Philippa Kaur

1.Dellwig O, Schnetger B, Brumsack H-J, Grossart H-P, Umlauf L. Dissolved reactive manganese at pelagic redoxclines (part II): hydrodynamic conditions for accumulation. J Mar Syst. 2012;90:31–41.
Google Scholar 
2.Taylor GT, Iabichella M, Ho T, Scranton MI, Thunell RC, Muller-Karger F, et al. Chemoautotrophy in the redox transition zone of the Cariaco Basin: a significant midwater source of organic carbon production. Limnol Oceanogr. 2001;46:148–63.CAS 

Google Scholar 
3.Zopfi J, Ferdelman TG, Jørgensen BB, Teske A, Thamdrup B. Influence of water column dynamics on sulfide oxidation and other major biogeochemical processes in the chemocline of Mariager Fjord (Denmark). Mar Chem. 2001;74:29–51.CAS 

Google Scholar 
4.Trefry JH, Presley BJ, Keeney-Kennicutt WL, Trocine RP. Distribution and chemistry of manganese, iron, and suspended particulates in Orca Basin. Geo-Mar Lett. 1984;4:125–30.
Google Scholar 
5.Dahl TW, Anbar AD, Gordon GW, Rosing MT, Frei R, Canfield DE. The behavior of molybdenum and its isotopes across the chemocline and in the sediments of sulfidic Lake Cadagno, Switzerland. Geochim Cosmochim Acta. 2010;74:144–63.CAS 

Google Scholar 
6.Özsoy E, Ünlüata Ü. Oceanography of the Black Sea: a review of some recent results. Earth-Sci Rev. 1997;42:231–72.
Google Scholar 
7.Wegwerth A, Eckert S, Dellwig O, Schnetger B, Severmann S, Weyer S, et al. Redox evolution during Eemian and Holocene sapropel formation in the Black Sea. Palaeogeogr Palaeoclimatol Palaeoecol. 2018;489:249–60.
Google Scholar 
8.Murray JW, Jannasch HW, Honjo S, Anderson RF, Reeburgh WS, Top Z, et al. Unexpected changes in the oxic/anoxic interface in the Black Sea. Nature. 1989;338:411–3.CAS 

Google Scholar 
9.Schulz-Vogt HN, Pollehne F, Jürgens K, Arz HW, Bahlo R, Dellwig O, et al. Effect of large magnetotactic bacteria with polyphosphate inclusions on the phosphate profile of the suboxic zone in the Black Sea. ISME J. 2019;13:1198–208.CAS 
PubMed 
PubMed Central 

Google Scholar 
10.Dellwig O, Wegwerth A, Schnetger B, Schulz H, Arz HW. Dissimilar behaviors of the geochemical twins W and Mo in hypoxic-euxinic marine basins. Earth-Sci Rev. 2019;193:1–23.CAS 

Google Scholar 
11.Stanev EV, Poulain PM, Grayek S, Johnson KS, Claustre H, Murray JW. Understanding the dynamics of the oxic-anoxic interface in the Black Sea. Geophys Res Lett. 2018;45:864–71.CAS 

Google Scholar 
12.Trouwborst RE. Soluble Mn(III) in suboxic zones. Science. 2006;313:1955–7.CAS 
PubMed 

Google Scholar 
13.Vliet DM, Meijenfeldt FAB, Dutilh BE, Villanueva L, Sinninghe Damsté JS, Stams AJM, et al. The bacterial sulfur cycle in expanding dysoxic and euxinic marine waters. Environ Microbiol. 2021;23:2834–57.PubMed 

Google Scholar 
14.Konovalov SK, Luther GW, Friederich GE, Nuzzio DB, Tebo BM, Murray JW, et al. Lateral injection of oxygen with the Bosporus plume-fingers of oxidizing potential in the Black Sea. Limnol Oceanogr. 2003;48:2369–76.CAS 

Google Scholar 
15.Lewis BL, Landing WM. The biogeochemistry of manganese and iron in the Black Sea. Deep Sea Res A Oceanogr Res Pap. 1991;38:S773–S803.
Google Scholar 
16.Yakushev EV, Pollehne F, Jost G, Kuznetsov I, Schneider B, Umlauf L. Analysis of the water column oxic/anoxic interface in the Black and Baltic seas with a numerical model. Mar Chem. 2007;107:388–410.CAS 

Google Scholar 
17.Gregg MC, Yakushev E. Surface ventilation of the Black Sea’s cold intermediate layer in the middle of the western gyre. Geophys Res Lett. 2005;32:1–4.
Google Scholar 
18.Schnetger B, Dellwig O. Dissolved reactive manganese at pelagic redoxclines (part I): a method for determination based on field experiments. J Mar Syst. 2012;90:23–30.
Google Scholar 
19.Tebo BM, Bargar JR, Clement BG, Dick GJ, Murray KJ, Parker D, et al. Biogenic manganese oxides: Properties and mechanisms of formation. Annu Rev Earth Planet Sci. 2004;32:287–328.CAS 

Google Scholar 
20.Glockzin M, Pollehne F, Dellwig O. Stationary sinking velocity of authigenic manganese oxides at pelagic redoxclines. Mar Chem. 2014;160:67–74.CAS 

Google Scholar 
21.Dellwig O, Leipe T, März C, Glockzin M, Pollehne F, Schnetger B, et al. A new particulate Mn-Fe-P-shuttle at the redoxcline of anoxic basins. Geochim Cosmochim Acta. 2010;74:7100–15.CAS 

Google Scholar 
22.Burdige DJ, Nealson KH. Chemical and microbiological studies of sulfide-mediated manganese reduction. Geomicrobiol J. 1986;4:361–87.CAS 

Google Scholar 
23.Yao W, Millero FJ. The rate of sulfide oxidation by δMnO2 in seawater. Geochim Cosmochim Acta. 1993;57:3359–65.CAS 

Google Scholar 
24.Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil PA, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36:996.CAS 
PubMed 

Google Scholar 
25.Henkel JV, Dellwig O, Pollehne F, Herlemann DPR, Leipe T, Schulz-Vogt HN. A bacterial isolate from the Black Sea oxidizes sulfide with manganese(IV) oxide. Proc Natl Acad Sci USA. 2019;116:12153–5.CAS 
PubMed 
PubMed Central 

Google Scholar 
26.Henkel JV, Vogts A, Werner J, Neu TR, Spröer C, Bunk B, et al. Candidatus Sulfurimonas marisnigri sp. nov. and Candidatus Sulfurimonas baltica sp. nov., thiotrophic manganese oxide reducing chemolithoautotrophs of the class Campylobacteria isolated from the pelagic redoxclines of the Black Sea and the Baltic Sea. Syst Appl Microbiol. 2021;44:1–11.27.Grote J, Jost G, Labrenz M, Herndl GJ, Jürgens K. Epsilonproteobacteria represent the major portion of chemoautotrophic bacteria in sulfidic waters of pelagic redoxclines of the Baltic and Black Seas. Appl Environ Microbiol. 2008;74:7546–51.CAS 
PubMed 
PubMed Central 

Google Scholar 
28.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 

Google Scholar 
29.Sekar R, Pernthaler A, Pernthaler J, Warnecke F, Posch T, Amann R. An improved protocol for quantification of freshwater Actinobacteria by fluorescence in situ hybridization. Appl Environ Microbiol. 2003;69:2928–35.CAS 
PubMed 
PubMed Central 

Google Scholar 
30.Grote J, Labrenz M, Pfeiffer B, Jost G, Jürgens K. Quantitative distributions of Epsilonproteobacteria and a Sulfurimonas subgroup in pelagic redoxclines of the central Baltic Sea. Appl Environ Microbiol. 2007;73:7155–61.CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Daims H, Bruhl A, Amann R, Schleifer K, Wagner M. The domain-specific probe EUB338 is insufficient for the detection of all bacteria: development and evaluation of a more comprehensive probe set. Syst Appl Microbiol. 1999;22:434–44.CAS 
PubMed 

Google Scholar 
32.Wallner G, Amann R, Beisker W. Optimizing fluorescent in situ hybridization with rRNA-targeted oligonucleotide probes for flow cytometric identification of microorganisms. Cytometry. 1993;11:136–43.
Google Scholar 
33.Glöckner FO, Yilmaz P, Quast C, Gerken J, Beccati A, Ciuprina A, et al. 25 years of serving the community with ribosomal RNA gene reference databases and tools. J Biotechnol. 2017;261:169–76.PubMed 

Google Scholar 
34.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:590–6.
Google Scholar 
35.Konstantinidis KT, Tiedje JM. Towards a genome-based taxonomy for prokaryotes. J Bacteriol. 2005;187:6258–64.CAS 
PubMed 
PubMed Central 

Google Scholar 
36.Buchfink B, Reuter K, Drost H-G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat Methods. 2021;18:366–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
37.Von Meijenfeldt FAB, Arkhipova K, Cambuy DD, Coutinho FH, Dutilh BE. Robust taxonomic classification of uncharted microbial sequences and bins with CAT and BAT. Genome Biol. 2019;20:1–14.
Google Scholar 
38.Schulz HD. Conceptual models and computer models. In: Schulz HD, Zabel M, editors. Marine geochemistry. Springer: Berlin, Heidelberg; 2006. p. 513–47.39.Diepenbroek M, Glöckner FO, Grobe P, Güntsch A, Huber R, König-Ries B, et al. Towards an integrated biodiversity and ecological research data management and archiving platform: the German federation for the curation of biological data (GFBio). In: Plödereder E, Grunske L, Schneider E, Ull D, editors. Informatik 2014. Bonn: Gesellschaft für Informatik e.V.; 2014.p. 1711–21.40.Yilmaz P, Kottmann R, Field D, Knight R, Cole JR, Amaral-Zettler L, et al. Minimum information about a marker gene sequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specifications. Nat Biotechnol. 2011;29:415–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
41.Revsbech NP, Thamdrup B, Dalsgaard T, Canfield DE. Construction of STOX oxygen sensors and their application for determination of O2 concentrations in oxygen minimum zones. Methods Enzymol. 2011;486:325–41.CAS 
PubMed 

Google Scholar 
42.Dahl C. A biochemical view on the biological sulfur cycle. In: Environmental technologies to treat sulphur pollution: principles and engineering. IWA Publishing: London; 2020;2:55–96.43.Murray JW, Yakushev EV. Past and present water column anoxia. Past and present water column anoxia. Dordrecht: Springer Netherlands; 2006.44.Schulz HD. Quantification of early diagenesis: dissolved constituents in pore water and signals in the solid phase. In: Schulz HD, Zabel M, editors. Marine geochemistry. Berlin/Heidelberg: Springer-Verlag; 2006. p. 73–124.45.Tebo BM. Manganese(II) oxidation in the suboxic zone of the Black Sea. Deep Res A. 1991;38:883–905.
Google Scholar 
46.Konovalov S, Samodurov A, Oguz T, Ivanov L. Parameterization of iron and manganese cycling in the Black Sea suboxic and anoxic environment. Deep Res Part I Oceanogr Res Pap. 2004;51:2027–45.CAS 

Google Scholar 
47.Lahme S, Callbeck CM, Eland LE, Wipat A, Enning D, Head IM, et al. Comparison of sulfide-oxidizing Sulfurimonas strains reveals a new mode of thiosulfate formation in subsurface environments. Environ Microbiol. 2020;22:1784–1800.CAS 
PubMed 

Google Scholar 
48.Grote J, Schott T, Bruckner CG, Glockner FO, Jost G, Teeling H, et al. Genome and physiology of a model Epsilonproteobacterium responsible for sulfide detoxification in marine oxygen depletion zones. Proc Natl Acad Sci USA. 2012;109:506–10.CAS 
PubMed 

Google Scholar 
49.Sievert SM, Scott KM, Klotz MG, Chain PSG, Hauser LJ, Hemp J, et al. Genome of the Epsilonproteobacterial chemolithoautotroph Sulfurimonas denitrificans. Appl Environ Microbiol. 2008;74:1145–56.CAS 
PubMed 

Google Scholar 
50.Friedrich CG, Bardischewsky F, Rother D, Quentmeier A, Fischer J. Prokaryotic sulfur oxidation. Curr Opin Microbiol. 2005;8:253–9.CAS 
PubMed 

Google Scholar 
51.Götz F, Pjevac P, Markert S, McNichol J, Becher D, Schweder T, et al. Transcriptomic and proteomic insight into the mechanism of cyclooctasulfur- versus thiosulfate-oxidation by the chemolithoautotroph Sulfurimonas denitrificans. Environ Microbiol. 2019;21:244–58.PubMed 

Google Scholar 
52.Pjevac P, Meier DV, Markert S, Hentschker C, Schweder T, Becher D, et al. Metaproteogenomic profiling of microbial communities colonizing actively venting hydrothermal chimneys. Front Microbiol. 2018;9:1–12.
Google Scholar 
53.Meier DV, Pjevac P, Bach W, Hourdez S, Girguis PR, Vidoudez C, et al. Niche partitioning of diverse sulfur-oxidizing bacteria at hydrothermal vents. ISME J. 2017;11:1545–58.CAS 
PubMed 
PubMed Central 

Google Scholar 
54.Wang S, Jiang L, Hu Q, Liu X, Yang S, Shao Z. Elemental sulfur reduction by a deep‐sea hydrothermal vent Campylobacterium Sulfurimonas sp. NW10. Environ Microbiol. 2021;23:965–79.CAS 
PubMed 

Google Scholar 
55.Yao W, Millero FH. Oxidation of hydrogen sulfide by Mn(IV) and Fe(III) (hydr)oxides in seawater. Mar Chem. 1996;52:1–16.CAS 

Google Scholar 
56.Herszage J, dos Santos Afonso M. Mechanism of hydrogen sulfide oxidation by manganese(IV) oxide in aqueous solutions. Langmuir. 2003;19:9684–92.CAS 

Google Scholar 
57.Glazer BT, Luther GW, Konovalov SK, Friederich GE, Nuzzio DB, Trouwborst RE, et al. Documenting the suboxic zone of the Black Sea via high-resolution real-time redox profiling. Deep Res II Top Stud Oceanogr. 2006;53:1740–55.
Google Scholar 
58.Jørgensen BB, Fossing H, Wirsen CO, Jannasch HW. Sulfide oxidation in the anoxic Black Sea chemocline. Deep Sea Res A Oceanogr Res Pap. 1991;38:1083–103.
Google Scholar 
59.Yiǧiterhan O, Murray JW. Trace metal composition of particulate matter of the Danube River and Turkish rivers draining into the Black Sea. Mar Chem. 2008;111:63–76.
Google Scholar 
60.Brewer PG, Spencer DW. Distribution of some trace elements in Black Sea and their flux between dissolved and particulate phases: water. In: The Black Sea–Geology, Chemistry, and Biology. AAPG Special Volumes. AAPG; 1974;137–43.61.Fuchsman CA, Kirkpatrick JB, Brazelton WJ, Murray JW, Staley JT. Metabolic strategies of free-living and aggregate-associated bacterial communities inferred from biologic and chemical profiles in the Black Sea suboxic zone. FEMS Microbiol Ecol. 2011;78:586–603.CAS 
PubMed 

Google Scholar 
62.Kelly DP. Biochemistry of the chemolithotrophic oxidation of inorganic sulphur. Philos Trans R Soc Lond B Biol Sci. 1982;298:499–528.CAS 
PubMed 

Google Scholar 
63.Kirkpatrick JB, Fuchsman CA, Yakushev EV, Egorov AV, Staley JT, Murray JW. Dark N2 fixation: nifH expression in the redoxcline of the Black Sea. Aquat Micro Ecol. 2018;82:43–58.
Google Scholar 
64.Glaubitz S, Kießlich K, Meeske C, Labrenz M, Jürgens K. SUP05 Dominates the gammaproteobacterial sulfur oxidizer assemblages in pelagic redoxclines of the central baltic and black seas. Appl Environ Microbiol. 2013;79:2767–76.CAS 
PubMed 
PubMed Central 

Google Scholar 
65.Shah V, Chang BX, Morris RM. Cultivation of a chemoautotroph from the SUP05 clade of marine bacteria that produces nitrite and consumes ammonium. ISME J. 2017;11:263–71.CAS 
PubMed 

Google Scholar 
66.Rogge A, Vogts A, Voss M, Jürgens K, Jost G, Labrenz M. Success of chemolithoautotrophic SUP05 and Sulfurimonas GD17 cells in pelagic Baltic Sea redox zones is facilitated by their lifestyles as K- and r -strategists. Environ Microbiol. 2017;19:2495–506.CAS 
PubMed 

Google Scholar 
67.Overmann J, Cypionka H, Pfennig N. An extremely low-light-adapted phototrophic sulfur bacterium from the Black Sea. Limnol Oceanogr. 1992;37:150–5.CAS 

Google Scholar 
68.Jensen MM, Kuypers MMM, Lavik G, Thamdrup B. Rates and regulation of anaerobic ammonium oxidation and denitrification in the Black Sea. Limnol Oceanogr. 2008;53:23–36.CAS 

Google Scholar 
69.Hannig M, Lavik G, Kuypers MMM, Woebken D, Martens-Habbena W, Jürgens K. Shift from denitrification to anammox after inflow events in the central Baltic Sea. Limnol Oceanogr. 2007;52:1336–45.CAS 

Google Scholar 
70.Engström P, Dalsgaard T, Hulth S, Aller RC. Anaerobic ammonium oxidation by nitrite (anammox): Implications for N2 production in coastal marine sediments. Geochim Cosmochim Acta. 2005;69:2057–65.
Google Scholar 
71.Dapena-Mora A, Fernández I, Campos JL, Mosquera-Corral A, Méndez R, Jetten MSM. Evaluation of activity and inhibition effects on Anammox process by batch tests based on the nitrogen gas production. Enzym Micro Technol. 2007;40:859–65.CAS 

Google Scholar 
72.Havig JR, McCormick ML, Hamilton TL, Kump LR. The behavior of biologically important trace elements across the oxic/euxinic transition of meromictic Fayetteville Green Lake, New York, USA. Geochim Cosmochim Acta. 2015;165:389–406.CAS 

Google Scholar 
73.Jürgens K, Taylor GT. Microbial ecology and biogeochemistry of oxygen-deficient water columns. Microbial Ecology of the Ocean, 3rd ed. Hoboken: Wiley; 2018. p. 231–88.74.Jost G, Martens-Habbena W, Pollehne F, Schnetger B, Labrenz M. Anaerobic sulfur oxidation in the absence of nitrate dominates microbial chemoautotrophy beneath the pelagic chemocline of the eastern Gotland Basin, Baltic Sea. FEMS Microbiol Ecol. 2010;71:226–36.CAS 
PubMed 

Google Scholar 
75.Aller RC, Rude PD. Complete oxidation of solid phase sulfides by manganese and bacteria in anoxic marine sediments. Geochim Cosmochim Acta. 1988;52:751–65.CAS 

Google Scholar 
76.King GM. Effects of added manganic and ferric oxides on sulfate reduction and sulfide oxidation in intertidal sediments. FEMS Microbiol Ecol. 1990;73:131–8.CAS 

Google Scholar  More

Experimental site, location and climateFive years’ field experimentation on ICM was started in 2014–15 at the ICAR-Indian Agricultural Research Institute (28°35′ N latitude, 77°12′ E longitude, 229 m MSL), New Delhi, India. The study site comes under the ‘Trans IGPs’, being semi-arid with an average annual rainfall of 650 mm, of which ~ 80% occurs in July–September (south-west monsoon). The mean max. / min. air temperature ranges between 20-40ºC and 4-28ºC, respectively. The five years (2014–2019) weather data were recorded from the observatory adjoining to the experimental field, and presented in Supplementary Table 1. Before start of the experiment, a rainy season Sesbania was grown in 2014 to ensure the uniform fertility across the blocks. Initial soil samples (0.0–0.15 m depth) were collected in October 2014 after incorporating the Sesbania residues in soil. The soil samples were processed for the chemical analysis. The study site had a pH of 7.9 (1:2.5 soil and water ratio)68, 3.8 g kg−1 soil organic-C69, 94.1 kg ha−1 KMnO4 oxidizable N70, 97 µg g−1 soil microbial biomass carbon71, 51.3 μg PNP g−1 soil h−1 alkaline phosphatase72, 53.0 μg TPF g−1 soil d−1 dehydrogenase73, and 13.5 μg NH4-N g−1 soil h−1urease74.Description of different ICM modulesThe eight ICM modules were tested, comprising of four conventional tillage (CT)-based (ICM1-4) and four conservation agriculture (CA)-based (ICM5-8) modules, replicated thrice in a complete randomized block design with the plot size of 60 m2 (15 m × 4.5 m) (Table 4). The crop residues were completely removed in the CT-based modules (ICM1-4), while in the ICM5-8 modules, in-situ wheat (~ 3 Mg ha−1 on dry weight basis)) and maize (~ 5 Mg ha−1, on dry weight basis) residues were retained on the soil surface during all the seasons of crops cultivation (Footnote Table 4, Fig. 6a,b).Table 4 Description of integrated crop management (ICM) modules adopted in maize and wheat crops during the five yearsˈ fixed plot experimentation.Full size tableIn the ICM1-4 modules, the field preparation was carried out by sequential tillage operations, such as, deep ploughing using the disc harrow, cultivator/rotavator twice (0.15–0.20 m), followed by levelling in each season. In the ICM3-4, the raised beds of 0.70 m bed width (bed top 0.40 m and furrow 0.30 m) were formed during each cropping cycle using the tractor mounted bed planter, and simultaneously wheat sowing was done (Fig. 6c). In the case of maize, ridges (0.67 m length) were prepared using the ridge maker. In the CA-based ICM5-8 modules, the tillage operations, such as, seed and fertilizer placement were restricted to the crop row-zone in maize and wheat both. In the ICM7&8, the permanent raised beds (0.67 m mid-furrow to mid-furrow, 0.37 m wide flat tops, and 0.15 m furrow depth), were prepared (Fig. 6d). However, these beds were reshaped using the disc coulter at the end of each cropping cycle without disturbing the surface residues. The sowing was accomplished using the raised bed multi-crop planter.Cultural operations and the fertilizer applicationDuring every season, the maize (cv. PMH 1) was sown in the first week of July using 20 kg seed ha−1. The wheat (cv. HD 2967) crop was sown in the first fortnight of November using the seed-cum fertilizer drill (ICM1-2), bed planter (ICM3-4) and zero-till seed drill (ICM5-8) at 100 kg seed ha−1. The chemical fertilizers (N, P and K) were applied as per the modules described in the footnote of Table 4. At sowing, the full doses of phosphorous (P) and potassium (K) were applied using the di-ammonium phosphate (DAP) and muriate of potash (MOP), and the nitrogen (N) supplied through DAP. The remaining N was top-dressed through urea in two equal splits after the first irrigation and tasseling / silking stages in maize, and crown root initiation and tillering stages of wheat. In the modules receiving ¾ fertilizers (ICM2,4,6,8), the seeds were treated with the NPK liquid bio-fertilizer (LBFs) (diluted 250 ml formulation 2.5 L of water ha−1), and an arbuscular mycorrhiza (AMF) was broadcasted at 12 kg ha−1 as has been described by75. This LBFs had the microbial consortia of N-fixer (Azotobacter chroococcum), P (Pseudomonas) and K (Bacillus decolorationis) solubilizers, procured from the commercial biofertilizer production unit of the Microbiology Division, ICAR-Indian Agricultural Research Institute, New Delhi (Patentee: ICAR, Govt. of India). Weeds were managed by integrating the pre- and post-emergence herbicides, and their combinations along with the hand weeding-mulching, as mentioned in the concerned modules (Footnote Table 4). However, in the CA-based modules (ICM5-8), the non-selective herbicide glyphosate (1 kg ha−1) was used 10 days before the sowing. The need-based integrated insect-pests and disease management practices were followed uniformly across the modules.Soil sampling and analysisBefore start of the experiment, the soil sampling was done from 0.0–0.15 m depth. Afterwards, five random samples from each module from 0.0–0.30 m soil depth were collected at the flowering stage of 5th season wheat. These samples were taken from the three soil depths (0.0 to 0.05, 0.05–0.15 and 0.150–0.30 m) using the core sampler. The ground, air-dried soil samples, passed through a 0.2 mm sieve were used for the determination of the Walkley and Black organic carbon (SOC), as described by76. For the soil biological properties, the soil samples were processed, and stored at 5ºC for 18–24 h, then analyzed the soil microbial biomass carbon (SMBC), dehydrogenase (SDH), alkaline phosphate (SAP) and the urease (URE) activities.The soil microbial biomass carbon (SMBC)The SMBC was measured using the fumigation extraction method as proposed by71. The pre-weighed samples from the respective soil depths were fumigated with the ethanol-free chloroform for the 24 h. Separately, a non–fumigated set was also maintained. Further, 0.5 M K2SO4 (soil: extractant 1:4) was added, and kept on a reciprocal shaker for 30 min. and then filtered through a Whatman No. 42 filter paper. OC of the filtrate was measured through the dichromate digestion, followed by the back titration with 0.05 N ferrous ammonium sulphate. The SMBC was then calculated using the equation:$${text{S}}_{{{text{MBC}}}} = {text{EC }} times { 2}.{64}$$where, EC = (Corg in fumigated soil – Corg in non-fumigated soil), and expressed in µg C g−1 soil.The dehydrogenase activity (SDH)The SDH activity (μg TPF g−1 soil d−1) was assessed using the method of73. The soil sample (~ 6 g) was saturated with 1.0 ml freshly prepared 3% triphenyltetrazolium chloride (TTC), and then incubated for 24 h under the dark. Later on, the methanol was added to stop the enzyme activity, and the absorbance of the filtered aliquot was read at 485 nm.The alkaline phosphatase activity (SAP)The APA activity was estimated in 1.0 g soil saturated with 4 ml of the modified universal buffer (MUB) along with 1 ml of p-nitrophenol phosphate followed by incubation at 37 °C for 1 h. After incubation, 1 ml of 0.5 M CaCl2 and 4 mL of NaOH were added and the contents filtered through Whatman No. 1 filter paper. The amount of p-nitrophenol in the sample was determined at 400 nm72 and the enzyme activity was expressed as µg p-NP g−1 soil h−1.The urease activityUrease activity was measured using 10 g soil suspended in 2.5 ml of urea solution (0.5%). After incubating for a day at 37 °C, 50 ml of 1 M KCl solution was added. This was kept on a shaker for 30 min and the aliquot was filtered through Whatman No. 1 filter paper. To the filtrate (10 ml), 5 ml of sodium salicylate and 2 ml of 0.1% sodium dichloro-isocyanide solution were added and the green color developed was measured at 690 nm74. These values are reported as µg NH4-N g−1 soil h−1.Water application and productivityIn experimental modules, water was given through the controlled border irrigation method. The current meter was fixed in the main lined rectangular channel, and the water velocity was measured. To get the flow discharge, then multiplied with area of cross section of the channel. The following formulae were used to calculate the applied irrigation water quantity and depth3:$${text{Irrigation water applied }}left( {text{L}} right) , = {text{ F }} times {text{ t (i)}}$$$${text{Depth }}left( {{text{mm}}} right) , = {text{ L}} div {text{A}}/{ 1}000$$where, F is flow rate (m3 s−1), t is time (s) taken in each irrigation in each module and A is area (m2).The effective precipitation (EP, difference between total rainfall and the actual evapotranspiration) was calculated, and then EP was added to the irrigation water applied to calculate the total water applied in each module. Across the maize and wheat modules (ICM1-8), irrigations were given at the critical growth stages, such as, knee high and silking / tasseling (maize) and crown root formation, maximum tillering, flowering, heading / milking (wheat) stages, and after long dry spell (≥ 10-days).On the basis of the soil water depletion pattern (at the depth of 0.60 m), in each season, 3–6 irrigations were given to maize, while wheat received 5–8 irrigations per season or crop including the pre-sowing irrigation. The rainfall data were obtained from the meteorological observatory located in the adjoining field. The water productivity (kg grains ha−1 mm−1 of water) was measured as per the equation given below:$${text{Water productivity }} = {text{ economic yield }}left( {{text{kg ha}}^{{ – {1}}} } right)/{text{ total water applied }}left( {{text{mm}}} right)$$Additionally, the systems water productivity (SWP) was also estimated by adding the water productivity (WP) of both maize and wheat crops grown under the MWR.Yield measurementsIn each season, the maize and wheat crops were harvested during the months of October and April, respectively, leaving 0.75 m border rows from all the corners of each module. The crops were harvested from the net sampling area (6 m × 3 m, 18 m2) located at the center of each plot. Maize crop was harvested manually and the wheat by using the plot combine harvester. All the harvested produce was sun dried before threshing and the grain and straw / stover yields were weighed separately. The stover/straw yields were measured by subtracting the grain weight from the total biomass. To compare the total (system) productivity of the different ICM modules, the system yield was computed, taking maize as the base crop, i.e., the maize equivalent yield (MGEY) using the equation20:$${text{M}}_{{{text{GEY}}}} left( {{text{Mg ha}}^{{ – {1}}} } right) , = {text{ Ym }} + , left{ {left( {{text{Yw }} times {text{ Pw}}} right) , div {text{ Pm}}} right}$$where, Ym = maize grain yield (Mg ha−1), Yw = wheat grain yield (Mg ha−1), Pm = price of maize grain (US$ Mg−1) and Pw = price of wheat grain (US$ Mg−1).Farm economicsUnder different ICM modules, the variable production costs and economic returns were worked out based on the prevailing market prices for the respective years. The production costs included the cost of various inputs, such as, rental value of land, seeds, pesticides, LBFs / consortia, AMF, labor, and machinery; tillage / sowing operations, irrigation, mineral fertilizers, plant protection, harvesting, and threshing etc. The costs for the crops’ residues were also considered. The system total returns were computed by adding the economic worth of the individual crop, however, the net returns were the differences between the total returns to the variable production costs of the respective module. The Govt. of India’s minimum support prices (MSP) were considered for the conversion of grain yield to the economic returns (profits) during the respective years. Further, the system net returns (SNR) were worked out by summing the net income from both maize and the wheat in Indian rupees (INR), and then converted to the US$, based on the exchange rates for different years.Sustainable yield index (SYI)77,78described the SYI as a quantitative measure of the sustainability of agricultural rotation/practice. The sustainability could be interpreted using the standard deviation (σ) values, where the lower values of the σ indicate the greater sustainability and vice-versa. Total crop productivity of maize and wheat under the different ICM modules was computed based on the five years’ mean yield data. SYI was calculated using equation78.$${text{S}}_{{{text{YI}}}} = , left( {{-}{overline{text{Y}}}_{{{text{a }}{-}}} sigma_{{text{n}}} {-}_{{1}} } right) , /{text{ Y}}^{{{-}{1}}}_{{text{m}}}$$where, –ȳa is the average yield of the crops across the years under the specific management practice, σn–1 is the standard deviation and Y–1 m is the maximum yield obtained under the set of an ICM module.Statistical analysisThe GLM procedure of the SAS 9.4 (SAS Institute, 2003, Cary, NC) was used for the statistical analysis of all the data obtained from different ICM modules to analyze the variance (ANOVA) under the randomized block design79. Tukey’s honest significant difference test was employed to compare the mean effect of the treatments at p = 0.05.Authors have confirmed that all the plant studies were carried out in accordance with relevant national, international or institutional guidelines. More

1.Andrews, K. L. Latin-American research on Spodoptera frugiperda (Lepidoptera, Noctuidae). Florida Entomol. 71, 630–653. https://doi.org/10.2307/3495022 (1988).Article 

Google Scholar 
2.Brevault, T. et al. First records of the fall armyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae), Senegal. Entomologia Generalis 37, 129–142. https://doi.org/10.1127/entomologia/2018/0553 (2018).Article 

Google Scholar 
3.Cock, M. J. W., Beseh, P. K., Buddie, A. G., Cafa, G. & Crozier, J. Molecular methods to detect Spodoptera frugiperda in Ghana, and implications for monitoring the spread of invasive species in developing countries. Sci. Rep. https://doi.org/10.1038/s41598-017-04238-y (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
4.Goergen, G., Kumar, P. L., Sankung, S. B., Togola, A. & Tamo, M. First report of outbreaks of the fall armyworm Spodoptera frugiperda (J E Smith) (Lepidoptera, Noctuidae), a new alien invasive pest in west and central Africa. PLoS ONE https://doi.org/10.1371/journal.pone.0165632 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
5.Jacobs, A., van Vuuren, A. & Rong, I. H. Characterisation of the fall armyworm (Spodoptera frugiperda JE Smith) (Lepidoptera: Noctuidae) from South Africa. Afr. Entomol. 26, 45–49. https://doi.org/10.4001/003.026.0045 (2018).Article 

Google Scholar 
6.Day, R. et al. Fall Armyworm: Impacts and Implications for Africa. Outlooks Pest Manag. 28, 196–201. https://doi.org/10.1564/v28_oct_02 (2017).Article 

Google Scholar 
7.Stokstad, E. New crop pest takes Africa at lightning speed. Science 356, 473–474. https://doi.org/10.1126/science.356.6337.473 (2017).ADS 
CAS 
Article 
PubMed 

Google Scholar 
8.Luginbill, P. The fall armyworm. US Dept. Agric. Tech. Bull. 34, 1–91 (1928).
Google Scholar 
9.Nagoshi, R. N., Meagher, R. L. & Hay-Roe, M. Inferring the annual migration patterns of fall armyworm (Lepidoptera: Noctuidae) in the United States from mitochondrial haplotypes. Ecol. Evol. 2, 1458–1467. https://doi.org/10.1002/ece3.268 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
10.Westbrook, J., Fleischer, S., Jairam, S., Meagher, R. & Nagoshi, R. Multigenerational migration of a pest insect. Ecosphere 10, e02919. https://doi.org/10.1002/ecs2.2919 (2019).Article 

Google Scholar 
11.Westbrook, J. K., Nagoshi, R. N., Meagher, R. L., Fleischer, S. J. & Jairam, S. Modeling seasonal migration of fall armyworm moths. Int. J. Biometeorol. 60, 255–267. https://doi.org/10.1007/s00484-015-1022-x (2016).ADS 
CAS 
Article 
PubMed 

Google Scholar 
12.Ge, S. S. et al. Laboratory-based flight performance of the fall armyworm, Spodoptera frugiperda. J. Integr. Agric. 20, 707–714. https://doi.org/10.1016/S2095-3119(20)63166-5 (2021).Article 

Google Scholar 
13.Nagoshi, R. N. et al. Southeastern Asia fall armyworms are closely related to populations in Africa and India, consistent with common origin and recent migration. Sci. Rep. 10, 1421. https://doi.org/10.1038/s41598-020-58249-3 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
14.Nagoshi, R. N. et al. Genetic characterization of fall armyworm infesting South Africa and India indicate recent introduction from a common source population. PLoS ONE https://doi.org/10.1371/journal.pone.0217755 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
15.Nagoshi, R. N., Goergen, G., Plessis, H. D., van den Berg, J. & Meagher, R. Jr. Genetic comparisons of fall armyworm populations from 11 countries spanning sub-Saharan Africa provide insights into strain composition and migratory behaviors. Sci. Rep. 9, 8311. https://doi.org/10.1038/s41598-019-44744-9 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
16.Nagoshi, R. N. et al. Analysis of strain distribution, migratory potential, and invasion history of fall armyworm populations in northern Sub-Saharan Africa. Sci. Rep. https://doi.org/10.1038/s41598-018-21954-1 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
17.Levy, H. C., Garcia-Maruniak, A. & Maruniak, J. E. Strain identification of Spodoptera frugiperda (Lepidoptera: Noctuidae) insects and cell line: PCR-RFLP of Cytochrome Oxidase Subunit I gene. Florida Entomol. 85, 186–190 (2002).CAS 
Article 

Google Scholar 
18.Nagoshi, R. N. The fall armyworm triose phosphate isomerase (Tpi) gene as a marker of strain identity and interstrain mating. Ann. Entomol. Soc. Am. 103, 283–292. https://doi.org/10.1603/An09046 (2010).CAS 
Article 

Google Scholar 
19.Prowell, D. P., McMichael, M. & Silvain, J. F. Multilocus genetic analysis of host use, introgression, and speciation in host strains of fall armyworm (Lepidoptera: Noctuidae). Ann. Entomol. Soc. Am. 97, 1034–1044 (2004).CAS 
Article 

Google Scholar 
20.Juárez, M. L. et al. Host association of Spodoptera frugiperda (Lepidoptera: Noctuidae) corn and rice strains in Argentina, Brazil, and Paraguay. J. Econ. Entomol. 105, 573–582. https://doi.org/10.1603/Ec11184 (2012).Article 
PubMed 

Google Scholar 
21.Murúa, M. G. et al. Demonstration using field collections that Argentina fall armyworm populations exhibit strain-specific host plant preferences. J. Econ. Entomol. 108, 2305–2315 (2015).Article 

Google Scholar 
22.Nagoshi, R. N. et al. Genetic characterization of fall armyworm (Lepidoptera: Noctuidae) host strains in Argentina. J. Econ. Entomol. 105, 418–428. https://doi.org/10.1603/Ec11332 (2012).CAS 
Article 
PubMed 

Google Scholar 
23.Nagoshi, R. N., Silvie, P., Meagher, R. L., Lopez, J. & Machados, V. Identification and comparison of fall armyworm (Lepidoptera: Noctuidae) host strains in Brazil, Texas, and Florida. Ann. Entomol. Soc. Am. 100, 394–402 (2007).CAS 
Article 

Google Scholar 
24.Nagoshi, R. N. Improvements in the identification of strains facilitate population studies of fall armyworm subgroups. Ann. Entomol. Soc. Am. 105, 351–358. https://doi.org/10.1603/AN11138 (2012).CAS 
Article 

Google Scholar 
25.Nagoshi, R. N. & Meagher, R. L. Using intron sequence comparisons in the triose-phosphate isomerase gene to study the divergence of the fall armyworm host strains. Insect Mol. Biol. 25, 324–337. https://doi.org/10.1111/imb.12223 (2016).CAS 
Article 
PubMed 

Google Scholar 
26.Nagoshi, R. N., Goergen, G., Du Plessis, H., van den Berg, J. & Meagher, R. Genetic comparisons of fall armyworm populations from 11 countries spanning sub-Saharan Africa provide insights into strain composition and migratory behaviors. Sci. Rep. https://doi.org/10.1038/s41598-019-44744-9 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
27.Nagoshi, R. N. et al. The fall armyworm strain associated with most rice, millet, and pasture infestations in the Western Hemisphere is rare or absent in Ghana and Togo. PLoS ONE 16, e0253528. https://doi.org/10.1371/journal.pone.0253528 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
28.Nagoshi, R. N. et al. Comparative molecular analyses of invasive fall armyworm in Togo reveal strong similarities to populations from the eastern United States and the Greater Antilles. PLoS ONE 12, e0181982. https://doi.org/10.1371/journal.pone.0181982 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
29.Koffi, D. et al. Trapping Spodoptera frugiperda (Lepidoptera: Noctuidae) moths in different crop habitats in Togo and Ghana. J. Econ. Entomol. 114, 1138–1144. https://doi.org/10.1093/jee/toab048 (2021).Article 
PubMed 

Google Scholar 
30.Thenkabail, P. S. et al. Assessing future risks to agricultural productivity, water Resources and food security: How can remote sensing help?. Photogramm. Eng. Remote. Sens. 78, 773–782 (2012).
Google Scholar 
31.Teluguntla, P. et al. (eds.). Global Cropland Area Database (GCAD) derived from remote sensing in support of food security in the twenty-first century: Current achievements and future possibilities. Chapter 7 Vol. II. Land Resources: Monitoring, Modelling, and Mapping, Remote Sensing Handbook edited by Prasad S. Thenkabail.32.Nagoshi, R. N. et al. Fall armyworm migration across the Lesser Antilles and the potential for genetic exchanges between North and South American populations. PLoS ONE 12, e0171743. https://doi.org/10.1371/journal.pone.0171743 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
33.Nagoshi, R. N., Fleischer, S. J. & Meagher, R. L. Texas is the overwintering source of fall armyworm in central Pennsylvania: Implications for migration into the northeastern United States. Environ. Entomol. 38, 1546–1554. https://doi.org/10.1603/022.038.0605 (2009).Article 
PubMed 

Google Scholar 
34.Nagoshi, R. N. et al. Haplotype profile comparisons between Spodoptera frugiperda (Lepidoptera: Noctuidae) populations from Mexico with those from Puerto Rico, South America, and the United States and their implications to migratory behavior. J. Econ. Entomol. 108, 135–144 (2015).CAS 
Article 

Google Scholar 
35.Assefa, Y., Mitchell, A. & Conlong, D. E. Phylogeography of Eldana saccharine Walker (Lepidoptera : Pyralidae). Annales de la Société Entomologique de France 42, 331–337. https://doi.org/10.1080/00379271.2006.10697465 (2006).Article 

Google Scholar 
36.Sezonlin, M. et al. Phylogeographic pattern and regional evolutionary history of the maize stalk borer Busseola fusca (Fuller) (Lepidoptera : Noctuidae) in sub-Saharan Africa. Annales de la Société Entomologique de France 42, 339–351. https://doi.org/10.1080/00379271.2006.10697466 (2006).Article 

Google Scholar 
37.Sezonlin, M. et al. Phylogeography and population genetics of the maize stalk borer Busseola fusca (Lepidoptera, Noctuidae) in sub-Saharan Africa. Mol. Ecol. 15, 407–420. https://doi.org/10.1111/j.1365-294X.2005.02761.x (2006).CAS 
Article 
PubMed 

Google Scholar 
38.Pashley, D. P. Host-associated genetic differentiation in fall armyworm (Lepidoptera, Noctuidae)—A sibling species complex. Ann. Entomol. Soc. Am. 79, 898–904 (1986).Article 

Google Scholar 
39.Nagoshi, R. N. & Meagher, R. Fall armyworm FR sequences map to sex chromosomes and their distribution in the wild indicate limitations in interstrain mating. Insect Mol. Biol. 12, 453–458 (2003).CAS 
Article 

Google Scholar 
40.Nagoshi, R. N. & Meagher, R. L. Seasonal distribution of fall armyworm (Lepidoptera: Noctuidae) host strains in agricultural and turf grass habitats. Environ. Entomol. 33, 881–889 (2004).Article 

Google Scholar 
41.Juárez, M. L. et al. Population structure of Spodoptera frugiperda maize and rice host forms in South America: Are they host strains?. Entomol. Exp. Appl. 152, 182–199. https://doi.org/10.1111/eea.12215 (2014).CAS 
Article 

Google Scholar 
42.Meagher, R. L. & Nagoshi, R. N. Differential feeding of fall armyworm (Lepidoptera: Noctuidae) host strains on meridic and natural diets. Ann. Entomol. Soc. Am. 105, 462–470. https://doi.org/10.1603/An11158 (2012).Article 

Google Scholar 
43.Pashley, D. P., Hardy, T. N. & Hammond, A. M. Host effects on developmental and reproductive traits in fall armyworm strains (Lepidoptera: Noctuidae). Ann. Entomol. Soc. Am. 88, 748–755 (1995).Article 

Google Scholar 
44.Groot, A. T., Marr, M., Heckel, D. G. & Schofl, G. The roles and interactions of reproductive isolation mechanisms in fall armyworm (Lepidoptera: Noctuidae) host strains. Ecol. Entomol. 35, 105–118. https://doi.org/10.1111/J.1365-2311.2009.01138.X (2010).Article 

Google Scholar 
45.Kost, S., Heckel, D. G., Yoshido, A., Marec, F. & Groot, A. T. A Z-linked sterility locus causes sexual abstinence in hybrid females and facilitates speciation in Spodoptera frugiperda. Evolution 70, 1418–1427. https://doi.org/10.1111/evo.12940 (2016).CAS 
Article 
PubMed 

Google Scholar 
46.Pashley, D. P., Hammond, A. M. & Hardy, T. N. Reproductive isolating mechanisms in fall armyworm host strains (Lepidoptera, Noctuidae). Ann. Entomol. Soc. Am. 85, 400–405 (1992).Article 

Google Scholar 
47.Nagoshi, R. N., Fleischer, S. & Meagher, R. L. Demonstration and quantification of restricted mating between fall armyworm host strains in field collections by SNP comparisons. J. Econ. Entomol. 110, 2568–2575. https://doi.org/10.1093/jee/tox229 (2017).CAS 
Article 
PubMed 

Google Scholar 
48.Gouin, A. et al. Two genomes of highly polyphagous lepidopteran pests (Spodoptera frugiperda, Noctuidae) with different host-plant ranges. Sci. Rep. 7, 11816. https://doi.org/10.1038/s41598-017-10461-4 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
49.Schlum, K. A. et al. Whole genome comparisons reveal panmixia among fall armyworm (Spodoptera frugiperda) from diverse locations. BMC Genom. 22, 179. https://doi.org/10.1186/s12864-021-07492-7 (2021).CAS 
Article 

Google Scholar 
50.Sperling, F. A. H. Sex-linked genes and species-differences in lepidoptera. Can. Entomol. 126, 807–818 (1994).Article 

Google Scholar 
51.Storer, N. P. et al. Discovery and characterization of field resistance to Bt maize: Spodoptera frugiperda (Lepidoptera:Noctuidae) in Puerto Rico. J. Econ. Entomol. 103, 1031–1038. https://doi.org/10.1603/Ec10040 (2010).Article 
PubMed 

Google Scholar 
52.Jeger, M. et al. Pest risk assessment of Spodoptera frugiperda for the European Union. Efsa J. https://doi.org/10.2903/j.efsa.2018.5351 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
53.Rwomushana, I. et al. Fall armyworm: Impacts and implications for Africa. In CABI Evidnece Notes (CABI, Oxfordshire, 2018) http://www.invasive-species.org/wp-content/uploads/sites/2/2019/02/FAW-Evidence-Note-October-2018.pdf54.Stanaway, M. A., Zalucki, M. P., Gillespie, P. S., Rodriguez, C. M. & Maynard, G. V. Pest risk assessment of insects in sea cargo containers. Aust. J. Entomol. 40, 180–192. https://doi.org/10.1046/j.1440-6055.2001.00215.x (2001).Article 

Google Scholar  More

1.Gaston, K. J. Global patterns of biodiversity. Nature 405, 220–227 (2000).CAS 
Article 

Google Scholar 
2.Friis, E. M., Crane, P. R. & Pedersen, K. R. Early Flowers and Angiosperm Evolution (Cambridge Univ. Press, 2011).3.Blomenkemper, P. et al. A hidden cradle of plant evolution in Permian tropical lowlands. Science 362, 1414–1416 (2018).CAS 
Article 

Google Scholar 
4.Kenrick, P. & Crane, P. R. The Origin and Early Diversification of Land Plants: A Cladistic Study (Smithsonian Institution Scholarly Press, 1997).5.Puttick, M. N. et al. The interrelationships of land plants and the nature of the nature of the ancestral embryophyte. Curr. Biol. 28, 733–745 (2018).CAS 
Article 

Google Scholar 
6.Morris, J. L. et al. The timescale of early land plant evolution. Proc. Natl Acad. Sci. USA 115, 2274–2283 (2018).Article 

Google Scholar 
7.Wellman, C. H., Steemans, P. & Vecoli, M. in Early Palaeozoic Biogeography and Palaeogeography (eds Harper, D. & Servais, T.) Ch. 29 (Geological Society of London, 2014).8.Edwards, D. et al. Piecing together the eophytes—a new group of ancient plants containing cryptospores. New Phytol. 233, 1440–1455 (2021).Article 

Google Scholar 
9.Gray, J. The microfossil record of early land plants; advances in understanding of early terrestrialization, 1970–1984. Philos. Trans. R. Soc. Lond. B 309, 167–195 (1985).Article 

Google Scholar 
10.Wellman, C. H. Cryptospores from the type area for the Caradoc Series (Ordovician) in southern Britain. Palaeontology 55, 103–136 (1996).
Google Scholar 
11.Torsvik, T. H. & Cocks, L. R. M. Earth History and Palaeogeography (Cambridge Univ. Press, 2017).12.Harland, W. B. The Geology of Svalbard (Geological Society of London, 1997).13.Davies, N. S., Berry, C. M., Marshall, J. E. A., Wellman, C. H. & Lindemann, F.-J. The Devonian landscape factory: plant–sediment interactions in the Old Red Sandstone of Svalbard and the rise of vegetation as a biogeomorphic agent. J. Geol. Soc. Lond. https://doi.org/10.1144/jgs2020-225 (2021).14.Blieck, A., Goujet, D. & Janvier, P. The vertebrate stratigraphy of the Lower Devonian (Red Bay Group and Wood Bay Formation) of Spitsbergen. Mod. Geol. 11, 197–217 (1987).
Google Scholar 
15.Blom, H. & Goujet, D. Thelodont scales from the Lower Devonian Red Bay Group, Spitsbergen. Palaeontology 45, 795–820 (2002).Article 

Google Scholar 
16.Pernègre, V. N. & Blieck, A. A revised heterostrachan-cased ichthyostratigraphy of the Wood Bay Formation (Lower Devonian, Spitsbergen), and correlation with Russian Arctic archipelagos. Geodiversitas 38, 5–20 (2016).Article 

Google Scholar 
17.Wellman, C. H. & Richardson, J. B. Sporomorph assemblages from the ‘Lower Old Red Sandstone’ of Lorne Scotland. Spec. Pap. Palaeontol. 55, 41–101.18.Richardson J. B. Taxonomy and classification of some new Early Devonian cryptospores from England. Spec. Pap. Palaeontol. 55, 7–40 (1996).19.Steemans, P. Etude palynostratgraphique du Devonian Inferieur dans l’Ouest de l’Europe. Mém. Soc. Géol. Minér. Bretagne 27, 1–453 (1989).
Google Scholar 
20.Rodriguez, R. M. Palinologia de las Formaciones del Silurico Superior-Devonico Inferior de la Cordillera Cantabrica, Noroeste de España (Institución Fray Bernardino de Sahagún, de la Excelentísima Diputación provincial de León y del Servicio de Publicaciones de la Universidad de León, 1983).21.Richardson, J. B., Rodriguez, R. M. & Sutherland, S. J. E. Palynological zonation of Mid-Palaeozoic sequences from the Cantabrian Mountains, NW Spain: implications for inter-regional and interfacies correlation of the Ludfor/Pridoli and Silurian/Devonian boundaries, and plant dispersal patterns. Bull. Nat. Hist. Mus. Lond. 57, 115–162 (2001).
Google Scholar 
22.Rubinstein, C. & Steemans, P. Miospore assemblages from the Silurian–Devonian boundary, in borehole A1–61, Ghadames Basin, Libya. Rev. Palaeobot. Palynol. 118, 397–412 (2002).Article 

Google Scholar 
23.Spina, A. & Vecoli, M. Palynostratigraphy and vegetational change in the Siluro-Devonian of the Ghadamis basin, North Africa. Palaeogeog. Palaeoclimatol. Palaeoecol. 282, 1–18 (2009).Article 

Google Scholar 
24.Hao, S. G. & Gensel, P. G. in Plants Invade the Land (eds Gensel, P. G. & Edwards, D.) 103–119 (Columbia Univ. Press, 2001).25.Wellman, C. H. et al. Spore assemblages from the Lower Devonian Xujiachong Formation from Qujing, Yunnan, China. Palaeontology 55, 583–611 (2012).Article 

Google Scholar 
26.Hao, S. & Xue, J. The Early Devonian Posongchong Flora of Yunnan (Science Press, 2013).27.Edwards, D., & Li, C.-S. Further insights into the Lower Devonian terrestrial vegetation of Sichuan Province, China. Rev. Palaeobot. Palynol. 253, 37–48 (2018).Article 

Google Scholar 
28.Gao, L. Early Devonian spore and acritarchs from the Guijiatum Formation of Qujing, China. Bull. Inst. Geol. Chin. Acad. Sci. 9, 125–136 (1984).
Google Scholar 
29.Tian, J. et al. Late Silurian to early Devonian palynomorphs from Qujing, Yunnan, southwest China. Acta Geol. Sin. 85, 559–568 (2011).Article 

Google Scholar 
30.Høeg, O. A. The Downtonian and Dittonian flora of Spitsbergen. Skr. Svalbard Ishavet 83, 1–229 (1942).
Google Scholar 
31.Morris, J. L., Edwards, D. & Richardson, J. B. in Transformative Paleobotany (eds Krings, M. et al.) 49–67 (Academic Press, 2018).32.McSweeney, F. R., Shimeta, J. & Buckeridge, J. St. J. S. Two new genera of early Tracheophyta (Zosterophyllaceae) from the upper Silurian–Lower Devonian of Victoria, Australia. Alcheringa https://doi.org/10.1080/03115518.2020.1744725 (2020).33.Xue, J. H. et al. Silurian–Devonian terrestrial revolution in South China: taxonomy, diversity, and character evolution of vascular plants in a paleogeographically isolated low-latitude region. Earth Sci. Rev. 180, 92–125 (2018).Article 

Google Scholar 
34.Hao, S. G. et al. Zosterophyllum Penhallow around the Silurian–Devonian boundary of northeastern Yunnan, China. Int. J. Plant Sci. 168, 477–489 (2007).Article 

Google Scholar 
35.Hao, S. G. et al. Earliest rooting system and root: shoot ratio from a new Zosterophyllum plant. New Phytol. 185, 217–225 (2009).Article 

Google Scholar 
36.Xue, J.-Z. Two zosterophyll plants from the Lower Devonian (Lochkovian) Xitun Formation of northeastern Yunnan, China. Acta Geol. Sin. 83, 504–512 (2009).Article 

Google Scholar 
37.Xue, J.-Z. Lochkovian plants from the Xitun Formation of Yunnan, China and their palaeophytogeographical significance. Geol. Mag. 149, 333–344 (2012).Article 

Google Scholar 
38.Sun, Y. et al. Lethally high temperatures during the early Triassic greenhouse. Science 6105, 366–370 (2012).Article 

Google Scholar 
39.Meng, X. Y. & Gai, Z. K. Falxcornus, a new genus of Tridensaspidae (Galeaspida, stem-Gnathostomata) from the Lower Devonian in Qujing, Yunnan, China. Hist. Biol. https://doi.org/10.1080/08912963.2021.1952198 (2021).40.Traverse, A. Paleopalynology 2nd edn (Springer, 2007). More

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

Google Scholar 
2.Caswell H, Cohen JE. Disturbance, interspecific interaction and diversity in metapopulations. Biol J Linn Soc. 1991;42:193–218.
Google Scholar 
3.Tolker-Nielsen T, Molin S. Spatial organization of microbial biofilm communities. Microb Ecol. 2000;40:75–84.CAS 
PubMed 

Google Scholar 
4.Yanni D, Márquez-Zacarías P, Yunker PJ, Ratcliff WC. Drivers of spatial structure in social microbial communities. Curr Biol. 2019;29:R545–50.CAS 
PubMed 

Google Scholar 
5.Ho A, Angel R, Veraart AJ, Daebeler A, Jia Z, Kim SY, et al. Biotic interactions in microbial communities as modulators of biogeochemical processes: methanotrophy as a model system. Front Microbiol. 2016;7:1–11.
Google Scholar 
6.Falkowski PG, Fenchel T, Delong EF. The microbial engines that drive earth’s biogeochemical cycles. Science. 2008;320:1034–9.CAS 
PubMed 

Google Scholar 
7.Overmann J, van Gemerden H. Microbial interactions involving sulfur bacteria: Implications for the ecology and evolution of bacterial communities. FEMS Microbiol Rev. 2000;24:591–9.CAS 
PubMed 

Google Scholar 
8.García-Bayona L, Comstock LE. Bacterial antagonism in host-associated microbial communities. Science. 2018;361:1–11.
Google Scholar 
9.Coyte KZ, Schluter J, Foster KR. The ecology of the microbiome: Networks, competition, and stability. Science. 2015;350:663–6.CAS 
PubMed 

Google Scholar 
10.Wang X, Li X, Ling J. Streptococcus gordonii LuxS/autoinducer-2 quorum-sensing system modulates the dual-species biofilm formation with Streptococcus mutans. J Basic Microbiol. 2017;57:605–16.CAS 
PubMed 

Google Scholar 
11.Hotterbeekx A, Kumar-Singh S, Goossens H, Malhotra-Kumar S. In vivo and In vitro interactions between Pseudomonas aeruginosa and Staphylococcus spp. Front Cell Infect Microbiol. 2017;7:1–13.
Google Scholar 
12.Dal Co A, van Vliet S, Kiviet DJ, Schlegel S, Ackermann M. Short-range interactions govern the dynamics and functions of microbial communities. Nat Ecol Evol. 2020;4:366–75. https://doi.org/10.1038/s41559-019-1080-2.Article 
PubMed 

Google Scholar 
13.Justice NB, Sczesnak A, Hazen TC, Arkin AP. Environmental selection, dispersal, and organism interactions shape community assembly in high-throughput enrichment culturing. Appl Environ Microbiol. 2017;83:1–16.
Google Scholar 
14.Hilker M. New synthesis: parallels between biodiversity and chemodiversity. J Chem Ecol. 2014;40:225–6.CAS 
PubMed 

Google Scholar 
15.Raguso R, Agrawal A, Douglas A, Jander G, Kessler A, Poveda K, et al. The raison d’être of chemical ecology. Ecology. 2015;96:617–30.PubMed 

Google Scholar 
16.Tilman D. Competition and biodiversity in spatially structured habitats. Ecology. 1994;75:2–16.
Google Scholar 
17.Geyrhofer L, Brenner N. Coexistence and cooperation in structured habitats. BMC Ecol. 2020;20:1–15. https://doi.org/10.1186/s12898-020-00281-y.Article 

Google Scholar 
18.Wakano JY, Nowak MA, Hauert C. Spatial dynamics of ecological public goods. Proc Natl Acad Sci USA. 2009;106:7910–4.CAS 
PubMed 
PubMed Central 

Google Scholar 
19.Nemergut DR, Schmidt SK, Fukami T, O’Neill SP, Bilinski TM, Stanish LF, et al. Patterns and processes of microbial community assembly. Microbiol Mol Biol Rev. 2013;77:342–56.PubMed 
PubMed Central 

Google Scholar 
20.Lowery NV, Ursell T. Structured environments fundamentally alter dynamics and stability of ecological communities. Proc Natl Acad Sci USA. 2019;116:379–88.CAS 

Google Scholar 
21.Lee JZ, Craig Everroad R, Karaoz U, Detweiler AM, Pett-Ridge J, Weber PK, et al. Metagenomics reveals niche partitioning within the phototrophic zone of a microbial mat. PLoS ONE. 2018;13:1–19.
Google Scholar 
22.Quinn RA, Comstock W, Zhang T, Morton JT, da Silva R, Tran A, et al. Niche partitioning of a pathogenic microbiome driven by chemical gradients. Sci Adv. 2018;4:1–12.
Google Scholar 
23.Fenchel T, Finlay B. Oxygen and the spatial structure of microbial communities. Biol Rev. 2008;83:553–69.PubMed 

Google Scholar 
24.Esteban DJ, Hysa B, Bartow-McKenney C. Temporal and spatial distribution of the microbial community of winogradsky columns. PLoS ONE. 2015;10:1–21.
Google Scholar 
25.Azam F. Microbial control of oceanic carbon flux: The plot thickens. Science. 1998;280:694–6.CAS 

Google Scholar 
26.McNally L, Brown SP. Building the microbiome in health and disease: niche construction and social conflict in bacteria. Philos Trans R Soc B Biol Sci. 2015;370:1–8.
Google Scholar 
27.Schreiber F, Ackermann M. Environmental drivers of metabolic heterogeneity in clonal microbial populations. Curr Opin Biotechnol. 2020;62:202–11. https://doi.org/10.1016/j.copbio.2019.11.018.CAS 
Article 
PubMed 

Google Scholar 
28.Lopez D, Vlamakis H, Kolter R. Biofilms. Cold Spring Harbor Perspectives in Biology. 2010;2:1–11.
Google Scholar 
29.Picketts STA, Cadenasso ML. Landscape ecology: spatial heterogeneity in ecological systems. NCASI Techn Bull. 1999;2:420.
Google Scholar 
30.Chao L, Levin BR. Structured habitats and the evolution of anticompetitor toxins in bacteria. Proc Natl Acad Sci USA. 1981;78:6324–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Rainey PB, Travisano M. Adaptive radiation in a heterogeneous environment. Nature. 1998;394:69–72.CAS 
PubMed 

Google Scholar 
32.Cardinale BJ. Biodiversity improves water quality through niche partitioning. Nature. 2011;472:86–91.CAS 
PubMed 

Google Scholar 
33.Loreau M, Naeem S, Inchausti P, Bengtsson J, Grime JP, Hector A, et al. Ecology: biodiversity and ecosystem functioning: current knowledge and future challenges. Science. 2001;294:804–8.CAS 
PubMed 

Google Scholar 
34.Wellborn GA, Langerhans RB. Ecological opportunity and the adaptive diversification of lineages. Ecol Evol. 2015;5:176–95.PubMed 

Google Scholar 
35.Czárán TL, Hoekstra RF. Killer-sensitive coexistence in metapopulations of micro-organisms. Proc R Soc B Biol Sci. 2003;270:1373–8.
Google Scholar 
36.West SA, Griffin AS, Gardner A, Diggle SP. Social evolution theory for microorganisms. Nat Rev Microbiol. 2006;4:597–607.CAS 
PubMed 

Google Scholar 
37.Wagner M, Loy A, Nogueira R, Purkhold U, Lee N, Daims H. Microbial community composition and function in wastewater treatment plants. Antonie Van Leeuwenhoek. 2002;81:665–80.CAS 
PubMed 

Google Scholar 
38.Johnson DR, Lee TK, Park J, Fenner K, Helbling DE. The functional and taxonomic richness of wastewater treatment plant microbial communities are associated with each other and with ambient nitrogen and carbon availability. Environ Microbiol. 2015;17:4851–60.CAS 
PubMed 

Google Scholar 
39.Liébana R, Arregui L, Santos A, Murciano A, Marquina D, Serrano S. Unravelling the interactions among microbial populations found in activated sludge during biofilm formation. FEMS Microbiol Ecol. 2016;92:1–13.
Google Scholar 
40.Reasoner DJ, Geldreich EE. A new medium for the enumeration and subculture of bacteria from potable water. Appl Environ Microbiol. 1985;49:1–7.CAS 
PubMed 
PubMed Central 

Google Scholar 
41.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. 2015;18:1403–14.PubMed 

Google Scholar 
42.Junkins EN, Stevenson BS. Using plate-wash PCR and high-throughput sequencing to measure cultivated diversity for natural product discovery efforts. Front Microbiol. 2021;12:1–14.
Google Scholar 
43.Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17:10–12.
Google Scholar 
44.Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
PubMed 
PubMed Central 

Google Scholar 
45.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:590–6.
Google Scholar 
46.Yilmaz P, Parfrey LW, Yarza P, Gerken J, Pruesse E, Quast C, et al. The SILVA and “all-species Living Tree Project (LTP)” taxonomic frameworks. Nucleic Acids Res. 2014;42:643–8.
Google Scholar 
47.Davis NM, Proctor DM, Holmes SP, Relman DA, Callahan BJ. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome. 2018;6:1–14.
Google Scholar 
48.Wright ES. DECIPHER: Harnessing local sequence context to improve protein multiple sequence alignment. BMC Bioinformatics. 2015;16:1–14. https://doi.org/10.1186/s12859-015-0749-z.CAS 
Article 

Google Scholar 
49.Wright ES. Using DECIPHER v2.0 to analyze big biological sequence data in R. R J. 2016;8:352–9.
Google Scholar 
50.Schliep KP. phangorn: phylogenetic analysis in R. Bioinformatics. 2011;27:592–3.CAS 
PubMed 

Google Scholar 
51.McMurdie PJ, Holmes S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:1–11.
Google Scholar 
52.Willis A, Bunge J. Estimating diversity via frequency ratios. Biometrics. 2015;71:1042–9.PubMed 

Google Scholar 
53.Pielou EC. The measurement of diversity in different types of biological collections. J Theor Biol. 1966;13:131–44.
Google Scholar 
54.Levene H. Robust tests for equality of variances. In: Olkin I, editor. Contributions to probability and statistics: essays in honor of Harold Hotelling. Stanford University Press, Palo Alto, California, USA; 1960. p. 278–92.55.Fox J, Weisberg S. An R companion to applied regression. 3rd ed. Thousand Oaks, CA: Sage; 2019.56.Lozupone C, Knight R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol. 2005;71:8228–35.CAS 
PubMed 
PubMed Central 

Google Scholar 
57.Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: community ecology package. R Package; 2019.58.Martin BD, Witten D, Willis AD. Modeling microbial abundances and dysbiosis with beta-binomial regression. Ann Appl Stat. 2020;14:94–115.PubMed 
PubMed Central 

Google Scholar 
59.Chambers MC, MacLean B, Burke R, Amodei D, Ruderman DL, Neumann S, et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat Biotechnol. 2012;30:918–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
60.Pluskal T, Castillo S, Villar-Briones A, Orešič M. MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics. 2010;11:1–11.61.Myers OD, Sumner SJ, Li S, Barne S, Du X. One step forward for reducing false positive and false negative compound identifications from mass spectrometry metabolomics data: new algorithms for constructing extracted ion chromatograms and detecting chromatographic peaks. Anal Chem. 2017;89:8696–703.CAS 
PubMed 

Google Scholar 
62.Wang M, Carver JJ, Phelan VV, Sanchez LM, Garg N;, Peng Y, et al. Sharing and community curation of mass spectrometry data with GNPS. Nat Biotechnol. 2017;34:828–37.
Google Scholar 
63.Nothias LF, Petras D, Schmid R, Dührkop K, Rainer J, Sarvepalli A, et al. Feature-based molecular networking in the GNPS analysis environment. Nat Methods. 2020;17:905–8. https://doi.org/10.1038/s41592-020-0933-6.CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
64.Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models. Genome Res. 2003;13:2498–504. http://ci.nii.ac.jp/naid/110001910481/.CAS 
PubMed 
PubMed Central 

Google Scholar 
65.R Core Team. R: a language and environment for R Foundation for Statistical Computing. 2018. https://www.r-project.org/.66.Bates D, Mächler M, Bolker BM, Walker SC. Fitting linear mixed-effects models using lme4. J Stat Softw. 2015;67:1–48.
Google Scholar 
67.Djoumbou Feunang Y, Eisner R, Knox C, Chepelev L, Hastings J, Owen G, et al. ClassyFire: automated chemical classification with a comprehensive, computable taxonomy. J Cheminform. 2016;8:1–20.
Google Scholar 
68.O’Brien J, Wright GD. An ecological perspective of microbial secondary metabolism. Curr Opin Biotechnol. 2011;22:552–8. https://doi.org/10.1016/j.copbio.2011.03.010.CAS 
Article 
PubMed 

Google Scholar 
69.Thierbach S, Wienhold M, Fetzner S, Hennecke U. Synthesis and biological activity of methylated derivatives of the Pseudomonas metabolites HHQ, HQNO and PQS. Beilstein J Org Chem. 2019;15:187–93.CAS 
PubMed 
PubMed Central 

Google Scholar 
70.Morales-Soto N, Dunham SJB, Baig NF, Ellis JF, Madukoma CS, Bohn PW, et al. Spatially dependent alkyl quinolone signaling responses to antibiotics in Pseudomonas aeruginosa swarms. J Biol Chem. 2018;293:9544–52.CAS 
PubMed 
PubMed Central 

Google Scholar 
71.Heeb S, Fletcher MP, Chhabra SR, Diggle SP, Williams P, Cámara M. Quinolones: from antibiotics to autoinducers. FEMS Microbiol Rev. 2011;35:247–74.CAS 
PubMed 

Google Scholar 
72.Grollman AP. Inhibitors of protein biosynthesis. II. Mode of action of anisomycin. J Biolog Chem. 1967;242:3226–33. https://doi.org/10.1016/S0021-9258(18)95953-3.CAS 
Article 

Google Scholar 
73.Sobin BA, Tanner FW Jr. Anisomycin, a new anti-protozoan antibiotic. J Am Chem Soc. 1954;76:4053–4053.CAS 

Google Scholar 
74.Gross H, Stockwell VO, Henkels MD, Nowak-Thompson B, Loper JE, Gerwick WH. The genomisotopic approach: a systematic method to isolate products of orphan biosynthetic gene clusters. Chem Biol. 2007;14:53–63.CAS 
PubMed 

Google Scholar 
75.Jang JY, Yang SY, Kim YC, Lee CW, Park MS, Kim JC, et al. Identification of orfamide A as an insecticidal metabolite produced by Pseudomonas protegens F6. J Agric Food Chem. 2013;61:6786–91.CAS 
PubMed 

Google Scholar 
76.Ma Z, Geudens N, Kieu NP, Sinnaeve D, Ongena M, Martins JC, et al. Biosynthesis, chemical structure, and structure-activity relationship of orfamide lipopeptides produced by Pseudomonas protegens and related species. Front Microbiol. 2016;7:1–16.
Google Scholar 
77.Figueira V, Vaz-Moreira I, Silva M, Manaia CM. Diversity and antibiotic resistance of Aeromonas spp. in drinking and waste water treatment plants. Water Res. 2011;45:5599–611.CAS 
PubMed 

Google Scholar 
78.Skwor T, Stringer S, Haggerty J, Johnson J, Duhr S, Johnson M, et al. Prevalence of potentially pathogenic antibiotic-resistant Aeromonas spp. in treated urban wastewater effluents versus recipient riverine populations: a 3-year comparative study. Appl Environ Microbiol. 2020;86:1–16.
Google Scholar 
79.Janda JM, Abbott SL. The genus Aeromonas: taxonomy, pathogenicity, and infection. Clin Microbiol Rev. 2010;23:35–73.CAS 
PubMed 
PubMed Central 

Google Scholar 
80.Rema T, Lawrence JR, Dynes JJ, Hitchcock AP, Korber DR. Microscopic and spectroscopic analyses of chlorhexidine tolerance in Delftia acidovorans biofilms. Antimicrob Agents Chemother. 2014;58:5673–86.PubMed 
PubMed Central 

Google Scholar 
81.Assanta MA, Roy D, Lemay MJ, Montpetit D. Attachment of Arcobacter butzleri, a new waterborne pathogen, to water distribution pipe surfaces. J Food Protect. 2002;65:1240–7.
Google Scholar 
82.Costerton JW, Stewart PS, Greenberg EP. Bacterial biofilms: a common cause of persistent infections. Science. 1999;284:1318–22.CAS 
PubMed 

Google Scholar 
83.Harrison F, Paul J, Massey RC, Buckling A. Interspecific competition and siderophore-mediated cooperation in Pseudomonas aeruginosa. ISME J. 2008;2:49–55.PubMed 

Google Scholar 
84.Inglis RF, Roberts PG, Gardner A, Buckling A. Spite and the scale of competition in Pseudomonas aeruginosa. Am Nat. 2011;178:276–85.PubMed 

Google Scholar 
85.van der Meij A, Worsley SF, Hutchings MI, van Wezel GP. Chemical ecology of antibiotic production by Actinomycetes. FEMS Microbiol Rev. 2017;41:392–416.PubMed 

Google Scholar 
86.Traxler MF, Kolter R. Natural products in soil microbe interactions and evolution. Nat Prod Rep. 2015;32:956–70.CAS 
PubMed 

Google Scholar 
87.Kinkel LL, Schlatter DC, Xiao K, Baines AD. Sympatric inhibition and niche differentiation suggest alternative coevolutionary trajectories among Streptomycetes. ISME J. 2014;8:249–56. https://doi.org/10.1038/ismej.2013.175. [Internet]Available fromCAS 
Article 
PubMed 

Google Scholar 
88.Pacala SW, Levin SA. Biologically generated spatial pattern and the coexistence of competing species. In: Tilman D, Kareiva P, editors. Spatial ecology: the role of space in population dynamics and interspecific interactions; Princeton University Press, Princeton, New Jersey, USA; 1997.89.Zhou J, Ning D. Stochastic community assembly: does it matter in microbial ecology? Microbiol Mol Biol Rev. 2017;81:1–32.
Google Scholar 
90.Haig SJ, Quince C, Davies RL, Dorea CC, Collinsa G. The relationship between microbial community evenness and function in slow sand filters. mBio. 2015;6:1–12.
Google Scholar 
91.Wittebolle L, Marzorati M, Clement L, Balloi A, Daffonchio D, Heylen K, et al. Initial community evenness favours functionality under selective stress. Nature. 2009;458:623–6.CAS 
PubMed 

Google Scholar 
92.Davies J, Ryan KS. Introducing the parvome: bioactive compounds in the microbial world. ACS Chem Biol. 2012;7:252–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
93.Bassler BL, Losick R. Bacterially speaking. Cell. 2006;125:237–46.CAS 
PubMed 

Google Scholar 
94.Venturi V. Regulation of quorum sensing in Pseudomonas. FEMS Microbiol Rev. 2006;30:274–91.CAS 
PubMed 

Google Scholar 
95.Granato ET, Meiller-Legrand TA, Foster KR. The evolution and ecology of bacterial warfare. Curr Biol. 2019;29:R521–37. https://doi.org/10.1016/j.cub.2019.04.024.CAS 
Article 
PubMed 

Google Scholar 
96.Estrela S, Brown SP. Community interactions and spatial structure shape selection on antibiotic resistant lineages. PLoS Comput Biol. 2018;14:1–21.CAS 

Google Scholar 
97.Hibbing ME, Fuqua C, Parsek MR, Peterson SB. Bacterial competition: surviving and thriving in the microbial jungle. Nat Rev Microbiol. 2010;8:15–25.CAS 
PubMed 
PubMed Central 

Google Scholar 
98.Garcia-Garcera M, Rocha EPC. Community diversity and habitat structure shape the repertoire of extracellular proteins in bacteria. Nat Commun. 2020;11:1–11. https://doi.org/10.1038/s41467-020-14572-x.CAS 
Article 

Google Scholar  More

1.Butchart, S. H. M. et al. Global biodiversity: indicators of recent declines. Science 328, 1164–1168 (2010).CAS 
Article 

Google Scholar 
2.Buchanan, G. M., Butchart, S. H. M., Chandler, G. & Gregory, R. D. Assessment of national-level progress towards elements of the Aichi Biodiversity Targets. Ecol. Indic. 116, 106497 (2020).Article 

Google Scholar 
3.Butchart, S. H. M. et al. in Global Assessment Report of the Intergovernmental Science–Policy Platform on Biodiversity and Ecosystem Services (eds Berkes, F. & Brooks, T. M.) Ch. 3 (IPBES Secretariat, 2019); https://doi.org/10.5281/zenodo.38320534.Maxwell, S. L. et al. Area-based conservation in the twenty-first century. Nature 586, 217–227 (2020).CAS 
Article 

Google Scholar 
5.Locke, H. Nature needs half: a necessary and hopeful new agenda for protected areas. Nat. N. S. W. 58, 7–17 (2014).
Google Scholar 
6.Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67, 534–545 (2017).Article 

Google Scholar 
7.Dinerstein, E. et al. A global deal for nature: guiding principles, milestones, and targets. Sci. Adv. 5, eaaw2869 (2019).CAS 
Article 

Google Scholar 
8.Mehrabi, Z., Ellis, E. C. & Ramankutty, N. The challenge of feeding the world while conserving half the planet. Nat. Sustain. 1, 409–412 (2018).Article 

Google Scholar 
9.Kok, M. T. J. et al. Assessing ambitious nature conservation strategies within a 2 degree warmer and food-secure world. Preprint at bioRxiv https://doi.org/10.1101/2020.08.04.236489 (2020).10.Rosa, I. M. D. et al. Multiscale scenarios for nature futures. Nat. Ecol. Evol. 1, 1416–1419 (2017).Article 

Google Scholar 
11.Obermeister, N. Local knowledge, global ambitions: IPBES and the advent of multi-scale models and scenarios. Sustain. Sci. 14, 843–856 (2019).Article 

Google Scholar 
12.Pereira, L. M. et al. Developing multiscale and integrative nature–people scenarios using the Nature Futures Framework. People Nat. 2, 1172–1195 (2020).Article 

Google Scholar 
13.Rabin, S. S. et al. Impacts of future agricultural change on ecosystem service indicators. Earth Syst. Dynam. 11, 357–376 (2019).Article 

Google Scholar 
14.Springmann, M. et al. Global and regional health effects of future food production under climate change: a modelling study. Lancet 387, 1937–1946 (2016).Article 

Google Scholar 
15.Springmann, M. et al. Health and nutritional aspects of sustainable diet strategies and their association with environmental impacts: a global modelling analysis with country-level detail. Lancet Planet. Health 2, e451–e461 (2018).Article 

Google Scholar 
16.Dinerstein, E. et al. A “Global Safety Net” to reverse biodiversity loss and stabilize Earth’s climate. Sci. Adv. 6, eabb2824 (2020).Article 

Google Scholar 
17.Locke, H. et al. Three global conditions for biodiversity conservation and sustainable use: an implementation framework. Natl Sci. Rev. https://doi.org/10.1093/nsr/nwz136 (2019).18.Waldron, A. et al. Protecting 30% of the Planet for Nature: Costs, Benefits and Economic Implications (Campaign for Nature, 2020).19.Strassburg, B. B. N. et al. Global priority areas for ecosystem restoration. Nature 586, 724–729 (2020).CAS 
Article 

Google Scholar 
20.O’Neill, B. C. et al. The roads ahead: narratives for Shared Socioeconomic Pathways describing world futures in the 21st century. Glob. Environ. Change 42, 169–180 (2015).21.Riahi, K. et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob. Environ. Change 42, 153–168 (2017).22.Tauli-Corpuz, V., Alcorn, J., Molnar, A., Healy, C. & Barrow, E. Cornered by PAs: adopting rights-based approaches to enable cost-effective conservation and climate action. World Dev. 130, 104923 (2020).Article 

Google Scholar 
23.Kashwan, P. V., Duffy, R., Massé, F., Asiyanbi, A. P. & Marijnen, E. From racialized neocolonial global conservation to an inclusive and regenerative conservation. Environ. Sci. Policy Sustain. Dev. 63, 4–19 (2021).Article 

Google Scholar 
24.The State of Food Security and Nutrition in the World 2017: Building Resilience for Peace and Food Security (FAO, 2017).25.Schleicher, J. et al. Protecting half of the planet could directly affect over one billion people. Nat. Sustain. 2, 1094–1096 (2019).Article 

Google Scholar 
26.Allan, J. R. et al. The minimum land area requiring conservation attention to safeguard biodiversity. Preprint at bioRxiv https://doi.org/10.1101/839977 (2021).27.Brockington, D. & Wilkie, D. Protected areas and poverty. Phil. Trans. R. Soc. B 370, 20140271 (2015).28.Protected Planet Report 2020 (UNEP–WCMC and IUCN, 2021).29.Naidoo, R. et al. Evaluating the impacts of protected areas on human well-being across the developing world. Sci. Adv. 5, eaav3006 (2019).CAS 
Article 

Google Scholar 
30.Dutta, A., Allan, J., Shimray, G., General, S. & Pact, A. I. P. RE: “A ‘Global Safety Net’ to reverse biodiversity loss and stabilize Earth’s climate”. Sci. Adv. 6, eabb2824 (2020).Article 

Google Scholar 
31.Simmons, B. A., Nolte, C. & McGowan, J. Tough questions for the “30 × 30” conservation agenda. Front. Ecol. Environ. 19, 322–323 (2021).Article 

Google Scholar 
32.Jung, M. et al. Areas of global importance for conserving terrestrial biodiversity, carbon and water. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-021-01528-7 (2021).33.The IUCN Red List of Threatened Species Version 2019.2 (IUCN, 2019).34.The World Database of Key Biodiversity Areas (KBA Partnership, 2019); www.keybiodiversityareas.org35.Mogg, S., Fastre, C. & Visconti, P. Targeted expansion of protected areas to maximise the persistence of terrestrial mammals. Preprint at bioRxiv https://doi.org/10.1101/608992 (2019).36.Gurobi Optimizer Reference Manual (Gurobi Optimization, 2019).37.Hanson, J. O. et al. prioritizr: Systematic Conservation Prioritization in R. R package version 5.0.3 https://CRAN.R-project.org/package=prioritizr (2020).38.Hurtt, G., Chini, L., Frolking, S. & Sahajpal, R. Land-Use Harmonization (LUH2) (Global Ecology Laboratory, Univ. Maryland, 2017).39.Protected Planet: The World Database on Protected Areas (WDPA) (UNEP-WCMC and IUCN, accessed April 2019); www.protectedplanet.net40.Dellink, R., Chateau, J., Lanzi, E. & Magné, B. Long-term economic growth projections in the Shared Socioeconomic Pathways. Glob. Environ. Change 42, 200–214 (2017).41.Jones, B. & O’Neill, B. C. Spatially explicit global population scenarios consistent with the Shared Socioeconomic Pathways. Environ. Res. Lett. 11, 84003 (2016).42.van Vuuren, D. P. et al. The representative concentration pathways: an overview. Climatic Change 109, 5–31 (2011).Article 

Google Scholar 
43.Engström, K. et al. Assessing uncertainties in global cropland futures using a conditional probabilistic modelling framework. Earth Syst. Dynam. 7, 893–915 (2016).44.Alexander, P. et al. Drivers for global agricultural land use change: the nexus of diet, population, yield and bioenergy. Glob. Environ. Change 35, 138–147 (2015).Article 

Google Scholar 
45.Popp, A. et al. Land-use transition for bioenergy and climate stabilization: model comparison of drivers, impacts and interactions with other land use based mitigation options. Climatic Change 123, 495–509 (2014).Article 

Google Scholar 
46.GBD Results Tool (IHME, 2020); http://ghdx.healthdata.org/gbd-results-tool47.KC, S. & Lutz, W. The human core of the Shared Socioeconomic Pathways: population scenarios by age, sex and level of education for all countries to 2100. Glob. Environ. Change 42, 181–192 (2017). More

1.Gattuso, J.-P. et al. Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios. Science 349, aac4722 (2015).
Google Scholar 
2.Caldeira, K. & Wickett, M. E. Anthropogenic carbon and ocean pH. Nature 425, 365 (2003).CAS 

Google Scholar 
3.Hönisch, B. et al. The geological record of ocean acidification. Science 335, 1058–1063 (2012).
Google Scholar 
4.Turley, C. & Gattuso, J.-P. Future biological and ecosystem impacts of ocean acidification and their socioeconomic-policy implications. Curr. Opin. Environ. Sustain. 4, 278–286 (2012).
Google Scholar 
5.San Martin, V. A. et al. Linking social preferences and ocean acidification impacts in mussel aquaculture. Sci. Rep. 9, 4719 (2019).
Google Scholar 
6.Falkenberg, L. et al. Ocean acidification and human health. Int. J. Environ. Res. Public Health 17, 4563 (2020).CAS 

Google Scholar 
7.Loewe, M. & Rippin, N. The Sustainable Development Goals of the Post-2015 Agenda. Comments on the OWG and SDSN Proposals (German Development Institute 2015).8.Doney, S. C. et al. The impacts of ocean acidification on marine ecosystems and reliant human communities. Annu. Rev. Environ. Resour. 45, 83–112 (2020).
Google Scholar 
9.Ekstrom, J. et al. Vulnerability and adaptation of US shellfisheries to ocean acidification. Nat. Clim. Change 5, 207–214 (2015).
Google Scholar 
10.Ponce Oliva, R. D. et al. Ocean acidification, consumers’ preferences, and market adaptation strategies in the mussel aquaculture industry. Ecol. Econ. 158, 42–50 (2019).
Google Scholar 
11.Quatrinni, A. M. et al. Palaeoclimate ocean conditions shaped the evolution of corals and their skeletons through deep time. Nat. Ecol. Evol. 4, 1531–1538 (2020).
Google Scholar 
12.Thomsen, J. et al. Naturally acidified habitat selects for ocean acidification-tolerant mussels. Sci. Adv. 3, e1602411 (2017).
Google Scholar 
13.Rastrick, S. S. P. et al. Using natural analogues to investigate the effects of climate change and ocean acidification on Northern ecosystems. ICES J. Mar. Sci. 75, 2299–2311 (2018).
Google Scholar 
14.Hall-Spencer, J. M. et al. Volcanic carbon dioxide vents reveal ecosystem effects of ocean acidification. Nature 454, 96–99 (2008).CAS 

Google Scholar 
15.Agostini, S. et al. Ocean acidification drives community shifts towards simplified non-calcified habitats in a subtropical–temperate transition zone. Sci. Rep. 8, 11354 (2018).
Google Scholar 
16.Riquelme-Bugueño, R. et al. Diel vertical migration into anoxic and high-pCO2 waters: acoustic and net-based krill observations in the Humboldt Current. Sci. Rep. 10, 17181 (2020).
Google Scholar 
17.Pérez et al. Riverine discharges impact physiological traits and carbon sources for shell carbonate in the marine intertidal mussel Perumytilus purpuratus. Limnol. Oceanogr. 61, 969–983 (2016).
Google Scholar 
18.Vargas, C. A. et al. Species-specific responses to ocean acidification should account for local adaptation and adaptive plasticity. Nat. Ecol. Evol. 1, 0084 (2017).
Google Scholar 
19.Saavedra et al. Local habitat influences on feeding and respiration of the intertidal mussels Perumytilus purpuratus exposed to increased pCO2 levels. Estuaries Coast. 41, 1118–1129 (2018).CAS 

Google Scholar 
20.Riebesell, U. & Gattuso, J.-P. Lessons learned from ocean acidification research. Nat. Clim. Change 5, 12–14 (2015).CAS 

Google Scholar 
21.Tilbrook, B. et al. An enhanced ocean acidification observing network: from people to technology to data synthesis and information exchange. Front. Mar. Sci. 6, 337 (2019).
Google Scholar 
22.Barry, J. P., Hall-Spencer, J. M. and Tyrrell, T. in Guide to Best Practices for Ocean Acidification Research and Data Reporting (eds Riebesell, U. et al.) Ch. 3 (Publications Office of the European Union, 2010).23.Vargas, C. A. et al. Influence of glacier melting and river discharges on the nutrient distribution and DIC recycling in the southern Chilean Patagonia. J. Geophys. Res. Biogeosci. 123, 256–270 (2018).
Google Scholar 
24.Feely, R. A. et al. Evidence for upwelling of corrosive ‘acidified’ water onto the Continental Shelf. Science 320, 1490–1492 (2008).CAS 

Google Scholar 
25.Vargas, C. A. et al. Riverine and corrosive upwelling waters influences on the carbonate system in the coastal upwelling area off Central Chile: implications for coastal acidification events. J. Geophys. Res. Biogeosci. 121, 1468–1483 (2016).
Google Scholar 
26.Cao, Z. et al. Dynamics of the carbonate system in a large continental shelf system under the influence of both a river plume and coastal upwelling. J. Geophys. Res. Oceans 116, G02010 (2010).
Google Scholar 
27.Feely, R. A. et al. The combined effects of ocean acidification, mixing, and respiration on pH and carbonate saturation in an urbanized estuary. Est. Coast. Shelf Sci. 88, 442–449 (2010).CAS 

Google Scholar 
28.Cai, W.-J. et al. Acidification of subsurface coastal waters enhanced by eutrophication. Nat. Geosci. 4, 766–770 (2011).CAS 

Google Scholar 
29.Kwiatkowski, L. et al. Nighttime dissolution in a temperate coastal ocean ecosystem increases under acidification. Sci. Rep. 6, 22984 (2016).CAS 

Google Scholar 
30.Wolfe, K., Nguyen, H. D., Davey, M. & Byrne, M. Characterizing biogeochemical fluctuations in a world of extremes: a synthesis for temperate intertidal habitats in the face of global change. Glob. Change Biol. 26, 3858–3879 (2020).
Google Scholar 
31.Shaw, E. C., Phinn, S. R., Tilbrook, B. & Steven, A. Natural in situ relationships suggest coral reef calcium carbonate production will decline with ocean acidification. Limnol. Oceanogr. 60, 777–788 (2015).
Google Scholar 
32.Takeshita, Y. et al. Coral reef carbonate chemistry variability at different functional scales. Front. Mar. Sci. 5, 175 (2018).
Google Scholar 
33.Brodeur, J. R. et al. Chesapeake Bay inorganic carbon: spatial distribution and seasonal variability. Front. Mar. Sci. 6, 99 (2019).
Google Scholar 
34.Hoshijima, U. & Hofmann, G. E. Variability of seawater chemistry in a kelp forest environment is linked to in situ transgenerational effects in the purple sea urchin, Strongylocentrotus purpuratus. Front. Mar. Sci. 6, 62 (2019).
Google Scholar 
35.Koweek, D. A. et al. A year in the life of a central California kelp forest: physical and biological insights into biogeochemical variability. Biogeosciences 14, 31–44 (2017).CAS 

Google Scholar 
36.Cornwall, C. E. & Hurd, C. L. Experimental design in ocean acidification research: problems and solutions. ICES J. Mar. Sci. 73, 572–581 (2016).
Google Scholar 
37.Kapsenberg, L. & Hofmann, G. E. Ocean pH time-series and drivers of variability along the northern Channel Islands, California, USA. Limnol. Oceanogr. 61, 953–968 (2016).
Google Scholar 
38.Hofmann, G. E. et al. High-frequency dynamics of ocean pH: a multi-ecosystem comparison. PLoS ONE 6, e28983 (2011).CAS 

Google Scholar 
39.Baumann, H. Experimental assessments of marine species sensitivities to ocean acidification and co-stressors: how far have we come? Can. J. Zool. 97, 399–408 (2019).
Google Scholar 
40.Cornwall, C. E. et al. Diurnal fluctuations in seawater pH influence the response of a calcifying macroalga to ocean acidification. Proc. R. Soc. B 280, 20132201 (2013).
Google Scholar 
41.Rivest, E. B., Comeau, S. & Cornwall, C. E. The role of natural variability in shaping the response of coral reef organisms to climate change. Curr. Clim. 3, 271–281 (2017).
Google Scholar 
42.Sanford, E. & Kelly, M. W. Local adaptation in marine invertebrates. Annu. Rev. Mar. Sci. 3, 509–535 (2011).
Google Scholar 
43.Lewis, C. N. et al. Sensitivity to ocean acidification parallels natural pCO2 gradients experienced by Arctic copepods under winter sea ice. Proc. Natl Acad. Sci. USA 110, E4960–E4967 (2013).CAS 

Google Scholar 
44.Spalding, M. D. et al. Marine ecoregions of the world: a bioregionalization of coastal and shelf areas. BioScience 57, 573–583 (2007).
Google Scholar 
45.Aguilera, V. M., Vargas, C. A. & Dewitte, B. Intraseasonal hydrographic variations and nearshore carbonates system off northern Chile during the 2015 El Niño event. J. Geophys. Res. Biogeosci. 125, e2020JG005704 (2020).CAS 

Google Scholar 
46.Fassbender, A. J. et al. Seasonal carbonate chemistry variability in marine surface waters of the US Pacific Northwest. Earth Syst. Sci. Data 10, 1367–1401 (2018).
Google Scholar 
47.Reum, J. C. P. et al. Seasonal carbonate chemistry covariation with temperature, oxygen, and salinity in a fjord estuary: implications for the design of ocean acidification experiments. PLoS ONE 9, e89619 (2014).
Google Scholar 
48.Wallace, R. B. et al. Coastal ocean acidification: the other eutrophication problem. Estuar. Coast. Shelf Sci. 148, 1–13 (2014).CAS 

Google Scholar 
49.Rutgersson, A. et al. The annual cycle of carbon dioxide and parameters influencing the air–sea carbon exchange in the Baltic Proper. J. Mar. Syst. 74, 381–394 (2008).
Google Scholar 
50.Clargo, N. M., Salt, L. A., Thomas, H. & de Baar, H. J. W. Rapid increase of observed DIC and pCO2 in the surface waters of the North Sea in the 2001–2011 decade ascribed to climate change superimposed by biological processes. Mar. Chem. 177, 566–581 (2015).CAS 

Google Scholar 
51.Ericson, Y. et al. Temporal variability in surface water pCO2 in Adventfjorden (West Spitsbergen) with emphasis on physical and biogeochemical drivers. J. Geophys. Res. Oceans 123, 4888–4905 (2018).CAS 

Google Scholar 
52.Geilfus, N.-X. et al. Spatial and temporal variability of seawater pCO2 within the Canadian Arctic Archipelago and Baffin Bay during the summer and autumn 2011. Cont. Shelf Res. 156, 1–10 (2018).
Google Scholar 
53.Islam, F. et al. Sea surface pCO2 and O2 dynamics in the partially ice-covered Arctic Ocean. J. Geophys. Res. Oceans 122, 1425–1438 (2016).
Google Scholar 
54.Copin-Montégut, C., Bégovic, M. & Merlivat, L. Variability of the partial pressure of CO2 on diel to annual time scales in the Northwestern Mediterranean Sea. Mar. Chem. 85, 169–189 (2004).
Google Scholar 
55.Pardo, P. C. et al. Surface ocean carbon dioxide variability in South Pacific boundary currents and Subantarctic waters. Sci. Rep. 9, 7592 (2019).
Google Scholar 
56.Gagliano, M., McCormick, M. I., Moore, J. A. & Depczynski, M. The basics of acidification: baseline variability of pH on Australian coral reefs. Mar. Biol. 157, 1849–1856 (2010).CAS 

Google Scholar 
57.Takeshita, Y. et al. Including high-frequency variability in coastal acidification projections. Biogeosciences 12, 5853–5870 (2015).
Google Scholar 
58.Carter, H. A., Ceballos-Osuna, L., Miller, N. A. & Stillman, J. H. Impact of ocean acidification on metabolism and energetics during early life stages of the intertidal porcelain crab Petrolisthes cinctipes. J. Exp. Biol. 216, 1412–1422 (2013).CAS 

Google Scholar 
59.Ceballos-Osuna, L., Carter, H. A., Miller, N. A. & Stillman, J. H. Effects of ocean acidification on early life-history stages of the intertidal porcelain crab Petrolisthes cinctipes. J. Exp. Biol. 216, 1405–1411 (2013).CAS 

Google Scholar 
60.Miller, S. H. et al. Effect of elevated pCO2 on metabolic responses of porcelain crab (Petrolisthes cinctipes) larvae exposed to subsequent salinity stress. PLoS ONE 9, e109167 (2014).
Google Scholar 
61.Bayne, B. L. Metabolic expenditure. Dev. Aquacult. Fish. Sci. 41, 331–415 (2017).
Google Scholar 
62.Waldbusser, G. G. et al. Slow shell building, a possible trait for resistance to the effects of acute ocean acidification. Limnol. Oceanogr. 61, 1969–1983 (2016).
Google Scholar 
63.Dorey, N., Lancon, P., Thorndyke, M. & Dupont, S. Assessing physiological tipping point for sea urchin larvae exposed to a broad range of pH. Glob. Change Biol. 19, 3355–3367 (2013).
Google Scholar 
64.Kelly, M. W., Padilla-Gamiño, J. L. & Hofmann, G. E. Natural variation and the capacity to adapt to ocean acidification in the keystone sea urchin Strongylocentrotus purpuratus. Glob. Change Biol. 19, 2536–2546 (2015).
Google Scholar 
65.Gaitán-Espitia, J. D. et al. Spatio–temporal environmental variation mediates geographical differences in phenotypic responses to ocean acidification. Biol. Lett. 13, 20160865 (2017).
Google Scholar 
66.Calosi, P. et al. Distribution of sea urchins living near shallow water CO2 vents is dependent upon species acid–base and ion-regulatory abilities. Mar. Pollut. Bull. 73, 470–484 (2013).CAS 

Google Scholar 
67.Foo, S. A., Dworjanyn, S. A., Poore, A. G. B. & Byrne, M. Adaptive capacity of the habitat modifying sea urchin Centrostephanus rodgersii to ocean warming and ocean acidification: performance of early embryos. PLoS ONE 7, e42497 (2012).CAS 

Google Scholar 
68.Chan, K. Y. K., Grünbaum, D., Arnberg, M. & Dupont, S. Impacts of ocean acidification on survival, growth, and swimming behaviours differ between larval urchins and brittlestars. ICES J. Mar. Sci. 73, 951–996 (2016).
Google Scholar 
69.Stumpp, M. et al. Acidified seawater impacts sea urchin larvae pH regulatory systems relevant for calcification. Proc. Natl Acad. Sci. USA 109, 18192–18197 (2012).CAS 

Google Scholar 
70.Stumpp, M. et al. Digestion in sea urchin larvae impaired under ocean acidification. Nat. Clim. Change 3, 1044–1049 (2013).CAS 

Google Scholar 
71.Thor, P. & Dupont, S. Transgenerational effects alleviate severe fecundity loss during ocean acidification in a ubiquitous planktonic copepod. Glob. Change Biol. 21, 2261–2271 (2015).
Google Scholar 
72.Gibbin, E. M. et al. The evolution of phenotypic plasticity under global change. Sci. Rep. 7, 17253 (2017).
Google Scholar 
73.Gibbin, E. M. et al. Can multi-generational exposure to ocean warming and acidification lead to the adaptation of life history and physiology in a marine metazoan? J. Exp. Biol. 220, 551–563 (2017).
Google Scholar 
74.Dam, H. G. et al. Rapid, but limited, zooplankton adaptation to simultaneous warming and acidification. Nat. Clim. Change 11, 780–786 (2021).
Google Scholar 
75.Byrne, M. Impact of ocean warming and ocean acidification on marine invertebrate life history stages: vulnerabilities and potential for persistence in a changing ocean. Oceanogr. Mar. Biol. 49, 1–42 (2011).
Google Scholar 
76.Kroeker, K. J., Kordas, R. L., Crim, R. N. & Singh, G. G. Meta-analysis reveals negative yet variable effects of ocean acidification on marine organisms. Ecol. Lett. 13, 1419–1434 (2010).
Google Scholar 
77.Kroeker et al. Impacts of ocean acidification on marine organisms: quantifying sensitivities and interaction with warming. Glob. Change Biol. 19, 1884–1896 (2013).
Google Scholar 
78.Takahashi, T., Sutherland, S. C. & Kozyr, A. LDEO Database (Version 2019): Global Ocean Surface Water Partial Pressure of CO2 Database: Measurements Performed During 1957–2019 (NCEI Accession 0160492) Version 9.9 (National Oceanic and Atmospheric Administration National Centers for Environmental Information); https://doi.org/10.3334/CDIAC/OTG.NDP088(V2015)79.Manly, B. F. J. Randomization, Bootstrap and Monte Carlo Methods in Biology (CRC Press, 1997).80.Martinez, W. L. & Martinez, A. R. Computational Statistics Handbook with MATLAB (CRC Press, 2002). More

1.Arora, V. K. & Boer, G. J. A parameterization of leaf phenology for the terrestrial ecosystem component of climate models. Glob. Change Biol. 11, 39–59 (2005).
Google Scholar 
2.Richardson, A. D. et al. Terrestrial biosphere models need better representation of vegetation phenology: results from the North American Carbon Program Site Synthesis. Glob. Change Biol. 18, 566–584 (2012).
Google Scholar 
3.Peñuelas, J., Rutishauser, T. & Filella, I. Phenology feedbacks on climate change. Science 324, 887–888 (2009).
Google Scholar 
4.Richardson, A. D. et al. Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philos. Trans. R. Soc. Lond. B Biol. Sci. 365, 3227–3246 (2010).
Google Scholar 
5.Diez, J. M. et al. Forecasting phenology: from species variability to community patterns. Ecol. Lett. 15, 545–553 (2012).
Google Scholar 
6.Hegland, S. J., Nielsen, A., Lazaro, A., Bjerknes, A. L. & Totland, O. How does climate warming affect plant-pollinator interactions? Ecol. Lett. 12, 184–195 (2009).
Google Scholar 
7.Fu, Y. H. et al. Declining global warming effects on the phenology of spring leaf unfolding. Nature 526, 104–107 (2015).CAS 

Google Scholar 
8.Zhang, H., Yuan, W., Liu, S. & Dong, W. Divergent responses of leaf phenology to changing temperature among plant species and geographical regions. Ecosphere 6, art250 (2015).
Google Scholar 
9.Zhang, G., Zhang, Y., Dong, J. & Xiao, X. Green-up dates in the Tibetan Plateau have continuously advanced from 1982 to 2011. Proc. Natl Acad. Sci. USA 110, 4309–4314 (2013).CAS 

Google Scholar 
10.Menzel, A. et al. European phenological response to climate change matches the warming pattern. Glob. Change Biol. 12, 1969–1976 (2006).
Google Scholar 
11.Cleland, E. E., Chuine, I., Menzel, A., Mooney, H. A. & Schwartz, M. D. Shifting plant phenology in response to global change. Trends Ecol. Evol. 22, 357–365 (2007).
Google Scholar 
12.Menzel, A., Sparks, T. H., Estrella, N. & Roy, D. B. Altered geographic and temporal variability in phenology in response to climate change. Glob. Ecol. Biogeogr. 15, 498–504 (2006).
Google Scholar 
13.Zhang, X., Tarpley, D. & Sullivan, J. T. Diverse responses of vegetation phenology to a warming climate. Geophys. Res. Lett. https://doi.org/10.1029/2007gl031447 (2007).14.Fitter, A. H. & Fitter, R. S. Rapid changes in flowering time in British plants. Science 296, 1689–1691 (2002).CAS 

Google Scholar 
15.Primack, R. B. et al. Spatial and interspecific variability in phenological responses to warming temperatures. Biol. Conserv. 142, 2569–2577 (2009).
Google Scholar 
16.Cleland, E. E., Chiariello, N. R., Loarie, S. R., Mooney, H. A. & Field, C. B. Diverse responses of phenology to global changes in a grassland ecosystem. Proc. Natl Acad. Sci. USA 103, 13740–13744 (2006).CAS 

Google Scholar 
17.Wang, H., Dai, J., Zheng, J. & Ge, Q. Temperature sensitivity of plant phenology in temperate and subtropical regions of China from 1850 to 2009. Int. J. Climatol. 35, 913–922 (2015).
Google Scholar 
18.Chuine, I. M., Morin, X. & Bugmann, H. Warming, photoperiods, and tree phenology. Science 329, 277–278 (2010).
Google Scholar 
19.Chuine, I. A unified model for budburst of trees. J. Theor. Biol. 207, 337–347 (2000).CAS 

Google Scholar 
20.Murray, M., Cannell, M. G. R. & Smith, R. I. Date of budburst of fifteen tree species in Britain following climatic warming. J. Appl. Ecol. 26, 693–700 (1989).
Google Scholar 
21.Man, R., Lu, P. & Dang, Q. L. Insufficient chilling effects vary among boreal tree species and chilling duration. Front. Plant Sci. 8, 1354 (2017).
Google Scholar 
22.Cannell, M. G. R. & Smith, R. I. L. Thermal time, chill days and prediction of budburst in Picea sitchensis. J. Appl. Ecol. 20, 951–963 (1983).
Google Scholar 
23.Fu, Y. H. et al. Increased heat requirement for leaf flushing in temperate woody species over 1980-2012: effects of chilling, precipitation and insolation. Glob. Change Biol. 21, 2687–2697 (2015).
Google Scholar 
24.Zhang, H., Liu, S., Regnier, P. & Yuan, W. New insights on plant phenological response to temperature revealed from long-term widespread observations in China. Glob. Change Biol. 24, 2066–2078 (2018).
Google Scholar 
25.Yu, H., Luedeling, E. & Xu, J. Winter and spring warming result in delayed spring phenology on the Tibetan Plateau. Proc. Natl Acad. Sci. USA 107, 22151–22156 (2010).CAS 

Google Scholar 
26.Asse, D. et al. Warmer winters reduce the advance of tree spring phenology induced by warmer springs in the Alps. Agric. For. Meteorol. 252, 220–230 (2018).
Google Scholar 
27.Ettinger, A. K. et al. Winter temperatures predominate in spring phenological responses to warming. Nat. Clim. Change 10, 1137–1142 (2020).
Google Scholar 
28.Chuine, I. & Régnière, J. Process-based models of phenology for plants and animals. Annu. Rev. Ecol. Evol. Syst. 48, 159–182 (2017).
Google Scholar 
29.Caffarra, A., Donnelly, A., Chuine, I. & Jones, M. B. Modelling the timing of Betula pubescens budburst. I. Temperature and photoperiod: a conceptual model. Clim. Res. 46, 147–157 (2011).
Google Scholar 
30.Luterbacher, J., Dietrich, D., Xoplaki, E., Grosjean, M. & Wanner, H. European seasonal and annual temperature variability, trends, and extremes since 1500. Science 303, 1499–1503 (2004).CAS 

Google Scholar 
31.Ciais, P. et al. in Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).32.Fu, Y. H. et al. Daylength helps temperate deciduous trees to leaf-out at the optimal time. Glob. Change Biol. 25, 2410–2418 (2019).
Google Scholar 
33.Wolkovich, E. M. et al. A simple explanation for declining temperature sensitivity with warming. Glob. Change Biol. 27, 4947–4949 (2021).CAS 

Google Scholar 
34.Templ, B. et al. Pan European Phenological database (PEP725): a single point of access for European data. Int. J. Biometeorol. 62, 1109–1113 (2018).
Google Scholar 
35.Kramer, K. Selecting a model to predict the onset of growth of Fagus sylvatica. J. Appl. Ecol. 31, 172–181 (1994).
Google Scholar 
36.Chuine, I., Cour, P. & Rousseau, D.-D. Selecting models to predict the timing of flowering of temperate trees: implications for tree phenology modelling. Plant Cell Environ. 22, 1–13 (1999).37.Savas, R. Investigations on the annual cycle of development of forest trees. II. Autumn dormancy and winter dormancy https://eurekamag.com/research/000/414/000414639.php (1974).38.Hänninen, H. Modelling bud dormancy release in trees from cool and temperate regions. Acta. Fenn. 14, 499–454 (1990).
Google Scholar 
39.Harrington, C. A., Gould, P. J. & St. Clair, J. B. Modeling the effects of winter environment on dormancy release of Douglas-fir. Ecol. Manag. 259, 798–808 (2010).
Google Scholar 
40.Zhang, H., Yuan, W., Liu, S., Dong, W. & Fu, Y. Sensitivity of flowering phenology to changing temperature in China. J. Geophys. Res. Biogeosci. 120, 1658–1665 (2015).
Google Scholar 
41.Richardson, A. D. et al. Influence of spring phenology on seasonal and annual carbon balance in two contrasting New England forests. Tree Physiol. 29, 321–331 (2009).CAS 

Google Scholar 
42.Piao, S. et al. Plant phenology and global climate change: current progresses and challenges. Glob. Change Biol. 25, 1922–1940 (2019).
Google Scholar 
43.Körner, C. & Basler, D. Phenology under global warming. Science 327, 1461–1462 (2010).
Google Scholar 
44.Zohner, C. M. & Renner, S. S. Common garden comparison of the leaf-out phenology of woody species from different native climates, combined with herbarium records, forecasts long-term change. Ecol. Lett. 17, 1016–1025 (2014).
Google Scholar 
45.Vitasse, Y. & Basler, D. What role for photoperiod in the bud burst phenology of European beech. Eur. J. For. Res. 132, 1–8 (2012).
Google Scholar 
46.Lenz, A., Hoch, G., Körner, C. & Vitasse, Y. Convergence of leaf-out towards minimum risk of freezing damage in temperate trees. Funct. Ecol. 30, 1480–1490 (2016).
Google Scholar 
47.Wang, Y. et al. Forest controls spring phenology of juvenile Smith fir along elevational gradients on the southeastern Tibetan Plateau. Int. J. Biometeorol. 63, 963–972 (2019).
Google Scholar 
48.Marquis, B., Bergeron, Y., Simard, M. & Tremblay, F. Probability of sping frosts, not growing degree-days, drives onset of spruce bud burst in plantations at the boreal-temperate forest ecotone. Front. Plant Sci. 11, 1031 (2020).
Google Scholar 
49.Shen, M., Piao, S., Cong, N., Zhang, G. & Jassens, I. A. Precipitation impacts on vegetation spring phenology on the Tiberan Plateau. Glob. Change Biol. 21, 3647–3656 (2015).
Google Scholar 
50.Liu et al. Temperature, precipitation, and insolation effects on autumn vegetation phenology in temperate China. Glob. Change Biol. 22, 644–655 (2016).CAS 

Google Scholar 
51.Minder, J. R., Mote, P. W. & Lundquist, J. D. Surface temperature lapse rates over complex terrain: lessons from the Cascade Mountains. J. Geophys. Res. 115, D14122 (2010).
Google Scholar 
52.Navarro-Serrano et al. Elevation effects on air temperature in a topographically complex mountain valley in the Spanish Pyrenees. Atmosphere 11, 656 (2020).
Google Scholar 
53.Chen, L. et al. Leaf senescence exhibits stronger climatic responses during warm than during cold autumns. Nat. Clim. Change 10, 777–780 (2020).CAS 

Google Scholar 
54.Leys, C., Ley, C., Klein, O., Bernard, P. & Licata, L. Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median. J. Exp. Soc. Psychol. 49, 764–766 (2013).
Google Scholar 
55.Beer, C. et al. Harmonized European long-term climate data for assessing the effect of changing temporal variability on land–atmosphere CO2 fluxes. J. Clim. 27, 4815–4834 (2014).
Google Scholar 
56.Olsson, C. & Jönsson, A. M. Process-based models not always better than empirical models for simulating budburst of Norway spruce and birch in Europe. Glob. Change Biol. 20, 3492–3507 (2014).
Google Scholar 
57.Duan, Q., Sorooshian, S. & Gupta, V. K. Optimal use of the SCE-UA global optimization method for calibrating watershed models. J. Hydrol. 158, 265–284 (1994).
Google Scholar 
58.Bluemel, K. & Chmielewski, F. Shortcomings of classical phenological forcing models and a way to overcome them. Agric. For. Meteorol. 164, 10–19 (2012).
Google Scholar  More