Ecology

Bond-Lamberty, B. & Thomson, A. Temperature-associated increases in the global soil respiration record. Nature 464, 579–582 (2010).Article 
CAS 
PubMed 

Google Scholar 
Crippa, M. et al. Food systems are responsible for a third of global anthropogenic GHG emissions. Nat. Food 2, 198–209 (2021).Article 
CAS 

Google Scholar 
Shakoor, A. et al. Effect of animal manure, crop type, climate zone, and soil attributes on greenhouse gas emissions from agricultural soils—A global meta-analysis. J. Clean Prod. 278, 124019. https://doi.org/10.1016/j.jclepro.2020.124019 (2021).Article 
CAS 

Google Scholar 
Wu, L. et al. Soil organic matter priming and carbon balance after straw addition is regulated by long-term fertilization. Soil Biol. Biochem. 135, 383–391 (2019).Article 
CAS 

Google Scholar 
Chen, F. et al. Effects of N addition and precipitation reduction on soil respiration and its components in a temperate forest. Agr. Forest. Meteorol. 271, 336–345 (2019).Article 

Google Scholar 
Lei, J. et al. Temporal changes in global soil respiration since 1987. Nat. Commun. 12, 1–9 (2021).
Google Scholar 
Wang, R. et al. Nitrogen application increases soil respiration but decreases temperature sensitivity: Combined effects of crop and soil properties in a semiarid agroecosystem. Geoderma 353, 320–330 (2019).Article 
CAS 

Google Scholar 
Du, K. et al. Influence of no-tillage and precipitation pulse on continuous soil respiration of summer maize affected by soil water in the North China Plain. Sci. Total Environ. 766, 144384. https://doi.org/10.1016/j.scitotenv.2020.144384 (2021).Article 
CAS 
PubMed 

Google Scholar 
Chen, X. & Chen, H. Y. Plant diversity loss reduces soil respiration across terrestrial ecosystems. Global Change Biol. 25, 1482–1492 (2019).Article 

Google Scholar 
Lang, A. K., Jevon, F. V., Ayres, M. P. & Matthes, J. H. Higher soil respiration rate beneath arbuscular mycorrhizal trees in a northern hardwood forest is driven by associated soil properties. Ecosystems 23, 1243–1253 (2020).Article 
CAS 

Google Scholar 
Huang, N. et al. Spatial and temporal variations in global soil respiration and their relationships with climate and land cover. Sci. Adv. 6, eabb8508 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Xiao, H. et al. The regulatory effects of biotic and abiotic factors on soil respiration under different land-use types. Ecol. Indic. 127, 107787. https://doi.org/10.1016/j.ecolind.2021.107787 (2021).Article 
CAS 

Google Scholar 
Liu, Y.-R. et al. New insights into the role of microbial community composition in driving soil respiration rates. Soil Biol. Biochem. 118, 35–41 (2018).Article 
CAS 

Google Scholar 
Wagg, C., Schlaeppi, K., Banerjee, S., Kuramae, E. E. & van der Heijden, M. G. Fungal-bacterial diversity and microbiome complexity predict ecosystem functioning. Nat. Commun. 10, 1–10 (2019).Article 
CAS 

Google Scholar 
Chen, L.-F. et al. Linkages between soil respiration and microbial communities following afforestation of alpine grasslands in the northeastern Tibetan Plateau. Appl. Soil Ecol. 161, 103882. https://doi.org/10.1016/j.apsoil.2021.103882 (2021).Article 

Google Scholar 
Choudhary, M. et al. Long-term effects of organic manure and inorganic fertilization on sustainability and chemical soil quality indicators of soybean-wheat cropping system in the Indian mid-Himalayas. Agr. Ecosyst. Environ. 257, 38–46 (2018).Article 

Google Scholar 
Zhang, M. et al. Increasing yield and N use efficiency with organic fertilizer in Chinese intensive rice cropping systems. Field Crop. Res. 227, 102–109 (2018).Article 

Google Scholar 
Bonanomi, G. et al. Repeated applications of organic amendments promote beneficial microbiota, improve soil fertility and increase crop yield. Appl. Soil Ecol. 156, 103714. https://doi.org/10.1016/j.apsoil.2020.103714 (2020).Article 

Google Scholar 
Gai, X. et al. Long-term benefits of combining chemical fertilizer and manure applications on crop yields and soil carbon and nitrogen stocks in North China Plain. Agr. Water Manage. 208, 384–392 (2018).Article 

Google Scholar 
Lai, R. et al. Manure fertilization increases soil respiration and creates a negative carbon budget in a Mediterranean maize (Zea mays L.)-based cropping system. Catena 151, 202–212 (2017).Article 
CAS 

Google Scholar 
Yan, T. et al. Negative effect of nitrogen addition on soil respiration dependent on stand age: Evidence from a 7-year field study of larch plantations in northern China. Agr. Forest Meteorol. 262, 24–33 (2018).Article 

Google Scholar 
Peng, Q. et al. Effects of nitrogen fertilization on soil respiration in temperate grassland in Inner Mongolia. China. Environ. Earth Sci. 62, 1163–1171 (2011).Article 
CAS 

Google Scholar 
Zeng, J. et al. Nitrogen fertilization directly affects soil bacterial diversity and indirectly affects bacterial community composition. Soil Biol. Biochem. 92, 41–49 (2016).Article 
CAS 

Google Scholar 
Levine, U. Y., Teal, T. K., Robertson, G. P. & Schmidt, T. M. Agriculture’s impact on microbial diversity and associated fluxes of carbon dioxide and methane. ISME J. 5, 1683–1691 (2011).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Chen, Q., Liu, Z., Zhou, J., Xu, X. & Zhu, Y. Long-term straw mulching with nitrogen fertilization increases nutrient and microbial determinants of soil quality in a maize–wheat rotation on China’s Loess Plateau. Sci. Total. Environ. 775, 145930. https://doi.org/10.1016/j.scitotenv.2021.145930 (2021).Article 
CAS 

Google Scholar 
Wang, J. et al. The impact of fertilizer amendments on soil autotrophic bacteria and carbon emissions in maize field on the semiarid Loess Plateau. Front. Microbiol. https://doi.org/10.3389/fmicb.2021.664120 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Subke, J. A., Inglima, I. & Francesca Cotrufo, M. Trends and methodological impacts in soil CO2 efflux partitioning: a metaanalytical review. Global Change Biol. 12, 921–943 (2006).Article 

Google Scholar 
Yan, W., Zhong, Y., Liu, J. & Shangguan, Z. Response of soil respiration to nitrogen fertilization: Evidence from a 6-year field study of croplands. Geoderma 384, 114829. https://doi.org/10.1016/j.geoderma.2020.114829 (2021).Article 
CAS 

Google Scholar 
Lamptey, S., Xie, J., Li, L., Coulter, J. A. & Jagadabhi, P. S. Influence of organic amendment on soil respiration and maize productivity in a semi-arid environment. Agronomy 9, 611. https://doi.org/10.3390/agronomy9100611 (2019).Article 
CAS 

Google Scholar 
Chen, Z. et al. Nitrogen fertilization stimulated soil heterotrophic but not autotrophic respiration in cropland soils: A greater role of organic over inorganic fertilizer. Soil Biol. Biochem. 116, 253–264 (2018).Article 
CAS 

Google Scholar 
Zheng, J., Zhang, X., Li, L., Zhang, P. & Pan, G. Effect of long-term fertilization on C mineralization and production of CH4 and CO2 under anaerobic incubation from bulk samples and particle size fractions of a typical paddy soil. Agr. Ecosyst. Environ. 120, 129–138 (2007).Article 
CAS 

Google Scholar 
Shen, J., Zhang, L., Guo, J., Ray, J. & He, J. Impact of long-term fertilization practices on the abundance and composition of soil bacterial communities in Northeast China. Appl. Soil Ecol. 46, 119–124 (2010).Article 

Google Scholar 
Chen, Q., An, X., Zheng, B., Ma, Y. & Su, J. Long-term organic fertilization increased antibiotic resistome in phyllosphere of maize. Sci. Total. Environ. 645, 1230–1237 (2018).Article 
CAS 
PubMed 

Google Scholar 
Zhang, W., Yu, C., Wang, X. & Hai, L. Increased abundance of nitrogen transforming bacteria by higher C/N ratio reduces the total losses of N and C in chicken manure and corn stover mix composting. Bioresource Technol. 297, 122410. https://doi.org/10.1016/j.biortech.2019.122410 (2020).Article 
CAS 

Google Scholar 
Chen, X. et al. Microbial carbon use efficiency, biomass turnover, and necromass accumulation in paddy soil depending on fertilization. Agr. Ecosyst. Environ. 292, 106816. https://doi.org/10.1016/j.agee.2020.106816 (2020).Article 
CAS 

Google Scholar 
Wang, J. et al. Nitrogen application increases soil microbial carbon fixation and maize productivity on the semiarid Loess Plateau. Plant Soil https://doi.org/10.1007/s11104-022-05457-7 (2022).Article 

Google Scholar 
Li, J. et al. The more straw we deep-bury, the more soil TOC will be accumulated: When soil bacteria abundance keeps growing. J. Soil Sediment 22, 162–171 (2022).Article 

Google Scholar 
Siczek, A., Frąc, M., Wielbo, J. & Kidaj, D. Benefits of flavonoids and straw mulch application on soil microbial activity in pea rhizosphere. Int. J. Environ. Sci. Te. 15, 755–764 (2018).Article 
CAS 

Google Scholar 
Zhao, S. et al. Change in straw decomposition rate and soil microbial community composition after straw addition in different long-term fertilization soils. Appl. Soil Ecol. 138, 123–133 (2019).Article 

Google Scholar 
Zhang, S. et al. Cow manure application effectively regulates the soil bacterial community in tea plantation. BMC Microbiol. 20, 1–11 (2020).Article 

Google Scholar 
Jiang, Y. et al. Crop rotations alter bacterial and fungal diversity in paddy soils across East Asia. Soil Biol. Biochem. 95, 250–261 (2016).Article 
CAS 

Google Scholar 
Drenovsky, R., Vo, D., Graham, K. & Scow, K. Soil water content and organic carbon availability are major determinants of soil microbial community composition. Microb. Ecol. 48, 424–430 (2004).Article 
CAS 
PubMed 

Google Scholar 
Rath, K. M., Fierer, N., Murphy, D. V. & Rousk, J. Linking bacterial community composition to soil salinity along environmental gradients. ISME J. 13, 836–846 (2019).Article 
CAS 
PubMed 

Google Scholar 
Zhao, F. et al. Changes of the organic carbon content and stability of soil aggregates affected by soil bacterial community after afforestation. CATENA 171, 622–631 (2018).Article 
CAS 

Google Scholar 
Goldfarb, K. C. et al. Differential growth responses of soil bacterial taxa to carbon substrates of varying chemical recalcitrance. Front. Microbiol. 2, 94. https://doi.org/10.3389/fmicb.2011.00094 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Zhao, J. et al. Response of soil microbial community to vegetation reconstruction modes in mining areas of the Loess Plateau, China. Front. Microbiol. https://doi.org/10.3389/fmicb.2021.714967 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Zhang, Y. et al. Fertilization shapes bacterial community structure by alteration of soil pH. Front. Microbiol. 8, 1325. https://doi.org/10.3389/fmicb.2017.01325 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, X. et al. Organic amendments drive shifts in microbial community structure and keystone taxa which increase C mineralization across aggregate size classes. Soil Biol. Biochem. 153, 108062. https://doi.org/10.1016/j.soilbio.2020.108062 (2021).Article 
CAS 

Google Scholar 
Lin, Y. et al. Long-term manure application increases soil organic matter and aggregation, and alters microbial community structure and keystone taxa. Soil Biol. Biochem. 134, 187–196 (2019).Article 
CAS 

Google Scholar 
Woyke, T. et al. Symbiosis insights through metagenomic analysis of a microbial consortium. Nature 443, 950–955 (2006).Article 
CAS 
PubMed 

Google Scholar 
Zhang, B., Zhang, J., Liu, Y., Shi, P. & Wei, G. Co-occurrence patterns of soybean rhizosphere microbiome at a continental scale. Soil Biol. Biochem. 118, 178–186 (2018).Article 
CAS 

Google Scholar 
Wiens, J. J. et al. Niche conservatism as an emerging principle in ecology and conservation biology. Ecol. Lett. 13, 1310–1324 (2010).Article 
PubMed 

Google Scholar 
Deng, Y. et al. Molecular ecological network analyses. BMC Bioinf. 13, 1–20 (2012).Article 

Google Scholar 
Liao, H. et al. Complexity of bacterial and fungal network increases with soil aggregate size in an agricultural Inceptisol. Appl. Soil Ecol. 154, 103640. https://doi.org/10.1016/j.apsoil.2020.103640 (2020).Article 

Google Scholar 
Herren, C. M. & McMahon, K. D. Keystone taxa predict compositional change in microbial communities. Environ. Microbiol. 20, 2207–2217 (2018).Article 
PubMed 

Google Scholar 
Zhang, C., Jiao, S., Shu, D. & Wei, G. Inter-phylum negative interactions affect soil bacterial community dynamics and functions during soybean development under long-term nitrogen fertilization. Stress Biol. 1, 1–13 (2021).Article 
CAS 

Google Scholar 
Su, Y. G., Huang, G., Lin, Y. J. & Zhang, Y. M. No synergistic effects of water and nitrogen addition on soil microbial communities and soil respiration in a temperate desert. CATENA 142, 126–133 (2016).Article 
CAS 

Google Scholar 
Yang, C. et al. Assessing the effect of soil salinization on soil microbial respiration and diversities under incubation conditions. Appl. Soil Ecol. 155, 103671. https://doi.org/10.1016/j.apsoil.2020.103671 (2020).Article 

Google Scholar 
Banerjee, S. et al. Network analysis reveals functional redundancy and keystone taxa amongst bacterial and fungal communities during organic matter decomposition in an arable soil. Soil Biol. Biochem. 97, 188–198 (2016).Article 
CAS 

Google Scholar 
Chen, L.-F., He, Z.-B., Zhao, W.-Z., Kong, J.-Q. & Gao, Y. Empirical evidence for microbial regulation of soil respiration in alpine forests. Ecol. Indic. 126, 107710. https://doi.org/10.1016/j.ecolind.2021.107710 (2021).Article 
CAS 

Google Scholar 
Liu, S. et al. Decoupled diversity patterns in bacteria and fungi across continental forest ecosystems. Soil Biol. Biochem. 144, 107763. https://doi.org/10.1016/j.soilbio.2020.107763 (2020).Article 
CAS 

Google Scholar 
Lynch, M. D. & Neufeld, J. D. Ecology and exploration of the rare biosphere. Nat. Rev. Microbiol. 13, 217–229 (2015).Article 
CAS 
PubMed 

Google Scholar 
Chen, L. et al. Competitive interaction with keystone taxa induced negative priming under biochar amendments. Microbiome 7, 1–18 (2019).
Google Scholar 
Chiba, A. et al. Soil bacterial diversity is positively correlated with decomposition rates during early phases of maize litter decomposition. Microorganisms 9, 357 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, S., Wang, S., Fan, M., Wu, Y. & Shangguan, Z. Interactions between biochar and nitrogen impact soil carbon mineralization and the microbial community. Soil Till. Res. 196, 104437. https://doi.org/10.1016/j.still.2019.104437 (2020).Article 

Google Scholar 
Bao, S. Soil agrochemical analysis 30 (China Agricultural Press, Beijing, Chinese, 2000).
Google Scholar 
Zhai, L., Liu, H., Zhang, J., Huang, J. & Wang, B. Long-term application of organic manure and mineral fertilizer on N2O and CO2 emissions in a red soil from cultivated maize-wheat rotation in China. Agr. Sci. China 10, 1748–1757 (2011).Article 

Google Scholar 
Xia, W. et al. Autotrophic growth of nitrifying community in an agricultural soil. ISME J. 5, 1226–1236 (2011).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Pruesse, E. et al. SILVA: A comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids. Res. 35, 7188–7196 (2007).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B. 57, 289–300 (1995).MathSciNet 
MATH 

Google Scholar 
Layeghifard, M., Hwang, D. M. & Guttman, D. S. Disentangling interactions in the microbiome: A network perspective. Trends Microbiol. 25, 217–228 (2017).Article 
CAS 
PubMed 

Google Scholar 
Liaw, A. & Wiener, M. Classification and regression by randomForest. R news 2, 18–22 (2002).
Google Scholar 
Archer, E. rfPermute: Estimate permutation p-values for random Forest importance metrics. R package version 2(1), 81 (2020).MathSciNet 

Google Scholar 
Hooper, D., Coughlan, J. & Mullen, M. Structural equation modelling: Guidelines for determining model fit. Electron. J. Bus. Res. Methods 6(1), 53–60 (2008).
Google Scholar  More

MaterialsTrunk wash was collected from one male (Tembo, born 1985) and five female (Tonga, 1984; Numbi, 1992; Mongu, 2003; Iqhwa, 2013; Kibali, 2019) African elephants at the Vienna Zoo during routine procedures. Briefly, 100 mL of a sterile 0.9% saline solution is injected in each nostril of the trunk, which is kept in a lifted position, so that the solution is running up to the base of the trunk. The mixture of the solution and trunk mucus is collected in sterile plastic bags by active blowing of the elephant. Chemicals were all from Merck, Austria, unless otherwise stated. Restriction enzymes and kits for DNA extraction and purification were from New England Biolabs, USA. Oligonucleotides and synthetic genes were custom synthesised at Eurofins Genomics, Germany.Ethics declarationWe confirm that the trunk wash performed to provide a sample of the mucus was carried out as a routine procedure to monitor the health of elephants at the Vienna Zoo and in accordance with relevant guidelines and regulations.Trunk wash fractionationTrunk wash was centrifuged for 1 h at 10,000 g, the supernatant was dialyzed against 50 mM Tris–HCl buffer, pH 7.4 and concentrated by ultrafiltration in the Amicon stirred cell, then fractionated by anion-exchange chromatography on HiPrep-Q 16/10 column, 20 mL (Bio-Rad), along with standard protocols.Protein alkylation and digestion, and mass spectrometry analysisSDS-PAGE gel portions of proteins from whole elephant trunk wash (for component identification), chromatographic fractions of the elephant trunk wash (for PTMs analysis) or SDS-PAGE gel bands of LafrOBP1 expressed in P. pastoris were in parallel triturated, washed with water, in gel-reduced, S-alkylated, and digested with trypsin (Sigma, sequencing grade). Resulting peptide mixtures were desalted by μZip-TipC18 (Millipore) using 50% (v/v) acetonitrile, 5% (v/v) formic acid as eluent, vacuum-dried by SpeedVac (Thermo Fisher Scientific, USA), and then dissolved in 20 μL of aqueous 0.1% (v/v) formic acid for subsequent MS analyses by means of a nanoLC-ESI-Q-Orbitrap-MS/MS system, comprising an UltiMate 3000 HPLC RSLC nano-chromatographer (Thermo Fisher Scientific) interfaced with a Q-ExactivePlus mass spectrometer (Thermo Fisher Scientific) mounting a nano-Spray ion source (Thermo Fisher Scientific). Chromatographic separations were obtained on an Acclaim PepMap RSLC C18 column (150 mm × 75 μm ID; 2 μm particle size; 100 Å pore size, Thermo Fisher Scientific), eluting the peptide mixtures with a gradient of solvent B (19.92/80/0.08 v/v/v water/acetonitrile/formic acid) in solvent A (99.9/0.1 v/v water/formic acid), at a flow rate of 300 nL/min. In particular, solvent B started at 3%, increased linearly to 40% in 45 min, then achieved 80% in 5 min, remaining at this percentage for 4 min, and finally returned to 3% in 1 min. The mass spectrometer operated in data-dependent mode in positive polarity, carrying out a full MS1 scan in the range m/z 345–1350, at a nominal resolution of 70,000, followed by MS/MS scans of the 10 most abundant ions in high energy collisional dissociation (HCD) mode. Tandem mass spectra were acquired in a dynamic m/z range, with a nominal resolution of 17,500, a normalized collision energy of 28%, an automatic gain control target of 50,000, a maximum ion injection time of 110 ms, and an isolation window of 1.2 m/z. Dynamic exclusion was set to 20 s36.Bioinformatics for peptide identification and post-translational modification assignmentRaw mass data files were searched by Proteome Discoverer v. 2.4 package (Thermo Fisher Scientific), running the search engine Mascot v. 2.6.1 (Matrix Science, UK), Byonic™ v. 2.6.46 (Protein Metrics, USA) and Peaks Studio 8.0 (BSI, Waterloo, Ontario, Canada) software, both for peptide assignment/protein identification and for post-translational modification analysis.In the first case, analyses were carried out against a customized database containing protein sequences downloaded from NCBI (https://www.ncbi.nlm.nih.gov/) for superorder Afrotheria (consisting of 192,838 protein sequences, December 2021) plus the most common protein contaminants and trypsin. Parameters for database searching were fixed carbamidomethylation at Cys, and variable oxidation at Met, deamidation at Asn/Gln, and pyroglutamate formation at Gln. Mass tolerance was set to ± 10 ppm for precursors and to ± 0.05 Da for MS/MS fragments. Proteolytic enzyme and maximum number of missed cleavages were set to trypsin and 3, respectively. All other parameters were kept at default values. In the latter case, raw mass data were analyzed against a customized database containing LafrOBP1 (XP_023395442.1) protein sequence plus the most common protein contaminants and trypsin, allowing to search Lys-acetylation (Δm =  + 42.01), Ser/Thr/Tyr-phosphorylation (Δm =  + 79.97), and the most common mammals N-linked glycans at Asn and O-linked glycans at Ser/Thr/Tyr, using the same parameters previously set. The max PTM sites per peptide was set to 2.Proteome Discoverer peptide candidates were considered confidently identified only when the following criteria were satisfied: (i) protein and peptide false discovery rate (FDR) confidence: high; (ii) peptide Mascot score:  > 30; (iii) peptide spectrum matches (PSMs): unambiguous; (iv) peptide rank (rank of the peptide match): 1; (v) Delta CN (normalized score difference between the selected PSM and the highest-scoring PSM for that spectrum): 0. Byonic peptide candidates were considered confidently identified only when the following criteria were satisfied: (i) PEP 2D and PEP 1D:  More

DNA sample collectionTo analyze the population structure of wild medaka populations, we selected samples from the DNA collection of Takehana et al.29, deposited in University of Shizuoka. The original DNA collection had been made throughout 1980s and 2000s. The selected samples covered the major mitotypes and contained more than three individuals of each population (Table S11, Fig. 3), which were collected from three collection sites for O. sakaizumii and 12 collection sites for O. latipes. We also examined several artificial strains: HNI and Hd-rR, which are inbred strains derived from O. sakaizumii and O. latipes, respectively, and four Himedaka individuals from commercial stock (Uruma city, Okinawa Prefecture, Japan).In addition, samples were newly collected at Kunigami Village, Okinawa Prefecture. Live fish were anesthetized with MS-222 (aminobenzene methanesulfonate, FUJIFILM Wako Pure Chemical Corporation, Osaka, Japan) and then fixed in 99% ethanol. Genomic DNA was extracted using a DNeasy kit (Qiagen Inc., Hilden, Germany) from ethanol-fixed pectoral fin samples according to the manufacturer’s protocol. The DNA concentration was measured using a spectrophotometer (Nanodrop 1000, Thermo Fisher Scientific, Waltham, Massachusetts, USA), and the DNA was diluted with PCR-grade water to a concentration of c.a. 10 ng/µl (UltraPure™ DNase/RNase-Free Distilled Water, Thermo Fisher Scientific).Ethic statementAll methods were carried out in accordance with the Regulation for Animal Experiments at University of the Ryukyus for handling live fish. All experiments were approved by the Animal Care Ethics Committee of University of the Ryukyus (R2019035). All experimental methods are reported in accordance with ARRIVE guidelines.PCR primer designThe following steps were used to select primers for MAAS (Fig. 1). (1) All possible 10-mer sequence combinations (i.e., 410 = 1,048,576 sequences) were generated in silico. (2) The sequences containing simple sequence repeats, some of which had been used in the MIG-seq method17, were excluded. (3) Sequences containing a functional motif, such as a transcription factor-binding site, were also excluded because they may not be suitable for examining neutral genetic markers. We obtained a catalog of motifs from the JASPAR CORE40 (http://jaspar.genereg.net). (4) To avoid taxon-dependency in primer performance, we used information about the k-mer (k = 10) frequency of reference genomes from multiple phyla. Sequences that showed marked differences in frequency among taxa were excluded. The frequencies of each 10-mer sequence in the reference genomes of 17 species belonging to 12 phyla of metazoa were counted (Table S12) using the “oligonucleotideFrequency” function in the “Biostrings” package ver. 2.441. In each of these taxa, the frequencies of sequences were stratified into three grades ( 103). We then selected the sequences that showed the same grade in more than 80% (14/17) of the species. (5) To avoid synthesizing primer dimers, self-complementary sequences were excluded, taking Illumina adapter sequences (5′-CGCTCTTCCGATCT-3′ and 5′-TGCTCTTCCGATCT-3′) into account. Self-complementation of two bases at the 3′-end or every three continuous bases in primer sequences was then evaluated using a custom script in R ver. 3.5.0 (R Development Core Team, http://cran.r-project.org). Based on the selected 10-mer sequences (i.e., 129 sequences, Fig. 1), 7-mer primer sequences were designed by removing the 3 bases at the 3′ end. Finally, we selected 24 candidate sequences for both 10-mer and 7-mer primers for the subsequent step (Table S1).The primer sequence consisted of three parts17: partial sequence of the Illimina adapter, 7 N bases, and a short priming sequence, e.g., 5′-CGCTCTTCCGATCTNNNNNNNGTCGCCC-3′. PCR amplification was performed using the candidate primers using the first PCR protocol described below (Table S1). Banding patterns were observed by electrophoresis on 1% agarose gels (agarose S; TaKaRa, Japan). Of the candidate primers, we selected four 7-mer primers and four 10-mer primers that each gave a smeared banding pattern with amplification products ranging from 500 to 2000 bp, indicating uniform amplification of multiple target sequences (Table S1).Library construction and sequencingThe library was constructed by a two-step PCR approach using a modification of a MIG-seq protocol14. In the first PCR step, multiple regions of genomic DNA were amplified using a cocktail of primers with a Multiplex PCR Assay Kit Ver.2 (TaKaRa) (Table 1). The volume of the PCR reaction mixture was 7 μl, containing 1 μl of template DNA, 2 μM of each PCR primer, 3.5 μl of 2 × Multiplex PCR Buffer, and 0.035 μl of Multiplex PCR Enzyme Mix. PCR was performed under the following conditions: denaturation at 94 °C for 1 min; 25 cycles of 94 °C for 30 s, 38 °C for 1 min, and 72 °C for 1 min, followed by a final extension step at 72 °C for 10 min.The primers in the second PCR step contained the Illumina sequencing adapter and an index sequence to identify each sample. Following the Truseq indexes, we used the combinations of eight forward indexes (i5) and 12 reverse indexes (i7), which resulted in a total of 96 combinations. To be used as a template for the second PCR, the first PCR product from each sample was diluted 50 times with PCR-grade water. The second PCR was performed in a 15-μl reaction mixture containing, 3 μl of diluted first PCR product, 3 μl of 5 × PrimeSTAR GXL Buffer, 200 μM of each dNTP, 0.2 μM of forward index primer and reverse index primer, 0.375 U of PrimeSTAR GXL DNA Polymerase (TaKaRa). The PCR conditions were as follows: 12 cycles at 98 °C for 10 s, 54 °C for 15 s, and 68 °C for 30 s.The second PCR product of each sample was pooled by equal volume and size-selected from 600 to 1000 bp using solid phase reversible immobilization (SPRI) select beads (Beckman Coulter Inc, Brea, California, USA) according to the manufacturer’s protocol. The DNA concentration of the pooled library was measured using a Qubit fluorometer (Thermo Fisher Scientific). We sequenced the libraries using two NGS platforms, MiSeq (Illumina, MiSeq Reagent Kit v2 Micro, Paired-End (PE), 150 bp) and HiSeq X (Illumina, PE, 150 bp). Sequencing using the HiSeq X platform was performed by Macrogen Japan (Tokyo, Japan).To compare primer performance, the DNA libraries constructed using the 7-mer and 10-mer primers for one individual were sequenced using MiSeq. Then, a 7-mer primer cocktail containing four sets of mixed primers was used for the subsequent analyses (Table 1). We also constructed DNA libraries using 7-mer and MIG-seq primer cocktails for three individuals and sequenced them using the HiSeq X platform. Finally, we constructed DNA libraries using 7-mer primer cocktails for 67 wild individuals and six artificial strain individuals for population genetics analyses (Table S11, Fig. 3).Mapping and SNV callingGenotyping was conducted using the following BWA-GATK best-practices pipeline for each sample42. Primer sequences were removed using cutadapt with the –b option selected43. The Illumina adapter sequences were also removed and quality filtering was performed using fastp ver. 0.20.0 with the “–detect_adapter_for_pe, –cut_front” option selected44. The remaining reads were mapped on the reference genome of medaka, Hd-rR strain, GCA_002234675.1; ASM223467v127 using Burrows-Wheeler Alignment tool, BWA mem ver. 0.7.1745. After mapping, output files were converted to Binary Alignment/Map (BAM) format using SAMtools ver. 1.746. SNVs and InDels in the sample were determined following the best practice guidelines set out in the Genome Analysis Tool Kit (GATK ver. 3.8.0)42. We then filtered out SNVs and InDels based on the following criteria: “QD  60.0 || MQ  More

Howe, H. F. & Smallwood, J. Ecology of seed dispersal. Annu. Rev. Ecol. Syst. 13, 201–228 (1982).Article 

Google Scholar 
Wang, B. C. & Smith, T. B. Closing the seed dispersal loop. Trends Ecol. Evol. 17, 379–385 (2002).Article 

Google Scholar 
Fleming, T. H., Breitwish, R. & Whitesides, G. H. Patterns of tropical vertebrate frugivore diversity. Annu. Rev. Ecol. Syst. 18, 91–109 (1987).Article 

Google Scholar 
Schupp, E. W. Quantity, quality and the effectiveness of seed dispersal by animals. Vegetatio 107(108), 15–29 (1993).Article 

Google Scholar 
Garber, P. A. & Lambert, J. E. Primates as seed dispersers: Ecological processes and directions for future research. Am. J. Primatol. 45, 3–8 (1998).Article 
CAS 
PubMed 

Google Scholar 
Godínez-Alvarez, H. & Jordano, P. An empirical approach to analysing the demographic consequences of seed dispersal by frugivores. In Seed Dispersal: Theory and its Application in a Changing World (eds Dennis, A. J. et al.) 391–406 (CAB International, 2007).Chapter 

Google Scholar 
Schupp, E. W., Jordano, P. & Gomez, J. M. Seed dispersal effectiveness revisited: A conceptual review. New Phytol. 188, 333–353 (2010).Article 
PubMed 

Google Scholar 
McConkey, K. R., Brockelman, W. Y. & Saralamba, C. Mammalian frugivores with different foraging behavior can show similar seed dispersal effectiveness. Biotropica 46, 647–651 (2014).Article 

Google Scholar 
Culot, L., Huynen, M. C. & Heymann, E. W. Partitioning the relative contribution of one-phase and two-phase seed dispersal when evaluating seed dispersal effectiveness. Methods Ecol. Evol. 6, 178–186 (2015).Article 

Google Scholar 
McConkey, K. R., Brockelman, W. Y., Saralamba, C. & Nathalang, A. Effectiveness of primate seed dispersers for an “oversized’’ fruit, Garcinia benthamii. Ecology 96, 2737–2747 (2015).Article 
PubMed 

Google Scholar 
Camargo, P., Martins, M. M., Feitosa, R. M. & Christianini, A. V. Bird and ant synergy increases the seed dispersal effectiveness of an ornithochoric shrub. Oecologia 181, 507–518 (2016).Article 
ADS 
PubMed 

Google Scholar 
McConkey, K. R. et al. Different megafauna vary in their seed dispersal effectiveness of the megafaunal fruit Platymitra macrocarpa (Annonaceae). PLoS One 13, e0198960 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Balcomb, S. R. & Chapman, C. A. Bridging the gap: Influence of seed deposition on seedling recruitment in a primate-tree interaction. Ecol. Monogr. 73, 625–642 (2003).Article 

Google Scholar 
Russo, S. E. Responses of dispersal agents to tree and fruit traits in Virola calophylla (Myristicaceae): Implications for selection. Oecologia 136, 80–87 (2003).Article 
ADS 
PubMed 

Google Scholar 
Gross-Camp, N. D., Masozera, M. & Kaplin, B. A. Chimpanzee seed dispersal quantity in a tropical montane forest of Rwanda. Am. J. Primatol. 71, 901–911 (2009).Article 
PubMed 

Google Scholar 
Jordano, P. & Schupp, E. W. Seed disperser effectiveness: The quantity component and patterns of seed rain for Prunus mahaleb. Ecol. Monogr. 70, 591–615 (2000).Article 

Google Scholar 
Leighton, M. Modeling dietary selectivity by Bornean orangutans: evidence for integration of multiple criteria in fruit selection. Int. J. Primatol. 14, 257–313 (1993).Article 

Google Scholar 
Stevenson, P. R. Fruit choice by woolly monkeys in Tinigua National Park, Colombia. Int. J. Primatol. 25, 367–381 (2004).Article 

Google Scholar 
Palacio, F. X. & Ordano, M. The strength and drivers of bird-mediated selection on fruit crop size: A meta-analysis. Front. Ecol. Evol. 6, 18 (2018).Article 

Google Scholar 
Flörchinger, M., Braun, J., Böhning-Gaese, K. & Schaefer, H. M. Fruit size, crop mass, and plant height explain differential fruit choice of primates and birds. Oecologia 164, 151–161 (2010).Article 
ADS 
PubMed 

Google Scholar 
Wenny, D. G. Seed dispersal, seed predation, and seedling recruitment of a neotropical montane tree. Ecol. Monogr. 70, 331–351 (2000).Article 

Google Scholar 
Calviño-Cancela, M. Spatial patterns of seed dispersal and seedling recruitment in Corema album (Empetraceae): The importance of unspecialized dispersers for regeneration. J. Ecol. 90, 775–784 (2002).Article 

Google Scholar 
Masaki, T. & Nakashizuka, A. Seedling demography of Swida controversa: Effect of light and distance to conspecifics. Ecology 83, 3497–3507 (2002).Article 

Google Scholar 
Vulinec, K. Dung beetle communities and seed dispersal in primary forest and disturbed land in Amazonia. Biotropica 34, 297–309 (2002).Article 

Google Scholar 
Janzen, D. H. Herbivores and the number of tree species in tropical forests. Am. Nat. 104, 501–528 (1970).Article 

Google Scholar 
Connell, J. H. On the role of natural enemies in preventing competitive exclusion in some marine animals and in rain forest trees. In Dynamics of Populations (eds Den Boer, P. J. & Gradwell, G.) 298–312 (PUDOC, 1971).
Google Scholar 
Howe, H. F., Schupp, E. W. & Westley, L. C. Early consequences of seed dispersal for a Neotropical tree (Virola surinamensis). Ecology 66, 781–791 (1985).Article 

Google Scholar 
Valenta, K. & Fedigan, L. M. Spatial patterns of seed dispersal by white-faced capuchins in Costa Rica: Evaluating distant-dependent seed mortality. Biotropica 42, 223–228 (2010).Article 

Google Scholar 
Andresen, E. Seed dispersal by monkeys and the fate of dispersed seeds in a Peruvian rain forest. Biotropica 31, 145–158 (1999).
Google Scholar 
Dausmann, K. H., Glos, J., Linsenmair, K. E. & Ganzhorn, J. U. Improved recruitment of a lemur-dispersed tree in Malagasy dry forests after the demise of vertebrates in forest fragments. Oecologia 157, 307–316 (2008).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Jordano, P. & Herrera, C. M. Shuffling the offspring: uncoupling and spatial discordance of multiple stages in vertebrate seed dispersal. Ecoscience 2, 230–237 (1995).Article 

Google Scholar 
Rey, P. J. & Alcantara, J. M. Recruitment dynamics of a fleshy-fruited plant (Olea europaea): Connecting patterns of seed dispersal to seedling establishment. J. Ecol. 88, 622–633 (2000).Article 

Google Scholar 
Traveset, A., Gulias, J., Riera, N. & Mus, M. Transition probabilities from pollination to establishment in a rare dioecious shrub species (Rhamnus ludovici salvatoris) in two habitats. J. Ecol. 91, 427–437 (2003).Article 

Google Scholar 
Cordeiro, N. J. & Howe, H. F. Forest fragmentation severs mutualism between seed dispersers and an endemic African tree. Proc. Natl. Acad. Sci. USA 100, 14052–14056 (2003).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Blendinger, P. G., Blake, J. G. & Loiselle, B. A. Connecting fruit production to seedling establishment in two co-occurring Miconia species: consequences of seed dispersal by birds in upper Amazonia. Oecologia 167, 61–73 (2011).Article 
ADS 
PubMed 

Google Scholar 
Corlett, R. T. The impact of hunting on the mammalian fauna of tropical Asian forests. Biotropica 39, 292–303 (2007).Article 

Google Scholar 
Nuñez-Iturri, G., Olsson, O. & Howe, H. F. Hunting reduces recruitment of primate-dispersed trees in Amazonian Peru. Biol. Conserv. 141, 1536–1546 (2008).Article 

Google Scholar 
Effiom, E. O., Nuñez-Iturri, G., Smith, H. G., Ottosson, U. & Olsson, O. Bushmeat hunting changes regeneration of African rainforests. Proc. R. Soc. B-Biol. Sci. 280, 20130246 (2013).Article 

Google Scholar 
Harrison, R. D. et al. Consequences of defaunation for a tropical tree community. Ecol. Lett. 16, 687–694 (2013).Article 
PubMed 

Google Scholar 
Fuentes, E. R., Hoffmann, A. J., Poiani, A. & Alliende, M. C. Vegetation change in large clearings: patterns in the Chilean matorral. Oecologia 68, 358–366 (1986).Article 
ADS 
PubMed 

Google Scholar 
Holl, K. D. Do bird perching structures elevate seed rain and seedling establishment in abandoned tropical pasture?. Restor. Ecol. 6, 253–261 (1998).Article 

Google Scholar 
Beltran, L. C. & Howe, H. F. The frailty of tropical restoration plantings. Restor. Ecol. 28, 16–21 (2020).Article 

Google Scholar 
Bollen, A., Van Elsacker, L. & Ganzhorn, J. U. Relations between fruits and disperser assemblages in a Malagasy littoral forest: a community-level approach. J. Trop. Ecol. 20, 599–612 (2004).Article 

Google Scholar 
Ganzhorn, J. U. et al. Possible fruit protein effects of primate communities in Madagascar and the Neotropics. PLoS One 4, e8253 (2009).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Albert-Daviaud, A. et al. The ghost fruits of Madagascar: identifying dysfunctional seed dispersal in Madagascar’s endemic flora. Biol. Conserv. 242, 108438 (2020).Article 

Google Scholar 
Dew, J. L. & Wright, P. Frugivory and seed dispersal by four species of primates in Madagascar’s eastern rain forest. Biotropica 30, 425–437 (1998).Article 

Google Scholar 
Moses, K. L. & Semple, S. Primary seed dispersal by the black-and-white ruffed lemur (Varecia variegata) in the Manombo forest, south-east Madagascar. J. Trop. Ecol. 27, 529–538 (2011).Article 

Google Scholar 
Sato, H. Frugivory and seed dispersal by brown lemurs in a Malagasy tropical dry forest. Biotropica 44, 479–488 (2012).Article 

Google Scholar 
Razafindratsima, O. H., Jones, T. A. & Dunham, A. E. Patterns of movement and seed dispersal by three lemur species. Am. J. Primatol. 76, 84–96 (2014).Article 
PubMed 

Google Scholar 
Steffens, K. J., Sanamo, J. & Razafitsalama, J. The role of lemur seed dispersal in restoring degraded forest ecosystems in Madagascar. Folia Primatol. 93, 1–19 (2022).Article 

Google Scholar 
Ganzhorn, J. U., Fietz, J., Rakotovao, E., Schwab, D. & Zinner, D. Lemurs and the regeneration of dry deciduous forest in Madagascar. Conserv. Biol. 13, 794–804 (1999).Article 

Google Scholar 
Razafindratsima, O. H. et al. Consequences of lemur loss for above-ground carbon stocks in a Malagasy rainforest. Int. J. Primatol. 39, 415–426 (2018).Article 

Google Scholar 
Razafindratsima, O. H. Seed dispersal by vertebrates in Madagascar’s forests: Review and future directions. Madag. Conserv. Dev. 9, 90–97 (2014).Article 

Google Scholar 
Nathan, R. & Muller-Landau, H. C. Spatial patterns of seed dispersal, their determinants and consequences for recruitment. Trends Ecol. Evol. 15, 278–285 (2000).Article 
CAS 
PubMed 

Google Scholar 
Pérez-Ramos, I. M., Urbieta, I. R., Maranon, T., Zavala, M. A. & Kobe, R. K. Seed removal in two coexisting oak species: Ecological consequences of seed size, plant cover and seed-drop timing. Oikos 117, 1386–1396 (2008).Article 

Google Scholar 
Sato, H. Seasonal fruiting and seed dispersal by the brown lemur in a tropical dry forest, north-western Madagascar. J. Trop. Ecol. 29, 61–69 (2013).Article 

Google Scholar 
Chapman, C. A. & Chapman, L. J. Determinants of group size in primates: the importance of travel costs. In On the Move: How and Why Animals Travel in Groups (eds Boinski, S. & Garber, P. A.) 24–42 (The University of Chicago Press, 2000).
Google Scholar 
Sato, H. Diurnal resting in brown lemurs in a dry deciduous forest, northwestern Madagascar: Implications for seasonal thermoregulation. Primates 53, 255–263 (2012).Article 
PubMed 

Google Scholar 
Razanaparany, P. T. & Sato, H. Abiotic factors affecting the cathemeral activity of Eulemur fulvus in the dry deciduous forest of north-western Madagascar. Folia Primatol. 91, 463–480 (2020).Article 

Google Scholar 
Sato, H. Predictions of seed shadows generated by common brown lemurs (Eulemur fulvus) and their relationship to seasonal behavioral strategies. Int. J. Primatol. 39, 377–396 (2018).Article 

Google Scholar 
Agetsuma, N. & Nakagawa, N. Effects of habitat differences on feeding behaviors of Japanese monkeys: Comparison between Yakushima and Kinkazan. Primates 39, 275–289 (1998).Article 

Google Scholar 
Stevenson, P. R. Seed dispersal by woolly monkeys (Lagothrix lagothricha) at Tinigua National Park, Colombia: Dispersal distance, germination rates, and dispersal quantity. Am. J. Primatol. 50, 275–289 (2000).Article 
CAS 
PubMed 

Google Scholar 
Hladik, A. & Miquel, S. Seedling types and plant establishment in an African rain forest. In Reproductive Ecology of Tropical Forest Plants (eds Bawa, K. S. & Hadley, M.) 261–282 (UNESCO and The Parthenon Publishing Group, 1990).
Google Scholar 
Ibarra-Manriquez, G., Ramos, M. M. & Oyama, K. Seedling functional types in a lowland rain forest in Mexico. Am. J. Bot. 88, 1801–1812 (2001).Article 
CAS 
PubMed 

Google Scholar 
Blate, G. M., Peart, D. R. & Leighton, M. Post-dispersal predation on isolated seeds: A comparative study of 40 tree species in a Southeast Asian rainforest. Oikos 82, 522–538 (1998).Article 

Google Scholar 
Hosaka, T. et al. Responses of pre-dispersal seed predators to sequential flowering of Dipterocarps in Malaysia. Biotropica 49, 177–185 (2017).Article 

Google Scholar 
Iku, A., Itioka, T., Shimizu-Kaya, U., Kishimoto-Yamada, K. & Meleng, P. Differences in the fruit maturation stages at which oviposition occurs among insect seed predators feeding on the fruits of five dipterocarp tree species. Entomol. Sci. 21, 412–422 (2018).Article 

Google Scholar 
Kitajima, K. Impact of cotyledon and leaf removal on seedling survival in three tree species with contrasting cotyledon functions. Biotropica 35, 429–434 (2003).Article 

Google Scholar 
Marchand, P. et al. Seed-to-seedling transitions exhibit distance-dependent mortality but no strong spacing effects in a Neotropical forest. Ecology 101, e02926 (2020).Article 
PubMed 

Google Scholar 
Moles, A. T. & Westoby, M. Seedling survival and seed size: a synthesis of the literature. J. Ecol. 92, 372–383 (2004).Article 

Google Scholar 
Sonesson, L. K. Growth and survival after cotyledon removal in Quercus robur seedlings, grown in different natural soil types. Oikos 69, 65–70 (1994).Article 

Google Scholar 
Hubbell, S. P., Condit, R. & Foster, R. B. Presence and absence of density dependence in a neotropical tree community. Philos. Trans. R. Soc. Lond. Ser. B-Biol. Sci. 330, 269–281 (1990).Article 
ADS 

Google Scholar 
Terborgh, J. Enemies maintain hyperdiverse tropical forests. Am. Nat. 179, 303–314 (2012).Article 
PubMed 

Google Scholar 
Godínez-Alvarez, H., Valiente-Banuet, A. & Rojas-Martínez, A. The role of seed dispersers in the population dynamics of the columnar cactus Neobuxbaumia tetetzo. Ecology 83, 2617–2629 (2002).Article 

Google Scholar 
Carson, W. P., Anderson, J. T., Leigh, E. G. Jr. & Schnitzer, S. A. Challenges associated with testing and falsofying the Janzen-Connell hypothesis: a review and critique. In Tropical Forest Community Ecology (eds Carson, W. P. & Schnitzer, S. A.) 210–241 (Wiley-Blackwell, 2008).
Google Scholar 
Swamy, V. et al. Are all seeds equal? Spatially explicit comparisons of seed fall and sapling recruitment in a tropical forest. Ecol. Lett. 14, 195–201 (2011).Article 
PubMed 

Google Scholar 
Reid, J. L. et al. Multi-scale habitat selection of key frugivores predicts large-seeded tree recruitment in tropical forest restoration. Ecosphere 12, e03868 (2021).Article 

Google Scholar 
Steffens, K. J. E. Lemur food plants as options for forest restoration in Madagascar. Restor. Ecol. 28, 1517–1527 (2020).Article 

Google Scholar 
Chapman, C. A. & Dunham, A. E. Primate seed dispersal and forest restoration: an African perspective for a brighter future. Int. J. Primatol. 39, 427–442 (2018).Article 

Google Scholar 
Morris, P. & Hawkins, F. Birds of Madagascar: A Photographic Guide (Yale University Press, 1998).
Google Scholar 
Garbutt, N. Mammals of Madagascar: A Complete Guide (Yale University Press, 2007).
Google Scholar 
Mittermeier, R. A. et al. Lemurs of Madagascar 3rd edn. (Conservation International, 2010).
Google Scholar 
Sato, H., Ichino, S. & Hanya, G. Dietary modification by common brown lemurs (Eulemur fulvus) during seasonal drought conditions in western Madagascar. Primates 55, 219–230 (2014).Article 
PubMed 

Google Scholar 
Laman, T. G. Ficus seed shadows in a Bornean rain forest. Oecologia 107, 347–355 (1996).Article 
ADS 
PubMed 

Google Scholar 
Clark, C. J., Poulsen, J. R., Connor, E. F. & Parker, V. T. Fruiting trees as dispersal foci in a semi-deciduous tropical forest. Oecologia 139, 66–75 (2004).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Sato, H. Gut passage time and size of swallowed seeds in the common brown lemur and the mongoose lemur. Primate Res. 25, 45–54 (2009) (In Japanese with English summary).Article 

Google Scholar 
Menard, S. W. Applied Logistic Regression Analysis 2nd edn. (Sage Publication, 2002).Book 

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

Google Scholar 
Richards, S. A. Testing ecological theory using the information-theoretic approach: Examples and cautionary results. Ecology 86, 2805–2814 (2005).Article 

Google Scholar 
Symonds, M. R. E. & Moussalli, A. A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike’s information criterion. Behav. Ecol. Sociobiol. 65, 13–21 (2011).Article 

Google Scholar  More

Laine RA. A calculation of all possible oligosaccharide isomers both branched and linear yields 1.05 x 10(12) structures for a reducing hexasaccharide: the Isomer Barrier to development of single-method saccharide sequencing or synthesis systems. Glycobiology. 1994;4:759–67.PubMed 

Google Scholar 
Becker S, Tebben J, Coffinet S, Wiltshire K, Iversen MH, Harder T, et al. Laminarin is a major molecule in the marine carbon cycle. Proc Natl Acad Sci. 2020;117:6599.PubMed 
PubMed Central 

Google Scholar 
Pauly M, Gille S, Liu L, Mansoori N, Souza A, de, Schultink A, et al. Hemicellulose biosynthesis. Planta. 2013;238:627–42.PubMed 

Google Scholar 
Domozych D. Algal Cell Walls. In: Lauc G, Wuhrer M High-throughput glycomics and glycoproteomics. Humana Press, New York, 2016. pp 1–11.Donlan RM. Biofilms: microbial life on surfaces. Emerg Infect Dis. 2002;8:881–90.PubMed 
PubMed Central 

Google Scholar 
Popper ZA, Michel G, Hervé C, Domozych DS, Willats WGT, Tuohy MG, et al. Evolution and diversity of plant cell walls: from algae to flowering plants. Annu Rev Plant Biol. 2011;62:567–90.PubMed 

Google Scholar 
Henrissat B. A classification of glycosyl hydrolases based on amino acid sequence similarities. Biochem J. 1991;280:309–16.PubMed 
PubMed Central 

Google Scholar 
Martens EC, Koropatkin NM, Smith TJ, Gordon JI. Complex glycan catabolism by the human gut microbiota: the Bacteroidetes Sus-like paradigm. J Biol Chem. 2009;284:24673–7.PubMed 
PubMed Central 

Google Scholar 
Cuskin F, Lowe EC, Temple MJ, Zhu Y, Cameron E, Pudlo NA, et al. Human gut Bacteroidetes can utilize yeast mannan through a selfish mechanism. Nature. 2015;517:165–9.PubMed 
PubMed Central 

Google Scholar 
Falkowski PG, Barber RT, Smetacek VV. Biogeochemical Controls and Feedbacks on Ocean Primary Production. Science. 1998;281:200–7.PubMed 

Google Scholar 
Smetacek V. Seeing is Believing: Diatoms and the Ocean Carbon Cycle Revisited. Protist. 2018;169:791–802.PubMed 

Google Scholar 
Teeling H, Fuchs BM, Becher D, Klockow C, Gardebrecht A, Bennke CM, et al. Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom. Science. 2012;336:608–11.PubMed 

Google Scholar 
Teeling H, Fuchs BM, Bennke CM, Krüger K, Chafee M, Kappelmann L, et al. Recurring patterns in bacterioplankton dynamics during coastal spring algae blooms. Elife. 2016;5:e11888.PubMed 
PubMed Central 

Google Scholar 
Kappelmann L, Krüger K, Hehemann J-H, Harder J, Markert S, Unfried F, et al. Polysaccharide utilization loci of North Sea Flavobacteriia as basis for using SusC/D-protein expression for predicting major phytoplankton glycans. ISME J. 2019;13:76–91.PubMed 

Google Scholar 
Vidal-Melgosa S, Sichert A, Francis TB, Bartosik D, Niggemann J, Wichels A, et al. Diatom fucan polysaccharide precipitates carbon during algal blooms. Nat Commun. 2021;12:1150.PubMed 
PubMed Central 

Google Scholar 
Chanzy H, Dube M, Marchessault RH, Revol JF. Single crystals and oriented crystallization of ivory nut mannan. Biopolymers 1979;18:887–98.
Google Scholar 
Katsuraya K, Okuyama K, Hatanaka K, Oshima R, Sato T, Matsuzaki K. Constitution of konjac glucomannan: chemical analysis and 13C NMR spectroscopy. Carbohydr Polym. 2003;53:183–9.
Google Scholar 
Melton L, Smith BG, Ibrahim R, Schröder R, Harris P, Schmitt U. Mannans in primary and secondary plant cell walls. NZ J forestry Sci. 2009;39:153–60.
Google Scholar 
Hannuksela T, Du Hervé Penhoat C. NMR structural determination of dissolved O-acetylated galactoglucomannan isolated from spruce thermomechanical pulp. Carbohydr Res. 2004;339:301–12.PubMed 

Google Scholar 
Gilbert HJ, Stålbrand H, Brumer H. How the walls come crumbling down: recent structural biochemistry of plant polysaccharide degradation. Curr Opin Plant Biol. 2008;11:338–48.PubMed 

Google Scholar 
Bågenholm V, Reddy SK, Bouraoui H, Morrill J, Kulcinskaja E, Bahr CM, et al. Galactomannan catabolism conferred by a polysaccharide utilization locus of Bacteroides ovatus: Enzyme synergy and crystal structure of a β-mannanase. J Biol Chem. 2017;292:229–43.PubMed 

Google Scholar 
Chen J, Robb CS, Unfried F, Kappelmann L, Markert S, Song T, et al. Alpha- and beta-mannan utilization by marine Bacteroidetes. Environ Microbiol. 2018;20:4127–40.PubMed 

Google Scholar 
Klemetsen T, Raknes IA, Fu J, Agafonov A, Balasundaram SV, Tartari G, et al. The MAR databases: development and implementation of databases specific for marine metagenomics. Nucl Acids Res. 2018;46:D692–99.PubMed 

Google Scholar 
Sayers EW, Bolton EE, Brister JR, Canese K, Chan J, Comeau DC, et al. Database resources of the national center for biotechnology information. Nucl Acids Res. 2022;50:D20–26.PubMed 

Google Scholar 
Gilchrist CL, Booth TJ, van Wersch B, van Grieken L, Medema MH, Chooi Y-H. cblaster: a remote search tool for rapid identification and visualisation of homologous gene clusters. Bioinformatics Advances. 2021;1:016.Zhang H, Yohe T, Huang L, Entwistle S, Wu P, Yang Z, et al. dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucl Acids Res. 2018;46:W95–W101.PubMed 
PubMed Central 

Google Scholar 
Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucl Acids Res. 1997;25:3389–402.PubMed 
PubMed Central 

Google Scholar 
Krüger K, Chafee M, Ben Francis T, Del Glavina Rio T, Becher D, Schweder T, et al. In marine Bacteroidetes the bulk of glycan degradation during algae blooms is mediated by few clades using a restricted set of genes. ISME J. 2019;13:2800–16.PubMed 
PubMed Central 

Google Scholar 
Francis TB, Bartosik D, Sura T, Sichert A, Hehemann J-H, Markert S, et al. Changing expression patterns of TonB-dependent transporters suggest shifts in polysaccharide consumption over the course of a spring phytoplankton bloom. ISME J. 2021;15:2336–50.PubMed 
PubMed Central 

Google Scholar 
Lex A, Gehlenborg N, Strobelt H, Vuillemot R, Pfister H. UpSet: Visualization of Intersecting Sets. IEEE Trans Vis Comput Graph. 2014;20:1983–92.PubMed 
PubMed Central 

Google Scholar 
Conway JR, Lex A, Gehlenborg N. UpSetR: an R package for the visualization of intersecting sets and their properties. Bioinformatics. 2017;33:2938–40.PubMed 
PubMed Central 

Google Scholar 
Krzywinski M, Schein J, Birol I, Connors J, Gascoyne R, Horsman D, et al. Circos: an information aesthetic for comparative genomics. Genome Res. 2009;19:1639–45.PubMed 
PubMed Central 

Google Scholar 
Madeira F, Pearce M, Tivey ARN, Basutkar P, Lee J, Edbali O, et al. Search and sequence analysis tools services from EMBL-EBI in 2022. Nucl Acids Res. 2022;50(W1):W276–79.Guindon S, Dufayard J-F, Lefort V, Anisimova M, Hordijk W, Gascuel O. New Algorithms and Methods to Estimate Maximum-Likelihood Phylogenies: Assessing the Performance of PhyML 3.0. Syst Biol. 2010;59:307–21.PubMed 

Google Scholar 
Lefort V, Longueville J-E, Gascuel O. SMS: Smart Model Selection in PhyML. Mol Biol Evol. 2017;34:2422–4.PubMed 
PubMed Central 

Google Scholar 
Letunic I, Bork P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 2019;47:W256–59.PubMed 
PubMed Central 

Google Scholar 
Hahnke RL, Harder J. Phylogenetic diversity of Flavobacteria isolated from the North Sea on solid media. Syst Appl Microbiol. 2013;36:497–504.PubMed 

Google Scholar 
Schut F, Vries EJ, de, Gottschal JC, Robertson BR, Harder W, Prins RA, et al. Isolation of Typical Marine Bacteria by Dilution Culture: Growth, Maintenance, and Characteristics of Isolates under Laboratory Conditions. Appl Environ Microbiol. 1993;59:2150–60.PubMed 
PubMed Central 

Google Scholar 
Otto A, Bernhardt J, Meyer H, Schaffer M, Herbst F-A, Siebourg J, et al. Systems-wide temporal proteomic profiling in glucose-starved Bacillus subtilis. Nat Commun. 2010;1:137.PubMed 

Google Scholar 
Cox J, Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol. 2008;26:1367–72.PubMed 

Google Scholar 
Tyanova S, Temu T, Sinitcyn P, Carlson A, Hein MY, Geiger T, et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat Methods. 2016;13:731–40.PubMed 

Google Scholar 
Yu NY, Wagner JR, Laird MR, Melli G, Rey S, Lo R, et al. PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics. 2010;26:1608–15.PubMed 
PubMed Central 

Google Scholar 
Yu C-S, Lin C-J, Hwang J-K. Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositions. Protein Sci. 2004;13:1402–6.PubMed 
PubMed Central 

Google Scholar 
Perez-Riverol Y, Bai J, Bandla C, García-Seisdedos D, Hewapathirana S, Kamatchinathan S, et al. The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences. Nucl Acids Res. 2022;50:D543–52.PubMed 

Google Scholar 
Studier F. Protein production by auto-induction in high density shaking cultures. Protein Expr. Purif. 2005;41:207–34.The CCP4 suite: programs for protein crystallography. Acta Crystallogr D Biol Crystallogr. 1994;50:760–3.Kabsch W. XDS. Acta Crystallogr D Biol Crystallogr. 2010;66:125–32.PubMed 
PubMed Central 

Google Scholar 
Cartmell A, Topakas E, Ducros VM-A, Suits MDL, Davies GJ, Gilbert HJ. The Cellvibrio japonicus mannanase CjMan26C displays a unique exo-mode of action that is conferred by subtle changes to the distal region of the active site. J Biol Chem. 2008;283:34403–13.PubMed 
PubMed Central 

Google Scholar 
Couturier M, Roussel A, Rosengren A, Leone P, Stålbrand H, Berrin J-G. Structural and Biochemical Analyses of Glycoside Hydrolase Families 5 and 26 β-(1,4)-Mannanases from Podospora anserina Reveal Differences upon Manno-oligosaccharide Catalysis*. J Biol Chem. 2013;288:14624–35.PubMed 
PubMed Central 

Google Scholar 
Afonine PV, Grosse-Kunstleve RW, Echols N, Headd JJ, Moriarty NW, Mustyakimov M, et al. Towards automated crystallographic structure refinement with phenix.refine. Acta Crystallogr D Biol Crystallogr. 2012;68:352–67.PubMed 
PubMed Central 

Google Scholar 
Murshudov GN, Skubák P, Lebedev AA, Pannu NS, Steiner RA, Nicholls RA, et al. REFMAC5 for the refinement of macromolecular crystal structures. Acta Crystallogr D Biol Crystallogr. 2011;67:355–67.PubMed 
PubMed Central 

Google Scholar 
Emsley P, Lohkamp B, Scott WG, Cowtan K. Features and development of Coot.Acta Crystallogr D Biol Crystallogr. 2010;66:486–501.PubMed 
PubMed Central 

Google Scholar 
Cowtan K. The Buccaneer software for automated model building. 1. Tracing protein chains. Acta Crystallogr D Biol Crystallogr. 2006;62:1002–11.PubMed 

Google Scholar 
DeLano WL. The PyMOL Molecular Graphics System Version 2.3.4. Schrödinger, LLC, New York; 2010.Fontes CM, Clarke JH, Hazlewood GP, Fernandes TH, Gilbert HJ, Ferreira LM. Possible roles for a non-modular, thermostable and proteinase-resistant cellulase from the mesophilic aerobic soil bacterium Cellvibrio mixtus. Appl Microbiol Biotechnol. 1997;48:473–9.PubMed 

Google Scholar 
Brändén C-I. The TIM barrel—the most frequently occurring folding motif in proteins: Current Opinion in Structural Biology. 1991;1:978–83.Marcus SE, Blake AW, Benians TAS, Lee KJD, Poyser C, Donaldson L, et al. Restricted access of proteins to mannan polysaccharides in intact plant cell walls. Plant J. 2010;64:191–203.PubMed 

Google Scholar 
Meikle PJ, Hoogenraad NJ, Bonig I, Clarke AE, Stone BA. A (1-3,1-4)-beta-glucan-specific monoclonal antibody and its use in the quantitation and immunocytochemical location of (1-3,1-4)-beta-glucans. Plant J. 1994;5:1–9.PubMed 

Google Scholar 
Kračun SK, Fangel JU, Rydahl MG, Pedersen HL, Vidal-Melgosa S, Willats WGT. Carbohydrate microarray technology applied to high-throughput mapping of plant cell wall glycans using comprehensive microarray polymer profiling (CoMPP). In: High-Throughput Glycomics and Glycoproteomics. Humana Press, New York, NY, 2017;1503:147–65.Moller I, Sørensen I, Bernal AJ, Blaukopf C, Lee K, Øbro J, et al. High-throughput mapping of cell-wall polymers within and between plants using novel microarrays. Plant J. 2007;50:1118–28.PubMed 

Google Scholar 
Yan X-X, An X-M, Gui L-L, Liang D-C. From structure to function: insights into the catalytic substrate specificity and thermostability displayed by Bacillus subtilis mannanase BCman. J Mol Biol. 2008;379:535–44.PubMed 

Google Scholar 
Tailford LE, Ducros VM-A, Flint JE, Roberts SM, Morland C, Zechel DL, et al. Understanding how diverse beta-mannanases recognize heterogeneous substrates. Biochemistry. 2009;48:7009–18.PubMed 

Google Scholar 
Nakae S, Ito S, Higa M, Senoura T, Wasaki J, Hijikata A, et al. Structure of Novel Enzyme in Mannan Biodegradation Process 4-O-β-d-Mannosyl-d-Glucose Phosphorylase MGP. J Mol Biol. 2013;425:4468–78.PubMed 

Google Scholar 
Scheller HV, Ulvskov P. Hemicelluloses. Annu Rev Plant Biol. 2010;61:263–89.PubMed 

Google Scholar 
Varki A, Cummings RD, Aebi M, Packer NH, Seeberger PH, Esko JD, et al. Symbol Nomenclature for Graphical Representations of Glycans. Glycobiology. 2015;25:1323–4.PubMed 
PubMed Central 

Google Scholar 
Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10.PubMed 

Google Scholar 
Finn RD, Clements J, Eddy SR. HMMER web server: interactive sequence similarity searching. Nucleic Acids Res. 2011;39:W29–W37.PubMed 
PubMed Central 

Google Scholar  More

We incorporated a macromolecular model of phytoplankton (CFM-Phyto) into the global ocean model (MITgcm). This combined model predicts cellular growth rate based on the macromolecular allocation, which in turn is used to determine the elemental stoichiometry of phytoplankton for the next model time step.The phytoplankton component of the model is executed using the following algorithm, which is illustrated graphically in Extended Data Fig. 2: (1) relate the growth rate and elemental stoichiometry of phytoplankton based on the macromolecular allocation; (2) evaluate the possible growth rates under four different limiting nutrient assumptions and select the lowest rate: Liebig’s Law of the Minimum; (3) evaluate storage of non-limiting elements; (4) evaluate excess of non-limiting elements relative to maximum quotas; (5) based on that excess, evaluate effective nutrient uptake rate; and (6) evaluate the change in the elemental stoichiometry based on the balance between the growth rate and effective nutrient uptake rate. We describe the procedural details in the following text. Parameter values are listed in Extended Data Table 1. See ref. 21 for further details and justification of each equation in CFM-Phyto; here we repeat equations essential to explain the model used in the current study.Connecting the elemental stoichiometry and the growth rateThe first step of the algorithm is to obtain the relationship between the current elemental stoichiometry and the growth rate (μ). To do that, we use CFM-Phyto21 (Extended Data Fig. 1). The model is based on the assumption of pseudo-steady state with respect to macromolecular allocation; in other words, the cellular-scale acclimation occurs rapidly relative to environmental changes. Laboratory studies show that macromolecular re-allocation occurs on the timescale of hours to days19. This is fast relative to the rates of environmental change in our coarse-resolution ocean simulations and so steady state solutions21 are used to relate growth rate, macromolecular allocation and elemental stoichiometry, as described in detail below. We first describe the case of N quota (here defined as QN; moles cellular N per mole cellular C) in detail, and then we briefly explain the case of P and C quotas as the overall procedures are similar. After that, we describe the case with Fe quota, which extends the previously published model21 for this study.Relating N quota and growth rateCFM-Phyto describes the allocation of N quota as follows, focusing on the quantitatively major molecules:$$Q_{mathrm{N}} = Q_{mathrm{N}}^{{mathrm{Pro}}} + Q_{mathrm{N}}^{{mathrm{RNA}}} + Q_{mathrm{N}}^{{mathrm{DNA}}} + Q_{mathrm{N}}^{{mathrm{Chl}}} + Q_{mathrm{N}}^{{mathrm{Sto}}}$$
(2)
where QN is total N quota (per cellular C: mol N (mol C)−1), the terms on the right-hand side are the contributions from protein, RNA, DNA, chlorophyll and N storage. We use empirically determined fixed elemental stoichiometry of macromolecules21 (Extended Data Table 1) to connect the macromolecular contributions of different elements (here C and P):$$Q_{mathrm{N}} = Q_{mathrm{C}}^{{mathrm{Pro}}}Y_{{mathrm{Pro}}}^{{mathrm{N:C}}} + Q_{mathrm{P}}^{{mathrm{RNA}}}Y_{{mathrm{RNA}}}^{{mathrm{N:P}}} + Q_{mathrm{C}}^{{mathrm{DNA}}}Y_{{mathrm{DNA}}}^{{mathrm{N:C}}} + Q_{mathrm{C}}^{{mathrm{Chl}}}Y_{{mathrm{Chl}}}^{{mathrm{N:C}}} + Q_{mathrm{N}}^{{mathrm{Nsto}}}$$
(3)
Here (Y_l^{j:k}) represents the imposed elemental ratio (elements j and k) for each macromolecular pool (l). (Q_{mathrm{C}}^x) and (Q_{mathrm{P}}^x) describe the contributions of macromolecule x to the total C quota (mol C (mol C)−1) and P quota (mol P (mol C)−1), respectively.CFM-Phyto uses the following empirically supported relationship to describe (Q_{mathrm{P}}^{{mathrm{RNA}}}) (ref. 21):$$Q_{mathrm{P}}^{{mathrm{RNA}}} = A_{{mathrm{RNA}}}^{mathrm{P}}mu Q_{mathrm{C}}^{{mathrm{Pro}}} + Q_{{mathrm{P,min}}}^{{mathrm{RNA}}}$$
(4)
where (A_{{mathrm{RNA}}}^{mathrm{P}}) is constant (below, A values represent constant except (A_{{mathrm{Chl}}}); see below), μ is growth rate (d−1) and (Q_{{mathrm{P,min}}}^{{mathrm{RNA}}}) represents the minimum amount of RNA in phosphorus per cellular C (mol P (mol C)−1). Substituting this equation into equation (3) gives:$$begin{array}{l}Q_{mathrm{N}} = Q_{mathrm{C}}^{{mathrm{Pro}}}Y_{{mathrm{Pro}}}^{{mathrm{N:C}}} + left( {A_{{mathrm{RNA}}}^{mathrm{P}}mu Q_{mathrm{C}}^{{mathrm{Pro}}} + Q_{{mathrm{P,min}}}^{{mathrm{RNA}}}} right)\Y_{{mathrm{RNA}}}^{{mathrm{N:P}}} + Q_{mathrm{C}}^{{mathrm{DNA}}}Y_{{mathrm{DNA}}}^{{mathrm{N:C}}} + Q_{mathrm{C}}^{{mathrm{Chl}}}Y_{{mathrm{Chl}}}^{{mathrm{N:C}}} + Q_{mathrm{N}}^{{mathrm{Nsto}}}end{array}$$
(5)
In CFM-Phyto, we resolve three types of protein, photosynthetic, biosynthetic and other:$$Q_{mathrm{C}}^{{mathrm{Pro}}} = Q_{mathrm{C}}^{{mathrm{Pro}}_{mathrm{Pho}}} + Q_{mathrm{C}}^{{mathrm{Pro}}_{mathrm{Bio}}} + Q_{mathrm{C}}^{{mathrm{Pro}}_{mathrm{Other}}}$$
(6)
Photosynthetic proteins represent those in chloroplasts largely responsible for light harvesting and electron transport. The model assumes a constant composition of chloroplasts; thus, the amount of photosynthetic protein is proportional to the amount of chlorophyll21:$$Q_{mathrm{C}}^{{mathrm{Pro}}_{mathrm{Pho}}} = A_{{mathrm{Pho}}}Q_{mathrm{C}}^{{mathrm{Chl}}}$$
(7)
Biosynthetic proteins represent proteins related to producing new material such as proteins, carbohydrates, lipids, RNAs, DNAs and other molecules. The models use the following empirically derived relationship21:$$Q_{mathrm{C}}^{{mathrm{Pro}}_{mathrm{Bio}}} = A_{{mathrm{Bio}}}mu$$
(8)
Substituting equations (6)–(8) (in this order) into equation (5) leads to the following equation:$$begin{array}{l}Q_{mathrm{N}} = left( {A_{{mathrm{Pho}}}Q_{mathrm{C}}^{{mathrm{Chl}}} + A_{{mathrm{Bio}}}mu + Q_{mathrm{C}}^{{mathrm{Pro}}_{mathrm{Other}}}} right)Y_{{mathrm{Pro}}}^{{mathrm{N:C}}}\ + left( {A_{{mathrm{RNA}}}^{mathrm{P}}mu left( {A_{{mathrm{Pho}}}Q_{mathrm{C}}^{{mathrm{Chl}}} + A_{{mathrm{Bio}}}mu + Q_{mathrm{C}}^{{mathrm{Pro}}_{mathrm{Other}}}} right) + Q_{{mathrm{P,min}}}^{{mathrm{RNA}}}} right)Y_{{mathrm{RNA}}}^{{mathrm{N:P}}}\ + Q_{mathrm{C}}^{{mathrm{DNA}}}Y_{{mathrm{DNA}}}^{{mathrm{N:C}}} + Q_{mathrm{C}}^{{mathrm{Chl}}}Y_{{mathrm{Chl}}}^{{mathrm{N:C}}} + Q_{mathrm{N}}^{{mathrm{Sto}}}end{array}$$
(9)
Empirically, chlorophyll depends on the growth rate and equation (10) accurately describes the relationship between the growth-rate dependences of chlorophyll under different light intensities21:$$Q_{mathrm{C}}^{{mathrm{Chl}}} = A_{{mathrm{Chl}}}mu + B_{{mathrm{Chl}}}$$
(10)
with (A_{{mathrm{Chl}}} = left( {1 + E} right)/v_I) and (B_{Chl} = m/v_I) with E (dimensionless) as a constant representing growth-rate-dependent respiration, and m (d−1) describing maintenance respiration. vI (mol C (mol C in Chl)−1 d−1) represents chlorophyll-specific photosynthesis rate based on an established function of light intensity I (μmol m−2 s−1)21,57:$$v_I = v_I^{{mathrm{max}}}left( {1 – e^{A_II}} right)$$
(11)
where (v_I^{{mathrm{max}}}) is the maximum chlorophyll-specific photosynthesis rate, e is the natural base and AI is a combined coefficient for absorption cross-section and turnover time. Substitution of equation (10) into equation (9) leads to the following quadratic relationship between QN and μ:$$Q_{mathrm{N}} = a_{mathrm{N}}mu ^2 + b_{mathrm{N}}mu + c_{mathrm{N}} + Q_{mathrm{N}}^{{mathrm{Sto}}}$$
(12)
where$$begin{array}{l}a_{mathrm{N}} = A_{{mathrm{RNA}}}^{mathrm{P}}left( {A_{{mathrm{Pho}}}A_{{mathrm{Chl}}} + A_{{mathrm{Bio}}}} right)Y_{{mathrm{RNA}}}^{{mathrm{N:P}}}\ b_{mathrm{N}} = left( {A_{{mathrm{Pho}}}A_{{mathrm{Chl}}} + A_{{mathrm{Bio}}}} right)Y_{{mathrm{Pro}}}^{{mathrm{N:C}}} + A_{{mathrm{Chl}}}Y_{{mathrm{Chl}}}^{{mathrm{N:C}}} + A_{{mathrm{RNA}}}^{mathrm{P}}left( {A_{{mathrm{Pho}}}B_{{mathrm{Chl}}} + Q_{mathrm{C}}^{{mathrm{Pro}}_{mathrm{Other}}}} right)Y_{mathrm{{RNA}}}^{{mathrm{N:P}}}\ c_{mathrm{N}} = B_{{mathrm{Chl}}}Y_{{mathrm{Chl}}}^{{mathrm{N:C}}} + left( {A_{{mathrm{Pho}}}B_{{mathrm{Chl}}} + Q_{mathrm{C}}^{{mathrm{Pro}}_{mathrm{Other}}}} right)Y_{{mathrm{Pro}}}^{{mathrm{N:C}}}\ + Q_{{mathrm{P}},{mathrm{min}}}^{{mathrm{RNA}}}Y_{{mathrm{RNA}}}^{{mathrm{N:P}}} + Q_{mathrm{C}}^{{mathrm{DNA}}}Y_{{mathrm{DNA}}}^{{mathrm{N:C}}}end{array}$$Relating P quota and growth rateSimilarly, CFM-Phyto describes the relationship between the current P quota QP and μ. P is allocated to its major molecular reservoirs:$$Q_{mathrm{P}} = Q_{mathrm{P}}^{{mathrm{RNA}}} + Q_{mathrm{C}}^{{mathrm{DNA}}}Y_{{mathrm{DNA}}}^{{mathrm{P:C}}} + Q_{mathrm{P}}^{{mathrm{Thy}}} + Q_{mathrm{P}}^{{mathrm{Other}}} + Q_{mathrm{P}}^{{mathrm{Sto}}}$$
(13)
Similar to equation (7), with the assumption of the constant composition of photosynthetic apparatus, the model connects the amount of the chlorophyll to phosphorus in thylakoid membranes:$$Q_{mathrm{P}}^{{mathrm{Thy}}} = A_{{mathrm{Pho}}}^{{mathrm{P:Chl}}}Q_{mathrm{C}}^{{mathrm{Chl}}}$$
(14)
As for N allocation, substitution of equations (14), (4), (6), (7), (8) and (10) (in this order) into equation (13) leads to a quadratic relationship between QP and μ:$$Q_{mathrm{P}} = a_{mathrm{P}}mu ^2 + b_{mathrm{P}}mu + c_{mathrm{P}} + Q_{mathrm{P}}^{{mathrm{Sto}}}$$
(15)
where$$begin{array}{l}a_{mathrm{P}} = A_{{mathrm{RNA}}}^{mathrm{P}}left( {A_{{mathrm{Pho}}}A_{{mathrm{Chl}}} + A_{{mathrm{Bio}}}} right)\ b_{mathrm{P}} = A_{{mathrm{RNA}}}^{mathrm{P}}left( {A_{{mathrm{Pho}}}B_{{mathrm{Chl}}} + Q_{mathrm{C}}^{{mathrm{Pro}}_{mathrm{Other}}}} right)Y_{{mathrm{RNA}}}^{{mathrm{N:P}}} + A_{{mathrm{Pho}}}^{{mathrm{P:Chl}}}A_{{mathrm{Chl}}}\ c_{mathrm{P}} = Q_{{mathrm{P,min}}}^{{mathrm{RNA}}} + Q_{mathrm{C}}^{{mathrm{DNA}}}Y_{{mathrm{DNA}}}^{{mathrm{P:C}}} + A_{{mathrm{Pho}}}^{{mathrm{P:Chl}}}B_{{mathrm{Chl}}} + Q_{mathrm{P}}^{{mathrm{Other}}}end{array}$$Relating C quota and growth rateSimilarly, CFM-Phyto describes C allocation as follows:$$begin{array}{l}Q_{mathrm{C}} = 1 = Q_{mathrm{C}}^{{mathrm{Pro}}} + Q_{mathrm{C}}^{{mathrm{RNA}}} + Q_{mathrm{C}}^{{mathrm{DNA}}} + Q_{mathrm{C}}^{{mathrm{Other}}} + Q_{mathrm{C}}^{{mathrm{Plip}} – {mathrm{Thy}}}\qquad + Q_{mathrm{C}}^{{mathrm{Csto}}} + Q_{mathrm{C}}^{{mathrm{Nsto}}}end{array}$$
(16)
where Plip−Thy indicates P lipid in thylakoid membranes. The equation represents the allocation per total cellular C in mol C (mol C)−1, so the sum of the macromolecules in C (QC) becomes 1. Using the imposed elemental ratios of macromolecular pools ((Y_l^{j:k})) we relate the elemental contributions:$$Q_{mathrm{C}} = Q_{mathrm{C}}^{{mathrm{Pro}}} + Q_{mathrm{P}}^{{mathrm{RNA}}}Y_{{mathrm{RNA}}}^{{mathrm{C:P}}} + Q_{mathrm{C}}^{{mathrm{DNA}}} + Q_{mathrm{C}}^{{mathrm{Other}}} + Q_{mathrm{P}}^{{mathrm{Thy}}}Y_{{mathrm{Plip}}}^{{mathrm{C:P}}} + Q_{mathrm{C}}^{{mathrm{Sto}}} + Q_{mathrm{N}}^{{mathrm{Sto}}}Y_{{mathrm{Nsto}}}^{{mathrm{C:N}}}$$
(17)
Following the steps similar to those for the N and P allocations, substituting equations (14), (4), (6), (7), (8) and (10) (in this order) into equation (17) leads to the following quadratic relationship between cellular C quota QC (=1 mol C (mol C)−1) and μ:$$Q_{mathrm{C}} = a_{mathrm{C}}mu ^2 + b_{mathrm{C}}mu + c_{mathrm{C}} + Q_{mathrm{C}}^{{mathrm{Sto}}} + Q_{mathrm{N}}^{{mathrm{Sto}}}Y_{{mathrm{Nsto}}}^{{mathrm{C:N}}}$$
(18)
where$$begin{array}{l}a_{mathrm{C}} = A_{{mathrm{RNA}}}^{mathrm{P}}left( {A_{{mathrm{Pho}}}A_{{mathrm{Chl}}} + A_{{mathrm{Bio}}}} right)Y_{{mathrm{RNA}}}^{{mathrm{C:P}}}\ b_{mathrm{C}} = A_{{mathrm{Chl}}}left( {1 + A_{{mathrm{Pho}}} + A_{{mathrm{Pho}}}^{{mathrm{P:Chl}}}Y_{{mathrm{Plip}}}^{{mathrm{C:P}}}} right) + A_{{mathrm{Bio}}} + A_{{mathrm{RNA}}}^{mathrm{P}}left( {A_{{mathrm{Pho}}}B_{{mathrm{Chl}}} + Q_{mathrm{C}}^{{mathrm{Pro}}_{mathrm{Other}}}} right)Y_{{mathrm{RNA}}}^{{mathrm{C:P}}}\ c_{mathrm{C}} = left( {1 + A_{{mathrm{Pho}}} + A_{{mathrm{Pho}}}^{{mathrm{P:Chl}}}Y_{{mathrm{Plip}}}^{{mathrm{C:P}}}} right)B_{{mathrm{Chl}}} + Q_{mathrm{C}}^{{mathrm{Pro}}_{rm{Other}}}\ + Q_{{mathrm{P}},{mathrm{min}}}^{{mathrm{RNA}}}Y_{{mathrm{RNA}}}^{{mathrm{C:P}}} + Q_{mathrm{C}}^{{mathrm{DNA}}} + Q_{mathrm{C}}^{{mathrm{Other}}}end{array}$$Relating Fe quota and growth rateIn order to capture global scale biogeochemical dynamics including the iron-limited high-nitrogen, low chlorophyll regimes, CFM-Phyto21 is extended to resolve Fe quota and allocation. The model is guided by a laboratory proteomic study58 in which the major Fe allocations are to photosystems, storage and nitrogen-fixing enzymes (nitrogenase). As we do not resolve nitrogen-fixing organisms here, Fe allocation (mol Fe (mol C)−1) represents only the first two:$$Q_{{mathrm{Fe}}} = Q_{{mathrm{Fe}}}^{{mathrm{Pho}}} + Q_{{mathrm{Fe}}}^{{mathrm{Sto}}}$$
(19)
As for equation (7) and equation (14), we relate the allocation of Fe to photosystems to the investment in chlorophyll, (Q_{mathrm{C}}^{{mathrm{Chl}}}):$$Q_{{mathrm{Fe}}}^{{mathrm{Pho}}} = A_{{mathrm{Pho}}}^{{mathrm{Fe}}}Q_{mathrm{C}}^{{mathrm{Chl}}}$$
(20)
This is a strong simplification because the pigment to photosystem ratio is observed to vary59, but enables an explicit, mechanistically motivated representation of Fe limitation, which, a posteriori, results in global scale regimes of iron limitation that resemble those observed43 (Extended Data Fig. 4). With equations (10), (19) and (20), we obtain the following relationship between QFe and μ:$$Q_{{mathrm{Fe}}} = A_{{mathrm{Pho}}}^{{mathrm{Fe}}}A_{{mathrm{Chl}}}mu + A_{{mathrm{Pho}}}^{{mathrm{Fe}}}B_{{mathrm{Chl}}} + Q_{{mathrm{Fe}}}^{{mathrm{Sto}}}$$
(21)
Evaluating the growth rateWe assume that the cellular growth rate is constrained by the most limiting element within the cell (and its associated functional macromolecules). Thus, at each time step and location, and for each cell type, the evaluation of growth rate is based on the following two steps: (1) computation of the growth rate for each element without storage; that is, the case when all of the elemental quotas are allocated to functional macromolecules; and (2) selection of the lowest growth rate among these; Liebig’s Law of the Minimum. For the first step, we define (mu _i) (i = C, N, P, Fe) as the growth rate, assuming that nutrient i is limiting. Under this condition, (Q_i^{{mathrm{Sto}}}) should be small as element i is allocated to other essential molecules. We assume that (Q_{mathrm{N}}^{{mathrm{Sto}}}) is also small under C limitation because N storage molecules are rich in carbon. With these assumptions, the solution for (mu _i) is obtained by solving the standard quadratic relationships of equations (12), (15) and (18) for N, P and C, respectively, neglecting any (Q_i^{{mathrm{Sto}}}) terms:$$mu _i = frac{{ – b_i + sqrt {b_i^2 – 4a_ileft( {c_i – Q_i} right)} }}{{2a_i}}$$
(22)
where QC = 1. For μFe, equation (21) without (Q_{{mathrm{Fe}}}^{{mathrm{Sto}}}) leads to$$mu _{{mathrm{Fe}}} = frac{{Q_{{mathrm{Fe}}} – A_{{mathrm{Pho}}}^{{mathrm{Fe}}}B_{{mathrm{Chl}}}}}{{A_{{mathrm{Pho}}}^{{mathrm{Fe}}}A_{{mathrm{Chl}}}}}$$
(23)
Once the μi values are obtained, we determine the effective growth rate, μ, based on the lowest value, which identifies the limiting element based on current intracellular quotas:$$mu = {mathrm{min}}left( {mu _{mathrm{N}},mu _{mathrm{P}},mu _{mathrm{C}},mu _{{mathrm{Fe}}}} right)$$
(24)
Evaluating nutrient storageIn CFM-Phyto, non-limiting nutrients can be stored in an intracellular reserve21, reflecting commonly observed luxury uptake. Storage is evaluated as the difference between the total elemental quota (updated later) and the functionally allocated portion of that element:$$Q_i^{{mathrm{Sto}}} = Q_i – Q_i^{{mathrm{Non}}_{mathrm{Sto}}}$$
(25)
Here (Q_i^{{mathrm{Non}}_{mathrm{Sto}}}) represents the contribution to element i by functional, non-storage molecules. For N, P and C, (Q_i^{{mathrm{Non}}_{mathrm{Sto}}}) is represented by the non-(Q_i^{{mathrm{Sto}}}) terms on the right-hand side in equations (12), (15) and (18), respectively:$$Q_i^{{mathrm{Non}}_{mathrm{Sto}}} = a_imu ^2 + b_imu + c_i$$
(26)
Similarly, for Fe, from equation (21):$$Q_{{mathrm{Fe}}}^{{mathrm{Non}}_{mathrm{Sto}}} = A_{{mathrm{Pho}}}^{{mathrm{Fe}}}A_{{mathrm{Chl}}}mu + A_{{mathrm{Pho}}}^{{mathrm{Fe}}}B_{{mathrm{Chl}}}$$
(27)
When there is N storage, (Q_{mathrm{C}}^{{mathrm{Sto}}}) must be recomputed to consider the allocation of C to it:$$Q_{mathrm{C}}^{{mathrm{Sto}}} = Q_{mathrm{C}} – Q_{mathrm{C}}^{{mathrm{Non}}_{mathrm{Sto}}} – Q_{mathrm{N}}^{{mathrm{Sto}}}Y_{{mathrm{Nsto}}}^{{mathrm{C:N}}}$$
(28)
Evaluating the excess nutrientStorage capacity for any element is finite and we define excess nutrient as a nutrient (N, P, Fe) that is in beyond an empirically informed, imposed maximum phytoplankton storage capacity. Excess nutrient is assumed to be excreted (see below). Excess of element i ((Q_i^{{mathrm{Exc}}})) is computed:$$Q_i^{{mathrm{Exc}}} = {mathrm{max}}left( {Q_i – Q_i^{{mathrm{max}}},0} right)$$
(29)
where (Q_i^{{mathrm{max}}}) is maximum capacity for nutrient i. For N, CFM-Phyto computes (Q_i^{{mathrm{max}}}) as a sum of non-storage molecules and prescribed maximum nutrient storing capacity according to model–data comparison21:$$Q_i^{{mathrm{max}}} = Q_i^{{mathrm{Non}}_{mathrm{Sto}}} + Q_i^{{mathrm{Sto}}_{mathrm{max}}}$$
(30)
Laboratory studies suggest that when P is not limiting, the phosphorus quota maximizes to a value that is almost independent of growth rate21,39,44. Storage of each element is finite and the upper limit to storage is imposed by specifying the maximum cellular quotas ((Q_{mathrm{P}}^{{mathrm{max}}}) (ref. 21) and also (Q_{{mathrm{Fe}}}^{{mathrm{max}}})) with size and taxonomic dependencies (for example, refs. 27,41). Thus, the maximum storage is represented by the difference between the prescribed maximum quota and the actual quota21:$$Q_i^{{mathrm{Sto}}_{mathrm{max}}} = Q_i^{{mathrm{max}}} – Q_i$$
(31)
In the case where (Q_i^{{mathrm{Sto}}}) computed in the previous section exceeds (Q_i^{{mathrm{Sto}}_{mathrm{max}}}), the value of (Q_i^{{mathrm{Sto}}}) is replaced by (Q_i^{{mathrm{Sto}}_{mathrm{max}}}) and the difference is placed in the excess pool, (Q_i^{{mathrm{Exc}}}).Computing effective nutrient uptake rateOne factor that influences the cellular elemental quota is the effective nutrient uptake rate (mol i (mol C)−1 d−1) of N, P and Fe, which we define as follows:$$V_i^{{mathrm{Eff}}} = V_i – frac{{Q_i^{{mathrm{Exc}}}}}{{tau _i^{{mathrm{Exu}}}}}$$
(32)
where Vi (mol i (mol C)−1 d−1) is nutrient uptake rate and the second term represents the exudation of the excess nutrient based on the timescale (tau _i^{{mathrm{Exu}}}) (d−1). For Vi, we use Monod kinetics60,61:$$V_i = V_i^{{mathrm{max}}}frac{{[i]}}{{left[ i right] + K_i}}$$
(33)
where (V_i^{{mathrm{max}}}) is maximum nutrient uptake, [i] (mmol m−3) is the environmental concentration of nutrient i and Ki (mmol m−3) is the half-saturation constant of i. Previous models have resolved the relationship between nutrient uptake and allocation to transporters31,62. Here we do not explicitly resolve allocation to transporters, as proteomic studies indicate that it is a relatively minor component of the proteome compared with photosystems and biosynthesis in phytoplankton63. Transporter proteins could be represented in a model with a finer-scale resolution of the proteome64.Differentiating small and large phytoplanktonIn this model, ‘small’ phytoplankton broadly represent picocyanobacteria, which have high nutrient affinities and low maximum growth rates (for example, Prochlorococcus), whereas ‘large’ phytoplankton represent eukaryotes with higher maximum growth rates (for example, diatoms). The former are associated with a gleaner strategy adapted to oligotrophic regimes, while the latter are opportunistic, adapted to variable and nutrient-enriched regimes. To encapsulate this, the large phytoplankton have overall higher imposed (V_i^{{mathrm{max}}}) (~µmaxQi), Ki and (v_I^{mathrm{max}}) than for the small phytoplankton (Extended Data Table 1), consistent with the previous models (for example, ref. 10). In addition, the larger cells are assigned a higher (Q_{mathrm{P}}^{{mathrm{max}}}) following the observed trends (Fig. 1 and Extended Data Table 1).Computing the change in the elemental stoichiometryThe computation of the change in the elemental quotas is done based on the balance between the effective nutrient uptake rate and the loss of nutrient to the new cells:$$frac{{{mathrm{d}}Q_i}}{{{mathrm{d}}t}} = V_i^{{mathrm{Eff}}} – mu Q_i$$
(34)
This change in the elemental quotas based on the cellular processes and the passive transport of elements in phytoplankton by the flow field created by MITgcm governs the elemental stoichiometry of the next time step at a specific grid box, as in other versions of ecological models with MITgcm10.Calculation of CV valuesWe computed the CV values based on the following equation:$${mathrm{CV}} = frac{sigma }{{bar x}}$$
(35)
where σ is the standard deviation and (bar x) is the mean. The purpose of this computation is to quantify the latitudinal variation of the averaged elemental stoichiometry. Thus, we used the averaged values for each latitude (as plotted in Fig. 2) for the calculation of σ and (bar x).MITgcm-CFMThe biogeochemical and ecological component of the model resolves the cycling of C, P, N and Fe through inorganic, living, dissolved and particulate organic phases. The biogeochemical and biological tracers are transported and mixed by the MIT general circulation model (MITgcm)35,36, constrained to be consistent with altimetric and hydrographic observations (the ECCO-GODAE state estimates)65. This three-dimensional configuration has a coarse resolution (1° × 1° horizontally) and 23 depth levels ranging from 5 m at the surface to 5450 m at depth. The model was run for three years, and the results of the third year were analysed, by which time the modelled plankton distribution becomes quasi-stable. Equations for the biogeochemical processes are as described by equations and parameters in previous studies10,38. Here, however, we include only nitrate for inorganic nitrogen, and do not resolve the silica cycle. We simulated eukaryotic and prokaryotic analogues of phytoplankton (as ‘large’ and ‘small’ phytoplankton). The eukaryotic analogue has a higher maximum C fixation rate for the same macromolecular composition and higher maximum nutrient uptake rates, but also has overall higher half-saturation constants for nutrient uptake. We used light absorption spectra of picoeukaryotes, which sits in-between small prokaryotes and large eukaryotes10. In MITgcm, the mortality of phytoplankton is represented by the sum of a linear term (ml), representing sinking and maintenance losses, and quadratic terms representing the action of unresolved next-trophic levels66,67, implicitly assuming that the higher-trophic-level biomass scales with that of its prey. We assumed that the latter term is small to avoid introducing additional uncertainties. Similarly, we do not resolve the stoichiometric effects of prey selection due to the nutritional status of prey, or viral partitioning of nutrients in the environment50. Atmospheric iron deposition varies by orders of magnitude around the globe and has a large margin of uncertainty, including the bio-availability of the deposited iron, which in turn depends on the source and chemical history of the deposited material68. To realize a realistic global net primary production, we doubled the atmospheric iron input from ref. 10; this factor is well within the uncertainty of the iron supply estimates. Each of the two phytoplankton groups has variable C:N:P:Fe as determined by the component macromolecules at each time step. The pools of C, N, P and Fe are tracked within the modelled three-dimensional flow fields. More

Tallis, H. M. et al. Front. Ecol. Environ. 16, 563–570 (2018).Article 

Google Scholar 
Tu, C. et al. Nat. Sustain. https://doi.org/10.1038/s41893-022-01008-1 (2022).Article 

Google Scholar 
Lloyd, W. F. Two lectures on the checks to population (S. Collingwood, The University of Oxford, 1833).Gordon, H. S. J. Polit. Econ. 62, 124–142 (1954).Article 

Google Scholar 
Nash, J. F. Jr Proc. Natl Acad. Sci. USA 36, 48–49 (1950).Article 
CAS 

Google Scholar 
Hardin, G. Science 162, 1243–1248 (1968).Article 
CAS 

Google Scholar 
Ostrom, E. Governing the Commons: The Evolution of Institutions for Collective Action (Cambridge University Press, 1990).Sethi, R. & Somanathan, E. Am. Econ. Rev. 86, 766–788 (1996).
Google Scholar 
Schill, C. et al. Nat. Sustain. 2, 1075–1082 (2019).Article 

Google Scholar 
Levin, S. Philos. Trans R. Soc. B Biol. Sci. 365, 13–18 (2010).Article 

Google Scholar  More

HFRS distribution in China, 2004–2018From January 1, 2004 to December 31, 2018, 190 203 cases of HFRS were reported nationwide in China, with an average annual incidence rate of 0.950 per 100,000 people, with the highest incidence in 2004 (1.926 per 100,000) and the lowest in 2018 (0.86 per 100,000) (Fig. 1A), and the cases showed obvious seasonal fluctuations (Fig. 1B). HFRS cases existed every month and showed an obvious dual-season mode every year, with a spring peak from May to June and a winter peak from November to December. The highest number of cases were in May and November, with the composition ratios accounting of 9.51% and 17.06%, respectively (Fig. 1B).Figure 1The incidence and number of HFRS cases reported in China, 2004–2018. (A) Number of cases and incidence by year. Trend of the incidence rate of HFRS between 2004 and 2018 shown by the joinpoint regression (upper right corner). The red squares represent the observed crude incidence of HFRS and the lines represent the slope of the annual percentage change (APC). (B) The pink line represents the monthly incidence of HFRS. The bar chart shows the number of cases at peak and trough.Full size imageThe incidence of HFRS in northern regions was higher than that in the south, especially in Heilongjiang, Liaoning, Jining, Shaanxi, Shandong and Hebei provinces. Relatively few cases existed in south China, which were mainly concentrated in Jiangxi, Zhejiang, Hunan and Fujian (Figs. S1 and S2). Spatial autocorrelation analysis indicated that HFRS cases were positively correlated (Moran’s I = 0.09, p  More