Ecology

Root morphological traitsThe wild-type parent ISR42-8 produced longer root length (RL) than the modern cultivar parent Golf and tested lines (Table S2, Fig. 1A.h,A.f [h = hydroponic; f = field]). The tested lines of the two evolving barley populations displayed significant variations under hydroponic conditions. Barley lines evolved under OCS had on average 3484 mm longer roots compared to CCS under hydroponic treatment (Fig. 1A.h, Table 2). Complementary results under field conditions show as well higher RL for the OCS lines, even though the variance was significantly less pronounced (Fig. 1A.f). In addition, a less evident variance was observed in the field within both groups compared to the hydroponic (Fig. 1A.d). Across both experimental setups, the observed range of RL was higher in the OCS lines [Standard deviation (SD)OCS = 883, SDCCS = 597] (Table 2).Figure 1Significantly variant root morphological phenotypes. Boxplots illustrate the overall distribution of observed data points for the parents Golf and ISR 42-8 as well as for the conventional (CCS) and organic (OCS) lines. Density plots highlight the overall distribution of organic and conventional adapted lines. (A)—Root length (RL)—the sum of all roots harvested in millimeters (mm), illustrated for all four groups. (A.h)—root length measured in the hydroponic experiment; (A.f)—field experiment; A.d—distribution histogram for root length in both field and hydroponic experiment for CCS and OCS adapted lines. (B)—the ratio of root length to volume (L/V). Data available for hydroponics (B.h), field (B.f), and distribution of the ratio of root length to volume illustrated in B.d. (C.f)—Root mass density (RMD) from the field; (D.f)—Root angle (RA) from the field, distribution of the root angle illustrated in (D.d); (E.f)—root tip per plant count from the field, corresponding histogram visualized in (E.d). (F.f)—root fork per plant count from the field.Full size imageTable 2 Comparison of organic and conventional population root phenotypes under field and hydroponic evaluation.Full size tableThe root length to volume (L/V) is an important indicator of the soil volume that can be explored by the roots. Under hydroponics conditions, variations were found for L/V between the parental genotypes as well as between the OCS and CCS populations (Tables 2 & S3). The organic lines were characterized by a significantly higher L/V, indicating a much more distinct exploration of the soil by these lines (Fig. 1B.h,B.f). In comparison to field, highest diversity in L/V was found under hydroponic experiments within both OCS and CCS populations (Fig. 1B.d).The root mass density (RMD) is the ratio of root volume for a given root mass and is a key indicator of root thickness. Although significant variations existed between ISR42-8 and Golf under hydroponics conditions, such significant variations were not found between the OCS and CCS groups (P = 0.09) (Fig. 1C.f and Tables 2 and S3).The root angle (RA) measurements were only performed under field conditions since plants grown under hydroponics conditions were placed in uniform growing vessels and the direction of root growth is restricted by tubes. Significant variation was observed for the RA between the two parental lines, which was also reflected in the CCS and OCS lines (Fig. 1D.f). ISR42-8 was characterized by an 11.5° average narrower RA than Golf (Table S3). The RA was 4.1° bigger in the OCS compared to the CCS population (P = 0.005) (Table 2). However, a higher diversity in RA was observed in the OCS compared to the CCS lines (Fig. 1D.d, Table 2).In addition to the RA, the number of root tips and forks was measured under field conditions only. Both tips and forks indicate a similar pattern, where the OCS lines produced on average more for both PForks = 0.014, PTips = 0.0041 (Fig. 1E.f,F.f). After applying a P-adjustment, the number of forks count was no longer significantly different between OCS and CCS (PForks = 0.07, Table 2). Complementary, ISR 42-8 was observed to produce more tips and forks than Golf, which remained highly significant even after probability adjustment (Fig. 1E.f,F.f, Table S3). The distribution and the standard deviation of observed phenotypes highlight once more the fact that the OCS lines tend to have a higher variation (Fig. 1E.d, Table 2). Similarly, a significant increasing trend was recorded in root surface area (RSA) and root average diameter (RAD) by ISR42-8 as compared to Golf under hydroponics (Table S3). Contrasting to the parental genotypes, no variation was observed between OCS and CCS lines for RSA (Table 2).Root anatomical traitsWithin the observed anatomical traits, four were considered due to their relevance and variation between the systems. In both hydroponic and field experiments, significant variations were observed for the late metaxylem number (LMXN) between the parental lines as well as OCS and CCS lines (Tables 2 & S3, Fig. 2A.h,A.f). An increased LMXN for ISR 42-8 compared to Golf was observed (Fig. 2A.h). Regarding the CCS and OCS lines, a heterogenic scenario was presented over both experimental setups. While the median LMXN under CCS was identical with ISR 42-8 in the seedling stages of plant development (Fig. 2A.h), it was much lower in flowering stages under field conditions (Fig. 1A.f). Additionally, the LMXN was significantly higher in the CCS lines in the seedling stage compared to OCS lines, vice-versa LMXN was observed at the flowering time point (Fig. 2A.d).Figure 2Significantly variant root anatomical traits. Boxplots illustrate the overall distribution of observed data points for the parents Golf and ISR 42-8 as well as for the conventional (CCS) and organic (OCS) lines. (A) –Late metaxylem number (LMN)—the sum of all roots harvested and expressed by plant−1, illustrated for all four groups. A.h—Late metaxylem number measured in the hydroponic experiment; A.f—field experiment; A.d—distribution histogram for late metaxylem number in both field and hydroponic experiment for CCS and OCS adapted lines. (B)—Aerenchyma area (AA). Data available for hydroponics (B.h), field (B.f), and distribution of the aerenchyma area illustrated in (B.d). (C.f)—Total cortical area (TCA) from the field; (D.f)—Root cross-section area (RA) from the field, distribution of the total cortical area and root cross-section area illustrated in C.d and D.d, respectively.Full size imageThe intercellular space, represented by the aerenchyma area (AA), was observed to be significantly more pronounced in the tested CCS compared to OCS lines in both environments (Fig. 2B.h,B.f). Furthermore, the OCS population did not show significant differences to both parents under hydroponics conditions, however, when grown under field conditions, it was noted that Golf had a significantly higher AA mean value as compared to the OCS population (Table S3). As illustrated by the values, the AA expended from early to late stages by a magnitude of 10-folds (Fig. 2B.d). In general, although the two parents did not indicate phenotypic variations, OCS and CCS lines showed significant variations (Table S4).A 0.12 mm2 decreased average total cortical area (TCA) was recorded in the OCS compared to the CCS population under field conditions (P = 0.003, Fig. 2C.f), although substantial variations for TCA was observed within OCS and CCS populations (Fig. 2C.d). The root cross-section area (RXA) is a two-dimensional axis of the root which is an important indicator of root thickness. In the hydroponic examination of the seedling stage, significant variations existed between the CCS and ISR42-8 as well as OCS population (Tables 2 and S3). The complementary study under field conditions observed a noticeable variation for OCS from both parental genotypes and the CCS (Table S3). About 0.13 mm2 increased average value for RXA was identified for CCS (Fig. 2D.f), while consistent significant variations were also observed between the populations in the under field experiment, where 0.13 mm2 increased average value for RXA was identified for CCS (Fig. 2D.f). Analog (Fig. 2D.d). Analogue to the AA, the RXA indicates a lower root extension in the OCS compared to the CCS population. For the stele area (SA), significant variations were only observed at the flowering stage, where ISR42-8 generally had the highest SA and varies significantly between Golf and its progeny lines (Tables 2 and S3).Shoot-related traitsBeyond the root-related phenotypic observations, above-ground characteristics were also recorded to assess the root-borne shoot dynamics (Figs. 3 and S2). Among the OCS and CCS populations and the parents, ISR42-8 had the longest duration of emergence. While CCS-adapted lines took on average 5.8 days of emergence (DE), OCS-adapted lines emerged 1.8 days later (7.6 days) (Fig. S2). No variation was observed for the tiller number (TN) throughout all tested groups, while ISR 42-8 tends to produce much more leaf number (LN), accompanied by a lower plant height (PH) and higher shoot dry weight (SDW) (Table S4, Fig. S2). The OCS and CCS plants significantly differed in PH as well as SDW (Fig. 3B,C). The LN was marginally above the probability threshold of 0.05 (p = 0.058, Fig. 3A), with a clear tendency of increased variability in phenotypic variation (Fig. 3D). Similar trend was recorded for the SDW (Fig. 3F).Figure 3Above-ground plant characteristics. Boxplots illustrate the overall distribution of observed data points for the parents Golf and ISR 42-8 as well as for the conventional (CCS) and organic (OCS) lines under the hydroponic experiment. (A)—Leaf number (LN) expressed by; (B)—Plant height (PH) and C-Shoot dry weight (SDW). The data distribution of the leaf number, plant height and shoot dry weight is illustrated in (D,E,F), respectively.Full size imageInterconnection of root-shoot traitsWe performed inter-trait correlation analysis to unravel association among root traits and in between root and shoot phenotypes (Fig. 4). Pearson correlation coefficient revealed significant correlations among root-shoot traits. LN, PH and SDW had strong positive associations with all root architectural traits under hydroponic conditions (P  0.30) in both CCS and OCS, while DE has negative association with all shoot traits (r = −0.17 to −0.48) (Fig. 4A.h,B.h). A consistent negative relationship was observed for L/V with shoot traits such as LN, PH and SDW and root morphological traits such as RL, RSA and RAD in both CCS and OSC populations (Fig. 4A.h,B.h). A strong negative association existed between RL and all shoot morphological, root architectural and anatomical traits in both populations, except for L/V where a weak negative (r = −0.09) association was displayed only in the OCS. Likewise, all above-ground traits and all root architectural traits exhibited significant positive associations with all root anatomical features in both groups with an exception for the AA (Fig. 4A.h,B.h). Moreover, correlation analysis revealed strong positive relationships in both groups of SDW and root dry weight (RDW) to all above-ground traits, below-ground traits including, RL, SA, and RAD, as well as in all root anatomical traits (Fig. 4A.h,B.h). This means that the growth of tissue and organ is proportional to the increase in total dry biomass. More importantly, we observed a significant positive correlation among most of the root morphological, architectural, and anatomical traits in both OCS and CCS adapted populations, with few exceptions such as L/V (Fig. 4A.h,B.h).Figure 4Correlation matrix for shoot morphological (only in hydroponic conditions; A.h and B.h), root architectural and anatomical traits in two groups of barley populations and their parental lines grown across two growing conditions. (A)—conventional and (B)—organic cropping systems. (A.h)—conventional under hydroponic, (B.h)—organic under hydroponic, (A.f)—conventional under field, and B.f—organic under field conditions. The color scale represents Spearman’s ranked correlation coefficient. A larger circle size indicates a smaller p-value; blank cells represent that correlation was non-significant at P  −0.90) and RDW (r =   > −0.80) (Fig. 4A.f,B.f) for CCS and OCS populations respectively, which means that narrower the angle of the nodal roots, the longer was the root system. The two root branching traits, the number of tips and number of roots forks which were known to be associated and dependent on the RL have a strong positive correlation reflected by r = 0.81 and 0.90 in CCS and r = 0.74 and 0.84 in OCS developed lines, respectively, while they have a significant negative correlation with RA (r = −0.72 to −0.77) in both barley groups. In addition, RA had also strong negative relationship to RDW contributing architectural traits including, RMD (r = −0.81 to −0.84) and L/V (r = −0.34 to −0.44). However, no positive associations were observed for RA and all root anatomical traits in both OCS and CCS populations (Fig. 4A.f,B.f).Allometry analysisThe correlation analysis identified interconnection among root and shoot-related traits. Therefore, we checked if these correlations can be explained by allometric relations (Tables 3 and 4).Table 3 Summary of allometric analysis of root-shoot system traits under hydroponic condition.Full size tableTable 4 Summary of allometric analysis of root-shoot system traits under field condition.Full size tableIn the hydroponic environment, we observed a total of ten allometric relations, from which six were annotated to the PH. The PH was allometrically related to the SDW, the RSA, the RV, the RDW, the SRL, and the RMD (Table 3). Besides, the SDW was allometrically associated with the RSD. Furthermore, the TCA was related to the RXA. Finally, an allometry relationship was detected between the LMXN and AA (Table 3).In the field experimental setup, we detected in total ten allometric relations (P  More

Data sourcesData for the trophic parameters and data categories listed in Tables 1 and 2 were gathered from peer-reviewed scientific publications, grey literature (e.g., agency reports, theses, and dissertations) and unpublished data by the authors of this paper. Data compilation on stomach contents, stable isotopes, FATM, and trophic positions, focussed on mesopelagic organisms, their potential prey and predators. For major and trace elements, energy density and estimates of diet proportions, our search concentrated on mesopelagic taxa. Nevertheless, we also gathered information from small or intermediate-sized epi-, bathy- or benthopelagic species found in the compiled data sources. These species were included because they play key roles in most marine ecosystems, both as important consumers of phytoplankton and zooplankton, and prey for many top predators, and can represent alternative energy pathways to mesopelagic organisms. However, we stress that the data coverage for these species in the current version of the database is very incomplete. Our main interest was on data from the central and eastern North Atlantic, and the Mediterranean, corresponding to the study regions of the SUMMER project. When we could not find suitable data within this region, we extended the geographic scope of our literature search to the western North Atlantic. We did not search for datasets in open access repositories since those data can be easily accessed and extracted. However, some of the data provided by the authors of this paper have been previously deposited in PANGAEA.DNA sequencing-based methods, such as metabarcoding and direct shotgun sequencing, are emerging as promising tools in dietary analyses due to the high resolution in taxonomic identification of many prey simultaneously, and the potential to provide quantitative diet estimates from relative read abundance29. However, recent studies have shown that various methodological and biological factors can break the correlation between the number and abundance of ingested prey and the prey DNA present in the sample, and lead to biased estimates of taxonomic diversity and composition of diet29,30. Given the uncertainties remaining in the interpretation of DNA sequencing-based diet data, we decided not to include these data in MesopTroph until additional research demonstrates that these techniques can be confidently applied for quantitative diet assessment.We identified available data sources in the literature through systematic searches on Web of Science, Google Scholar, ResearchGate, and the Google search engine. We used multiple combinations of terms related to specific data categories (Table 3), in conjunction with the common or scientific taxon names (from genus to order), and the ocean basin. For example, the search for stomach content data of fishes belonging to the family Myctophidae was undertaken using the following terms: “stomach content” OR “gut content” OR “prey composition” OR “diet composition”, AND “mesopelagic fish” OR “myctophid” OR “Myctophiformes” OR “Myctophidae”, AND “Atlantic” or “Mediterranean”. For the mesopelagic and predator species known to be numerically abundant in the SUMMER study regions, we performed a second literature search using the common or scientific name of the species, along with the terms “diet”, “feeding habits”, “trophic ecology”, “trophic markers”, or “food web”. We also examined the literature cited within each collected publication to locate additional data sources.Table 3 Terms used in the literature search for each data category.Full size tableWe next screened the full text of the compiled studies and retained data sources that: (1) were collected within the region of interest, (2) reported quantitative data for the trophic parameters of interest, (3) reported the number of samples for pooled or aggregated data, and (4) provided sufficient details on the methodology to enable a quality check. In the case of stable isotope data, we only included data sources reporting both δ13C and δ15N measurements.Data extraction, cleaning, and formattingWe created a template table for each data category in Microsoft Excel to assemble all datasets into a single file, and to facilitate cleaning and standardization of data records. We added a large number of metadata fields to the tables to annotate details about the sampling (e.g., location, date, methods), sampled specimen(s) (e.g., taxonomy, number and size of individuals, number of replicates, tissue analysed), and data source (e.g., full reference, DOI) for every record.Data contributors formatted and incorporated their datasets directly into the tables. For published sources, the data and associated metadata were extracted manually or digitized from the article text, tables, or supplementary material into the tables. Extraneous or hidden characters, and values such as “NA” (Not Available) or “ND” (Not Determined), were deleted from the parameter and metadata fields. Measurements of trophic parameters were standardized to the same units (see Tables 1 and 2). Parameter values that were clearly incorrect (e.g., δ15N  > 20, or the frequency of occurrence of a prey higher than the number of stomachs sampled) were corrected by searching for the value within the data source. When values could not be corrected, we deleted that data record.When available, we extracted information at the individual level. However, most studies reported data obtained from pooled samples of the same species. In some cases (e.g., small specimens such as planktonic organisms), a minimum and maximum number of individuals in the sample was provided instead of the actual number of individuals sampled. We added two columns to the tables presenting the minimum and maximum number of individuals in the sample. By filtering the column “Ind No (maximum per sample)” for values >1, users can easily identify records with aggregated data and differentiate them from records where information was drawn from a single individual (i.e., where “Ind No (maximum per sample)” =1). In addition, the tables Stomach contents and Estimates of diet proportions include a field “Sample ID” with a unique identifier of the sample. If data are reported at the individual level (i.e., “Ind No (maximum per sample)” =1) then Sample ID is the individual animal ID. If the data are from a group of individuals (i.e., “Ind No (maximum per sample)” >1), then Sample ID identifies that group.We standardized the taxonomic classification and nomenclature of fishes and elasmobranchs following the Eschmeyer’s Catalog of Fishes (http://researcharchive.calacademy.org/research/ichthyology/catalog/fishcatmain.asp)31,32. For the remaining taxa, we used the World Register of Marine Species (http://www.marinespecies.org/)33. Unaccepted or alternate taxon names were replaced by the most up-to-date valid name. When the identification of a taxon was uncertain, the taxonomic level of identification was decreased to a satisfactory level. For example, prey reported as “Cephalopods” were changed to “Cephalopoda”, “Sepiolids” to “Sepiolidae”, and “Myctophum punctatum?” to the genus “Myctophum”.Stomach contentsStomach contents analysis is a standard dietary assessment method that potentially enables quantifying diet components with high taxonomic resolution34. Three parameters are typically used to describe diet composition from stomach contents: the number of individuals of a prey type as a proportion of the total number of prey items (%N), the proportion of a prey item by weight or volume (%W), and the proportion of stomachs containing a particular prey item (i.e., percent frequency of occurrence, %F)35. When available, we collected data on the three parameters, as well as on the absolute number, weight, and frequency of occurrence of each prey type in the stomachs of each sampled individual or group of individuals. If stated in the data source, we indicate if prey weights were directly measured or reconstructed from hard remains (fish otoliths and vertebrae, cephalopod beaks), and if they represent dry or wet weight. Some datasets contained records of prey items without corresponding weights or numbers. As a result, the cumulative percent of all prey items did not sum to 100%. This occurred in 11 data records for the cumulative %W, and nine for the cumulative %N. While we checked the accuracy of percentage values and adjusted rounding errors, we did not attempt to fill in missing values nor did we remove records with missing values. When prey values were reported by an upper bound (e.g., “ 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