Philippa Kaur

1.Irwin AJ, Oliver MJ. Are ocean deserts getting larger? Geophys Res Lett. 2009;36:L18609.Article 

Google Scholar 
2.McClain CR, Signorini SR, Christian JR. Subtropical gyre variability observed by ocean-color satellites. Deep Sea Res Part II Topical Stud Oceanogr. 2004;51:281–301.CAS 
Article 

Google Scholar 
3.Signorini SR, Franz BA, McClain CR. Chlorophyll variability in the oligotrophic gyres: Mechanisms, seasonality and trends. Front Mar Sci. 2015;2:1–11.Article 

Google Scholar 
4.Polovina JJ, Howell EA, Abecassis M. Ocean’s least productive waters are expanding. Geophys Res Lett. 2008;35:L03618.Article 

Google Scholar 
5.Sharma P, Marinov I, Cabre A, Kostadinov T, Singh A. Increasing biomass in the warm oceans: unexpected new insights from SeaWIFS. Geophys Res Lett. 2019;46:3900–10.Article 

Google Scholar 
6.Flombaum P, Wang W-L, Primeau FW, Martiny AC. Global picophytoplankton niche partitioning predicts overall positive response to ocean warming. Nat Geosci. 2020;13:116–20.CAS 
Article 

Google Scholar 
7.Carr M-E, Friedrichs MAM, Schmeltz M, Noguchi Aita M, Antoine D, Arrigo KR, et al. A comparison of global estimates of marine primary production from ocean color. Deep Sea Res Part II Topical Stud Oceanogr. 2006;53:741–70.Article 

Google Scholar 
8.Field CB, Behrenfeld MJ, Randerson JT, Falkowski P. Primary production of the biosphere: Integrating terrestrial and oceanic components. Science. 1998;281:237–40.CAS 
PubMed 
Article 

Google Scholar 
9.DeVries T, Primeau F, Deutsch C. The sequestration efficiency of the biological pump. Geophys Res Lett. 2012;39:L13601.Article 
CAS 

Google Scholar 
10.Cabré A, Marinov I, Leung S. Consistent global responses of marine ecosystems to future climate change across the IPCC AR5 earth system models. Clim Dyn. 2015;45:1253–80.Article 

Google Scholar 
11.Behrenfeld MJ, O’Malley RT, Boss ES, Westberry TK, Graff JR, Halsey KH, et al. Revaluating ocean warming impacts on global phytoplankton. Nat Clim Change. 2015;6:323–30.Article 

Google Scholar 
12.Richardson K, Bendtsen J. Vertical distribution of phytoplankton and primary production in relation to nutricline depth in the open ocean. Mar Ecol Prog Ser. 2019;620:33–46.CAS 
Article 

Google Scholar 
13.Roshan S, DeVries T. Efficient dissolved organic carbon production and export in the oligotrophic ocean. Nat Commun. 2017;8:1–8.CAS 
Article 

Google Scholar 
14.Marañón E, Holligan PM, Barciela R, González N, Mouriño B, Pazó MJ, et al. Patterns of phytoplankton size structure and productivity in contrasting open-ocean environments. Mar Ecol Prog Ser. 2001;216:43–56.Article 

Google Scholar 
15.Pérez V, Fernández E, Marañón E, Morán XAG, Zubkov MV. Vertical distribution of phytoplankton biomass, production and growth in the Atlantic subtropical gyres. Deep Sea Res Part I Oceanographic Res Pap. 2006;53:1616–34.Article 

Google Scholar 
16.Teira E, Mouriño B, Marañón E, Pérez V, Pazó MJ, Serret P, et al. Variability of chlorophyll and primary production in the Eastern North Atlantic subtropical gyre: potential factors affecting phytoplankton activity. Deep Sea Res Part I Oceanographic Res Pap. 2005;52:569–88.CAS 
Article 

Google Scholar 
17.Chisholm SW, Frankel SL, Goericke R, Olson RJ, Palenik B, Waterbury JB, et al. Prochlorococcus marinus nov. Gen. Nov. Sp.: an oxyphototrophic marine prokaryote containing divinyl chlorophyll a and b. Arch Microbiol. 1992;157:297–300.CAS 
Article 

Google Scholar 
18.Flombaum P, Gallegos JL, Gordillo RA, Rincón J, Zabala LL, Jiao N, et al. Present and future global distributions of the marine cyanobacteria Prochlorococcus and Synechococcus. PNAS. 2013;110:9824–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
19.Partensky F, Hess WR, Vaulot D. Prochlorococcus, a marine photosynthetic prokaryote of global significance. Microbiol Mol Biol Rev. 1999;63:106–27.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Li WK. Primary production of prochlorophytes, cyanobacteria, and eucaryotic ultraphytoplankton: Measurements from flow cytometric sorting. Limnol Oceanogr. 1994;39:169–75.CAS 
Article 

Google Scholar 
21.Jardillier L, Zubkov MV, Pearman J, Scanlan DJ. Significant CO2 fixation by small prymnesiophytes in the subtropical and tropical Northeast Atlantic Ocean. ISME J. 2010;4:1180–92.CAS 
PubMed 
Article 

Google Scholar 
22.Irion S, Christaki U, Berthelot H, L’Helguen S, Jardillier L. Small phytoplankton contribute greatly to CO2-fixation after the diatom bloom in the Southern Ocean. ISME J. 2021;15:1–14.Article 
CAS 

Google Scholar 
23.Liu K, Suzuki K, Chen B, Liu H. Are temperature sensitivities of Prochlorococcus and Synechococcus impacted by nutrient availability in the subtropical Northwest Pacific? Limnol Oceanogr. 2020;66:639–51.Article 
CAS 

Google Scholar 
24.D’Hondt S, Spivack AJ, Pockalny R, Ferdelman TG, Fischer JP, Kallmeyer J, et al. Subseafloor sedimentary life in the South Pacific gyre. PNAS. 2009;106:11651–6.PubMed 
PubMed Central 
Article 

Google Scholar 
25.Longhurst A, Sathyendranath S, Platt T, Caverhill C. An estimate of global primary production in the ocean from satellite radiometer data. J Plankton Res. 1995;17:1245–71.Article 

Google Scholar 
26.Morel A, Gentili B, Claustre H, Babin M, Bricaud A, Ras J, et al. Optical properties of the “clearest” natural waters. Limnol Oceanogr. 2007;52:217–29.CAS 
Article 

Google Scholar 
27.Halm H, Lam P, Ferdelman TG, Lavik G, Dittmar T, LaRoche J, et al. Heterotrophic organisms dominate nitrogen fixation in the south pacific gyre. ISME J. 2012;6:1238–49.CAS 
PubMed 
Article 

Google Scholar 
28.Raimbault P, Garcia N. Evidence for efficient regenerated production and dinitrogen fixation in nitrogen-deficient waters of the South Pacific Ocean: impact on new and export production estimates. Biogeosciences. 2008;5:323–38.CAS 
Article 

Google Scholar 
29.Shiozaki T, Bombar D, Riemann L, Sato M, Hashihama F, Kodama T, et al. Linkage between dinitrogen fixation and primary production in the oligotrophic South Pacific Ocean. Glob Biogeochem Cyc. 2018;32:1028–44.CAS 
Article 

Google Scholar 
30.Reintjes G, Tegetmeyer HE, Bürgisser M, Orlić S, Tews I, Zubkov M, et al. On-site analysis of bacterial communities of the ultraoligotrophic South Pacific gyre. Appl Environ Microbiol. 2019;85:e00184–19.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Zielinski O, Henkel R, Voß D, Ferdelman TG. Physical oceanography during Sonne cruise SO245 (Ultrapac). PANGAEA. 2018. https://doi.org/10.1594/PANGAEA.890394.32.Ferdelman TG, Klockgether G, Downes P, Lavik G. Nutrient data from CTD Nisken bottles from Sonne expedition SO-245 “Ultrapac”. PANGAEA. 2019. https://doi.org/10.1594/PANGAEA.899228.33.Arar EJ, Collins GB. Method 445.0: In vitro determination of chlorophyll a and pheophytin a in marine and freshwater algae by fluorescence: U.S. Environmental Protection Agency, Washington, DC; 1997. https://cfpub.epa.gov/si/si_public_record_report.cfm?Lab=NERL&dirEntryId=309417.34.Welschmeyer N, Naughton S. Improved chlorophyll a analysis: single fluorometric measurement with no acidification. Lake Reserv Manag. 1994;9:123.
Google Scholar 
35.Osterholz H, Kilgour D, Storey DS, Lavik G, Ferdelman T, Niggemann J, et al. Accumulation of DOC in the South Pacific subtropical gyre from a molecular perspective. Mar Chem. 2021;231:103955.CAS 
Article 

Google Scholar 
36.Voß D, Henkel R, Wollschläger J, Zielinski O. Hyperspectral underwater light field measured during the cruise SO245 with R/V Sonne. PANGAEA. 2020. https://doi.org/10.1594/PANGAEA.911558.37.Martínez-Pérez C, Mohr W, Löscher CR, Dekaezemacker J, Littmann S, Yilmaz P, et al. The small unicellular diazotrophic symbiont, UCYN-A, is a key player in the marine nitrogen cycle. Nat Microbiol. 2016;1:1–7.Article 
CAS 

Google Scholar 
38.Marra J. Net and gross productivity: weighing in with 14C. Aquat Microb Ecol. 2009;56:123–31.Article 

Google Scholar 
39.Ribeiro CG, Marie D, Santos ALD, Brandini FP, Vaulot D. Estimating microbial populations by flow cytometry: comparison between instruments. Limnol Oceanogr Methods. 2016;14:750–8.Article 

Google Scholar 
40.Pernthaler A, Pernthaler J, Amann R. Fluorescence in situ hybridization and catalyzed reporter deposition for the identification of marine bacteria. Appl Environ Microbiol. 2002;68:3094–101.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.West NJ, Schönhuber WA, Fuller NJ, Amann RI, Rippka R, Post AF, et al. Closely related Prochlorococcus genotypes show remarkably different depth distributions in two oceanic regions as revealed by in situ hybridization using 16 S rRNA-targeted oligonucleotides. Microbiology. 2001;147:1731–44.CAS 
PubMed 
Article 

Google Scholar 
42.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 
43.Verity PG, Robertson CY, Tronzo CR, Andrews MG, Nelson JR, Sieracki ME. Relationships between cell volume and the carbon and nitrogen content of marine photosynthetic nanoplankton. Limnol Oceanogr. 1992;37:1434–46.CAS 
Article 

Google Scholar 
44.Khachikyan A, Milucka J, Littmann S, Ahmerkamp S, Meador T, Könneke M, et al. Direct cell mass measurements expand the role of small microorganisms in nature. Appl Environ Microbiol. 2019;85:AEM00493–19.Article 

Google Scholar 
45.Walters W, Hyde ER, Berg-Lyons D, Ackermann G, Humphrey G, Parada A, et al. Improved bacterial 16 S rRNA gene (v4 and v4-5) and fungal internal transcribed spacer marker gene primers for microbial community surveys. MSystems. 2016;1:e00009–15.PubMed 
Article 

Google Scholar 
46.Parada AE, Needham DM, Fuhrman JA. Every base matters: assessing small subunit rrna primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol. 2016;18:1403–14.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Comeau AM, Douglas GM, Langille MG. Microbiome helper: a custom and streamlined workflow for microbiome research. MSystems. 2017;2:e00127–16.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.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.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Haas S, Desai DK, LaRoche J, Pawlowicz R, Wallace DW. Geomicrobiology of the carbon, nitrogen and sulphur cycles in Powell Lake: a permanently stratified water column containing ancient seawater. Environ Microbiol. 2019;21:3927–52.CAS 
PubMed 
Article 

Google Scholar 
50.Zhang J, Kobert K, Flouri T, Stamatakis A. Pear: a fast and accurate Illumina paired-end read merger. Bioinformatics. 2013;30:614–20.51.Rognes T, Flouri T, Nichols B, Quince C, Mahé F. Vsearch: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584.PubMed 
PubMed Central 
Article 

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

Google Scholar 
53.Mercier C, Boyer F, Bonin A, Coissac E (eds). Sumatra and Sumaclust: fast and exact comparison and clustering of sequences. SeqBio 2013 Workshop 2013: (abstract).54.DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, et al. Greengenes, a chimera-checked 16 S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006;72:5069–72.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.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. 2012;41:D590–6.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
56.Decelle J, Romac S, Stern RF, Bendif EM, Zingone A, Audic S, et al. PhytoREF: A reference database of the plastidial 16 S rRNA gene of photosynthetic eukaryotes with curated taxonomy. Molec Ecol Res. 2015;15:1435–45.CAS 
Article 

Google Scholar 
57.Guillou L, Bachar D, Audic S, Bass D, Berney C, Bittner L, et al. The protist ribosomal reference database (PR2): a catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 2012;4:D597–604.Article 
CAS 

Google Scholar 
58.Del Campo J, Kolisko M, Boscaro V, Santoferrara LF, Nenarokov S, Massana R, et al. EukRef: phylogenetic curation of ribosomal RNA to enhance understanding of eukaryotic diversity and distribution. PLoS Biol. 2018;16:e2005849.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
59.Ludwig W, Strunk O, Westram R, Richter L, Meier H, Yadhukumar, et al. ARB: A software environment for sequence data. Nucleic Acids Res. 2004;32:1363–71.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Gruber-Vodicka HR, Seah BK, Pruesse E. Phyloflash: rapid small-subunit rRNA profiling and targeted assembly from metagenomes. Msystems. 2020;5:e00920.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Farrant GK, Doré H, Cornejo-Castillo FM, Partensky F, Ratin M, Ostrowski M, et al. Delineating ecologically significant taxonomic units from global patterns of marine picocyanobacteria. PNAS. 2016;113:E3365–74.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
62.Oggerin de Orube M, Fuchs BM. Personal communication: Unpublished shotgun metagenomes collected from in situ pump samples during R/V Sonne expedition SO245. Bremen, Germany. 2021.63.Schlitzer R. Ocean Data View. Bremerhaven, Germany. 2021. https://odv.awi.de.64.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing,Vienna, Austria. 2017. https://www.R-project.org/.65.Wickham H. Ggplot2: elegant graphics for data analysis. Springer-Verlag, New York. 2016.66.McMurdie PJ, Holmes S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLOS ONE. 2013;8:e61217.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
67.Oksanen J, Kindt R, Legendre P, O’Hara B, Stevens MHH, Oksanen MJ, et al. The vegan package: community ecology package. R package version 2.5–7. 2019. https://CRAN.R-project.org/package=vegan.68.Chaigneau A, Pizarro O. Surface circulation and fronts of the South Pacific Ocean, east of 120°W. Geophys Res Lett. 2005;32:L08605.69.Logares R, Sunagawa S, Salazar G, Cornejo‐Castillo FM, Ferrera I, Sarmento H, et al. Metagenomic 16 S rRNA Illumina tags are a powerful alternative to amplicon sequencing to explore diversity and structure of microbial communities. Environ Microbiol. 2014;16:2659–71.CAS 
PubMed 
Article 

Google Scholar 
70.Shi XL, Lepère C, Scanlan DJ, Vaulot D. Plastid 16 s rRNA gene diversity among eukaryotic picophytoplankton sorted by flow cytometry from the South Pacific Ocean. PLOS ONE. 2011;6:e18979.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
71.Fuller NJ, Campbell C, Allen DJ, Pitt FD, Zwirglmaier K, Le Gall F, et al. Analysis of photosynthetic picoeukaryote diversity at open ocean sites in the Arabian Sea using a pcr biased towards marine algal plastids. Aquat Micro Ecol. 2006;43:79–93.Article 

Google Scholar 
72.Raes EJ, Bodrossy L, Kamp JVD, Bissett A, Ostrowski M, Brown MV, et al. Oceanographic boundaries constrain microbial diversity gradients in the South Pacific Ocean. PNAS. 2018;115:E8266–75.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
73.Campbell L, Liu H, Nolla HA, Vaulot D. Annual variability of phytoplankton and bacteria in the subtropical North Pacific Ocean at station ALOHA during the 1991-4 ENSO event. Deep Sea Res Part I Oceanogr Res Pap. 1997;44:167–92.CAS 
Article 

Google Scholar 
74.Viviani DA, Church MJ. Decoupling between bacterial production and primary production over multiple time scales in the North Pacific subtropical gyre. Deep Sea Res Part I Oceanogr Res Pap. 2017;121:132–42.CAS 
Article 

Google Scholar 
75.Rii YM, Duhamel S, Bidigare RR, Karl DM, Repeta DJ, Church MJ. Diversity and productivity of photosynthetic picoeukaryotes in biogeochemically distinct regions of the south east pacific ocean. Limnol Oceanogr. 2016;61:806–24.Article 

Google Scholar 
76.Shi XL, Marie D, Jardillier L, Scanlan DJ, Vaulot D. Groups without cultured representatives dominate eukaryotic picophytoplankton in the oligotrophic South East Pacific Ocean. PLOS ONE. 2009;4:e7657.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
77.Kirkham AR, Lepere C, Jardillier LE, Not F, Bouman H, Mead A, et al. A global perspective on marine photosynthetic picoeukaryote community structure. ISME J. 2013;7:922–36.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
78.Lepère C, Vaulot D, Scanlan DJ. Photosynthetic picoeukaryote community structure in the South East Pacific Ocean encompassing the most oligotrophic waters on earth. Environ Microbiol. 2009;11:3105–17.PubMed 
Article 
CAS 

Google Scholar 
79.Bender ML, Jönsson B. Is seasonal net community production in the South Pacific subtropical gyre anomalously low? Geophys Res Lett. 2016;43:9757–63.Article 

Google Scholar 
80.Montégut CDB, Madec G, Fischer AS, Lazar A, Iudicone D. Mixed layer depth over the global ocean: an examination of profile data and a profile-based climatology. J Geophys Res Oceans. 2004;109:C12003.Article 

Google Scholar 
81.Liu Q, Lu Y. Role of horizontal density advection in seasonal deepening of the mixed layer in the subtropical Southeast Pacific. Adv Atmospher Sci. 2016;33:442–51.Article 

Google Scholar 
82.Sato K, Suga T. Structure and modification of the South Pacific eastern subtropical mode water. J Phys Oceanogr. 2009;39:1700–14.Article 

Google Scholar 
83.Jung J, Furutani H, Uematsu M. Atmospheric inorganic nitrogen in marine aerosol and precipitation and its deposition to the north and south pacific oceans. J Atmospher Chem. 2011;68:157–81.CAS 
Article 

Google Scholar 
84.Pavia FJ, Anderson RF, Winckler G, Fleisher MQ. Atmospheric dust inputs, iron cycling, and biogeochemical connections in the South Pacific Ocean from thorium isotopes. Glob Biogeochem Cycles. 2020;34:e2020GB006562.CAS 

Google Scholar 
85.Bonnet S, Guieu C, Bruyant F, Prášil O, Van Wambeke F, Raimbault P, et al. Nutrient limitation of primary productivity in the Southeast Pacific (Biosope Cruise). Biogeosciences. 2008;5:215–25.CAS 
Article 

Google Scholar 
86.Mahaffey C, Björkman KM, Karl DM. Phytoplankton response to deep seawater nutrient addition in the North Pacific subtropical gyre. Mar Ecol Prog Ser. 2012;460:13–34.CAS 
Article 

Google Scholar 
87.Grob C, Jardillier L, Hartmann M, Ostrowski M, Zubkov MV, Scanlan DJ. Cell-specific CO2 fixation rates of two distinct groups of plastidic protists in the Atlantic Ocean remain unchanged after nutrient addition. Environ Microbiol Rep. 2015;7:211–8.CAS 
PubMed 
Article 

Google Scholar 
88.Vaulot D, Marie D, Olson RJ, Chisholm SW. Growth of Prochlorococcus, a photosynthetic prokaryote, in the equatorial pacific ocean. Science. 1995;268:1480–2.CAS 
PubMed 
Article 

Google Scholar 
89.Grob C, Hartmann M, Zubkov MV, Scanlan DJ. Invariable biomass-specific primary production of taxonomically discrete picoeukaryote groups across the Atlantic Ocean. Environ Microbiol. 2011;13:3266–74.PubMed 
Article 

Google Scholar 
90.Berthelot H, Duhamel S, L’Helguen S, Maguer J-F, Wang S, Cetinić I, et al. NanoSIMS single cell analyses reveal the contrasting nitrogen sources for small phytoplankton. ISME J. 2019;13:651.CAS 
PubMed 
Article 

Google Scholar 
91.Zubkov MV, Fuchs BM, Tarran GA, Burkill PH, Amann R. High rate of uptake of organic nitrogen compounds by Prochlorococcus cyanobacteria as a key to their dominance in oligotrophic oceanic waters. Appl Environ Microbiol. 2003;69:1299–304.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
92.Muñoz-Marín MC, Gómez-Baena G, López-Lozano A, Moreno-Cabezuelo JA, Díez J, García-Fernández JM. Mixotrophy in marine picocyanobacteria: use of organic compounds by Prochlorococcus and Synechococcus. ISME J. 2020;14:1065–73.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
93.Timmermans K, Van der Wagt B, Veldhuis M, Maatman A, De Baar H. Physiological responses of three species of marine pico-phytoplankton to ammonium, phosphate, iron and light limitation. J Sea Res. 2005;53:109–20.CAS 
Article 

Google Scholar 
94.Vaulot D, Eikrem W, Viprey M, Moreau H. The diversity of small eukaryotic phytoplankton (≤ 3 μm) in marine ecosystems. FEMS Microbiol Rev. 2008;32:795–820.CAS 
PubMed 
Article 

Google Scholar 
95.Worden AZ, Janouskovec J, McRose D, Engman A, Welsh RM, Malfatti S, et al. Global distribution of a wild alga revealed by targeted metagenomics. Curr Biol. 2012;22:R675–7.CAS 
PubMed 
Article 

Google Scholar 
96.Le Gall F, Rigaut-Jalabert F, Marie D, Garczarek L, Viprey M, Gobet A, et al. Picoplankton diversity in the South-east Pacific Ocean from cultures. Biogeosciences. 2008;5:203–14.Article 

Google Scholar 
97.NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group. Moderate-resolution Imaging Spectroradiometer (MODIS) Aqua Chlorophyll Data; Reprocessing. NASA OB.DAAC, Greenbelt, MD, USA. 2018. https://oceancolor.gsfc.nasa.gov/data/10.5067/AQUA/MODIS/L3M/CHL/2018/ Accessed 2019/08/01. More

1.Borer ET, Seabloom EW, Mitchell CE, Cronin JP. Multiple nutrients and herbivores interact to govern diversity, productivity, composition, and infection in a successional grassland. Oikos. 2014;123:214–24.Article 

Google Scholar 
2.Isbell F, Reich PB, Tilman D, Hobbie SE, Polasky S, Binder S. Nutrient enrichment, biodiversity loss, and consequent declines in ecosystem productivity. Proc Natl Acad Sci. 2013;110:11911–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
3.Robinson RJ, Fraaije BA, Clark IM, Jackson RW, Hirsch PR, Mauchline TH. Endophytic bacterial community composition in wheat (Triticum aestivum) is determined by plant tissue type developmental stage and soil nutrient availability. Plant Soil. 2016;405:381–96.CAS 
Article 

Google Scholar 
4.Ratzke C, Barrere J, Gore J. Strength of species interactions determines biodiversity and stability in microbial communities. Nat Ecol Evol. 2020;4:376–83.PubMed 
Article 

Google Scholar 
5.Lambers JHR, Harpole WS, Tilman D, Knops J, Reich PB. Mechanisms responsible for the positive diversity–productivity relationship in minnesota grasslands. Ecol Lett. 2004;7:661–8.Article 

Google Scholar 
6.Essarioui A, LeBlanc N, Kistler HC, Kinkel LL. Plant community richness mediates inhibitory interactions and resource competition between Streptomyces and fusarium populations in the rhizosphere. Micro Ecol. 2017;74:157–67.Article 

Google Scholar 
7.Pan Y, Cassman N, de Hollander M, Mendes LW, Korevaar H, Geerts RH, et al. Impact of long-term n, p, k, and npk fertilization on the composition and potential functions of the bacterial community in grassland soil. FEMS Microbiol Ecol. 2014;90:195–205.CAS 
PubMed 
Article 

Google Scholar 
8.Schlatter DC, DavelosBaines AL, Xiao K, Kinkel LL. Resource use of soilborne Streptomyces varies with location phylogeny, and nitrogen amendment. Micro Ecol. 2013;66:961–71.Article 

Google Scholar 
9.Firn J, McGree JM, Harvey E, Flores-Moreno H, Schütz M, Buckley YM, et al. Leaf nutrients, not specific leaf area, are consistent indicators of elevated nutrient inputs. Nat Ecol Evol. 2019;3:400–6.PubMed 
Article 

Google Scholar 
10.Anderson TM, Griffith DM, Grace JB, Lind EM, Adler PB, Biederman LA, et al. Herbivory and eutrophication mediate grassland plant nutrient responses across a global climatic gradient. Ecol. 2018;99:822–31.Article 

Google Scholar 
11.Bernstein N, Gorelick J, Zerahia R, Koch S. Impact of n, p, k, and humic acid supplementation on the chemical profile of medical cannabis (Cannabis sativa L.). Front Plant Sci. 2019;10:736.PubMed 
PubMed Central 
Article 

Google Scholar 
12.Tangolar S, Tangolar S, Torun AA, Ada M, Göçmez S. Influence of supplementation of vineyard soil with organic substances on nutritional status, yield and quality of ‘black magic’ grape (Vitis vinifera L.) and soil microbiological and biochemical characteristics. OENO One. 2020;54:1143–57.Article 
CAS 

Google Scholar 
13.De Long JR, Sundqvist MK, Gundale MJ, Giesler R, Wardle DA. Effects of elevation and nitrogen and phosphorus fertilization on plant defence compounds in subarctic tundra heath vegetation. Funct Ecol. 2016;30:314–25.Article 

Google Scholar 
14.Dietrich R, Ploss K, Heil M. Constitutive and induced resistance to pathogens in Arabidopsis thaliana depends on nitrogen supply. Plant Cell Environ. 2004;27:896–906.CAS 
Article 

Google Scholar 
15.Bryant JP, Chapin III FS, Klein DR. Carbon/nutrient balance of boreal plants in relation to vertebrate herbivory. Oikos. 1983;40:357–68.16.Kinkel LL, Otto-Hanson LK, Otto-Hansen Z, Johnson M, Spawn S, Song Z, et al. Foliar endophytic microbiome composition and functional capacities vary with soil nutrient inputs. Phytopathol. 2018;108:77.
Google Scholar 
17.Seabloom EW, Condon B, Kinkel L, Komatsu KJ, Lumibao CY, May G, et al. Effects of nutrient supply, herbivory, and host community on fungal endophyte diversity. Ecol. 2019;100:e02758.Article 

Google Scholar 
18.Vandenkoornhuyse P, Quaiser A, Duhamel M, Le Van A, Dufresne A. The importance of the microbiome of the plant holobiont. N. Phytol. 2015;206:1196–206.Article 

Google Scholar 
19.Stulberg E, Fravel D, Proctor LM, Murray DM, LoTempio J, Chrisey L, et al. An assessment of US microbiome research. Nat Microbiol. 2016;1:15015.CAS 
PubMed 
Article 

Google Scholar 
20.Hanson BM, Weinstock GM. The importance of the microbiome in epidemiologic research. Ann Epidemiol. 2016;26:301–5.PubMed 
Article 
PubMed Central 

Google Scholar 
21.Bell TH, Hockett KL, Alcalá-Briseño RI, Barbercheck M, Beattie GA, Bruns MA, et al. Manipulating wild and tamed phytobiomes: Challenges and opportunities. Phytobiomes J 2019;3:3–21.Article 

Google Scholar 
22.Henning JA, Kinkel L, May G, Lumibao CY, Seabloom EW, Borer ET. Plant diversity and litter accumulation mediate the loss of foliar endophyte fungal richness following nutrient addition. Ecol. 2021;102:e03210.Article 

Google Scholar 
23.Vacher C, Hampe A, Porté AJ, Sauer U, Compant S, Morris CE. The phyllosphere: microbial jungle at the plant–climate interface. Annu Rev Ecol Evol Syst. 2016;47:1–24.Article 

Google Scholar 
24.Berendsen RL, Pieterse CM, Bakker PA. The rhizosphere microbiome and plant health. Trends Plant Sci. 2012;17:478–86.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Turner TR, James EK, Poole PS. The plant microbiome. Genome Biol. 2013;14:1–10.Article 
CAS 

Google Scholar 
26.Trivedi P, Leach JE, Tringe SG, Sa T, Singh BK. Plant–microbiome interactions: from community assembly to plant health. Nat Rev Microbiol. 2020;18:607–21.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Sanchez-Gorostiaga A, Bajić D, Osborne ML, Poyatos JF, Sanchez A. High-order interactions distort the functional landscape of microbial consortia. PLOS Biol. 2019;17:e3000550.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
28.Gould AL, Zhang V, Lamberti L, Jones EW, Obadia B, Korasidis N, et al. Microbiome interactions shape host fitness. Proc Natl Acad Sci. 2018;115:E11951–E11960.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
29.O’Keeffe KR. Within-host Microbial Interactions and Plant Parasites: From Pairwise Interactions to the Microbiome. PhD thesis, The University of North Carolina at Chapel Hill, 2019.30.Wemheuer F, Kaiser K, Karlovsky P, Daniel R, Vidal S, Wemheuer B. Bacterial endophyte communities of three agricultural important grass species differ in their response towards management regimes. Sci Rep. 2017;7:1–13.Article 
CAS 

Google Scholar 
31.Wemheuer B, Thomas T, Wemheuer F. Fungal endophyte communities of three agricultural important grass species differ in their response towards management regimes. Microorg. 2019;7:37.CAS 
Article 

Google Scholar 
32.Layeghifard M, Hwang DM, Guttman DS. Disentangling interactions in the microbiome: a network perspective. Trends Microbiol. 2017;25:217–28.CAS 
PubMed 
Article 

Google Scholar 
33.Barabási AL Network science. (Cambridge University Press, Cambridge, 2016).
Google Scholar 
34.Scott J. Social network analysis. Sociol. 1988;22:109–27.Article 

Google Scholar 
35.Borgatti SP, Mehra A, Brass DJ, Labianca G. Network analysis in the social sciences. Science. 2009;323:892–5.CAS 
PubMed 
Article 

Google Scholar 
36.Nelson GD, Rae A. An economic geography of the United States: from commutes to megaregions. PLOS ONE. 2016;11:e0166083.Article 
CAS 

Google Scholar 
37.Danon L, Ford AP, House T, Jewell CP, Keeling MJ, Roberts GO, et al. Networks and the epidemiology of infectious disease. Interdiscip Perspectives on Infect Dis. 2011.38.Expert P, Evans TS, Blondel VD, Lambiotte R. Uncovering space-independent communities in spatial networks. Proc Natl Acad Sci. 2011;108:7663–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
39.Röttjers L, Faust K. From hairballs to hypotheses—biological insights from microbial networks. FEMS Microbiol Rev. 2018;42:761–80.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
40.Naqvi A, Rangwala H, Keshavarzian A, Gillevet P. Network-based modeling of the human gut microbiome. Chem Biodivers. 2010;7:1040–50.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Coyte KZ, Schluter J, Foster KR. The ecology of the microbiome: networks, competition, and stability. Sci. 2015;350:663–6.CAS 
Article 

Google Scholar 
42.Poudel R, Jumpponen A, Schlatter DC, Paulitz TC, McSpadden Gardener BB, Kinkel LL, et al. Microbiome networks: a systems framework for identifying candidate microbial assemblages for disease management. Phytopathol. 2016;106:1083–96.CAS 
Article 

Google Scholar 
43.Bakker MG, Schlatter DC, Otto-Hanson L, Kinkel LL. Diffuse symbioses: roles of plant–plant, plant–microbe and microbe–microbe interactions in structuring the soil microbiome. Mol Ecol. 2014;23:1571–83.PubMed 
Article 

Google Scholar 
44.van der Heijden MG, Hartmann M. Networking in the plant microbiome. PLOS Biol. 2016;14:e1002378.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
45.Lau MK, Borrett SR, Baiser B, Gotelli NJ, Ellison AM. Ecological network metrics: opportunities for synthesis. Ecosphere. 2017;8:e01900.Article 

Google Scholar 
46.Billick I, Case TJ. Higher order interactions in ecological communities: what are they and how can they be detected? Ecol. 1994;75:1529–43.Article 

Google Scholar 
47.Carr A, Diener C, Baliga NS, Gibbons SM. Use and abuse of correlation analyses in microbial ecology. ISME J. 2019;13:2647–55.PubMed 
PubMed Central 
Article 

Google Scholar 
48.Vaz Jauri P, Bakker MG, Salomon CE, Kinkel LL. Subinhibitory antibiotic concentrations mediate nutrient use and competition among soil Streptomyces. PLOS ONE. 2013;8:e81064.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
49.Borer ET, Harpole WS, Adler PB, Lind EM, Orrock JL, Seabloom EW, et al. Finding generality in ecology: a model for globally distributed experiments. Methods Ecol Evol. 2014;5:65–73.Article 

Google Scholar 
50.Borer ET, Grace JB, Harpole WS, MacDougall AS, Seabloom EW. A decade of insights into grassland ecosystem responses to global environmental change. Nat Ecol Evol. 2017;1:1–7.Article 

Google Scholar 
51.Essarioui A, LeBlanc N, Kistler HC, Kinkel LL. Plant host and community diversity impact the dynamics of resource use by soil Streptomyces. Phytopathol. 2014;104:38.
Google Scholar 
52.LeBlanc N, Essarioui A, Kinkel LL, Kistler HC. Fusarium community structure and carbon metabolism phenotypes respond to grassland plant community richness and plant host. Phytopathol. 2014;104:67.Article 

Google Scholar 
53.Essarioui A, Kistler HC, Kinkel LL. Nutrient use preferences among soil Streptomyces suggest greater resource competition in monoculture than polyculture plant communities. Plant Soil. 2016;409:329–43.CAS 
Article 

Google Scholar 
54.Essarioui A, LeBlanc N, Otto-Hanson L, Schlatter DC, Kistler HC, Kinkel LL. Inhibitory and nutrient use phenotypes among coexisting fusarium and Streptomyces populations suggest local coevolutionary interactions in soil. Environ Microbiol. 2020;22:976–85.PubMed 
Article 
PubMed Central 

Google Scholar 
55.Schlatter D, Fubuh A, Xiao K, Hernandez D, Hobbie S, Kinkel L. Resource amendments influence density and competitive phenotypes of Streptomyces in soil. Micro Ecol. 2009;57:413–20.Article 

Google Scholar 
56.Kinkel LL, Schlatter DC, Xiao K, Baines AD. Sympatric inhibition and niche differentiation suggest alternative coevolutionary trajectories among Streptomycetes. ISME J 2013;8:249–56.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
57.Reichardt J, Bornholdt S. Statistical mechanics of community detection. Phys Rev E 2006;74:016110.Article 
CAS 

Google Scholar 
58.Watts DJ, Strogatz SH. Collective dynamics of ‘small-world’ networks. Nat. 1998;393:440–2.CAS 
Article 

Google Scholar 
59.Allesina S, Levine JM. A competitive network theory of species diversity. Proc Natl Acad Sci. 2011;108:5638–42.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Maynard DS, Bradford MA, Lindner DL, van Diepen LT, Frey SD, Glaeser JA, et al. Diversity begets diversity in competition for space. Nat Ecol Evol. 2017;1:1–8.Article 

Google Scholar 
61.Maynard DS, Crowther TW, Bradford MA. Competitive network determines the direction of the diversity–function relationship. Proc Natl Acad Sci. 2017;114:11464–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
62.Gallien L, Zimmermann NE, Levine JM, Adler PB. The effects of intransitive competition on coexistence. Ecol Lett. 2017;20:791–800.PubMed 
Article 
PubMed Central 

Google Scholar 
63.Schlatter DC, Song Z, Vaz-Jauri P, Kinkel LL. Inhibitory interaction networks among coevolved Streptomyces populations from prairie soils. PLOS ONE. 2019;14:e0223779.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.Milo R. Network motifs: simple building blocks of complex networks. Sci. 2002;298:824–7.CAS 
Article 

Google Scholar 
65.Case TJ, Bender EA. Testing for higher order interactions. Am Nat. 1981;118:920–9.Article 

Google Scholar 
66.Levine JM, Bascompte J, Adler PB, Allesina S. Beyond pairwise mechanisms of species coexistence in complex communities. Nat. 2017;546:56–64.CAS 
Article 

Google Scholar 
67.Mayfield MM, Stouffer DB. Higher-order interactions capture unexplained complexity in diverse communities. Nat Ecol Evol. 2017;1:0062.Article 

Google Scholar 
68.Friedman J, Higgins LM, Gore J. Community structure follows simple assembly rules in microbial microcosms. Nat Ecol Evol. 2017;1:0109.Article 

Google Scholar 
69.Bender EA, Canfield E. The asymptotic number of labeled graphs with given degree sequences. J Comb Theory Ser A 1978;24:296–307.Article 

Google Scholar 
70.Newman ME. Modularity and community structure in networks. Proc Natl Acad Sci. 2006;103:8577–82.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
71.Guo X, Boedicker JQ. The contribution of high-order metabolic interactions to the global activity of a four-species microbial community. PLOS Comput Biol. 2016;12:e1005079.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
72.Borrelli JJ, Allesina S, Amarasekare P, Arditi R, Chase I, Damuth J, et al. Selection on stability across ecological scales. Trends Ecol Evol. 2015;30:417–25.PubMed 
Article 
PubMed Central 

Google Scholar 
73.Davis GH, Crofoot MC, Farine DR. Estimating the robustness and uncertainty of animal social networks using different observational methods. Anim Behav. 2018;141:29–44.Article 

Google Scholar 
74.Gilbertson ML, White LA, Craft ME. Trade-offs with telemetry-derived contact networks for infectious disease studies in wildlife. Methods Ecol Evol. 2020;12:76–87.PubMed 
Article 
PubMed Central 

Google Scholar 
75.Grilli J, Barabás G, Michalska-Smith MJ, Allesina S. Higher-order interactions stabilize dynamics in competitive network models. Nat. 2017;548:210–3.CAS 
Article 

Google Scholar 
76.Letten AD, Stouffer DB. The mechanistic basis for higher-order interactions and non-additivity in competitive communities. Ecol Lett. 2019;22:423–36.PubMed 
Article 

Google Scholar 
77.Dormann CF, Roxburgh SH. Experimental evidence rejects pairwise modelling approach to coexistence in plant communities. Proc R Soc B Biol Sci. 2005;272:1279–85.Article 

Google Scholar 
78.Staniczenko PP, Kopp JC, Allesina S. The ghost of nestedness in ecological networks. Nat Commun. 2013;4:1–6.Article 
CAS 

Google Scholar 
79.Großkopf T, Soyer OS. Synthetic microbial communities. Curr Opin Microbiol. 2014;18:72–77.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
80.Holm S. A simple sequentially rejective multiple test procedure. Scand J Stat. 1979;6:65–70.
Google Scholar  More

1.Hubbell, S. The Unified Neutral Theory of Biodiversity and Biogeography (Princeton Univ. Press, 2001).2.Tilman, D. et al. Diversity and productivity in a long-term grassland experiment. Science 294, 843–845 (2001).CAS 
PubMed 
Article 

Google Scholar 
3.Hutchinson, G. E. The paradox of the plankton. Am. Nat. 95, 137–145 (1961).Article 

Google Scholar 
4.Koskella, B., Hall, L. J. & Metcalf, C. J. E. The microbiome beyond the horizon of ecological and evolutionary theory. Nat. Ecol. Evol. 1, 1606–1615 (2017).PubMed 
Article 

Google Scholar 
5.Flemming, H. C. & Wuertz, S. Bacteria and archaea on Earth and their abundance in biofilms. Nat. Rev. Microbiol. 17, 247–260 (2019).CAS 
PubMed 
Article 
PubMed Central 

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

Google Scholar 
7.Salazar, G. & Sunagawa, S. Marine microbial diversity. Curr. Biol. 27, R489–R494 (2017).CAS 
PubMed 
Article 

Google Scholar 
8.Huttenhower, C. et al. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).CAS 
Article 

Google Scholar 
9.Falkowski, P. G., Fenchel, T. & Delong, E. F. The microbial engines that drive earth’s biogeochemical cycles. Science 320, 1034–1039 (2008).CAS 
PubMed 
Article 

Google Scholar 
10.Kau, A. L., Ahern, P. P., Griffin, N. W., Goodman, A. L. & Gordon, J. I. Human nutrition, the gut microbiome and the immune system. Nature 474, 327–336 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
11.Cavicchioli, R. et al. Scientists’ warning to humanity: microorganisms and climate change. Nat. Rev. Microbiol. 17, 569–586 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Blasche, S. et al. Metabolic cooperation and spatiotemporal niche partitioning in a kefir microbial community. Nat. Microbiol. 6, 196–208 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
13.Gude, S. et al. Bacterial coexistence driven by motility and spatial competition. Nature 578, 588–592 (2020).CAS 
PubMed 
Article 

Google Scholar 
14.Kommineni, S. et al. Bacteriocin production augments niche competition by enterococci in the mammalian gastrointestinal tract. Nature 526, 719–722 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.Granato, E. T., Meiller-Legrand, T. A. & Foster, K. R. The evolution and ecology of bacterial warfare. Curr. Biol. 29, R521–R537 (2019).CAS 
PubMed 
Article 

Google Scholar 
16.Ratzke, C., Barrere, J. & Gore, J. Strength of species interactions determines biodiversity and stability in microbial communities. Nat. Ecol. Evol. 4, 376–383 (2020).PubMed 
Article 

Google Scholar 
17.Hoek, T. A. et al. Resource availability modulates the cooperative and competitive nature of a microbial cross-feeding mutualism. PLoS Biol. 14, e1002540 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
18.Goldford, J. E. et al. Emergent simplicity in microbial community assembly. Science 361, 469–474 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
19.Tilman, D. Resource Competition and Community Structure Vol. 17 (Princeton Univ. Press, 1982).20.Gause, G. F. The Struggle for Existence (Hafner Press, 1934).21.MacArthur, R. Species packing and competitive equilibrium for many species. Theor. Popul. Biol. 1, 1–11 (1970).CAS 
PubMed 
Article 

Google Scholar 
22.Levin, S. A. Community equilibria and stability, and an extension of the competitive exclusion principle. Am. Nat. 104, 413–423 (1970).Article 

Google Scholar 
23.Estrela, S. et al. Metabolic rules of microbial community assembly. Preprint at bioRxiv https://doi.org/10.1101/2020.03.09.984278 (2020).24.Enke, T. N. et al. Modular assembly of polysaccharide-degrading marine microbial communities. Curr. Biol. 29, 1528–1535 (2019).CAS 
PubMed 
Article 

Google Scholar 
25.Fu, H., Uchimiya, M., Gore, J. & Moran, M. A. Ecological drivers of bacterial community assembly in synthetic phycospheres. Proc. Natl Acad. Sci. USA 117, 3656–3662 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
26.Gralka, M., Szabo, R., Stocker, R. & Cordero, O. X. Trophic interactions and the drivers of microbial community assembly. Curr. Biol. 30, R1176–R1188 (2020).CAS 
PubMed 
Article 

Google Scholar 
27.Enke, T. N. et al. Modular assembly of polysaccharide-degrading marine microbial communities. Curr. Biol. 29, 1528–1535.e6 (2019).CAS 
PubMed 
Article 

Google Scholar 
28.Martiny, J. B. H., Jones, S. E., Lennon, J. T. & Martiny, A. C. Microbiomes in light of traits: a phylogenetic perspective. Science 350, aac9323 (2015).PubMed 
Article 
CAS 

Google Scholar 
29.Naylor, D. et al. Deconstructing the soil microbiome into reduced-complexity functional modules. mBio 11, e01349-20 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
30.MacArthur, R. H. Geographical Ecology. Patterns in the Distribution of Species (Harper & Row, 1972) .31.Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
32.Caspi, R. et al. The MetaCyc database of metabolic pathways and enzymes. Nucleic Acids Res. 46, D633–D639 (2018).CAS 
PubMed 
Article 

Google Scholar 
33.Wang, T., Goyal, A., Dubinkina, V. & Maslov, S. Evidence for a multi-level trophic organization of the human gut microbiome. PLoS Comput. Biol. 15, e1007524 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
34.Goyal, A. & Maslov, S. Diversity, stability, and reproducibility in stochastically assembled microbial ecosystems. Phys. Rev. Lett. 120, 158102 (2018).CAS 
PubMed 
Article 

Google Scholar 
35.Marsland, R. et al. Available energy fluxes drive a transition in the diversity, stability, and functional structure of microbial communities. PLoS Comput. Biol. 15, e1006793 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Levine, J. M. & HilleRisLambers, J. The importance of niches for the maintenance of species diversity. Nature 461, 254–257 (2009).CAS 
PubMed 
Article 

Google Scholar 
37.Tromas, N. et al. Niche separation increases with genetic distance among bloom-forming Cyanobacteria. Front. Microbiol. 9, 438 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
38.Sriswasdi, S., Yang, C. C. & Iwasaki, W. Generalist species drive microbial dispersion and evolution. Nat. Commun. 8, 1162 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
39.Logares, R. et al. Biogeography of bacterial communities exposed to progressive long-term environmental change. ISME J. 7, 937–948 (2013).CAS 
PubMed 
Article 

Google Scholar 
40.Monard, C., Gantner, S., Bertilsson, S., Hallin, S. & Stenlid, J. Habitat generalists and specialists in microbial communities across a terrestrial-freshwater gradient. Sci. Rep. 6, 37719 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Székely, A. J. & Langenheder, S. The importance of species sorting differs between habitat generalists and specialists in bacterial communities. FEMS Microbiol. Ecol. 87, 102–112 (2014).PubMed 
Article 
CAS 

Google Scholar 
42.Pandit, S. N., Kolasa, J. & Cottenie, K. Contrasts between habitat generalists and specialists: an empirical extension to the basic metacommunity framework. Ecology 90, 2253–2262 (2009).PubMed 
Article 

Google Scholar 
43.Muscarella, M. E., Boot, C. M., Broeckling, C. D. & Lennon, J. T. Resource heterogeneity structures aquatic bacterial communities. ISME J. 13, 2183–2195 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
44.Roller, B. R. K., Stoddard, S. F. & Schmidt, T. M. Exploiting rRNA operon copy number to investigate bacterial reproductive strategies. Nat. Microbiol. 1, 16160 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
45.Goldfarb, K. C. et al. Differential growth responses of soil bacterial taxa to carbon substrates of varying chemical recalcitrance. Front. Microbiol. 2, 94 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
46.Klappenbach, J. A., Dunbar, J. M. & Schmidt, T. M. rRNA operon copy number reflects ecological strategies of bacteria. Appl. Environ. Microbiol. 66, 1328–1333 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Rojo, F. Carbon catabolite repression in Pseudomonas: optimizing metabolic versatility and interactions with the environment. FEMS Microbiol. Rev. 34, 658–684 (2010).CAS 
PubMed 
Article 

Google Scholar 
48.Mills, C. G., Allen, R. J. & Blythe, R. A. Resource spectrum engineering by specialist species can shift the specialist-generalist balance. Theor. Ecol. 13, 149–163 (2020).Article 

Google Scholar 
49.Bajic, D. & Sanchez, A. The ecology and evolution of microbial metabolic strategies. Curr. Opin. Biotechnol. 62, 123–128 (2020).CAS 
PubMed 
Article 

Google Scholar 
50.Basan, M. et al. A universal trade-off between growth and lag in fluctuating environments. Nature 584, 470–474 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Paczia, N. et al. Extensive exometabolome analysis reveals extended overflow metabolism in various microorganisms. Microb. Cell Fact. 11, 122 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
52.Pinu, F. R. et al. Metabolite secretion in microorganisms: the theory of metabolic overflow put to the test. Metabolomics 14, 43 (2018).PubMed 
Article 
CAS 

Google Scholar 
53.Douglas, A. E. The microbial exometabolome: ecological resource and architect of microbial communities. Phil. Trans. R. Soc. B 375, 20190250 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
54.Machado, D., Andrejev, S., Tramontano, M. & Patil, K. R. Fast automated reconstruction of genome-scale metabolic models for microbial species and communities. Nucleic Acids Res. 46, 7542–7553 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Zelezniak, A. et al. Metabolic dependencies drive species co-occurrence in diverse microbial communities. Proc. Natl Acad. Sci. USA 112, 6449–6454 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
56.Diener, C., Gibbons, S. M. & Resendis-Antonio, O. MICOM: metagenome-scale modeling to infer metabolic interactions in the gut microbiota. mSystems 5, e00606-19 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
57.Garza, D. R., van Verk, M. C., Huynen, M. A. & Dutilh, B. E. Towards predicting the environmental metabolome from metagenomics with a mechanistic model. Nat. Microbiol. 3, 456–460 (2018).CAS 
PubMed 
Article 

Google Scholar 
58.Pacheco, A. R., Osborne, M. L. & Segrè, D. Non-additive microbial community responses to environmental complexity. Nat Commun. 12, 2365 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
59.Wang, X., Xia, K., Yang, X. & Tang, C. Growth strategy of microbes on mixed carbon sources. Nat. Commun. 10, 1279 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Louca, S. et al. High taxonomic variability despite stable functional structure across microbial communities. Nat. Ecol. Evol. 1, 0015 (2017).Article 

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

Google Scholar 
62.Callahan, B. J., Sankaran, K., Fukuyama, J. A., McMurdie, P. J. & Holmes, S. P. Bioconductor workflow for microbiome data analysis: from raw reads to community analyses. F1000Research 5, 1492 (2016).PubMed 
PubMed Central 
Article 

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

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

Google Scholar 
65.R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).66.McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
67.Chao, A. et al. Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies. Ecol. Monogr. 84, 45–67 (2014).Article 

Google Scholar 
68.Chao, A., Chiu, C. H. & Jost, L. Phylogenetic diversity measures based on Hill numbers. Phil. Trans. R. Soc. B 365, 3599–3609 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
69.Underwood, A. J. Experiments in Ecology (Cambridge Univ. Press, 1996); https://doi.org/10.1017/cbo978051180640770.Saeedghalati, M. et al. Quantitative comparison of abundance structures of generalized communities: from B-cell receptor repertoires to microbiomes. PLoS Comput. Biol. 13, e1005362 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
71.Goldford, J. E., Hartman, H., Smith, T. F. & Segrè, D. Remnants of an ancient metabolism without phosphate. Cell 168, 1126–1134.e9 (2017).CAS 
PubMed 
Article 

Google Scholar 
72.Raymond, J. & Segrè, D. The effect of oxygen on biochemical networks and the evolution of complex life. Science 311, 1764–1767 (2006).CAS 
PubMed 
Article 

Google Scholar 
73.Handorf, T., Ebenhoh, O. E. & Heinrich, R. Expanding metabolic networks: scopes of compounds, robustness, and evolution. J. Mol. Evol. 61, 498–512 (2005).CAS 
PubMed 
Article 

Google Scholar 
74.Ebenhoh, O., Handorf, T. & Heinrich, R. Structural analysis of expanding metabolic networks. Genome Inform. 15, 35–45 (2004).PubMed 

Google Scholar 
75.Orth, J. D., Thiele, I. & Palsson, B. O. What is flux balance analysis? Nat. Biotechnol. 28, 245–248 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
76.Johnson, M. et al. NCBI BLAST: a better web interface. Nucleic Acids Res. 36, 5–9 (2008).Article 
CAS 

Google Scholar 
77.Machado, D. et al. Polarization of microbial communities between competitive and cooperative metabolism. Nat. Ecol. Evol. 5, 195–203 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
78.Stoddard, S. F., Smith, B. J., Hein, R., Roller, B. R. K. & Schmidt, T. M. rrnDB: improved tools for interpreting rRNA gene abundance in bacteria and archaea and a new foundation for future development. Nucleic Acids Res. 43, D593–D598 (2015).CAS 
PubMed 
Article 

Google Scholar 
79.Douglas, G. M. et al. PICRUSt2: an improved and extensible approach for metagenome inference. Preprint at bioRxiv https://doi.org/10.1101/672295 (2019).80.Chen, T. & Guestrin, C. XGBoost: a scalable tree boosting system. In Proc. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785–794 (Association for Computing Machinery, 2016).81.Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2nd edn. (Springer Series in Statistics, Springer, 2009).82.Lundberg, S. M. et al. From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2, 56–67 (2020).PubMed 
PubMed Central 
Article 

Google Scholar  More

1.Arnold, M. L. Natural Hybridization and Evolution (Oxford University Press, 1997).
Google Scholar 
2.Stelkens, R. & Seehausen, O. Genetic distance between species predicts novel trait expression in their hybrids. Evolution 63, 884–897 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
3.Grant, P. R. & Grant, B. R. Hybridization of bird species. Science 256, 193–197 (1992).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Saino, N. & Villa, S. Pair composition and reproductive success across a hybrid zone of carrion crows and hooded crows. Auk 109, 543–555 (1992).
Google Scholar 
5.Good, T. P., Ellis, J. C., Annett, C. A. & Pierotti, R. Bounded hybrid superiority in an avian hybrid zone: effects of mate, diet, and habitat choice. Evolution 54, 1774–1783 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Bartley, D. M., Rana, K. & Immink, A. J. The use of inter-specific hybrids in aquaculture and fisheries. Rev. Fish Biol. Fisher. 10, 325–337 (2001).Article 

Google Scholar 
7.Rosenfield, J. A., Nolasco, S., Lindauer, S., Sandoval, C. & Kodric-Brown, A. The role of hybrid vigor in the replacement of Pecos pupfish by its hybrids with sheepshead minnow. Conserv. Biol. 18, 1589–1598 (2004).Article 

Google Scholar 
8.Sun, Y. et al. Comparative transcriptomic study of muscle provides new insights into the growth superiority of a novel grouper hybrid. PLoS ONE 11, e0168802 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
9.Scribner, K. T., Page, K. S. & Bartron, M. L. Hybridization in freshwater fishes: A review of case studies and cytonuclear methods of biological inference. Rev. Fish Biol. Fisher. 10, 293–323 (2001).Article 

Google Scholar 
10.Ottová, E. et al. Evolution and trans-species polymorphism of MHC class IIB genes in cyprinid fish. Fish Shellfish Immun. 18, 199–222 (2005).Article 
CAS 

Google Scholar 
11.Šimková, A. et al. Does invasive Chondrostoma nasus shift the parasite community structure of endemic Parachondrostoma toxostoma in sympatric zones?. Parasite. Vector. 5, 200 (2012).Article 

Google Scholar 
12.Klein, J. & OhUigin, C. MHC polymorphism and parasites. Philos. Trans. R. Soc. Lond. B Biol. Sci. 346, 351–358 (1994).ADS 
CAS 
PubMed 
Article 

Google Scholar 
13.Klein, J., Klein, D., Figueroa, F., OhUigin, C. & Sato, A. Major histocompatibility complex genes in the study of fish phylogeny. In Molecular Systematic of Fishes (eds Kocher, T. D. & Stepien, C. A.) 271–283 (Academic Press, 1997).Chapter 

Google Scholar 
14.Hughes, A. L. & Nei, M. Nucleotide substitution at major histocompatibility complex class II loci: Evidence for overdominant selection. Proc. Nat. Acad. Sci. USA 56, 958–962 (1989).ADS 
Article 

Google Scholar 
15.Klein, J. & OhUigin, C. Composite origin of major histocompatibility complex genes. Curr. Opin. Genet. Dev. 3, 923–930 (1993).CAS 
PubMed 
Article 

Google Scholar 
16.Hughes, A. L. & Nei, M. Models of host-parasite interactions and MHC polymorphism. Genetics 132, 863–864 (1992).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
17.Klein, J. Of HLA, tryps, and selection? An essay on coevolution of MHC and parasites. Hum. Immunol. 30, 247–258 (1991).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Hughes, A. L., Hughes, M. K., Howell, C. Y. & Nei, M. Natural selection at the class II major histocompatibility complex loci of mammals. Philos. Trans. R. Soc. Lond. B Biol. Sci. 346, 359–367 (1994).ADS 
CAS 
Article 

Google Scholar 
19.Hedrick, P. W. Pathogen resistence and genetic variation at MHC loci. Evolution 56, 1902–1908 (2002).PubMed 
Article 
PubMed Central 

Google Scholar 
20.Nowak, M. A., Tarczy-Hornoch, K. & Austyn, J. M. The optimal number of major histocompatibility complex molecules in an individual. Proc. Nat. Acad. Sci. U.S.A. 89, 10896–10899 (1992).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.Wegner, K. M., Reusch, T. B. H. & Kalbe, M. Multiple parasites are driving major histocompatibility complex polymorphism in the wild. J. Evol. Biol. 16, 224–232 (2003).CAS 
PubMed 
Article 

Google Scholar 
22.Eizaguirre, C., Lenz, T. L., Traulsen, A. & Milinski, M. Speciation accelerated and stabilized by pleiotropic major histocompatibility complex immunogenes. Ecol. Lett. 12, 5–12 (2009).PubMed 
Article 

Google Scholar 
23.Nadachowska-Brzyska, K., Zielinski, P., Radwan, J. & Babiks, W. Interspecific hybridization increases MHC class II diversity in two sister species of newts. Mol. Ecol. 21, 887–906 (2012).CAS 
PubMed 
Article 

Google Scholar 
24.Wegner, K. M. & Eizaguirre, C. New(t)s and views from hybridizing MHC genes: Introgression rather than trans-species polymorphism may shape allelic repertoires. Mol. Ecol. 21, 779–781 (2012).CAS 
PubMed 
Article 

Google Scholar 
25.Dudek, K., Gaczorek, T. S., Zielinski, P. & Babik, W. Massive introgression of major histocompatibility complex (MHC) genes in newt hybrid zones. Mol. Ecol. 28, 4798–4810 (2019).CAS 
PubMed 
Article 

Google Scholar 
26.Šimková, A., Civáňová, K., Gettová, L. & Gilles, A. Genomic porosity between invasive Chondrostoma nasus and endangered endemic Parachondrostoma toxostoma (Cyprinidae): The evolution of MHC IIB genes. PLoS ONE 8, e65883 (2013).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
27.Zhang, S., Wang, Z. & Wang, H. Maternal immunity in fish. Dev. Comp. Immunol. 39, 72–78 (2013).ADS 
PubMed 
Article 
CAS 

Google Scholar 
28.Šimková, A., Vojtek, L., Halačka, K., Hyršl, P. & Vetešník, L. The effect of hybridization on fish physiology, immunity and blood biochemistry: A case study in hybridizing Cyprinus carpio and Carassius gibelio (Cyprinidae). Aquaculture 435, 381–389 (2015).Article 
CAS 

Google Scholar 
29.Cowx, I. G. The biology of bream, Abramis brama (L), and its natural hybrid with roach, Rutilus rutilus (L), in the River Exe. J. Fish Biol. 22, 631–646 (1983).Article 

Google Scholar 
30.Economidis, P. S. & Wheeler, A. Hybrids of Abramis brama with Scardinius erythrophthalmus and Rutilus rutilus from Lake Volvi, Macedonia, Greece. J. Fish Biol. 35, 295–299 (1989).Article 

Google Scholar 
31.Toscano, B. J. et al. An ecomorphological framework for the coexistence of two cyprinid fish and their hybrids in a novel environment. Biol. J. Linn. Soc. 99, 768–783 (2010).Article 

Google Scholar 
32.Hayden, B. et al. Hybridisation between two cyprinid fishes in a novel habitat: Genetics, morphology and life-history traits. BMC Evol. Biol. 10, 169 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
33.Kuparinen, A., Vinni, M., Teacher, A. G. F., Kähkönen, K. & Merilä, J. Mechanism of hybridization between bream Abramis brama and roach Rutilus rutilus in their native range. J. Fish Biol. 84, 237–242 (2014).CAS 
PubMed 
Article 

Google Scholar 
34.Konopinski, M. K. & Amirowicz, A. Genetic composition of a population of natural common bream Abramis brama x roach Rutilus rutilus hybrids and their morphological characteristics in comparison with parent species. J. Fish Biol. 92, 365–385 (2018).CAS 
PubMed 
Article 

Google Scholar 
35.Krasnovyd, V., Vetešník, L., Gettová, L., Civáňová, K. & Šimková, A. Patterns of parasite distribution in the hybrids of non-congeneric cyprinid fish species: Is asymmetry in parasite infection the result of limited coadaptation?. Int. J. Parasitol. 47, 471–483 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
36.Hayden, B. et al. Trophic dynamics within a hybrid zone—interactions between an abundant cyprinid hybrid and sympatric parental species. Freshwater Biol. 56, 1723–1735 (2011).Article 

Google Scholar 
37.Nzau Matondo, B. et al. Hybridization success of three common European cyprinid species, Rutilus rutilus, Blicca bjoerkna and Abramis brama and larval resistance to stress tests. Fish. Sci. 73, 1137–1146 (2007).Article 
CAS 

Google Scholar 
38.Hayden, B., McLoone, P., Coyne, J. & Caffrey, J. M. Extensive hybridization between roach, Rutilus rutilus L., and common bream, Abramis brama L. Irish lakes and rivers. Biol. Environ. 114B, 35–39 (2014).
Google Scholar 
39.Eizaguirre, C. et al. Parasite diversity, patterns of MHC II variation and olfactory based mate choice in diverging threespined stickleback ecotypes. Evol. Ecol. 25, 605–622 (2011).Article 

Google Scholar 
40.Hubbs, C. L. Hybridization between fish species in nature. Syst. Zool. 4, 1–20 (1955).Article 

Google Scholar 
41.Rauch, G., Kalbe, M. & Reusch, T. B. H. Relative importance of MHC and genetic background for parasite load in a field experiment. Evol. Ecol. Res. 8, 373–386 (2006).
Google Scholar 
42.Eizaguirre, C., Lenz, T. L., Kalbe, M. & Milinski, M. Divergent selection on locally adapted major histocompatibility complex immune genes experimentally proven in the field. Ecol. Lett. 15, 723–731 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
43.Šimková, A., Dávidová, M., Papoušek, I. & Vetešník, L. Does interspecies hybridization affect the host specificity of parasites in cyprinid fish?. Parasite. Vector. 6, 95 (2013).Article 

Google Scholar 
44.Seifertová, M., Jarkovský, J. & Šimková, A. Does the parasite-mediated selection drive the MHC class IIB diversity in wild populations of European chub (Squalius cephalus)?. Parasitol. Res. 115, 1401–1415 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
45.Nzau Matondo, B., Ovidio, M., Philippart, J. C. & Poncin, P. Reproductive behaviour and sexual production in the first-generation hybrids of roach Rutilus rutilus L. × common bream Abramis brama L. J. Appl. Ichthyol. 27, 859–867 (2011).Article 

Google Scholar 
46.Graser, R., OhUigin, C., Vincek, V., Meyer, A. & Klein, J. Trans-species polymorphism of class II Mhc loci in danio fishes. Immunogenetics 44, 36–48 (1996).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
47.Figueroa, F. et al. MHC class IIB gene evolution in East African cichlid fishes. Immunogenetics 51, 556–575 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
48.Migalska, M., Sebastian, A. & Radwan, J. Major histocompatibility complex class I diversity limits the repertoire of T cell
receptors.Proc. Natl. Acad. Sci. USA 116, 5021–5026 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Šimková, A., Košař, M., Vetešník, L. & Vyskočilová, M. MHC genes and parasitism in Carassius gibelio, a diploid-triploid fish species with dual reproduction strategies. BMC Evol. Biol. 13, 122 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
50.Borghans, J. A. M., Beltman, J. B. & De Boer, J. B. MHC polymorphism under host-pathogen coevolution. Immunogenetics 55, 732–739 (2004).CAS 
PubMed 
Article 

Google Scholar 
51.Ejsmond, M. J. & Radwan, J. Red queen processes drive positive selection on major histocompatibility complex (MHC) genes. PLoS Comput. Biol. 11, e1004627 (2015).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
52.Phillips, K. P. et al. Immunogenetic novelty confers a selective advantage in host-pathogen coevolution. Proc. Natl. Acad. Sci. USA 115, 1552–1557 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
53.Gaigher, A., Burri, R., San-Jose, L. M., Roulin, A. & Fumagalli, L. Lack of statistical power as a major limitation in understanding MHC-mediated immunocompetence in wild vertebrate populations. Mol. Ecol. 28, 5115–5132 (2019).PubMed 
Article 

Google Scholar 
54.Šimková, A., Ottová, E. & Morand, S. MHC variability, life-traits and parasite diversity of European cyprinid fish. Evol. Ecol. 20, 465–477 (2006).Article 

Google Scholar 
55.Clarke, B. & Kirby, D. R. S. Maintenance of histocompatibility polymorphisms. Nature 211, 999–1000 (1966).ADS 
CAS 
PubMed 
Article 

Google Scholar 
56.Meglécz, E. et al. SESAME (SEquence Sorter & AMplicon Explorer): Genotyping based on high throughput multiplex amplicon sequencing. Bioinformatics 27, 277–278 (2011).PubMed 
Article 
CAS 

Google Scholar 
57.Zagalska-Neubauer, M. et al. 454 sequencing reveals extreme complexity of the class II major histocompatibility complex in the collared flycatcher. BMC Evol. Biol. 10, 395 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
58.Van Erp, S. H. M., Egberts, E. & Stet, R. J. M. Characterization of class II A and B genes in a gynogenetic carp clone. Immunogenetics 44, 192–202 (1996).PubMed 
Article 

Google Scholar 
59.Šimková, A. Major histocompatibility complex genes and parasites in cyprinid fish. Vie Milieu 67, 139–148 (2017).
Google Scholar 
60.Klein, J. et al. Nomenclature for the major histocompatibility complexes of different species: A proposal. Immunogenetics 31, 217–219 (1990).CAS 
PubMed 
PubMed Central 

Google Scholar 
61.Dixon, B., Nagelkerke, L. A. J., Sibbing, F. A., Egberts, E. & Stet, R. J. M. Evolution of MHC class II beta chain-encoding genes in the Lake Tana barbel species flock (Barbus intermedius complex). Immunogenetics 44, 419–431 (1996).CAS 
PubMed 

Google Scholar 
62.Rakus, K. L. et al. Major histocompatibility (MH) class IIB gene polymorphism influences disease resistance of common carp (Cyprinus carpio L). Aquaculture 288, 44–50 (2009).CAS 
Article 

Google Scholar 
63.Seifertová, M. & Šimková, A. Structure, diversity and evolutionary patterns of expressed MHC class IIB genes in chub (Squalius cephalus), a cyprinid fish species from Europe. Immunogenetics 63, 167–181 (2011).PubMed 
Article 

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

Google Scholar 
65.Darriba, D., Taboala, G. L., Doallo, R. & Posada, D. J. ModelTest2: More models, new heuristics and parallel computing. Nat. Methods 9, 772 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
66.Yang, Z. H. PAML4: Phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–1591 (2007).CAS 
PubMed 
Article 

Google Scholar 
67.Doledec, S. & Chessel, D. Co-inertia analysis—an alternative method for studying species environment relationships. Freshwater Biol. 31, 277–294 (1994).Article 

Google Scholar 
68.Dray, S., Chessel, D. & Thioulouse, J. Co-inertia analysis and the linking of ecological data tables. Ecology 84, 3078–3089 (2003).Article 

Google Scholar 
69.Deter, J. et al. Association between the DQA MHC class II gene and puumala virus infection in Myodes glareolus, the bank vole. Infect. Genet. Evol. 8, 450–458 (2008).CAS 
PubMed 
Article 

Google Scholar 
70.Evans, M. L. & Neff, B. D. Major histocompatibility complex heterozygote advantage and widespread bacterial infections in populations of Chinook salmon (Oncorhynchus tshawytscha). Mol. Ecol. 18, 4716–4729 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
71.Zuur, A. et al. Mixed Effects Models and Extensions in Ecology With R (Springer, 2009).MATH 
Book 

Google Scholar 
72.Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach 2nd edn. (Springer, 2002).MATH 

Google Scholar 
73.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/(2018).74.Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
75.Bartoń, K. MuMIn: Multi-Model Inference. R package version 1.15.1. http://CRAN.R-project.org/package=MuMIn (2018).76.Thioulouse, J. & Dray, S. Interactive multivariate data analysis in R with the ade4 and ade4tkgui packages. J. Stat. Softw. 22, 1–14 (2007).Article 

Google Scholar  More

1.Chapman, B. B. et al. Partial migration in fishes: Causes and consequences. J. Fish Biol. 81, 456–478 (2012).CAS 
Article 

Google Scholar 
2.Araújo, C. V. M. et al. Habitat fragmentation caused by contaminants: Atrazine as a chemical barrier isolating fish populations. Chemosphere 193, 24–31 (2018).ADS 
Article 

Google Scholar 
3.Flitcroft, R. L., Arismendi, I. & Santelmann, M. V. A review of habitat connectivity research for pacific salmon in marine, estuary, and freshwater environments. J. Am. Water Resour. Assoc. 55, 430–441 (2019).ADS 
Article 

Google Scholar 
4.Maire, A., Thierry, E., Viechtbauer, W. & Daufresne, M. Poleward shift in large-river fish communities detected with a novel meta-analysis framework. Freshw. Biol. 64, 1143–1156 (2019).Article 

Google Scholar 
5.van Vliet, M. T. H. et al. Coupled daily streamflow and water temperature modelling in large river basins. Hydrol. Earth Syst. Sci. 16, 4303–4321 (2012).ADS 
Article 

Google Scholar 
6.Palmer, M. A. et al. Climate change and the world’s river basins: Anticipating management options. Front. Ecol. Environ. 6, 81–89 (2008).Article 

Google Scholar 
7.Jonsson, B. & Jonsson, N. A review of the likely effects of climate change on anadromous Atlantic salmon Salmo salar and brown trout Salmo trutta, with particular reference to water temperature and flow. J. Fish Biol. 75, 2381–2447 (2009).CAS 
Article 

Google Scholar 
8.Arevalo, E. et al. An innovative bivariate approach to detect joint temporal trends in environmental conditions: Application to large French rivers and diadromous fish. Sci. Total Environ. 748, 141260 (2020).ADS 
CAS 
Article 

Google Scholar 
9.Hughes, L. Biological consequences of global warming: Is the signal already apparent?. Trends Ecol. Evol. 15, 56–61 (2000).CAS 
Article 

Google Scholar 
10.Tesch, F.-W. & Bartsch, P. The Eel (Blackwell Science, 2003).Book 

Google Scholar 
11.Durif, C. M. F. et al. Age of European silver eels during a period of declining abundance in Norway. Ecol. Evol. 10, 4801–4815 (2020).Article 

Google Scholar 
12.Poole, R. W. & Reynolds, J. D. Growth rate and age at migration of Anguilla anguilla. J. Fish Biol. 48, 633–642 (1996).
Google Scholar 
13.Durif, C. M. F., Travade, F., Rives, J., Elie, P. & Gosset, C. Relationship between locomotor activity, environmental factors, and timing of the spawning migration in the European eel Anguilla anguilla. Aquat. Living Resour. 21, 163–170 (2008).Article 

Google Scholar 
14.Fontaine, M. Physiological mechanisms in the migration of marine and amphihaline fish. Adv. Mar. Biol. 13, 241–355 (1975).Article 

Google Scholar 
15.Bruijs, M. C. M. & Durif, C. M. F. Silver Eel migration and behaviour. Spawning Migr. Eur. Eel https://doi.org/10.1007/978-1-4020-9095-0_4 (2009).Article 

Google Scholar 
16.ICES. Workshop on the temporal migration patterns of European eel (WKEELMIGRATION). vol. 2 http://doi.org/https://doi.org/10.17895/ices.pub.5993 (2020).17.Vøllestad, L. A., Jonsson, B., Hvidsten, N. A. & Naesje, T. F. Experimental test of environmental factors influencing the seaward migration of European silver eels. J. Fish Biol. 45, 641–651 (1994).Article 

Google Scholar 
18.Sandlund, O. T. et al. Timing and pattern of annual silver eel migration in two European watersheds are determined by similar cues. Ecol. Evol. 7, 5956–5966 (2017).Article 

Google Scholar 
19.Drouineau, H. et al. Freshwater eels: A symbol of the effects of global change. Fish Fish. 19, 903–930 (2018).Article 

Google Scholar 
20.Pike, C., Crook, V., & Gollock, M. Anguilla anguilla. The IUCN Red List of Treatened Species 2020. https://dx.doi.org/https://doi.org/10.2305/IUCN.UK.2020-539 2.RLTS.T60344A152845178.en (2020).21.Dankers, R. & Feyen, L. Climate change impact on flood hazard in Europe: An assessment based on high-resolution climate simulations. J. Geophys. Res. Atmos. 113, 1–17 (2008).Article 

Google Scholar 
22.Reid, A. J. et al. Emerging threats and persistent conservation challenges for freshwater biodiversity. Biol. Rev. 94, 849–873 (2019).Article 

Google Scholar 
23.Trenberth, K. E. et al. Global warming and changes in drought. Nat. Clim. Chang. 4, 17–22 (2014).ADS 
Article 

Google Scholar 
24.Durif, C., Elie, P., Gosset, C. & Rives, J. Behavioral Study of Downstream Migrating Eels by Radio-telemetry at a Small Hydroelectric Power Plant. Am. Fish. Soc. Symp. 1–14 (2002).25.Flitcroft, R. L. et al. Linking hydroclimate to fish phenology and habitat use with ichthyographs. PLoS ONE 11, 1–12 (2016).Article 

Google Scholar 
26.Bossard, M., Feranec, J. & Othael, J. CORINE Land Cover Technical Guide – Addendum 2000. European Environment Agency. Technical Report. Available online at: http://www.eea.europa.eu/publications/tech40add. (2000).27.de Eyto, E. et al. Characterisation of salmonid food webs in the rivers and lakes of an Irish peatland ecosystem. Biol. Environ. Proc. R. Irish Acad. 120, 1–17 (2020).
Google Scholar 
28.Poole, W. R., Reynolds, J. D. & Moriarty, C. Observations on the Silver Eel Migrations of the Burrishoole River System, Ireland, 1959 to 1988. Int. Rev. der gesamten Hydrobiol. und Hydrogr 75, 807–815 (1990).Article 

Google Scholar 
29.Poole, W. R. et al. Long-term variation in numbers and biomass of silver eels being produced in two European river systems. ICES J. Mar. Sci. 75, 1627–1637 (2018).Article 

Google Scholar 
30.Chacón, J. E. & Duong, T. Multivariate plug-in bandwidth selection with unconstrained pilot bandwidth matrices. TEST 19, 375–398 (2010).MathSciNet 
Article 

Google Scholar 
31.Lechowicz, M. The sampling characteristics of electivity indices. Oecologia 52, 22–30 (1982).ADS 
Article 

Google Scholar 
32.Ivlev, V. S. Experimental ecology of the feeding fishes (Yale University Press, 1961).
Google Scholar 
33.R Development Core Team. R: A Language and Environment for Statistical Computing. (2020).34.Drouineau, H., Arevalo, E., Lassalle, G., Tétard, S. & Maire, A. chocR: Exploring the temporal CHange of OCcurence of events in multivariate time series. R package version 0.0.0.9000. (2020).35.Hutchinson, G. E. Concluding Remarks. in Cold Spring Harbor Symposia on Quantitative Biology 415–442 (1957).36.Schneider, C., Laizé, C. L. R., Acreman, M. C. & Flörke, M. How will climate change modify river flow regimes in Europe?. Hydrol. Earth Syst. Sci. 17, 325–339 (2013).ADS 
Article 

Google Scholar 
37.Hannaford, J., Laize, C. L. R. & Marsh, T. J. An assessment of runoff trends in undisturbed catchments in the Celtic regions of North West Europe. IAHS-AISH Publ. 78–85 (2007).38.Engen-Skaugen, T. Refinement of dynamically downscaled precipitation and temperature scenarios. Clim. Change 84, 365–382 (2007).ADS 
Article 

Google Scholar 
39.Lawrence, D. & Hisdal, H. Hydrological projections for floods in Norway under a future climate. NVE Report http://webby.nve.no/publikasjoner/report/2011/report2011_05.pdf (2011).40.Woolway, R. I. et al. Substantial increase in minimum lake surface temperatures under climate change. Clim. Change 155, 81–94 (2019).ADS 
CAS 
Article 

Google Scholar 
41.Fealy, R. et al. RESCALE : Review and Simulate Climate and Catchment Responses at Burrishoole. Review Literature and Arts of the Americas (2014).42.Als, T. D. et al. All roads lead to home: Panmixia of European eel in the Sargasso Sea. Mol. Ecol. 20, 1333–1346 (2011).Article 

Google Scholar 
43.Acou, A., Laffaille, P., Legault, A. & Feunteun, E. Migration pattern of silver eel (Anguilla anguilla, L.) in an obstructed river system. Ecol. Freshw. Fish 17, 432–442 (2008).Article 

Google Scholar 
44.Fernandes, W. P. A. et al. Does relatedness influence migratory timing and behaviour in Atlantic salmon smolts?. Anim. Behav. 106, 191–199 (2015).Article 

Google Scholar 
45.Vøllestad, L. A. et al. Environmental Factors Regulating the Seaward Migration of European Silver Eels (Anguilla anguilla). Can. J. Fish. Aquat. Sci. 43, 1909–1916 (1986).Article 

Google Scholar 
46.Daverat, F. et al. One century of eel growth: Changes and implications. Ecol. Freshw. Fish 21, 325–336 (2012).Article 

Google Scholar 
47.Vøllestad, L. A. Geographic variation in age and length at metamorphosis of maturing European eel: Environmental effects and phenotypic plasticity. J. Anim. Ecol. 61, 41 (1992).Article 

Google Scholar 
48.Vaughan, L. et al. Growth rates in a European eel Anguilla anguilla (L., 1758) population show a complex relationship with temperature over a seven-decade otolith biochronology. ICES J. Mar. Sci. (2021).49.Lassalle, G. & Rochard, E. Impact of twenty-first century climate change on diadromous fish spread over Europe, North Africa and the Middle East. Glob. Chang. Biol. 15, 1072–1089 (2009).ADS 
Article 

Google Scholar 
50.Monteiro, R. M. et al. Migration and escapement of silver eel males, Anguilla anguilla, from a southwestern European river. Ecol. Freshw. Fish https://doi.org/10.1111/eff.12545 (2020).Article 

Google Scholar 
51.Mateo, M. et al. Cause or consequence? Exploring the role of phenotypic plasticity and genetic polymorphism in the emergence of phenotypic spatial patterns of the European eel. Can. J. Fish. Aquat. Sci. 74, 987–999 (2017).Article 

Google Scholar  More

Model A: abundances and δ13C of short alkanesConsidering the cleavage at position m (between the no. m and no. m + 1 carbon atoms) of an n-alkyl chain with n carbon atoms (1 ≤ m 15), the isotopic compositions of gas products are insensitive to the initial kerogen side chain length distribution. For initial values, a δ13C value of −35‰ is applied. The initial chain length is in a normal distribution with a peak of C17 and a standard deviation of σ = 2 carbon atoms. The initial alkane concentrations are assumed to be 0.For simplicity, we assume that since there is no isotopic fractionation within or between the alkyl chains at the beginning of hydrogenolysis, the probability of 13C substitution at any position of any side chain is identical and determined by the initial carbon isotopic composition δ13C. Multiple 13C substitutions on a C–C chain are omitted because consideration of multiple substitutions would drastically increase the modelling complexity. This approximation is valid when the C–C chain is not too long. For example, the ratio between the probabilities of double and single 13C substitution in a C20 chain is ({left[{left(begin{array}{c}20\ 2end{array}right)}{{left(frac{{,}^{13}{{{{{rm{C}}}}}}}{{,}^{12}{{{{{rm{C}}}}}}}right)}}^{2}right]}/{left[{left(begin{array}{c}20\ 1end{array}right)}{left(frac{{,}^{13}{{{{{rm{C}}}}}}}{{,}^{12}{{{{{rm{C}}}}}}}right)}right]}) ≈ 10% for 13C/12C ~ 0.01. Such a chain is long enough that the δ13C of gas products is insensitive to C–C chain length. Numerical simulation was conducted with Mathworks MATLAB 2020a.Model B: bulk and clumped isotopic fractionations of CH4
Conversion of methylene in a long C–C chain to methane is generalised into two steps:$${{{{{rm{R}}}}}}{mbox{-}}{{{{{{rm{CH}}}}}}}_{2}{mbox{-}}{{{{{rm{R}}}}}}{^prime} mathop{longrightarrow}limits^{{{{{{{rm{r}}}}}}}_{a}}_{+{{{{{rm{H}}}}}}}{{{{{rm{R}}}}}}{mbox{-}}{{{{{{rm{CH}}}}}}}_{3}mathop{longrightarrow}limits^{{{{{{{rm{r}}}}}}}_{b}}_{+{{{{{rm{H}}}}}}}{{{{{{rm{CH}}}}}}}_{4}$$
(8)
The first step (step a) is the conversion of the methylene group R-CH2-R′ to a methyl group (RCH3) by accepting a capping hydrogen atom from the hydrogen donor (activated H2); the second step (step b) is the conversion of the methyl group to methane by accepting another capping hydrogen atom. This scheme is highly generalised, and each step may involve multiple elementary biochemical reaction steps, such as the binding of H2 and long alkyl chains to the enzyme, activation of H–H and C–C bonds, and release of the short alkane products from the enzyme. It is beyond the scope of this work to discuss the detailed biochemical reaction steps. But the cleavage and formation of chemical bonds in these steps should be constrained by the observed isotopic patterns.Due to the computational complexity, we did not use the random scission model (Model A) in the simulation involving clumped isotopic fractionation, as explained in the following. A conventional kinetic model of the decomposition of organic matter without considering the constraints of C–C chain lengths is a zero-dimensional problem. Modelling the random cutting of long C–C chains without considering isotopes is a one-dimensional problem, and modelling bulk carbon isotopic fractionation during random cutting (Model A) is a two-dimensional problem. If 13C–13C coupling is included in random cutting, the modelling is a three-dimensional problem; a complex Monte Carlo method has been applied to deal with this problem19. If the 13C–D or D–D coupling is included in Model B, as we wish, it is a problem above the fourth dimension. The complexity of programming and the difficulty of computation make the model unattainable; even if it is achievable, solving this problem is far beyond the scope of this work.Reaction equation Eq. 8 is expanded to the scheme in Fig. 3a to quantify the five most abundant isotopologues in methane (three or more substitutions such as 13CH2D2 or 12CHD3 are ignored due to their low abundances). For the subscripts in Fig. 3a (m, i, and j in ramij or rbmij), the first digit (m = 0 or 1) is the number of 13C atoms involved in the reaction, the second digit (i = 0, 1, or 2) is the number of deuterium atoms connected in the methylene or methyl group, and the third digit (j = 0 or 1) is the number of deuterium atoms in the hydrogen donor.Clumped isotopic compositions of methylene and methane are defined as the following:$$left{begin{array}{l}{{Delta}} {{{{{rm{R}}}}}}{,}^{13}{{{{{rm{C}}}}}}{{{{{rm{HDR}}}}}}^{prime} =frac{({{{{{rm{R}}}}}}{,}^{13}{{{{{rm{C}}}}}}{{{{{rm{HDR}}}}}}^{prime} )({{{{{rm{R}}}}}}{,}^{12}{{{{{rm{C}}}}}}{{{{{{rm{H}}}}}}}_{2}{{{{{rm{R}}}}}}^{prime} )}{({{{{{rm{R}}}}}}{,}^{13}{{{{{rm{C}}}}}}{{{{{{rm{H}}}}}}}_{2}{{{{{rm{R}}}}}}^{prime} )({{{{{rm{R}}}}}}{,}^{12}{{{{{rm{C}}}}}}{{{{{rm{HDR}}}}}}^{prime} )}-1hfill\ {{Delta}} {{{{{rm{R}}}}}}{,}^{12}{{{{{rm{C}}}}}}{{{{{{rm{D}}}}}}}_{2}{{{{{rm{R}}}}}}^{prime} =4frac{({{{{{rm{R}}}}}}{,}^{12}{{{{{rm{C}}}}}}{{{{{{rm{D}}}}}}}_{2}{{{{{rm{R}}}}}}^{prime} )({{{{{rm{R}}}}}}{,}^{12}{{{{{rm{C}}}}}}{{{{{{rm{H}}}}}}}_{2}{{{{{rm{R}}}}}}^{prime} )}{{({{{{{rm{R}}}}}}{,}^{12}{{{{{rm{C}}}}}}{{{{{rm{HDR}}}}}}^{prime} )}^{2}}-1hfill\ {{Delta}} {,}^{13}{{{{{rm{C}}}}}}{{{{{{rm{H}}}}}}}_{3}{{{{{rm{D}}}}}}=frac{({,}^{13}{{{{{rm{C}}}}}}{{{{{{rm{H}}}}}}}_{3}{{{{{rm{D}}}}}})({,}^{12}{{{{{rm{C}}}}}}{{{{{{rm{H}}}}}}}_{4})}{({,}^{12}{{{{{rm{C}}}}}}{{{{{{rm{H}}}}}}}_{3}{{{{{rm{D}}}}}})({,}^{13}{{{{{rm{C}}}}}}{{{{{{rm{H}}}}}}}_{4})}-1hfill\ {{Delta}} {,}^{12}{{{{{rm{C}}}}}}{{{{{{rm{H}}}}}}}_{2}{{{{{{rm{D}}}}}}}_{2}=frac{8}{3} frac{({,}^{12}{{{{{rm{C}}}}}}{{{{{{rm{H}}}}}}}_{2}{{{{{{rm{D}}}}}}}_{2})({,}^{12}{{{{{rm{C}}}}}}{{{{{{rm{H}}}}}}}_{4})}{{({,}^{12}{{{{{rm{C}}}}}}{{{{{{rm{H}}}}}}}_{3}{{{{{rm{D}}}}}})}^{2}}-1 hfillend{array}right.$$
(9)
Note that the isotopic compositions here are expressed in decimals; they should be multiplied by 1000 to give per mil values.The deuterium isotope ratio between the hydrogen donor (denoted with subscript B) and the methylene group (subscript A) is expressed as:$${alpha }_{{{{{{rm{A}}}}}}}^{{{{{{rm{B}}}}}}}=frac{1+{{{delta }}{{{{{rm{D}}}}}}}_{{{{{{rm{B}}}}}}}}{1+{{{delta }}{{{{{rm{D}}}}}}}_{{{{{{rm{A}}}}}}}}$$
(10)
For each reaction step in Fig. 3a, the corresponding rate constants are denoted as kamij for step a or kbmij for step b. Kinetic fractionation factors αkamij = kamij/ka000 and αkbmij = kbmij/kb000 define KIEs. Note that a DKIE is often expressed as kH/kD, which is the reciprocal of the αk nomenclature here. A DKIE may be primary or secondary; a primary DKIE results in αka001 ≠ 1 and αkb001 ≠ 1, and a secondary one results in αka010 ≠ 1 and αkb010 ≠ 1. Kinetic clumped isotope fractionation factors γamij = αkamij/(αka100mαka010iαka001j) and γbmij = αkbmij/ (αkb100mαkb010iαkb001j) define the excessive KIE due to isotope clumping in steps a and b, respectively30.Conversion of the reactant R-CH2-R′ is defined as 1 − f, where f is the residual fraction of R-CH2-R′:$$f=({{{{{rm{R}}}}}}{mbox{-}}{{{{{{rm{CH}}}}}}}_{2}{mbox{-}}{{{{{rm{R}}}}}}{^prime} )/{({{{{{rm{R}}}}}}{mbox{-}}{{{{{{rm{CH}}}}}}}_{2}{mbox{-}}{{rm{R}}}{^prime} )}_{{{{{{rm{initial}}}}}}}$$
(11)
Considering the isotope abundance of D  1 or γb011  > 1, as shown by the Δ12CH2D2 expression in Eq. (13). With this prerequisite, either an inverse primary DKIE (1° DKIE, αka001  > 1, αkb001  > 1) or an inverse secondary DKIE (2° DKIE, αka010  > 1, αkb010  > 1) is necessary, and through numerical simulation, we found that only the inverse 1° DKIE satisfies the above-mentioned δDA, δDB, and Δ12CH2D2 values.Two scenarios (one is the pure stochastic condition, the other is with an inverse 1° DKIE) are modelled (Fig. 3). The parameters are listed in Table 1. For comparison, analytical solutions at the beginning and end of reactions from Eqs. (12) and (13) are presented. The numerical and analytical solutions are nearly identical at the beginning of conversion. There are small differences between the numerical and analytical solutions at the endpoint because the abundance of the hydrogen donor is not extremely excessive. A weak 13C fractionation between the organic precursor and the methane product is obtained with the KIE parameters (Fig. 3b). With such a weak 13C KIE, Δ13CH3D is nearly constant for reaction extent (Fig. 3c). Note that we applied an inverse 13C KIE, as required by the δ13C distribution of the alkane gases (Method 1, Model A). The δD and Δ12CH2D2 values are independent of 13C KIE. Both the bulk and clumped isotopic compositions of methane within the range of reported values are obtained at the organic precursor conversion of 0.65–0.70 as constrained by Fig. 2. More

1.Rao, G. D., Sui, J. K. & Zhang, J. G. Metabolomics reveals significant variations in metabolites and correlations regarding the maturation of walnuts (Juglans regia L.). Biol Open 5, 829–836 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
2.Li, T. et al. Influence of green manure and rice straw management on soil organic carbon, enzyme activities, and rice yield in red paddy soil. Soil Till Res 195, 104428 (2019).3.Sharma, P. et al. Green manure as part of organic management cycle: effects on changes in organic matter characteristics across the soil profile. Geoderma 305, 197–207 (2017).ADS 
CAS 
Article 

Google Scholar 
4.Nivelle, E. et al. Functional response of soil microbial communities to tillage, cover crops and nitrogen fertilization. Appl. Soil Ecol. 108, 147–155 (2016).Article 

Google Scholar 
5.Mbuthia, L. W. et al. Long term tillage, cover crop, and fertilization effects on microbial community structure, activity: Implications for soil quality. Soil Biol Biochem. 89, 24–34 (2015).CAS 
Article 

Google Scholar 
6.Chavarria, D. N. et al. Effect of cover crops on microbial community structure and related enzyme activities and macronutrient availability. Eur. J. Soil Biol. 76, 74–82 (2016).CAS 
Article 

Google Scholar 
7.Zhao, S. et al. Changes in soil microbial community, enzyme activities and organic matter fractions under long-term straw return in north-central China. Agric. Ecosyst. Environ. 216, 82–88 (2016).CAS 
Article 

Google Scholar 
8.Tian, Y., Zhang, X., Wang, J. & Gao, L. Soil microbial communities associated with the rhizosphere of cucumber under different summer cover crops and residue management: A 4-year field experiment. Sci. Hort. 150, 100–109 (2013).Article 

Google Scholar 
9.Capo-Bauca, S., Marques, A., Llopis-Vidal, N., Bota, J. & Baraza, E. Long-term establishment of natural green cover provides agroecosystem services by improving soil quality in a Mediterranean vineyard. Ecol. Eng. 127, 285–291 (2019).Article 

Google Scholar 
10.Saikia, R., Sharma, S., Thind, H. S., Sidhu, H. S. & Yadvinder, S. Temporal changes in biochemical indicators of soil quality in response to tillage, crop residue and green manure management in a rice-wheat system. Ecol. Indic. 103, 383–394 (2019).CAS 
Article 

Google Scholar 
11.Acosta-Martinez, V. et al. Dryland cropping systems influence the microbial biomass and enzyme activities in a semiarid sandy soil. Biol. Fert. Soils 47, 655–667 (2011).CAS 
Article 

Google Scholar 
12.Sharma, S. & Dhaliwal, S. S. Conservation agriculture based practices enhanced micronutrients transformation in earthworm cast soil under rice-wheat cropping system. Ecol Eng 163, 106195 (2021).13.Nunes, M. R., Karlen, D. L., Veum, K. S., Moorman, T. B. & Cambardella, C. A. Biological soil health indicators respond to tillage intensity: A US metaanalysis. Geoderma 369, 114335 (2020).14.Roper, M. M. & Gupta, V. V. S. R. Management practices and soil biota. Aust. J. Soil Res. 33, 321–339 (1995).Article 

Google Scholar 
15.Wortman, S. E., Drijber, R. A., Francis, C. A. & Lindquist, J. L. Arable weeds, cover crops, and tillage drive soil microbial community composition in organic cropping systems. Appl. Soil Ecol. 72, 232–241 (2013).Article 

Google Scholar 
16.Drijber, R. A., Doran, J. W., Parkhurst, A. M. & Lyon, D. J. Changes in soil microbial community structure with tillage under long-term wheat-fallow management. Soil. Biol. Biochem. 32, 1419–1430 (2000).CAS 
Article 

Google Scholar 
17.Qian, X. et al. Effects of living mulches on the soil nutrient contents, enzyme activities, and bacterial community diversities of apple orchard soils. Eur. J. Soil Biol. 70, 23–30 (2015).CAS 
Article 

Google Scholar 
18.Verzeaux, J. et al. Cover crops prevent the deleterious effect of nitrogen fertilisation on bacterial diversity by maintaining the carbon content of ploughed soil. Geoderma 281, 49–57 (2016).ADS 
CAS 
Article 

Google Scholar 
19.Rothe, M., Darnaudery, M. & Thuries, L. Organic fertilizers, green manures and mixtures of the two revealed their potential as substitutes for inorganic fertilizers used in pineapple cropping. Sci. Hort. 257, 108691 (2019)20.Lupwayi, N. Z., Larney, F. J., Blackshaw, R. E., Kanashiro, D. A. & Pearson, D. C. Phospholipid fatty acid biomarkers show positive soil microbial community responses to conservation soil management of irrigated crop rotations. Soil Till Res. 168, 1–10 (2017).Article 

Google Scholar 
21.Li, L., Larney, F. J., Angers, D. A., Pearson, D. C. & Blackshaw, R. E. Surface soil quality attributes following 12 years of conventional and conservation management on irrigated rotations in Southern Alberta. Soil Sci. Soc. Am. J. 79, 930–942 (2015).ADS 
CAS 
Article 

Google Scholar 
22.Xu, Z. et al. The variations in soil microbial communities, enzyme activities and their relationships with soil organic matter decomposition along the northern slope of Changbai Mountain. Appl. Soil Ecol. 86, 19–29 (2015).Article 

Google Scholar 
23.Cusack, D. F., Silver, W. L., Torn, M. S., Burton, S. D. & Firestone, M. K. Changes in microbial community characteristics and soil organic matter with nitrogen additions in two tropical forests. Ecology 92, 621–632 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
24.Sinsabaugh, R. L. Phenol oxidase, peroxidase and organic matter dynamics of soil. Soil Biol. Biochem. 42, 391–404 (2010).CAS 
Article 

Google Scholar 
25.Masai, E., Katayama, Y. & Fukuda, M. Genetic and biochemical investigations on bacterial catabolic pathways for lignin-derived aromatic compounds. Biosci. Biotechnol. Biochem. 71, 1–15 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Falchini, L., Naumova, N., Kuikman, P. J., Bloem, J. & Nannipieri, P. CO2 evolution and denaturing gradient gel electrophoresis profiles of bacterial communities in soil following addition of low molecular weight substrates to simulate root exudation. Soil Biol. Biochem. 35, 775–782 (2003).CAS 
Article 

Google Scholar 
27.Liang, S., Grossman, J. & Shi, W. Soil microbial responses to winter, legume cover crop management during organic transition. Eur. J. Soil Biol. 65, 15–22 (2014).CAS 
Article 

Google Scholar 
28.Cruz, C., Green, J. J., Watson, C. A., Wilson, F. & Martins-Loução, M. A. Functional aspects of root architecture and mycorrhizal inoculation with respect to nutrient uptake capacity. Mycorrhiza 14, 177–184 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Wu, Q. S., Zou, Y. N., He, X. H. & Luo, P. Arbuscular mycorrhizal fungi can alter some root characters and physiological status in trifoliate orange (Poncirus trifoliata L. Raf) seedlings. Plant Growth Regul. 65, 273–278 (2011).CAS 
Article 

Google Scholar 
30.Gutjahr, C. & Paszkowski, U. Multiple control levels of root system remodeling in arbuscular mycorrhizal symbiosis. Front. Plant Sci. 4, 204 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
31.Endlweber, K. & Scheu, S. Interactions between mycorrhizal fungi and Collembola: effects on root structure of competing plant species. Biol. Fertil. Soils 43, 741–749 (2007).Article 

Google Scholar 
32.Stevens, K. J., Wall, C. B. & Janssen, J. A. Effects of arbuscular mycorrhizal fungi on seedling growth and development of two wetland plants, Bidens frondosa L., and Eclipta prostrate L., grown under three levels of water availability. Mycorrhiza 21, 279–288 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
33.Peng, L., Wen, Z., An, X., Han, J. & Jiang, Y. M. Effects of interplanting grass on utilization, loss and accumulation of 15N in apple orchard. Acta Pedol. Sin. 52, 950–956 (2015).
Google Scholar 
34.Sánchez, E. E. et al. Cover crops influence soil properties and tree performance in an organic apple (Malus domestica Borkh) orchard in northern Patagonia. Plant Soil 292, 193–203 (2007).Article 
CAS 

Google Scholar 
35.Zhang, C. P., Meng, P., Zhang, J. S. & Wan, X. C. Effects of a nitrogen fixing plant Vigna radiata on growth, leaf stomatal gas exchange and hydraulic characteristics of the intercropping Juglans regia seedlings. Chin. J. Plant Ecol. 38, 499–506 (2014).Article 

Google Scholar 
36.Li, Y. Y., Hu, H. S., Cheng, X., Sun, J. H. & Li, L. Effects of interspecific interactions and nitrogen fertilization rates on above-and below-growth in faba bean/mazie intercropping system. Acta Ecol. Sin. 31, 1617–1630 (2011).
Google Scholar 
37.Nyamadzawo, G., Nyamangara, J., Nyamugafata, P. & Muzulu, A. Soil microbial biomass and mineralization of aggregate protected carbon in fallow-maize systems under conventional and no-tillage in Central Zimbabwe. Soil Tillage Res. 102, 151–157 (2009).Article 

Google Scholar 
38.Xiao, D. et al. Microbial biomass, metabolic functional diversity, and activity are affected differently by tillage disturbance and maize planting in a typical karst calcareous soil. J. Soil Sediment. 19, 809–821 (2019).CAS 
Article 

Google Scholar 
39.Elfstrand, S., Bath, B. & Martensson, A. Influence of various forms of green manure amendment on soil microbial community composition, enzyme activity and nutrient levels in leek. Appl. Soil Ecol. 36, 70–82 (2007).Article 

Google Scholar 
40.Waring, B. G., Averill, C. & Hawkes, C. V. Differences in fungal and bacterial physiology alter soil carbon and nitrogen cycling: insights from meta-analysis and theoretical models. Ecol. Lett. 16, 887–894 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
41.Sinsabaugh, R. L. & Moorhead, D. L. Resource allocation to extracellular enzyme production: a model for nitrogen and phosphorus control of litter decomposition. Soil Biol. Biochem. 26, 1305–1311 (1994).Article 

Google Scholar 
42.DeForest, J. The influence of time, storage temperature, and substrate age on potential soil enzyme activity in acidic forest soils using MUB-linked substrates and L-DOPA. Soil Biol. Biochem. 41, 1180–1186 (2009).CAS 
Article 

Google Scholar 
43.Saiya-Cork, K. R., Sinsabaugh, R. L. & Zak, D. R. The effects of long term nitrogen deposition on extracellular enzyme activity in an Acer saccharum forest soil. Soil. Biol. Biochem. 34, 1309–1315 (2002).CAS 
Article 

Google Scholar 
44.Schutter, M. E. & Dick, R. P. Comparison of fatty acid methyl ester (FAME) methods for characterizing microbial communities. Soil Sci. Soc. Am. J. 64, 1659–1668 (2000).ADS 
CAS 
Article 

Google Scholar 
45.Bowles, T. M., Acosta-Martinez, V., Calderon, F. & Jackson, L. E. Soil enzyme activities, microbial communities, and carbon and nitrogen availability in organic agroecosystems across an intensively-managed agricultural landscape. Soil Biol. Biochem. 68, 252–262 (2014).CAS 
Article 

Google Scholar  More

1.Cury, P. M. et al. Global seabird response to forage fish depletion—one-third for the birds. Science 23(6063), 1703–1706 (2011).ADS 
Article 

Google Scholar 
2.Pikitch, K. E. The risks of overfishing. Science 338, 474–475. https://doi.org/10.1126/science.1229965 (2012).ADS 
CAS 
Article 
PubMed 

Google Scholar 
3.F.A.O. The State of World Fisheries and Aquaculture. Meeting the Sustainable Development Goals. Italy, Rome, http://www.fao.org/documents/card/en/c/I9540EN/ (2018).4.Branch, G. M. & Steffani, C. N. Can we predict the effects of alien species? A case-history of the invasion of South Africa by Mytilus galloprovincialis (Lamarck). J. Exp. Mar. Biol. Ecol. 300, 189–215. https://doi.org/10.1016/j.jembe.2003.12.007 (2004).Article 

Google Scholar 
5.de Graaf, M. M. et al. Declining stocks of Lake Tana’s endemic Barbus species flock (Pisces, Cyprinidae): natural variation or human impact?. Biol. Conserv. 116, 277–287. https://doi.org/10.1016/S0006-3207(03)00198-8 (2004).Article 

Google Scholar 
6.Davidson, N. C. How much wetland has the world lost? Long-term and recent trends in the global wetland area. Mar. Freshwater. Res. 65, 934–941. https://doi.org/10.1071/MF14173 (2014).Article 

Google Scholar 
7.Landrigan, P. J. et al. The lancet commission on pollution and health. Lancet 91, 462–512. https://doi.org/10.1016/S0140-6736(17)32345-0 (2018).Article 

Google Scholar 
8.Döll, P. et al. Integrating risks of climate change into water management. Hydrol. Sci. J. 60, 4–13. https://doi.org/10.1080/02626667.2014.967250 (2015).CAS 
Article 

Google Scholar 
9.Whitehead, P. R. et al. A review of the potential impacts of climate change on surface water quality. Hydrol. Sci. J. 54, 101–123. https://doi.org/10.1623/hysj.54.1.101 (2009).Article 

Google Scholar 
10.Knouft, J. H. & Ficklin, D. L. The potential impacts of climate change on biodiversity in flowing freshwater systems. Annu. Rev. Ecol Evol. Syst. 48, 111–133 (2017).Article 

Google Scholar 
11.Claireaux, G. & Chabot, G. Responses by fishes to environmental hypoxia: integration through Fry’s concept of aerobic metabolic scope. J. Fish. Biol. 88, 232–251. https://doi.org/10.1111/jfb.12833 (2016).CAS 
Article 
PubMed 

Google Scholar 
12.Hadjinikolova, L., Nikolova, L. & Stoeva, A. Comparative investigations on the nutritive value of carp fish meat (Cyprinidae), grown at organic aquaculture conditions. Bulg. J. Agric. Sci. 14, 127–132 (2008).
Google Scholar 
13.Ljubojević, D. et al. Fat quality of marketable fresh water fish species in the Republic of Serbia. Czech. J. Food Sci. 31, 445–450. https://doi.org/10.17221/53/2013-CJFS (2013).Article 

Google Scholar 
14.Pyz-Łukasik, R. & Paszkiewicz, W. Species variations in the proximate composition, amino acid profile, and protein quality of the muscle tissue of grass carp, bighead carp, Siberian sturgeon, and wels catfish. J. Food. Qual. 2018, 2625401. https://doi.org/10.1155/2018/2625401 (2018).CAS 
Article 

Google Scholar 
15.Enders, E. C. & Boisclair, D. Effects of environmental fluctuations on fish metabolism: Atlantic salmon Salmo salar as a case study. J. Fish. Biol. 88, 344–358. https://doi.org/10.1111/jfb.12786 (2016).CAS 
Article 
PubMed 

Google Scholar 
16.Zheng, J. L. et al. Dietary L-carnitine supplementation increases lipid deposition in the liver and muscle of yellow catfish (Pelteobagrus fulvidraco) through changes in lipid metabolism. Br. J. Nutr. 112, 698–708. https://doi.org/10.1017/S0007114514001378 (2014).CAS 
Article 
PubMed 

Google Scholar 
17.Geda, F. et al. β-Alanine does not act through branched-chain amino acid catabolism in carp, a species with low muscular carnosine storage. Fish. Physiol. Biochem. 41, 281–287. https://doi.org/10.1007/s10695-014-0024-7 (2015).CAS 
Article 
PubMed 

Google Scholar 
18.Sabzi, E., Mohammadiazarm, H. & Salati, A. P. Effect of dietary L-carnitine and lipid levels on growth performance, blood biochemical parameters, and antioxidant status in juvenile common carp (Cyprinus carpio). Aquaculture 480, 89–93. https://doi.org/10.3390/antiox10010036 (2017).CAS 
Article 

Google Scholar 
19.Geda, F. et al. Changes in intestinal morphology and amino acid catabolism in common carp at mildly elevated temperature as affected by dietary mannan oligosaccharides. Anim. Feed. Sci. Technol. 178, 95–102 (2012).CAS 
Article 

Google Scholar 
20.Geda, F. et al. The metabolic response in fish to mildly elevated water temperature relates to species-dependent muscular concentrations of imidazole compounds and free amino acids. J. Therm. Biol. 65, 57–63 (2017).CAS 
Article 

Google Scholar 
21.Li, J. M. et al. Corrigendum: systemic regulation of L-carnitine in nutritional metabolism in zebrafish. Danio rerio. Sci. Rep. 7, 44970. https://doi.org/10.1038/srep44970 (2017).ADS 
CAS 
Article 
PubMed 

Google Scholar 
22.Butler, P. J., Green, J. A., Boyd, I. L. & Speakman, J. R. Measuring metabolic rate in the field: the pros and cons of the doubly labelled water and heart rate methods. Funct. Ecol. 18, 168–183. https://doi.org/10.1111/j.0269-8463.2004.00821.x (2004).Article 

Google Scholar 
23.Forster, J., Hirst, A. G. & Atkinson, D. Warming-induced reductions in body size are greater in aquatic than terrestrial species. Proc. Natl. Acad. Sci. 109, 19310–19314 (2012).ADS 
CAS 
Article 

Google Scholar 
24.McKnight, C. L. et al. Introduction to metabolism. In Surgical Metabolism (eds Davis, K. & Rosenbaum, S.) (Springer, Cham, 2020). https://doi.org/10.1007/978-3-030-39781-4_1.Chapter 

Google Scholar 
25.Li, L. Y. et al. Mitochondrial fatty acid β-oxidation inhibition promotes glucose utilization and protein deposition through energy homeostasis remodeling in fish. J. Nutr. 150, 2322–2335 (2020).Article 

Google Scholar 
26.Miyaaki, H. et al. Blood carnitine profiling on tandem mass spectrometry in liver cirrhotic patients. BMC Gastroenterol. 20, 41. https://doi.org/10.1186/s12876-020-01190-6 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
27.Worku, K. et al. Measuring seasonal and agro-ecological effects on nutritional status in tropical ranging dairy cows. J. Dairy Sci. 104, 4341–4349 (2021).CAS 
Article 

Google Scholar 
28.Brenes-Soto, A. et al. Gaining insights in the nutritional metabolism of amphibians: analyzing body nutrient profiles of the African clawed frog. Xenopus laevis. PeerJ. 7, e7365 (2019).Article 

Google Scholar 
29.Tilahun, G. & Ahlgren, G. Seasonal variations in phytoplankton biomass and primary production in the Ethiopian Rift Valley lakes Ziway, Awassa, and Chamo-The basis for fish production. Limnlogica 40, 330–342. https://doi.org/10.1016/j.limno.2009.10.005 (2010).CAS 
Article 

Google Scholar 
30.Vijverberg, J. et al. Zooplankton, fish communities and the role of planktivory in nine Ethiopian lakes. Hydrobiology 722, 45–60. https://doi.org/10.1007/s10750-013-1674-7 (2014).CAS 
Article 

Google Scholar 
31.Dagne, A., Herzig, A., Jersabek, C. & Tadesse, Z. Abundance, species composition and spatial distribution of planktonic rotifers and crustaceans in Lake Ziway (Rift Valley, Ethiopia). Int. Rev. Hydrobiol. 93, 210–226. https://doi.org/10.1002/iroh.200711005 (2008).Article 

Google Scholar 
32.Engdaw, F., Dadebo, E. & Nagappan, R. Morphometric relationships and feeding habits of Nile tilapia Oreochromis niloticus (L.) (Pisces: Cichlidae) from Lake Koka, Ethiopia. Int. J. Fish. Aquat. Sci. 2, 65–71 (2013).
Google Scholar 
33.Gouni, M. M. & Sommer, U. Review: effects of harmful blooms of large-sized and colonial cyanobacteria on aquatic food webs. Water 12, 1587. https://doi.org/10.3390/w12061587 (2020).Article 

Google Scholar 
34.Menezes, R. F., Attayde, J. L. & Vasconcelos, F. R. Effects of omnivorous filter-feeding fish and nutrient enrichment on the plankton community and water transparency of a tropical reservoir. Freshw. Biol. 55, 767–779 (2010).CAS 
Article 

Google Scholar 
35.Ibrahim, A. F. N., Noll, M. S. C. & Valenti, W. C. Zooplankton capturing by Nile Tilapia, Oreochromis niloticus (Teleostei: Cichlidae) throughout post-larval development. Zologica 32, 469–475. https://doi.org/10.1590/S1984-46702015000600006 (2015).Article 

Google Scholar 
36.Ambelu, A., Lock, K. & Goethals, P. L. M. Hydrological and anthropogenic influence in the Gilgel Gibe I reservoir (Ethiopia) on macroinvertebrate assemblages. Lake. Reserv. Manag. 29, 143–150. https://doi.org/10.1080/10402381.2013.806971 (2013).CAS 
Article 

Google Scholar 
37.Bayissa, T. N. et al. The impact of lake ecosystems on mineral concentrations in tissues of Nile tilapia (Oreochromis Niloticus L.). Animals 11, 1000 (2021).Article 

Google Scholar 
38.Puvvada, Y., Vankayalapati, S. & Sukhavasi, S. Extraction of chitin from chitosan from the exoskeleton of shrimp for application in the pharmaceutical industry. Int. Curr. Pharm. J. 1, 258–263. https://doi.org/10.3329/icpj.v1i9.11616 (2012).CAS 
Article 

Google Scholar 
39.Zhang, D., Jin, Y., Deng, Y., Wang, D. & Zhao, Y. Production of chitin from shrimp shell powders using Serratia Marcescens B742 and Lactobacillus Plantarum ATCC 8014 successive two-step fermentation. Carbohydr. Res. 362, 13–20. https://doi.org/10.1016/j.carres.2012.09.011 (2012).CAS 
Article 
PubMed 

Google Scholar 
40.Philibert, T., Lee, B. H. & Fabien, N. Current status and new perspectives on chitin and chitosan as functional biopolymers. Appl. Biochem. Biotechnol. 181, 1314–1337. https://doi.org/10.1007/s12010-016-2286-2 (2017).CAS 
Article 
PubMed 

Google Scholar 
41.Matsumiya, M. & Mochizuki, A. Distribution of chitinase and β-N-acetylhexosaminidase in the organs of several fishes. Fish Res. 62, 150–151. https://doi.org/10.2331/fishsci.62.150 (1996).CAS 
Article 

Google Scholar 
42.Gutowska, M. A., Drazen, J. C. & Robison, B. H. Digestive chitinolytic activity in marine fishes of Monterey Bay, California. Comput. Biochem. Physiol. 139, 351–358 (2004).Article 

Google Scholar 
43.Molinari, L. M. et al. Identification and partial characterization of a chitinase from Nile tilapia, Oreochromis niloticus. Comput. Biochem. Physiol. 146, 81–87 (2007).Article 

Google Scholar 
44.Cauchie, H. M. Chitin production by arthropods in the hydrosphere. Hydrobiol. 470, 63–96. https://doi.org/10.1023/A:1015615819301 (2002).CAS 
Article 

Google Scholar 
45.Merga, L. B. et al. Trends in chemical pollution and ecological status of Lake Ziway, Ethiopia: a review focusing on nutrients, metals and pesticides. Afr. J. Aquat. Sci. 45, 386–400 (2020).CAS 
Article 

Google Scholar 
46.Clark, T. D. et al. The efficacy of field techniques for obtaining and storing blood samples from fishes. J. Fish. Biol. 79, 1322–1333. https://doi.org/10.1111/j.1095-8649.2011.03118.x (2011).CAS 
Article 
PubMed 

Google Scholar 
47.Ferguson, H. Blood sampling standard operating procedure. Aquatic Animal Diseases Lab Manual, Department of Integrative Biology, University of Guelph, Quelph, Canada. https://www.uoguelph.ca/ib/sites/uoguelph.ca.ib/files/public/fishbloodsamplingSOP.pdf (2005).48.Arends, R. J., Mancera, J. M., Muñoz, J. L., Wendelaar Bonga, S. E. & Flik, G. The stress response of the gilthead seabream (Sparus aurata L.) to air exposure and confinement. J. Endocr. 163, 149–157. https://doi.org/10.1677/joe.0.1630149 (1999).CAS 
Article 
PubMed 

Google Scholar 
49.Zytkovicz, T. H. et al. Tandem mass spectrometric analysis for amino, organic, and fatty acid disorders in newborn dried blood spots: a two-year summary from the New England Newborn Screening Program. Clin. Chem. 47, 1945–1955 (2001).CAS 
Article 

Google Scholar 
50.Vieira Neto, E. et al. Analysis of acylcarnitine profiles in umbilical cord blood and during the early neonatal period by electrospray ionization tandem mass spectrometry. Braz. J. Med. Biol. Res. 45, 546–556. https://doi.org/10.1590/S0100-879X2012007500056 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
51.AOAC. Official methods of analysis of the Association of Official Analytical Chemists, 15th ed. Methods 962.09, 954.01. AOAC, Arlington, VA, USA. https://archive.org/stream/gov.law.aoac.methods.1.1990/aoac.methods.1.1990_djvu.txt (1990).52.Pearson, D. Pearson Composition and Analysis of Foods (University of Reading, Reading, 1999).
Google Scholar  More