Philippa Kaur

1.Knittel K, Boetius A. Anaerobic oxidation of methane: progress with an unknown process. Annu Rev Microbiol. 2009;63:311–34.CAS 
PubMed 
Article 

Google Scholar 
2.Reeburgh WS. Oceanic methane biogeochemistry. Chem Rev. 2007;107:486–513.CAS 
PubMed 
Article 

Google Scholar 
3.Hatzenpichler R, Connon SA, Goudeau D, Malmstrom RR, Woyke T, Orphan VJ. Visualizing in situ translational activity for identifying and sorting slow-growing archaeal−bacterial consortia. Proc Natl Acad Sci USA. 2016;113:E4069–E4078.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
4.Skennerton CT, Chourey K, Iyer R, Hettich RL, Tyson GW, Orphan VJ. Methane-fueled syntrophy through extracellular electron transfer: uncovering the genomic traits conserved within diverse bacterial partners of anaerobic methanotrophic archaea. mBio. 2017;8:e00530–e00517.PubMed 
PubMed Central 

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

Google Scholar 
6.Wegener G, Krukenberg V, Ruff SE, Kellermann MY, Knittel K. Metabolic capabilities of microorganisms involved in and associated with the anaerobic oxidation of methane. Front Microbiol. 2016;7:869.Article 

Google Scholar 
7.Metcalfe KS, Murali R, Mullin SW, Connon SA, Orphan VJ. Experimentally-validated correlation analysis reveals new anaerobic methane oxidation partnerships with consortium-level heterogeneity in diazotrophy. ISME J. 2020;15:1–20.8.Krukenberg V, Riedel D, Gruber Vodicka HR, Buttigieg PL, Tegetmeyer HE, Boetius A, et al. Gene expression and ultrastructure of meso- and thermophilic methanotrophic consortia. Environ Microbiol. 2018;20:1651–6.9.Milucka J, Ferdelman TG, Polerecky L, Franzke D, Wegener G, Schmid M, et al. Zero-valent sulphur is a key intermediate in marine methane oxidation. Nature. 2012;491:541–6.CAS 
PubMed 
Article 

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

Google Scholar 
11.Yu H, Susanti D, McGlynn SE, Skennerton CT, Chourey K, Iyer R, et al. Comparative genomics and proteomic analysis of assimilatory sulfate reduction pathways in anaerobic methanotrophic archaea. Front Microbiol. 2018;9:2917.12.Scheller S, Yu H, Chadwick GL, McGlynn SE, Orphan VJ. Artificial electron acceptors decouple archaeal methane oxidation from sulfate reduction. Science. 2016;351:703–7.CAS 
PubMed 
Article 

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

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

Google Scholar 
15.Liu Y, Beer LL, Whitman WB. Sulfur metabolism in archaea reveals novel processes. Environ Microbiol. 2012;14:2632–44.CAS 
PubMed 
Article 

Google Scholar 
16.Perona JJ, Rauch BJ, Driggers CM. Sulfur assimilation and trafficking in methanogens. In: Rampelotto PH, editor. Molecular Mechanisms of Microbial Evolution. Cham: Springer International Publishing; 2018. p. 371–408.17.White RH, Allen KD, Wegener G. Identification of a redox active thioquinoxalinol sulfate compound produced by an anaerobic methane-oxidizing microbial consortium. ACS Omega. 2019;4:22613–22.18.Cline JD. Spectrophotometric determination of hydrogen sulfide in natural waters. Limnol Oceanogr. 1969;14:454–8.CAS 
Article 

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

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

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

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

Google Scholar 
23.Laczny CC, Sternal T, Plugaru V, Gawron P, Atashpendar A, Margossian HH, et al. VizBin – an application for reference-independent visualization and human-augmented binning of metagenomic data. Microbiome. 2015;3:1.PubMed 
PubMed Central 
Article 

Google Scholar 
24.Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 2010;11:119.Article 
CAS 

Google Scholar 
26.Chen I-MA, Chu K, Palaniappan K, Pillay M, Ratner A, Huang J, et al. IMG/M v.5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes. Nucleic Acids Res. 2019;47:D666–D677.CAS 
PubMed 
Article 

Google Scholar 
27.Agarwala R, Barrett T, Beck J, Benson DA, Bollin C, Bolton E, et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2018;46:D8–D13.Article 
CAS 

Google Scholar 
28.Saier MH, Reddy VS, Tsu BV, Ahmed MS, Li C, Moreno-Hagelsieb G. The Transporter Classification Database (TCDB): recent advances. Nucleic Acids Res. 2016;44:D372–D379.CAS 
PubMed 
Article 

Google Scholar 
29.Li H, Durbin R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics. 2009;25:1754–60.CAS 
PubMed 
PubMed Central 
Article 

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

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

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

Google Scholar 
33.Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Kopylova E, Noe L, Touzet H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics. 2012;28:3211–7.CAS 
Article 

Google Scholar 
35.Bray NL, Pimentel H, Melsted P, Pachter L. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol. 2016;34:525–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Pimentel H, Bray NL, Puente S, Melsted P, Pachter L. Differential analysis of RNA-seq incorporating quantification uncertainty. Nat Methods. 2017;14:687–90.CAS 
PubMed 
Article 

Google Scholar 
37.McGee WA, Pimentel H, Pachter L, Wu JY. Compositional Data Analysis is necessary for simulating and analyzing RNA-Seq data. bioRxiv 2019;564955.38.Rocha DJP, Santos CS, Pacheco LGC. Bacterial reference genes for gene expression studies by RT-qPCR: survey and analysis. Antonie van Leeuwenhoek. 2015;108:685–93.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
39.Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol. 2011;29:644–52.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
40.Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12:59–60.CAS 
Article 
PubMed 

Google Scholar 
41.Rinke C, Chuvochina M, Mussig AJ, Chaumeil P-A, Waite DW, Whitman WB, et al. A rank-normalized archaeal taxonomy based on genome phylogeny resolves widespread incomplete and uneven classifications. bioRxiv. 2020. https://doi.org/10.1101/2020.03.01.972265.42.Orphan VJ, Turk KA, Green AM, House CH. Patterns of 15N assimilation and growth of methanotrophic ANME-2 archaea and sulfate-reducing bacteria within structured syntrophic consortia revealed by FISH-SIMS. Environ Microbiol. 2009;11:1777–91.CAS 
PubMed 
Article 

Google Scholar 
43.Girguis PR, Cozen AE, DeLong EF. Growth and population dynamics of anaerobic methane-oxidizing archaea and sulfate-reducing bacteria in a continuous-flow bioreactor. Appl Environ Microbiol. 2005;71:3725–33.CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
45.Meulepas RJW, Jagersma CG, Khadem AF, Buisman CJN, Stams AJM, Lens PNL. Effect of environmental conditions on sulfate reduction with methane as electron donor by an Eckernförde Bay enrichment. Environ Sci Technol. 2009;43:6553–9.CAS 
PubMed 
Article 

Google Scholar 
46.McGlynn SE. Energy metabolism during anaerobic methane oxidation in ANME archaea. Microbes Environ. 2017;32:5–13.PubMed 
PubMed Central 
Article 

Google Scholar 
47.Wang F-P, Zhang Y, Chen Y, He Y, Qi J, Hinrichs K-U, et al. Methanotrophic archaea possessing diverging methane-oxidizing and electron-transporting pathways. ISME J. 2014;8:1069–78.CAS 
PubMed 
Article 

Google Scholar 
48.Meyerdierks A, Kube M, Kostadinov I, Teeling H, Glöckner FO, Reinhardt R, et al. Metagenome and mRNA expression analyses of anaerobic methanotrophic archaea of the ANME-1 group. Environ Microbiol. 2010;12:422–39.CAS 
PubMed 
Article 

Google Scholar 
49.Cai C, Leu AO, Xie G-J, Guo J, Feng Y, Zhao J-X, et al. A methanotrophic archaeon couples anaerobic oxidation of methane to Fe(III) reduction. ISME J. 2018;1:285.
Google Scholar 
50.Leu AO, Cai C, McIlroy SJ, Southam G, Orphan VJ, Yuan Z, et al. Anaerobic methane oxidation coupled to manganese reduction by members of the Methanoperedenaceae. ISME J. 2020;14:1030–41.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Yanagawa K, Sunamura M, Lever MA, Morono Y, Hiruta A, Ishizaki O, et al. Niche separation of methanotrophic archaea (ANME-1 and-2) in methane-seep sediments of the eastern Japan Sea offshore Joetsu. Geomicrobiol J. 2011;28:118–29.CAS 
Article 

Google Scholar 
52.Biddle JF, Cardman Z, Mendlovitz H, Albert DB, Lloyd KG, Boetius A, et al. Anaerobic oxidation of methane at different temperature regimes in Guaymas Basin hydrothermal sediments. ISME J. 2012;6:1018–31.CAS 
PubMed 
Article 

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

Google Scholar 
54.Roalkvam I, Jørgensen SL, Chen Y, Stokke R, Dahle H, Hocking WP, et al. New insight into stratification of anaerobic methanotrophs in cold seep sediments. FEMS Microbiol Ecol. 2011;78:233–43.CAS 
PubMed 
Article 

Google Scholar 
55.Timmers PHA, Widjaja-Greefkes HCA, Ramiro-Garcia J, Plugge CM, Stams AJM. Growth and activity of ANME clades with different sulfate and sulfide concentrations in the presence of methane. Front Microbiol. 2015;6:988.56.Nauhaus K, Treude T, Boetius A, Krüger M. Environmental regulation of the anaerobic oxidation of methane: a comparison of ANME-I and ANME-II communities. Environ Microbiol. 2005;7:98–106.CAS 
PubMed 
Article 

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

Google Scholar 
58.Wegener G, Niemann H, Elvert M, Hinrichs K-U, Boetius A. Assimilation of methane and inorganic carbon by microbial communities mediating the anaerobic oxidation of methane. Environ Microbiol. 2008;10:2287–98.CAS 
PubMed 
Article 

Google Scholar 
59.Scherer P, Lippert H, Wolff G. Composition of the major elements and trace elements of 10 methanogenic bacteria determined by inductively coupled plasma emission spectrometry. Biol Trace Elem Res. 1983;5:149–63.CAS 
PubMed 
Article 

Google Scholar 
60.Marsili E, Baron DB, Shikhare ID, Coursolle D, Gralnick JA, Bond DR. Shewanella secretes flavins that mediate extracellular electron transfer. Proc Natl Acad Sci USA. 2008;105:3968–73.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Kotloski NJ, Gralnick JA. Flavin electron shuttles dominate extracellular electron transfer by Shewanella oneidensis. mBio 2013;4:e00553–12.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
62.Mevers E, Su L, Pishchany G, Baruch M, Cornejo J, Hobert E, et al. An elusive electron shuttle from a facultative anaerobe. eLife. 2019;8:e48054.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
63.Anderson AG, Iii FBC, Odom JM, Weimer PJ. Anthraquinones as inhibitors of sulfide production from sulfate-reducing bacteria. 1991.64.Wang X, Cheng X, Ren Y, Xu G, Tang J. Humic analog AQDS can act as a selective inhibitor to enable anoxygenic photosynthetic bacteria to outcompete sulfate-reducing bacteria under microaerobic conditions. J Chem Technol Biotechnol. 2016;91:2103–10.CAS 
Article 

Google Scholar 
65.Lee YH, Pavlostathis SG. Decolorization and toxicity of reactive anthraquinone textile dyes under methanogenic conditions. Water Res. 2004;38:1838–52.CAS 
PubMed 
Article 

Google Scholar 
66.Wu Y-W, Ouyang J, Xiao X-H, Gao W-Y, Liu Y. Antimicrobial properties and toxicity of anthraquinones by microcalorimetric bioassay. Chin J Chem. 2006;24:45–50.CAS 
Article 

Google Scholar 
67.Novotný Č, Dias N, Kapanen A, Malachová K, Vándrovcová M, Itävaara M, et al. Comparative use of bacterial, algal and protozoan tests to study toxicity of azo- and anthraquinone dyes. Chemosphere. 2006;63:1436–42.PubMed 
Article 
CAS 

Google Scholar 
68.Shyu JBH, Lies DP, Newman DK. Protective role of tolC in efflux of the electron shuttle anthraquinone-2,6-disulfonate. J Bacteriol. 2002;184:1806–10.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
69.Lovley DR, Coates JD, Blunt-Harris EL, Phillips EJP, Woodward JC. Humic substances as electron acceptors for microbial respiration. Nature. 1996;382:445–8.CAS 
Article 

Google Scholar 
70.Newman DK, Kolter R. A role for excreted quinones in extracellular electron transfer. Nature. 2000;405:94–7.CAS 
PubMed 
Article 

Google Scholar 
71.Holmes DE, Ueki T, Tang H-Y, Zhou J, Smith JA, Chaput G, et al. A membrane-bound cytochrome enables Methanosarcina acetivorans to conserve energy from extracellular electron transfer. mBio. 2019;10:e00789–19.CAS 
PubMed 
PubMed Central 

Google Scholar 
72.Neuberger A, Du D, Luisi BF. Structure and mechanism of bacterial tripartite efflux pumps. Res Microbiol. 2018;169:401–13.CAS 
PubMed 
Article 

Google Scholar 
73.Crow A, Greene NP, Kaplan E, Koronakis V. Structure and mechanotransmission mechanism of the MacB ABC transporter superfamily. Proc Natl Acad Sci USA. 2017;114:12572–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
74.Jiménez-Otero F, Chan CH, Bond DR. Identification of different putative outer membrane electron conduits necessary for Fe (III) citrate, Fe (III) oxide, Mn (IV) oxide, or electrode reduction by Geobacter sulfurreducens. J Bacteriol. 2018;200:3061.Article 

Google Scholar 
75.Plugge CM, Scholten JCM, Culley DE, Nie L, Brockman FJ, Zhang W. Global transcriptomics analysis of the Desulfovibrio vulgaris change from syntrophic growth with Methanosarcina barkeri to sulfidogenic metabolism. Microbiology. 2010;156:2746–56.CAS 
PubMed 
Article 

Google Scholar 
76.Walker CB, He Z, Yang ZK, Ringbauer JAJ, He Q, Zhou J, et al. The electron transfer system of syntrophically grown Desulfovibrio vulgaris. J Bacteriol. 2009;191:5793–801.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
77.Wenter R, Hütz K, Dibbern D, Li T, Reisinger V, Plöscher M, et al. Expression-based identification of genetic determinants of the bacterial symbiosis ‘Chlorochromatium aggregatum’. Environ Microbiol. 2010;12:2259–76.CAS 
PubMed 

Google Scholar  More

1.SERNAPESCA (Servicio Nacional de Pesca y Acuicultura). Informe Sanitario de Salmonicultura. http://www.sernapesca.cl/sites/default/files/informe_sanitario_salmonicultura_en_centros_marinos_2018_final.pdf (2018).2.Bang Jensen, B., Qviller, L. & Toft, N. Spatio-temporal variations in mortality during the seawater production phase of Atlantic salmon (Salmo salar) in Norway. J. Fish Dis. 43, 445–457 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
3.Moriarty, M. et al. Modelling temperature and fish biomass data to predict annual Scottish farmed salmon, Salmo salar L., losses: Development of an early warning tool. Prev. Vet. Med. 178, 104985. https://doi.org/10.1016/j.prevetmed.2020.104985 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
4.Sommerset, I. et al. The Health Situation in Norwegian Aquaculture 2019. https://www.vetinst.no/rapporter-og-publikasjoner/rapporter/2020/fiskehelserapporten-2019 (2020).5.Grefsrud, E. S. et al. Risikorapport norsk fiskeoppdrett 2021—risikovurdering. https://www.hi.no/hi/nettrapporter/rapport-fra-havforskningen-2021-8 (2021).6.Overton, K. et al. Salmon lice treatments and salmon mortality in Norwegian aquaculture: A review. Rev. Aquac. 11, 1398–1417 (2019).Article 

Google Scholar 
7.Soares, S., Green, D. M., Turnbull, J. F., Crumlish, M. & Murray, A. G. A baseline method for benchmarking mortality losses in Atlantic salmon (Salmo salar) production. Aquaculture 314, 7–12 (2011).Article 

Google Scholar 
8.Santurtun, E., Broom, D. & Phillips, C. A review of factors affecting the welfare of Atlantic salmon (Salmo salar). Anim. Welf. 27, 193–204 (2018).Article 

Google Scholar 
9.Ellis, T., Berrill, I., Lines, J., Turnbull, J. F. & Knowles, T. G. Mortality and fish welfare. Fish Physiol. Biochem. 38, 189–199 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Crockford, T., Menzies, F., McLoughlin, M., Wheatley, S. & Goodall, E. Aspects of the epizootiology of pancreas disease in farmed Atlantic salmon Salmo salar in Ireland. Dis. Aquat. Organ. 36, 113–119 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.Stormoen, M., Kristoffersen, A. B. & Jansen, P. A. Mortality related to pancreas disease in Norwegian farmed salmonid fish, Salmo salar L. and Oncorhynchus mykiss (Walbaum). J. Fish Dis. 36, 639–645 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Taksdal, T. et al. Mortality and weight loss of Atlantic salmon, Salmon salar L., experimentally infected with salmonid alphavirus subtype 2 and subtype 3 isolates from Norway. J. Fish Dis. 38, 1047–1061 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Hammell, K. L. & Dohoo, I. R. Mortality patterns in infectious salmon anaemia virus outbreaks in New Brunswick, Canada. J. Fish Dis. 28, 639–650 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Glover, K. A. et al. Size-dependent susceptibility to infectious salmon anemia virus (ISAV) in Atlantic salmon (Salmo salar L.) of farm, hybrid and wild parentage. Aquaculture 254, 82–91 (2006).Article 

Google Scholar 
15.Roberts, R. J. & Pearson, M. D. Infectious pancreatic necrosis in Atlantic salmon, Salmo salar L. J. Fish Dis. 28, 383–390 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
16.Bang Jensen, B. & Kristoffersen, A. Risk factors for outbreaks of infectious pancreatic necrosis (IPN) and associated mortality in Norwegian salmonid farming. Dis. Aquat. Organ. 114, 177–187 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
17.Brun, E., Poppe, T., Skrudland, A. & Jarp, J. Cardiomyopathy syndrome in farmed Atlantic salmon Salmo salar: Occurrence and direct financial losses for Norwegian aquaculture. Dis. Aquat. Organ. 56, 241–247 (2003).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Bang Jensen, B., Brun, E., Fineid, B., Larssen, R. & Kristoffersen, A. Risk factors for cardiomyopathy syndrome (CMS) in Norwegian salmon farming. Dis. Aquat. Organ. 107, 141–150 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
19.Løvoll, M. et al. Atlantic salmon bath challenged with Moritella viscosa—Pathogen invasion and host response. Fish Shellfish Immunol. 26, 877–884 (2009).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
20.Delghandi, M. R., El-Matbouli, M. & Menanteau-Ledouble, S. Renibacterium salmoninarum—The causative agent of bacterial kidney disease in salmonid fish. Pathogens 9, 845. https://doi.org/10.3390/pathogens9100845 (2020).CAS 
Article 
PubMed Central 

Google Scholar 
21.Lhorente, J. P., Gallardo, J. A., Villanueva, B., Carabaño, M. J. & Neira, R. Disease resistance in Atlantic Salmon (Salmo salar): Coinfection of the intracellular bacterial pathogen Piscirickettsia salmonis and the Sea Louse Caligus rogercresseyi. PLoS ONE 9, e95397. https://doi.org/10.1371/journal.pone.0095397 (2014).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
22.Kristoffersen, A. B. et al. Quantitative risk assessment of salmon louse-induced mortality of seaward-migrating post-smolt Atlantic salmon. Epidemics 23, 19–33 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
23.Vollset, K. W. Parasite induced mortality is context dependent in Atlantic salmon: Insights from an individual-based model. Sci. Rep. 9, 17377. https://doi.org/10.1038/s41598-019-53871-2 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
24.Taylor, R. S., Kube, P. D., Muller, W. J. & Elliott, N. G. Genetic variation of gross gill pathology and survival of Atlantic salmon (Salmo salar L.) during natural amoebic gill disease challenge. Aquaculture 294, 172–179 (2009).Article 

Google Scholar 
25.Carvalho, L. A. et al. Impact of co-infection with Lepeophtheirus salmonis and Moritella viscosa on inflammatory and immune responses of Atlantic salmon (Salmo salar). J. Fish Dis. 43, 459–473 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Barker, S. E. et al. Sea lice, Lepeophtheirus salmonis (Krøyer 1837), infected Atlantic salmon (Salmo salar L.) are more susceptible to infectious salmon anemia virus. PLoS ONE 14, e0209178. https://doi.org/10.1371/journal.pone.0209178 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
27.Staurnes, M., Sigholt, T., Åsgård, T. & Baeverfjord, G. Effects of a temperature shift on seawater challenge test performance in Atlantic salmon (Salmo salar) smolt. Aquaculture 201, 153–159 (2001).Article 

Google Scholar 
28.Ytrestøyl, T. et al. Performance and welfare of Atlantic salmon, Salmo salar L. post-smolts in recirculating aquaculture systems: Importance of salinity and water velocity. J. World Aquac. Soc. 51, 373–392 (2020).Article 
CAS 

Google Scholar 
29.Montes, R. M., Rojas, X., Artacho, P., Tello, A. & Quiñones, R. A. Quantifying harmful algal bloom thresholds for farmed salmon in southern Chile. Harmful Algae 77, 55–65 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
30.León-Muñoz, J., Urbina, M. A., Garreaud, R. & Iriarte, J. L. Hydroclimatic conditions trigger record harmful algal bloom in western Patagonia (summer 2016). Sci. Rep. 8, 1330. https://doi.org/10.1038/s41598-018-19461-4 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
31.Groner, M. L., McEwan, G. F., Rees, E. E., Gettinby, G. & Revie, C. W. Quantifying the influence of salinity and temperature on the population dynamics of a marine ectoparasite. Can. J. Fish. Aquat. Sci. 73, 1281–1291 (2016).Article 

Google Scholar 
32.Sievers, M., Oppedal, F., Ditria, E. & Wright, D. W. The effectiveness of hyposaline treatments against host-attached salmon lice. Sci. Rep. 9, 6976. https://doi.org/10.1038/s41598-019-43533-8 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
33.Tunsjø, H. S. et al. Adaptive response to environmental changes in the fish pathogen Moritella viscosa. Res. Microbiol. 158, 244–250 (2007).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
34.Guomundsdóttir, S. et al. Measures applied to control Renibacterium salmoninarum infection in Atlantic salmon: A retrospective study of two sea ranches in Iceland. Aquaculture 186, 193–203 (2000).Article 

Google Scholar 
35.Jansen, M. D. et al. Salmonid alphavirus (SAV) and pancreas disease (PD) in Atlantic salmon, Salmo salar L., in freshwater and seawater sites in Norway from 2006 to 2008. J. Fish Dis. 33, 391–402 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
36.Jensen, B. B., Kristoffersen, A. B., Myr, C. & Brun, E. Cohort study of effect of vaccination on pancreas disease in Norwegian salmon aquaculture. Dis. Aquat. Organ. 102, 23–31 (2012).Article 

Google Scholar 
37.Karlsen, C., Thorarinsson, R., Wallace, C., Salonius, K. & Midtlyng, P. J. Atlantic salmon winter-ulcer disease: Combining mortality and skin ulcer development as clinical efficacy criteria against Moritella viscosa infection. Aquaculture 473, 538–544 (2017).Article 

Google Scholar 
38.Jansen, P. A. et al. Sea lice as a density-dependent constraint to salmonid farming. Proc. R. Soc. B Biol. Sci. 279, 2330–2338 (2012).Article 

Google Scholar 
39.Overton, K., Samsing, F., Oppedal, F., Stien, L. H. & Dempster, T. Lowering treatment temperature reduces salmon mortality: A new way to treat with hydrogen peroxide in aquaculture. Pest Manag. Sci. 74, 535–540 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
40.Helgesen, K. O., Romstad, H., Aaen, S. M. & Horsberg, T. E. First report of reduced sensitivity towards hydrogen peroxide found in the salmon louse Lepeophtheirus salmonis in Norway. Aquac. Rep. 1, 37–42 (2015).Article 

Google Scholar 
41.Walde, C. S., Bang Jensen, B., Pettersen, J. M. & Stormoen, M. Estimating cage level mortality distributions following different delousing treatments of Atlantic salmon (Salmo salar) in Norway. J. Fish Dis. 44, jfd.13348. https://doi.org/10.1111/jfd.13348 (2021).Article 

Google Scholar 
42.Aaen, S. M., Helgesen, K. O., Bakke, M. J., Kaur, K. & Horsberg, T. E. Drug resistance in sea lice: A threat to salmonid aquaculture. Trends Parasitol. 31, 72–81 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
43.Gismervik, K., Nielsen, K. V., Lind, M. B. & Viljugrein, H. Mekanisk avlusing med FLS-avlusersystem—dokumentasjon av fiskevelferd og effekt mot lus. Veterinærinstituttets rapportserie 6–2017. See https://www.vetinst.no/rapporter-og-publikasjoner/rapporter/2017/mekanisk-avlusing-dokumentasjon-av-fiskevelferd-og-effekt-mot-lus (2017).44.Grøntvedt, R. N. et al. Thermal de-licing of salmonid fish—Documentation of fish welfare and effect. Norwegian Veterinary Institute`s Report series 13–2015. https://www.vetinst.no/rapporter-og-publikasjoner/rapporter/2015/thermal-de-licing-of-salmonid-fish-documentation-of-fish-welfare-and-effect (2015).45.Gismervik, K. et al. Thermal injuries in Atlantic salmon in a pilot laboratory trial. Vet. Anim. Sci. 8, 100081. https://doi.org/10.1016/j.vas.2019.100081 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
46.Wickham, H. et al. Welcome to the Tidyverse. J. Open Source Softw. 4, 1686. https://doi.org/10.21105/joss.01686 (2019).ADS 
Article 

Google Scholar 
47.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).
Google Scholar 
48.Auguie B. gridExtra: Miscellaneous functions for “grid” graphics. R package version 2.3. https://CRAN.R-project.org/package=gridExtra (2017).49.Salama, N. K. G., Murray, A. G., Christie, A. J. & Wallace, I. S. Using fish mortality data to assess reporting thresholds as a tool for detection of potential disease concerns in the Scottish farmed salmon industry. Aquaculture 450, 283–288 (2016).Article 

Google Scholar 
50.Aunsmo, A. et al. Methods for investigating patterns of mortality and quantifying cause-specific mortality in sea-farmed Atlantic salmon Salmo salar. Dis. Aquat. Organ. 81, 99–107 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
51.Usher, M. L., Talbot, C. & Eddy, F. B. Effects of transfer to seawater on growth and feeding in Atlantic salmon smolts (Salmo salar L.). Aquaculture 94, 309–326 (1991).Article 

Google Scholar 
52.Johansson, L.-H., Timmerhaus, G., Afanasyev, S., Jørgensen, S. M. & Krasnov, A. Smoltification and seawater transfer of Atlantic salmon (Salmo salar L.) is associated with systemic repression of the immune transcriptome. Fish Shellfish Immunol. 58, 33–41 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
53.Ellis, T., Turnbull, J. F., Knowles, T. G., Lines, J. A. & Auchterlonie, N. A. Trends during development of Scottish salmon farming: An example of sustainable intensification?. Aquaculture 458, 82–99 (2016).Article 

Google Scholar 
54.Kristensen, T. et al. Effects of production intensity and production strategies in commercial Atlantic salmon smolt (Salmo salar L.) production on subsequent performance in the early sea stage. Fish Physiol. Biochem. 38, 273–282 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
55.Handeland, S. O., Björnsson, B. T., Arnesen, A. M. & Stefansson, S. O. Seawater adaptation and growth of post-smolt Atlantic salmon (Salmo salar) of wild and farmed strains. Aquaculture 220, 367–384 (2003).Article 

Google Scholar 
56.Bjørndal, T. & Tusvik, A. Economic analysis of on-growing of salmon post-smolts. Aquac. Econ. Manag. 24, 355–386 (2020).Article 

Google Scholar 
57.Bang Jensen, B., Mårtensson, A. & Kristoffersen, A. B. Estimating risk factors for the daily risk of developing clinical cardiomyopathy syndrome (CMS) on a fishgroup level. Prev. Vet. Med. 175, 104852. https://doi.org/10.1016/j.prevetmed.2019.104852 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
58.Iversen, M. et al. Stress responses in Atlantic salmon (Salmo salar L.) smolts during commercial well boat transports, and effects on survival after transfer to sea. Aquaculture 243, 373–382 (2005).Article 

Google Scholar 
59.Intorre, L. Safety of azamethiphos in eel, seabass and trout. Pharmacol. Res. 49, 171–176 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
60.Olsvik, P. A., Ørnsrud, R., Lunestad, B. T., Steine, N. & Fredriksen, B. N. Transcriptional responses in Atlantic salmon (Salmo salar) exposed to deltamethrin, alone or in combination with azamethiphos. Comp. Biochem. Physiol. Part C Toxicol. Pharmacol. 162, 23–33 (2014).CAS 
Article 

Google Scholar 
61.Johnson, S., Constible, J. & Richard, J. Laboratory investigations on the efficacy of hydrogen peroxide against the salmon louse Lepeophtheirus salmonis and its lexicological and histopathological effects on Atlantic salmon Salmo salar and Chinook salmon Oncorhynchus tshawytscha. Dis. Aquat. Organ. 17, 197–204 (1993).CAS 
Article 

Google Scholar 
62.Nilsson, J. et al. Sudden exposure to warm water causes instant behavioural responses indicative of nociception or pain in Atlantic salmon. Vet. Anim. Sci. 8, 100076. https://doi.org/10.1016/j.vas.2019.100076 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
63.Stien, L. H., Lind, M. B., Oppedal, F., Wright, D. W. & Seternes, T. Skirts on salmon production cages reduced salmon lice infestations without affecting fish welfare. Aquaculture 490, 281–287 (2018).Article 

Google Scholar 
64.Barrett, L. T., Oppedal, F., Robinson, N. & Dempster, T. Prevention not cure: A review of methods to avoid sea lice infestations in salmon aquaculture. Rev. Aquac. 12, 2527–2543 (2020).Article 

Google Scholar 
65.Overton, K., Barrett, L. T., Oppedal, F., Kristiansen, T. S. & Dempster, T. Sea lice removal by cleaner fish in salmon aquaculture: A review of the evidence base. Aquac. Environ. Interact. 12, 31–44 (2020).Article 

Google Scholar 
66.Tully, O., Daly, P., Lysaght, S., Deady, S. & Varian, S. J. A. Use of cleaner-wrasse (Centrolabrus exoletus (L.) and Ctenolabrus rupestris (L.)) to control infestations of Caligus elongatus Nordmann on farmed Atlantic salmon. Aquaculture 142, 11–24 (1996).Article 

Google Scholar 
67.Imsland, A. K. D. et al. It works! Lumpfish can significantly lower sea lice infestation in large-scale salmon farming. Biol. Open 7, bio036301. https://doi.org/10.1242/bio.036301 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
68.Erkinharju, T., Dalmo, R. A., Hansen, M. & Seternes, T. Cleaner fish in aquaculture: Review on diseases and vaccination. Rev. Aquac. 13, 189–237 (2021).Article 

Google Scholar 
69.Elliott, J. M. & Elliott, J. A. Temperature requirements of Atlantic salmon Salmo salar, brown trout Salmo trutta and Arctic charr Salvelinus alpinus: Predicting the effects of climate change. J. Fish Biol. 77, 1793–1817 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
70.Finstad, T. & Sigholt, B. Effect of low temperature on seawater tolerance in Atlantic salmon (Salmo salar) smolts. Aquaculture 84, 167–172 (1990).Article 

Google Scholar 
71.Grefsrud, E. S. et al. Risikorapport norsk fiskeoppdrett 2018. https://www.hi.no/resources/publikasjoner/risikorapport-norsk-fiskeoppdrett/2018/risikorapport_2018.pdf (2018).72.Hvas, M., Folkedal, O. & Oppedal, F. Fish welfare in offshore salmon aquaculture. Rev. Aquac. 13, 836–852 (2021).Article 

Google Scholar 
73.Dórea, F. C. & Vial, F. Animal health syndromic surveillance: A systematic literature review of the progress in the last 5 years (2011–2016). Vet. Med. Res. Rep. 7, 157–170 (2016).
Google Scholar 
74.Fernández-Fontelo, A. et al. Enhancing the monitoring of fallen stock at different hierarchical administrative levels: An illustration on dairy cattle from regions with distinct husbandry, demographical and climate traits. BMC Vet. Res. 16, 110 (2020).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
75.Samsing, F., Johnsen, I., Dempster, T., Oppedal, F. & Treml, E. A. Network analysis reveals strong seasonality in the dispersal of a marine parasite and identifies areas for coordinated management. Landsc. Ecol. 32, 1953–1967 (2017).Article 

Google Scholar 
76.Samsing, F., Johnsen, I., Treml, E. A. & Dempster, T. Identifying ‘firebreaks’ to fragment dispersal networks of a marine parasite. Int. J. Parasitol. 49, 277–286 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
77.Myksvoll, M. S. et al. Evaluation of a national operational salmon lice monitoring system—From physics to fish. PLoS ONE 13, e0201338. https://doi.org/10.1371/journal.pone.0201338 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
78.Pebesma, E. Simple features for R: Standardized support for spatial vector data. R J. 10, 439–446 (2018).Article 

Google Scholar 
79.Tennekes, M. tmap: Thematic maps in R. J. Stat. Softw. 84, 1–39 (2018).Article 

Google Scholar 
80.NFD (Nærings- og fiskeridepartementet). Forskrift om bekjempelse av lakselus i akvakulturanlegg. Lovdata. https://lovdata.no/dokument/SF/forskrift/2012-12-05-1140 (2012).81.NFD (Nærings- og fiskeridepartementet). Forskrift om bekjempelse av lakselus i akvakulturanlegg. Lovdata. https://lovdata.no/dokument/LTI/forskrift/2012-12-05-1140 (2012).82.Asplin, L., Albretsen, J., Johnsen, I. A. & Sandvik, A. D. The hydrodynamic foundation for salmon lice dispersion modeling along the Norwegian coast. Ocean Dyn. 70, 1151–1167 (2020).ADS 
Article 

Google Scholar 
83.Pierce, D. ncdf4: Interface to Unidata netCDF (Version 4 or Earlier) Format Data Files. CRAN. https://cran.r-project.org/web/packages/ncdf4/ncdf4.pdf (2019).84.Dohoo I., Martin W. & Stryhn H. Veterinary Epidemiologic Research (2nd edn) Charlottetown, Prince Edward Island (VER Inc., 2009).85.Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).Article 

Google Scholar 
86.Lüdecke, D., Makowski, D., Waggoner, P. & Patil, I. performance: Assessment of regression models performance. CRAN. https://easystats.github.io/performance (2020).87.Hartig, F. DHARMa: Residual diagnostics for hierarchical (multi-level/mixed) regression models. CRAN. https://cran.r-project.org/web/packages/DHARMa/DHARMa.pdf (2020). More

1.van Klink, R. et al. Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances. Science 368, 417–420 (2020).PubMed 
PubMed Central 

Google Scholar 
2.Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574, 671–674 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
3.Wepprich, T., Adrion, J. R., Ries, L., Wiedmann, J. & Haddad, N. M. Butterfly abundance declines over 20 years of systematic monitoring in Ohio, USA. PLoS ONE 14, e0216270 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Wagner, D. L., Grames, E. M., Forister, M. L., Berenbaum, M. R. & Stopak, D. Insect decline in the Anthropocene: death by a thousand cuts. Proc. Natl Acad. Sci. USA 118, e2023989118 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
5.Dirzo, R. et al. Defaunation in the Anthropocene. Science 345, 401–406 (2014).CAS 
PubMed 

Google Scholar 
6.Winfree, R., Fox, J. W., Williams, N. M., Reilly, J. R. & Cariveau, D. P. Abundance of common species, not species richness, drives delivery of a real-world ecosystem service. Ecol. Lett. 18, 626–635 (2015).PubMed 
PubMed Central 

Google Scholar 
7.Cardoso, P. et al. Scientists’ warning to humanity on insect extinctions. Biol. Conserv. 242, 108426 (2020).
Google Scholar 
8.Brower, L. P. et al. Decline of monarch butterflies overwintering in Mexico: is the migratory phenomenon at risk? Insect Conserv. Divers. 5, 95–100 (2012).
Google Scholar 
9.Agrawal, A. A. & Inamine, H. Mechanisms behind the monarch’s decline. Science 360, 1294–1296 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
10.Schultz, C. B., Brown, L. M., Pelton, E. & Crone, E. E. Citizen science monitoring demonstrates dramatic declines of monarch butterflies in western North America. Biol. Conserv. 214, 343–346 (2017).
Google Scholar 
11.Thogmartin, W. E. et al. Monarch butterfly population decline in North America: identifying the threatening processes. R. Soc. Open Sci. 4, 170760 (2017).PubMed 
PubMed Central 

Google Scholar 
12.Boyle, J. H., Dalgleish, H. J. & Puzey, J. R. Monarch butterfly and milkweed declines substantially predate the use of genetically modified crops. Proc. Natl Acad. Sci. USA 116, 3006–3011 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
13.Hann, N. L. & Landis, D. A. The importance of shifting disturbance regimes in monarch butterfly decline and recovery. Front. Ecol. Evol. 7, 191 (2019).
Google Scholar 
14.Oberhauser, K. S. et al. Temporal and spatial overlap between monarch larvae and corn pollen. Proc. Natl Acad. Sci. USA 98, 11913–11918 (2001).CAS 
PubMed 
PubMed Central 

Google Scholar 
15.Pleasants, J. M. & Oberhauser, K. S. Milkweed loss in agricultural fields because of herbicide use: effect on the monarch butterfly population. Insect Conserv. Divers. 6, 135–144 (2013).
Google Scholar 
16.Ries, L., Taron, D. J. & Rendón-Salinas, E. The disconnect between summer and winter monarch trends for the eastern migratory population: possible links to differing drivers. Ann. Entomol. Soc. Am. 108, 691–699 (2015).
Google Scholar 
17.Inamine, H., Ellner, S. P., Springer, J. P. & Agrawal, A. A. Linking the continental migratory cycle of the monarch butterfly to understand its population decline. Oikos 125, 1081–1091 (2016).
Google Scholar 
18.Saunders, S. P. et al. Multiscale seasonal factors drive the size of winter monarch colonies. Proc. Natl Acad. Sci. USA 116, 8609–8614 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
19.Zalucki, M. P. Temperature and rate of development in Danaus plexippus L. and D. chrysippus L. (Lepidoptera: Nymphalidae). Aust. J. Entomol. 21, 241–246 (1982).
Google Scholar 
20.Zipkin, E. F., Ries, L., Reeves, R., Regetz, J. & Oberhauser, K. S. Tracking climate impacts on the migratory monarch butterfly. Glob. Change Biol. 18, 3039–3049 (2012).
Google Scholar 
21.Saunders, S. P., Ries, L., Oberhauser, K. S., Thogmartin, W. E. & Zipkin, E. F. Local and cross-seasonal associations of climate and land use with abundance of monarch butterflies. Ecography 41, 278–290 (2018).
Google Scholar 
22.Batalden, R. V., Oberhauser, K. & Peterson, A. T. Ecological niches in sequential generations of eastern North American monarch butterflies: the ecology of migration and likely climate change implications. Environ. Entomol. 36, 1365–1373 (2007).PubMed 
PubMed Central 

Google Scholar 
23.Lemoine, N. P. Climate change may alter breeding ground distributions of eastern migratory monarchs via range expansion of Asclepias host plants. PLoS ONE 10, e0118614 (2015).PubMed 
PubMed Central 

Google Scholar 
24.Vidal, O. & Rendón-Salinas, E. Dynamics and trends of overwintering colonies of the monarch butterfly in Mexico. Biol. Conserv. 180, 165–175 (2014).
Google Scholar 
25.Thogmartin, W. E. et al. Density estimates of monarch butterflies overwintering in central Mexico. PeerJ 5, e3221 (2017).PubMed 
PubMed Central 

Google Scholar 
26.Flockhart, D. T. T., Pichancourt, J.-B., Norris, D. R. & Martin, T. G. Unravelling the annual cycle in a migratory animal: breeding-season habitat loss drives population declines of monarch butterflies. J. Anim. Ecol. 84, 155–165 (2015).PubMed 
PubMed Central 

Google Scholar 
27.Oberhauser, K. et al. A trans-national monarch butterfly population model and implications for regional conservation priorities. Ecol. Entomol. 42, 51–60 (2017).
Google Scholar 
28.Wilcox, A. A. E., Flockhart, D. T. T., Newman, A. E. M. & Norris, D. R. An evaluation of studies on the potential threats contributing to the decline of eastern migratory North American monarch butterflies (Danaus plexippus). Front. Ecol. Evol. 7, 99 (2019).
Google Scholar 
29.Chevan, A. & Sutherland, M. Hierarchical partitioning. Am. Stat. 45, 90–96 (1991).
Google Scholar 
30.Dai, S., Shulski, M. D., Hubbard, K. G. & Takle, E. S. A spatiotemporal analysis of Midwest US temperature and precipitation trends during the growing season from 1980 to 2013. Int. J. Climatol. 36, 517–525 (2016).
Google Scholar 
31.Feng, Z. et al. More frequent intense and long-lived storms dominate the springtime trend in central US rainfall. Nat. Commun. 7, 13429 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
32.Crimmins, T. M. & Crimmins, M. A. Biologically-relevant trends in springtime temperatures across the United States. Geophys. Res. Lett. 46, 12377–12387 (2019).
Google Scholar 
33.Roy, D. B., Rothery, P., Moss, D., Pollard, E. & Thomas, J. A. Butterfly numbers and weather: predicting historical trends in abundance and the future effects of climate change. J. Anim. Ecol. 70, 201–217 (2001).
Google Scholar 
34.Nelson, W. A., Bjørnstad, O. N. & Yamanaka, T. Recurrent insect outbreaks caused by temperature-driven changes in system stability. Science 341, 796–799 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
35.IPCC Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds. Field, C. B. et al.) (Cambridge Univ. Press, 2014).36.Diffenbaugh, N. S. & Giorgi, F. Climate change hotspots in the CMIP5 global climate model ensemble. Clim. Change 114, 813–822 (2012).PubMed 
PubMed Central 

Google Scholar 
37.Cook, K. H., Vizy, E. K., Launer, Z. S. & Patricola, C. M. Springtime intensification of the Great Plains low-level jet and Midwest precipitation in GCM simulations of the twenty-first century. J. Clim. 21, 6321–6340 (2008).
Google Scholar 
38.Diffenbaugh, N. S. & Field, C. B. Changes in ecologically critical terrestrial climate conditions. Science 341, 486–492 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
39.Wagner, D. L. Insect declines in the Anthropocene. Ann. Rev. Entomol. 65, 457–480 (2020).CAS 

Google Scholar 
40.Forister, M. L. et al. Fewer butterflies seen by community scientists across the warming and drying landscapes of the American West. Science 371, 1042–1045 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
41.Janzen, D. H. & Hallwachs, W. To us insectometers, it is clear that insect decline in our Costa Rican tropics is real, so let’s be kind to the survivors. Proc. Natl Acad. Sci. USA 118, e2002546117 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
42.Flockhart, D. T. T. et al. Regional climate on the breeding grounds predicts variation in the natal origin of monarch butterflies overwintering in Mexico over 38 years. Glob. Change Biol. 23, 2565–2576 (2017).
Google Scholar 
43.Wassenaar, L. I. & Hobson, K. A. Natal origins of migratory monarch butterflies at wintering colonies in Mexico: new isotopic evidence. Proc. Natl Acad. Sci. USA 95, 15436–15439 (1998).CAS 
PubMed 
PubMed Central 

Google Scholar 
44.Oberhauser, K. S. et al. in Monarchs in a Changing World: Biology and Conservation of an Iconic Butterfly (eds. Oberhauser, K. S. et al.) 13–30 (Cornell Univ. Press, 2015).45.Pollard, E. A method for assessing changes in the abundance of butterflies. Biol. Conserv. 12, 115–134 (1977).
Google Scholar 
46.Saunders, S. P., Ries, L., Obserhauser, K. S. & Zipkin, E. F. Evaluating confidence in climate-based predictions of population change in a migratory species. Glob. Ecol. Biogeogr. 25, 1000–1012 (2016).
Google Scholar 
47.Missrie, M. in The Monarch Butterfly: Biology and Conservation (eds. Obserhauser, K. S. & Solensky, M. J.) 141–150 (Cornell Univ. Press, 2004).48.García-Serrano, E., Reyes, J. L. & Alvarez, B. X. M. in The Monarch Butterfly: Biology and Conservation (eds. Obserhauser, K. S. & Solensky, M. J.) 129–133 (Cornell Univ. Press, 2004).49.Ramírez, M. I., Sáenz-Romero, C., Rehfeldt, G. & Salas-Canela, L. in Monarchs in a Changing World: Biology and Conservation of an Iconic Butterfly (eds. Oberhauser, K. S. et al.) 157–168 (Cornell Univ. Press, 2015).50.Howard, E. & Davis, A. K. Investigating long-term changes in the spring migration of monarch butterflies (Lepidoptera: Nymphalidae) using 18 years of data from Journey North, a citizen science program. Ann. Entomol. Soc. Am. 108, 664–669 (2015).
Google Scholar 
51.McMaster, G. S. & Wilhelm, W. Growing degree-days: one equation, two interpretations. Agric. Meteorol. 87, 291–300 (1997).
Google Scholar 
52.Thornton, P. E. et al. Daymet: Daily Surface Weather Data on a 1-km Grid for North America Version 3 (ORNL DAAC, 2018); https://doi.org/10.3334/ORNLDAAC/132853.Hartzler, R. G. & Buhler, D. D. Occurrence of common milkweed (Asclepias syriaca) in cropland and adjacent areas. Crop Prot. 19, 363–366 (2000).
Google Scholar 
54.Hartzler, R. G. Reduction in common milkweed (Asclepias syriaca) occurrence in Iowa cropland from 1999 to 2009. Crop Prot. 29, 1542–1544 (2010).
Google Scholar 
55.Homer, C. G. et al. Completion of the 2011 National Land Cover Database for the conterminous United States—representing a decade of land cover change information. Photogramm. Eng. Remote Sens. 81, 345–354 (2015).
Google Scholar 
56.Douglas, M. R., Sponsler, D. B., Lonsdorf, E. V. & Grozinger, C. M. County-level analysis reveals a rapidly shifting landscape of insecticide hazard to honey bees (Apis mellifera) on US farmland. Sci. Rep. 10, 797 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
57.Benbrook, C. M. Trends in glyphosate herbicide use in the United States and globally. Environ. Sci. Eur. 28, 3 (2016).PubMed 
PubMed Central 

Google Scholar 
58.Pesticide National Synthesis Project (US Geological Survey, 2020); https://water.usgs.gov/nawqa/pnsp/usage/maps/county-level/59.Quick Stats (US Department of Agriculture, National Agricultural Statistics Service, 2020); http://quickstats.nass.usda.gov60.Crops (Ontario Ministry of Agriculture, Food and Rural Affairs, 2020); http://www.omafra.gov.on.ca/english/crops/61.Batalden, R. V. & Oberhauser, K. S. in Monarchs in a Changing World: Biology and Conservation of an Iconic Butterfly (eds. Oberhauser, K. S. et al.) 215–224 (Cornell Univ. Press, 2015).62.Alonso-Mejía, A., Rendón-Salinas, E., Montesinos-Patiño, E. & Brower, L. P. Use of lipid reserves by monarch butterflies overwintering in Mexico: implications for conservation. Ecol. Appl. 7, 934–947 (1997).
Google Scholar 
63.Brower, L. P., Fink, L. S. & Walford, P. Fueling the fall migration of the monarch butterfly. Integr. Comp. Biol. 46, 1123–1142 (2006).PubMed 
PubMed Central 

Google Scholar 
64.Tracy, J. L., Kantola, T., Baum, K. A. & Coulson, R. N. Modeling fall migration pathways and spatially identifying potential migratory hazards for the eastern monarch butterfly. Landsc. Ecol. 34, 443–458 (2019).
Google Scholar 
65.Feldman, R. E. & McGill, B. J. How important is nectar in shaping spatial variation in the abundance of temperate breeding hummingbirds? J. Biogeogr. 41, 489–500 (2014).
Google Scholar 
66.Didan, K. MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global 250 m SIN Grid V006 [Data set] (NASA EOSDIS Land Processes DAAC, 2015); https://doi.org/10.5067/MODIS/MOD13Q1.00667.Vidal, O., López-García, J. & Rendón-Salinas, E. Trends in deforestation and forest degradation after a decade of monitoring in the Monarch Butterfly Biosphere Reserve in Mexico. Conserv. Biol. 28, 177–186 (2013).PubMed 
PubMed Central 

Google Scholar 
68.Williams, E. H. & Brower, L. P. in Monarchs in a Changing World: Biology and Conservation of an Iconic Butterfly (eds. Oberhauser, K. S. et al.) 109–116 (Cornell Univ. Press, 2015).69.Brower, L. P. et al. in The Monarch Butterfly: Biology and Conservation (eds. Obserhauser, K. S. & Solensky, M. J.) 151–166 (Cornell Univ. Press, 2004).70.Brower, L. P. et al. Butterfly mortality and salvage logging from the March 2016 storm in the Monarch Butterfly Biosphere Reserve in Mexico. Am. Entom. 63, 151–164 (2017).
Google Scholar 
71.Farfán-Gutiérrez, M. et al. Modeling anthropic factors as drivers of wildfire occurrence at the Monarch Butterfly Biosphere. Madera y Bosques 24, e2431591 (2018).
Google Scholar 
72.Ramírez, M. I., López-Sánchez, J. G. & Barrasa, S. Mapa de Vegetación y Cubiertas del Suelo, Reserva de la Biosfera Mariposa Monarca Vol. II (CIGA-UNAM, 2019).73.Flores-Martínez, J. J. et al. Recent forest cover loss in the core zones of the Monarch Butterfly Biosphere Reserve in Mexico. Front. Ecol. Evol. 7, 167 (2019).
Google Scholar 
74.Ramírez, M. I., Gímenez-Azcárate, J. & Luna, L. Effects of human activities on monarch butterfly habitat in protected mountain forests, Mexico. For. Chron. 79, 242–246 (2003).
Google Scholar 
75.Ramírez, M. I., Miranda, R., Zubieta, R. & Jiménez, M. Land cover and road network map for the Monarch Butterfly Biosphere Reserve in Mexico 2003. J. Maps 3, 181–190 (2007).
Google Scholar 
76.Zuur, A. F. & Ieno, E. N. Beginner’s Guide to Zero-Inflated Models with R (Highland Statistics Ltd, 2016).77.Yackulic, C. B., Dodrill, M., Dzul, M., Sanderlin, J. S. & Reid, J. A. A need for speed in Bayesian population models: a practical guide to marginalizing and recovering discrete latent states. Ecol. Appl. 30, e02112 (2020).PubMed 
PubMed Central 

Google Scholar 
78.Murray, K. & Conner, M. M. Methods to quantify variable importance: implications for the analysis of noisy ecological data. Ecology 90, 348–355 (2009).PubMed 
PubMed Central 

Google Scholar 
79.Mac Nally, R. Hierarchical partitioning as an interpretative tool in multivariate inference. Austral Ecol. 21, 224–228 (1996).
Google Scholar 
80.Gelman, A., Goodrich, B., Gabry, J. & Vehtari, A. R-squared for Bayesian regression models. Am. Stat. 73, 307–309 (2019).
Google Scholar 
81.Carpenter, B. et al. Stan: a probabilistic programming language. J. Stat. Softw. 76, 1 (2017).
Google Scholar 
82.Stan Development Team. rstan: the R Interface to Stan. R package version 2.17.3 http://mc-stan.org/ (2018).83.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019); https://www.R-project.org/84.Gelman, A. & Rubin, D. B. Inference from iterative simulation using multiple sequences. Stat. Sci. 7, 457–472 (1992).
Google Scholar 
85.Gelman, A. & Hill, J. Data Analysis Using Regression and Multilevel/Hierarchical Models (Cambridge Univ. Press, 2007). More

1.Giles-Corti, B. et al. City planning and population health: a global challenge. Lancet 388, 2912–2924 (2016).Article 

Google Scholar 
2.World Urbanization Prospects: The 2018 Revision ST/ESA/SER.A/420 (UN DESA, 2019).3.Okkels, N., Kristiansen, C. B., Munk-Jørgensen, P. & Sartorius, N. Urban mental health. Curr. Opin. Psychiatry 31, 258–264 (2018).Article 

Google Scholar 
4.Robbins, R. N., Scott, T., Joska, J. A. & Gouse, H. Impact of urbanization on cognitive disorders. Curr. Opin. Psychiatry 32, 210–217 (2019).Article 

Google Scholar 
5.Sarkar, C., Webster, C. & Gallacher, J. Residential greenness and prevalence of major depressive disorders: a cross-sectional, observational, associational study of 94 879 adult UK Biobank participants. Lancet Planet. Health 2, e162–e173 (2018).Article 

Google Scholar 
6.Engemann, K. et al. Residential green space in childhood is associated with lower risk of psychiatric disorders from adolescence into adulthood. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/PNAS.1807504116 (2019).7.Dadvand, P. et al. Green spaces and cognitive development in primary schoolchildren. Proc. Natl Acad. Sci. USA 112, 7937–7942 (2015).CAS 
Article 

Google Scholar 
8.Franco, L. S., Shanahan, D. F. & Fuller, R. A. A review of the benefits of nature experiences: more than meets the eye. Int. J. Environ. Res. Public Health 14, 864 (2017).Article 

Google Scholar 
9.Cox, D. T. C. et al. Skewed contributions of individual trees to indirect nature experiences. Landsc. Urban Plan. 185, 28–34 (2019).Article 

Google Scholar 
10.Irvine, K. N. et al. Green space, soundscape and urban sustainability: an interdisciplinary, empirical study. Local Environ. 14, 155–172 (2009).Article 

Google Scholar 
11.Weber, S. T. & Heuberger, E. The impact of natural odors on affective states in humans. Chem. Senses 33, 441–447 (2008).Article 

Google Scholar 
12.Li, Q. Effect of forest bathing trips on human immune function. Environ. Health Prev. Med. 15, 9–17 (2010).CAS 
Article 

Google Scholar 
13.Rook, G. A., Raison, C. L. & Lowry, C. A. Can we vaccinate against depression? Drug Discov. Today 17, 451–458 (2012).Article 

Google Scholar 
14.Markevych, I. et al. Access to urban green spaces and behavioural problems in children: results from the GINIplus and LISAplus studies. Environ. Int. 71, 29–35 (2014).Article 

Google Scholar 
15.Taylor, M. S., Wheeler, B. W., White, M. P., Economou, T. & Osborne, N. J. Research note: urban street tree density and antidepressant prescription rates—a cross-sectional study in London, UK. Landsc. Urban Plan. 136, 174–179 (2015).16.Akpinar, A., Barbosa-Leiker, C. & Brooks, K. R. Does green space matter? Exploring relationships between green space type and health indicators. Urban For. Urban Green. 20, 407–418 (2016).Article 

Google Scholar 
17.Cox, D. T. C., Shanahan, D. F., Hudson, H. L., Fuller, R. A. & Gaston, K. J. The impact of urbanisation on nature dose and the implications for human health. Landsc. Urban Plan. 179, 72–80 (2018).Article 

Google Scholar 
18.Amoly, E. et al. Green and blue spaces and behavioral development in Barcelona schoolchildren: the BREATHE Project. Environ. Health Perspect. 122, 1351–1358 (2014).Article 

Google Scholar 
19.Astell-Burt, T. & Feng, X. Association of urban green space with mental health and general health among adults in Australia. JAMA Netw. Open 2, e198209 (2019).Article 

Google Scholar 
20.Barton, J. & Pretty, J. What is the best dose of nature and green exercise for improving mental health? A multi-study analysis. Environ. Sci. Technol. 44, 3947–3955 (2010).CAS 
Article 

Google Scholar 
21.Gascon, M. et al. Mental health benefits of long-term exposure to residential green and blue spaces: a systematic review. Int. J. Environ. Res. Public Health 12, 4354–4379 (2015).Article 

Google Scholar 
22.The Mental Health of Children and Young People in London (PHE, 2016).23.Bijnens, E. M., Derom, C., Thiery, E., Weyers, S. & Nawrot, T. S. Residential green space and child intelligence and behavior across urban, suburban, and rural areas in Belgium: a longitudinal birth cohort study of twins. PLoS Med. 17, e1003213 (2020).Article 

Google Scholar 
24.Milligan, C. & Bingley, A. Restorative places or scary spaces? The impact of woodland on the mental well-being of young adults. Health Place 13, 799–811 (2007).Article 

Google Scholar 
25.Toledano, M. B. et al. Cohort profile: the study of cognition, adolescents and mobile phones (SCAMP). Int. J. Epidemiol. 48, 25–26l (2018).Article 

Google Scholar 
26.Afifi, M. Gender differences in mental health. Singapore Med. J. 48, 385–391 (2007).CAS 

Google Scholar 
27.Guhn, M., Emerson, S. D., Mahdaviani, D. & Gadermann, A. M. Associations of birth factors and socio-economic status with indicators of early emotional development and mental health in childhood: a population-based linkage study. Child Psychiatry Hum. Dev. 51, 80–93 (2020).Article 

Google Scholar 
28.Morita, E. et al. Psychological effects of forest environments on healthy adults: shinrin-yoku (forest-air bathing, walking) as a possible method of stress reduction. Public Health 121, 54–63 (2007).CAS 
Article 

Google Scholar 
29.Thompson, C. W. et al. Health impacts of environmental and social interventions designed to increase deprived communities’ access to urban woodlands: a mixed-methods study. Public Health Res. 27, 1–172 (2019).Article 

Google Scholar 
30.Hedblom, M., Heyman, E., Antonsson, H. & Gunnarsson, B. Bird song diversity influences young people’s appreciation of urban landscapes. Urban For. Urban Green. 13, 469–474 (2014).Article 

Google Scholar 
31.Liao, J. et al. Residential exposure to green space and early childhood neurodevelopment. Environ. Int. 128, 70–76 (2019).Article 

Google Scholar 
32.Picavet, H. S. J. et al. Greener living environment healthier people? Exploring green space, physical activity and health in the Doetinchem Cohort Study. Prev. Med. 89, 7–14 (2016).Article 

Google Scholar 
33.Francis, J., Wood, L. J., Knuiman, M. & Giles-Corti, B. Quality or quantity? Exploring the relationship between public open space attributes and mental health in Perth, Western Australia. Soc. Sci. Med. 74, 1570–1577 (2012).Article 

Google Scholar 
34.Nutsford, D., Pearson, A. L., Kingham, S. & Reitsma, F. Residential exposure to visible blue space (but not green space) associated with lower psychological distress in a capital city. Health Place 39, 70–78 (2016).Article 

Google Scholar 
35.Little, S. & Derr, V. in Research Handbook on Childhoodnature (eds Cutter-Mackenzie-Knowles, A. et al.) 151–178 (Springer, 2020).36.Bell, S. L., Phoenix, C., Lovell, R. & Wheeler, B. W. Seeking everyday wellbeing: the coast as a therapeutic landscape. Soc. Sci. Med. 142, 56–67 (2015).Article 

Google Scholar 
37.Bratman, G. N. et al. Nature and mental health: an ecosystem service perspective. Sci. Adv. 5, 903–927 (2019).Article 

Google Scholar 
38.Hartig, T., Mitchell, R., de Vries, S. & Frumkin, H. Nature and health. Annu. Rev. Public Health 35, 207–228 (2014).Article 

Google Scholar 
39.Tarling, R. & Roger, R. D. Socio-economic determinants of crime rates: modelling local area police-recorded crime. Howard J. Crime Justice 55, 207–225 (2016).Article 

Google Scholar 
40.Rose, D., Pevalin, D. J. & O’Reilly, K. The National Statistics Socio-economic Classification: Origins, Development and Use (Palgrave MacMillan, 2005).41.Carstairs, V. & Morris, R. Deprivation and health in Scotland. Health Bull. 48, 162–175 (1990).CAS 

Google Scholar 
42.2011 Census Aggregate Data (Office of National Statistics, 2012); https://www.ons.gov.uk/census/2011census43.Luciana, M. & Nelson, C. A. Assessment of neuropsychological function through use of the Cambridge Neuropsychological Testing Automated Battery: performance in 4- to 12-year-old children. Dev. Neuropsychol. 22, 595–624 (2002).Article 

Google Scholar 
44.Tombaugh, T. N. Trail Making Test A and B: Normative data stratified by age and education. Arch. Clin. Neuropsychol. 19, 203–214 (2004).Article 

Google Scholar 
45.Wechsler, D. The Measurement of Adult Intelligence (Williams & Wilkins, 1944).46.Burgess, P. W. in Methodology of Frontal and Executive Function (ed. Rabbitt, P.) 79–113 (Taylor and Francis, 2004).47.Goodman, R., Meltzer, H. & Bailey, V. The Strengths and Difficulties Questionnaire: a pilot study on the validity of the self-report version. Int. Rev. Psychiatry 15, 173–177 (2003).CAS 
Article 

Google Scholar 
48.Cronbach, L. J. Coefficient alpha and the internal structure of tests. Psychometrika 16, 297–334 (1951).Article 

Google Scholar 
49.The KIDSCREEN Group Europe The Kidscreen Questionnaires: Quality of Life Questionnaires for Children and Adolescents (Pabst Science, 2006).50.Berman, A. H., Liu, B., Ullman, S., Jadbäck, I. & Engström, K. Children’s quality of life based on the KIDSCREEN-27: child self-report, parent ratings and child–parent agreement in a Swedish random population sample. PLoS ONE 11, e0150545 (2016).Article 
CAS 

Google Scholar 
51.Sentinel-2 User Handbook (ESA, 2015).52.Gascon, M. et al. Normalized Difference Vegetation Index (NDVI) as a marker of surrounding greenness in epidemiological studies: the case of Barcelona city. Urban For. Urban Green. 19, 88–94 (2016).Article 

Google Scholar 
53.Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).Article 

Google Scholar 
54.Open Map—Local (Ordnance Survey, 2019); http://os.uk55.Miura, N. & Jones, S. D. Characterizing forest ecological structure using pulse types and heights of airborne laser scanning. Remote Sens. Environ. 114, 1069–1076 (2010).Article 

Google Scholar 
56.Dadvand, P. et al. The association between greenness and traffic-related air pollution at schools. Sci. Total Environ. 523, 59–63 (2015).CAS 
Article 

Google Scholar 
57.Sunyer, J. et al. Association between traffic-related air pollution in schools and cognitive development in primary school children: a prospective cohort study. PLoS Med. 12, e1001792 (2015).Article 
CAS 

Google Scholar 
58.Roberts, S. et al. Exploration of NO2 and PM2.5 air pollution and mental health problems using high-resolution data in London-based children from a UK longitudinal cohort study. Psychiatry Res. 272, 8–17 (2019).CAS 
Article 

Google Scholar 
59.Tzivian, L. et al. Effect of long-term outdoor air pollution and noise on cognitive and psychological functions in adults. Int. J. Hyg. Environ. Health 218, 1–11 (2015).Article 

Google Scholar 
60.Rue, H., Martino, S. & Chopin, N. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J. R. Stat. Soc. B 71, 319–392 (2009).Article 

Google Scholar  More

1.2012 Recreational Water Quality Criteria. (U. S. Environmental Protection Agency, 2012).2.Lee, C. M. et al. Persistence of fecal indicator bacteria in Santa Monica Bay beach sediments. Water Res. 40, 2593–2602 (2006).CAS 
PubMed 
Article 

Google Scholar 
3.Luo, C. et al. Genome sequencing of environmental Escherichia coli expands understanding of the ecology and speciation of the model bacterial species. Proc. Natl. Acad. Sci. 108, 7200–7205 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
4.Ishii, S., Ksoll, W. B., Hicks, R. E. & Sadowsky, M. J. Presence and growth of naturalized Escherichia coli in temperate soils from lake superior watersheds. Appl. Environ. Microbiol. 72, 612–621 (2006).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.Rochelle-Newall, E., Nguyen, T. M. H., Le, T. P. Q., Sengtaheuanghoung, O. & Ribolzi, O. A short review of fecal indicator bacteria in tropical aquatic ecosystems: Knowledge gaps and future directions. Front. Microbiol. 6, 1–15 (2015).Article 

Google Scholar 
6.Tymensen, L. D. et al. Comparative accessory gene fingerprinting of surface water Escherichia coli reveals genetically diverse naturalized population. J. Appl. Microbiol. 119, 263–277 (2015).CAS 
PubMed 
Article 

Google Scholar 
7.Ishii, S. & Sadowsky, M. J. Escherichia coli in the environment: Implications for water quality and human health. Microbes and environments / JSME 23, 101–108 (2008).Article 

Google Scholar 
8.Surbeck, C. Q., Jiang, S. C. & Grant, S. B. Ecological control of fecal indicator bacteria in an urban stream. Environ. Sci. Technol. 44, 631–637 (2010).ADS 
CAS 
PubMed 
Article 

Google Scholar 
9.Jang, J. et al. Environmental Escherichia coli: Ecology and public health implications—A review. J. Appl. Microbiol. 123(3), 570–581. https://doi.org/10.1111/jam.13468 (2017).CAS 
Article 
PubMed 

Google Scholar 
10.Van Elsas, J. D., Semenov, A. V., Costa, R. & Trevors, J. T. Survival of Escherichia coli in the environment: Fundamental and public health aspects. ISME J. 5, 173–183 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
11.Jaureguy, F. et al. Phylogenetic and genomic diversity of human bacteremic Escherichia coli strains. BMC Genomics 9, 560 (2008).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
12.Clermont, O. et al. Characterization and rapid identification of phylogroup G in Escherichia coli, a lineage with high virulence and antibiotic resistance potential. Environ. Microbiol. 21, 3107–3117 (2019).CAS 
PubMed 
Article 

Google Scholar 
13.Clermont, O., Christenson, J. K., Denamur, E. & Gordon, D. M. The Clermont Escherichia coli phylo-typing method revisited: Improvement of specificity and detection of new phylo-groups. Environ. Microbiol. Rep. https://doi.org/10.1111/1758-2229.12019 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
14.Ratajczak, M. et al. Influence of hydrological conditions on the Escherichia coli population structure in the water of a creek on a rural watershed. BMC Microbiol. 10, 1–10 (2010).Article 
CAS 

Google Scholar 
15.Johnson, J. R. et al. Phylogenetic backgrounds and virulence associated traits of Escherichia coli isolates from surface waters and diverse animals in Minnesota and Wisconsin. Appl. Environ. Microbiol. 83, 1–33 (2017).CAS 

Google Scholar 
16.Petit, F. et al. Change in the structure of Escherichia coli population and the pattern of virulence genes along a rural aquatic continuum. Front. Microbiol. 8, 1–14 (2017).Article 

Google Scholar 
17.Kraft, N. J. B. et al. Community assembly, coexistence and the environmental filtering metaphor. Funct. Ecol. 29, 592–599 (2015).Article 

Google Scholar 
18.Berthe, T., Ratajczak, M., Clermont, O., Denamur, E. & Petit, F. Evidence for coexistence of distinct Escherichia coli populations in various aquatic environments and their survival in estuary water. Appl. Environ. Microbiol. 79, 4684–4693 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
19.Méric, G., Kemsley, E. K., Falush, D., Saggers, E. J. & Lucchini, S. Phylogenetic distribution of traits associated with plant colonization in Escherichia coli. Environ. Microbiol. 15, 487–501 (2013).PubMed 
Article 
CAS 

Google Scholar 
20.Walk, S. T. The “Cryptic” Escherichia. EcoSal Plus 6, 2 (2015).21.Ingle, D. J. et al. Biofilm formation by and thermal niche and virulence characteristics of Escherichia spp. Appl. Environ. Microbiol. 77, 2695–2700 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
22.Elmqvist, T. The Urban Planet: Knowledge Towards Sustainable Cities (Cambridge University Press, 2018).Book 

Google Scholar 
23.Hosen, J. D., Febria, C. M., Crump, B. C. & Palmer, M. A. Watershed urbanization linked to differences in stream bacterial community composition. Front. Microbiol. 8, 1–17 (2017).Article 

Google Scholar 
24.Wang, S.-Y., Sudduth, E. B., Wallenstein, M. D., Wright, J. P. & Bernhardt, E. S. Watershed urbanization alters the composition and function of stream bacterial communities. PLoS ONE 6, e22972 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Bernhardt, E. S., Band, L. E., Walsh, C. J. & Berke, P. E. Understanding, managing, and minimizing urban impacts on surface water nitrogen loading. Ann. N. Y. Acad. Sci. 1134, 61–96 (2008).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Hosen, J. D., McDonough, O. T., Febria, C. M. & Palmer, M. A. Dissolved organic matter quality and bioavailability changes across an urbanization gradient in headwater streams. Environ. Sci. Technol. 48, 7817–7824 (2014).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Hatt, B. E., Fletcher, T. D., Walsh, C. J. & Taylor, S. L. The influence of urban density and drainage infrastructure on the concentrations and loads of pollutants in small streams. Environ. Manage. 34, 112–124 (2004).PubMed 
Article 
PubMed Central 

Google Scholar 
28.Smith, R. M., Kaushal, S. S., Beaulieu, J. J., Pennino, M. J. & Welty, C. Influence of infrastructure on water quality and greenhouse gas dynamics in urban streams. Biogeosciences 14, 2831–2849 (2017).ADS 
CAS 
Article 

Google Scholar 
29.Handler, N. B., Paytan, A., Higgins, C. P., Luthy, R. G. & Boehm, A. B. Human development is linked to multiple water body impairments along the California coast. Estuar. Coasts 29, 860–870 (2006).CAS 
Article 

Google Scholar 
30.Ishii, S. et al. Factors controlling long-term survival and growth of naturalized Escherichia coli populations in temperate field soils. Microbes Environ. 25, 8–14 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
31.Whitman, R. L. et al. Microbes in beach sands: Integrating environment, ecology and public health. Rev. Environ. Sci. Biotechnol. 13, 329–368 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
32.Kleinheinz, G. et al. Effect of aquatic macrophytes on the survival of Escherichia coli in a laboratory microcosm. Lake Reserv. Manage. 25, 149–154 (2009).Article 

Google Scholar 
33.Moreira, S. et al. Persistence of Escherichia coli in freshwater periphyton: Biofilm-forming capacity as a selective advantage. FEMS Microbiol. Ecol. 79, 608–618 (2012).CAS 
PubMed 
Article 

Google Scholar 
34.Pachepsky, Y. A. & Shelton, D. R. Escherichia coli and fecal coliforms in freshwater and estuarine sediments. Crit. Rev. Environ. Sci. Technol. 41, 1067–1110 (2011).CAS 
Article 

Google Scholar 
35.Walsh, C. J. & Kunapo, J. The importance of upland flow paths in determining urban effects on stream ecosystems. J. N. Am. Benthol. Soc. 28, 977–990 (2009).Article 

Google Scholar 
36.McLellan, S. L., Fisher, J. C. & Newton, R. J. The microbiome of urban waters. Int. Microbiol. 18, 141–149 (2015).PubMed 
PubMed Central 

Google Scholar 
37.Newton, R. J. et al. Sewage reflects the microbiomes of human populations. MBio 6, 1–9 (2015).CAS 
Article 

Google Scholar 
38.Richards, S., Paterson, E., Withers, P. J. A. & Stutter, M. Septic tank discharges as multi-pollutant hotspots in catchments. Sci. Total Environ. 542, 854–863 (2016).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
39.Sowah, R. A., Habteselassie, M. Y., Radcliffe, D. E., Bauske, E. & Risse, M. Isolating the impact of septic systems on fecal pollution in streams of suburban watersheds in Georgia, United States. Water Res. 108, 330–338 (2017).CAS 
PubMed 
Article 

Google Scholar 
40.Ly, D. K. & Chui, T. F. M. Modeling sewage leakage to surrounding groundwater and stormwater drains. Water Sci. Technol. 66, 2659–2665 (2012).PubMed 
Article 

Google Scholar 
41.Graziano, M., Giorgi, A. & Feijoó, C. Science of the Total Environment Multiple stressors and social-ecological traps in Pampean streams (Argentina): A conceptual model. Sci. Total Environ. 765, 142785 (2020).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
42.Graziano, M. et al. Fostering urban transformations in Latin America: Lessons around the ecological management of an urban stream in coproduction with a social movement (Buenos Aires, Argentina). Ecol. Soc. 24, 13 (2019).Article 

Google Scholar 
43.Cirelli, A. F. & Ojeda, C. Wastewater management in Greater Buenos Aires, Argentina. Desalination 218, 52–61 (2008).CAS 
Article 

Google Scholar 
44.Elordi, M. L., Lerner, J. E. C. & Porta, A. Evaluación del impacto antrópico sobre la calidad del agua del arroyo Las Piedras, Quilmes, Buenos Aires, Argentina. Acta Bioquimica Clinica Latinoamericana 50, 669–677 (2016).
Google Scholar 
45.Censo nacional de población, hogares y viviendas 2010 : censo del Bicentenario : resultados definitivos, Serie B nº 2. (Instituto Nacional de Estadística y Censos, 2012).46.Gordon, N. D., McMahon, T. A., Finlayson, B. L., Gippel, C. J. & Nathan, R. J. Stream Hydrology: An Introduction for Ecologists (Wiley, 2004).
Google Scholar 
47.Elosegui, A., Sabater, S. (eds.). Conceptos y técnicas en ecología fluvial. 243-251. (Fundación BBVa, 2009)48.Baird, R. B., Eaton, A. D., Rice, E. W., & Bridgewater, L. (eds.)Standard methods for the examination of water and wastewater, 23. (American Public Health Association, 2017).
49.Clermont, O., Bonacorsi, S., Bingen, E. & Bonacorsi, P. Rapid and simple determination of the Escherichia coli phylogenetic group. Appl. Environ. Microbiol. 66, 4555–4558 (2000).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
50.Clermont, O., Gordon, D. M., Brisse, S., Walk, S. T. & Denamur, E. Characterization of the cryptic Escherichia lineages: Rapid identification and prevalence. Environ. Microbiol. 13, 2468–2477 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
51.Lescat, M. et al. Commensal Escherichia coli strains in Guiana reveal a high genetic diversity with host-dependant population structure. Environ. Microbiol. Rep. 5, 9–57 (2013).Article 
CAS 

Google Scholar 
52.Clermont, O. et al. Evidence for a human-specific Escherichia coli clone. Environ. Microbiol. 10, 1000–1006 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
53.Congedo, L. Semi-automatic classification plugin for QGIS. Sapienza Univ, 1-25 (2013).54.Blanchet, F. G., Legendre, P. & Borcard, D. Forward selection of explanatory variables. Ecology 89, 2623–2632 (2008).PubMed 
Article 

Google Scholar 
55.Borcard, D., Gillet, F. & Lengendre, P. Numerical Ecology with R (Springer, 2018).MATH 
Book 

Google Scholar 
56.Legendre, P., Borcard, D. & Roberts, D. W. Variation partitioning involving orthogonal spatial eigenfunction submodels. Ecology 93, 1234–1240 (2012).PubMed 
Article 

Google Scholar 
57.Bivand, R. S. & Wong, D. W. S. Comparing implementations of global and local indicators of spatial association. TEST 27, 716–748 (2018).MathSciNet 
MATH 
Article 

Google Scholar 
58.Magurran, A. E. Measuring Biological Diversity (Wiley, Hoboken, 2004).
Google Scholar 
59.Oksanen, J. et al. Vegan: Ecological Diversity. R Project, 368. http://cran.r-project.org (2013)
60.Wilkinson, L. & Friendly, M. History corner the history of the cluster heat map. Am. Stat. 63, 179–184 (2009).Article 

Google Scholar 
61.Wei, T. et al. Visualization of a correlation matrix. Statistician 56, 316–324 (2017).
Google Scholar 
62.Robinaugh, D. J., Millner, A. J. & McNally, R. J. Identifying highly influential nodes in the complicated grief network. J. Abnormal Psychol. 125(6), 747 (2016).Article 

Google Scholar 
63.Peres-Neto, P. R., Legendre, P. L., Dray, S. & Borcard, D. Variation partitioning of species data matrices: Estimation and comparison of fractions. Ecology 87, 2614–2625 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
64.Legendre, P. & Gallagher, E. D. Ecologically meaningful transformations for ordination of species data. Oecologia 129, 271–280 (2001).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
65.Lepš, J. & Šmilauer, P. Multivariate Analysis of Ecological Data Using CANOCO (Cambridge University Press, Cambridge, 2003).MATH 
Book 

Google Scholar 
66.Simpson, G. Restricted permutations; using the permute package. http://cran.r-project.org (2012).67.Booth, D. B., Roy, A. H., Smith, B. & Capps, K. A. Global perspectives on the urban stream syndrome. Freshw. Sci. 35, 412–420 (2016).Article 

Google Scholar 
68.Peipoch, M., Brauns, M., Hauer, F. R., Weitere, M. & Valett, H. M. Ecological simplification: Human influences on Riverscape complexity. Bioscience 65, 1057–1065 (2015).Article 

Google Scholar 
69.Stoppe, N. D. C. et al. Worldwide phylogenetic group patterns of Escherichia coli from commensal human and wastewater treatment plant isolates. Front. Microbiol. 8, 2512 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
70.Escobar-Páramo, P. et al. Large-scale population structure of human commensal Escherichia coli isolates. Appl. Environ. Microbiol. 70, 5698–5700 (2004).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
71.Walk, S. T., Alm, E. W., Calhoun, L. M., Mladonicky, J. M. & Whittam, T. S. Genetic diversity and population structure of Escherichia coli isolated from freshwater beaches. Environ. Microbiol. 9, 2274–2288 (2007).PubMed 
Article 

Google Scholar 
72.Touchon, M. et al. Phylogenetic background and habitat drive the genetic diversification of Escherichia coli. PLoS Genet. 16, e1008866 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
73.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).
Google Scholar  More

Jennifer Grenz is currently a sessional lecturer at the University of British Columbia and owns a land healing company, Greener This Side. Her recently completed PhD dissertation explores the science of invasive species management and restoration through the lens of an ‘Indigenous ecology’, which she defines as “relationally guided healing of our lands, waters, and relations through intentional shaping of ecosystems by humans to bring a desired balance that meets the fluid needs of communities while respecting and honouring our mutual dependence through reciprocity.” Here we ask about her research and experiences as an Indigenous woman in ecology. More

1.Kruckeberg AR, Rabinowitz D. Biological aspects of endemism in higher plants. Annu Rev Ecol Syst. 1985;16:447–79.Article 

Google Scholar 
2.Ceballos G, Brown JH. Global patterns of mammalian diversity, endemism, and endangerment. Conserv Biol. 1995;9:559–68.Article 

Google Scholar 
3.Mueller GM, Schmit JP, Leacock PR, Buyck B, Cifuentes J, Desjardin DE, et al. Global diversity and distribution of macrofungi. Biodivers Conserv. 2007;16:37–48.Article 

Google Scholar 
4.Prideaux GJ, Warburton NM. An osteology-based appraisal of the phylogeny and evolution of kangaroos and wallabies (macropodidae: Marsupialia). Zool J Linn Soc. 2010;159:954–87.Article 

Google Scholar 
5.Finlay BJ, Clarke KJ. Ubiquitous dispersal of microbial species. Nature. 1999;400:828.CAS 
Article 

Google Scholar 
6.Whitaker RJ, Grogan DW, Taylor JW. Geographic barriers isolate endemic populations of hyperthermophilic archaea. Science. 2003;301:976–978.CAS 
PubMed 
Article 

Google Scholar 
7.Whitfield J. Is everything everywhere? Science. 2005;310:960–61.CAS 
PubMed 
Article 

Google Scholar 
8.Boenigk J, Pfandl K, Garstecki T, Harms H, Novarino G, Chatzinotas A. Evidence for geographic isolation and signs of endemism within a protistan morphospecies. Appl Environ Microbiol. 2006;72:5159–64.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
9.DeWit R, Bouvier T. ‘Everything is everywhere, but, the environment selects’; what did Baas Becking and Beijerinck really say? Environ Microbiol. 2006;8:755–8.Article 

Google Scholar 
10.van der Gast CJ. Microbial biogeography: the end of the ubiquitous dispersal hypothesis? Environ Microbiol. 2015;17:544–6.PubMed 
Article 
PubMed Central 

Google Scholar 
11.Whittaker KA, Rynearson TA. Evidence for environmental and ecological selection in a microbe with no geographic limits to gene flow. Proc Natl Acad Sci USA. 2017;114:2651–56.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Louca S, Shih PM, Pennell MW, Fischer WW, Parfrey LW, Doebeli M. Bacterial diversification through geological time. Nat Ecol Evol. 2018;2:1458–67.PubMed 
Article 
PubMed Central 

Google Scholar 
13.Martiny JBH, Bohannan BJ, Brown JH, Colwell RK, Fuhrman JA, Green JL, et al. Microbial biogeography: putting microorganisms on the map. Nat Rev Microbiol. 2006;4:102–12.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Martiny JBH, Eisen JA, Penn K, Allison SD, Horner-Devine MC. Drivers of bacterial β-diversity depend on spatial scale. Proc Natl Acad Sci USA. 2011;108:7850–54.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.Jungblut AD, Lovejoy C, Vincent WF. Global distribution of cyanobacterial ecotypes in the cold biosphere. ISME J. 2010;4:191–202.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
16.Gibbons SM, Caporaso JG, Pirrung M, Field D, Knight R, Gilbert JA. Evidence for a persistent microbial seed bank throughout the global ocean. Proc Natl Acad Sci USA. 2013;110:4651–55.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
17.Ramirez KS, Leff JW, Barberán A, Bates ST, Betley J, Crowther TW, et al. Biogeographic patterns in below-ground diversity in New York City’s Central Park are similar to those observed globally. Proc R Soc Lond B Biol Sci. 2014;281:20141988.18.Gonnella G, Böhnke S, Indenbirken D, Garbe-Schönberg D, Seifert R, Mertens C, et al. Endemic hydrothermal vent species identified in the open ocean seed bank. Nat Microbiol. 2016;1:16086 EP.Article 
CAS 

Google Scholar 
19.Louca S, Mazel F, Doebeli M, Parfrey WL. A census-based estimate of Earth’s bacterial and archaeal diversity. PLoS Biol. 2019;17:e3000106.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Ochman H, Wilson A. Evolution in bacteria: evidence for a universal substitution rate in cellular genomes. J Mol Evol. 1987;26:74–86.CAS 
PubMed 
Article 

Google Scholar 
21.Kuo CH, Ochman H. Inferring clocks when lacking rocks: the variable rates of molecular evolution in bacteria. Biol Direct. 2009;4:35–35.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
22.Roberts MS, Cohan FM. Recombination and migration rates in natural populations of Bacillus subtilis and Bacillus mojavensis. Evolution. 1995;49:1081–94.PubMed 
Article 

Google Scholar 
23.van Gremberghe I, Leliaert F, Mergeay J, Vanormelingen P, Van der Gucht K, Debeer AE, et al. Lack of phylogeographic structure in the freshwater cyanobacterium Microcystis aeruginosa suggests global dispersal. PLoS ONE. 2011;6:e19561.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
24.Papke RT, Ramsing NB, Bateson MM, Ward DM. Geographical isolation in hot spring cyanobacteria. Environ Microbiol. 2003;5:650–9.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Hongmei J, Aitchison JC, Lacap DC, Peerapornpisal Y, Sompong U, Pointing SB. Community phylogenetic analysis of moderately thermophilic cyanobacterial mats from China, the Philippines and Thailand. Extremophiles. 2005;9:325–32.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
26.Miller SR, Castenholz RW, Pedersen D. Phylogeography of the thermophilic cyanobacterium Mastigocladus laminosus. Appl Environ Microbiol. 2007;73:4751–59.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
27.Takacs-Vesbach C, Mitchell K, Jackson-Weaver O, Reysenbach AL. Volcanic calderas delineate biogeographic provinces among Yellowstone thermophiles. Environ Microbiol. 2008;10:1681–89.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
28.Reno ML, Held NL, Fields CJ, Burke PV, Whitaker RJ. Biogeography of the Sulfolobus islandicus pan-genome. Proc Natl Acad Sci USA. 2009;106:8605–10.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
29.Bahl J, Lau MCY, Smith GJD, Vijaykrishna D, Cary SC, Lacap DC, et al. Ancient origins determine global biogeography of hot and cold desert cyanobacteria. Nat Commun. 2011;2:163.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
30.Anderson RE, Kouris A, Seward CH, Campbell KM, Whitaker RJ. Structured populations of Sulfolobus acidocaldarius with susceptibility to mobile genetic elements. Genome Biol Evol. 2017;9:1699–710.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Podar PT, Yang Z, Björnsdóttir SH, Podar M. Comparative analysis of microbial diversity across temperature gradients in hot springs from Yellowstone and Iceland. Front Microbiol. 2020;11:1625.PubMed 
PubMed Central 
Article 

Google Scholar 
32.Clark K, Karsch-Mizrachi I, Lipman DJ, Ostell J, Sayers EW. Genbank. Nucleic Acids Res. 2015;44:D67–D72.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
33.Konstantinidis KT, Tiedje JM. Genomic insights that advance the species definition for prokaryotes. Proc Natl Acad Sci USA. 2005;102:2567–72.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Kim M, Oh HS, Park SC, Chun J. Towards a taxonomic coherence between average nucleotide identity and 16S rRNA gene sequence similarity for species demarcation of prokaryotes. Int J Syst Evol Microbiol. 2014;64:346–51.CAS 
Article 

Google Scholar 
35.Olm MR, Crits-Christoph A, Diamond S, Lavy A, Carnevali PBM, Banfield JF. Consistent metagenome-derived metrics verify and delineate bacterial species boundaries. mSystems. 2020;5:e00731-19.36.Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:5114.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
37.Shapiro BJ. What microbial population genomics has taught us about speciation. In: Polz MF, Rajora OP, editors. Population Genomics: Microorganisms. Cham, Switzerland: Springer International Publishing; 2019. p. 31–47.38.Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil PA, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36:996–1004.CAS 
PubMed 
Article 

Google Scholar 
39.Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 2020;36:1925–27.CAS 

Google Scholar 
40.Felsenstein J. Phylogenies and the comparative method. Am Nat. 1985;125:1–15.Article 

Google Scholar 
41.Louca S. Phylogeographic estimation and simulation of global diffusive dispersal. Syst Biol. 2021;70:340–59.PubMed 
Article 
PubMed Central 

Google Scholar 
42.Comas I, Coscolla M, Luo T, Borrell S, Holt KE, Kato-Maeda M, et al. Out-of-Africa migration and Neolithic coexpansion of Mycobacterium tuberculosis with modern humans. Nat Genet. 2013;45:1176–82.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Denef VJ, Banfield JF. In situ evolutionary rate measurements show ecological success of recently emerged bacterial hybrids. Science. 2012;336:462–6.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
44.Bouckaert R, Cartwright R. Phylogeography by diffusion on a sphere: whole world phylogeography. PeerJ. 2016;4:e2406.PubMed 
PubMed Central 
Article 

Google Scholar 
45.Brillinger DR. A particle migrating randomly on a sphere. In: Selected Works of David Brillinger. Cham, Switzerland: Springer; 2012. p. 73–87.46.Ghosh A, Samuel J, Sinha SA. “Gaussian” for diffusion on the sphere. Europhys Lett. 2012;98:30003.Article 
CAS 

Google Scholar 
47.Castenholz RW. The biogeography of hot spring algae through enrichment cultures. SIL Commun. 1978;21:296–315. 1953-1996
Google Scholar 
48.Valentine DL. Adaptations to energy stress dictate the ecology and evolution of the archaea. Nat Rev Micro. 2007;5:316–23.CAS 
Article 

Google Scholar 
49.Louca S, Parfrey LW, Doebeli M. Decoupling function and taxonomy in the global ocean microbiome. Science. 2016;353:1272–77.CAS 
PubMed 
Article 

Google Scholar 
50.Smith DJ, Jaffe DA, Birmele MN, Griffin DW, Schuerger AC, Hee J, et al. Free tropospheric transport of microorganisms from Asia to North America. Micro Ecol. 2012;64:973–85.CAS 
Article 

Google Scholar 
51.Pagel M. Detecting correlated evolution on phylogenies: a general method for the comparative analysis of discrete characters. Proc R Soc Lond B Biol Sci. 1994;255:37–45.Article 

Google Scholar 
52.Whitman WB, Coleman DC, Wiebe WJ. Prokaryotes: the unseen majority. Proc Natl Acad Sci USA. 1998;95:6578–83.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
53.Anderson D. The regulation of fishing and related activities in exclusive economic zones. In: Modern Law Sea, Publications on Ocean Development, vol. 59, chap. 11. Leiden, The Netherlands: Brill Nijhoff; 2008. p. 209–27.54.Bullock JM, Clarke RT. Long distance seed dispersal by wind: measuring and modelling the tail of the curve. Oecologia. 2000;124:506–21.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
55.Brynjarsdóttir J, O’Hagan A. Learning about physical parameters: the importance of model discrepancy. Inverse Probl. 2014;30:114007.Article 

Google Scholar 
56.Bell T. Experimental tests of the bacterial distance-decay relationship. ISME J. 2010;4:1357–65.PubMed 
Article 
PubMed Central 

Google Scholar 
57.Hyatt D, Chen GL, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinforma. 2010;11:119.Article 
CAS 

Google Scholar 
58.Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. Assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2014;25:1043–55.Article 
CAS 

Google Scholar 
59.Chambat F, Valette B. Mean radius, mass, and inertia for reference Earth models. Phys Earth Planet Inter. 2001;124:237–53.Article 

Google Scholar 
60.Data NS, (SEDAC) AC Gridded Population of the World, Version 4 (GPW v4): Population Density, Revision 11. Tech. rep., Palisades, NY: Center for International Earth Science Information Network – CIESIN – Columbia University. 2018. Accessed November 23, 2020.61.Price MN, Dehal PS, Arkin AP. FastTree 2: approximately maximum-likelihood trees for large alignments. PLoS ONE. 2010;5:e9490.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
62.Britton T, Anderson CL, Jacquet D, Lundqvist S, Bremer K. Estimating divergence times in large phylogenetic trees. Syst Biol. 2007;56:741–52.PubMed 
Article 

Google Scholar 
63.Zhu Q, Mai U, Pfeiffer W, Janssen S, Asnicar F, Sanders JG, et al. Phylogenomics of 10,575 genomes reveals evolutionary proximity between domains bacteria and archaea. Nat Commun. 2019;10:5477.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.Perrin F. Étude mathématique du movement brownien de rotation. In: Annales scientifiques del’École Normale Supérieure, vol. 45. Paris, France: Elsevier; with 1928. p. 1–51.65.Louca S, Doebeli M. Efficient comparative phylogenetics on large trees. Bioinformatics. 2018;34:1053–55.CAS 
PubMed 
Article 

Google Scholar 
66.Bloomquist EW, Lemey P, Suchard MA. Three roads diverged? routes to phylogeographic inference. Trends Ecol Evol. 2010;25:626–32.PubMed 
PubMed Central 
Article 

Google Scholar 
67.Lemey P, Rambaut A, Welch JJ, Suchard MA. Phylogeography takes a relaxed random walk in continuous space and time. Mol Biol Evol. 2010;27:1877–85.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
68.Faria NR, Suchard MA, Rambaut A, Lemey P. Toward a quantitative understanding of viral phylogeography. Curr Opin Virol. 2011;1:423–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
69.Faria NR, Suchard MA, Abecasis A, Sousa JD, Ndembi N, Bonfim I, et al. Phylodynamics of the HIV-1 CRF02_AG clade in Cameroon. Infect Genet Evol. 2012;12:453–60.PubMed 
Article 
PubMed Central 

Google Scholar 
70.Lange K. Diffusion processes. In: Applied Probability, chap. 11. New York, NY: Springer New York; 2010. p. 269–95.71.Ondov BD, Treangen TJ, Melsted P, Mallonee AB, Bergman NH, Koren S, et al. Mash: fast genome and metagenome distance estimation using minhash. Genome Biol. 2016;17:132.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
72.Pasolli E, Asnicar F, Manara S, Zolfo M, Karcher N, Armanini F, et al. Extensive unexplored human microbiome diversity revealed by over 150,000 genomes from metagenomes spanning age, geography, and lifestyle. Cell 2019;176:649–62.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
73.Criscuolo A, Gascuel O. Fast NJ-like algorithms to deal with incomplete distance matrices. BMC Bioinforma. 2008;9:166.Article 
CAS 

Google Scholar 
74.Paradis E, Claude J, Strimmer K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics. 2004;20:289–90.75.Kinene T, Wainaina J, Maina S, Boykin LM, Kliman RM. Methods for rooting trees, vol. 3. Oxford: Academic Press; 2016. p. 489–93.76.van Rossum G. Python tutorial. Tech. Rep. CS-R9526, Amsterdam: Centrum voor Wiskunde en Informatica (CWI); 1995. More

1.Cheverud, J. M. Developmental integration and the evolution of pleiotropy. Am. Zool. 36, 44–50 (1996).Article 

Google Scholar 
2.Wagner, G. P. & Altenberg, L. Perspective: complex adaptations and the evolution of evolvability. Evolution 50, 967–976 (1996).PubMed 
Article 
PubMed Central 

Google Scholar 
3.Wagner, G. P., Pavlicev, M. & Cheverud, J. M. The road to modularity. Nat. Rev. Genet. 8, 921–931 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Klingenberg, C. P. Morphological integration and developmental modularity. Annu. Rev. Ecol. Evol. Syst. 39, 115–132 (2008).Article 

Google Scholar 
5.Klingenberg, C. P. Studying morphological integration and modularity at multiple levels: concepts and analysis. Phil. Trans. R. Soc. B 369, 20130249 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
6.Hallgrímsson, B. et al. Deciphering the palimpsest: studying the relationship between morphological integration and phenotypic covariation. Evol. Biol. 36, 355–376 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
7.Olson, E. & Miller, R. Morphological Integration (Univ. of Chicago Press, 1958).8.Pigliucci, M. Phenotypic integration: studying the ecology and evolution of complex phenotypes. Ecol. Lett. 6, 265–272 (2003).Article 

Google Scholar 
9.Eble, G. J. in Phenotypic Integration: Studying the Ecology and Evolution of Complex Phenotypes (eds Pigliucci, M. & Preston, K.) 253–273 (Oxford Univ. Press, 2004).10.Goswami, A., Smaers, J. B., Soligo, C. & Polly, P. D. The macroevolutionary consequences of phenotypic integration: from development to deep time. Phil. Trans. R. Soc. B 369, 20130254 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
11.Goswami, A., Binder, W. J., Meachen, J. & O’Keefe, F. R. The fossil record of phenotypic integration and modularity: a deep-time perspective on developmental and evolutionary dynamics. Proc. Natl Acad. Sci. USA 112, 4891–4896 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Wagner, G. P. & Schwenk, K. Evolutionarily stable configurations: functional integration and the evolution of phenotypic stability. Evol. Biol. 31, 155–217 (2000).
Google Scholar 
13.Hallgrímsson, B., Willmore, K. & Hall, B. K. Canalization, developmental stability, and morphological integration in primate limbs. Am. J. Phys. Anthropol. 119, 131–158 (2002).Article 

Google Scholar 
14.Gould, S. J. A developmental constraint in cerion, with comments on the definition and interpretation of constraint in evolution. Evolution 43, 516–539 (1989).PubMed 

Google Scholar 
15.Arthur, W. Developmental drive: an important determinant of the direction of phenotypic evolution. Evol. Dev. 3, 271–278 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
16.Klingenberg, C. P. in Variation: A Central Concept in Biology (eds Hallgrímsson, B. & Hall, B.) 219–247 (Elsevier, 2005).17.Felice, R. N. & Goswami, A. Developmental origins of mosaic evolution in the avian cranium. Proc. Natl Acad. Sci. USA 115, 555–560 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Bell, E., Andres, B. & Goswami, A. Integration and dissociation of limb elements in flying vertebrates: a comparison of pterosaurs, birds and bats. J. Evol. Biol. 24, 2586–2599 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
19.Gatesy, S. M. & Dial, K. P. Locomotor modules and the evolution of avian flight. Evolution 50, 331–340 (1996).PubMed 
Article 
PubMed Central 

Google Scholar 
20.Gatesy, S. M. & Middleton, K. M. Bipedalism, flight, and the evolution of theropod locomotor diversity. J. Vertebr. Paleontol. 17, 308–329 (1997).Article 

Google Scholar 
21.Kulemeyer, C., Asbahr, K., Gunz, P., Frahnert, S. & Bairlein, F. Functional morphology and integration of corvid skulls—a 3D geometric morphometric approach. Front. Zool. 6, 2 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
22.Bright, J. A., Marugán-Lobón, J., Rayfield, E. J. & Cobb, S. N. The multifactorial nature of beak and skull shape evolution in parrots and cockatoos (Psittaciformes). BMC Evol. Biol. 19, 104 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
23.Bright, J. A., Marugán-Lobón, J., Cobb, S. N. & Rayfield, E. J. The shapes of bird beaks are highly controlled by nondietary factors. Proc. Natl Acad. Sci. USA 113, 5352–5357 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
24.Navalón, G., Marugán-Lobón, J., Bright, J. A., Cooney, C. R. & Rayfield, E. J. The consequences of craniofacial integration for the adaptive radiations of Darwin’s finches and Hawaiian honeycreepers. Nat. Ecol. Evol. 4, 270–278 (2020).PubMed 
Article 

Google Scholar 
25.Felice, R. N., Randau, M. & Goswami, A. A fly in a tube: macroevolutionary expectations for integrated phenotypes. Evolution 72, 2580–2594 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
26.Shatkovska, O. V. & Ghazali, M. Integration of skeletal traits in some passerines: impact (or the lack thereof) of body mass, phylogeny, diet and habitat. J. Anat. 236, 274–287 (2020).PubMed 
Article 

Google Scholar 
27.Hieronymus, T. L. Qualitative skeletal correlates of wing shape in extant birds (Aves: Neoaves). BMC Evol. Biol. 15, 30 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
28.Felice, R. N., Tobias, J. A., Pigot, A. L. & Goswami, A. Dietary niche and the evolution of cranial morphology in birds. Proc. R. Soc. B 286, 20182677 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
29.Navalón, G., Bright, J. A., Marugán-Lobón, J. & Rayfield, E. J. The evolutionary relationship among beak shape, mechanical advantage, and feeding ecology in modern birds. Evolution 73, 422–435 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
30.Pigot, A. L. et al. Macroevolutionary convergence connects morphological form to ecological function in birds. Nat. Ecol. Evol. 4, 230–239 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
31.Grant, R. B. & Grant, P. R. What Darwin’s finches can teach us about the evolutionary origin and regulation of biodiversity. BioScience 53, 965–975 (2003).Article 

Google Scholar 
32.Van de Ven, T., Martin, R., Vink, T., McKechnie, E. & Cunningham, S. Regulation of heat exchange across the hornbill beak: functional similarities with toucans? PLoS ONE 11, e0154768 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
33.Lamichhaney, S. et al. Rapid hybrid speciation in Darwin’s finches. Science 359, 224–228 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
34.Klingenberg, C. P. & Marugán-Lobón, J. Evolutionary covariation in geometric morphometric data: analyzing integration, modularity, and allometry in a phylogenetic context. Syst. Biol. 62, 591–610 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
35.Dececchi, T. A. & Larsson, H. C. Body and limb size dissociation at the origin of birds: uncoupling allometric constraints across a macroevolutionary transition. Evolution 67, 2741–2752 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
36.Nudds, R., Dyke, G. & Rayner, J. Forelimb proportions and the evolutionary radiation of Neornithes. Proc. R. Soc. Lond. B 271, S324–S327 (2004).
Google Scholar 
37.Benson, R. B. & Choiniere, J. N. Rates of dinosaur limb evolution provide evidence for exceptional radiation in Mesozoic birds. Proc. R. Soc. B 280, 20131780 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
38.Videler, J. J. Avian Flight (Oxford Univ. Press, 2006).39.Carrano, M. T. & Sidor, C. A. Theropod hind limb disparity revisited: comments on Gatesy and Middleton (1997). J. Vertebr. Paleontol. 19, 602–605 (1999).Article 

Google Scholar 
40.Middleton, K. M. & Gatesy, S. M. Theropod forelimb design and evolution. Zool. J. Linn. Soc. 128, 149–187 (2000).Article 

Google Scholar 
41.Young, N. M., Linde-Medina, M., Fondon, J. W., Hallgrímsson, B. & Marcucio, R. S. Craniofacial diversification in the domestic pigeon and the evolution of the avian skull. Nat. Ecol. Evol. 1, 0095 (2017).Article 

Google Scholar 
42.Martín-Serra, A. & Benson, R. B. Developmental constraints do not influence long-term phenotypic evolution of marsupial forelimbs as revealed by interspecific disparity and integration patterns. Am. Nat. 195, 547–560 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
43.Dumont, E. R. et al. Selection for mechanical advantage underlies multiple cranial optima in New World leaf-nosed bats. Evolution 68, 1436–1449 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
44.Hedrick, B. P. et al. Morphological diversification under high integration in a hyper diverse mammal clade. J. Mamm. Evol. 27, 563–575 (2020).Article 

Google Scholar 
45.Rossoni, D. M., Costa, B. M., Giannini, N. P. & Marroig, G. A multiple peak adaptive landscape based on feeding strategies and roosting ecology shaped the evolution of cranial covariance structure and morphological differentiation in phyllostomid bats. Evolution 73, 961–981 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
46.Prum, R. O. et al. A comprehensive phylogeny of birds (Aves) using targeted next-generation DNA sequencing. Nature 526, 569–573 (2015).CAS 
Article 

Google Scholar 
47.Bjarnason, A. & Benson, R. A 3D geometric morphometric dataset quantifying skeletal variation in birds. MorphoMuseuM 7, e125 (2021).Article 

Google Scholar 
48.Adams, D. C., Rohlf, F. J. & Slice, D. E. Geometric morphometrics: ten years of progress following the ‘revolution’. Ital. J. Zool. 71, 5–16 (2004).Article 

Google Scholar 
49.R Core Team R: A Language and Environment for Statistical Computing v.3.6.3 (R Foundation for Statistical Computing, 2020).50.Birds of the World (The Cornell Lab of Ornithology, 2021); https://birdsoftheworld.org/bow/home51.Dunning, J. B. Jr CRC Handbook of Avian Body Masses (CRC, 1992).52.The IUCN Red List of Threatened Species (IUCN, 2019); https://www.iucnredlist.org/53.Wilman, H. et al. EltonTraits 1.0: species-level foraging attributes of the world’s birds and mammals. Ecology 95, 2027 (2014).Article 

Google Scholar 
54.Taylor, G. & Thomas, A. Evolutionary Biomechanics (Oxford Univ. Press, 2014).55.Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M. & Hornik, K. cluster: Cluster analysis basics and extensions. R package version 2.1.0 (2019).56.Grafen, A. The phylogenetic regression. Phil. Trans. R. Soc. Lond. B 326, 119–157 (1989).CAS 
Article 

Google Scholar 
57.Revell, L. J. Size-correction and principal components for interspecific comparative studies. Evolution 63, 3258–3268 (2009).PubMed 
Article 

Google Scholar 
58.Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & R Core Team nlme: Linear and nonlinear mixed effects models. R package version 3.1-145 (2020).59.Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2018).Article 
CAS 

Google Scholar 
60.Goodall, C. Procrustes methods in the statistical analysis of shape. J. R. Stat. Soc. B 53, 285–321 (1991).
Google Scholar 
61.Adams, D., Collyer, M. & Kaliontzopoulou, A. Geomorph: Software for geometric morphometric analyses. R package version 3.2.1 (2020).62.Felsenstein, J. Phylogenies and the comparative method. Am. Nat. 125, 1–15 (1985).Article 

Google Scholar 
63.Adams, D. C. & Felice, R. N. Assessing trait covariation and morphological integration on phylogenies using evolutionary covariance matrices. PLoS ONE 9, e94335 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
64.Rohlf, F. J. & Corti, M. Use of two-block partial least-squares to study covariation in shape. Syst. Biol. 49, 740–753 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
65.Adams, D. C. & Collyer, M. L. On the comparison of the strength of morphological integration across morphometric datasets. Evolution 70, 2623–2631 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
66.Melo, D., Garcia, G., Hubbe, A., Assis, A. P. & Marroig, G. Evolqg—an R package for evolutionary quantitative genetics [version 3; referees: 2 approved, 1 approved with reservations]. F1000Research 4, 925 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
67.Goswami, A. & Polly, P. D. Methods for studying morphological integration and modularity. Paleontol. Soc. Pap. 16, 213–243 (2010).Article 

Google Scholar 
68.Oksanen, J. et al. vegan: Community ecology package. R package version 2.5-6 (2019). More