. Potential for low-cost carbon dioxide removal through tropical reforestation. Nat. Clim. Change 9, 463–466 (2019).
CAS  Google Scholar 

5.
Brancalion, P. H. S. et al. Global restoration opportunities in tropical rainforest landscapes. Sci. Adv. 5, eaav3223 (2019).
PubMed  PubMed Central  Google Scholar 

6.
Strassburg, B. B. N. et al. Strategic approaches to restoring ecosystems can triple conservation gains and halve costs. Nat. Ecol. Evol. 3, 62–70 (2019).
PubMed  Google Scholar 

7.
Friedlingstein, P., Allen, M., Canadell, J. G., Peters, G. P. & Seneviratne, S. I. Comment on “The global tree restoration potential”. Science 366, eaay8060 (2019).
PubMed  Google Scholar 

8.
Lewis, S. L. et al. Comment on “The global tree restoration potential”. Science 366, eaaz0388 (2019).
CAS  PubMed  Google Scholar 

9.
Veldman, J. W. et al. Comment on “The global tree restoration potential”. Science 366, eaay7976 (2019).
PubMed  Google Scholar 

10.
Anderson, C. M. et al. Natural climate solutions are not enough. Science 363, 933–934 (2019).
CAS  PubMed  Google Scholar 

11.
Maron, M. et al. Global no net loss of natural ecosystems. Nat. Ecol. Evol. 4, 46–49 (2019).
PubMed  Google Scholar 

12.
Schleicher, J. et al. Protecting half of the planet could directly affect over one billion people. Nat. Sustain. 2, 1094–1096 (2019).
Google Scholar 

13.
Mappin, B. et al. Restoration priorities to achieve the global protected area target. Conserv. Lett. 12, e12646 (2019).
Google Scholar 

14.
Pritchard, R. & Brockington, D. Forests: regrow with locals’ participation. Nature 569, 630 (2019).
CAS  PubMed  Google Scholar 

15.
Potapov, P., Laestadius, L. & Minnemeyer, S. Global map of forest landscape restoration opportunities. World Resources Institute https://www.wri.org/applications/maps/flr-atlas/#&init=y (2011).

16.
Oldekop, J. A., Holmes, G., Harris, W. E. & Evans, K. L. A global assessment of the social and conservation outcomes of protected areas. Conserv. Biol. 30, 133–141 (2016).
CAS  PubMed  Google Scholar 

17.
Agrawal, A. & Redford, K. Conservation and displacement: an overview. Conservat. Soc. 7, 1–10 (2009).
Google Scholar 

18.
Chazdon, R. L. Protecting intact forests requires holistic approaches. Nat. Ecol. Evol. 2, 915 (2018).
PubMed  Google Scholar 

19.
IPCC Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds Field, C. B. et al.) (Cambridge Univ. Press, 2014).

20.
Loft, L. et al. Risks to REDD+: potential pitfalls for policy design and implementation. Environ. Conserv. 44, 44–55 (2017).
Google Scholar 

21.
Brancalion, P. H. S. & Chazdon, R. L. Beyond hectares: four principles to guide reforestation in the context of tropical forest and landscape restoration. Restor. Ecol. 25, 491–496 (2017).
Google Scholar 

22.
Mansourian, S. in Forest Restoration in Landscapes: Beyond Planting Trees (eds Mansourian, S., Vallauri, D. & Dudley, N.) 8–13 (Springer, 2005).

23.
Sabogal, C., Besacier, C. & McGuire, D. Forest and landscape restoration: concepts, approaches and challenges for implementation. Unasylva 66, 3–10 (2015).
Google Scholar 

24.
Stanturf, J. A. et al. Implementing forest landscape restoration under the Bonn Challenge: a systematic approach. Ann. For. Sci. 76, 50 (2019).
Google Scholar 

25.
Sayer, J. et al. Ten principles for a landscape approach to reconciling agriculture, conservation, and other competing land uses. Proc. Natl Acad. Sci. USA 110, 8349–8356 (2013).
CAS  PubMed  Google Scholar 

26.
S, Mansourian. In the eye of the beholder: reconciling interpretations of forest landscape restoration. Land. Degrad. Dev. 29, 2888–2898 (2018).
Google Scholar 

27.
Fagan, M. E., Reid, J. L., Holland, M. B., Drew, J. G. & Zahawi, R. A. How feasible are global forest restoration commitments? Conserv. Lett. 13, e12700 (2020).
Google Scholar 

28.
Mansourian, S., Stanturf, J. A., Derkyi, M. A. A. & Engel, V. L. Forest landscape restoration: increasing the positive impacts of forest restoration or simply the area under tree cover? Restor. Ecol. 25, 178–183 (2017).
Google Scholar 

29.
Mansourian, S. et al. Putting the pieces together: integration for forest landscape restoration implementation. Land. Degrad. Dev. 31, 419–429 (2020).
Google Scholar 

30.
Erbaugh, J. T. & Oldekop, J. A. Forest landscape restoration for livelihoods and well-being. Curr. Opin. Environ. Sustain. 32, 76–83 (2018).
Google Scholar 

31.
Mansourian, S. & Parrotta, J. Forest landscape restoration: integrated approaches to support effective implementation (Routledge, 2018).

32.
Adams, C., Rodrigues, S. T., Calmon, M. & Kumar, C. Impacts of large-scale forest restoration on socioeconomic status and local livelihoods: what we know and do not know. Biotropica 48, 731–744 (2016).
Google Scholar 

33.
Fox, H. & Cundill, G. Towards increased community-engaged ecological restoration: a review of current practice and future directions. Ecol. Restor. 36, 208–218 (2018).
Google Scholar 

34.
Persha, L., Agrawal, A. & Chhatre, A. Social and ecological synergy: local rulemaking, forest livelihoods, and biodiversity conservation. Science 331, 1606–1608 (2011).
CAS  PubMed  Google Scholar 

35.
Brooks, J. S., Waylen, K. A. & Mulder, M. B. How national context, project design, and local community characteristics influence success in community-based conservation projects. Proc. Natl Acad. Sci. USA 109, 21265–21270 (2012).
CAS  PubMed  Google Scholar 

36.
Boedhihartono, A. K. & Sayer, J. in Forest Landscape Restoration Vol. 15 (eds Stanturf, J. et al.) 309–323 (Springer, 2012).

37.
Chazdon, R. L. et al. A policy-driven knowledge agenda for global forest and landscape restoration. Conserv. Lett. 10, 125–132 (2017).
Google Scholar 

38.
Bennett, M. M. & Smith, L. C. Advances in using multitemporal night-time lights satellite imagery to detect, estimate, and monitor socioeconomic dynamics. Remote Sens. Environ. 192, 176–197 (2017).
Google Scholar 

39.
Proville, J., Zavala-Araiza, D. & Wagner, G. Night-time lights: a global, long term look at links to socio-economic trends. PLoS ONE 12, e0174610 (2017).
PubMed  PubMed Central  Google Scholar 

40.
Kyba, C. C. M. et al. Artificially lit surface of Earth at night increasing in radiance and extent. Sci. Adv. 3, e1701528 (2017).
PubMed  PubMed Central  Google Scholar 

41.
Soares-Filho, B. S. et al. Modelling conservation in the Amazon basin. Nature 440, 520–523 (2006).
CAS  PubMed  Google Scholar 

42.
Oldekop, J. A., Sims, K. R. E., Karna, B. K., Whittingham, M. J. & Agrawal, A. Reductions in deforestation and poverty from decentralized forest management in Nepal. Nat. Sustain. 2, 421–428 (2019).
Google Scholar 

43.
At a Crossroads. Trends in Recognition of Community-Based Forest Tenure from 2002–2017 (Rights and Resources Initiative, 2018).

44.
Jones, K. R. et al. One-third of global protected land is under intense human pressure. Science 360, 788–791 (2018).
CAS  PubMed  Google Scholar 

45.
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).
CAS  PubMed  Google Scholar 

46.
Veldman, J. W. et al. Where tree planting and forest expansion are bad for biodiversity and ecosystem services. Bioscience 65, 1011–1018 (2015).
Google Scholar 

47.
Veldman, J. W. et al. Toward an old-growth concept for grasslands, savannas, and woodlands. Front. Ecol. Environ. 13, 154–162 (2015).
Google Scholar 

48.
Gridded Population of the World, version 4 (GPWv4): Population Count Adjusted to Match 2015 Revision of UN WPP Country Totals, Revision 11 (CIESIN, 2018).

49.
Version 1 VIIRS Day/Night Band Nighttime Lights (NOAA, 2019).

50.
World Development Indicators (The World Bank, 2019).

51.
Schlager, E. & Ostrom, E. Property-rights regimes and natural resources: a conceptual analysis. Land Econ. 68, 249–262 (1992).
Google Scholar 

52.
Sikor, T., He, J. U. N. & Lestrelin, G. Property rights regimes and natural resources: a conceptual analysis revisited. World Dev. 93, 337–349 (2017).
Google Scholar 

53.
Who Owns the World’s Land? A Global Baseline of Formally Recognized Indigenous and Community Land Rights (Rights and Resources Initiative, 2015). More

1.
Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).
CAS  PubMed  PubMed Central  Google Scholar 
2.
Tilman, D., Isbell, F. & Cowles, J. M. Biodiversity and ecosystem functioning. Annu. Rev. Ecol. Evol. Syst. 45, 471–493 (2014).
Google Scholar 

3.
Isbell, F. et al. Linking the influence and dependence of people on biodiversity across scales. Nature 546, 65–72 (2017).
CAS  PubMed  PubMed Central  Google Scholar 

4.
van der Plas, F. Biodiversity and ecosystem functioning in naturally assembled communities. Biol. Rev. 94, 1220–1245 (2019).
PubMed  Google Scholar 

5.
Schulze, E.-D. & Mooney, H. Biodiversity and Ecosystem Functioning (Springer, 1993).

6.
Naeem, S., Thompson, L. J., Lawler, S. P., Lawton, J. H. & Woodfin, R. M. Declining biodiversity can alter the performance of ecosystems. Nature 368, 734–737 (1994).
Google Scholar 

7.
Balvanera, P. et al. Quantifying the evidence for biodiversity effects on ecosystem functioning and services. Ecol. Lett. 9, 1146–1156 (2006).
PubMed  Google Scholar 

8.
Hines, J. et al. Mapping change in biodiversity and ecosystem function research: food webs foster integration of experiments and science policy. Adv. Ecol. Res. 61, 297–322 (2019).
Google Scholar 

9.
Tilman, D., Wedin, D. & Knops, J. Productivity and sustainability influenced by biodiversity in grassland ecosystems. Nature 379, 718–720 (1996).
CAS  Google Scholar 

10.
Roscher, C., Schumacher, J. & Baade, J. The role of biodiversity for element cycling and trophic interactions: an experimental approach in a grassland community. Basic Appl. Ecol. 121, 107–121 (2004).
Google Scholar 

11.
Tilman, D. et al. Diversity and productivity in a long-term grassland experiment. Science 294, 843–845 (2001).
CAS  PubMed  Google Scholar 

12.
Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).
Google Scholar 

13.
Cardinale, B. J. et al. The functional role of producer diversity in ecosystems. Am. J. Bot. 98, 572–592 (2011).
PubMed  Google Scholar 

14.
O’Connor, M. I. et al. A general biodiversity–function relationship is mediated by trophic level. Oikos 126, 18–31 (2017).
Google Scholar 

15.
Loreau, M. et al. Biodiversity and ecosystem functioning: current knowledge and future challenges. Science 294, 804–808 (2001).
CAS  PubMed  Google Scholar 

16.
Reich, P. B. et al. Impacts of biodiversity loss escalate through time as redundancy fades. Science 336, 589–592 (2012).
CAS  PubMed  Google Scholar 

17.
Huston, M. A. Hidden treatments in ecological experiments: re-evaluating the ecosystem function of biodiversity. Oecologia 110, 449–460 (1997).
PubMed  Google Scholar 

18.
Grime, J. P. Benefits of plant diversity to ecosystems: immediate, filter and founder effects. J. Ecol. 86, 902–910 (1998).
Google Scholar 

19.
Wardle, D. A. et al. Biodiversity and ecosystem function: an issue in ecology. Bull. Ecol. Soc. Am. 81, 235–239 (2000).
Google Scholar 

20.
Leps, J. What do the biodiversity experiments tell us about consequences of plant species loss in the real world? Basic Appl. Ecol. 5, 529–534 (2004).
Google Scholar 

21.
Srivastava, D. S. & Vellend, M. Biodiversity–ecosystem function research: is it relevant to conservation? Annu. Rev. Ecol. Evol. Syst. 36, 267–294 (2005).
Google Scholar 

22.
Duffy, J. E. Why biodiversity is important to the functioning of real-world ecosystems. Front. Ecol. Environ. 7, 437–444 (2008).
Google Scholar 

23.
Duffy, J. E. Biodiversity effects: trends and exceptions—a reply to Wardle and Jonsson. Front. Ecol. Environ. 8, 11–12 (2010).
Google Scholar 

24.
Wardle, D. A. & Jonsson, M. Biodiversity effects in real ecosystems—a response to Duffy. Front. Ecol. Environ. 8, 10–11 (2010).
Google Scholar 

25.
Wardle, D. A. Do experiments exploring plant diversity–ecosystem functioning relationships inform how biodiversity loss impacts natural ecosystems? J. Veg. Sci. 27, 646–653 (2016).
Google Scholar 

26.
Manning, P. et al. Transferring biodiversity-ecosystem function research to the management of ‘real-world’ ecosystems. Adv. Ecol. Res. 61, 323–356 (2019).
Google Scholar 

27.
Wilsey, B. J. & Potvin, C. Biodiversity and ecosystem functioning: importance of species evenness in an old field. Ecology 81, 887–892 (2000).
Google Scholar 

28.
Wilsey, B. J. & Polley, H. W. Realistically low species evenness does not alter grassland species-richness–productivity relationships. Ecology 85, 2693–2700 (2004).
Google Scholar 

29.
Hillebrand, H., Bennett, D. & Cadotte, M. Consequences of dominance: a review of evenness effects on local and regional ecosystem processes. Ecology 89, 1510–1520 (2008).
PubMed  Google Scholar 

30.
Schmitz, M. et al. Consistent effects of biodiversity on ecosystem functioning under varying density and evenness. Folia Geobot. 48, 335–353 (2013).
Google Scholar 

31.
Finn, J. A. et al. Ecosystem function enhanced by combining four functional types of plant species in intensively managed grassland mixtures: a 3-year continental-scale field experiment. J. Appl. Ecol. 50, 365–375 (2013).
Google Scholar 

32.
Weisser, W. W. et al. Biodiversity effects on ecosystem functioning in a 15-year grassland experiment: patterns, mechanisms, and open questions. Basic Appl. Ecol. 23, 1–73 (2017).
Google Scholar 

33.
Schmid, B. & Hector, A. The value of biodiversity experiments. Basic Appl. Ecol. 5, 535–542 (2004).
Google Scholar 

34.
Eisenhauer, N. et al. Biodiversity–ecosystem function experiments reveal the mechanisms underlying the consequences of biodiversity change in real world ecosystems. J. Veg. Sci. 27, 1061–1070 (2016).
Google Scholar 

35.
Isbell, F. et al. Nutrient enrichment, biodiversity loss, and consequent declines in ecosystem productivity. Proc. Natl Acad. Sci. USA 110, 11911–11916 (2013).
CAS  PubMed  Google Scholar 

36.
Duffy, J. E., Godwin, C. M. & Cardinale, B. J. Biodiversity effects in the wild are common and as strong as key drivers of productivity. Nature 549, 261–264 (2017).
CAS  PubMed  Google Scholar 

37.
Buchmann, T. et al. Connecting experimental biodiversity research to real-world grasslands. Perspect. Plant Ecol. Evol. Syst. 33, 78–88 (2018).
Google Scholar 

38.
Tilman, D. et al. The influence of functional diversity and composition on ecosystem processes. Science 277, 1300–1302 (1997).
CAS  Google Scholar 

39.
Tilman, D., Reich, P. B. & Isbell, F. Biodiversity impacts ecosystem productivity as much as resources, disturbance, or herbivory. Proc. Natl Acad. Sci. USA 109, 10394–10397 (2012).
CAS  PubMed  Google Scholar 

40.
Isbell, F. et al. Biodiversity increases the resistance of ecosystem productivity to climate extremes. Nature 526, 574–577 (2015).
CAS  PubMed  Google Scholar 

41.
Fischer, M. et al. Implementing large-scale and long-term functional biodiversity research: the biodiversity exploratories. Basic Appl. Ecol. 11, 473–485 (2010).
Google Scholar 

42.
Soliveres, S. et al. Biodiversity at multiple trophic levels is needed for ecosystem multifunctionality. Nature 536, 456–459 (2016).
CAS  Google Scholar 

43.
Tilman, D. Secondary succession and the pattern of plant dominance along experimental nitrogen gradients. Ecol. Monogr. 57, 189–214 (1987).
Google Scholar 

44.
Clark, C. M. & Tilman, D. Loss of plant species after chronic low-level nitrogen deposition to prairie grasslands. Nature 451, 712–715 (2008).
CAS  PubMed  Google Scholar 

45.
Inouye, R. et al. Old-field succession on a Minnesota sand plain. Ecology 68, 12–26 (1987).
Google Scholar 

46.
Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2015).
PubMed  Google Scholar 

47.
Craven, D. et al. Multiple facets of biodiversity drive the diversity–stability relationship. Nat. Ecol. Evol. 2, 1579–1587 (2018).
PubMed  Google Scholar 

48.
Nakamura, G., Gonçalves, L. O. & da Silva Duarte, L. Revisiting the dimensionality of biological diversity. Ecography (Cop.) 43, 539–548 (2020).
Google Scholar 

49.
Stevens, R. D. & Tello, J. S. On the measurement of dimensionality of biodiversity. Glob. Ecol. Biogeogr. 23, 1115–1125 (2014).
Google Scholar 

50.
Manning, P. et al. Simple measures of climate, soil properties and plant traits predict national-scale grassland soil carbon stocks. J. Appl. Ecol. 52, 1188–1196 (2015).
CAS  Google Scholar 

51.
Adler, D. & Kelly, T. vioplot: Violin plot. R package version 0.3.0 (2018).

52.
Loreau, M. & Hector, A. Partitioning selection and complementarity in biodiversity experiments. Nature 412, 72–76 (2001).
CAS  PubMed  PubMed Central  Google Scholar 

53.
Allan, E. et al. Land use intensification alters ecosystem multifunctionality via loss of biodiversity and changes to functional composition. Ecol. Lett. 18, 834–843 (2015).
PubMed  PubMed Central  Google Scholar 

54.
Le Bagousse-Pinguet, Y. et al. Phylogenetic, functional, and taxonomic richness have both positive and negative effects on ecosystem multifunctionality. Proc. Natl Acad. Sci. USA 116, 8419–8424 (2019).
PubMed  Google Scholar 

55.
Venail, P. et al. Species richness, but not phylogenetic diversity, influences community biomass production and temporal stability in a re-examination of 16 grassland biodiversity studies. Funct. Ecol. 29, 615–626 (2015).
Google Scholar 

56.
Hillebrand, H. & Matthiessen, B. Biodiversity in a complex world: consolidation and progress in functional biodiversity research. Ecol. Lett. 12, 1405–1419 (2009).
PubMed  Google Scholar 

57.
Grace, J. B. et al. Integrative modelling reveals mechanisms linking productivity and plant species richness. Nature 529, 390–393 (2016).
CAS  PubMed  Google Scholar 

58.
Liang, J. et al. Positive biodiversity–productivity relationship predominant in global forests. Science 354, aaf8957 (2016).
PubMed  Google Scholar 

59.
Oehri, J., Schmid, B., Schaepman-Strub, G. & Niklaus, P. A. Biodiversity promotes primary productivity and growing season lengthening at the landscape scale. Proc. Natl Acad. Sci. USA 114, 10160–10165 (2017).
CAS  PubMed  Google Scholar 

60.
Díaz, S. et al. Incorporating plant functional diversity effects in ecosystem service assessments. Proc. Natl Acad. Sci. USA 104, 20684–20689 (2007).
PubMed  Google Scholar 

61.
Lavorel, S. et al. Using plant functional traits to understand the landscape distribution of multiple ecosystem services. J. Ecol. 99, 135–147 (2011).
Google Scholar 

62.
Schmid, B. The species richness–productivity controversy. Trends Ecol. Evol. 17, 113–114 (2002).
Google Scholar 

63.
Loreau, M. Biodiversity and ecosystem functioning: a mechanistic model. Proc. Natl Acad. Sci. USA 95, 5632–5636 (1998).
CAS  PubMed  Google Scholar 

64.
Maestre, F. T. et al. Plant species richness and ecosystem multifunctionality in global drylands. Science 335, 214–218 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

65.
van der Plas, F. et al. Jack-of-all-trades effects drive biodiversity–ecosystem multifunctionality relationships in European forests. Nat. Commun. 7, 11109 (2016).
PubMed  PubMed Central  Google Scholar 

66.
Socher, S. A. et al. Direct and productivity-mediated indirect effects of fertilization, mowing and grazing on grassland species richness. J. Ecol. 100, 1391–1399 (2012).
Google Scholar 

67.
Hobbs, R. J., Higgs, E. & Harris, J. A. Novel ecosystems: implications for conservation and restoration. Trends Ecol. Evol. 24, 599–605 (2009).
PubMed  Google Scholar 

68.
Klaus, V. H. et al. Do biodiversity–ecosystem functioning experiments inform stakeholders how to simultaneously conserve biodiversity and increase ecosystem service provisioning in grasslands? Biol. Conserv. 245, 108552 (2020).
Google Scholar 

69.
Roscher, C. et al. Convergent high diversity in naturally colonized experimental grasslands is not related to increased productivity. Perspect. Plant Ecol. Evol. Syst. 20, 32–45 (2016).
Google Scholar 

70.
Ellenberg, H. & Leuschner, C. Vegetation Mitteleuropas mit den Alpen: In Ökologischer, Dynamischer und Historischer Sicht (UTB, 2010).

71.
Blüthgen, N. et al. A quantitative index of land-use intensity in grasslands: integrating mowing, grazing and fertilization. Basic Appl. Ecol. 13, 207–220 (2012).
Google Scholar 

72.
Tilman, D., Reich, P. B. & Knops, J. M. H. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 441, 629–632 (2006).
CAS  PubMed  PubMed Central  Google Scholar 

73.
Tilman, D. Community invasibility, recruitment limitation, and grassland biodiversity. Ecology 78, 81–92 (1997).
Google Scholar 

74.
Catford, J. A. et al. Traits linked with species invasiveness and community invasibility vary with time, stage and indicator of invasion in a long-term grassland experiment. Ecol. Lett. 22, 593–604 (2019).
PubMed  Google Scholar 

75.
Fargione, J. et al. From selection to complementarity: shifts in the causes of biodiversity–productivity relationships in a long-term biodiversity experiment. Proc. R. Soc. B 274, 871–876 (2007).
PubMed  Google Scholar 

76.
Londo, G. The decimal scale for releves of permanent quadrats. Vegetatio 33, 61–64 (1976).
Google Scholar 

77.
Roscher, C. et al. What happens to the sown species if a biodiversity experiment is not weeded? Basic Appl. Ecol. 14, 187–198 (2013).
Google Scholar 

78.
Kattge, J. et al. TRY—a global database of plant traits. Glob. Change Biol. 17, 2905–2935 (2011).
Google Scholar 

79.
Cayuela, L., Stein, A. & Oksanen, J. Taxonstand: Taxonomic standardization of plant species names. R package version 2.1 (2017).

80.
The Plant List version 1.1 (2013); http://www.theplantlist.org/

81.
Qian, H. & Jin, Y. An updated megaphylogeny of plants, a tool for generating plant phylogenies and an analysis of phylogenetic community structure. J. Plant Ecol. 9, 233–239 (2016).
Google Scholar 

82.
Martins, W. S., Carmo, W. C., Longo, H. J., Rosa, T. C. & Rangel, T. F. SUNPLIN: simulation with uncertainty for phylogenetic investigations. BMC Bioinform. 14, 324 (2013).
Google Scholar 

83.
Rangel, T. F. et al. Phylogenetic uncertainty revisited: implications for ecological analyses. Evolution 69, 1301–1312 (2015).
PubMed  Google Scholar 

84.
Cornelissen, J. H. C. et al. A handbook of protocols for standardised and easy measurement of plant functional traits worldwide. Aust. J. Bot. 51, 335–380 (2003).
Google Scholar 

85.
Goolsby, E. W., Bruggeman, J. & Ane, C. Rphylopars: Phylogenetic comparative tools for missing data and within-species variation. R package version 0.2.9 (2016).

86.
Penone, C. et al. Imputation of missing data in life-history trait datasets: which approach performs the best? Methods Ecol. Evol. 5, 961–970 (2014).
Google Scholar 

87.
Oksanen, J. et al. Vegan: Community ecology package. R package version 2.3-4 (2016).

88.
Hill, M. Diversity and evenness: a unifying notation and its consequences. Ecology 54, 427–432 (1973).
Google Scholar 

89.
Smith, B. & Wilson, J. B. A consumer’s guide to evenness indices. Oikos 76, 70–82 (1996).
Google Scholar 

90.
Magurran, A. Measuring Biological Diversity (Blackwell, 2004).

91.
Morris, E. K. et al. Choosing and using diversity indices: insights for ecological applications from the German biodiversity exploratories. Ecol. Evol. 4, 3514–3524 (2014).
PubMed  PubMed Central  Google Scholar 

92.
Tucker, C. M. et al. A guide to phylogenetic metrics for conservation, community ecology and macroecology. Biol. Rev. 92, 698–715 (2017).
PubMed  Google Scholar 

93.
Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).
CAS  PubMed  Google Scholar 

94.
Villéger, S., Mason, N. W. H. & Mouillot, D. New multidimensional functional diversity indices for a multifaceted framework in functional ecology. Ecology 89, 2290–2301 (2008).
PubMed  Google Scholar 

95.
Laliberte, E. & Legendre, P. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91, 299–305 (2010).
PubMed  Google Scholar 

96.
Mouchet, M. A., Villéger, S., Mason, N. W. H. & Mouillot, D. Functional diversity measures: an overview of their redundancy and their ability to discriminate community assembly rules. Funct. Ecol. 24, 867–876 (2010).
Google Scholar 

97.
Laliberté, E., Legendre, P. & Shipley, B. FD: Measuring functional diversity from multiple traits, and other tools for functional ecology. R package version 1.0-12 (2014).

98.
R: A Language and Environment for Statistical Computing v.3.4.2 (R Core Team, 2019); https://doi.org/10.1007/978-3-540-74686-7

99.
Dormann, C. F. et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography (Cop.) 36, 27–46 (2013).
Google Scholar 

100.
Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).
Google Scholar 

101.
Jochum, M. et al. R-code and aggregated data from: The results of biodiversity-ecosystem functioning experiments are realistic. iDiv Data Repository https://doi.org/10.25829/idiv.1869-11-3082 (2020).

102.
Fox, J. & Weisberg, S. An R Companion to Applied Regression (SAGE, 2011).

103.
Pebesma, E. & Bivand, R. Classes and methods for spatial data in R. R News 5, 9–13 (2005).
Google Scholar 

104.
Bivand, R. S., Pebesma, E. & Gomez-Rubio, V. Applied Spatial Data Analysis with R (Springer, 2013).

105.
Habel, K., Grasman, R., Gramacy, R. B., Stahel, A. & Sterratt, D. C. geometry: Mesh generation and surface tessellation. R package version 0.4.1 (2019).

106.
Blonder, B. & Harris, D. hypervolume: High dimensional geometry and set operations using kernel density estimation, support vector machines, and convex hulls. R package version 2.0.11 (2018).

107.
Meyer, S. T. et al. Effects of biodiversity strengthen over time as ecosystem functioning declines at low and increases at high biodiversity. Ecosphere 7, e01619 (2016).
Google Scholar 

108.
Brownrigg, R. mapdata: Extra map databases. R package version 2.3.0 (2018). More

1.
Bell, M. A., Baumgartner, J. V. & Olson, E. C. Patterns of temporal change in single morphological characters of a Miocene stickleback fish. Paleobiology 11, 258–271 (1985).
Google Scholar 
2.
Bell, M. A. Implications of a fossil stickleback assemblage for Darwinian gradualism. J. Fish. Biol. 75, 1977–1999 (2009).
CAS  PubMed  Google Scholar 

3.
Bell, M. A., Travis, M. P. & Blouw, D. M. Inferring natural selection in a fossil threespine stickleback. Paleobiology 32, 562–577 (2006).
Google Scholar 

4.
Hunt, G., Bell, M. A. & Travis, M. P. Evolution toward a new adaptive optimum: phenotypic evolution in a fossil stickleback lineage. Evolution 62, 700–710 (2008).
PubMed  Google Scholar 

5.
Klepaker, T., Østbye, K. & Bell, M. A. Regressive evolution of the pelvic complex in stickleback fishes: a study of convergent evolution. Evol. Ecol. Res. 15, 413–435 (2013).
Google Scholar 

6.
Bell, M. A., Ortí, G., Walker, J. A. & Koenings, J. P. Evolution of pelvic reduction in threespine stickleback fish: a test of competing hypotheses. Evolution 47, 906–914 (1993).
PubMed  Google Scholar 

7.
Chan, Y. F. et al. Adaptive evolution of pelvic reduction in sticklebacks by recurrent deletion of a Pitx1 enhancer. Science 327, 302–305 (2010).
CAS  PubMed  Google Scholar 

8.
Cole, N. J., Tanaka, M., Prescott, A. & Tickle, C. Expression of limb initiation genes and clues to the morphological diversification of threespine stickleback. Curr. Biol. 13, R951–R952 (2003).
CAS  PubMed  Google Scholar 

9.
Shapiro, M. D. et al. Genetic and developmental basis of evolutionary pelvic reduction in threespine sticklebacks. Nature 428, 717–723 (2004).
CAS  PubMed  Google Scholar 

10.
Cresko, W. A. et al. Parallel genetic basis for repeated evolution of armor loss in Alaskan threespine stickleback populations. Proc. Natl Acad. Sci. USA 101, 6050–6055 (2004).
CAS  PubMed  Google Scholar 

11.
Coyle, S. M., Huntingford, F. A. & Peichel, C. L. Parallel evolution of Pitx1 underlies pelvic reduction in Scottish threespine stickleback (Gasterosteus aculeatus). J. Hered. 98, 581–586 (2007).
CAS  PubMed  Google Scholar 

12.
Xie, K. T. et al. DNA fragility in the parallel evolution of pelvic reduction in stickleback fish. Science 363, 81–84 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

13.
Bell, M. A., Khalef, V. & Travis, M. P. Directional asymmetry of pelvic vestiges in threespine stickleback. J. Exp. Zool. B Mol. Dev. Evol. 308B, 189–199 (2007).
Google Scholar 

14.
Palmer, A. R. Symmetry breaking and the evolution of development. Science 306, 828–833 (2004).
CAS  PubMed  Google Scholar 

15.
Marcil, A., Dumontier, É., Chamberland, M., Camper, S. A. & Drouin, J. Pitx1 and Pitx2 are required for development of hindlimb buds. Development 130, 45–55 (2003).
CAS  PubMed  Google Scholar 

16.
Shapiro, M. D., Bell, M. A. & Kingsley, D. M. Parallel genetic origins of pelvic reduction in vertebrates. Proc. Natl Acad. Sci. USA 103, 13753–13758 (2006).
CAS  PubMed  Google Scholar 

17.
Bell, M. A. in The Evolutionary Biology of the Threespine Stickleback (eds Bell, M. A. & Foster, S. A.) 438–471 (Oxford Univ. Press, 1994).

18.
Peichel, C. L. et al. The genetic architecture of divergence between threespine stickleback species. Nature 414, 901–905 (2001).
CAS  PubMed  Google Scholar 

19.
Rollins, J. L., Lohman, B. K. & Bell, M. A. Does ion limitation select for pelvic reduction in threespine stickleback (Gasterosteus aculeatus)? Evol. Ecol. Res. 16, 101–120 (2014).
Google Scholar 

20.
Reimchen, T. E. Spine deficiency and polymorphism in a population of Gasterosteus aculeatus: an adaptation to predators? Can. J. Zool. 58, 1232–1244 (1980).
Google Scholar 

21.
Reimchen, T. E. in The Evolutionary Biology of the Threespine Stickleback (eds Bell, M. A. & Foster, S. A.) 240–276 (Oxford Univ. Press, 1994).

22.
Hoogland, R., Morris, D. & Tinbergen, N. The spines of sticklebacks (Gasterosteus and Pygosteus) as a means of defense against predators (Perca and Esox). Behaviour 10, 205–236 (1956).
Google Scholar 

23.
Baumgartner, J. V. A new fossil ictalurid catfish from the Miocene middle member of the Truckee Formation, Nevada. Copeia 1982, 38–46 (1982).
Google Scholar 

24.
Stearley, R. F. & Smith, G. R. Fishes of the Mio-Pliocene western snake river plain and vicinity. Misc. Publ. Mus. Zool. Univ. Mich. 204, 1–43 (2016).
Google Scholar 

25.
Baker, J. A. in The Evolutionary Biology of the Threespine Stickleback (eds Bell, M. A. & Foster, S. A.) 144–187 (Oxford Univ. Press, 1994).

26.
Bell, M. A. Interacting evolutionary constraints in pelvic reduction of threespine sticklebacks, Gasterosteus aculeatus (Pisces, Gasterosteidae). Biol. J. Linn. Soc. Lond. 31, 347–382 (1987).
Google Scholar 

27.
Bell, M. A. & Harris, E. I. Developmental osteology of the pelvic complex of Gasterosteus aculeatus. Copeia 1985, 789–792 (1985).
Google Scholar 

28.
Schmid, L. & Sánchez-Villagra, M. R. Potential genetic bases of morphological evolution in the Triassic fish Saurichthys. J. Exp. Zool. B Mol. Dev. Evol. 314B, 519–526 (2010).
Google Scholar 

29.
Meredith, R. W., Gatesy, J., Murphy, W. J., Ryder, O. A. & Springer, M. S. Molecular decay of the tooth gene Enamelin (ENAM) mirrors the loss of enamel in the fossil record of placental mammals. PLoS Genet. 5, e1000634 (2009).
PubMed  PubMed Central  Google Scholar 

30.
Qu, Q., Haitina, T., Zhu, M. & Ahlberg, P. E. New genomic and fossil data illuminate the origin of enamel. Nature 526, 108–111 (2015).
CAS  PubMed  Google Scholar 

31.
Zhu, M. et al. A Silurian placoderm with osteichthyan-like marginal jaw bones. Nature 502, 188–193 (2013).
CAS  PubMed  Google Scholar 

32.
Zhu, M. Bone gain and loss: insights from genomes and fossils. Natl Sci. Rev. 1, 490–492 (2014).
CAS  Google Scholar 

33.
Hunt, G. Evolutionary divergence in directions of high phenotypic variance in the ostracode genus Poseidonamicus. Evolution 61, 1560–1576 (2007).
PubMed  Google Scholar 

34.
Thompson, J. R. et al. Reorganization of sea urchin gene regulatory networks at least 268 million years ago as revealed by oldest fossil cidaroid echinoid. Sci. Rep. 5, 15541 (2015).
CAS  PubMed  PubMed Central  Google Scholar 

35.
Organ, C. L., Janes, D. E., Meade, A. & Pagel, M. Genotypic sex determination enabled adaptive radiations of extinct marine reptiles. Nature 461, 389–392 (2009).
CAS  Google Scholar 

36.
Organ, C. L., Shedlock, A. M., Meade, A., Pagel, M. & Edwards, S. V. Origin of avian genome size and structure in non-avian dinosaurs. Nature 446, 180–184 (2007).
CAS  PubMed  Google Scholar 

37.
Organ, C. L. & Shedlock, A. M. Palaeogenomics of pterosaurs and the evolution of small genome size in flying vertebrates. Biol. Lett. 5, 47–50 (2009).
PubMed  Google Scholar 

38.
Conte, G. L., Arnegard, M. E., Peichel, C. L. & Schluter, D. The probability of genetic parallelism and convergence in natural populations. Proc. Biol. Sci. 279, 5039–5047 (2012).
PubMed  PubMed Central  Google Scholar 

39.
Rennison, D. J., Stuart, Y. E., Bolnick, D. I. & Peichel, C. L. Ecological factors and morphological traits are associated with repeated genomic differentiation between lake and stream stickleback. Philos. Trans. R. Soc. Lond., B, Biol. Sci. 374, 20180241 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

40.
Szeto, D. P. et al. Role of the Bicoid-related homeodomain factor Pitx1 in specifying hindlimb morphogenesis and pituitary development. Genes Dev. 13, 484–494 (1999).
CAS  PubMed  PubMed Central  Google Scholar 

41.
Thompson, A. C. et al. A novel enhancer near the Pitx1 gene influences development and evolution of pelvic appendages in vertebrates. eLife 7, e38555 (2018).
PubMed  PubMed Central  Google Scholar 

42.
Bell, M. A., Stewart, J. D. & Park, P. J. The world’s oldest fossil threespine stickleback fish. Copeia 2009, 256–265 (2009).
Google Scholar 

43.
Rawlinson, S. E. & Bell, M. A. A stickleback fish (Pungitius) from the Neogene Sterling Formation, Kenai Peninsula, Alaska. J. Paleontol. 56, 583–588 (1982).
Google Scholar 

44.
Rohlf, F. J. tpsDIG v.2.10 (2006).

45.
Bowne, P. S. in The Evolutionary Biology of the Threespine Stickleback (eds Bell, M. A. & Foster, S. A.) 28–60 (Oxford Univ. Press, 1994).

46.
R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).

47.
Lleonart, J., Salat, J. & Torres, G. J. Removing allometric effects of body size in morphological analysis. J. Theor. Biol. 205, 85–93 (2000).
CAS  PubMed  Google Scholar 

48.
Oke, K. B. et al. Does plasticity enhance or dampen phenotypic parallelism? A test with three lake-stream stickleback pairs. J. Evol. Biol. 29, 126–143 (2016).
CAS  PubMed  Google Scholar 

49.
Stuart, Y. E. et al. Contrasting effects of environment and genetics generate a continuum of parallel evolution. Nat. Ecol. Evol. 1, 0158 (2017).
Google Scholar 

50.
Hartigan, J. A. & Hartigan, P. M. The dip test of unimodality. Ann. Stat. 13, 70–84 (1985).
Google Scholar 

51.
Maechler, M. diptest: Hartigan’s dip statistic for unimodality—corrected v.0.75-7 (2016); https://rdrr.io/cran/diptest/ More

1.
Owen, A. W. Trilobite abnormalities. Earth Environ. Sci. Trans. R. Soc. Edinb. 76(2–3), 255–272 (1985).
Google Scholar 
2.
Daley, A. C. & Drage, H. B. The fossil record of ecdysis, and trends in the moulting behaviour of trilobites. Arthropod Struct. Dev. 45(2), 71–96 (2016).
ADS  PubMed  Google Scholar 

3.
Clarkson, E. N. On the schizochroal eyes of three species of Reedops (Trilobita: Phacopidae) from the Lower Devonian of Bohemia. Earth Environ. Sci. Trans. R. Soc. Edinb. 68(8), 183–205 (1969).
Google Scholar 

4.
Henningsmoen, G. Moulting in trilobites. Fossils Strata. 4(1), 79–200 (1975).
Google Scholar 

5.
Howe, N. R. Partial molting synchrony in the giant Malaysian prawn, Macrobrachium rosenbergii: A chemical communication hypothesis. J. Chem. Ecol. 7(3), 487–500 (1981).
PubMed  CAS  Google Scholar 

6.
Drage, H. B. Quantifying intra-and interspecific variability in trilobite moulting behaviour across the Palaeozoic. Paleontol. Electron. 22(2) (2019).

7.
Pates, S. & Bicknell, R. D. Elongated thoracic spines as potential predatory deterrents in olenelline trilobites from the lower Cambrian of Nevada. Palaeogeogr. Palaeoclimatol. Palaeoecol. 516, 295–306 (2019).
Google Scholar 

8.
Webster, S. G. Seasonal anecdysis and moulting synchrony in field populations of Palaemon elegans (Rathke). Estuar. Coast. Shelf Sci. 15(1), 85–94 (1982).
ADS  Google Scholar 

9.
Leinaas, H. P. Synchronized moulting controlled by communication in group-living Collembola. Science 219(4581), 193–195 (1983).
ADS  PubMed  CAS  Google Scholar 

10.
Stone, R. P. Mass molting of tanner crabs Chionoecetes bairdi in a Southeast Alaska-Estuary. Alaska Fish. Res. Bull. 6(1), 19–28 (1999).
Google Scholar 

11.
Kim, K. W. Social facilitation of synchronized molting behavior in the spider Amaurobius ferox (Araneae, Amaurobiidae). J. Insect Behav. 14(3), 401–409 (2001).
CAS  Google Scholar 

12.
Haug, J. T., Caron, J. B. & Haug, C. Demecology in the Cambrian: Synchronized molting in arthropods from the Burgess Shale. BMC Biol. 11(1), 64 (2013).
PubMed  PubMed Central  Google Scholar 

13.
Braddy, S. J. Eurypterid palaeoecology: Palaeobiological, ichnological and comparative evidence for a ‘mass–moult–mate’ hypothesis. Palaeogeogr. Palaeoclimatol. Palaeoecol. 172(1–2), 115–132 (2001).
Google Scholar 

14.
Karim, T. & Westrop, S. R. Taphonomy and paleoecology of Ordovician trilobite clusters, Bromide Formation, south-central Oklahoma. Palaios 17, 394–402 (2002).
ADS  Google Scholar 

15.
Vrazo, M. B. & Braddy, S. J. Testing the ‘mass-moult-mate’hypothesis of eurypterid palaeoecology. Palaeogeogr. Palaeoclimatol. Palaeoecol. 311(1–2), 63–73 (2011).
Google Scholar 

16.
Paterson, J. R., Jago, J. B., Brock, G. A. & Gehling, J. G. Taphonomy and palaeoecology of the emuellid trilobite Balcoracania dailyi (early Cambrian, South Australia). Palaeogeogr. Palaeoclimatol. Palaeoecol. 249(3–4), 302–321 (2007).
Google Scholar 

17.
Błażejowski, B., Brett, C. E., Kin, A., Radwański, A. & Gruszczyński, M. Ancient animal migration: A case study of eyeless, dimorphic Devonian trilobites from Poland. Palaeontology 59(5), 743–751 (2016).
Google Scholar 

18.
Vannier, J. et al. Collective behaviour in 480-million-year-old trilobite arthropods from Morocco. Sci. Rep. 9(1), 1–10 (2019).
CAS  Google Scholar 

19.
Pocock, K. J. The Emuellidae, a new family of trilobites from the Lower Cambrian of South Australia. Palaeontology 13(4), 522–562 (1970).
Google Scholar 

20.
Esker, G. C. New species of trilobites from the Bromide Formation (Pooleville Member) of Oklahoma. Oklahoma Geology Notes. 24(9), 195–209 (1964).
Google Scholar 

21.
Thoral, M. Contribution à l’étude paléontologique de l’Ordovicien inférieur de la Montagne Noire et révision sommaire de la faune cambrienne de la Montagne Noire. (Imprimerie de la Charité, Montpellier, 1935).

22.
Passano, L. M. Molting and its control. In Metabolism and Growth (1960).

23.
Webster, M., Gaines, R. R. & Hughes, N. C. Microstratigraphy, trilobite biostratinomy, and depositional environment of the “lower Cambrian” Ruin Wash Lagerstätte, Pioche Formation, Nevada. Palaeogeogr. Palaeoclimatol. Palaeoecol. 264(1–2), 100–122 (2008).
Google Scholar 

24.
Esteve, J. & Zamora, S. Enrolled agnostids from Cambrian of Spain provide new insights about the mode of life in these forms. Bull. Geosci. 89(2), 283–291 (2014).
Google Scholar 

25.
Speyer, S. E. Comparative taphonomy and palaeoecology of trilobite lagerstätten. Alcheringa 11(3), 205–232 (1987).
Google Scholar 

26.
Geyer, G. & Peel, J. S. The Henson Gletscher Formation, North Greenland, and its bearing on the global Cambrian Series 2–Series 3 boundary. Bull. Geosci. 86(3), 465–534 (2011).
Google Scholar 

27.
Zhou, T. M., Liu, Y. R., Meng, X. S & Sun, Z. H. Palaeontological atlas of central and southern China. In Early Palaeonzoic, vol. 1 (eds. Hubei Institute of Geological Sciences, Geological Bureau of Henan Province, Geological Bureau of Hubei Province, Geological Bureau of Hunan Province, Geological Bureau of Guangdong Province & Geological Bureau of Guangxi Province) 104–266 (Geological Publishing House, Beijing, 1977).

28.
Yuan, J. L. & Esteve, J. The earliest species of Burlingia Walcott, 1908 (Trilobita) from South China: Biostratigraphical and palaeogeographical significance. Geol. Mag. 152(2), 358–366 (2015).
ADS  Google Scholar 

29.
Hughes, N. C., Minelli, A. & Fusco, G. The ontogeny of trilobite segmentation: A comparative approach. Paleobiology. 32(4), 602–627 (2006).
Google Scholar 

30.
Brett, C. E. & Baird, G. C. Taphonomic approaches to temporal resolution in stratigraphy: Examples from Paleozoic marine mudrocks. Short Courses Paleontol. 6, 251–274 (1993).
Google Scholar 

31.
Brandt, D. S. Taphonomic grades as a classification for fossiliferous assemblages and implications for paleoecology. Palaios 4(4), 303–309 (1989).
ADS  Google Scholar 

32.
Schäfer, W. & Oertel, I. Ecology and Palaeoecology of Marine Environments (University of Chicago Press, Illinois, 1972).
Google Scholar 

33.
Brett, C. E. & Baird, G. C. Comparative taphonomy: A key to paleoenvironmental interpretation based on fossil preservation. Palaios 1(3), 207–227 (1986).
ADS  Google Scholar 

34.
Plotnick, R. E. Taphonomy of a modern shrimp: Implications for the arthropod fossil record. Palaios. 286–293 (1986).

35.
Plotnick, R. E., Baumiller, T. & Wetmore, K. L. Fossilization potential of the mud crab, Panopeus (Brachyura: Xanthidae) and temporal variability in crustacean taphonomy. Palaeogeogr. Palaeoclimatol. Palaeoecol. 63(1–3), 27–43 (1988).
Google Scholar 

36.
Babcock, L. E. & Chang, W. Comparative taphonomy of two nonmineralized arthropods: Naraoia (Nektaspida; Early Cambrian, Chengjiang Biota, China) and Limulus (Xiphosurida; Holocene, Atlantic Ocean). Collect. Res. 10, 233–250 (1997).
Google Scholar 

37.
Speyer, S. E. & Brett, C. E. Clustered trilobite assemblages in the Middle Devonian Hamilton group. Lethaia. 18(2), 85–103 (1985).
Google Scholar 

38.
Paterson, J. R. et al. Trilobite clusters: What do they tell us? A preliminary investigation. Adv. Trilobite Res. 9, 313–318 (2008).
Google Scholar 

39.
Gaines, R. R. & Droser, M. L. Paleoecology of the familiar trilobite Elrathia kingii: An early exaerobic zone inhabitant. Geology 31(11), 941–944 (2003).
ADS  Google Scholar 

40.
Gutiérrez-Marco, J. C., Sá, A. A., García-Bellido, D. C., Rábano, I. & Valério, M. Giant trilobites and trilobite clusters from the Ordovician of Portugal. Geology 37(5), 443–446 (2009).
ADS  Google Scholar 

41.
Esteve, J., Hughes, N. C. & Zamora, S. Purujosa trilobite assemblage and the evolution of trilobite enrollment. Geology 39(6), 575–578 (2011).
ADS  Google Scholar 

42.
Brett, C. E., Zambito, J. J. IV., Schindler, E. & Becker, R. T. Diagenetically-enhanced trilobite obrution deposits in concretionary limestones: The paradox of “rhythmic events beds”. Palaeogeogr. Palaeoclimatol. Palaeoecol. 367, 30–43 (2012).
Google Scholar 

43.
Hoare, B. Animal Migration: Remarkable Journeys in the Wild. (University of California Press, 2009).

44.
Chatterton, B. D. E. & Fortey, R. A. Linear clusters of articulated trilobites from Lower Ordovician (Arenig) strata at Bini Tinzoulin, North Zagora, Southern Morocco. Adv. Trilobite Res. (Cuadernos del Museo Geominero) 9, 73–77 (2008).

45.
Trenchard, H., Brett, C. E. & Perc, M. Trilobite ‘pelotons’: Possible hydrodynamic drag effects between leading and following trilobites in trilobite queues. Palaeontology 60(4), 557–569 (2017).
Google Scholar 

46.
Kim, K. W. & Horel, A. Matriphagy in the spider Amaurobius ferox (Araneidae, Amaurobiidae): an example of mother-offspring interactions. Ethology 104(12), 1021–1037 (1998).
Google Scholar 

47.
Kim, K. W. & Roland, C. Trophic egg laying in the spider, Amaurobius ferox: mother–offspring interactions and functional value. Behav. Proc. 50(1), 31–42 (2000).
CAS  Google Scholar 

48.
Drage, H. B., Holmes, J. D., García-Bellido, D. C. & Daley, A. C. An exceptional record of Cambrian trilobite moulting behaviour preserved in the Emu Bay Shale, South Australia. Lethaia 51(4), 473–492 (2018).
Google Scholar 

49.
Zhao, Y. L. et al. Balang section, Guizhou, China: Stratotype section for the Taijiangian Stage and candidate for GSSP of an unnamed Cambrian Series. Camb. Syst. China Korea Guide Field Excursions 62–83 (2005).

50.
Zhao, Y. L. et al. Kaili Biota: A taphonomic window on diversification of metazoans from the basal Middle Cambrian: Guizhou, China. Acta Geol. Sin.-English Ed. 79(6), 751–765 (2005).
Google Scholar 

51.
Yang, X. L., Zhao, Y. L., Peng, J., Yang, Y. N. & Yang, K. D. Discovery of Oryctocephalid trilobites from the Tsinghsutung Formation (Duyunian Stage, Qiandongian Series, Cambrian), Jianhe County, Guizhou Province. Geol. J. China Univ. 16(3), 309–316 (2010).
Google Scholar 

52.
Yuan, J. L., Esteve, J. & Ng, T. W. Articulation, interlocking devices and enrolment in Monkaspis daulis (W alcott, 1905) from the Guzhangian, middle Cambrian of North China. Lethaia. 47(3), 405–417 (2014).
Google Scholar 

53.
Zhao, Y. L., Yuan, J. L., Esteve, J. & Peng, J. The oryctocephalid trilobite zonation across the Cambrian Series 2-Series 3 boundary at Balang, South China: A reappraisal. Lethaia. 50(3), 400–406 (2017).
Google Scholar 

54.
Abràmoff, M. D., Magalhães, P. J. & Ram, S. J. Image processing with ImageJ. Biophoton. Int. 11(7), 36–42 (2004).
Google Scholar 

55.
Esteve, J., Zhao, Y. L., Maté-González, M. A., Gómez-Heras, M. & Peng, J. A new high-resolution 3-D quantitative method for analysing small morphological features: An example using a Cambrian trilobite. Sci. Rep. 8(1), 1–10 (2018).
CAS  Google Scholar 

56.
Lask, P. B. The hydrodynamic behavior of sclerites from the trilobite Flexicalymene meeki. Palaios, 219–225 (1993).

57.
Hesselbo, S. P. The biostratinomy of Dikelocephalus sclerites: implications for the use of trilobite attitude data. Palaios. 605–608 (1987).

58.
Mikulic, D. G. The arthropod fossil record: biologic and taphonomic controls on its composition. Short Courses Paleontol. 3, 1–23 (1990).
Google Scholar 

59.
Speyer, S. E. & Donovan, S. K. Trilobite taphonomy: A basis for comparative studies of arthropod preservation, functional anatomy and behaviour. Processes Fossil., 194–219 (1991).

60.
Speyer, S. E. & Brett, C. E. Trilobite taphonomy and Middle Devonian taphofacies. Palaios., 312–327 (1986).

61.
Schumacher, G. A. & Shrake, D. L. Paleoecology and comparative taphonomy of an Isotelus (Trilobita) fossil lagerstätten from the Waynesville Formation (Upper Ordovician, Cincinnatian Series) of southwestern Ohio. In Paleontological Events: Stratigraphic, Ecological, and Evolutionary Implications. 131–161 (Columbia University Press, New York, 1997).

62.
Hickerson, W. J. Middle Devonian (Givetian) trilobite clusters from eastern Iowa and northwestern Illinois. In Paleontological Events: Stratigraphic, Ecological, and Evolutionary Implications. 224–246 (Columbia University Press, New York, 1997).

63.
Hughes, N. C. & Cooper, D. L. Paleobiologic and taphonomic aspects of the “granulosa” trilobite cluster, Kope Formation (Upper Ordovician, Cincinnati region). J. Paleontol. 73(2), 306–319 (1999).
Google Scholar 

64.
Hunda, B. R., Hughes, N. C. & Flessa, K. W. Trilobite taphonomy and temporal resolution in the Mt. Orab shale bed (Upper Ordovician, Ohio, USA). Palaios. 21(1), 26–45 (2006).

65.
Hunter, J. D. Matplotlib: A 2D graphics environment. Comput. Sci. Eng. 9(3), 90–95 (2007).
Google Scholar 

66.
Davis, J. C. Statistics and data analysis In Geology 289–291 (Wiley, New York, 1986).

67.
Roubeyrie, L. & Celles, S. Windrose: A Python Matplotlib, Numpy library to manage wind and pollution data, draw windrose. J Open Source Softw. 3(29), 268 (2018).
ADS  Google Scholar 

68.
Sun, H.-J., Zhao, Y.-L., Peng, J. & Yang, Y.-N. New Wiwaxia material from the Tsinghsutung Formation (Cambrian Series 2) of Eastern Guizhou, China. Geol. Mag. 151(2), 339–348 (2014).
ADS  CAS  Google Scholar  More