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.IBGE. Instituto Brasileiro de Geografia e Estatística – Censo Agro 2017. IBGE | Censo Agro 2017, Dados preliminares https://censos.ibge.gov.br/agro/2017/ (2017).2.Boldrini, I. I. et al. Flora. In Biodiversidade dos Campos do Planalto das Araucárias 39–94 (2009).3.Iganci, J. R. V., Heiden, G., Miotto, S. T. S. & Pennington, R. T. Campos de Cima da Serra: The Brazilian subtropical highland Grasslands show an unexpected level of plant endemism. Bot. J. Linn. Soc. 167, 378–393 (2011).Article 

Google Scholar 
4.Borer, E. T. et al. Herbivores and nutrients control grassland plant diversity via light limitation. Nature 508, 517–520 (2014).ADS 
CAS 
Article 

Google Scholar 
5.Alhamad, M. N. & Alrababah, M. A. Defoliation and competition effects in a productivity gradient for a semiarid Mediterranean annual grassland community. Basic Appl. Ecol. 9, 224–232 (2008).Article 

Google Scholar 
6.Fedrigo, J. K. et al. Temporary grazing exclusion promotes rapid recovery of species richness and productivity in a long-term overgrazed Campos grassland. Restor. Ecol. https://doi.org/10.1111/rec.12635 (2017).Article 

Google Scholar 
7.Mavromihalis, J. A., Dorrough, J., Clark, S. G., Turner, V. & Moxham, C. Manipulating livestock grazing to enhance native plant diversity and cover in native grasslands. Rangel. J. 35, 95–108 (2013).Article 

Google Scholar 
8.Bircham, J. S. & Hodgson, J. The influence of sward condition on rates of herbage growth and senescence in mixed swards under continuous stocking management. Grass Forage Sci. 38, 323–331 (1983). Article 

Google Scholar 
9.Sbrissia, A. F. et al. Defoliation strategies in pastures submitted to intermittent stocking method: Underlying mechanisms buffering forage accumulation over a range of grazing heights. Crop Sci. 58, 945–954 (2018).Article 

Google Scholar 
10.Jaurena, M. et al. Native grasslands at the core: A new paradigm of intensification for the Campos of Southern South America to increase economic and environmental sustainability. Front. Sustain. Food Syst. 5, 11 (2021).Article 

Google Scholar 
11.Cruz, P. et al. Leaf traits as functional descriptors of the intensity of continuous grazing in native grasslands in the South of Brazil. Rangel. Ecol. Manag. 63, 350–358 (2010).Article 

Google Scholar 
12.Benitez, C. A. & Fernandez, J. G. Espécies forrageiras de la pradera natural: Fenologia y respuesta a la frequência e severidad de corte (1970).13.Herve, A. M. B. & Valls, J. F. M. Genêro Andropogon L. (Gramineae) no Rio Grande do Sul. Anuario tecnico do Instituto de Pesquisas Zootecnicas Francisco Osorio (1980).14.Zanin, A. & Longhi-Wagner, H. M. Revisão de Andropogon (Poaceae – Andropogoneae) para o Brasil. Rodriguesia 62, 171–202 (2011).Article 

Google Scholar 
15.Augustine, D. J. & McNaughton, S. J. Ungulate effects on the functional species composition of plant communities: Herbivore selectivity and plant tolerance. J. Wildl. Manag. 62, 1165 (1998).Article 

Google Scholar 
16.Fraser, L. H. et al. Worldwide evidence of a unimodal relationship between productivity and plant species richness. Science 350, 1177b (2015).ADS 
Article 

Google Scholar 
17.Connell, J. H. Diversity in tropical rain forests and coral reefs: High diversity of trees and corals is maintained only in a nonequilibrium state. Science 199, 1302–1310 (1978).ADS 
CAS 
Article 

Google Scholar 
18.Milchunas, D. G., Sala, O. E. & Lauenroth, W. K. A generalized model of the effects of grazing by large herbivores on grassland community structure. Am. Nat. 132, 87–106 (1988).Article 

Google Scholar 
19.Liu, J. et al. Impacts of grazing by different large herbivores in grassland depend on plant species diversity. J. Appl. Ecol. 52(4), 1053–1062 (2015).Article 

Google Scholar 
20.Ren, H., Schönbach, P., Wan, H., Gierus, M. & Taube, F. Effects of grazing intensity and environmental factors on species composition and diversity in typical Steppe of Inner Mongolia, China. PLoS ONE 7(12), e52180 (2012).ADS 
CAS 
Article 

Google Scholar 
21.Sbrissia, A. F., Silva, S. C., Schmitt, D. & Duchini, P. G. Unravelling the relationship between a seasonal environment and the dynamics of forage growth in grazed swards. J. Agron. Crop Sci. 206, 630–639 (2020).Article 

Google Scholar 
22.Hernández-Lambraño, R. E., González-Moreno, P. & Sánchez-Agudo, J. Á. Towards the top: Niche expansion of Taraxacum officinale and Ulex europaeus in mountain regions of South America. Austral. Ecol. 42, 577–589 (2017).Article 

Google Scholar 
23.Pinto, L. F. M. et al. Dinâmica do acúmulo de matéria seca em pastagens de Tifton 85 sob pastejo. Sci. Agric. 58, 439–447 (2001).Article 

Google Scholar 
24.Duchini, P. G., Guzatti, G. C., Ribeiro Filho, H. M. N. & Sbrissia, A. F. Tiller size/density compensation in temperate climate grasses grown in monoculture or in intercropping systems under intermittent grazing. Grass Forage Sci. 69, 655–665 (2014).CAS 
Article 

Google Scholar 
25.Briske, D. D. & Anderson, V. J. Competitive ability of the bunchgrass Schizachyrium scoparium as affected by grazing history and defoliation. Vegetatio 103, 41–49 (1992).
Google Scholar 
26.Altesor, A., Oesterheld, M., Leoni, E., Lezama, F. & Rodriguez, C. Effect of grazing on community structure and productivity of a Uruguayan grassland. Plant Ecol. 179, 83–91 (2005).Article 

Google Scholar 
27.Lezama, F. et al. Variation of grazing-induced vegetation changes across a large-scale productivity gradient. J. Veg. Sci. 25, 8–21 (2014).Article 

Google Scholar 
28.Lattanzi, F. A. et al. 13C-labeling shows the effect of hierarchy on the carbon gain of individuals and functional groups in dense field stands. Ecology 93, 169–179 (2012).Article 

Google Scholar 
29.Roscher, C. et al. Functional composition has stronger impact than species richness on carbon gain and allocation in experimental grasslands. PLoS ONE 14(1), e0204715 (2019).CAS 
Article 

Google Scholar 
30.Wan, C. & Sosebee, R. E. Central dieback of the dryland bunchgrass Eragrostis curvula (weeping lovegrass) re-examined: The experimental clearance of tussock centres. J. Arid Environ. 46, 69–78 (2000).ADS 
Article 

Google Scholar 
31.Angassa, A. Effects of grazing intensity and bush encroachment on herbaceous species and rangeland condition in Southern Ethiopia. L. Degrad. Dev. 25, 438–451 (2014).Article 

Google Scholar 
32.Schultz, N. L., Morgan, J. W. & Lunt, I. D. Effects of grazing exclusion on plant species richness and phytomass accumulation vary across a regional productivity gradient. J. Veg. Sci. 22, 130–142 (2011).Article 

Google Scholar 
33.Chaneton, E. J. & Facelli, J. M. Disturbance effects on plant community diversity: Spatial scales and dominance hierarchies. Vegetatio 93, 143–155 (1991).Article 

Google Scholar 
34.Tow, P. G. & Lazenby, A. Competition and Succession in Pastures (CAB International, 2001). doi:https://doi.org/10.1079/9780851994413.0000.35.Briske, D. D. & Hendrickson, J. R. Does selective defoliation mediate competitive interactions in a semiarid savannah? A demographic evaluation. J. Veg. Sci. 9, 611–622 (1998).Article 

Google Scholar 
36.Baer, S. G., Blair, J. M. & Collins, S. L. Environmental heterogeneity has a weak effect on diversity during community assembly in tallgrass prairie. Ecol. Monogr. 86, 94–106 (2016).Article 

Google Scholar 
37.Alvares, C. A., Stape, J. L., Sentelhas, P. C., Gonçalves, J. L. M. & Sparovek, G. Köppen’s climate classification map for Brazil. Meteorol. Zeitschrift 22, 711–728 (2013).ADS 
Article 

Google Scholar 
38.Pallarés, O. R., Berretta, E. J. & Maraschin, G. The South American Campos ecosystem BT—Grasslands of the World. Grasslands of the World 1–49 (2005). 39.Allen, V. G. et al. An international terminology for grazing lands and grazing animals. Grass Forage Sci. 66, 2–28 (2011).Article 

Google Scholar 
40.Zanini, G. D., Santos, G. T., Schmitt, D. & Padilha, D. A. Distribuição de colmo na estrutura vertical de pastos de capim Aruana e azevém anual submetidos a pastejo intermitente por ovinos. Ciênc. Rural 42, 882–887 (2012).Article 

Google Scholar 
41.Carvalho, P. C. F. Harry Stobbs Memorial Lecture: Can grazing behaviour support innovations in grassland management?. Trop. Grassl. Forrajes Trop. 1, 137–155 (2013).Article 

Google Scholar 
42.Barthram, G. T. Experimental techniques: The HFRO sward stick. In The Hill Farming Research Organization Biennial Report 1984/1985 29–30 (HFRO, 1985).43.Haydock, K. P. & Shaw, N. H. The comparative yield method for estimating dry matter yield of pasture. Aust. J. Exp. Agric. 15, 663–670 (1975).
Google Scholar 
44.Williams, R. J. Gap dynamics in subalpine heathland and grassland vegetation in south-eastern Australia. J. Ecol. 80, 343–352 (1992).Article 

Google Scholar 
45.Derner, J. D., Briske, D. D. & Polley, H. W. Tiller organization within the tussock grass Schizachyrium scoparium: A field assessment of competition–cooperation tradeoffs. Botany 90, 669–677 (2012).Article 

Google Scholar 
46.Mueller-Dombois, D. & Ellenberg, D. Aims and methods of vegetation ecology. In Community Sampling: The Relevé Method 45–66 (1974).47.Tothill, J. C., Hargreaves, J. N. G., Jones, R. M. & McDonald, C. K. Botanal—A comprehensive sampling and computing procedure for estimating pasture yield and composition. 1. Field sampling. Trop. Agron. Tech. Mem. 78, 1–24 (1992).
Google Scholar 
48.’t Mannetje, L. Measuring biomass of grassland vegetation. In Field and Laboratory Methods for Grassland and Animal Production Research 151–177 (CABI, 2000). doi:https://doi.org/10.1079/9780851993515.0151.49.Oksanen, F.J., Blanchet, G., Friendly, M., Kindt, R., Legendre, P. et al. vegan: Community Ecology Package. R package version 2.5-7. (2020). https://CRAN.R-project.org/package=vegan.50.Kindt, R. & Coe, R. Tree diversity analysis. A manual and software for common statistical methods for ecological and biodiversity studies. World Agroforestry Centre (ICRAF), Nairobi. ISBN: 92-9059-179-X (2005).51.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (2021). https://www.R-project.org/.52.Watkins, A. J. & Wilson, J. B. Plant community structure, and its relation to the vertical complexity of communities: dominance/diversity and spatial rank consistency. Oikos 70, 91–98 (1994).Article 

Google Scholar 
53.Bates, D., Mächler, M., Zurich, E., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
54.Sbrissia, A. F., Zanella, P. G., Pinto, C. E., Baldissera, T. C. & Garagorry, F. C. Natural grasslands experiment – 2015 – 2017 – Pablo. figshare. https://doi.org/10.6084/m9.figshare.14055419.v1 (2021). More

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

Google Scholar 
2.Lefcheck, J. S. et al. Biodiversity enhances ecosystem multifunctionality across trophic levels and habitats. Nat. Commun. 6, 6936 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Fanin, N. et al. Consistent effects of biodiversity loss on multifunctionality across contrasting ecosystems. Nat. Ecol. Evol. 2, 269–278 (2018).PubMed 
Article 
PubMed Central 

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

Google Scholar 
5.Sánchez-Bayo, F. & Wyckhuys, K. A. G. Worldwide decline of the entomofauna: A review of its drivers. Biol. Conserv. 232, 8–27 (2019).Article 

Google Scholar 
6.IPBES. Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. (IPBES secretariat, Bonn, Germany, 2019).7.Smith, H. F. & Sullivan, C. A. Ecosystem services within agricultural landscapes—farmers’ perceptions. Ecol. Econ. 98, 72–80 (2014).Article 

Google Scholar 
8.Barnes, A. D. et al. Biodiversity enhances the multitrophic control of arthropod herbivory. Sci. Adv. 6, eabb6603 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
9.Dainese, M. et al. A global synthesis reveals biodiversity-mediated benefits for crop production. Sci. Adv. 5, eaax0121 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
10.Costanza, R. et al. The value of the world’s ecosystem services and natural capital. Nature 387, 253–260 (1997).CAS 
Article 

Google Scholar 
11.Oliver, T. H. et al. Declining resilience of ecosystem functions under biodiversity loss. Nat. Commun. 6, 10122 (2015).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
12.Naranjo, S. E., Ellsworth, P. C. & Frisvold, G. B. Economic value of biological control in integrated pest management of managed plant systems. Annu. Rev. Entomol. 60, 621–645 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Frishkoff, L. O. et al. Loss of avian phylogenetic diversity in neotropical agricultural systems. Science 345, 1343–1346 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Mendenhall, C. D., Karp, D. S., Meyer, C. F. J., Hadly, E. A. & Daily, G. C. Predicting biodiversity change and averting collapse in agricultural landscapes. Nature 509, 213–217 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.Karp, D. S. et al. Crop pests and predators exhibit inconsistent responses to surrounding landscape composition. Proc. Natl Acad. Sci. USA 115, E7863–E7870 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
16.Tamburini, G. et al. Agricultural diversification promotes multiple ecosystem services without compromising yield. Sci. Adv. 6, eaba1715 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
17.Redlich, S., Martin, E. A. & Steffan-Dewenter, I. Landscape-level crop diversity benefits biological pest control. J. Appl. Ecol. 55, 2419–2428 (2018).Article 

Google Scholar 
18.Muneret, L. et al. Evidence that organic farming promotes pest control. Nat. Sustain. 1, 361–368 (2018).Article 

Google Scholar 
19.Roubos, C. R., Rodriguez-Saona, C. & Isaacs, R. Mitigating the effects of insecticides on arthropod biological control at field and landscape scales. Biol. Control 75, 28–38 (2014).CAS 
Article 

Google Scholar 
20.Roschewitz, I., Hucker, M., Tscharntke, T. & Thies, C. The influence of landscape context and farming practices on parasitism of cereal aphids. Agric. Ecosyst. Environ. 108, 218–227 (2005).Article 

Google Scholar 
21.Frago, E., Pujadevillar, J., Guara, M. & Selfa, J. Hyperparasitism and seasonal patterns of parasitism as potential causes of low top-down control in Euproctis chrysorrhoea L. (Lymantriidae). Biol. Control 60, 123–131 (2012).Article 

Google Scholar 
22.Rosenheim, J. A., Kaya, H. K., Ehler, L. E., Marois, J. J. & Jaffee, B. A. Intraguild predation among biological-control agents: theory and evidence. Biol. Control 5, 303–335 (1995).Article 

Google Scholar 
23.Brobyn, P. J., Clark, S. J. & Wilding, N. The effect of fungus infection of Metopolophium dirhodum [Hom.: Aphididae] on the oviposition behaviour of the aphid parasitoid Aphidius rhopalosiphi [Hym.: Aphidiidae]. Entomophaga 33, 333–338 (1988).Article 

Google Scholar 
24.Tscharntke, T. et al. Conservation biological control and enemy diversity on a landscape scale. Biol. Control 43, 294–309 (2007).Article 

Google Scholar 
25.Rand, T. A., van Veen, F. J. F. & Tscharntke, T. Landscape complexity differentially benefits generalized fourth, over specialized third, trophic level natural enemies. Ecography 35, 97–104 (2012).Article 

Google Scholar 
26.Zhao, Z. H., Hui, C., He, D. H. & Li, B. L. Effects of agricultural intensification on ability of natural enemies to control aphids. Sci. Rep. 5, 8024 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
27.Vollhardt, I. M. G., Tscharntke, T., Wäckers, F. L., Bianchi, F. J. J. A. & Thies, C. Diversity of cereal aphid parasitoids in simple and complex landscapes. Agric. Ecosyst. Environ. 126, 289–292 (2008).Article 

Google Scholar 
28.Tomanović, Z. et al. Regional tritrophic relationship patterns of five aphid parasitoid species (Hymenoptera: Braconidae: Aphidiinae) in agroecosystem-dominated landscapes of southeastern Europe. J. Econ. Entomol. 102, 836–854 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
29.Kaartinen, R. & Roslin, T. Shrinking by numbers: landscape context affects the species composition but not the quantitative structure of local food webs. J. Anim. Ecol. 80, 622–631 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
30.Wang, S. & Brose, U. Biodiversity and ecosystem functioning in food webs: the vertical diversity hypothesis. Ecol. Lett. 21, 9–20 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
31.Garzke, J., Connor, S. J., Sommer, U. & O’Connor, M. I. Trophic interactions modify the temperature dependence of community biomass and ecosystem function. PLoS Biol. 17, e2006806 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
32.Pocock, M. J. O. et al. The visualisation of ecological networks, and their use as a tool for engagement, advocacy and management. Adv. Ecol. Res. 54, 41–85 (2016).Article 

Google Scholar 
33.Bersier, L.-F., Banašek-Richter, C. & Cattin, M.-F. Quantitative descriptors of food-web matrices. Ecology 83, 2394–2407 (2002).Article 

Google Scholar 
34.Tylianakis, J. M., Laliberté, E., Nielsen, A. & Bascompte, J. Conservation of species interaction networks. Biol. Conserv. 143, 2270–2279 (2010).Article 

Google Scholar 
35.Gilbert, A. J. Connectance indicates the robustness of food webs when subjected to species loss. Ecol. Indic. 9, 72–80 (2009).Article 

Google Scholar 
36.Williams, R. J. & Martinez, N. D. Simple rules yield complex food webs. Nature 404, 180–183 (2000).CAS 
PubMed 
Article 

Google Scholar 
37.Galiana, N., Hawkins, B. A. & Montoya, J. M. The geographical variation of network structure is scale dependent: understanding the biotic specialization of host–parasitoid networks. Ecography 42, 1175–1187 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
38.Banašek-Richter, C., Cattin, M.-F. & Bersier, L.-F. Sampling effects and the robustness of quantitative and qualitative food-web descriptors. J. Theor. Biol. 226, 23–32 (2004).PubMed 
Article 

Google Scholar 
39.Varennes, Y. D., Boyer, S. & Wratten, S. D. Un-nesting DNA Russian dolls—the potential for constructing food webs using residual DNA in empty aphid mummies. Mol. Ecol. 23, 3925–3933 (2014).CAS 
PubMed 
Article 

Google Scholar 
40.Zhu, Y. L. et al. A molecular detection approach for a cotton aphid-parasitoid complex in northern China. Sci. Rep. 9, 15836 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
41.Staniczenko, P. P. A. et al. Predicting the effect of habitat modification on networks of interacting species. Nat. Commun. 8, 792 (2018).Article 
CAS 

Google Scholar 
42.Thies, C. & Tscharntke, T. In Biocontrol-Based Integrated Management of Oilseed Rape Pests (ed. Williams, I.H.). (Springer Netherlands, 2010).43.Tylianakis, J. M., Tscharntke, T. & Lewis, O. T. Habitat modification alters the structure of tropical host-parasitoid food webs. Nature 445, 202–205 (2007).CAS 
Article 

Google Scholar 
44.Grass, I., Jauker, B., Steffandewenter, I., Tscharntke, T. & Jauker, F. Past and potential future effects of habitat fragmentation on structure and stability of plant-pollinator and host-parasitoid networks. Nat. Ecol. Evol. 2, 1408–1417 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
45.Gagic, V. et al. Food web structure and biocontrol in a four-trophic level system across a landscape complexity gradient. Proc. Roy. Soc. B. 278, 2946–2953 (2011).Article 

Google Scholar 
46.Lundgren, J. G. & Fausti, S. W. Trading biodiversity for pest problems. Sci. Adv. 1, e1500558 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
47.Zhou, K. et al. Effects of land use and insecticides on natural enemies of aphids in cotton: first evidence from smallholder agriculture in the North China Plain. Agric. Ecosyst. Environ. 183, 176–184 (2014).Article 

Google Scholar 
48.Zhang, Z. Q. The natural enemies of Aphis gossypii Glover (Hom., Aphididae) in China. J. Appl. Entomol. 114, 251–262 (2009).Article 

Google Scholar 
49.Gagic, V. et al. Agricultural intensification and cereal aphid–parasitoid–hyperparasitoid food webs: network complexity, temporal variability and parasitism rates. Oecologia 170, 1099–1109 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
50.Vollhardt, I. M. G. et al. Influence of plant fertilisation on cereal aphid-primary parasitoid-secondary parasitoid networks in simple and complex landscapes. Agric. Ecosyst. Environ. 281, 47–55 (2019).CAS 
Article 

Google Scholar 
51.Sullivan, D. J. & Völkl, W. Hyperparasitism: multitrophic ecology and behavior. Annu. Rev. Entomol. 44, 291–315 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.Dainese, M., Montecchiari, S., Sitzia, T., Sigura, M. & Marini, L. High cover of hedgerows in the landscape supports multiple ecosystem services in Mediterranean cereal fields. J. Appl. Ecol. 54, 380–388 (2016).Article 

Google Scholar 
53.Landis, D. A., Wratten, S. D. & Gurr, G. M. Habitat management to conserve natural enemies of arthropod pests in agriculture. Annu. Rev. Entomol. 45, 175–201 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Thies, C., Roschewitz, I. & Tscharntke, T. The landscape context of cereal aphid-parasitoid interactions. Proc. Roy. Soc. B. 272, 203–210 (2005).Article 

Google Scholar 
55.Plećaš, M. et al. Landscape composition and configuration influence cereal aphid–parasitoid–hyperparasitoid interactions and biological control differentially across years. Agric. Ecosyst. Environ. 183, 1–10 (2014).Article 

Google Scholar 
56.Sirami, C. et al. Increasing crop heterogeneity enhances multitrophic diversity across agricultural regions. Proc. Natl Acad. Sci. USA 116, 16442–16447 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
57.Lichtenberg, E. M. et al. A global synthesis of the effects of diversified farming systems on arthropod diversity within fields and across agricultural landscapes. Glob. Change Biol. 23, 4946–4957 (2017).Article 

Google Scholar 
58.Osorio, S., Arnan, X., Bassols, E., Vicens, N. & Bosch, J. Local and landscape effects in a host-parasitoid interaction network along a forest-cropland gradient. Ecol. Appl. 25, 1869–1879 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
59.Dunne, J., Williams, R. & Martinez, N. Network topology and biodiversity loss in food webs: robustness increases with connectance. Ecol. Lett. 5, 558–567 (2002).Article 

Google Scholar 
60.Montoya, J. M., Rodríguez, M. A. & Hawkins, B. A. Food web complexity and higher-level ecosystem services. Ecol. Lett. 6, 587–593 (2003).Article 

Google Scholar 
61.Hawkins, B. A. Parasitoid-host food webs and donor control. Oikos 65, 159–162 (1992).Article 

Google Scholar 
62.Yeakel, J. D. et al. Diverse interactions and ecosystem engineering can stabilize community assembly. Nat. Commun. 11, 3307 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
63.Poisot, T., Mouquet, N. & Gravel, D. Trophic complementarity drives the biodiversity-ecosystem functioning relationship in food webs. Ecol. Lett. 16, 853–861 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
64.White, L., O’Connor, N. E., Yang, Q., Emmerson, M. C. & Donohue, I. Individual species provide multifaceted contributions to the stability of ecosystems. Nat. Ecol. Evol. 4, 1594–1601 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
65.Ho, H.-C., Tylianakis, J. M. & Pawar, S. Behaviour moderates the impacts of food-web structure on species coexistence. Ecol. Lett. 24, 298–309 (2021).PubMed 
Article 
PubMed Central 

Google Scholar 
66.Holland, J. M. et al. Agri-environment scheme enhancing ecosystem services: A demonstration of improved biological control in cereal crops. Agric. Ecosyst. Environ. 155, 147–152 (2012).Article 

Google Scholar 
67.Batary, P., Dicks, L., Kleijn, D. & Sutherland, W. The role of agri-environment schemes in conservation and environmental management: European Agri-Environment Schemes. Conserv. Biol. 29, 1006–1016 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
68.McGarigal, K., Cushman, S., Maile, N. & Ene, E. FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. http://www.umass.edu/landeco/research/fragstats/fragstats.html (2012).69.Liu, B. et al. Secondary crops and non-crop habitats within landscapes enhance the abundance and diversity of generalist predators. Agric. Ecosyst. Environ. 258, 30–39 (2018).Article 

Google Scholar 
70.Lu, Y. H., Qi, F. J. & Zhang, Y. J. Integrated Management of Diseases and Insect Pests in Cotton (Golden Shield Press, Beijing 2010).71.Shannon, C. E., Weaver, W., Blahut, R. E. & Hajek, B. The Mathematical Theory of Communications (University of Illinois Press, Urbana, 1949).72.Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).CAS 
Article 

Google Scholar 
73.R Development Core Team. R: A language and environment for statistical computing, Version 4.0.2. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org (2020).74.Dormann, C. F., Fründ, J. & Gruber, B. Package ‘bipartite’: Visualising bipartite networks and calculating some (ecological) indices. (2019).75.Huang, H. Y., Zhou, L., Chen, J. & Wei, T. Y. ggcor: Extended tools for correlation analysis and visualization. R package version 0.9.7. (2020).76.Oksanen, J. et al. vegan: community ecology package. R. package version 2, 5–6 (2020).
Google Scholar 
77.Kassambara, A. & Fabian, M. factoextra: Extract and Visualize the Results of Multivariate Data analyses. R package version 1.0.7. (2020).78.Akaike, H. An information criterion (AIC). Math. Sci. 14, 5–9 (1976).
Google Scholar 
79.Burnham, K. P. & Anderson, D. R. Multimodel inference understanding AIC and BIC in model selection. Sociol. Method. Res. 33, 261–304 (2004).Article 

Google Scholar 
80.Cardinale, B. J. et al. Effects of biodiversity on the functioning of trophic groups and ecosystems. Nature 443, 989–992 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
81.Fox, J. & Weisberg, S. An R Companion to Applied Regression, Third Edition. (Thousand Oaks CA: Sage., 2011).82.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
83.Bartoń, K. MuMIn: Multi-Model Inference. R package version 1.43.17. (2020).84.Thompson, R. M. et al. Food webs: reconciling the structure and function of biodiversity. Trends Ecol. Evol. 27, 689–697 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
85.Tylianakis, J. M. & Binzer, A. Effects of global environmental changes on parasitoid–host food webs and biological control. Biol. Control 75, 77–86 (2014).Article 

Google Scholar 
86.Lefcheck, J. S. piecewiseSEM: Piecewise structural equation modelling in r for ecology evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).Article 

Google Scholar 
87.Shipley, B. The AIC model selection method applied to path analytic models compared using a d-separation test. Ecology 94, 560–564 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
88.Yang, F. et al. The data for “Species diversity and food web structure jointly shape natural biological control in agricultural landscapes”. Dryad, Dataset https://doi.org/10.5061/dryad.pc866t1kz (2021).Article 

Google Scholar  More

To defend against predators, insects often modify their morphology, flexibly, to enhance survival and reproductive advantages. Here, we report that predation risks from either isolated predator or predator odor cues, induce a higher proportion of nymphs to developed into long-winged females among the parent generation, as well as among F1 generation offspring. Surprisingly, these previously threatened long-winged adults survived better when attacked by a predator owing to the enhanced agility level gained from risk experience. The long wing, and increased agility level, provide adaptive benefits for SBPHs to escape from predation and so are able to go on to reproduce.SBPHs responded more strongly to the caged predators (visual + odor risk cues) and predator odor cues, than just the visual cue of the predator. Different risk cues can elicit different levels of responses in prey33,34,35,36. For example, in the case of the Colorado potato beetle, volatile odor cues from the predator stronger reduced the beetle feeding on plants than predator visual and tactile cues35. But a visual cue has been shown to be crucial for insect pollinators detecting and avoiding flowers with predators37. Insect herbivores frequently communicate via chemical odors33,38. Exploiting the odor cue to perceive the presence of predators should have advantages, because the odor cue can be sensed from a long distance and penetrate the blocking effect of foliage or canopy structure39, enabling the prior detection of risks and the preparation of antipredation behaviors.In densely planted rice paddies, the active foraging behavior of rove beetle may serve as a selective pressure favouring the development in SBPHs of a chemical instead of visual pathway to detect the approach of a rove beetle. However, in the F1 generation, the influence of a predator odor cue on the proportion of winged forms was weaker than that of caged predators, indicating the combined effects of odor and visual cues might be stronger than only an odor cue, suggesting that visual cues cannot be ignored. In our experiments, sealed predator cadavers may have weakened the visual cue of the rove beetles, because the lack of motion did not fully represent the normal visual cue.SBPHs frequently exhibit wing plasticity in response to population density and food quality28,29. When nymph density is higher, or food has deteriorated, a higher proportion of macropters will arise28,29. The development of the winged form is thought to be a strategy for SBPHs to emigrate from inhospitable environments. However, we assumed, predation risks could also induce the occurrence of the winged form, because long wings might enable SBPHs to escape from predation. As expected, the results presented here show that a higher proportion of long-winged females and their offspring arose when nymphs or adults were previously exposed to predation risk, demonstrating that SBPHs can express morphologically plastic defenses in response to prior predation risk. Additionally, the higher proportion of wing forms was not only due to the increasing number of winged females (see Fig. 1, the number of winged females in “caged rove beetle” treatment was lower), but also the increasing proportion of winged females among female groups (the decreasing numbers and proportions of wingless females, Fig. 1). To date, similar patterns have only been shown in pea aphids, in which when predation risk (foot prints from lady beetles) is higher during the parent generation, a higher proportion of winged morphs arise in the offspring40,41. In our experiments, we tested the risk effects passing from nymphs to adults and from parents to their offspring with combined risk cues, an odor cue or a visual cue, which better reveals the capacity for flexible defense strategies within SBPHs and the nature of predation risks in the perpetual ‘arms race’ against predation. This is the first example of how insects can express both within- generational and transgenerational morphological plasticity as a defense strategy in response to prior predator threat, and we suggest that this phenomenon is likely to occur more widely.However, SBPHs do not only face a single lethal pressure from their environment as we discussed above. Nymph density, food quality, even the temperatures or photoperiods may play or interplay roles in the induction of wing plasticity in SBPHs28,29. In these situations, the responses of SBPHs may differ from present results, or opposite results can occur. As an example, the growth rates of snails vary depending on snail densities, food supply and the strength of predation risks. Growth rates were higher when snails were reared on high nutrients and in low densities, but decreased steeply as the predation risk increased. Conversely, the growth rate was lower at high densities and with high predation risk, but increased as nutrient availability increased42. As for SBPHs, the proportion of winged adults may be higher if we reared in higher densities combined with high predation risk, or may be lower if the nutrient condition of the rice plants increases (for example, higher fertilizer inputs benefit the development of planthoppers43) and predators are removed. This hypothesis needs to be tested. Further, the rice plant phenotypes (resistant or sensitive phenotypes) are important to the development of planthoppers or leafhoppers44,45,46, and tests of the interactive effects of plant phenotype, plant quality/quantity, nymph density and predation risk on the wing plasticity of SBPHs should provide insights into the evolution of insects within changing environments.Induced transgenerational defense plasticity as shown in SBPHs may be common in many organisms20,47. It allows parents to transfer their risk experience to offspring and promotes their evolutionary fitness20. When SBPH nymphs are exposed to predation risk, they are likely to develop into long-winged females, because it is an advantageous form for them in the current risk environment. However, such predation risk is variable in time and space, and SBPH parents cannot predict when or whether the predators will disappear. Thus, an appropriate strategy to enhance the survival rate of offspring in an unpredictable environment is to continue producing a higher proportion of long-winged forms. Within-generational and transgenerational plasticity of defense should be a successful adaptive defense strategy for SBPHs, given that rove beetle and other groups of predators such as predatory spiders are abundant all around the year in rice paddies.The higher mortality of SBPH nymphs when they experience predation risk, has been broadly addressed before24,48,49. Reduced food intake during risk periods may contribute to this poorer survival outcome, because insects are likely to alter their feeding behavior50,51, or shift from a high-risk host to a safer, but nutritionally inferior, one52, when they detect the presence of predators. However, we did not observe an apparent behavior change in threatened nymphs in our experiments, even those going on to be macropters, compared to the non-threatened ones. For example, changing feeding location, non-feeding related motility, an increase in jump frequency, etc. did not occur in threatened nymphs. Thus, behavior plasticity seems not to be invoked to explain this phenomenon. However, considering the food consumption of sap-sucking SBPHs is difficult to determine, experiments employing electrical penetration graph (EPG) techniques should be conducted to quantify the amounts of sap consumption during risk periods53. This will help to explain whether the higher mortality is due to a change of feeding behavior (less food intake). Furthermore, some obscure internal physiological plasticity may also cause the higher mortality of SBPH nymphs at risk. For example, increased oxidative damage and decreased assimilation efficiency during the risk period may weaken the survival success of SBPH nymphs. Unfortunately, few studies have verified this assumption, although it has been shown that different assimilation efficiencies may arise under predation risk17, or oxidative damage may be induced by predation risk resulting in a slower growth rate54 and decreased escape performance55.SBPHs exhibit sexual differences in both with- and trans- generational morphological plasticity in relation to defense, i.e., threatened nymphs/parents are more likely to develop into long-winged females, due to the different vulnerability of females and males to predation. This predation difference is particularly acute between short-winged females and males, given that the proportion of short-winged females is lower than that seen in control settings (Fig. 1), and we assume the level of vulnerability may depend on their body size and reproductive role. The body sizes of short-winged females are larger than those of long-winged females or males, causing them to be more vulnerable to predation because they are more highly preferred targets for predator. Also, the short-winged female needs to stay and deposit eggs in the bare rice stem, which increases the time window of exposure to predators while, by contrast, long-winged males are slim and are not required to lay eggs, and so should be not be heavily predated. It follows that short-winged females should be more vulnerable to predation than long-winged females or males. Hence, in SHPBs, increasing the proportion of long-wing females in a population creates greater opportunities to migrate to predator-free habitats for reproduction, while at the same time reducing their vulnerability to predation. We hypothesize that the sexual difference in responses should be adaptive, and might be inheritable if predation pressure frequently favors the long-winged forms among populations over multiple generations.Results presented here also show that previously threatened long-winged offspring survived better than previosuly non-threatened ones when attacked by P. fuscipes. Studies suggest prey-altered morphology in response to predation risks should confer a survival advantage (fitness gained), i.e., a better-developed defensive structure13,24, or refuge in having a larger size that increase survival success57. However, wings themselves are without protective functions for SBPHs, as seen in pea aphids41. Thus, we setup behavioral experiments to reveal how threatened long-winged adults may increase their survival when attacked by a predator. Results show threatened long- winged offspring (but not parents) are more active, and respond more quickly, than unthreatened ones, i.e., a higher number of attacks are needed for P. fuscipes to capture a previously threatened long-winged offspring than one that has not been threatened before. We suggest the increased agility level is not because of the long wing itself, but due to the enhanced muscle strength in the legs of long-winged adults, because in our observation, long-winged adults avoid attack mainly by jumping but not by flight, probably because a jump needs less reaction time than flight.We only observed transgenerational plasticity of induced behavioral defense in SBPHs. This generational difference (within- and trans-generational) in behavioral defense in SBPHs may reflect potential carry-over effects from parents. To our knowledge, the generational difference in defense has rarely been shown in insects, though in pea aphids a fluctuating expression of transgenerational defensive traits (long wing) over generations when predation risk was present or absent has been reported58. We also expect there will be cumulative effects59 accumulated by SBPHs from the parent generation to the F1 generation. However, we are not certain whether these effects exist in our experiments. To determine this, experiments examining defensive traits across multigeneration should be conducted.However, if predation risk increases the number of agile, long-winged SBPH adults, which are of benefit in respect of dispersal, migration, and thus spreading rice viruses, the application of P. fuscipes in biological control appears ultimately to weaken the control effectiveness. Also, a study with field experiments found that predatory ladybugs increase the number of dispersed aphid nymphs, especially in plants with lower resistance. However, surprising results show that the higher number of dispersed aphid nymphs will not necessarily translate into population growth because dispersed aphids are weak (less food intake) and more easily predated by predators60. Thus, the benefits of anti-predator defense in aphids will, over time, translate into negative developmental costs that suppress the aphid population. As for SBPHs, threatened long-winged females perform well in dispersal and defense, but worse in development and reproduction. Recent experiments reveal that previously threatened long-winged females have a longevity that is three days shorter, and produces about 60 fewer eggs per female, than non-threatened long-winged females (unpublished data). Consequently, these negative effects would eventually translate into lower population growth rates within SBPHs. Thus, the introduction of the predation risk from P. fuscipes to control SBPHs is workable, since field experiments in controlling western flower thrips and grasshoppers by exposure to predation risk have been successful49,61, and the main purpose of biological control is to suppress the pest population beneath the relevant economic threshold, and reduce plant mass loss without necessarily eliminating the pest altogether.This study advances the importance of predation risk on the induction of flexible anti-predation defenses in insect parents and their offspring, uncovers the mediating mechanisms, shows how this anti-predation defense expresses differently between sexes, and further explores the adaptation significance of these defense traits for insects exposed to unpredictable environments. These findings should prove important for predicting SBPH migration or dispersal, conducting effective pest control, and better understanding prey-predator interactions. However, future work should examine the effects of predation risks from other groups of predators or parasites on the physiological and behavioral plasticity of SBPHs. More

WORLD VIEW
18 August 2021

Great Barrier Reef: accept ‘in danger’ status, there’s more to gain than lose

The Australian government must embrace UNESCO’s assessment to marshal the resources needed to protect the unique coral ecosystem.

Tiffany H. Morrison

0

Tiffany H. Morrison

Tiffany H. Morrison is a political geographer specializing in marine interventions at James Cook University in Townsville, Australia.

View author publications

You can also search for this author in PubMed
 Google Scholar

Share on Twitter
Share on Twitter

Share on Facebook
Share on Facebook

Share via E-Mail
Share via E-Mail

Download PDF

No one denies the cascade of climate-induced coral bleaching that devastated huge portions of the Great Barrier Reef in 2016, nor the subsequent bleaching. No one questions the Queensland government’s 2019 report (see go.nature.com/3ckg) that the reef’s condition near the shore is poor.Yet last month, the World Heritage Committee of the United Nations organization UNESCO caved to lobbying from the Australian government — pressured by fossil-fuel, agricultural and mining interests — and kept the Great Barrier Reef off its list of ecosystems ‘in danger’. In my view, this decision is wrong, factually and strategically. It leaves both UNESCO and Australia weaker against the climate crisis.I study the governance of approximately 250 ecosystems with World Heritage status because of their outstanding value to humanity — including attempts to curtail runaway industrial development of Vietnam’s Ha Long Bay and overzealous urbanization along Florida’s Everglades wetlands.There are benefits to an in-danger listing: the Belize Barrier Reef Reserve System was placed on the list in 2009. The World Heritage Fund then provided technical and financial assistance for its restoration. By 2018, mangrove coverage was restored nearly to 1996 levels, with clearing in protected areas almost entirely curtailed. The whole maritime zone was under a moratorium on oil and gas production. Restoration work is ongoing, but the Belize reef is no longer on the list.
Save reefs to rescue all ecosystems
This July, UNESCO proposed to list the Great Barrier Reef as in danger owing to severe coral bleaching, poor water quality and inaction on climate change.In arguing against the listing, the Australian government did not directly deny the reef’s parlous state, but did play down its condition. The government also argued that the listing would decrease tourism revenues, that Australia had too little time to respond and should not be held responsible for global change, and that UNESCO should not supersede national sovereignty on climate-change policy.Australian environment minister Sussan Ley lobbied committee members from more than a dozen countries to override UNESCO’s recommendation. Australia avoided an in-danger listing in 2015 using similar tactics and by touting a sustainability plan. The following year saw the worst coral bleaching in the world’s history.But changes are in the wind. After back-to-back coral bleaching in 2016–17 and the tragic 2020 bush fires, more Australian voters, industries and even conservative politicians are calling for strong efforts against climate change.Accepting an in-danger listing for the reef could tip the balance past gridlock. More than 70% of Australians think that formally acknowledging the reef’s endangered state would spur action. In 1993, former US president Bill Clinton’s administration requested that UNESCO certify Florida’s Everglades as in danger. This helped to bring industry opponents on board to better manage coastal development. Had the Great Barrier Reef been listed as in danger in 2015, fossil-fuel developments in the catchment areas draining into the reef would have struggled to get approval.Australia’s most conservative politicians will argue that avoiding an in-danger listing in 2022 is necessary to boost economic development. But this will embarrass Australia later. As more marine heating occurs globally, Australia will struggle to defend its inaction on climate to the UN climate-change conference in November and to the World Heritage Committee next year. Even the Queensland Tourism Industry Council has said keeping the reef’s status under the spotlight is a “call to the world to do more on climate change”.
Fevers are plaguing the oceans — and climate change is making them worse
And undercutting the listing undermines the purpose of the World Heritage Committee. Since 1972, 41 ecosystems have been considered for the in-danger list — 27 of them more than once — but not officially inscribed, even though UNESCO and its advisory body had assessed these ecosystems as threatened, or more threatened than those already listed. The number of sites on the list has declined by almost one-third since 2001, although threats continue to grow and there are more ecosystems on the overall World Heritage List.However, destabilizing strategies are mainly due to a small group of nations — including countries in the Organisation for Economic Co-operation and Development, such as Australia and Spain. World Heritage status and in-danger listings often work as intended: the managers of 73% of sites do comply with their responsibilities.Concerned observers are helping the World Heritage Committee to protect itself from political manipulation. In February 2020, a consortium of 76 organizations and individuals petitioned UNESCO to consider climate change in its World Heritage decisions. A nascent international network known as World Heritage Watch hopes to provide more oversight and monitoring of self-interested states. Ecologists and non-profit organizations are using remote sensing and citizen science to track and expose degradation of protected areas (see go.nature.com/2xn1) and hold governments accountable.UNESCO and its World Heritage Committee grasp the stakes. A new draft policy clearly states that climate-related degradation of a World Heritage Area can be used as the basis for in-danger listing; it will probably be ratified later this year at the UNESCO General Assembly. This policy will shine a harsh light on the intensifying geopolitics of climate change. Advanced economies, such as Australia, with high per-capita emissions but limited climate action, will need to find alternative ways to protect resources and jobs.

Nature 596, 319 (2021)
doi: https://doi.org/10.1038/d41586-021-02220-3

Related Articles

Fevers are plaguing the oceans — and climate change is making them worse

Save reefs to rescue all ecosystems

Subjects

Climate change

Conservation biology

Government

Latest on:

Climate change

‘Polluter pays’ policy could speed up emission reductions and removal of atmospheric CO2
News & Views 16 AUG 21

Warming world, women in science — the week in infographics
News 13 AUG 21

IPCC climate report: Earth is warmer than it’s been in 125,000 years
News 09 AUG 21

Government

Brazilian road proposal threatens famed biodiversity hotspot
News 17 AUG 21

The world must cooperate to avoid a catastrophic space collision
Editorial 11 AUG 21

From the archive
News & Views 10 AUG 21

Jobs

Locum Associate or Senior Editor, Immunology

Springer Nature
London, United Kingdom

Position title: Postdoctoral Research Fellows – Air Force Science & Technology Fellowship Program

National Academies of Sciences, Engineering, and Medicine
Various locations in the U.S., United States

Postdoctoral Research Associates – NRC Research Associateship Programs

National Academies of Sciences, Engineering, and Medicine
Various locations in the U.S., United States

Research Associate in the Centre for Developmental Neurobiology

King’s College London (KCL)
London, United Kingdom

Nature Briefing
An essential round-up of science news, opinion and analysis, delivered to your inbox every weekday.

Email address

Yes! Sign me up to receive the daily Nature Briefing email. I agree my information will be processed in accordance with the Nature and Springer Nature Limited Privacy Policy.

Sign up More

1.Dell, J. et al. Interaction diversity maintains resiliency in a frequently disturbed ecosystem. Front. Ecol. Evol. 7, 145 (2019).Article 

Google Scholar 
2.White, P. S. & Pickett, S. T. A. In The Ecology of Natural Disturbance and Patch Dynamics (eds S. T. A. Pickett & P. S. White) 3–13 (Academic Press, 1985).3.Newman, E. A. Disturbance ecology in the anthropocene. Front. Ecol. Evolut. https://doi.org/10.3389/fevo.2019.00147 (2019).Article 

Google Scholar 
4.Barnosky, A. D. et al. Approaching a state shift in Earth’s biosphere. Nature 486, 52–58 (2012).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Yuan, Z., Jiao, F., Li, Y. & Kallenbach, R. L. Anthropogenic disturbances are key to maintaining the biodiversity of grasslands. Sci. Rep. 6, 22132 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
6.Hughes, A. R., Byrnes, J. E., Kimbro, D. L. & Stachowicz, J. J. Reciprocal relationships and potential feedbacks between biodiversity and disturbance. Ecol. Lett. 10, 849–864. https://doi.org/10.1111/j.1461-0248.2007.01075.x (2007).Article 
PubMed 
PubMed Central 

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

Google Scholar 
8.Connell, J. H. Diversity in tropical rain forests and coral reefs. Science 199, 1302–1310 (1978).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Fox, J. W. The intermediate disturbance hypothesis should be abandoned. Trends Ecol. Evol. 28, 86–92. https://doi.org/10.1016/j.tree.2012.08.014 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
10.Sheil, D. & Burslem, D. F. Disturbing hypotheses in tropical forests. Trends Ecol. Evol. 18, 18–26 (2003).Article 

Google Scholar 
11.Teixidó, N., Garrabou, J., Gutt, J. & Arntz, W. E. Recovery in Antarctic benthos after iceberg disturbance: Trends in benthic composition, abundance and growth forms. Mar. Ecol. Prog. Ser. 278, 1–16. https://doi.org/10.3354/meps278001 (2004).ADS 
Article 

Google Scholar 
12.Teixidó, N., Garrabou, J., Gutt, J. & Arntz, W. Iceberg disturbance and successional spatial patterns: the case of the shelf Antarctic benthic communities. Ecosystems 10, 143–158 (2007).Article 

Google Scholar 
13.Johst, K., Gutt, J., Wissel, C. & Grimm, V. Diversity and disturbances in the Antarctic megabenthos: Feasible versus theoretical disturbance ranges. Ecosystems 9, 1145–1155 (2006).Article 

Google Scholar 
14.Mackey, R. L. & Currie, D. J. The diversity-disturbance relationship: Is it generally strong and peaked?. Ecology 82, 3479–3492. https://doi.org/10.1890/0012-9658(2001) (2001).Article 

Google Scholar 
15.Huston, M. A. Disturbance, productivity, and species diversity: Empiricism vs. logic in ecological theory. Ecology 95, 2382–2396. https://doi.org/10.1890/13-1397.1 (2014).Article 

Google Scholar 
16.Smale, D. A., Brown, K. M., Barnes, D. K., Fraser, K. P. & Clarke, A. Ice scour disturbance in Antarctic waters. Science 321, 371. https://doi.org/10.1126/science.1158647 (2008).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
17.Griffiths, H. J., Danis, B. & Clarke, A. Quantifying Antarctic marine biodiversity: The SCAR-MarBIN data portal. Deep Sea Res. Part II 58, 18–29. https://doi.org/10.1016/j.dsr2.2010.10.008 (2011).ADS 
Article 

Google Scholar 
18.Grange, L. J. & Smith, C. R. Megafaunal communities in rapidly warming fjords along the West Antarctic Peninsula: Hotspots of abundance and beta diversity. PLoS ONE 8, e77917 (2013).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
19.Gutt, J., Griffiths, H. J. & Jones, C. D. Circumpolar overview and spatial heterogeneity of Antarctic macrobenthic communities. Mar. Biodivers. 43, 481–487. https://doi.org/10.1007/s12526-013-0152-9 (2013).Article 

Google Scholar 
20.Potthoff, M., Johst, K. & Gutt, J. How to survive as a pioneer species in the Antarctic benthos: Minimum dispersal distance as a function of lifetime and disturbance. Polar Biol. 29, 543–551 (2006).Article 

Google Scholar 
21.Convey, P. et al. The spatial structure of Antarctic biodiversity. Ecol. Monogr. 84, 203–244 (2014).Article 

Google Scholar 
22.Peck, L. S., Brockington, S., Vanhove, S. & Beghyn, M. Community recovery following catastrophic iceberg impacts in a soft-sediment shallow-water site at Signy Island, Antarctica. Mar. Ecol Progr. Ser. 186, 1–8 (1999).ADS 
Article 

Google Scholar 
23.Lee, H., Vanhove, S., Peck, L. & Vincx, M. Recolonisation of meiofauna after catastrophic iceberg scouring in shallow Antarctic sediments. Polar Biol. 24, 918–925. https://doi.org/10.1007/s003000100300 (2001).Article 

Google Scholar 
24.Armstrong, T. World Meteorological Organization. WMO sea-ice nomenclature. Terminology, codes and illustrated glossary. Edition 1970. Geneva, Secretariat of the World Meteorological Organization, 1970. [ix], 147 p. [including 175 photos]+ corrigenda slip. (WMO/OMM/BMO, No. 259, TP. 145.). J. Glaciol. 11, 148–149 (1972).25.Robinson, B. J., Barnes, D. K. & Morley, S. A. Disturbance, dispersal and marine assemblage structure: A case study from the nearshore Southern Ocean. Mar. Environ. Res. 160, 105025 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Gutt, J., Starmans, A. & Dieckmann, G. Impact of iceberg scouring on polar benthic habitats. Mar. Ecol. Prog. Ser. 137, 311–316 (1996).ADS 
Article 

Google Scholar 
27.Barnes, D. K. A. & Conlan, K. E. Disturbance, colonization and development of Antarctic benthic communities. Philos. Trans. R. Soc. Lond. B Biol. Sci. 362, 11–38. https://doi.org/10.1098/rstb.2006.1951 (2007).Article 
PubMed 
PubMed Central 

Google Scholar 
28.Smale, D. A. Ecological traits of benthic assemblages in shallow Antarctic waters: Does ice scour disturbance select for small, mobile, secondary consumers with high dispersal potential?. Polar Biol. 31, 1225–1231. https://doi.org/10.1007/s00300-008-0461-9 (2008).Article 

Google Scholar 
29.Barnes, D. K. A. The influence of ice on polar nearshore benthos. J. Mar. Biol. Assoc. U.K. 79, 401–407 (1999).Article 

Google Scholar 
30.Gutt, J. On the direct impact of ice on marine benthic communities, a review. Polar Biol. 24, 553–564 (2001).Article 

Google Scholar 
31.Barnes, D. K. A. & Tarling, G. A. Polar oceans in a changing climate. Curr. Biol. 27, R454–R460. https://doi.org/10.1016/j.cub.2017.01.045 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
32.Barnes, D. K. A., Fleming, A., Sands, C. J., Quartino, M. L. & Deregibus, D. Icebergs, sea ice, blue carbon and Antarctic climate feedbacks. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 376, 20170176. https://doi.org/10.1098/rsta.2017.0176 (2018).ADS 
Article 

Google Scholar 
33.Cook, A. J., Fox, A. J., Vaughan, D. G. & Ferrigno, J. G. Retreating glacier fronts on the Antarctic Peninsula over the past half-century. Science 308, 541–544. https://doi.org/10.1126/science.1104235 (2015).ADS 
CAS 
Article 

Google Scholar 
34.Cook, A. et al. Ocean forcing of glacier retreat in the western Antarctic Peninsula. Science 353, 283–286 (2016).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
35.Clarke, A. et al. Climate change and the marine ecosystem of the western Antarctic Peninsula. Philos. Trans. R. Soc. Lond. B Biol. Sci. 362, 149–166. https://doi.org/10.1098/rstb.2006.1958 (2007).Article 
PubMed 

Google Scholar 
36.Turner, J. & Comiso, J. Solve Antarctica’s sea-ice puzzle. Nat. News 547, 275 (2017).CAS 
Article 

Google Scholar 
37.Meredith, M. P. & King, J. C. Rapid climate change in the ocean west of the Antarctic Peninsula during the second half of the 20th century. Geophys. Res. Lett. https://doi.org/10.1029/2005GL024042 (2005).Article 

Google Scholar 
38.Barnes, D. K. A. & Souster, T. Reduced survival of Antarctic benthos linked to climate-induced iceberg scouring. Nat. Clim. Chang. 1, 365–368. https://doi.org/10.1038/nclimate1232 (2011).ADS 
Article 

Google Scholar 
39.Parkinson, C. L. Global sea ice coverage from satellite data: Annual cycle and 35-yr trends. J. Clim. 27, 9377–9382. https://doi.org/10.1175/jcli-d-14-00605.1 (2014).ADS 
Article 

Google Scholar 
40.Rogers, A. et al. Antarctic futures: An assessment of climate-driven changes in ecosystem structure, function, and service provisioning in the Southern Ocean. Ann. Rev. Mar. Sci. 12, 87–120 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
41.Morley, S. A. et al. Global drivers on Southern Ocean ecosystems: Changing physical environments and anthropogenic pressures in an Earth system. Front. Mar. Sci. 7, 1097 (2020).Article 

Google Scholar 
42.Barnes, D. K. et al. Blue carbon gains from glacial retreat along Antarctic fjords: What should we expect?. Glob. Change Biol. 26, 2750–2755 (2020).ADS 
Article 

Google Scholar 
43.Barnes D. K. A. Blue carbon on polar and subpolar seabeds. In Carbon capture, utilization and sequestration (InTech, 2018). https://doi.org/10.5772/intechopen.78237.44.Bowler, D. et al. The geography of the Anthropocene differs between the land and the sea. bioRxiv https://doi.org/10.1101/432880 (2019).Article 

Google Scholar 
45.Arntz, W., Brey, T. & Gallardo, V. Antarctic zoobenthos. Oceanogr. Mar. Biol. 32, 241–304 (1994).
Google Scholar 
46.Clarke, A. Marine benthic populations in Antarctica: Patterns and processes. Antarct. Res. Ser. 70, 373–388 (1996).Article 

Google Scholar 
47.Fillinger, L., Janussen, D., Lundälv, T. & Richter, C. Rapid glass sponge expansion after climate-induced Antarctic ice shelf collapse. Curr. Biol. 23, 1330–1334 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
48.Clarke, A., Meredith, M. P., Wallace, M. I., Brandon, M. A. & Thomas, D. N. Seasonal and interannual variability in temperature, chlorophyll and macronutrients in northern Marguerite Bay, Antarctica. Deep Sea Res. Part II 55, 1988–2006. https://doi.org/10.1016/j.dsr2.2008.04.035 (2008).ADS 
Article 

Google Scholar 
49.Barnes, D. K. A. Iceberg killing fields limit huge potential for benthic blue carbon in Antarctic shallows. Glob. Chang. Biol. 23, 2649–2659. https://doi.org/10.1111/gcb.13523 (2017).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
50.Pinkerton, M., Bradford-Grieve, J., Bowden, D. & Cummings, V. Benthos: Trophic modelling of the Ross Sea. Support. Docum. CCAMLR Sci. 17, 1–31 (2010).
Google Scholar 
51.Pielou, E. Shannon’s formula as a measurement of species diversity: It’s use and disuse. Am. Nat. 100, 463–465 (1966).Article 

Google Scholar 
52.Fisher, R. A., Corbet, A. S. & Williams, C. B. The relation between the number of species and the number of individuals in a random sample of an animal population. J. Anim. Ecol. 1, 42–58 (1943).Article 

Google Scholar 
53.Everitt, B. & Skrondal, A. The Cambridge Dictionary of Statistics Vol. 106 (Cambridge University Press, Cambridge, 2002).MATH 

Google Scholar 
54.Smale, D. A., Barnes, D. K. A. & Fraser, K. P. P. The influence of ice scour on benthic communities at three contrasting sites at Adelaide Island, Antarctica. Aust. Ecol. 32, 878–888. https://doi.org/10.1111/j.1442-9993.2007.01776.x (2007).Article 

Google Scholar 
55.Peck, L. S., Convey, P. & Barnes, D. K. A. Environmental constraints on life histories in Antarctic ecosystems: Tempos, timings and predictability. Biol. Rev. 81, 75–109. https://doi.org/10.1017/s1464793105006871 (2006).Article 
PubMed 
PubMed Central 

Google Scholar 
56.Waller, C., Worland, M., Convey, P. & Barnes, D. Ecophysiological strategies of Antarctic intertidal invertebrates faced with freezing stress. Polar Biol. 29, 1077–1083 (2006).Article 

Google Scholar 
57.Barnes, D. K. A. Polar zoobenthos blue carbon storage increases with sea ice losses, because across-shelf growth gains from longer algal blooms outweigh ice scour mortality in the shallows. Glob. Chang Biol. 23, 5083–5091. https://doi.org/10.1111/gcb.13772 (2017).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
58.Smith, C. R., Mincks, S. & DeMaster, D. J. A synthesis of bentho-pelagic coupling on the Antarctic shelf: Food banks, ecosystem inertia and global climate change. Deep Sea Res. Part II 53, 875–894 (2006).ADS 
Article 

Google Scholar 
59.Jansen, J. et al. Abundance and richness of key Antarctic seafloor fauna correlates with modelled food availability. Nat. Ecol. Evolut. 2, 71–80 (2018).Article 

Google Scholar 
60.Henley, S. F. et al. Changing biogeochemistry of the Southern Ocean and its ecosystem implications. Front. Mar. Sci. 7, 581 (2020).Article 

Google Scholar 
61.Marshall, G. J. et al. Causes of exceptional atmospheric circulation changes in the Southern Hemisphere. Geophys. Res. Lett. 31, 14 (2004).Article 

Google Scholar 
62.Ashton, G. V., Morley, S. A., Barnes, D. K., Clark, M. S. & Peck, L. S. Warming by 1 C drives species and assemblage level responses in Antarctica’s marine shallows. Curr. Biol. 27, 2698-2705e2693 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
63.Riesgo, A. et al. Some like it fat: Comparative ultrastructure of the embryo in two demosponges of the genus Mycale (order poecilosclerida) from Antarctica and the Caribbean. PLoS ONE 10, e0118805 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
64.Toszogyova, A. & Storch, D. Global diversity patterns are modulated by temporal fluctuations in primary productivity. Glob. Ecol. Biogeogr. 28, 1827–1838 (2019).Article 

Google Scholar 
65.Clark, G. F. et al. Light-driven tipping points in polar ecosystems. Glob. Change Biol. 19, 3749–3761 (2013).ADS 
Article 

Google Scholar 
66.Brockington, S., Clarke, A. & Chapman, A. Seasonality of feeding and nutritional status during the austral winter in the Antarctic sea urchin Sterechinus neumayeri. Mar. Biol. 139, 127–138 (2001).Article 

Google Scholar 
67.Fratt, D. B. & Dearborn, J. Feeding biology of the Antarctic brittle star Ophionotus victoriae (Echinodermata: Ophiuroidea). Polar Biol. 3, 127–139 (1984).Article 

Google Scholar 
68.Sahade, R., Tatián, M. & Esnal, G. B. Reproductive ecology of the ascidian Cnemidocarpa verrucosa at Potter Cove, South Shetland Islands, Antarctica. Mar. Ecol. Progr. Ser. 272, 131–140 (2004).ADS 
Article 

Google Scholar 
69.Dayton, P. K. et al. Recruitment, growth and mortality of an Antarctic hexactinellid sponge, Anoxycalyx joubini. PLoS ONE 8, e56939 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
70.Vacchi, M., Cattaneo-Vietti, R., Chiantore, M. & Dalù, M. Predator-prey relationship between the nototheniid fish Trematomus bernacchii and the Antarctic scallop Adamussium colbecki at Terra Nova Bay (Ross Sea). Antarct. Sci. 12, 64–68 (2000).ADS 
Article 

Google Scholar 
71.Sheil, D. & Burslem, D. F. Defining and defending Connell’s intermediate disturbance hypothesis: a response to Fox. Trends Ecol. Evol. 28, 571–572. https://doi.org/10.1016/j.tree.2013.07.006 (2013).Article 
PubMed 
PubMed Central 

Google Scholar  More

A. Strain sampling and isolationBradyrhizobium is a commonly occurring genus in soil [21]. Closely related Bradyrhizobium diazoefficiens (previously Bradyrhizobium japonicum) strains were isolated from soil, as previously described [20, 22]. In brief, Bradyrhizobium isolates that formed symbiotic associations with a foundational legume species in the censused region, Acacia acuminata, were isolated from soil sampled along a large region spanning ~300,000 km2 in South West Australia, a globally significant biodiversity hotspot [23]. In total 60 soil samples were collected from twenty sites (3 soil samples per site; Supplementary Fig. S1) and 380 isolates were sequenced (19 isolates per site, 5 or 6 isolates per soil sample, each isolate re-plated from a single colony at least 2 times). Host A. acuminata legume plants were inoculated with field soil in controlled chamber conditions and isolates were cultured on Mannitol Yeast agar plates from root nodules (see [20, 22] for details). A total of 374 strains were included in this study after removing 5 contaminated samples and one sample that was a different Bradyrhizobium species; non- Bradyrhizobium diazoefficiens sample removal was determined from 16S rRNA sequences extracted from draft genome assemblies (Method C) using RNAmmer [24].B. Environmental variation among sampled sitesIn this study, I focus on environmental factors (temperature, rainfall, soil pH and salinity) previously identified to impact either rhizobia growth performance, functional fitness or persistence in soil [25,26,27,28] and where a directionality of rhizobial stress response could be attributed with respect to environmental variation present in the sampled region (i.e. stress occurs at high temperatures, low rainfall, high acidity and high salinity). Each environmental factor was standardised to a mean of 0 and a standard deviation of 1, and pH and rainfall scales were reversed to standardise stress responses directions so that low stress is at low values and high stress is at high values for all factors (Supplementary Fig. S2). Additionally, salinity was transformed using a log transformation (log(x + 0.01) to account for some zeroes) prior to standardisation.C. Isolate sequencing and pangenome annotationIllumina short reads (150 bp paired-end) were obtained and draft genome assemblies were generated using Unicycler from a previous study [29]. Resulting assemblies were of good assembly quality (99.2% of all strains had >95.0% genome completeness score according to BUSCO [30]; Table S1; assembled using reads that contained nominal 0.016 ± 0.00524% non-prokaryotic DNA content across all 374 isolates, according to Kraken classification [31]). Protein coding regions (CDS regions) were identified using Prokka [32] and assembled into a draft pangenome using ROARY [33], which produced a matrix of counts of orthologous gene clusters (i.e. here cluster refers to a set of protein-coding sequences containing all orthologous variants from all the different strains, grouped together and designated as a single putative gene). Gene clusters that occurred in 99% of strains were designated as ‘core genes’ and used to calculate the ‘efficiency of selection’ [34, 35] (measured as dN/dS, Method G.2) and population divergence measured as Fixation Index ‘Fst’, Method H) across each environmental stress factor. The identified gene clusters were then annotated using eggNOG-mapper V2 [36] and the strain by gene cluster matrix was reaggregated using the Seed ortholog ID returned by eggNOG-mapper as the protein identity. Out of the total 2,744,533 CDS regions identified in the full sample of 374 strains, eggNOG-mapper was able to assign 2,612,345 of them to 91,230 unique Seed orthologs. These 91,230 protein coding genes constituted the final protein dataset for subsequent analyses.D. Calculation and statistical analysis of gene richness and pangenome diversity along the stress gradientGene richness was calculated as the total number of unique seed orthologues for each strain (i.e. genome). Any singleton genes that occurred in only a single strain, as well as ‘core’ genes that occurred in every strain (for symmetry, and because these are equally uninformative with respect to variation between strains) were removed, leaving 74,089 genes in this analysis. Gene richness (being count data) was modelled on a negative binomial distribution (glmer.nb function) as a function of each of the four environmental stressors as predictors using the lme4 package in R [37], also accounting for hierarchical structure in the data by including site and soil sample as random effects.To rule out potentially spurious effects of assembly quality (i.e. missed gene annotations due to incomplete draft genomes) on key findings, I confirmed no significant association between gene richness and genome completeness (r = 0.042, p = 0.4224, Fig. S3).Finally, pangenome diversity was calculated as the total number of unique genes that occurred in each soil sample (since multiple strains were isolated from a single soil sample). Pangenome diversity was modelled the same as gene richness, except here soil sample was not included as a random effect.E. Calculation of network and duplication traits for each geneI used the seed orthologue identifier from eggNOG-mapper annotations to query matching genes within StringDB ([38]; https://string-db.org/), which collects information on protein-protein interactions. Out of 91,230 query seed orthologues, 73,126 (~80%) returned a match in STRING. For matching seed orthologue hits, a network was created by connecting any proteins that were annotated as having pairwise interactions in the STRING database using the igraph package in R [39]. Two vertex-based network metrics were calculated for each gene: betweenness centrality, which measures a genes tendency to connect other genes in the gene network, and mean cosine similarity, which is a measure of how much a gene’s links to other genes are similar to other genes.Betweenness centrality was calculated using igraph (functional betweenness). For mean cosine similarity, a pairwise cosine similarity was first calculated between all genes. To do this, the igraph network object was converted into a (naturally sparse yet large) adjacency matrix and the cosSparse function in qlcMatrix in R [40] was used to calculate cosine similarity between all pairs of genes. To obtain an overall cosine similarity trait value for each gene, the average pairwise cosine similarity to all other genes in the network was calculated.Finally, gene duplication level was calculated for each gene as one additional measure of ‘redundancy’, by calculating the average number of gene duplicates found within the same strain. Duplicates were identified as CDS regions with the same Seed orthologue ID.F. Gene distribution modelsTo determine how gene traits predict accessory genome distributions patterns along the stress gradients, I first calculated a model-based metric (hereafter and more specifically a standardised coefficient, ‘z-score’) of the relative tendency of each gene to be found in different soil samples across the four stress gradients (heat, salinity, acidity, and aridity). This was achieved by modelling each gene’s presence or absence in a strain as a function of the four stress gradients, with site and soil sample as a random effect, using a binomial model in lme4 (the structure of the model being the same as the gene richness model, only the response is different). To reduce computational overhead, these models were only run for the set of genes that were used in the gene richness analysis (e.g. after removing singletons and core genes), and which had matching network traits calculated (e.g. they occurred in the STRING database; n = 64,867 genes). Distribution models were run in tandem for each gene using the manyany function in the R package mvabund [41]. Standardised coefficients, or z-scores (coefficient/standard error) indicating the change in the probability of occurrence for each gene across each of the stress gradients were extracted. More negative coefficients correspond to genes that are more likely to be absent in high stress (and vice versa for positive coefficients).To determine how network and duplication traits influence the distribution of genes across the stress gradient, I performed a subsequent linear regression model where the gene’s z-scores was the response and gene traits as predictors. The environmental stress type (i.e. acidity, aridity, heat and salinity) was included as a categorical predictor, and the interaction between stress category and gene function traits were used to infer the influence of gene function traits on gene distributions in a given stress type (see Supplementary Methods S1 for z-score transformation).G. Quantifying molecular signals of natural selection on accessory and core genesTo examine molecular signatures of selection in accessory and core genes, I calculated dN/dS for a subsample of the total pool (n=74,089 genes), which estimates the efficiency of selection [34, 35]. Two major questions relevant to dN/dS that are addressed here require a different gene subsampling approach:(1) Do variable environmental stress responses lead to different dN/dS patterns among accessory genes?Here, I subsampled accessory genes (total accessory gene pool across 374 strains, 74,089) to generate and compare dN/dS among 3 categorical groups, each representing a different level of stress response based on their z-scores (accessory genes that either strongly increase, decrease or have no change in occurrence as stress increases; n = 1000 genes/category; see Supplementary Methods S2 for subsample stratification details).For each gene, sequences were aligned using codon-aware alignment tool, MACSE v2 [42]. dN/dS was estimated by codon within each gene using Genomegamap’s Bayesian model-based approach [43], which is a phylogeny-free method optimised for within bacterial species dN/dS calculation (see Supplementary Methods S3 for dN/dS calculation/summarisation; S9 for xml configuration). The proportion of codons with dN/dS that were credibly less than 1 (purifying selection) and those credibly greater than 1 (positive selection) were analysed, with respect to the genes’ occurrence response to stress. Specifically, I modelled the proportion of codons with dN/dS  1 was overall too low to analyse in this way, so the binary outcome (a gene with any codons with dN/dS  > 1 or not) was modelled using a binomial response model with the response categories as predictors (see Supplementary Methods S4 for details of both models).(2) Does dN/dS among microbial populations vary across environmental stress?Here, I compared the average change in dN/dS in core genes present across all environments at the population level (i.e. all isolates from one soil sample), which is used here to measure the change in the efficiency of selection across each stress gradient. Core genes were used since they occur in all soil samples, allowing a consistent set and sample size of genes to be used in the population-level dN/dS calculation. This contrasts with the previous section, which quantifies gene-level dN/dS on extant accessory genes that intrinsically have variable presence or absence across soil samples. For computational feasibility, 500 core genes were subsampled (total core 1015 genes) and, for each gene, individual strain variants were collated into a single fasta file based on soil sample membership, such that dN/dS could be calculated separately for each gene within each soil sample (i.e. 60 soil samples × 500 genes = 30,000 fasta files). Each fasta file was then aligned in MACSE and dN/dS calculated using the same methodology for accessory genes (Supplementary Method S3). This enabled the average dN/dS in a sample to be associated with soil-sample level environmental stress variables. Specifically, I modelled the mean proportion of codons with dN/dS  1 due to overall rarity of positive selection (average proportion of genes where at least 1 codon with dN/dS  > 1 was ~0.006). This low level of positive selection is expected for core genes which tend to be under strong selective constraint.H. Calculation and analysis of Fixation index (Fst) along stress gradientsUsing the core genome alignment (all SNPs among 1015 core genes) generated previously with ROARY, I computed pairwise environmentally-stratified Fst. Each soil sample (n = 60) was first placed into one of 5 bins based on their distances in total environmental stress space (using all four stress gradients), with the overall goal of generating roughly evenly sized bins such that the environmental similarity of stress was greater within bins than between (see Supplementary Methods S6 and Fig. S4 for clustering algorithm details). Next, fasta alignments were converted to binary SNPs using the adegenet package. Pairwise Fst between all strains (originating from a particular soil sample) within a single bin was calculated using StAMPP in R [44]. For each strain pair, the average of the two stress gradient values was assigned.The relationship between pairwise Fst and the average stress value was evaluated using a linear regression model with each of the four stress values as predictors. Since the analysis uses pairwise data (thus violating standard independence assumptions), the significance of the relationship was determined using a permutation test (see Supplementary Methods S7 for details).I. Chromosomal structure analysis of gene loss patternsTo gain insight into structural variation and test for regional hotspots in gene loss along the chromosome, I mapped each gene’s stress response (i.e. probability of loss or gain indicated by each genes z-score) onto a completed Bradyrhizobium genome (strain ‘36_1’ from the same set of 374 strains (Genbank CP067102.1; [45]). Putative CDS positions on the complete genome were determined using Prokka and annotated with SEED orthologue ID’s using eggNOG-mapper. Matching accessory genes derived from the full set of 374 incomplete draft genomes (n = 74,089) were mapped to positions on the complete genome (6274 matches). The magnitude of gene loss or gain (as measured by their standardised z-scores for each environment from the gene distribution models; see Method F) was then modelled across the genome using a one-dimensional spatial smoothing model. This model was implemented in R INLA [46] (www.r-inla.org), and models a response in a one-dimensional space using a Matern covariance-based random effect. The method uses an integrated nested Laplace approximation to a Bayesian posterior distribution, with a cyclical coordinate system to accommodate circular genomes. The model accounts for spatial non-independence among sites and produces a continuous posterior distribution of average z-score predictions along the entire genome, which was then used to visualise potential ‘hotspots’ of gene loss or gain. The modelling procedure was repeated, instead with gene network traits, such that model predictions of similarity and betweenness could be visualised on the reference chromosome. More