Philippa Kaur

Green and red lighting might be good for migratory birds and sea turtles, but could have undesirable effects if marine algae are present. Credit: Getty

Ecology
24 June 2021
Red light, green light: both signal ‘go’ to deadly algae

Artificial lighting thought to be more wildlife-friendly than white light could encourage algal blooms.

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Green or red lights in seaside areas have been proposed as alternatives to white light to protect wildlife. But new experiments show that exposure to red or green light at night boosts the growth of some ocean algae — including species known to rob waters of oxygen.Little is known about the impact of artificial light on marine life, even though many brightly lit cities are coastal. To address that knowledge gap, Sofie Spatharis at the University of Glasgow, UK, and her colleagues exposed a mix of microscopic marine algae collected from Scottish waters to standard white light. They also exposed the mixture to red and green lights, which have been proposed to minimize impacts on sea turtles and migratory seabirds, respectively.The team found that all light colours enhanced growth of the microalgae mix. Red light had the most pronounced effect, doubling the number of cells produced. The proportions of species in the mixture also shifted: both red and green light especially favoured growth of harmful species in the Skeletonema genus, which form dense blooms that are deadly to fish.

Proc. R. Soc. B (2021)

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1.Mace, G. M. et al. Aiming higher to bend the curve of biodiversity loss. Nat. Sustain. 1, 448–451 (2018).Article 

Google Scholar 
2.Leclère, D. et al. Bending the curve of terrestrial biodiversity needs an integrated strategy. Nature 585, 551–556 (2020).PubMed 

Google Scholar 
3.Updated Zero Draft of the Post-2020 Global Biodiversity Framework (Convention on Biological Diversity, 2020); https://www.cbd.int/doc/c/3064/749a/0f65ac7f9def86707f4eaefa/post2020-prep-02-01-en.pdf4.Pereira, H. M. et al. Essential biodiversity variables. Science 339, 277–278 (2013).CAS 
Article 

Google Scholar 
5.Loh, J. et al. The Living Planet Index: using species population time series to track trends in biodiversity. Philos. Trans. R. Soc. B 360, 289–295 (2005).Article 

Google Scholar 
6.Collen, B. et al. Monitoring change in vertebrate abundance: the Living Planet Index. Conserv. Biol. 23, 317–327 (2009).Article 

Google Scholar 
7.McRae, L., Deinet, S. & Freeman, R. The diversity-weighted Living Planet Index: controlling for taxonomic bias in a global biodiversity indicator. PLoS ONE 12, e0169156 (2017).Article 

Google Scholar 
8.Almond, R.E.A., Grooten M. & Petersen, T. (eds) Living Planet Report 2020—Bending the Curve of Biodiversity Loss (WWF, 2020).9.Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES, 2019).10.Global Biodiversity Outlook 5 (Convention on Biological Diversity, 2020).11.Jaspers, A. Can a single index track the state of global biodiversity? Biol. Conserv. 246, 108524 (2020).Article 

Google Scholar 
12.Leung, B. et al. Clustered versus catastrophic global vertebrate declines. Nature 588, 267–271 (2020).CAS 
Article 

Google Scholar 
13.Buckland, S. T., Studeny, A. C., Magurran, A. E., Illian, J. & Newson, S. E. The geometric mean of relative abundance indices: a biodiversity measure with a difference. Ecosphere 2, 100 (2011).14.de Valpine, P. & Hastings, A. Fitting population models incorporating process noise and observation error. Ecol. Monogr. 72, 57–76.15.Daskalova, G. N., Myers-Smith, I. H. & Godlee, J. L. Rare and common vertebrates span a wide spectrum of population trends. Nat. Commun. 11, 4394 (2020).CAS 
Article 

Google Scholar 
16.Living Planet Report 2020. Technical Supplement: Living Planet Index (WWF, 2020); https://f.hubspotusercontent20.net/hubfs/4783129/LPR/PDFs/ENGLISH%20-%20TECH%20SUPPLIMENT.pdf17.Vellend, M. Conceptual synthesis in community ecology. Quart. Rev. Biol. 85, 183–206 (2010).Article 

Google Scholar 
18.Vellend, M. et al. Assessing the relative importance of neutral stochasticity in ecological communities. Oikos 123, 1420–1430 (2014).Article 

Google Scholar 
19.Lande, R. Risks of population extinction from demographic and environmental stochasticity and random catastrophes. Am. Nat. 142, 911–927 (1993).Article 

Google Scholar 
20.Gravel, D., Guichard, F. & Hochberg, M. E. Species coexistence in a variable world. Ecol. Lett. 14, 828–839 (2011).Article 

Google Scholar 
21.Kotze, D. J., O’Hara, R. B. & Lehvävirta, S. Dealing with varying detection probability, unequal sample sizes and clumped distributions in count data. PLoS ONE 7, e40923 (2012).CAS 
Article 

Google Scholar 
22.Kellner, K. F. & Swihart, R. K. Accounting for imperfect detection in ecology: a quantitative review. PLoS ONE 9, e111436 (2014).Article 

Google Scholar 
23.Di Fonzo, M., Collen, B. & Mace, G. M. A new method for identifying rapid decline dynamics in wild vertebrate populations. Ecol. Evol. 3, 2378–2391 (2013).Article 

Google Scholar 
24.Maxwell, S. L. et al. Being smart about SMART environmental targets. Science 347, 1075–1076 (2015).CAS 
Article 

Google Scholar 
25.Butchart, S. H. M., Di Marco, M. & Watson, J. E. M. Formulating SMART commitments on biodiversity: lessons from the Aichi Targets. Conserv Lett. 9, 457–468 (2016).Article 

Google Scholar 
26.Green, E. J. et al. Relating characteristics of global biodiversity targets to reported progress. Conserv. Biol. 33, 1360–1369 (2019).Article 

Google Scholar 
27.Dornelas, M. et al. A balance of winners and losers in the Anthropocene. Ecol. Lett. 22, 847–854 (2019).Article 

Google Scholar 
28.Fournier, A. M. V., White, E. R. & Heard, S. B. Site‐selection bias and apparent population declines in long‐term studies. Conserv. Biol. 33, 1370–1379 (2019).Article 

Google Scholar 
29.Pauly, D. Anecdotes and the shifting baseline syndrome of fisheries. Trends Ecol. Evol. 10, 430 (1995).CAS 
Article 

Google Scholar 
30.Papworth, S. K., Rist, J., Coad, L. & Milner-Gulland, E. J. Evidence for shifting baseline syndrome in conservation. Conserv Lett. 2, 93–100 (2009).
Google Scholar 
31.Barnosky, A. D. et al. Approaching a state shift in Earth’s biosphere. Nature 486, 52–58 (2012).CAS 
Article 

Google Scholar 
32.Nicholson, E. et al. Scenarios and models to support global conservation targets. Trends Ecol. Evol. 34, 57–68 (2019).Article 

Google Scholar 
33.Bull, J. W., Strange, N., Smith, R. J. & Gordon, A. Reconciling multiple counterfactuals when evaluating biodiversity conservation impact in social-ecological systems. Conserv. Biol. 35, 510–521 (2021).Article 

Google Scholar 
34.van Strien, A. J. et al. Modest recovery of biodiversity in a western European country: The Living Planet Index for the Netherlands. Biol. Conserv. 200, 44–50 (2016).Article 

Google Scholar 
35.Wauchope, H. S., Amano, T., Sutherland, W. J. & Johnston, A. When can we trust population trends? A method for quantifying the effects of sampling interval and duration. Methods Ecol. Evol. 10, 2067–2078 (2019).Article 

Google Scholar 
36.Wauchope, H. S. et al. Evaluating impact using time-series data. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2020.11.001 (2020).37.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).38.Buschke, F. T. Biodiversity trajectories and the time needed to achieve no net loss through averted-loss biodiversity offsets. Ecol. Model 352, 54–57 (2017).Article 

Google Scholar  More

1.De’Ath G, Fabricius KE, Sweatman H, Puotinen M. The 27-year decline of coral cover on the Great Barrier Reef and its causes. Proc Natl Acad Sci U.S.A. 2012;109:17995–9.PubMed 
PubMed Central 
Article 

Google Scholar 
2.Randall CJ, van Woesik R. Contemporary white-band disease in Caribbean corals driven by climate change. Nat Clim Chang. 2015;5:375–9.Article 

Google Scholar 
3.Maynard J, van Hooidonk R, Eakin CM, Puotinen M, Garren M, Williams G, et al. Projections of climate conditions that increase coral disease susceptibility and pathogen abundance and virulence. Nat Clim Chang. 2015;5:688–95.Article 

Google Scholar 
4.Cziesielski MJ, Schmidt-Roach S, Aranda M. The past, present, and future of coral heat stress studies. Ecol Evol. 2019;9:10055–66.PubMed 
PubMed Central 
Article 

Google Scholar 
5.Bourne D, Iida Y, Uthicke S, Smith-Keune C. Changes in coral-associated microbial communities during a bleaching event. ISME J. 2008;2:350–63.CAS 
PubMed 
Article 

Google Scholar 
6.van de Water JAJM, Chaib De Mares M, Dixon GB, Raina JB, Willis BL, Bourne DG, et al. Antimicrobial and stress responses to increased temperature and bacterial pathogen challenge in the holobiont of a reef-building coral. Mol Ecol. 2018;27:1065–80.PubMed 
Article 
CAS 

Google Scholar 
7.Sussman M, Mieog JC, Doyle J, Victor S, Willis BL, Bourne DG. Vibrio zinc-metalloprotease causes photoinactivation of coral endosymbionts and coral tissue lesions. PLoS ONE. 2009;4:1–14.8.Ben-Haim Y, Zicherman-Keren M, Rosenberg E. Temperature-regulated bleaching and lysis of the coral Pocillopora damicornis by the novel pathogen Vibrio coralliilyticus. Appl Environ Microbiol. 2003;69:4236–41.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
9.Garren M, Son K, Raina J-B, Rusconi R, Menolascina F, Shapiro OH, et al. A bacterial pathogen uses dimethylsulfoniopropionate as a cue to target heat-stressed corals. ISME J. 2014;8:999–1007.CAS 
PubMed 
Article 

Google Scholar 
10.Garren M, Son K, Tout J, Seymour JR, Stocker R. Temperature-induced behavioral switches in a bacterial coral pathogen. ISME J. 2016;10:1363–72.CAS 
PubMed 
Article 

Google Scholar 
11.Barbara GM, Mitchell JG. Marine bacterial organisation around point-like sources of amino acids. FEMS Microbiol Ecol. 2003;43:99–109.CAS 
PubMed 
Article 

Google Scholar 
12.Seymour JR, Marcos, Stocker R. Resource patch formation and exploitation throughout the marine microbial food web. Am Nat. 2009;173:E15–29.CAS 
PubMed 
Article 

Google Scholar 
13.Son K, Menolascina F, Stocker R. Speed-dependent chemotactic precision in marine bacteria. Proc Natl Acad Sci U.S.A. 2016;113:8624–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Meron D, Efrony R, Johnson WR, Schaefer AL, Morris PJ, Rosenberg E, et al. Role of Flagella in virulence of the coral pathogen Vibrio coralliilyticus. Appl Environ Microbiol. 2009;75:5704–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.Ushijima B, Häse CC. Influence of chemotaxis and swimming patterns on the virulence of the coral pathogen Vibrio coralliilyticus. J Bacteriol. 2018;200:1–16.Article 

Google Scholar 
16.Crossland CJ, Barnes DJ, Borowitzka MA. Diurnal lipid and mucus production in the staghorn coral Acropora acuminata. Mar Biol. 1980;60:81–90.17.Davies PS. The role of zooxanthellae in the nutritional energy requirements of Pocillopora eydouxi. Coral Reefs. 1984;2:181–6.18.Rix L, de Goeij JM, Mueller CE, Struck U, Middelburg JJ, van Duyl FC, et al. Coral mucus fuels the sponge loop in warm-and cold-water coral reef ecosystems. Sci Rep. 2016;6:1–11.Article 
CAS 

Google Scholar 
19.Naumann MS, Haas A, Struck U, Mayr C, El-Zibdah M, Wild C. Organic matter release by dominant hermatypic corals of the Northern Red Sea. Coral Reefs. 2010;29:649–59.Article 

Google Scholar 
20.Wild C, Huettel M, Klueter A, Kremb SG, Rasheed MYM, Jørgensen BB. Coral mucus functions as an energy carrier and particle trap in the reef ecosystem. Nature. 2004;428:66–70.CAS 
PubMed 
Article 

Google Scholar 
21.Bythell JC, Wild C. Biology and ecology of coral mucus release. J Exp Mar Bio Ecol. 2011;408:88–93.Article 

Google Scholar 
22.Bakshani CR, Morales-Garcia AL, Althaus M, Wilcox MD, Pearson JP, Bythell JC, et al. Evolutionary conservation of the antimicrobial function of mucus: a first defence against infection. NPJ Biofilms Microbiomes. 2018;14:1–12.
Google Scholar 
23.Gibbin E, Gavish A, Krueger T, Kramarsky-Winter E, Shapiro O, Guiet R, et al. Vibrio coralliilyticus infection triggers a behavioural response and perturbs nutritional exchange and tissue integrity in a symbiotic coral. ISME J. 2019;13:989–1003.24.Gavish AR, Shapiro OH, Kramarsky-Winter E, Vardi A. Microscale tracking of coral-vibrio interactions. ISME Communications. 2021;1:1–18.25.Shapiro OH, Fernandez VI, Garren M, Guasto JS, Debaillon-Vesque FP, Kramarsky-Winter E, et al. Vortical ciliary flows actively enhance mass transport in reef corals. Proc Natl Acad Sci U.S.A. 2014;111:13391–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
26.Seymour JR, Ahmed T, Stocker R. A microfluidic chemotaxis assay to study microbial behavior in diffusing nutrient patches. Limnol Oceanogr Methods. 2008;6:477–88.CAS 
Article 

Google Scholar 
27.Penn K, Wang J, Fernando SC, Thompson JR. Secondary metabolite gene expression and interplay of bacterial functions in a tropical freshwater cyanobacterial bloom. ISME J. 2014;8:1866–78.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
28.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:1–21.Article 
CAS 

Google Scholar 
29.Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010;11:1–12.30.Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U.S.A. 2005;102:15545–50.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Mootha VK, Lindgren CM, Eriksson K-F, Subramanian A, Sihag S, Lehar J, et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003;34:267–73.CAS 
PubMed 
Article 

Google Scholar 
32.Schneider WR, Doetsch RN. Effect of viscosity on bacterial motility. J Bacteriol. 1974;117:696–701.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
33.Martinez VA, Schwarz-Linek J, Reufer M, Wilson LG, Morozov AN, Poon WCK. Flagellated bacterial motility in polymer solutions. Proc Natl Acad Sci U.S.A. 2014;111:17771–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Kimes NE, Grim CJ, Johnson WR, Hasan NA, Tall BD, Kothary MH, et al. Temperature regulation of virulence factors in the pathogen Vibrio coralliilyticus. ISME J. 2012;6:835–46.CAS 
PubMed 
Article 

Google Scholar 
35.Kojima S, Yamamoto K, Kawagishi I, Homma M. The polar flagellar motor of Vibrio cholerae is driven by an Na+ motive force. J Bacteriol. 1999;181:1927–30.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Sowa Y, Hotta H, Homma M, Ishijima A. Torque-speed relationship of the Na+-driven flagellar motor of Vibrio alginolyticus. J Mol Biol. 2003;327:1043–51.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
37.Milo R, Phillips R. Cell biology by the numbers. 1st ed. New York, NY: Garland Science; 2016.38.Crossland CJ. In situ release of mucus and DOC-lipid from the corals Acropora variabilis and Stylophora pistillata in different light regimes. Coral Reefs. 1987;6:35–42.CAS 
Article 

Google Scholar 
39.Wild C, Woyt H, Huettel M. Influence of coral mucus on nutrient fluxes in carbonate sands. Mar Ecol Prog Ser. 2005;287:87–98.40.Ducklow HW, Mitchell R. Composition of mucus released by coral reef coelenterates. Limnol Oceanogr. 1979;24:706–14.CAS 
Article 

Google Scholar 
41.Meikle P, Richards GN, Yellowlees D. Structural determination of the oligosaccharide side chains from a glycoprotein isolated from the mucus of the coral Acropora formosa. J Biol Chem. 1987;262:16941–7.CAS 
PubMed 
Article 

Google Scholar 
42.Coddeville B, Maes E, Ferrier-Pagès C, Guerardel Y. Glycan profiling of gel forming mucus layer from the scleractinian symbiotic coral Oculina arbuscula. Biomacromolecules. 2011;12:2064–73.CAS 
PubMed 
Article 

Google Scholar 
43.Hasegawa H, Häse CC. TetR-type transcriptional regulator VtpR functions as a global regulator in Vibrio tubiashii. Appl Environ Microbiol. 2009;75:7602–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Ball AS, Chaparian RR, van Kessel JC. Quorum sensing gene regulation by LuxR/HapR master regulators in Vibrios. J Bacteriol. 2017;199:1–13.45.Rutherford ST, Van Kessel JC, Shao Y, Bassler BL. AphA and LuxR/HapR reciprocally control quorum sensing in vibrios. Genes Dev. 2011;25:397–408.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Hammer BK, Bassler BL. Quorum sensing controls biofilm formation in Vibrio cholerae. Mol Microbiol. 2003;50:101–4.CAS 
PubMed 
Article 

Google Scholar 
47.Waters CM, Lu W, Rabinowitz JD, Bassler BL. Quorum sensing controls biofilm formation in Vibrio cholerae through modulation of cyclic Di-GMP levels and repression of vpsT. J Bacteriol. 2008;190:2527–36.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Burger AH. Quorum Sensing in the Hawai’ian Coral Pathogen Vibrio coralliilyticus strain OCN008. University of Hawaii at Manoa; 2017.49.Yildiz FH, Schoolnik GK. Vibrio cholerae O1 El Tor: identification of a gene cluster required for the rugose colony type, exopolysaccharide production, chlorine resistance, and biofilm formation. Proc Natl Acad Sci U.S.A. 1999;96:4028–33.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
50.Fong JCN, Syed KA, Klose KE, Yildiz FH. Role of Vibrio polysaccharide (vps) genes in VPS production, biofilm formation and Vibrio cholerae pathogenesis. Microbiology. 2010;156:2757–69.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Fong JCN, Karplus K, Schoolnik GK, Yildiz FH. Identification and characterization of RbmA, a novel protein required for the development of rugose colony morphology and biofilm structure in Vibrio cholerae. J Bacteriol. 2006;188:1049–59.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
52.Fong JCN, Yildiz FH. The rbmBCDEF gene cluster modulates development of rugose colony morphology and biofilm formation in Vibrio cholerae. J Bacteriol. 2007;189:2319–30.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
53.DiRita VJ, Mekalanos JJ. Periplasmic interaction between two membrane regulatory proteins, ToxR and ToxS, results in signal transduction and transcriptional activation. Cell. 1991;64:29–37.CAS 
PubMed 
Article 

Google Scholar 
54.Almagro-Moreno S, Root MZ, Taylor RK. Role of ToxS in the proteolytic cascade of virulence regulator ToxR in Vibrio cholerae. Mol Microbiol. 2015;98:963–76.CAS 
PubMed 
Article 

Google Scholar 
55.Lee SE, Ryu PY, Kim SY, Kim YR, Koh JT, Kim OJ, et al. Production of Vibrio vulnificus hemolysin in vivo and its pathogenic significance. Biochem Biophys Res Commun. 2004;324:86–91.56.Senoh M, Okita Y, Shinoda S, Miyoshi S. The crucial amino acid residue related to inactivation of Vibrio vulnificus hemolysin. Micro Pathog. 2008;44:78–83.CAS 
Article 

Google Scholar 
57.Bröms JE, Ishikawa T, Wai SN, Sjöstedt A. A functional VipA-VipB interaction is required for the type VI secretion system activity of Vibrio cholerae O1 strain A1552. BMC Microbiol. 2013;13:1–12.Article 
CAS 

Google Scholar 
58.Vizcaino MI, Johnson WR, Kimes NE, Williams K, Torralba M, Nelson KE, et al. Antimicrobial resistance of the coral pathogen Vibrio coralliilyticus and Caribbean sister phylotypes isolated from a diseased octocoral. Micro Ecol. 2010;59:646–57.Article 

Google Scholar 
59.Ritchie KB. Regulation of microbial populations by coral surface mucus and mucus-associated bacteria. Mar Ecol Prog Ser. 2006;322:1–14.CAS 
Article 

Google Scholar 
60.Nissimov J, Rosenberg E, Munn CB. Antimicrobial properties of resident coral mucus bacteria of Oculina patagonica. FEMS Microbiol Lett. 2009;292:210–5.CAS 
PubMed 
Article 

Google Scholar 
61.Shnit-Orland M, Kushmaro A. Coral mucus-associated bacteria: a possible first line of defense. FEMS Microbiol Ecol. 2009;67:371–80.CAS 
PubMed 
Article 

Google Scholar 
62.Rypien KL, Ward JR, Azam F. Antagonistic interactions among coral-associated bacteria. Environ Microbiol. 2010;12:28–39.CAS 
PubMed 
Article 

Google Scholar 
63.Alagely A, Krediet CJ, Ritchie KB, Teplitski M. Signaling-mediated cross-talk modulates swarming and biofilm formation in a coral pathogen Serratia marcescens. ISME J. 2011;5:1609–20.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.Stocker R, Seymour JR, Samadani A, Hunt DE, Polz MF. Rapid chemotactic response enables marine bacteria to exploit ephemeral microscale nutrient patches. Proc Natl Acad Sci U.S.A. 2008;105:4209–14.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
65.Polz MF, Hunt DE, Preheim SP, Weinreich DM. Patterns and mechanisms of genetic and phenotypic differentiation in marine microbes. Philos Trans R Soc B Biol Sci. 2006;361:2009–21.Article 

Google Scholar 
66.Taylor JR, Stocker R. Trade-offs of chemotactic foraging in turbulent water. Science. 2012;338:675–9.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
67.Krediet CJ, Ritchie KB, Cohen M, Lipp EK, Patterson Sutherland K, Teplitski M. Utilization of mucus from the coral Acropora palmata by the pathogen Serratia marcescens and by environmental and coral commensal bacteria. Appl Environ Microbiol. 2009;75:3851–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
68.Krediet CJ, Ritchie KB, Alagely A, Teplitski M. Members of native coral microbiota inhibit glycosidases and thwart colonization of coral mucus by an opportunistic pathogen. ISME J. 2013;7:980–90.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
69.Packer HL, Armitage JP. The chemokinetic and chemotactic behavior of Rhodobacter sphaeroides: two independent responses. J Bacteriol. 1994;176:206–12.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
70.Deepika D, Karmakar R, Tirumkudulu MS, Venkatesh KV. Variation in swimming speed of Escherichia coli in response to attractant. Arch Microbiol. 2015;197:211–22.CAS 
PubMed 
Article 

Google Scholar 
71.Zhulin IB, Armitage JP. Motility, chemokinesis, and methylation-independent chemotaxis in Azospirillum brasilense. J Bacteriol. 1993;175:952–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
72.Ramos HC, Rumbo M, Sirard J-C. Bacterial flagellins: mediators of pathogenicity and host immune responses in mucosa. Trends Microbiol. 2004;12:509–17.CAS 
PubMed 
Article 

Google Scholar 
73.Reed KC, Muller EM, van Woesik R. Coral immunology and resistance to disease. Dis Aquat Organ. 2010;90:85–92.CAS 
PubMed 
Article 

Google Scholar 
74.Ushijima B, Videau P, Poscablo D, Stengel JW, Beurmann S, Burger AH, et al. Mutation of the toxR or mshA genes from Vibrio coralliilyticus strain OCN014 reduces infection of the coral Acropora cytherea. Environ Microbiol. 2016;18:4055–67.CAS 
PubMed 
Article 

Google Scholar 
75.Ushijima B, Richards GP, Watson MA, Schubiger CB, Häse CC. Factors affecting infection of corals and larval oysters by Vibrio coralliilyticus. PLoS ONE. 2018;13:e0199475.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
76.Peterson KM, Mekalanos JJ. Characterization of the Vibrio cholerae ToxR regulon: identification of novel genes involved in intestinal colonization. Infect Immun. 1988;56:2822–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
77.Provenzano D, Klose KE. Altered expression of the ToxR-regulated porins OmpU and OmpT diminishes Vibrio cholerae bile resistance, virulence factor expression, and intestinal colonization. Proc Natl Acad Sci U.S.A. 2000;97:10220–4.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
78.Waters CM, Bassler BL. The Vibrio harveyi quorum-sensing system uses shared regulatory components to discriminate between multiple autoinducers. Genes Dev. 2006;20:2754–67.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
79.Mukherjee S, Bassler BL. Bacterial quorum sensing in complex and dynamically changing environments. Nat Rev Microbiol. 2019;17:371–82.80.Sikora AE, Zielke RA, Lawrence DA, Andrews PC, Sandkvist M. Proteomic analysis of the Vibrio cholerae type II secretome reveals new proteins, including three related serine proteases. J Biol Chem. 2011;286:16555–66.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
81.Korotkov KV, Sandkvist M, Hol WGJ. The type II secretion system: biogenesis, molecular architecture and mechanism. Nat Rev Microbiol. 2012;10:336–51.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
82.Stathopoulos C, Hendrixson DR, Thanassi DG, Hultgren SJ, St. Geme III JW, Curtiss III R. Secretion of virulence determinants by the general secretory pathway in Gram-negative pathogens: an evolving story. Microbes Infect. 2000;2:1061–72.83.Hood RD, Singh P, Hsu FS, Güvener T, Carl MA, Trinidad RRS, et al. A Type VI secretion system of Pseudomonas aeruginosa targets a toxin to bacteria. Cell Host Microbe. 2010;7:25–37.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
84.Zheng J, Ho B, Mekalanos JJ. Genetic analysis of anti-amoebae and anti-bacterial activities of the Type VI secretion system in Vibrio cholerae. PLoS ONE. 2011;6:e23876.85.MacIntyre DL, Miyata ST, Kitaoka M, Pukatzki S. The Vibrio cholerae type VI secretion system displays antimicrobial properties. Proc Natl Acad Sci U.S.A. 2010;107:19520–4.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
86.Lee SH, Hava DL, Waldor MK, Camilli A. Regulation and temporal expression patterns of Vibrio cholerae virulence genes during infection. Cell. 1999;99:625–34.87.Pennetzdorfer N, Lembke M, Pressler K, Matson JS, Reidl J, Schild S. Regulated proteolysis in Vibrio cholerae allowing rapid adaptation to stress conditions. Front Cell Infect Microbiol. 2019;9:1–9.Article 
CAS 

Google Scholar 
88.Liu R, Chen H, Zhang R, Zhou Z, Hou Z, Gao D, et al. Comparative transcriptome analysis of Vibrio splendidus JZ6 reveals the mechanism of its pathogenicity at low temperatures. Appl Environ Microbiol. 2016;82:2050–61.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
89.Hughes TP, Anderson KD, Connolly SR, Heron SF, Kerry JT, Lough JM, et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science. 2018;359:80–3.90.Vezzulli L, Previati M, Pruzzo C, Marchese A, Bourne DG, Cerrano C, et al. Vibrio infections triggering mass mortality events in a warming Mediterranean Sea. Environ Microbiol. 2010;12:2007–19.91.Zaneveld JR, Burkepile DE, Shantz AA, Pritchard CE, McMinds R, Payet JP, et al. Overfishing and nutrient pollution interact with temperature to disrupt coral reefs down to microbial scales. Nat Commun. 2016;7:1–12.Article 
CAS 

Google Scholar  More

1.Erb, K. H. et al. Unexpectedly large impact of forest management and grazing on global vegetation biomass. Nature 553, 73–76 (2018).CAS 
PubMed 
Article 

Google Scholar 
2.Luyssaert, S. et al. Old-growth forests as global carbon sinks. Nature 455, 213–215 (2008).CAS 
PubMed 
Article 

Google Scholar 
3.Drake, J. B. et al. Above-ground biomass estimation in closed canopy Neotropical forests using lidar remote sensing: factors affecting the generality of relationships. Glob. Ecol. Biogeogr. 12, 147–159 (2003).Article 

Google Scholar 
4.Lefsky, M. A. et al. Lidar remote sensing of above-ground biomass in three biomes. Glob. Ecol. Biogeogr. 11, 393–399 (2002).Article 

Google Scholar 
5.Duncanson, L. et al. The importance of consistent global forest aboveground biomass product validation. Surv. Geophys. 40, 979–999 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
6.Spawn, S. A., Sullivan, C. C., Lark, T. J. & Gibbs, H. K. Harmonized global maps of above and belowground biomass carbon density in the year 2010. Sci. Data 7, 112 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
7.Ottaviani, G. et al. The neglected belowground dimension of plant dominance. Trends Ecol. Evol. 35, 763–766 (2020).PubMed 
Article 

Google Scholar 
8.Jackson, L. E., Burger, M. & Cavagnaro, T. R. Roots, nitrogen transformations, and ecosystem services. Annu. Rev. Plant Biol. 59, 341–363 (2008).CAS 
PubMed 
Article 

Google Scholar 
9.Gill, R. A. & Jackson, R. B. Global patterns of root turnover for terrestrial ecosystems. New Phytol. 147, 13–31 (2000).Article 

Google Scholar 
10.Robinson, D. Implications of a large global root biomass for carbon sink estimates and for soil carbon dynamics. Proc. R. Soc. Lond. B 274, 2753–2759 (2007).CAS 

Google Scholar 
11.Bardgett, R. D., Mommer, L. & De Vries, F. T. Going underground: root traits as drivers of ecosystem processes. Trends Ecol. Evol. 29, 692–699 (2014).PubMed 
Article 

Google Scholar 
12.Ribeiro, S. C. et al. Above- and belowground biomass in a Brazilian Cerrado. For. Ecol. Manage. 262, 491–499 (2011).Article 

Google Scholar 
13.Mokany, K., Raison, R. J. & Prokushkin, A. S. Critical analysis of root:shoot ratios in terrestrial biomes. Glob. Chang. Biol. 12, 84–96 (2006).Article 

Google Scholar 
14.Saatchi, S. S. et al. Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl Acad. Sci. USA 108, 9899–9904 (2011).CAS 
PubMed 
Article 

Google Scholar 
15.Ruesch, A. S. & Gibbs, H. H. K. New IPCC Tier-1 Global Biomass Carbon Map for the Year 2000 (Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, 2008).16.Chen, J. L. & Reynolds, J. F. A coordination model of whole-plant carbon allocation in relation to water stress. Ann. Bot. 80, 45–55 (1997).CAS 
Article 

Google Scholar 
17.Franklin, O. et al. Modeling carbon allocation in trees: a search for principles. Tree Physiol. 32, 648–666 (2012).CAS 
PubMed 
Article 

Google Scholar 
18.Bloom, A. J., Chapin, F. S. & Mooney, H. A. Resource limitation in plants—an economic analogy. Annu. Rev. Ecol. Syst. 16, 363–392 (1985).Article 

Google Scholar 
19.Poorter, H. et al. Biomass allocation to leaves, stems and roots: meta-analyses of interspecific variation and environmental control. New Phytol. 193, 30–50 (2012).CAS 
PubMed 
Article 

Google Scholar 
20.Reich, P. in Plant Roots: The Hidden Half (eds. Waisel, Y. et al.) 205–220 (Marcel Dekker, 2006).21.Ledo, A. et al. Tree size and climatic water deficit control root to shoot ratio in individual trees globally. New Phytol. 217, 8–11 (2018).PubMed 
Article 

Google Scholar 
22.Qi, Y., Wei, W., Chen, C. & Chen, L. Plant root-shoot biomass allocation over diverse biomes: a global synthesis. Glob. Ecol. Conserv. 18, e00606 (2019).Article 

Google Scholar 
23.Reich, P. B. et al. Temperature drives global patterns in forest biomass distribution in leaves, stems, and roots. Proc. Natl Acad. Sci. USA 111, 13721–13726 (2014).CAS 
PubMed 
Article 

Google Scholar 
24.De Frenne, P. et al. Latitudinal gradients as natural laboratories to infer species’ responses to temperature. J. Ecol. 101, 784–795 (2013).Article 

Google Scholar 
25.Luo, Y. Terrestrial carbon-cycle feedback to climate warming. Annu. Rev. Ecol. Evol. Syst. 38, 683–712 (2007).Article 

Google Scholar 
26.Jackson, R. B. et al. A global analysis of root distributions for terrestrial biomes. Oecologia 108, 389–411 (1996).CAS 
PubMed 
Article 

Google Scholar 
27.Malhi, Y., Doughty, C. & Galbraith, D. The allocation of ecosystem net primary productivity in tropical forests. Philos. Trans. R. Soc. Lond. B 366, 3225–3245 (2011).CAS 
Article 

Google Scholar 
28.Roberts, D. R. et al. Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography 40, 913–929 (2017).Article 

Google Scholar 
29.Cairns, M. A., Brown, S., Helmer, E. H. & Baumgardner, G. A. Root biomass allocation in the world’s upland forests. Oecologia 111, 1–11 (1997).PubMed 
Article 

Google Scholar 
30.McCarthy, M. C. & Enquist, B. J. Consistency between an allometric approach and optimal partitioning theory in global patterns of plant biomass allocation. Funct. Ecol. 21, 713–720 (2007).Article 

Google Scholar 
31.Barton, C. V. M. & Montagu, K. D. Effect of spacing and water availability on root:shoot ratio in Eucalyptus camaldulensis. For. Ecol. Manage. 221, 52–62 (2006).Article 

Google Scholar 
32.Enquist, B. J. & Niklas, K. J. Global allocation rules for patterns of biomass partitioning in seed plants. Science 295, 1517–1520 (2002).CAS 
PubMed 
Article 

Google Scholar 
33.Goward, S. N., Tucker, C. J. & Dye, D. G. North American vegetation patterns observed with the NOAA-7 advanced very high resolution radiometer. Vegetatio 64, 3–14 (1985).Article 

Google Scholar 
34.Manzoni, S., Jackson, R. B., Trofymow, J. A. & Porporato, A. The global stoichiometry of litter nitrogen mineralization. Science 321, 684–686 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
35.Kaiser, C., Franklin, O., Dieckmann, U. & Richter, A. Microbial community dynamics alleviate stoichiometric constraints during litter decay. Ecol. Lett. 17, 680–690 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
36.Jiao, F., Shi, X. R., Han, F. P. & Yuan, Z. Y. Increasing aridity, temperature and soil pH induce soil C-N-P imbalance in grasslands. Sci. Rep. 6, 19601 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Sitch, S. et al. Recent trends and drivers of regional sources and sinks of carbon dioxide. Biogeosciences 12, 653–679 (2015).Article 

Google Scholar 
38.De Deyn, G. B., Cornelissen, J. H. C. & Bardgett, R. D. Plant functional traits and soil carbon sequestration in contrasting biomes. Ecol. Lett. 11, 516–531 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
39.Tjoelker, M. G., Craine, J. M., Wedin, D., Reich, P. B. & Tilman, D. Linking leaf and root trait syndromes among 39 grassland and savannah species. New Phytol. 167, 493–508 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
40.Personeni, E. & Loiseau, P. How does the nature of living and dead roots affect the residence time of carbon in the root litter continuum? Plant Soil 267, 129–141 (2004).CAS 
Article 

Google Scholar 
41.Tuanmu, M. N. & Jetz, W. A global 1-km consensus land-cover product for biodiversity and ecosystem modelling. Glob. Ecol. Biogeogr. 23, 1031–1045 (2014).Article 

Google Scholar 
42.Pan, Y., Birdsey, R. A., Phillips, O. L. & Jackson, R. B. The structure, distribution, and biomass of the world’s forests. Annu. Rev. Ecol. Evol. Syst. 44, 593–622 (2013).Article 

Google Scholar 
43.Jackson, R. B., Mooney, H. A. & Schulze, E. D. A global budget for fine root biomass, surface area, and nutrient contents. Proc. Natl Acad. Sci. USA 94, 7362–7366 (1997).CAS 
PubMed 
Article 

Google Scholar 
44.Genet, H., Bréda, N. & Dufrêne, E. Age-related variation in carbon allocation at tree and stand scales in beech (Fagus sylvatica L.) and sessile oak (Quercus petraea (Matt.) Liebl.) using a chronosequence approach. Tree Physiol. 30, 177–192 (2009).PubMed 
Article 

Google Scholar 
45.De Castro, E. A. & Kauffman, J. B. Ecosystem structure in the Brazilian Cerrado: a vegetation gradient of aboveground biomass, root mass and consumption by fire. J. Trop. Ecol. 14, 263–283 (1998).Article 

Google Scholar 
46.Ding, B. & Sun, J. Study on biomass of Korean pine plantation in east mountain areas of northeast China. Bull. Bot. Res. 9, 149–157 (1989).
Google Scholar 
47.Ding, B., Liu, S. & Cai, T. Studies on biological productivity of artificial forests of Dahurian larches. Chin. J. Plant Ecol. 14, 226–236 (1990).
Google Scholar 
48.Ding, B. & Sun, J. Accumulation and distribution of productivity and nutrient element in natural Manchurian ash. J. Northeast For. Univ. 4, 1–9 (1989).
Google Scholar 
49.Dossa, E. L., Fernandes, E. C. M., Reid, W. S. & Ezui, K. Above- and belowground biomass, nutrient and carbon stocks contrasting an open-grown and a shaded coffee plantation. Agrofor. Syst. 72, 103–115 (2008).Article 

Google Scholar 
50.Epron, D. et al. Do changes in carbon allocation account for the growth response to potassium and sodium applications in tropical Eucalyptus plantations? Tree Physiol. 32, 667–679 (2012).CAS 
PubMed 
Article 

Google Scholar 
51.Fonseca, W., Rey Benayas, J. M. & Alice, F. E. Carbon accumulation in the biomass and soil of different aged secondary forests in the humid tropics of Costa Rica. For. Ecol. Manage. 262, 1400–1408 (2011).Article 

Google Scholar 
52.Goodman, R. C. et al. Amazon palm biomass and allometry. For. Ecol. Manage. 310, 994–1004 (2013).Article 

Google Scholar 
53.Greenland, D. J. & Kowal, J. M. L. Nutrient content of the moist tropical forest of Ghana. Plant Soil 12, 154–173 (1960).CAS 
Article 

Google Scholar 
54.He, Y. et al. Carbon storage capacity of monoculture and mixed-species plantations in subtropical China. For. Ecol. Manage. 295, 193–198 (2013).Article 

Google Scholar 
55.Aiba, M. & Nakashizuka, T. Variation in juvenile survival and related physiological traits among dipterocarp species co‐existing in a Bornean forest. J. Veg. Sci. 18, 379–388 (2007).Article 

Google Scholar 
56.Jha, K. K. Carbon storage and sequestration rate assessment and allometric model development in young teak plantations of tropical moist deciduous forest, India. J. For. Res. 26, 589–604 (2015).CAS 
Article 

Google Scholar 
57.Kalita, R. M., Das, A. K. & Nath, A. J. Allometric equations for estimating above- and belowground biomass in Tea (Camellia sinensis (L.) O. Kuntze) agroforestry system of Barak Valley, Assam, northeast India. Biomass Bioenergy 83, 42–49 (2015).Article 

Google Scholar 
58.Kenzo, T. et al. Development of allometric relationships for accurate estimation of above- and below-ground biomass in tropical secondary forests in Sarawak, Malaysia. J. Trop. Ecol. 25, 371–386 (2009).Article 

Google Scholar 
59.Kenzo, T. et al. Allometric equations for accurate estimation of above-ground biomass in logged-over tropical rainforests in Sarawak, Malaysia. J. For. Res. 14, 365–372 (2009).CAS 
Article 

Google Scholar 
60.Kraenzel, M., Castillo, A., Moore, T. & Potvin, C. Carbon storage of harvest-age teak (Tectona grandis) plantations, Panama. For. Ecol. Manage. 173, 213–225 (2003).Article 

Google Scholar 
61.Kuyah, S., Dietz, J., Muthuri, C., van Noordwijk, M. & Neufeldt, H. Allometry and partitioning of above- and below-ground biomass in farmed eucalyptus species dominant in Western Kenyan agricultural landscapes. Biomass Bioenergy 55, 276–284 (2013).Article 

Google Scholar 
62.Liu, S., Cai, Y. & Cai, T. in Long-term Research on Forest Ecosystems (ed. Zhou, X.) 419–427 (Northeast Forestry Univ. Press, 1991).63.Luo, T. et al. Root biomass along subtropical to alpine gradients: global implication from Tibetan transect studies. For. Ecol. Manage. 206, 349–363 (2005).Article 

Google Scholar 
64.Markesteijn, L. & Poorter, L. Seedling root morphology and biomass allocation of 62 tropical tree species in relation to drought- and shade-tolerance. J. Ecol. 97, 311–325 (2009).Article 

Google Scholar 
65.McNicol, I. M. et al. Development of allometric models for above and belowground biomass in swidden cultivation fallows of northern Laos. For. Ecol. Manage. 357, 104–116 (2015).Article 

Google Scholar 
66.Aiba, M. & Nakashizuka, T. Sapling structure and regeneration strategy in 18 Shorea species co-occurring in a tropical rainforest. Ann. Bot. 96, 313–321 (2005).PubMed 
PubMed Central 
Article 

Google Scholar 
67.Menaut, J. C. & Cesar, J. Structure and primary productivity of Lamto savannas, Ivory Coast. Ecology 60, 1197–1210 (1979).Article 

Google Scholar 
68.Morais, V. A. et al. Estoques de carbono e biomassa de um fragmento de cerradão em Minas Gerais, Brasil. Cerne 19, 237–245 (2013).Article 

Google Scholar 
69.Mugasha, W. A. et al. Allometric models for prediction of above- and belowground biomass of trees in the miombo woodlands of Tanzania. For. Ecol. Manage. 310, 87–101 (2013).Article 

Google Scholar 
70.Návar, J. Plasticity of biomass component allocation patterns in semiarid Tamaulipan thornscrub and dry temperate pine species of northeastern Mexico. Polibotánica 31, 121–141 (2011).
Google Scholar 
71.Njana, M. A., Eid, T., Zahabu, E. & Malimbwi, R. Procedures for quantification of belowground biomass of three mangrove tree species. Wetl. Ecol. Manage. 23, 749–764 (2015).Article 

Google Scholar 
72.Nogueira Junior, L. R., Engel, V. L., Parrotta, J. A., de Melo, A. C. G. & Ré, D. S. Equações alométricas para estimativa da biomassa arbórea em plantios mistos com espécies nativas na restauração da Mata Atlântica. Biota Neotrop. 14, 1–9 (2014).Article 

Google Scholar 
73.Peichl, M. & Arain, M. A. Above- and belowground ecosystem biomass and carbon pools in an age-sequence of temperate pine plantation forests. Agric. For. Meteorol. 140, e20130084 (2006).Article 

Google Scholar 
74.Battles, J. J. et al. Vegetation composition, structure, and biomass of two unpolluted watersheds in the Cordillera de Piuchué, Chiloé Island, Chile. Plant Ecol. 158, 5–19 (2002).Article 

Google Scholar 
75.Ryan, C. M., Williams, M. & Grace, J. Above- and belowground carbon stocks in a miombo woodland landscape of Mozambique. Biotropica 43, 423–432 (2011).Article 

Google Scholar 
76.Saint-André, L. et al. Age-related equations for above- and below-ground biomass of a Eucalyptus hybrid in Congo. For. Ecol. Manage. 205, 199–214 (2005).Article 

Google Scholar 
77.Aryal, D. R., De Jong, B. H. J., Ochoa-Gaona, S., Esparza-Olguin, L. & Mendoza-Vega, J. Carbon stocks and changes in tropical secondary forests of southern Mexico. Agric. Ecosyst. Environ. 195, 220–230 (2014).Article 

Google Scholar 
78.Schepaschenko, D. et al. A dataset of forest biomass structure for Eurasia. Sci. Data 4, 170070 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
79.Schroth, G., D’Angelo, S. A., Teixeira, W. G., Haag, D. & Lieberei, R. Conversion of secondary forest into agroforestry and monoculture plantations in Amazonia: consequences for biomass, litter and soil carbon stocks after 7 years. For. Ecol. Manage. 163, 131–150 (2002).Article 

Google Scholar 
80.Schulze, E. D. et al. Rooting depth, water availability, and vegetation cover along an aridity gradient in Patagonia. Oecologia 108, 503–511 (1996).Article 

Google Scholar 
81.Stolbovoi, V. & McCallum, I. Land resources of Russia [CD] (International Institute for Applied Systems Analysis and the Russian Academy of Science, 2002); http://www.iiasa.ac.at/Research/FOR/russia_cd/guide.htm82.Wang, L. et al. Biomass allocation patterns across China’s terrestrial biomes. PLoS ONE 9, e93566 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
83.Wauters, J. B., Coudert, S., Grallien, E., Jonard, M. & Ponette, Q. Carbon stock in rubber tree plantations in Western Ghana and Mato Grosso (Brazil). For. Ecol. Manage. 255, 2347–2361 (2008).Article 

Google Scholar 
84.Williams-Linera, G. Biomass and nutrient content in two successional stages of tropical wet forest in Uxpanapa, Mexico. Biotropica 15, 275–284 (1983).Article 

Google Scholar 
85.Xu, Y. et al. Improving allometry models to estimate the above- and belowground biomass of subtropical forest, China. Ecosphere 6, 289 (2015).Article 

Google Scholar 
86.Youkhana, A. H. & Idol, T. W. Allometric models for predicting above- and belowground biomass of Leucaena-KX2 in a shaded coffee agroecosystem in Hawaii. Agrofor. Syst. 83, 331–345 (2011).Article 

Google Scholar 
87.Zhang, H. et al. Biogeographical patterns of biomass allocation in leaves, stems, and roots in China’s forests. Sci. Rep. 5, 15997 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
88.Castellanos, J., Maass, M. & Kummerow, J. Root biomass of a dry deciduous tropical forest in Mexico. Plant Soil 131, 225–228 (1991).Article 

Google Scholar 
89.Zheng, Z., Feng, Z., Cao, M., Li, Z. & Zhang, J. Forest structure and biomass of a tropical seasonal rain forest in Xishuangbanna, southwest China. Biotropica 38, 318–327 (2006).Article 

Google Scholar 
90.Návar, J. Root stock biomass and productivity assessments of reforested pine stands in northern Mexico. For. Ecol. Manage. 338, 139–147 (2015).Article 

Google Scholar 
91.Wang, X., Fang, J. & Zhu, B. Forest biomass and root–shoot allocation in northeast China. For. Ecol. Manage. 255, 4007–4020 (2008).Article 

Google Scholar 
92.Chen, D. K., Zhou, X. F., Zhao, H. X., Wang, Y. H. & Jing, Y. Y. Study on the structure, function and succession of the four types in natural secondary forest. J. Northeast For. Univ. 2, 1–20 (1982).
Google Scholar 
93.Chidumayo, E. N. Estimating tree biomass and changes in root biomass following clear-cutting of Brachystegia-Julbernardia (miombo) woodland in central Zambia. Environ. Conserv. 41, 54–63 (2014).Article 

Google Scholar 
94.Coll, L., Potvin, C., Messier, C. & Delagrange, S. Root architecture and allocation patterns of eight native tropical species with different successional status used in open-grown mixed plantations in Panama. Trees 22, 585–596 (2008).Article 

Google Scholar 
95.Das, D. K. & Chaturvedi, O. P. Structure and function of Populus deltoides agroforestry systems in eastern India: 1. dry matter dynamics. Agrofor. Syst. 65, 215–221 (2005).Article 

Google Scholar 
96.Ni, J. Estimating net primary productivity of grasslands from field biomass measurements in temperate northern China. Plant Ecol. 174, 217–234 (2011).Article 

Google Scholar 
97.Olson, R. et al. NPP Multi-Biome: Summary Data from Intensive Studies at 125 Sites, 1936–2006 (ORNL DAAC, accessed 19 June 2019); https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=135298.Perez, C. A. & Frangi, J. L. Grassland biomass dynamics along an altitudinal gradient in the pampa. J. Range Manage. 53, 518–528 (2007).Article 

Google Scholar 
99.Perez-Quezada, J. F. F., Delpiano, C. A. A., Snyder, K. A. A., Johnson, D. A. A. & Franck, N. Carbon pools in an arid shrubland in Chile under natural and afforested conditions. J. Arid Environ. 75, 29–37 (2011).Article 

Google Scholar 
100.Pornon, A., Boutin, M. & Lamaze, T. Contribution of plant species to the high N retention capacity of a subalpine meadow undergoing elevated N deposition and warming. Environ. Pollut. 245, 235–242 (2019).CAS 
PubMed 
Article 

Google Scholar 
101.Ramakrishnan, P. S. & Ram, S. C. Vegetation, biomass and productivity of seral grasslands of Cherrapunji in north-east India. Vegetatio 74, 47–53 (1988).Article 

Google Scholar 
102.Shaver, G. R., Laundre, J. A., Giblin, A. E. & Nadelhoffer, K. J. Changes in live plant biomass, primary production, and species composition along a riverside toposequence in Arctic Alaska, USA. Arct. Alp. Res. 28, 363–379 (2006).Article 

Google Scholar 
103.Smith, J. M. B. & Klinger, L. F. Aboveground:belowground phytomass ratios in Venezuelan paramo vegetation and their significance. Arct. Alp. Res. 17, 189–198 (2006).Article 

Google Scholar 
104.Sun, J. et al. Effects of grazing regimes on plant traits and soil nutrients in an alpine steppe, northern Tibetan Plateau. PLoS ONE 9, e108821 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
105.Wang, P. et al. Belowground plant biomass allocation in tundra ecosystems and its relationship with temperature. Environ. Res. Lett. 11, 055003 (2016).Article 
CAS 

Google Scholar 
106.Yang, Y., Fang, J., Ji, C. & Han, W. Above- and belowground biomass allocation in Tibetan grasslands. J. Veg. Sci. 20, 177–184 (2009).Article 

Google Scholar 
107.Yang, Y., Fang, J., Ma, W., Guo, D. & Mohammat, A. Large-scale pattern of biomass partitioning across China’s grasslands. Glob. Ecol. Biogeogr. 19, 268–277 (2010).Article 

Google Scholar 
108.Geng, H. L., Wang, Y. H., Wang, F. Y. & Jia, B. R. The dynamics of root-shoot ratio and its environmental effective factors of recovering Leymus chinensis steppe vegetation in Inner Mongolia, China. Acta Ecol. Sin. 28, 4629–4634 (2008).Article 

Google Scholar 
109.Hui, D. & Jackson, R. B. Geographical and interannual variability in biomass partitioning in grassland ecosystems: a synthesis of field data. New Phytol. 169, 85–93 (2006).CAS 
PubMed 
Article 

Google Scholar 
110.Jouquet, P., Tavernier, V., Abbadie, L. & Lepage, M. Nests of subterranean fungus-growing termites (Isoptera, Macrotermitinae) as nutrient patches for grasses in savannah ecosystems. Afr. J. Ecol. 43, 191–196 (2005).Article 

Google Scholar 
111.Leonid, U. et al. Impact of climate and grazing on biomass components of eastern Russia typical steppe. J. Integr. Agric. 13, 1183–1192 (2014).Article 

Google Scholar 
112.Lucash, M. S., Farnsworth, B. & Winner, W. E. Response of sagebrush steppe species to elevated CO2 and soil temperature. West. N. Am. Nat. 65, 80–86 (2005).
Google Scholar 
113.Luo, W. et al. Patterns of plant biomass allocation in temperate grasslands across a 2500-km transect in northern China. PLoS ONE 8, e71749 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
114.Barbour, M. G. Desert dogma reexamined: root/shoot productivity and plant spacing. Am. Midl. Nat. 89, 41–57 (1973).Article 

Google Scholar 
115.Becker, P., Sharbini, N. & Yahya, R. Root architecture and root:shoot allocation of shrubs and saplings in two lowland tropical forests: implications for life-form composition. Biotropica 31, 93–101 (1999).
Google Scholar 
116.Becker, P. & Castillo, A. Root architecture of shrubs and saplings in the understory of a tropical moist forest in lowland Panama. Biotropica 22, 242–249 (1990).Article 

Google Scholar 
117.Beier, C. et al. Carbon and nitrogen balances for six shrublands across Europe. Glob. Biogeochem. Cycles 23, GB4008 (2009).Article 
CAS 

Google Scholar 
118.Bhatt, Y. D., Rawat, Y. S. & Singh, S. P. Changes in ecosystem functioning after replacement of forest by Lantana shrubland in Kumaun Himalaya. J. Veg. Sci. 5, 67–70 (1994).Article 

Google Scholar 
119.Caldwell, M. M., White, R. S., Moore, R. T. & Camp, L. B. Carbon balance, productivity, and water use of cold-winter desert shrub communities dominated by C3 and C4 species. Oecologia 29, 275–300 (1977).PubMed 
Article 

Google Scholar 
120.De Viñas, I. C. R. et al. Biomass of root and shoot systems of Quercus coccifera shrublands in eastern Spain. Ann. For. Sci. 57, 803–810 (2000).Article 

Google Scholar 
121.Caravaca, F., Figueroa, D., Alguacil, M. M. & Roldán, A. Application of composted urban residue enhanced the performance of afforested shrub species in a degraded semiarid land. Bioresour. Technol. 90, 65–70 (2003).CAS 
PubMed 
Article 

Google Scholar 
122.Caravaca, F., Figueroa, D., Azcón-Aguilar, C., Barea, J. M. & Roldán, A. Medium-term effects of mycorrhizal inoculation and composted municipal waste addition on the establishment of two Mediterranean shrub species under semiarid field conditions. Agric. Ecosyst. Environ. 97, 95–105 (2003).Article 

Google Scholar 
123.Carrasco, L., Azcón, R., Kohler, J., Roldán, A. & Caravaca, F. Comparative effects of native filamentous and arbuscular mycorrhizal fungi in the establishment of an autochthonous, leguminous shrub growing in a metal-contaminated soil. Sci. Total Environ. 409, 1205–1209 (2011).CAS 
PubMed 
Article 

Google Scholar 
124.Carrillo-Garcia, Á., Bashan, Y. & Bethlenfalvay, G. J. Resource-island soils and the survival of the giant cactus, cardon, of Baja California Sur. Plant Soil 218, 207–214 (2000).CAS 
Article 

Google Scholar 
125.Carrión-Prieto, P. et al. Mediterranean shrublands as carbon sinks for climate change mitigation: new root-to-shoot ratios. Carbon Manage. 8, 67–77 (2017).Article 
CAS 

Google Scholar 
126.Deng, L., Han, Q. S., Zhang, C., Tang, Z. S. & Shangguan, Z. P. Above-ground and below-ground ecosystem biomass accumulation and carbon sequestration with Caragana korshinskii Kom plantation development. Land Degrad. Dev. 28, 906–917 (2017).Article 

Google Scholar 
127.Perkins, S. R. & Owens, M. K. Growth and biomass allocation of shrub and grass seedlings in response to predicted changes in precipitation seasonality. Plant Ecol. 168, 107–120 (2003).Article 

Google Scholar 
128.Gargaglione, V., Peri, P. L. & Rubio, G. Allometric relations for biomass partitioning of Nothofagus antarctica trees of different crown classes over a site quality gradient. For. Ecol. Manage. 259, 1118–1126 (2010).Article 

Google Scholar 
129.Hao, H. M. et al. Effects of shrub patch size succession on plant diversity and soil water content in the water-wind erosion crisscross region on the Loess Plateau. Catena 144, 177–183 (2016).Article 

Google Scholar 
130.Herwitz, S. R. & Olsvig-Whittaker, L. Preferential upslope growth of Zygophyllum dumosum Boiss. (Zygophyllaceae) roots into bedrock fissures in the northern Negev desert. J. Biogeogr. 16, 457–460 (1989).Article 

Google Scholar 
131.Hoffmann, A. & Kummerow, J. Root studies in the Chilean matorral. Oecologia 32, 57–69 (1978).PubMed 
Article 

Google Scholar 
132.Holl, K. D. Effects of above- and below-ground competition of shrubs and grass on Calophyllum brasiliense (Camb.) seedling growth in abandoned tropical pasture. For. Ecol. Manage. 109, 187–195 (1998).Article 

Google Scholar 
133.Hollister, R. D. & Flaherty, K. J. Above- and below-ground plant biomass response to experimental warming in northern Alaska. Appl. Veg. Sci. 13, 378–387 (2010).
Google Scholar 
134.Kizito, F. et al. Seasonal soil water variation and root patterns between two semi-arid shrubs co-existing with pearl millet in Senegal, West Africa. J. Arid Environ. 67, 436–455 (2006).Article 

Google Scholar 
135.Kummerow, J., Krause, D. & Jow, W. Root systems of chaparral shrubs. Oecologia 29, 163–177 (1977).PubMed 
Article 

Google Scholar 
136.León, M. F., Squeo, F. A., Gutiérrez, J. R. & Holmgren, M. Rapid root extension during water pulses enhances establishment of shrub seedlings in the Atacama Desert. J. Veg. Sci. 22, 120–129 (2011).Article 

Google Scholar 
137.Li, C. P. & Xiao, C. W. Above- and belowground biomass of Artemisia ordosica communities in three contrasting habitats of the Mu Us Desert, northern China. J. Arid Environ. 70, 195–207 (2007).Article 

Google Scholar 
138.Liang, Y. M., Hazlett, D. L. & Lauenroth, W. K. Biomass dynamics and water use efficiencies of five plant communities in the shortgrass steppe. Oecologia 80, 148–153 (1989).CAS 
PubMed 
Article 

Google Scholar 
139.Zan, Q., Wang, Y., Liao, B. & Zheng, D. Biomass and net productivity of Sonneratia apetala, S. caseolaris mangrove man-made forest. Wuhan Bot. Res. 19, 391–396 (2001).
Google Scholar 
140.Liao, B., Zheng, D. & Zheng, S. Studies on the biomass of Sonneratia caseolaris stand. For. Res. 3, 47–54 (1990).
Google Scholar 
141.Lufafa, A. et al. Allometric relationships and peak-season community biomass stocks of native shrubs in Senegal’s Peanut Basin. J. Arid Environ. 73, 260–266 (2009).Article 

Google Scholar 
142.Lusk, C. H. Leaf area and growth of juvenile temperate evergreens in low light: species of contrasting shade tolerance change rank during ontogeny. Funct. Ecol. 18, 820–828 (2004).Article 

Google Scholar 
143.Marsh, A. S., Arnone, J. A., Bormann, B. T. & Gordon, J. C. The role of Equisetum in nutrient cycling in an Alaskan shrub wetland. J. Ecol. 88, 999–1011 (2000).Article 

Google Scholar 
144.Martínez, F. et al. Belowground structure and production in a Mediterranean sand dune shrub community. Plant Soil 201, 209–216 (1998).Article 

Google Scholar 
145.Marziliano, P. A. et al. Estimating belowground biomass and root/shoot ratio of Phillyrea latifolia L. in the Mediterranean forest landscapes. Ann. For. Sci. 72, 585–593 (2015).Article 

Google Scholar 
146.Mauchamp, A., Montaña, C., Lepart, J., Rambal, S. & Montana, C. Ecotone dependent recruitment of a desert shrub, Flourensia cernua, in vegetation stripes. Oikos 68, 107–116 (1993).Article 

Google Scholar 
147.Mendoza-Ponce, A. & Galicia, L. Aboveground and belowground biomass and carbon pools in highland temperate forest landscape in central Mexico. Forestry 83, 497–506 (2010).Article 

Google Scholar 
148.Miller, P. C. & Ng, E. Root:shoot biomass ratios in shrubs in southern California and central Chile. Madrono 24, 215–223 (1977).
Google Scholar 
149.Mooney, H. A. & Rundel, P. W. Nutrient relations of the evergreen shrub, Adenostoma fasciculatum, in the California chaparral. Bot. Gaz. 140, 109–113 (1979).CAS 
Article 

Google Scholar 
150.Moro, M. J., Pugnaire, F. I., Haase, P. & Puigdefábregas, J. Effect of the canopy of Retama sphaerocarpa on its understorey in a semiarid environment. Funct. Ecol. 11, 425–431 (1997).Article 

Google Scholar 
151.Negreiros, D., Fernandes, G. W., Silveira, F. A. O. & Chalub, C. Seedling growth and biomass allocation of endemic and threatened shrubs of rupestrian fields. Acta Oecol. 35, 301–310 (2009).Article 

Google Scholar 
152.Nie, X., Yang, Y., Yang, L. & Zhou, G. Above- and belowground biomass allocation in shrub biomes across the northeast Tibetan Plateau. PLoS ONE 11, e0154251 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
153.Nobel, P. S., Quero, E. & Linares, H. Root versus shoot biomass: responses to water, nitrogen, and phosphorus applications for Agave lechuguilla. Bot. Gaz. 150, 411–416 (1989).Article 

Google Scholar 
154.Pacaldo, R. S., Volk, T. A. & Briggs, R. D. Greenhouse gas potentials of shrub willow biomass crops based on below- and aboveground biomass inventory along a 19-year chronosequence. Bioenergy Res. 6, 252–262 (2013).CAS 
Article 

Google Scholar 
155.Padilla, F. M., Miranda, J. D., Jorquera, M. J. & Pugnaire, F. I. Variability in amount and frequency of water supply affects roots but not growth of arid shrubs. Plant Ecol. 204, 261–270 (2009).Article 

Google Scholar 
156.Portsmuth, A., Niinemets, Ü., Truus, L. & Pensa, M. Biomass allocation and growth rates in Pinus sylvestris are interactively modified by nitrogen and phosphorus availabilities and by tree size and age. Can. J. For. Res. 35, 2346–2359 (2005).CAS 
Article 

Google Scholar 
157.Roth, G. A., Whitford, W. G. & Steinberger, Y. Jackrabbit (Lepus californicus) herbivory changes dominance in desertified Chihuahuan Desert ecosystems. J. Arid Environ. 70, 418–426 (2007).Article 

Google Scholar 
158.Ruiz-Peinado, R., Moreno, G., Juarez, E., Montero, G. & Roig, S. The contribution of two common shrub species to aboveground and belowground carbon stock in Iberian dehesas. J. Arid Environ. 91, 22–30 (2013).Article 

Google Scholar 
159.Rundel, P. W. Biomass, productivity, and nutrient allocation in subalpine shrublands and meadows of the Emerald Lake Basin, Sequoia National Park, California. Arct. Antarct. Alp. Res. 47, 115–123 (2015).Article 

Google Scholar 
160.Millikin, C. S. & Bledsoe, C. S. Biomass and distribution of fine and coarse roots from blue oak (Quercus douglasii) trees in the northern Sierra Nevada foothills of California. Plant Soil 214, 27–38 (1999).CAS 
Article 

Google Scholar 
161.Saura-Mas, S. & Lloret, F. Adult root structure of Mediterranean shrubs: relationship with post-fire regenerative syndrome. Plant Biol. 16, 147–154 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
162.Schenk, H. J. & Mahall, B. E. Positive and negative plant interactions contribute to a north-south-patterned association between two desert shrub species. Oecologia 132, 402–410 (2002).PubMed 
Article 

Google Scholar 
163.Silva, J. S., Rego, F. C. & Martins-Loução, M. A. Belowground traits of Mediterranean woody plants in a Portuguese shrubland. Ecol. Mediterr. 28, 5–13 (2002).Article 

Google Scholar 
164.Simões, M. P., Madeira, M. & Gazarini, L. Biomass and nutrient dynamics in Mediterranean seasonal dimorphic shrubs: strategies to face environmental constraints. Plant Biosyst. 146, 500–510 (2012).
Google Scholar 
165.Tao, Y., Zhang, Y. M. & Downing, A. Similarity and difference in vegetation structure of three desert shrub communities under the same temperate climate but with different microhabitats. Bot. Stud. 54, 59 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
166.Toscano, S., Scuderi, D., Giuffrida, F. & Romano, D. Responses of Mediterranean ornamental shrubs to drought stress and recovery. Sci. Hortic. 178, 145–153 (2014).Article 

Google Scholar 
167.Trubat, R., Cortina, J. & Vilagrosa, A. Nutrient deprivation improves field performance of woody seedlings in a degraded semi-arid shrubland. Ecol. Eng. 37, 1164–1173 (2011).Article 

Google Scholar 
168.Van Wijk, M. T., Williams, M., Gough, L., Hobbie, S. E. & Shaver, G. R. Luxury consumption of soil nutrients: a possible competitive strategy in above-ground and below-ground biomass allocation and root morphology for slow-growing arctic vegetation? J. Ecol. 91, 664–676 (2003).Article 

Google Scholar 
169.Walker, L. R., Clarkson, B. D., Silvester, W. B. & Clarkson, B. R. Colonization dynamics and facilitative impacts of a nitrogen-fixing shrub in primary succession. J. Veg. Sci. 14, 277–290 (2003).Article 

Google Scholar 
170.Wang, B. & Yang, X. S. Comparison of biomass and species diversity of four typical zonal vegetations. J. Fujian Coll. For. 29, 345–350 (2009).
Google Scholar 
171.Wang, M. & Li, H. Quantitative study on the soil water dynamics of various forest plantations in the Loess Plateau region in northwestern Shanxi. Acta Ecol. Sin. 2, 178–184 (1995).
Google Scholar 
172.Wang, P. et al. Seasonal changes and vertical distribution of root standing biomass of graminoids and shrubs at a Siberian tundra site. Plant Soil 407, 55–65 (2016).CAS 
Article 

Google Scholar 
173.Whittaker, R. H. & Woodwell, G. M. Dimension and production relations of trees and shrubs in the Brookhaven Forest, New York. J. Ecol. 56, 1–25 (1968).Article 

Google Scholar 
174.Xu, H., Li, Y., Xu, G. & Zou, T. Ecophysiological response and morphological adjustment of two Central Asian desert shrubs towards variation in summer precipitation. Plant Cell Environ. 30, 399–409 (2007).CAS 
PubMed 
Article 

Google Scholar 
175.Yan, Z. Biomass and its allocation in a 28-year-old Castanopsis kawakamii plantation. J. Fujian Coll. For. 2, 114–118 (1996).
Google Scholar 
176.Gong, Y. et al. Carbon storage and vertical distribution in three shrubland communities in Gurbantünggüt Desert, Uygur Autonomous Region of Xinjiang, northwest China. Chin. Geogr. Sci. 22, 541–549 (2012).Article 

Google Scholar 
177.Yu, Y., Shi, D., Qiuyi, J., He, L. & Cheng, G. On the biomass of secondary Schima superba forest in Hangzhou. J. Zhejiang For. Coll. 2, 157–161 (1993).
Google Scholar 
178.Kato, T. et al. Carbon dioxide exchange between the atmosphere and an alpine meadow ecosystem on the Qinghai-Tibetan Plateau, China. Agric. Meteorol. 124, 121–134 (2004).Article 

Google Scholar 
179.Li, Z., Zhu, Q. & Li, J. A comparison of photosynthetic carbon sequestration of four shrubs in Ningxia. Pratacultural Sci. 29, 352–357 (2012).CAS 

Google Scholar 
180.Zhu, X., Shi, Q. & Li, Y. A preliminary study on the Qinghai’s treasure house of the forest biomass and shrubs. Sci. Technol. Qinghai Agric. For. 1, 15–20 (1993).
Google Scholar 
181.Liao, B. & Zheng, D. Study on the forest biomass and productivity of olive wood. For. Res. 4, 22–29 (1991).
Google Scholar 
182.Liu, B., Liu, Z., Lü, X., Maestre, F. T. & Wang, L. Sand burial compensates for the negative effects of erosion on the dune-building shrub Artemisia wudanica. Plant Soil 374, 263–273 (2014).CAS 
Article 

Google Scholar 
183.Alguacil, M. M., Hernández, J. A., Caravaca, F., Portillo, B. & Roldán, A. Antioxidant enzyme activities in shoots from three mycorrhizal shrub species afforested in a degraded semi-arid soil. Physiol. Plant. 118, 562–570 (2003).CAS 
Article 

Google Scholar 
184.Axe, M. S., Grange, I. D. & Conway, J. S. Carbon storage in hedge biomass—a case study of actively managed hedges in England. Agric. Ecosyst. Environ. 250, 81–88 (2017).Article 

Google Scholar 
185.van den Hoogen, J. et al. Soil nematode abundance and functional group composition at a global scale. Nature 572, 194–198 (2019).PubMed 
Article 
CAS 

Google Scholar 
186.Erin, L. et al. h2o: R Interface for the ‘H2O’ Scalable Machine Learning Platform. R package v.3.32.0.2 (2020); https://github.com/h2oai/h2o-3187.Sagi, O. & Rokach, L. Ensemble learning: a survey. WIREs Data Min. Knowl. Discov. 8, e1249 (2018).
Google Scholar 
188.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).189.Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).Article 

Google Scholar 
190.Heiberger, R. M. HH: Statistical Analysis and Data Display: Heiberger and Holland (2020).191.Hothorn, T. & Zeileis, A. partykit: A modular toolkit for recursive partytioning in R. J. Mach. Learn. Res. 16, 3905–3909 (2015).
Google Scholar 
192.Borkovec, M. & Madin, N. ggparty: ‘ggplot’ visualizations for the ‘partykit’ package (2019).193.Dormann, C. F. Effects of incorporating spatial autocorrelation into the analysis of species distribution data. Glob. Ecol. Biogeogr. 16, 129–138 (2007).Article 

Google Scholar 
194.Hutchinson, M., Xu, T., Houlder, D., Nix, H. & McMahon, J. ANUCLIM 6.0 User’s Guide (Australian National Univ., 2009).195.Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
196.Global Aridity and PET database (CGIAR-CSI, accessed 15 May 2018); http://www.cgiarcsi.community/data/global-aridity-and-pet-database197.CIESIN Gridded Population of the World, version 4 (GPWv4): Population Density Adjusted to Match 2015 Revision UN WPP Country Totals (NASA SEDAC, 2018); https://doi.org/10.7927/H4HX19NJ198.Venter, O. et al. Global terrestrial human footprint maps for 1993 and 2009. Sci. Data 3, 160067 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
199.SoilGrids (ISRIC, accessed 15 May 2018); https://www.soilgrids.org200.Entekhabi, D. et al. The soil moisture active passive (SMAP) mission. Proc. IEEE 98, 704–716 (2010).Article 

Google Scholar 
201.Fan, Y., Li, H. & Miguez-Macho, G. Global patterns of groundwater table depth. Science 339, 940–943 (2013).CAS 
PubMed 
Article 

Google Scholar 
202.Batjes, N. H. Harmonized soil property values for broad-scale modelling (WISE30sec) with estimates of global soil carbon stocks. Geoderma 269, 61–68 (2016).CAS 
Article 

Google Scholar 
203.Schaaf, C. & Wang, Z. MCD43A1 MODIS/Terra+Aqua BRDF/Albedo Model Parameters Daily L3 Global – 500m V006 (NASA LP DAAC, 2015); https://doi.org/10.5067/MODIS/MCD43A1C.006204.Didan, K. MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V006 (NASA LP DAAC, 2015).205.Crowther, T. W. et al. Mapping tree density at a global scale. Nature 525, 201–205 (2015).CAS 
PubMed 
Article 

Google Scholar  More

1.Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).PubMed 
Article 
CAS 

Google Scholar 
2.Diffenbaugh, N. S. & Field, C. B. Changes in ecologically critical terrestrial climate conditions. Science 341, 486–492 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
3.Viana, D. S., Santamaría, L. & Figuerola, J. Migratory birds as global dispersal vectors. Trends Ecol. Evol. 31, 763–775 (2016).PubMed 
Article 

Google Scholar 
4.Bauer, S. & Hoye, B. J. Migratory animals couple biodiversity and ecosystem functioning worldwide. Science 344, 1242552 (2014).CAS 
PubMed 
Article 

Google Scholar 
5.Corlett, R. T. & Westcott, D. A. Will plant movements keep up with climate change? Trends Ecol. Evol. 28, 482–488 (2013).PubMed 
Article 

Google Scholar 
6.Lenoir, J. & Svenning, J. C. Climate-related range shifts – a global multidimensional synthesis and new research directions. Ecography 38, 15–28 (2015).Article 

Google Scholar 
7.Lenoir, J. et al. Species better track climate warming in the oceans than on land. Nat. Ecol. Evol. 4, 1044–1059 (2020).PubMed 
Article 

Google Scholar 
8.Loarie, S. R. et al. The velocity of climate change. Nature 462, 1052–1055 (2009).ADS 
CAS 
PubMed 
Article 

Google Scholar 
9.Chen, I.-C., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).ADS 
CAS 
Article 

Google Scholar 
10.González-Varo, J. P., López-Bao, J. V. & Guitián, J. Seed dispersers help plants to escape global warming. Oikos 126, 1600–1606 (2017).Article 

Google Scholar 
11.Urban, M. C. et al. Improving the forecast for biodiversity under climate change. Science 353, aad8466 (2016).PubMed 
Article 
CAS 

Google Scholar 
12.Thuiller, W. et al. Predicting global change impacts on plant species’ distributions: future challenges. Perspect. Plant Ecol. Evol. Syst. 9, 137–152 (2008).Article 

Google Scholar 
13.Nadeau, C. P. & Urban, M. C. Eco-evolution on the edge during climate change. Ecography 42, 1280–1297 (2019).
Google Scholar 
14.Bacles, C. F. E., Lowe, A. J. & Ennos, R. A. Effective seed dispersal across a fragmented landscape. Science 311, 628 (2006).PubMed 
Article 

Google Scholar 
15.Jordano, P., García, C., Godoy, J. A. & García-Castaño, J. L. Differential contribution of frugivores to complex seed dispersal patterns. Proc. Natl Acad. Sci. USA 104, 3278–3282 (2007).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
16.Breitbach, N., Böhning-Gaese, K., Laube, I. & Schleuning, M. Short seed-dispersal distances and low seedling recruitment in farmland populations of bird-dispersed cherry trees. J. Ecol. 100, 1349–1358 (2012).Article 

Google Scholar 
17.Cain, M. L., Damman, H. & Muir, A. Seed dispersal and the Holocene migration of woodland herbs. Ecol. Monogr. 68, 325–347 (1998).Article 

Google Scholar 
18.Nathan, R. et al. Spread of North American wind-dispersed trees in future environments. Ecol. Lett. 14, 211–219 (2011).PubMed 
Article 

Google Scholar 
19.Nathan, R. et al. Mechanisms of long-distance seed dispersal. Trends Ecol. Evol. 23, 638–647 (2008).PubMed 
Article 

Google Scholar 
20.Viana, D. S., Gangoso, L., Bouten, W. & Figuerola, J. Overseas seed dispersal by migratory birds. Proc. R. Soc. Lond. B 283, 20152406 (2016).
Google Scholar 
21.Viana, D. S., Santamaría, L., Michot, T. C. & Figuerola, J. Migratory strategies of waterbirds shape the continental-scale dispersal of aquatic organisms. Ecography 36, 430–438 (2013).Article 

Google Scholar 
22.Carlquist, S. The biota of long-distance dispersal. V. Plant dispersal to Pacific islands. Bull. Torrey Bot. Club 94, 129–162 (1967).Article 

Google Scholar 
23.Esteves, C. F., Costa, J. M., Vargas, P., Freitas, H. & Heleno, R. H. On the limited potential of Azorean fleshy fruits for oceanic dispersal. PLoS ONE 10, e0138882 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
24.Viana, D. S., Santamaría, L., Michot, T. C. & Figuerola, J. Allometric scaling of long-distance seed dispersal by migratory birds. Am. Nat. 181, 649–662 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
25.Martínez-López, V., García, C., Zapata, V., Robledano, F. & De la Rúa, P. Intercontinental long-distance seed dispersal across the Mediterranean basin explains population genetic structure of a bird-dispersed shrub. Mol. Ecol. 29, 1408–1420 (2020).PubMed 
Article 

Google Scholar 
26.Newton, I. The Migration Ecology of Birds (Elsevier, 2010).27.Sorensen, A. E. Interactions between birds and fruit in a temperate woodland. Oecologia 50, 242–249 (1981).ADS 
CAS 
PubMed 
Article 

Google Scholar 
28.González-Varo, J. P., Arroyo, J. M. & Jordano, P. The timing of frugivore-mediated seed dispersal effectiveness. Mol. Ecol. 28, 219–231 (2019).PubMed 
Article 

Google Scholar 
29.Jordano, P. in Seeds: The Ecology of Regeneration of Plant Communities (ed. Gallagher, R. S.) 18–61 (CABI, 2014).30.Bascompte, J. & Jordano, P. Mutualistic Networks (Princeton Univ. Press, 2013).31.Gallinat, A. S. et al. Patterns and predictors of fleshy fruit phenology at five international botanical gardens. Am. J. Bot. 105, 1824–1834 (2018).PubMed 
Article 

Google Scholar 
32.Cadotte, M. W. Experimental evidence that evolutionarily diverse assemblages result in higher productivity. Proc. Natl Acad. Sci. USA 110, 8996–9000 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
33.Mitter, C., Farrell, B. & Futuyma, D. J. Phylogenetic studies of insect–plant interactions: insights into the genesis of diversity. Trends Ecol. Evol. 6, 290–293 (1991).CAS 
PubMed 
Article 

Google Scholar 
34.Siemann, E., Tilman, D., Haarstad, J. & Ritchie, M. Experimental tests of the dependence of arthropod diversity on plant diversity. Am. Nat. 152, 738–750 (1998).CAS 
PubMed 
Article 

Google Scholar 
35.Sanderson, F. J., Donald, P. F., Pain, D. J., Burfield, I. J. & van Bommel, F. P. J. Long-term population declines in Afro-Palearctic migrant birds. Biol. Conserv. 131, 93–105 (2006).Article 

Google Scholar 
36.Beresford, A. E. et al. Phenology and climate change in Africa and the decline of Afro-Palearctic migratory bird populations. Remote Sens. Ecol. Conserv. 5, 55–69 (2019).Article 

Google Scholar 
37.Nilsson, C., Bäckman, J. & Alerstam, T. Seasonal modulation of flight speed among nocturnal passerine migrants: differences between short- and long-distance migrants. Behav. Ecol. Sociobiol. 68, 1799–1807 (2014).Article 

Google Scholar 
38.Gaston, K. J. Valuing common species. Science 327, 154–155 (2010).ADS 
CAS 
PubMed 
Article 

Google Scholar 
39.Horton, K. G. et al. Phenology of nocturnal avian migration has shifted at the continental scale. Nat. Clim. Change 10, 63–68 (2020).ADS 
Article 

Google Scholar 
40.Miller-Rushing, A. J., Lloyd-Evans, T. L., Primack, R. B. & Satzinger, P. Bird migration times, climate change, and changing population sizes. Glob. Change Biol. 14, 1959–1972 (2008).ADS 
Article 

Google Scholar 
41.Brochet, A.-L. et al. Preliminary assessment of the scope and scale of illegal killing and taking of birds in the Mediterranean. Bird Conserv. Int. 26, 1–28 (2016).Article 

Google Scholar 
42.Kays, R., Crofoot, M. C., Jetz, W. & Wikelski, M. Terrestrial animal tracking as an eye on life and planet. Science 348, aaa2478 (2015).PubMed 
Article 
CAS 

Google Scholar 
43.Stiles, E. W. Patterns of fruit presentation and seed dispersal in bird-disseminated woody plants in the eastern deciduous forest. Am. Nat. 116, 670–688 (1980).Article 

Google Scholar 
44.Noma, N. & Yumoto, T. Fruiting phenology of animal-dispersed plants in response to winter migration of frugivores in a warm temperate forest on Yakushima Island, Japan. Ecol. Res. 12, 119–129 (1997).Article 

Google Scholar 
45.Lovas-Kiss, Á. et al. Shorebirds as important vectors for plant dispersal in Europe. Ecography 42, 956–967 (2019).Article 

Google Scholar 
46.Coughlan, N. E., Kelly, T. C., Davenport, J. & Jansen, M. A. K. Up, up and away: bird-mediated ectozoochorous dispersal between aquatic environments. Freshw. Biol. 62, 631–648 (2017).Article 

Google Scholar 
47.Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth: a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. Bioscience 51, 933–938 (2001).Article 

Google Scholar 
48.Rivas-Martínez, S., Penas, A. & Díaz, T. Bioclimatic Map of Europe, Thermoclimatic Belts (Cartographic Service, Univ. León, 2004).49.Olesen, J. M. et al. Missing and forbidden links in mutualistic networks. Proc. R. Soc. Lond. B 278, 725–732 (2011).
Google Scholar 
50.Snow, B. & Snow, D. Birds and Berries (T. and A. D. Poyser, 1988).51.Stiebel, H. & Bairlein, F. Frugivory in central European birds I: diet selection and foraging. Vogelwarte 46, 1–23 (2008).
Google Scholar 
52.González-Varo, J. P., Arroyo, J. M. & Jordano, P. Who dispersed the seeds? The use of DNA barcoding in frugivory and seed dispersal studies. Methods Ecol. Evol. 5, 806–814 (2014).Article 

Google Scholar 
53.Simmons, B. I. et al. Moving from frugivory to seed dispersal: incorporating the functional outcomes of interactions in plant–frugivore networks. J. Anim. Ecol. 87, 995–1007 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
54.Plein, M. et al. Constant properties of plant–frugivore networks despite fluctuations in fruit and bird communities in space and time. Ecology 94, 1296–1306 (2013).PubMed 
Article 

Google Scholar 
55.Albrecht, J. et al. Variation in neighbourhood context shapes frugivore-mediated facilitation and competition among co-dispersed plant species. J. Ecol. 103, 526–536 (2015).Article 

Google Scholar 
56.García, D. Birds in ecological networks: insights from bird–plant mutualistic interactions. Ardeola 63, 151–180 (2016).Article 

Google Scholar 
57.Farwig, N., Schabo, D. G. & Albrecht, J. Trait-associated loss of frugivores in fragmented forest does not affect seed removal rates. J. Ecol. 105, 20–28 (2017).Article 

Google Scholar 
58.Torroba Balmori, P., Zaldívar García, P. & Hernández Lázaro, Á. Semillas de Frutos Carnosos del Norte Ibérico: Guía de Identificación (Ediciones Univ. Valladolid, 2013).59.Stiebel, H. Frugivorie bei Mitteleuropäischen Vögeln. PhD thesis, Univ. Oldenburg (2003).60.Jordano, P. Data from: Angiosperm fleshy fruits and seed dispersers: a comparative analysis of adaptation and constraints in plant-animal interactions. Dryad https://doi.org/10.5061/dryad.9tb73 (2013).61.González-Varo, J. P., Carvalho, C. S., Arroyo, J. M. & Jordano, P. Unravelling seed dispersal through fragmented landscapes: frugivore species operate unevenly as mobile links. Mol. Ecol. 26, 4309–4321 (2017).PubMed 
Article 

Google Scholar 
62.Ratnasingham, S. & Hebert, P. D. N. bold: the Barcode of Life data system (http://www.barcodinglife.org). Mol. Ecol. Notes 7, 355–364 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
63.CBOL Plant Working Group et al. A DNA barcode for land plants. Proc. Natl Acad. Sci. USA 106, 12794–12797 (2009).PubMed Central 
Article 
PubMed 

Google Scholar 
64.Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
65.González-Varo, J. P., Díaz-García, S., Arroyo, J. M. & Jordano, P. Seed dispersal by dispersing juvenile animals: a source of functional connectivity in fragmented landscapes. Biol. Lett. 15, 20190264 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
66.Fuentes, M. Latitudinal and elevational variation in fruiting phenology among western European bird-dispersed plants. Ecography 15, 177–183 (1992).Article 

Google Scholar 
67.Herrera, C. M. A study of avian frugivores, bird-dispersed plants, and their interaction in Mediterranean scrublands. Ecol. Monogr. 54, 1–23 (1984).Article 

Google Scholar 
68.Hampe, A. & Bairlein, F. Modified dispersal-related traits in disjunct populations of bird-dispersed Frangula alnus (Rhamnaceae): a result of its Quaternary distribution shifts? Ecography 23, 603–613 (2000).Article 

Google Scholar 
69.Thomas, P. A. & Mukassabi, T. A. Biological flora of the British Isles: Ruscus aculeatus. J. Ecol. 102, 1083–1100 (2014).Article 

Google Scholar 
70.Jordano, P. Biología de la reproducción de tres especies del género Lonicera (Caprifoliaceae) en la Sierra de Cazorla. An. Jardin Botanico Madr. 1979 48, 31–52 (1990).
Google Scholar 
71.Debussche, M. & Isenmann, P. A Mediterranean bird disperser assemblage: composition and phenology in relation to fruit availability. Rev. Ecol. 47, 411–432 (1992).
Google Scholar 
72.Jordano, P. Diet, fruit choice and variation in body condition of frugivorous warblers in Mediterranean scrubland. Ardea 76, 193–209 (1988).
Google Scholar 
73.Barroso, Á., Amor, F., Cerdá, X. & Boulay, R. Dispersal of non-myrmecochorous plants by a “keystone disperser” ant in a Mediterranean habitat reveals asymmetric interdependence. Insectes Soc. 60, 75–86 (2013).Article 

Google Scholar 
74.González-Varo, J. P. Fragmentation, habitat composition and the dispersal/predation balance in interactions between the Mediterranean myrtle and avian frugivores. Ecography 33, 185–197 (2010).Article 

Google Scholar 
75.Sánchez-Salcedo, E. M., Martínez-Nicolás, J. J. & Hernández, F. Phenological growth stages of mulberry tree (Morus sp.) codification and description according to the BBCH scale. Ann. Appl. Biol. 171, 441–450 (2017).Article 

Google Scholar 
76.García-Castaño, J. L. Consecuencias Demográficas de la Dispersión de Semillas por Aves y Mamíferos Frugívoros en la Vegetación Mediterránea de Montaña. PhD thesis, Univ. Sevilla (2001).77.Gilbert, O. L. Symphoricarpos albus (L.) S. F. Blake (S. rivularis Suksd., S. racemosus Michaux). J. Ecol. 83, 159–166 (1995).Article 

Google Scholar 
78.Billerman, S. M. et al. (eds) Birds of the World (Cornell Laboratory of Ornithology, 2020).79.Tellería, J., Asensio, B. & Díaz, M. Aves Ibéricas: II. Paseriformes (J. M. Reyero Editor, 1999).80.Díaz, M., Asensio, B. & Tellería, J. L. Aves Ibéricas: I. No paseriformes (J. M. Reyero Editor, 1996).81.SEO/Birdlife. La Enciclopedia de las Aves de España (SEO/Birdlife-Fundación BBVA, 2019).82.Spina, F. & Volponi, S. Atlante della Migrazione degli Uccelli in Italia. 2. Passeriformi (Ministero dell’Ambiente e della Tutela del Territorio e del Mare, Istituto Superiore per la Protezione e la Ricerca Ambientale (ISPRA), Tipografia SCR-Roma, 2008).83.Spina, F. & Volponi, S. Atlante della Migrazione degli Uccelli in Italia. 1. Non-Passeriformi (Ministero dell’Ambiente e della Tutela del Territorio e del Mare, Istituto Superiore per la Protezione e la Ricerca Ambientale (ISPRA), Tipografia CSR-Roma, 2008).84.Wernham, C. et al. The Migration Atlas: Movements of the Birds of Britain and Ireland (T. & A. D. Poyser, 2002).85.Cramp, S. The Complete Birds of the Western Paleartic (CD-ROM) (Oxford Univ. Press, 1998).86.Bairlein, F. et al. Atlas des Vogelzugs – Ringfunde deutscher Brut- und Gastvögel (Aula, 2014).87.Tomiałojć, L. & Stawarczyk, T. Awifauna Polski: Rozmieszczenie, Liczebność i Zmiany (PTPP pro. Natura, 2003).88.Busse, P., Gromadzki, M. & Szulc, B. Obserwacje przelotu jesiennego ptaków w roku 1960 w Górkach Wschodnich koło Gdańska (Observations on bird migration at Górki Wschodnie near Gdańsk Autumn 1960). Acta Ornithologica 7, 305–336 (1963).
Google Scholar 
89.Bobrek, R. et al. Międzysezonowa powtarzalność dynamiki jesiennej migracji wróblowych Passeriformes nad Jeziorem Rakutowskim. Ornis Polonica 57, 39–57 (2016).
Google Scholar 
90.Keller, M. et al. Ptaki Środkowej Wisły (M-ŚTO, 2017).91.Bocheński, M. et al. Awifauna przelotna i zimująca środkowego odcinka doliny Odry. Ptaki Śląska 16, 123–161 (2006).
Google Scholar 
92.BTO. BirdTrack. http://www.birdtrack.net (accessed October 2018).93.Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).Article 

Google Scholar 
94.Fox, J. & Weisberg, S. An R Companion to Applied Regression 2nd edn (SAGE, 2011).95.Douma, J. C. & Weedon, J. T. Analysing continuous proportions in ecology and evolution: a practical introduction to beta and Dirichlet regression. Methods Ecol. Evol. 10, 1412–1430 (2019).Article 

Google Scholar 
96.Smith, S. A. & Brown, J. W. Constructing a broadly inclusive seed plant phylogeny. Am. J. Bot. 105, 302–314 (2018).PubMed 
Article 

Google Scholar 
97.Magallón, S., Gómez-Acevedo, S., Sánchez-Reyes, L. L. & Hernández-Hernández, T. A metacalibrated time-tree documents the early rise of flowering plant phylogenetic diversity. New Phytol. 207, 437–453 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
98.Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
99.Pagel, M. Inferring the historical patterns of biological evolution. Nature 401, 877–884 (1999).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
100.Freckleton, R. P., Harvey, P. H. & Pagel, M. Phylogenetic analysis and comparative data: a test and review of evidence. Am. Nat. 160, 712–726 (2002).CAS 
PubMed 
Article 

Google Scholar 
101.Molina-Venegas, R. & Rodríguez, M. Á. Revisiting phylogenetic signal; strong or negligible impacts of polytomies and branch length information? BMC Evol. Biol. 17, 53 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
102.Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).Article 

Google Scholar 
103.Dormann, C. F., Fründ, J., Blüthgen, N. & Gruber, B. Indices, graphs and null models: analyzing bipartite ecological networks. Open Ecol. J. 2, 7–24 (2009).Article 

Google Scholar 
104.Bascompte, J., Jordano, P. & Olesen, J. M. Asymmetric coevolutionary networks facilitate biodiversity maintenance. Science 312, 431–433 (2006).ADS 
CAS 
PubMed 
Article 

Google Scholar 
105.Bates, D., Maechler, M. & Bolker, B. lme4: linear mixed-effects models using ‘Eigen’ and S4. R package version 1.1-19 https://CRAN.R-project.org/package=lme4 (2013). More

Study areaThe study was carried out in Bunnefjorden, Norway. The fjord froze over from January to April and we here analyze data from ice-free conditions in early winter (12 Nov to 2 Dec 2009). Bunnefjorden is a 150 m deep inner branch of the Oslofjord, and a 57 m deep sill at the entrance restricts water exchange with the outer part of the fjord. Klevjer and Kaartvedt24 provide a map of the study area. The fjord branch normally becomes hypoxic in the lower part of the water column. During the current study, oxygen contents were 2–3 ml l−1 between 15 and 60 m, while waters below 70–80 m were severely hypoxic and devoid of fishes9.Studies of overwintering sprat have been undertaken in Bunnefjorden during several winters, and the biology of sprat as well as the identity of the main acoustic targets in the fjord are well established9,18,24. In the winter of the current study, catches from 33 trawl samples were dominated by sprat; with ~ 40 times higher catches than the next most abundant species, herring (Clupea harengus)9.Study designSolberg and Kaartvedt9 and Solberg et al.18 provide details on methods, and we here only give a summary of the acoustic setup. In short, upward-looking Simrad EK 60 echosounders kept in pressure-proof casings were deployed at the bottom (150 m) and in buoys (80 and 30 m) for enhanced resolution in shallower part of the water column. Cables for electricity and transfer of data to a PC on shore enabled continuous operation of the systems. We here use the data from the shallowest echosounder (200 kHz) that provided superior resolution in near-surface water, though did not cover the full depth range of the population distribution. Echograms from the deeper located echosounders covering the whole (inhabited) water column and showing the full diel population behavior are given in Solberg and Kaartvedt9 and Solberg et al.18.Records of gas releaseReleased gas appeared as ascending lines in the echogram (Fig. 4). We quantified the release as explained by Solberg and Kaartvedt9. We only included ascending traces connected to the acoustic record of a fish, but without enumerating the release per individual fish. Since the same fish may release several bursts of bubbles within a short time interval, we here pooled any sequences of gas release within a 10-s period as one event. This procedure will also exclude cases with several different individuals releasing bubbles in the course of this short time interval, yet we chose this conservative approach not to generate an artificial high connection of gas releases between the fishes.Figure 4Echogram showing sprat releasing gas, with every oblique line representing one release event and lines with a different angle to the release events representing swimming sprat. Colors represent the volume-backscatter coefficient (Sv).Full size imageAnalyses of dataThe frequency of gas releases varied with time, both within a day and between the weeks. Such patterns compare to service systems like call centres and hospital emergency rooms25 that can be modelled as a Poisson process26,27. We therefore started our analysis with the statistical procedure suggested by Brown et al.28 in their influential analysis of the call dynamics in a banking call centre. The first step is to subdivide the day into time intervals, which are short enough to consider event rates as approximately constant. Here we chose to investigate alternative periods of respectively 1, 5, and 30 min, as well as 1, 2, 4, and 6 h. At the longest interval, the peaks in the gas release intensity are expected to be the result of a non-constant Poisson parameter, and therefore more likely to induce rejection of the null-hypothesis of a homogenous random process. In contrast, we expect to find higher concordance with a random process for the short intervals of 5 min. In assessing connectivity among gas bubble releases, we formulate a new model allowing for a formal test of non-randomness (summarized in Fig. 5). We name this approach the simulated connectivity test (S-CON test), which we implemented in R29, with the code being available in the Supplementary appendix.Figure 5Illustration of the steps related to the simulated connectivity test (S-CON test). Bubbles occurring within 10 s are pooled into single release events. We then determine the connectivity of each release event—aka the number of release events within the following 30 s time window. From these, we calculate the average connectivity for a specified period (1, 5, 30 min, 1-, 2-, 4- and 6-h intervals). In a final step, we compare the observed average connectivity to the critical value which is defined as the 95th percentile of 1000 simulations of random placements of the same number of releases. If the observed connectivity is larger than the critical value, we reject the null hypothesis of random gas releases for the specified time interval.Full size imageIf there is a common physiological reason or some form of communication among sprat, a burst of gas release is likely followed by subsequent releases. Thus, it is reasonable to assume that the total number of releases within a short time interval like 30 s would be effective in detecting dependencies between the releases. We therefore define the concept of connectivity as follows:Let the gas be released at times T1, T2, …, Tn and define the connectivity at each event as the numbers of records within the following 30 s. The average connectivity in any considered window of the investigated time-period (for example a window of 1 h) is defined as the average connectivity of all cases of connectivity within the considered window (see also Fig. 5).In order to test the null hypothesis of no dependency between gas bubble releases, we compare the measured average connectivity in the data set with the simulation of 1000 random placements of the total number of observations in a given time window. For example, if we consider a window of 30 min with 15 release events having an average connectivity of 2.1, we performed 1000 random placements of 15 points between 1 and 1800. In this way, we get 1000 simulated values of the average connectivity, from which we pick out the critical 95th percentile, following the common significance level of 0.05 in biology. If the observed average connectivity is larger than this critical value, we reject the null-hypothesis and conclude that the releases of gas bubbles are dependent random variables. Thus, if our example obtains a critical value of 1.7, the null-hypothesis of random arrival times of bubbles is rejected (because the observed value of 2.1 is larger than the critical value of 1.7).Since a dependency between the fish will induce a higher concentration of release events than produced by random releases, we expect the average connectivity to be quite sensitive to the alternative hypothesis of dependent arrival times. Also, note that the concept of connectivity has a combinatory nature, so we need only require that the considered window contains at least two releases of gas bubbles. In contrast, alternative approaches using Kolmogorov–Smirnov tests28 are based on the cumulative distribution function and therefore require at least five observed bubble releases.To test the dependency of the results on the chosen time interval, we also ran the analysis using connectivity intervals of 25 and 35 s, which revealed some variability to the estimates of non-random bubble release (Fig. 3) but did not influence the general pattern. We also tested whether the interval within which we consider subsequent bubbles to be part of one single release event influences our results. The more we consider sequential bubbles to be independent of each other, i.e. their own release event, the higher the proportion of non-random gas release and vice versa.Fish abundanceTo exclude the possibility that apparent connectivity would be a mere result of fluctuating fish abundance, we tested whether the number of released bubbles is a function of fish biomass. For this, we first calculated the total number of gas release events within 30-min periods. We then compared these values to the summed surface integrated acoustic scattering coefficient (SA) for the same periods and for the same depth interval (upper 30 m), assuming that the integrated scattering coefficient (SA) serves as a proxy for the total fish biomass9. We filtered the scattering data to remove noise from non-biological sources prior to use. Both variables were log-transformed prior to analysis. We then fitted a linear model of the two variables using generalized least squares. To account for temporal autocorrelation in the data, we also included a correlation structure of type 1 (corAR1). The analysis was done in R29 using the nlme package30.Ethics declarationsLive animals (fish) were not used in this study. More

1.Schaffelke, B., Carleton, J., Skuza, M., Zagorskis, I. & Furnas, M. J. Water quality in the inshore Great Barrier Reef lagoon: implications for long-term monitoring and management. Mar. Pollut. Bull. 65, 249–260 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Kleypas, J. A., McManus, J. W. & Meñez, L. A. B. Environmental limits to coral reef development: Where do we draw the line?. Am. Zool. 39, 146–159 (1999).Article 

Google Scholar 
3.Barnes, D. J. & Devereux, M. J. Productivity and calcification on a coral reef: A survey using pH and oxygen electrode techniques. J. Exp. Mar. Biol. Ecol. 79, 213–231 (1984).Article 

Google Scholar 
4.Hoegh-Guldberg, O. & Williamson, J. Availability of two forms of dissolved nitrogen to the coral Pocillopora damicornis and its symbiotic zooxanthellae. Mar. Biol. 133, 561–570 (1999).CAS 
Article 

Google Scholar 
5.Koop, K. et al. ENCORE: The effect of nutrient enrichment on coral reefs. Synthesis of results and conclusions. Mar. Pollut. Bull. 42, 91–120 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Grover, R., Maguer, J.-F., Reynaud-Vaganay, S. & Ferrier-Pagès, C. Uptake of ammonium by the scleractinian coral Stylophora pistillata : effect of feeding, light, and ammonium concentrations. Limnol. Oceanogr. 47, 782–790 (2002).ADS 
Article 

Google Scholar 
7.Grover, R., Maguer, J.-F., Allemand, D. & Ferrier-Pagès, C. Uptake of dissolved free amino acids by the scleractinian coral Stylophora pistillata. J. Exp. Biol. 211, 860–865 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.Godinot, C., Ferrier-Pagés, C. & Grover, R. Control of phosphate uptake by zooxanthellae and host cells in the scleractinian coral Stylophora pistillata. Limnol. Oceanogr. 54, 1627–1633 (2009).ADS 
Article 

Google Scholar 
9.Wang, J. & Douglas, A. Nitrogen recycling or nitrogen conservation in an alga-invertebrate symbiosis?. J. Exp. Biol. 201, 2445–2453 (1998).PubMed 
Article 
PubMed Central 

Google Scholar 
10.Davy, S. K., Allemand, D. & Weis, V. M. Cell biology of cnidarian-dinoflagellate symbiosis. Microbiol. Mol. Biol. Rev. 76, 229–261 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.Tanaka, Y., Suzuki, A. & Sakai, K. The stoichiometry of coral-dinoflagellate symbiosis: carbon and nitrogen cycles are balanced in the recycling and double translocation system. ISME J. https://doi.org/10.1038/s41396-017-0019-3 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
12.Lesser, M. P. et al. Nitrogen fixation by symbiotic cyanobacteria provides a source of nitrogen for the scleractinian coral Montastraea cavernosa. Mar. Ecol. Prog. Ser. 346, 143–152 (2007).ADS 
CAS 
Article 

Google Scholar 
13.Miller, D. J. & Yellowlees, D. Inorganic nitrogen uptake by symbiotic marine cnidarians: a critical review. Proc. R. Soc. B Biol. Sci. 237, 109–125 (1989).ADS 

Google Scholar 
14.Pernice, M. et al. A single-cell view of ammonium assimilation in coral–dinoflagellate symbiosis. ISME J. 6, 1314–1324 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.Yellowlees, D., Rees, T. A. V. & Leggat, W. Metabolic interactions between algal symbionts and invertebrate hosts. Plant, Cell Environ. 31, 679–694 (2008).CAS 
Article 

Google Scholar 
16.Krueger, T. et al. Intracellular competition for nitrogen controls dinoflagellate population density in corals. Proc. R. Soc. B Biol. Sci. 287, 20200049 (2020).CAS 
Article 

Google Scholar 
17.Godinot, C., Houlbrèque, F., Grover, R., Ferrier-Pagès, C. & Larsen, A. Coral uptake of inorganic phosphorus and nitrogen negatively affected by simultaneous changes in temperature and pH. PLoS ONE 6, e25024 (2011).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Ferrier-Pagès, C., Godinot, C., D’Angelo, C., Wiedenmann, J. & Grover, R. Phosphorus metabolism of reef organisms with algal symbionts. Ecol. Monogr. 86, 262–277 (2016).Article 

Google Scholar 
19.Snidvongs, A. & Kinzie, R. A. Effects of nitrogen and phosphorus enrichement on in vivo symbiotic zooxanthellae of Pocillopora damicornis. Mar. Biol. 118, 705–711 (1994).CAS 
Article 

Google Scholar 
20.Ferrier-Pagès, C., Gattuso, J. P., Dallot, S. & Jaubert, J. Effect of nutrient enrichment on growth and photosynthesis of the zooxanthellate coral Stylophora pistillata. Coral Reefs 19, 103–113 (2000).Article 

Google Scholar 
21.Roberty, S., Béraud, E., Grover, R. & Ferrier-Pagès, C. Coral productivity is co-limited by bicarbonate and ammonium availability. Microorganisms 8, 640 (2020).CAS 
PubMed Central 
Article 

Google Scholar 
22.Muller-Parker, G., Cook, C. B. & D’elia, C. F. Elemental composition of the coral Pocillopora damicornis exposed to elevated seawater ammonium. Pac. Sci. 48, 234–246 (1994).CAS 

Google Scholar 
23.Muller-Parker, G., McCloskey, L., Hoegh-Guldberg, O. & McAuley, P. Effect of ammonium enrichment on animal and algal biomass of the coral Pocillopora damicornis. Pac. Sci. 48, 273–283 (1994).CAS 

Google Scholar 
24.Dubinsky, Z. et al. The effect of external nutrient resources on the optical properties and photosynthetic efficiency of Stylophora pistillata. Proc. R. Soc. B Biol. Sci. 239, 231–246 (1990).ADS 

Google Scholar 
25.Marubini, F. & Davies, P. S. Nitrate increases zooxanthellae population density and reduces skeletogenesis in corals. Mar. Biol. 127, 319–328 (1996).CAS 
Article 

Google Scholar 
26.Silbiger, N. J. et al. Nutrient pollution disrupts key ecosystem functions on coral reefs. Proc. R. Soc. B Biol. Sci. 285, 20172718 (2018).Article 
CAS 

Google Scholar 
27.Morris, L. A., Voolstra, C. R., Quigley, K. M., Bourne, D. G. & Bay, L. K. Nutrient availability and metabolism affect the stability of coral-symbiodiniaceae symbioses. Trends Microbiol. 27, 678–689 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
28.Baker, D. M., Freeman, C. J., Wong, J. C. Y., Fogel, M. L. & Knowlton, N. Climate change promotes parasitism in a coral symbiosis. ISME J. 12, 921–930 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
29.Stambler, N., Popper, N., Dubinsky, Z. & Stimson, J. Effects of nutrient enrichment and water motion on the coral Pocillopora damicornis. Pac. Sci. 45, 299–307 (1991).
Google Scholar 
30.Rädecker, N. et al. Heat stress destabilizes symbiotic nutrient cycling in corals. Proc. Natl. Acad. Sci. U. S. A. 118, e2022653118 (2021).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
31.Bassim, K. & Sammarco, P. Effects of temperature and ammonium on larval development and survivorship in a scleractinian coral (Diploria strigosa). Mar. Biol. 142, 241–252 (2003).CAS 
Article 

Google Scholar 
32.Langdon, C. & Atkinson, M. J. Effect of elevated pCO2 on photosynthesis and calcification of corals and interactions with seasonal change in temperature/irradiance and nutrient enrichment. J. Geophys. Res. 110, C09S07 (2005).ADS 

Google Scholar 
33.Rosset, S., Wiedenmann, J., Reed, A. J. & D’Angelo, C. Phosphate deficiency promotes coral bleaching and is reflected by the ultrastructure of symbiotic dinoflagellates. Mar. Pollut. Bull. 118, 180–187 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Lapointe, B. E., Brewton, R. A., Herren, L. W., Porter, J. W. & Hu, C. Nitrogen enrichment, altered stoichiometry, and coral reef decline at Looe Key, Florida Keys, USA: a 3-decade study. Mar. Biol. 166, 108 (2019).Article 
CAS 

Google Scholar 
35.Wiedenmann, J. et al. Nutrient enrichment can increase the susceptibility of reef corals to bleaching. Nat. Clim. Chang. 3, 160–164 (2013).ADS 
CAS 
Article 

Google Scholar 
36.Meyer, J. L. & Schultz, E. T. Migrating haemulid fishes as a source of nutrients and organic matter on coral reefs1. Limnol. Oceanogr. 30, 146–156 (1985).ADS 
Article 

Google Scholar 
37.Holbrook, S. J., Brooks, A. J., Schmitt, R. J. & Stewart, H. L. Effects of sheltering fish on growth of their host corals. Mar. Biol. 155, 521–530 (2008).Article 

Google Scholar 
38.Shantz, A. A., Ladd, M. C., Schrack, E. & Burkepile, D. E. Fish-derived nutrient hotspots shape coral reef benthic communities. Ecol. Appl. 25, 2142–2152 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
39.Schmidt, S., Dennison, W. C., Moss, G. J. & Stewart, G. R. Nitrogen ecophysiology of Heron Island, a subtropical coral cay of the Great Barrier Reef, Australia. Funct. Plant Biol. 31, 517–528 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
40.Staunton Smith, J. & Johnson, C. R. Nutrient inputs from seabirds and humans on a populated coral cay. Mar. Ecol. Prog. Ser. 124, 189–200 (1995).ADS 
Article 

Google Scholar 
41.Ezzat, L. et al. Nutrient starvation impairs the trophic plasticity of reef-building corals under ocean warming. Funct. Ecol. 33, 643–653 (2019).Article 

Google Scholar 
42.Ezzat, L., Maguer, J. F., Grover, R. & Ferrier-Pagès, C. Limited phosphorus availability is the Achilles heel of tropical reef corals in a warming ocean. Sci. Rep. 6, 1–11 (2016).Article 
CAS 

Google Scholar 
43.Meyer, J. L. & Schultz, E. T. Tissue condition and growth rate of corals associated with schooling fish1. Limnol. Oceanogr. 30, 157–166 (1985).ADS 
Article 

Google Scholar 
44.Liberman, T., Genin, A. & Loya, Y. Effects on growth and reproduction of the coral Stylophora pistillata by the mutualistic damselfish Dascyllus marginatus. Mar. Biol. 121, 741–746 (1995).Article 

Google Scholar 
45.Burkepile, D. E. et al. Nutrient supply from fishes facilitates macroalgae and suppresses corals in a Caribbean coral reef ecosystem. Sci. Rep. 3, 1493 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.Burkepile, D. E. et al. Nitrogen identity drives differential impacts of nutrients on coral bleaching and mortality. Ecosystems https://doi.org/10.1007/s10021-019-00433-2 (2019).Article 

Google Scholar 
47.Ezzat, L., Maguer, J. F., Grover, R. & Ferrier-Pagés, C. New insights into carbon acquisition and exchanges within the coral–dinoflagellate symbiosis under NH4+ and NO3− supply. Proc. R. Soc. B Biol. Sci. 282, 20150610 (2015).Article 
CAS 

Google Scholar 
48.Shantz, A. A. & Burkepile, D. E. Context-dependent effects of nutrient loading on the coral–algal mutualism. Ecology 95, 1995–2005 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
49.Devlin, M. J. & Brodie, J. Terrestrial discharge into the Great Barrier Reef Lagoon: nutrient behavior in coastal waters. Mar. Pollut. Bull. 51, 9–22 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
50.Bender, D., Diaz-Pulido, G. & Dove, S. The impact of CO 2 emission scenarios and nutrient enrichment on a common coral reef macroalga is modified by temporal effects. J. Phycol. 50, 203–215 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
51.Wild, C., Woyt, H. & Huettel, M. Influence of coral mucus on nutrient fluxes in carbonate sands. Mar. Ecol. Prog. Ser. 287, 87–98 (2005).ADS 
CAS 
Article 

Google Scholar 
52.Bender, E. A., Case, T. J. & Gilpin, M. E. Perturbation experiments in community ecology: theory and practice. Ecology 65, 1–13 (1984).Article 

Google Scholar 
53.Parsons, T. R., Maita, Y. & Lalli, C. M. A Manual of Chemical and Biological Methods for Seawater Analysis (Pergamon Press, 1984).
Google Scholar 
54.Chisholm, J. R. M. & Gattuso, J.-P. Validation of the alkalinity anomaly technique for investigating calcification of photosynthesis in coral reef communities. Limnol. Oceanogr. 36, 1232–1239 (1991).ADS 
CAS 
Article 

Google Scholar 
55.Dickson, A. G., Afghan, J. D. & Anderson, G. C. Reference materials for oceanic CO2 analysis: a method for the certification of total alkalinity. Mar. Chem. 80, 185–197 (2003).CAS 
Article 

Google Scholar 
56.Maier, C., Watremez, P., Taviani, M., Weinbauer, M. G. & Gattuso, J. P. Calcification rates and the effect of ocean acidification on Mediterranean cold-water corals. Proc. R. Soc. B Biol. Sci. 279, 1716–1723 (2012).CAS 
Article 

Google Scholar 
57.Whitaker, J. R. & Granum, P. E. An absolute method for protein determination based on difference in absorbance at 235 and 280 nm. Anal. Biochem. 109, 156–159 (1980).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
58.Dunn, S. R., Thomas, M. C., Nette, G. W., Dove, S. G. & Blackburn, S. A lipidomic approach to understanding free fatty acid lipogenesis derived from dissolved inorganic carbon within Cnidarian-Dinoflagellate symbiosis. PLoS ONE 7, e46801 (2012).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
59.van der Zande, R. M. et al. Paradise lost: end-of-century warming and acidification under business-as-usual emissions have severe consequences for symbiotic corals. Glob. Change Biol. 26, 2203–2219 (2020).ADS 
Article 

Google Scholar 
60.Gaffey, S. J. & Bronnimann, C. E. Effects of bleaching on organic and mineral phases in biogenic carbonates. J. Sediment. Res. 63, 752–754 (1993).ADS 
Article 

Google Scholar 
61.Veal, C. J., Carmi, M., Fine, M. & Hoegh-Guldberg, O. Increasing the accuracy of surface area estimation using single wax dipping of coral fragments. Coral Reefs 29, 893–897 (2010).ADS 
Article 

Google Scholar 
62.Underwood, A. Experiments in Ecology: Their Logical Design and Interpretation Using Analysis of Variance (Cambridge University Press, 1997). .63.Wooldridge, S., Brodie, J. & Furnas, M. Exposure of inner-shelf reefs to nutrient enriched runoff entering the Great Barrier Reef Lagoon: post-European changes and the design of water quality targets. Mar. Pollut. Bull. 52, 1467–1479 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
64.Ferrier-Pagès, C., Schoelzke, V., Jaubert, J., Muscatine, L. & Hoegh-Guldberg, O. Response of a scleractinian coral, Stylophora pistillata, to iron and nitrate enrichment. J. Exp. Mar. Bio. Ecol. 259, 249–261 (2001).Article 

Google Scholar 
65.Atkinson, M. J., Carlson, B. & Crow, G. L. Coral growth in high-nutrient, low-pH seawater: a case study of corals cultured at the Waikiki Aquarium, Honolulu, Hawaii. Coral Reefs 14, 215–223 (1995).ADS 
Article 

Google Scholar 
66.Godinot, C., Ferrier-Pagès, C., Montagna, P. & Grover, R. Tissue and skeletal changes in the scleractinian coral Stylophora pistillata Esper 1797 under phosphate enrichment. J. Exp. Mar. Biol. Ecol. 409, 200–207 (2011).Article 

Google Scholar 
67.Dunn, J. G., Sammarco, P. W. & LaFleur, G. Effects of phosphate on growth and skeletal density in the scleractinian coral Acropora muricata: a controlled experimental approach. J. Exp. Mar. Biol. Ecol. 411, 34–44 (2012).CAS 
Article 

Google Scholar 
68.Marshall, P. A. Skeletal damage in reef corals: relating resistance to colony morphology. Mar. Ecol. Prog. Ser. 200, 177–189 (2000).ADS 
Article 

Google Scholar 
69.Andrews, J. C. & Gentien, P. Upwelling as a source of nutrients for the Great Barrier Reef ecosystems: A solution to Darwin’s question?. Mar. Ecol. Prog. Ser. 8, 257–269 (1982).ADS 
Article 

Google Scholar 
70.Marubini, F. & Thake, B. Bicarbonate addition promotes coral growth. Limnol. Oceanogr. 44, 716–720 (1999).ADS 
CAS 
Article 

Google Scholar 
71.Hoegh-Guldberg, O. & Smith, G. J. The effect of sudden changes in temperature, light and salinity on the population density and export of zooxanthellae from the reef corals Stylophora pistillata Esper and Seriatopora hystrix Dana. J. Exp. Mar. Bio. Ecol. 129, 279–303 (1989).Article 

Google Scholar 
72.Quinlan, Z. A. et al. Fluorescent organic exudates of corals and algae in tropical reefs are compositionally distinct and increase with nutrient enrichment. Limnol. Oceanogr. Lett. 3, 331–340 (2018).CAS 
Article 

Google Scholar 
73.Tanaka, Y., Grottoli, A., Matsui, Y., Suzuki, A. & Sakai, K. Effects of nitrate and phosphate availability on the tissues and carbonate skeleton of scleractinian corals. Mar. Ecol. Prog. Ser. 570, 101–112 (2017).ADS 
CAS 
Article 

Google Scholar 
74.Siboni, N., Ben-Dov, E., Sivan, A. & Kushmaro, A. Global distribution and diversity of coral-associated Archaea and their possible role in the coral holobiont nitrogen cycle. Environ. Microbiol. 10, 2979–2990 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
75.Rädecker, N., Pogoreutz, C., Voolstra, C. R., Wiedenmann, J. & Wild, C. Nitrogen cycling in corals: the key to understanding holobiont functioning?. Trends Microbiol. 23, 490–497 (2015).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
76.Kopp, C. et al. Highly dynamic cellular-level response of symbiotic coral to a sudden increase in environmental nitrogen. MBio 4, e00052-e113 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
77.Meyer, J. L., Schultz, E. T. & Helfman, G. S. Fish schools: an asset to corals. Science 220, 1047–1049 (1983).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
78.Fong, C. R. & Fong, P. Nutrient fluctuations in marine systems: press versus pulse nutrient subsidies affect producer competition and diversity in estuaries and coral reefs. Estuaries Coasts 41, 421–429 (2018).CAS 
Article 

Google Scholar 
79.Allgeier, J. E., Burkepile, D. E. & Layman, C. A. Animal pee in the sea: consumer-mediated nutrient dynamics in the world’s changing oceans. Glob. Change Biol. 23, 2166–2178 (2017).ADS 
Article 

Google Scholar 
80.Gil, M. A. Unity through nonlinearity: a unimodal coral–nutrient interaction. Ecology 94, 1871–1877 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
81.McAuley, P. J. & Smith, V. J. Effect of diel photoperiod on nitrogen metabolism of cultured and symbiotic zooxanthellae. Mar. Biol. 123, 145–152 (1995).CAS 
Article 

Google Scholar 
82.Bruggeman, F. J., Boogerd, F. C. & Westerhoff, H. V. The multifarious short-term regulation of ammonium assimilation of Escherichia coli: dissection using an in silico replica. FEBS J. 272, 1965–1985 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
83.D’Angelo, C. & Wiedenmann, J. Impacts of nutrient enrichment on coral reefs: new perspectives and implications for coastal management and reef survival. Curr. Opin. Environ. Sustain. 7, 82–93 (2014).Article 

Google Scholar 
84.Vega Thurber, R. L. et al. Chronic nutrient enrichment increases prevalence and severity of coral disease and bleaching. Glob. Change Biol. 20, 544–554 (2014).ADS 
Article 

Google Scholar  More

NEWS AND VIEWS
23 June 2021

Migratory birds aid the redistribution of plants to new climates

Birds that travel long distances can disperse seeds far and wide. An assessment of the timing and direction of European bird migration reveals how these patterns might affect seed dispersal as the planet warms.

Barnabas H. Daru

 ORCID: http://orcid.org/0000-0002-2115-0257

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Barnabas H. Daru

Barnabas H. Daru is in the Department of Life Sciences, Texas A&M University-Corpus Christi, Corpus Christi, Texas 78412, USA.

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The rapid pace of global warming and its effects on habitats raise the question of whether species are able to keep up so that they remain in suitable living conditions. Some animals can move fast to adjust to a swiftly changing climate. Plants, being less mobile, rely on means such as seed dispersal by animals, wind or water to move to new areas, but this redistribution typically occurs within one kilometre of the original plant1. Writing in Nature, González-Varo et al.2 shed light on the potential capacity of migratory birds to aid seed dispersal.When the climate in a plant’s usual range becomes hotter than it can tolerate, it must colonize new, cooler areas that might lie many kilometres away. It is not fully clear how plants distribute their seeds across great distances, let alone how they cross geographical barriers. One explanation for long-distance seed dispersal is through transport by migratory birds. Such birds ingest viable seeds when eating fruit (Fig. 1) and can move them tens or hundreds of kilometres outside the range of a plant species3. In this mode of dispersal, the seeds pass through the bird’s digestive tract unharmed4,5 and are deposited in faeces, which provides fertilizer that aids plant growth. In the case of European migratory birds, for example, the direction of seed dispersal will depend on whether the timing of fruit production coincides with a bird’s southward trip to warmer regions around the Equator, or northward to cooler regions. Many aspects of this process have been a mystery until now.

Figure 1 | A young blackcap bird (Sylvia atricapilla) eating elderberries.Credit: Getty

González-Varo and colleagues report how plants might be able to keep pace with rapid climate change through the help of migrating birds. The authors analysed the fruiting times of plants, patterns of bird migration and the interactions between fruit-eating birds and fleshy-fruited plants across Europe. Plants with fleshy fruits were chosen for this study because most of their seed transport is by migratory birds6, and because fleshy-fruited plants are an important component of the woody-plant community in Europe. The common approach until now has been to predict plant dispersal and colonization using models fitted to abiotic factors, such as the current climate. González-Varo et al. instead analysed an impressive data set of 949 different seed-dispersal interactions between bird and plant communities, together with data on entire fruiting times and migratory patterns of birds across Europe. The researchers also analysed DNA traces from bird faeces to identify the plants and birds responsible for seed dispersal.
Read the paper: Limited potential for bird migration to disperse plants to cooler latitudes
The authors hypothesized that the direction of seed migration depends on how the plants interact with migratory birds, the frequency of these interactions or the number of bird species that might transport seeds from each plant species. González-Varo and colleagues found that 86% of plant species studied might have seeds dispersed by birds during their southward trip towards drier and hotter equatorial regions in autumn, whereas only about one-third of the plant species might be dispersed by birds migrating north in spring. This dispersal trend was more pronounced in temperate plants than in the Mediterranean plant communities examined. These results are in general agreement with well-known patterns of fruiting times and bird migrations. For example, the fruit of most fleshy-fruited plants in Europe ripens at a time that coincides with when birds migrate south towards the Equator7.Perhaps the most striking feature of these inferred seed movements is the observation that 35% of plant species across European communities, which are closely related on the evolutionary tree (phylogenetically related), might benefit from long-distance dispersal by the northward journey of migratory birds. This particular subset of plants tends to fruit over a long period of time, or has fruits that persist over the winter. This means that the ability of plants to keep up with climate change could be shaped by their evolutionary history — implying that future plant communities in the Northern Hemisphere will probably come from plant species that are phylogenetically closely related and that have migrated from the south. Or, to put it another way, the overwhelming majority of plant species that are dispersed south towards drier and hotter regions at the Equator will probably be less able to keep pace with rapid climate change in their new locations than will the few ‘winners’ that are instead dispersed north to cooler climates. This has implications for understanding how plants will respond to climate change, and for assessing ecosystem functions and community assembly at higher levels of the food chain. However, for seeds of a given plant species, more evidence is needed to assess whether passing through the guts of birds affects germination success.To determine which birds might be responsible for the plant redistributions to cooler climates in the north, the authors categorized European bird migrants into Palaearctic (those that fly to southern Europe and northern Africa during their non-breeding season) and Afro-Palaearctic (those that winter in sub-Saharan Africa). Only a few common Palaearctic migrants, such as the blackcap (Sylvia atricapilla; Fig. 1) or blackbird (Turdus merula), provide most of this crucial dispersal service northwards to cooler regions across Europe. Because migratory birds are able to relocate a small, non-random subset of plants, this could well have a strong influence on the types of plant community that will form under climate-change conditions.
A bird’s migration decoded
A major problem, however, is that the role of these birds in dispersing seeds over long distances is already at risk from human pressures and environmental changes8. Understanding these large-scale seed-dispersal interactions offers a way for targeted conservation actions to protect the areas that are most vulnerable to climate change. This could include boosting protection efforts in and around the wintering grounds of migratory birds — locations that are already experiencing a rise in human pressures, such as illegal bird hunting.González-Varo and colleagues’ focus on seed dispersal across a Northern Hemisphere region means that, as with most ecological analyses, the results are dependent on scale, which can cause issues when interpreting data9. Because the Northern Hemisphere has more land area and steeper seasonal temperature gradients than the Southern Hemisphere does, seed-dispersal interactions might have different patterns from those occurring in the Southern Hemisphere or in aquatic systems.For example, seed-eating birds from the genus Quelea migrate from the Southern Hemisphere to spend the dry season in equatorial West Africa, then move southwards again when the rains arrive. Their arrival in southern Africa usually coincides with the end of the wet season in this region, when annual grass seeds are in abundance. It will be worth investigating whether migratory birds in the Southern Hemisphere also influence the redistribution of plant communities during global warming. Likewise, exploring the long-distance dispersal of seeds of aquatic plants, such as seagrasses10 by water birds, is another area for future research that might benefit from González-Varo and colleagues’ methods.This study provides a great example of how migratory birds might assist plant redistribution to new locations that would normally be difficult for them to reach on their own, and which might offer a suitable climate. As the planet warms, understanding how such biological mechanisms reorganize plant communities complements the information available from climate-projection models, which offer predictions of future species distributions.

doi: https://doi.org/10.1038/d41586-021-01547-1

References1.Jordano, P., García, C., Godoy, J. A. & García-Castaño, J. L. Proc. Natl Acad. Sci. USA 104, 3278–3282 (2007).PubMed 
Article 

Google Scholar 
2.González-Varo, J. P. et al. Nature https://doi.org/10.1038/s41586-021-03665-2 (2021).Article 

Google Scholar 
3.Viana, D. S., Santamaría, L. & Figuerola, J. Trends Ecol. Evol. 31, 763–775 (2016).PubMed 
Article 

Google Scholar 
4.Lehouck, V., Spanhove, T. & Lens, L. Plant Ecol. Evol. 144, 96–100 (2011).Article 

Google Scholar 
5.Shikang, S., Fuqin, W. & Yuehua, W. Sci. Rep. 5, 11615 (2015).PubMed 
Article 

Google Scholar 
6.Jordano, P. in Seeds: The Ecology of Regeneration in Plant Communities 3rd edn (ed. Gallagher, R. S.) 18–61 (CAB Int., 2014).
Google Scholar 
7.Dingle, H. Migration: The Biology of Life on the Move (Oxford Univ. Press, 2014).
Google Scholar 
8.Bairlein, F. Science 354, 547–548 (2016).PubMed 
Article 

Google Scholar 
9.Daru, B. H., Farooq, H., Antonelli, A. & Faurby, S. Nature Commun. 11, 2115 (2020).PubMed 
Article 

Google Scholar 
10.Rock, B. M. & Daru B. H. Front. Mar. Sci. 8, 608867 (2021).Article 

Google Scholar 
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Competing Interests
The author declares no competing interests.

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