Ecology

1.Magnabosco, C. et al. The biomass and biodiversity of the continental subsurface. Nat. Geosci. 11, 707–717 (2018).Article 

Google Scholar 
2.Merino, N. et al. Living at the extremes: extremophiles and the limits of life in a planetary context. Front. Microbiol. 10, 780 (2019).Article 

Google Scholar 
3.Colman, D. R. et al. Geobiological feedbacks and the evolution of thermoacidophiles. ISME J. 12, 225–236 (2018).Article 

Google Scholar 
4.Reveillaud, J. et al. Subseafloor microbial communities in hydrogen-rich vent fluids from hydrothermal systems along the Mid-Cayman Rise. Environ. Microbiol. 18, 1970–1987 (2016).Article 

Google Scholar 
5.Lau, M. C. Y. et al. An oligotrophic deep-subsurface community dependent on syntrophy is dominated by sulfur-driven autotrophic denitrifiers. Proc. Natl Acad. Sci. USA 113, 7927–7936 (2016).Article 

Google Scholar 
6.Momper, L., Jungbluth, S. P., Lee, M. D. & Amend, J. P. Energy and carbon metabolisms in a deep terrestrial subsurface fluid microbial community. ISME J. 11, 2319–2333 (2017).Article 

Google Scholar 
7.Brazelton, W. J. et al. Metagenomic identification of active methanogens and methanotrophs in serpentinite springs of the Voltri Massif, Italy. PeerJ 5, e2945 (2017).Article 

Google Scholar 
8.Havig, J. R., Raymond, J., Meyer-Dombard, D. R., Zolotova, N. & Shock, E. L. Merging isotopes and community genomics in a siliceous sinter-depositing hot spring. J. Geophys. Res. Biogeosci. 116, G01005 (2011).Article 

Google Scholar 
9.Power, J. F. et al. Microbial biogeography of 925 geothermal springs in New Zealand. Nat. Commun. 9, 2876 (2018).Article 

Google Scholar 
10.Lauber, C. L., Hamady, M., Knight, R. & Fierer, N. Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl. Environ. Microbiol. 75, 5111–5120 (2009).Article 

Google Scholar 
11.Kelemen, P. B. & Manning, C. E. Reevaluating carbon fluxes in subduction zones, what goes down, mostly comes up. Proc. Natl Acad. Sci. USA 112, E3997–E4006 (2015).Article 

Google Scholar 
12.Brovarone, A. V. et al. Subduction hides high-pressure sources of energy that may feed the deep subsurface biosphere. Nat. Commun. 11, 3880 (2020).Article 

Google Scholar 
13.Plümper, O. et al. Subduction zone forearc serpentinites as incubators for deep microbial life. Proc. Natl Acad. Sci. USA 114, 4324–4329 (2017).Article 

Google Scholar 
14.Syracuse, E. M. & Abers, G. A. Global compilation of variations in slab depth beneath arc volcanoes and implications. Geochem. Geophys. Geosyst. 7, Q05017 (2006).Article 

Google Scholar 
15.Shaw, A. M., Hilton, D. R., Fischer, T. P., Walker, J. A. & Alvarado, G. E. Contrasting He–C relationships in Nicaragua and Costa Rica: insights into C cycling through subduction zones. Earth Planet. Sci. Lett. 214, 499–513 (2003).Article 

Google Scholar 
16.Barry, P. H. et al. Forearc carbon sink reduces long-term volatile recycling into the mantle. Nature 568, 487–492 (2019).Article 

Google Scholar 
17.Arce-Rodríguez, A. et al. Thermoplasmatales and sulfur-oxidizing bacteria dominate the microbial community at the surface water of a CO2-rich hydrothermal spring located in Tenorio Volcano National Park, Costa Rica. Extremophiles 23, 177–187 (2019).Article 

Google Scholar 
18.Crespo-Medina, M. et al. Methane dynamics in a tropical serpentinizing environment: the Santa Elena ophiolite, Costa Rica. Front. Microbiol. 8, 916 (2017).Article 

Google Scholar 
19.Probst, A. J. & Moissl-Eichinger, C. “Altiarchaeales”: uncultivated Archaea from the subsurface. Life https://doi.org/10.3390/life5021381 (2015).20.Giggenbach, W. F. Geothermal solute equilibria, derivation of Na-K-Mg-Ca geoindicators. Geochim. Cosmochim. Acta 52, 2749–2765 (1988).Article 

Google Scholar 
21.Giggenbach, W. F. & Soto, R. C. Isotopic and chemical composition of water and steam discharges from volcanic–magmatic–hydrothermal systems of the Guanacaste Geothermal Province, Costa Rica. Appl. Geochem. 7, 309–332 (1992).Article 

Google Scholar 
22.Rodríguez, A. & van Bergen, M. J. Superficial alteration mineralogy in active volcanic systems: an example of Poás volcano, Costa Rica. J. Volcanol. Geotherm. Res. 346, 54–80 (2017).Article 

Google Scholar 
23.Chan, C. S., Fakra, S. C., Emerson, D., Fleming, E. J. & Edwards, K. J. Lithotrophic iron-oxidizing bacteria produce organic stalks to control mineral growth: implications for biosignature formation. ISME J. 5, 717–727 (2011).Article 

Google Scholar 
24.Lücke, O. H. & Arroyo, I. G. Density structure and geometry of the Costa Rican subduction zone from 3-D gravity modeling and local earthquake data. Solid Earth 6, 1169–1183 (2015).Article 

Google Scholar 
25.Protti, M., Gündel, F. & McNally, K. The geometry of the Wadati–Benioff zone under southern Central America and its tectonic significance: results from a high-resolution local seismographic network. Phys. Earth Planet. Inter. 84, 271–287 (1994).Article 

Google Scholar 
26.de Moor, J. M. et al. A new sulfur and carbon degassing inventory for the Southern Central American Volcanic Arc: the importance of accurate time-series data sets and possible tectonic processes responsible for temporal variations in arc-scale volatile emissions: new volatile budget for Central America. Geochem. Geophys. Geosyst. 18, 4437–4468 (2017).Article 

Google Scholar 
27.Delgado-Baquerizo, M. et al. A global atlas of the dominant bacteria found in soil. Science 359, 320–325 (2018).Article 

Google Scholar 
28.Kim, M. S., Jo, S. K., Roh, S. W. & Bae, J. W. Alishewanella agri sp. nov., isolated from landfill soil. Int. J. Syst. Evol. Microbiol. 60, 2199–2203 (2010).Article 

Google Scholar 
29.Chen, W. M. et al. Aquabacterium limnoticum sp. nov., isolated from a freshwater spring. Int. J. Syst. Evol. Microbiol. 62, 698–704 (2012).Article 

Google Scholar 
30.Garrity, G. M. & Bell, J. A. Bergey’s Manual of Systematics of Archaea and Bacteria (Bergey’s Manual Trust, 2015).31.Hayashi, N. R., Ishida, T., Yokota, A., Kodama, T. & Igarashi, Y. Hydrogenophilus thermoluteolus gen. nov., sp. nov., a thermophilic, facultatively chemolithoautotrophic, hydrogen-oxidizing bacterium. Int. J. Syst. Evol. Microbiol. 49, 783–786 (1999).Article 

Google Scholar 
32.Berg, I. A. et al. Autotrophic carbon fixation in archaea. Nat. Rev. Microbiol. 8, 447–460 (2010).Article 

Google Scholar 
33.Giovannelli, D. et al. Insight into the evolution of microbial metabolism from the deep-branching bacterium, Thermovibrio ammonificans. eLife 6, e18990 (2017).Article 

Google Scholar 
34.Yokochi, R. et al. Noble gas radionuclides in Yellowstone geothermal gas emissions: a reconnaissance. Chem. Geol. 339, 43–51 (2013).Article 

Google Scholar 
35.Harris, R. N. & Wang, K. Thermal models of the Middle America Trench at the Nicoya Peninsula, Costa Rica. Geophys. Res. Lett. 29, 6-1–6-4 (2010).
Google Scholar 
36.Jelen, B. I., Giovannelli, D. & Falkowski, P. G. The role of microbial electron transfer in the coevolution of the biosphere and geosphere. Annu. Rev. Microbiol. 70, 45–62 (2016).Article 

Google Scholar 
37.Falkowski, P. G., Fenchel, T. & Delong, E. F. The microbial engines that drive Earth’s biogeochemical cycles. Science 320, 1034–1039 (2008).Article 

Google Scholar 
38.Tassi, F. et al. The geothermal resource in the Guanacaste region (Costa Rica): new hints from the geochemistry of naturally discharging fluids. Front. Earth Sci. 6, 69 (2018).Article 

Google Scholar 
39.Tassi, F., Vaselli, O., Barboza, V., Fernandez, E. & Duarte, E. Fluid geochemistry and seismic activity in the period 1998–2002 at Turrialba Volcano (Costa Rica). Ann. Geophys. 47, 4 (2004).
Google Scholar 
40.Barry, P. H. et al. Helium, inorganic and organic carbon isotopes of fluids and gases across the Costa Rica convergent margin. Sci. Data https://doi.org/10.1038/s41597-019-0302-4 (2019).41.Vetriani, C., Jannasch, H. W., MacGregor, B. J., Stahl, D. A. & Reysenbach, A.-L. Population structure and phylogenetic characterization of marine benthic archaea in deep-sea sediments. Appl. Environ. Microbiol. 65, 4375–4384 (1999).Article 

Google Scholar 
42.Wright, J. J., Lee, S., Zaikova, E., Walsh, D. A. & Hallam, S. J. DNA extraction from 0.22 μm Sterivex filters and cesium chloride density gradient centrifugation. JOVE https://doi.org/10.3791/1352 (2009).43.Teare, J. M. et al. Measurement of nucleic acid concentrations using the DyNA QuantTM and the GeneQuantTM. Biotechniques 22, 1170–1174 (1997).Article 

Google Scholar 
44.Simbolo, M. et al. DNA qualification workflow for next generation sequencing of histopathological samples. PLoS ONE 8, e62692 (2013).Article 

Google Scholar 
45.Giovannelli, D. et al. Diversity and distribution of prokaryotes within a shallow-water pockmark field. Front. Microbiol. 7, 941 (2016).Article 

Google Scholar 
46.Huse, S. M. et al. VAMPS: a website for visualization and analysis of microbial population structures. BMC Bioinformatics 15, 41 (2014).Article 

Google Scholar 
47.Huse, S. M. et al. Comparison of brush and biopsy sampling methods of the ileal pouch for assessment of mucosa-associated microbiota of human subjects. Microbiome 2, 5 (2014).Article 

Google Scholar 
48.Schloss, P. D. et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).Article 

Google Scholar 
49.Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2012).Article 

Google Scholar 
50.Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).Article 

Google Scholar 
51.Zhu, C. et al. Functional sequencing read annotation for high precision microbiome analysis. Nucleic Acids Res. 46, e23 (2018).Article 

Google Scholar 
52.R Core Team, R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2013).53.McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).Article 

Google Scholar 
54.vegan (CRAN, 2019).55.Hamilton, N. E. & Ferry, M. ggtern: ternary diagrams using ggplot2. J. Stat. Softw. 87, 1–17 (2018).Article 

Google Scholar 
56.Stekhoven, D. J. & Buhlmann, P. MissForest—non-parametric missing value imputation for mixed-type data. Bioinformatics 28, 112–118 (2012).Article 

Google Scholar 
57.Genuer, R., Poggi, J.-M. & Tuleau-Malot, C. VSURF: an R package for variable selection using random forests. R J. 7, 1–19 (2015).Article 

Google Scholar 
58.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).59.Sheik, C. S. et al. Identification and removal of contaminant sequences from ribosomal gene databases: lessons from the Census of Deep Life. Front. Microbiol. 9, 840 (2018).Article 

Google Scholar 
60.Sugimori, K. et al. Microbial life in the acid lake and hot springs of Poas Volcano, Costa Rica. In Proc. Colima Volcano International Meeting (2002).61.Mcmurdie, P. J. & Holmes, S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput. Biol. 10, e1003531 (2014).Article 

Google Scholar 
62.Weiss, S. et al. Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome 5, 27 (2017).Article 

Google Scholar 
63.Giovannelli, D. et al. Large-scale distribution and activity of prokaryotes in deep-sea surface sediments of the Mediterranean Sea and the adjacent Atlantic Ocean. PLoS ONE 8, e72996 (2013).64.Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70 (1979).
Google Scholar 
65.Schruben, P. G. Geology and Resource Assessment of Costa Rica DDS-19-R (USGS, 1987).66.Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8, e1002687 (2012).Article 

Google Scholar 
67.Kurtz, Z. D. et al. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput. Biol. 11, e1004226 (2015).Article 

Google Scholar 
68.Schwager, E., Mallick, H., Ventz, S. & Huttenhower, C. A Bayesian method for detecting pairwise associations in compositional data. PLoS Comput. Biol. 13, e1005852 (2017).Article 

Google Scholar 
69.Zar, J. H. Significance testing of the spearman rank correlation coefficient. J. Am. Stat. Assoc. 67, 578–580 (1972).Article 

Google Scholar 
70.Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJournal 1695, 1–9 (2006).
Google Scholar 
71.Braun, S. et al. Microbial turnover times in the deep seabed studied by amino acid racemization modelling. Sci. Rep. 7, 5680 (2017).Article 

Google Scholar 
72.Whitman, W. B., Coleman, D. C. & Wiebe, W. J. Prokaryotes: the unseen majority. Proc. Natl Acad. Sci. USA 95, 6578–6583 (1998).Article 

Google Scholar 
73.McMahon, S. & Parnell, J. Weighing the deep continental biosphere. FEMS Microbiol. Ecol. 87, 113–120 (2013).Article 

Google Scholar  More

Soil resident microbesWe chose sand as a realistic source of a mixed microbial community (which is referred to as the sand community or SC). Because the sand community cannot be preserved as a whole by freezing, we collected fresh material for better consistency for each experiment from the same spot in St. Sulpice near Lake Geneva (GPS coordinates: 46.508032N, 6.544050 E) as described in Moreno et al.51. Sampled sand at different seasons thus likely carried slightly different starting communities and cell densities. The sand was sieved through 2 mm2 pores to remove large particles. The sieved sand was stored at room temperature and used within 7 days for extraction of resident microbial cells.Microbial cells were extracted from four aliquots of 200 g of sand. Each 200 g aliquot was transferred into a 1-l conical flask and submerged in 400 ml of 21 C minimal media salts (MMS) (containing, per litre: 1 g NH4Cl, 3.49 g Na2HPO4·2H2O, 2.77 g KH2PO4, pH 6.8)21. Flasks were incubated at 25 °C under rotary shaking at 120 rpm for 1 h. The sand was allowed to settle and the supernatant was decanted into a set of 50 ml Falcon tubes, which were centrifuged at 800 rpm with an A-4-81 rotor and a 5810R centrifuge (Eppendorf AG.) for 10 min to precipitate heavy soil particles. Supernatants were decanted into clean 50 ml Falcon tubes and centrifuged at 4000 rpm for 30 min to pellet cells. The supernatants were carefully discarded and the cell pellets were resuspended and pooled from the four aliquots (i.e., from the initial 800 g of sand) in one tube using 5 ml of MMS. The pooled liquid suspension was further sieved through a 40 µm Falcon cell strainer (Corning Inc.) in order to remove any particles and large eukaryotic cells that may obstruct flow cytometry analysis (see below). A small proportion of the sieved liquid suspension was used to quantify the numbers of recovered cells (see below); the remainder was used within 12 h for bead encapsulation or for mixed liquid suspended growth (see below). With this gentle method, we extracted approximately 3 × 105 cells g−1 of sand.Flow cytometry cell countingCell numbers in extracted soil communities and in the mixed liquid suspended growth experiments were counted by flow cytometry. SC-suspensions were diluted 100 times in MMS and stained in 200 µl aliquots with 2 µl of diluted SYBR Green I solution (1:100 in DMSO; Molecular Probes) in the dark for 30 min at room temperature. In some experiments, cells were additionally stained with 2 µl propidium iodide solution (10 µg ml–1, Molecular Probes). Aliquots of 20 µl were aspired at 14 µl min–1 on a Novocyte flow cytometer with absolute volumetric cell counting (ACEA Biosciences, USA). Cells were thresholded above a forward scatter signal (FSC-H) of 20 and further gated for propidium iodide-staining (excited at 535 nm and its fluorescence was collected at 617 ± 30 nm) and for SYBR Green I (excitation 488 nm, 530 ± 30 nm band-pass filter; channel voltage at 441 V) above values of 1000 (Supplementary Fig. 5).Cell samples from the mixed liquid suspension growth experiments were diluted to approximately 106 ml−1 and subsampled to aliquots of 100 µl. The subsamples were then mixed with 100 µl of 8 g l–1 sodium azide in phosphate buffered saline and incubated for 1 h at 4 °C to arrest cell respiration and growth. Samples were then stained with SYBR Green I as above and quantified by flow cytometry using the same thresholds and gates as describe above.Bacterial strains and pre-culturing proceduresP. veronii 1YdBTEX2 is a toluene, benzene, m-xylene and p-xylene degrading bacterium isolated from contaminated soil20. The strain was tagged with a single-copy chromosomally inserted mini-Tn7 transposon carrying a Ptac–mCherry cassette (Pve, strain 3433) as described in the ref. 52. A single P. veronii colony from a selective plate with toluene as the sole carbon substrate after 48 h incubation at 30 °C was inoculated into 10 ml of liquid MMS containing 5 mM sodium succinate as the sole carbon source and grown for 24 h at 30 °C with rotary shaking at 180 rpm21. After 24 h, the cells were harvested and washed for bead encapsulation or for comparative liquid mixed suspension growth, as described below.Agarose bead encapsulationSC cell suspensions containing between 2 × 107 to 108 cells ml–1 were encapsulated in agarose using rapid mixing with pluronic acid in dimethylpolysiloxane and subsequent cooling, followed by sieving to achieve beads with a diameter range of 40–70 µm53. The entire procedure was carried at room temperature and near a gas flame to maintain antiseptic conditions. 1% (w/v) low melting agarose (GEPAGA04-62, Eurobio ingen, France) was prepared in PBS solution (PBS contains per L H2O: 8 g NaCl, 0.2 g KCl, 1.44 g Na2HPO4, 0.24 g KH2PO4, pH 7.4) and dissolved by heating in a microwave. The molten agarose solution was cooled down and equilibrated in a 37 °C water bath. Separately, 15 ml of dimethylpolysiloxane (Sigma-Aldrich, DMPS5X-500G) was poured in a 30 ml glass test tube. 1 ml of the 37 °C-agarose solution was mixed with 30 µl of pluronic acid (10% Pluronic® F-68, Gibco, Life Technologies) by vortexing at the highest speed (Vortex-Genie 2, Scientific Industries, Inc.) for a minute. Into this mixture of agarose and pluronic acid, 200 µl of prepared SC cell suspension at 0.2–1.0 × 108 cells ml–1 was pipetted and vortexed again at the highest speed for another minute. Five hundred microliter of this mixture was added drop-wise into the glass tube with dimethylpolysiloxane that was being vortexed at maximum speed. Vortexing was continued for 2 min. The tube was then immediately plunged into crushed ice and allowed to stand for a minimum of 10 min. After this, the total content of the tube was transferred into a 50 ml Falcon tube. The tube was centrifuged for 10 min at 2000 rpm using an A-4-81 swinging-bucket rotor (Eppendorf). The oil was carefully decanted while retaining the beads pellet. Fifteen milliliter of sterile PBS was added to the pellet and the beads were resuspended by vortexing at a speed set to 5. The tubes were again centrifuged at 2000 rpm for 10 min and any visible oil phase on the top was removed using a pipette. The process was repeated once more to remove any visible oil phase. Beads of diameter between 40 and 70 µm were then recovered by passing the PBS-resuspended bead content of the tube first over a 70-µm cell strainer (Corning Inc.). A further 5 ml of PBS was added to the cell strainer to flush remaining beads ( 0.25). Finally, we tested further interaction terms that influenced attributed growth rates. The initial carbon concentration was set to 50 mg ml–1, which allowed similar community development in terms of size (i.e., cell numbers) as in the experiments.Growth was simulated for 120 time steps, corresponding to 60 h in the experiments, at which point the substrate is depleted and cells stop dividing (stationary phase). Based on the attributed growth rates to every OTU (i.e., every cell and genotype of the vector or double vector), the model calculates per time step how much substrate is converted into biomass (we allow continuous biomass formation) and lost in form of CO2, which is subtracted to calculate the remaining substrate concentration for the next time step. When the overall substrate concentration is lower than ({S}_{{min }}) = 3 × 10–6 g ml–1, growth stops. The production of cell biomass is converted to cell numbers, which is then subsampled at the last time step per OTU (to an equivalent of 50,000 sequence reads), per single or pair (to an equivalent of 5000 beads) to calculate developed microcolony sizes, diversity measures, and interaction effects (as in Figs. 5 and 6).The following interaction scenarios were simulated. Although we allowed growth penalties and interspecific interactions to influence attributed OTU growth rates, a threshold of ~0.6 h–1 was imposed as the maximum individual OTU growth rate in all simulations.High connectivity random vs. OTU-abundance growth rates. OTU-specific growth rates (µmax,sp) were drawn randomly between 0.01 and 0.6 h–1. Alternatively, growth rates were attributed to the vector of OTUs according to the probability distribution function reflecting their measured log10 empiric abundance at t = 0 h (Supplementary methods, Section 2).Low connectivity single founder cell growth penalty. We contrasted simulations with single founder cells growing according to their OTU-proportional attributed growth rate and those in which that growth rate was multiplied by a penalty, composed by a factor equal to the inverse proportion of the initially attributed µmax,sp per OTU. The assumptiton is that the slower the inherent growth rate, the more likely that OTU is penalized when it is alone (Supplementary methods, Sections 2 and 3). This was combined with testing the effect of random or biased death on the starting community.$${{rm{mu }}}_{{{rm{max}} },{{rm{sp}}}}=frac{1.2}{{{log }}_{10}({{rm{mu }}}_{{{rm{max}} },{{rm{sp}}}})}$$
(3)
Low connectivity paired interspecific interaction effects. We further tested different assumptions on the nature of interspecific interactions and simulated how these affected community growth rates and diversity outcomes. These effects directly influenced the OTU attributed growth rates in the doubles (Supplementary methods, Sections 3.1–3.3). In the bimodal scenario (Supplementary methods, Section 3.4.1), we assumed that the community is composed of two underlying distributions; rare and abundant members (the threshold being placed at log10 measured OTU relative abundance = 2.8), with abundant members having a higher probability to be positively influenced in pairs. The probability is drawn from a bimodal interaction curve that attributes an interaction factor (between 0.01 and 2.2), which is multiplied with the assigned OTU growth rate at start.In the biased positive model (Supplementary methods, Section 3.4.2) we allowed a 40% chance for an interaction term imposed independently on each founder cell in a pair to lower the attributed OTU growth rate (factor range 0.4–0.6), and 60% chance for a factor in between 0.6 and 1.4 to modulate or increase the growth rate.In the positive on slow model (Supplementary methods, Section 3.4.3), the attributed OTU growth rates on each founder cell in a pair had a chance of 40% to become improved inversely proportionally to its initial growth rate, thereby favoring slow growers$${{rm{mu }}}_{{max },{{rm{sp}}}}={{mbox{-}}}{rm{ln}}({{rm{mu }}}_{{max },{{rm{sp}}}}),times {{rm{mu }}}_{{max },{{rm{sp}}}}$$
(4)
In the biased negative model (Supplemtary methods, Section 3.4.4), we attributed OTU-abundance proportional growth rates to each partner of the founder pair, but penalized faster growers (µ  > 0.15) at 20% chance and the others at 40% chance that their growth rate would be multiplied by a negative interaction factor (range 0.01–0.1).Finally, in a random model (Supplementary methods, Section 3.4.5), we allowed OTU-abundance proportional growth rates in pairs to be multiplied with a factor randomly drawn in the range of 0.01–1.25, independently for each partner in a pair. The models were contrasted to those without any assumed interspecific interactions, and without or with assumed random or fast-growing genotype biased cell death at start (Supplementary methods, Section 2.3.1).All simulations were run five times from the beginning, independently producing five derived parameter values for alpha-diversity, OTU- and microcolony size distributions in stationary phase and partner interactions.Statistics and reproducibilityLiquid suspension growth experiments were carried out in biological quadruplates and all bead experiments were carried out in biological triplicates. Total numbers of analyzed bead and those of beads with single or double occupancy are reported. Derived community growth rates and P. veronii-normalized yields were compared using t-tests (n as reported, two-sided test, unequal variance). Normalized PBP bin-size distributions were globally compared using Fisher’s exact test implemented in R (2000 replicates). Median and 75th percentile aggregate PBPs across different experiments were compared using the non-parametric Wilcoxon signed-rank test. Correlations between simulated species abundance distributions and empirical OTU relative abundances were calculated by bootstrapping (n = 1000) in MATLAB. Correlation coefficients from five independent simulations were compared using t-tests. The proportion correctly predicted OTU abundances by simulation was calculated as the ratio to observed values within a two-fold or four-fold range, and compared by two-sided t-tests on five independent simulations. Simulated and observed microcolony size distributions for single or paired founder cells among different models were compared by principal component analysis in MATLAB (pca), and by Spearman rank correlation (spear) from five independent simulations. Single and paired productivity was then compared between each other using two-sided t-tests of the 75th percentiles of microcolony size distribution (n = 5). Simulated and observed paired growth (excluding pairs with dead cells) was categorized and counted in a grid of 12 × 12 (each bin covering 0.5 log10-distance) using MATLAB’s hist3d function, and then compared by pca from five independent simulations. Confidence intervals on ratios of paired simulated microcolony sizes (excluding those pairs with a non-growing or dead partner) were determined by subsampling (n = 1000) from mean ratio distributions, which were then used to calculate the fractions deviating from experimentally observed paired size ratios.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

1.Schlesinger, W. H. & Bernhardt, E. S. Biogeochemistry: an Analysis of Global Change (Elsevier/Academic Press, 2012).2.Fernandez, C. W., Langley, J. A., Chapman, S., McCormack, M. L. & Koide, R. T. The decomposition of ectomycorrhizal fungal necromass. Soil Biol. Biochem. 93, 38–49 (2016).CAS 
Article 

Google Scholar 
3.Glassman, S. I. et al. Decomposition responses to climate depend on microbial community composition. Proc. Natl Acad. Sci. USA 115, 11994–11999 (2018).CAS 
PubMed 
Article 

Google Scholar 
4.Mushinski, R. M. et al. Microbial mechanisms and ecosystem flux estimation for aerobic NOy emissions from deciduous forest soils. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1814632116 (2019).5.Prosser, J. I. Dispersing misconceptions and identifying opportunities for the use of ‘omics’ in soil microbial ecology. Nat. Rev. Microbiol. 13, 439–446 (2015).CAS 
PubMed 
Article 

Google Scholar 
6.Delgado-Baquerizo, M. et al. A global atlas of the dominant bacteria found in soil. Science 359, 320–325 (2018).CAS 
PubMed 
Article 

Google Scholar 
7.Tedersoo, L. et al. Global diversity and geography of soil fungi. Science 346, 1256688 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
8.Bahram, M. et al. Structure and function of the global topsoil microbiome. Nature 560, 233–237 (2018).CAS 
PubMed 
Article 

Google Scholar 
9.Drews, G. The roots of microbiology and the influence of Ferdinand Cohn on microbiology of the 19th century. FEMS Microbiol. Rev. 24, 225–249 (2000).CAS 
PubMed 
Article 

Google Scholar 
10.Chase, J. M. Spatial scale resolves the niche versus neutral theory debate. J. Veg. Sci. 25, 319–322 (2014).Article 

Google Scholar 
11.Ricklefs, R. E. & Renner, S. S. Global correlations in tropical tree species richness and abundance reject neutrality. Science 335, 464–467 (2012).CAS 
PubMed 
Article 

Google Scholar 
12.Cavender-Bares, J., Keen, A. & Miles, B. Phylogenetic structure of Floridian plant communities depends on taxonomic and spatial scale. Ecology 87, S109–S122 (2006).PubMed 
Article 

Google Scholar 
13.Cavender-Bares, J., Kozak, K. H., Fine, P. V. A. & Kembel, S. W. The merging of community ecology and phylogenetic biology. Ecol. Lett. 12, 693–715 (2009).PubMed 
Article 

Google Scholar 
14.Ladau, J. & Eloe-Fadrosh, E. A. Spatial, temporal, and phylogenetic scales of microbial ecology. Trends Microbiol. 27, 662–669 (2019).CAS 
PubMed 
Article 

Google Scholar 
15.Elena, S. F. & Lenski, R. E. Evolution experiments with microorganisms: the dynamics and genetic bases of adaptation. Nat. Rev. Genet. 4, 457–469 (2003).CAS 
PubMed 
Article 

Google Scholar 
16.Diaz, S. & Cabido, M. Plant functional types and ecosystem function in relation to global change. J. Veg. Sci. 8, 463–474 (1997).Article 

Google Scholar 
17.Violle, C. et al. Let the concept of trait be functional! Oikos 116, 882–892 (2007).Article 

Google Scholar 
18.Fierer, N., Bradford, M. A. & Jackson, R. B. Toward an ecological classification of soil bacteria. Ecology 88, 1354–1364 (2007).PubMed 
Article 

Google Scholar 
19.Nguyen, N. H. et al. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 20, 241–248 (2016).Article 

Google Scholar 
20.Whittaker, R. H. Communities and Ecosystems (Macmillan, 1975).21.Gibbons, S. M. Microbial community ecology: function over phylogeny. Nat. Ecol. Evol. 1, 0032 (2017).Article 

Google Scholar 
22.Locey, K. J. & Lennon, J. T. Scaling laws predict global microbial diversity. Proc. Natl Acad. Sci. USA 113, 5970–5975 (2016).CAS 
PubMed 
Article 

Google Scholar 
23.Dietze, M. C. Ecological Forecasting (Princeton Univ. Press, 2017).24.Losos, J. B. Phylogenetic niche conservatism, phylogenetic signal and the relationship between phylogenetic relatedness and ecological similarity among species. Ecol. Lett. 11, 995–1003 (2008).PubMed 
Article 

Google Scholar 
25.Ramirez, K. S. et al. Detecting macroecological patterns in bacterial communities across independent studies of global soils. Nat. Microbiol. 3, 189–196 (2018).CAS 
PubMed 
Article 

Google Scholar 
26.Smets, W. et al. A method for simultaneous measurement of soil bacterial abundances and community composition via 16S rRNA gene sequencing. Soil Biol. Biochem. 96, 145–151 (2016).CAS 
Article 

Google Scholar 
27.Hubbell, S. P. The Unified Neutral Theory of Biodiversity and Biogeography (Princeton Univ. Press, 2001).28.Leibold, M. A., Urban, M. C., De Meester, L., Klausmeier, C. A. & Vanoverbeke, J. Regional neutrality evolves through local adaptive niche evolution. Proc. Natl Acad. Sci. USA 116, 2612–2617 (2019).CAS 
PubMed 
Article 

Google Scholar 
29.Dietze, M. & Lynch, H. Forecasting a bright future for ecology. Front. Ecol. Environ. 17, 3 (2019).Article 

Google Scholar 
30.Thompson, L. R. et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 551, 457–463 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Todd-Brown, K. E. O. et al. Causes of variation in soil carbon simulations from CMIP5 Earth system models and comparison with observations. Biogeosciences 10, 1717–1736 (2013).Article 

Google Scholar 
32.Todd-Brown, K. E. O. et al. Changes in soil organic carbon storage predicted by Earth system models during the 21st century. Biogeosciences 10, 18969–19004 (2013).Article 

Google Scholar 
33.Lekberg, Y. et al. More bang for the buck? Can arbuscular mycorrhizal fungal communities be characterized adequately alongside other fungi using general fungal primers? New Phytol. 220, 971–976 (2018).PubMed 
Article 

Google Scholar 
34.Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
35.Running, S., Mu, Q. & Zhao, M. MOD17A3 MODIS/Terra Net Primary Production Yearly L4 Global 1km SIN Grid V055. NASA EOSDIS Land Processes DAAC (NASA, 2011); https://cmr.earthdata.nasa.gov/search/concepts/C198653829-LPDAAC_ECS.html36.Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.Kõljalg, U. et al. Towards a unified paradigm for sequence-based identification of fungi. Mol. Ecol. 22, 5271–5277 (2013).PubMed 
Article 
CAS 

Google Scholar 
39.Steidinger, B. S. et al. Climatic controls of decomposition drive the global biogeography of forest-tree symbioses. Nature 569, 404–408 (2019).CAS 
PubMed 
Article 

Google Scholar 
40.DeSantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Albright, M. B. N., Chase, A. B. & Martiny, J. B. H. Experimental evidence that stochasticity contributes to bacterial composition and functioning in a decomposer community. mBio 10, e00568-19 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
42.Berlemont, R. & Martiny, A. C. Phylogenetic distribution of potential cellulases in bacteria. Appl. Environ. Microbiol. 79, 1545–1554 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Ho, A., Lonardo, D. P. D. & Bodelier, P. L. E. Revisiting life strategy concepts in environmental microbial ecology. Microbiol. Ecol. https://doi.org/10.1093/femsec/fix006 (2017).44.Wang, L. & Wise, M. J. Glycogen with short average chain length enhances bacterial durability. Naturwissenschaften 98, 719–729 (2011).CAS 
PubMed 
Article 

Google Scholar 
45.Soil Microbe Community Composition (DP1.10081.001) (National Ecological Observatory Network (NEON)); https://data.neonscience.org46.Averill, C., Dietze, M. C. & Bhatnagar, J. M. Continental-scale nitrogen pollution is shifting forest mycorrhizal associations and soil carbon stocks. Glob. Change Biol. 24, 4544–4553 (2018).Article 

Google Scholar 
47.Pawlowsky-Glahn, V., Egozcue, J. J. & Tolosana-Delgado, R. Modelling and Analysis of Compositional Data (John Wiley & Sons, 2015).48.Smithson, M. & Verkuilen, J. A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables. Psychol. Methods 11, 54–71 (2006).PubMed 
Article 

Google Scholar 
49.Cribari-Neto, F. & Zeileis, A. Beta regression in R. J. Stat. Softw. 34, 1–22 (2010).
Google Scholar 
50.Johnson, N. L., Kotz, S. & Balakrishnan, N. Discrete Multivariate Distributions (Wiley, 1997).51.Plummer, M. JAGS: a program for analysis of Bayesian graphical models using Gibbs sampling. In Proc. 3rd International Workshop on Distributed Statistical Computing 1–8 (2003); http://www.ci.tuwien.ac.at/Conferences/DSC-2003/Drafts/Plummer.pdf52.Denwood, M. J. runjags: an R package providing interface utilities, model templates, parallel computing methods and additional distributions for MCMC models in JAGS. J. Stat. Softw. 71, 1–25 (2016).Article 

Google Scholar 
53.Gelman, A. & Hill, J. Data Analysis Using Regression and Multilevel/Hierarchical Models (Cambridge Univ. Press, 2007).54.R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).55.Moran, P. A. P. Notes on continuous stochastic phenomena. Biometrika 37, 17–23 (1950).CAS 
PubMed 
Article 

Google Scholar 
56.Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

1.Baylis, A. M. et al. Disentangling the cause of a catastrophic population decline in a large marine mammal. Ecology 96, 2834–2847 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
2.Hernández-Camacho, C. & Trites, A. Population viability analysis of Guadalupe fur seals Arctocephalus townsendi. Endanger. Species Res. 37, 255–267 (2018).Article 

Google Scholar 
3.Lee, O. A., Burkanov, V. & Neill, W. H. Population trends of northern fur seals (Callorhinus ursinus) from a metapopulation perspective. J. Exp. Mar. Biol. Ecol. 451, 25–34 (2014).Article 

Google Scholar 
4.Williams, P. J., Hooten, M. B., Womble, J. N., Esslinger, G. G. & Bower, M. R. Monitoring dynamic spatio-temporal ecological processes optimally. Ecology 99(3), 524–535 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
5.Wege, M. et al. Trend changes in sympatric Subantarctic and Antarctic fur seal pup populations at Marion Island. Southern Ocean. Mar. Mam. Sci. 32(3), 960–982 (2016).Article 

Google Scholar 
6.McIntosh, R. R. et al. Understanding meta-population trends of the Australian fur seal, with insights for adaptive monitoring. PLoS ONE 13(9), e0200253. https://doi.org/10.1371/journal.pone.0200253 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
7.Harwood, J. & Prime, J. H. Some factors affecting the size of British grey seal populations. J. Appl. Ecol. 15, 401–411 (1978).Article 

Google Scholar 
8.Trillmich, F. et al. On the challenge of interpreting census data: Insights from a study of an endangered pinniped. PLoS ONE 12(5), e0154588. https://doi.org/10.1371/journal.pone.0154588 (2016).CAS 
Article 

Google Scholar 
9.Taylor, R. H., Barton, K. J., Wilson, P. R., Thomas, B. W. & Karl, B. Population status and breeding of New Zealand fur seals (Arctocephalus forsteri) in the Nelson–northern Marlborough region, 1991–94. New. Zeal. J. Mar. Fresh. 29, 223–234 (1995).Article 

Google Scholar 
10.Riofrío-Lazo, M., Arreguín-Sánchez, F. & Páez-Rosas, D. Population abundance of the endangered galapagos Sea Lion Zalophus wollebaeki in the Southeastern galapagos archipelago. PLoS ONE 12(1), e0168829. https://doi.org/10.1371/journal.pone.0168829 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
11.García-Aguilar, M. C., Elorriaga-Verplancken, F. R., Rosales-Nanduca, H. & Schramm, Y. Population status of the Guadalupe fur seal (Arctocephalus townsendi). J. Mammal. 99(6), 1522–1528 (2018).
Google Scholar 
12.Forcada, J., Trathan, P. N., Reid, K. & Murphy, E. J. The effects of global climate variability in pup production of Antarctic fur seals. Ecology 86(9), 2408–2417 (2005).Article 

Google Scholar 
13.Taylor, B. L., Martinez, M., Gerrodette, T. & Barlow, J. Lessons from monitoring trends in abundance of marine mammals. Mar. Mam. Sci. 23, 157–175 (2007).Article 

Google Scholar 
14.Soto, K. H., Trites, A. W. & Arias-Schreiber, M. The effects of prey availability on pup mortality and the timing of birth of South American sea lions (Otaria flavescens) in Peru. J. Zool. 264, 419–428 (2004).Article 

Google Scholar 
15.Elorriaga-Verplancken, F., Sierra-Rodríguez, G., Rosales-Nanduca, H., Acevedo-Whitehouse, K. & Sandoval-Sierra, J. Impact of the 2015 El Niño-Southern Oscillation on the Abundance and Foraging Habits of Guadalupe Fur Seals and California Sea Lions from the San Benito Archipelago. Mexico. PLoS ONE. 11(5), e0155034. https://doi.org/10.1371/journal.pone.0155034 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
16.Villegas-Amtmann, S., Simmons, S. E., Kuhn, C. E., Huckstadt, L. A. & Costa, D. P. Latitudinal range influences the seasonal variation in the foraging behavior of marine top predators. PLoS ONE 6(8), e23166. https://doi.org/10.1371/journal.pone.0023166 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
17.Hoskins, A. J., Costa, D. P., Wheatley, K. E., Gibbens, J. R. & Arnould, J. P. Influence of intrinsic variation on foraging behaviour of adult female Australian fur seals. Mar. Ecol. Prog. Ser. 526, 227–239 (2015).ADS 
Article 

Google Scholar 
18.Guinet, C., Jouventin, P. & Georges, J. Y. Long term population changes of fur seals Arctocephalus gazella and Arctocephalus tropicalis on subantarctic (Crozet) and subtropical (St. Paul and Amsterdam) islands and their possible relationship to El Niño southern oscillation. Antarct. Sci. 6(4), 473–478 (1994).19.Chilvers, B. L., Wilkinson, I. S. & Childerhouse, S. New Zealand sea lion, Phocarctos hookeri, pup production 1995 to 2006. New. Zeal. J. Mar. Fresh. 41, 205–213 (2007).Article 

Google Scholar 
20.Weise, M. J. & Harvey, J. T. Temporal variability in ocean climate and California sea lion diet and biomass consumption: implications for fisheries management. Mar. Ecol. Prog. Ser. 373, 157–172 (2008).ADS 
Article 

Google Scholar 
21.Robinson, H., Thayer, J., Sydeman, W. J. & Weise, M. J. Changes in California sea lion diet during a period of substantial climate variability. Mar. Biol. 165, 169. https://doi.org/10.1007/s00227-018-3424-x (2018).Article 

Google Scholar 
22.Trillmich, F. & Dellinger, T. The Effects of El Niño on Galapagos Pinnipeds. In: Pinnipeds and El Niño. Ecological Studies (Analysis and Synthesis) (eds. Trillmich, F., & Ono, K. A.) 66–74 (Springer, Heidelberg, 1991).23.Churchill, M., Boessenecker, R. & Clementz, M. Colonization of the Southern Hemisphere by fur seals and sea lions (Carnivora: otariidae), revealed by combined evidence phylogenetic and Bayesian biogeographic analysis. Zool. J. Linn. Soc. 172, 200–225 (2014).Article 

Google Scholar 
24.Páez-Rosas, D., Aurioles-Gamboa, D., Alava, J. J. & Palacios, D. Stable isotopes indicate differing foraging strategies in two sympatric otariids of the Galapagos Islands. J. Exp. Mar. Bio. Ecol. 425, 44–52 (2012).Article 
CAS 

Google Scholar 
25.Villegas-Amtmann, S., Jeglinski, J. W. E., Costa, D. P., Robinson, P. W. & Trillmich, F. Individual foraging strategies reveal niche overlap between endangered Galapagos Pinnipeds. PLoS ONE 8(8), e70748. https://doi.org/10.1371/journal.pone.0070748 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
26.Alava, J. J. & Salazar, S. Status and conservation of otariids in Ecuador and the Galapagos Islands. In: Sea lions of the world (eds. Trites, A. W., et al.) 495–519 (Fairbanks, 2006).27.Trillmich, F. Zalophus wollebaeki: The IUCN Red List of Threatened Species. e.T41668A45230540; https://dx.doi.org/https://doi.org/10.2305/IUCN.UK.2015-2.RLTS.T41668A45230540.en (2015).28.Trillmich, F. Arctocephalus galapagoensis. The IUCN Red List of Threatened Species. e.T2057A45223722; https://dx.doi.org/https://doi.org/10.2305/IUCN.UK.2015-2.RLTS.T2057A45223722.en (2015).29.Páez-Rosas, D. & Guevara, N. Management strategies and conservation status of Galapagos sea lion populations at San Cristóbal Island, Galapagos, Ecuador. In: Tropical Pinnipeds: Bio- Ecology, Threats and Conservation (ed. Alava, J. J.) 159–175 (Taylor & Francis Group, Abingdon, 2017).30.Trillmich, F. Galapagos sea lions and fur seals. Noticias de Galápagos. 29, 8–14 (1979).
Google Scholar 
31.Wolf, J. B. et al. Tracing early stages of species differentiation: Ecological, morphological and genetic divergence of Galápagos sea lion populations. BMC Evol. Biol. 8, 150. https://doi.org/10.1186/1471-2148-8-150 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
32.Palacios, D. Seasonal patterns of sea surface temperature and ocean color around the Galapagos: regional and local influences. Deep Sea Res. Part II Top. Stud. Oceanogr. 51, 43–57 (2004).33.Palacios, D., Bograd, S., Foley, D. & Schwing, F. Oceanographic characteristics of biological hot spots in the North Pacific: A remote sensing perspective. Deep Sea Res. Part II Top. Stud. Oceanogr. 53(3), 250–269 (2006).34.Schaeffer, B. et al. Phytoplankton biomass distribution and identification of productive habitats within the Galapagos Marine Reserve by MODIS, a surface acquisition system. Remote Sens. Environ. 112(6), 3044–3054 (2008).ADS 
Article 

Google Scholar 
35.Trillmich, F. & Wolf, J. B. Parent-offspring and sibling conflict in Galapagos fur seals and sea lions. Behav. Ecol. Sociobiol. 62, 363–375 (2008).Article 

Google Scholar 
36.Mueller, B., Pörschmann, U., Wolf, J. B. & Trillmich, F. Growth under uncertainty: the influence of marine variability on early development of Galapagos sea lions. Mar. Mam. Sci. 27, 350–365 (2011).Article 

Google Scholar 
37.Bonnell, M. L. & Ford, R. G. California sea lion distribution: a statistical analysis of aerial transect data. J. Wildl. Manage. 51, 13–20 (1987).Article 

Google Scholar 
38.Edgar, G. J. et al. Conservation of threatened species in the Galapagos Marine Reserve through identification and protection of marine key biodiversity areas. Aqua. Conserv. Mar. Freshw. Ecosyst. 18, 955–968 (2008).Article 

Google Scholar 
39.Edgar, G. J., Banks, S., Fariña, J. M., Calvopiña, M. & Martínez, C. Regional biogeography of shallow reef fish and macro-invertebrate communities in the Galapagos archipelago. J. Biogeogr. 31, 1107–1124 (2004).Article 

Google Scholar 
40.Ruttenberg, B. I., Haupt, A. J., Chiriboga, A. I. & Warner, R. R. Patterns, causes and consequences of regional variation in the ecology and life history of a reef fish. Oecologia 145, 394–403 (2005).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
41.Wellington, G. M., Strong, A. E. & Merlen, G. Sea surface temperature variation in the Galápagos Archipelago: a comparison between AVHRR nighttime satellite data and in-situ instrumentation (1982–1988). Bull. Mar. Res. 69, 27–42 (2001).
Google Scholar 
42.Le Boeuf, B. J. et al. Size and distribution of the California sea lion population in Mexico. Proc. Calif. Acad. Sci. 43, 77–85 (1983).
Google Scholar 
43.Fielder, P. C. & Talley, L. D. Hydrography of the tropical Eastern Pacific: a review. Prog. In Ocean. 69, 143–180 (2006).ADS 
Article 

Google Scholar 
44.Brand, L. E., Guillard, R. R. & Murphy, L. S. A method for therapid and precise determination of acclimated phytoplankton reproduction rates. J. Plankton. Res. 3, 193–201 (1981).Article 

Google Scholar 
45.Riedman, M. The Pinnipeds: seals, sea lions and walruses (University of California Press, 1990).Book 

Google Scholar 
46.Costa, D. P., Weise, M. J. & Arnould, J. P. Potential influences of whaling on the status and trends of pinniped populations (University of California Press, 2006).
Google Scholar 
47.Kalberer, S., Meise, K., Trillmich, F. & Krüger, O. Reproductive performance of a tropical apex predator in an unpredictable habitat. Behav. Ecol. Sociobiol. 72, 108. https://doi.org/10.1007/s00265-018-2521-7 (2018).Article 

Google Scholar 
48.Trillmich, F. & Limberger, D. Drastic effects of El Niño on Galapagos pinnipeds. Oecologia 67(1), 19–22 (1985).ADS 
PubMed 
Article 

Google Scholar 
49.Cai, W. et al. Increasing frequency of extreme El Niño events due to greenhouse warming. Nat. Clim. Change. 4, 111–116 (2014).ADS 
Article 

Google Scholar 
50.Wang, G., et al. Continued increase of extreme El Niño frequency long after 1.5 C warming stabilization. Nat. Clim. Change. 7, 568–572 (2017).51.Páez-Rosas, D., Rodríguez-Pérez, M. & Riofrío-Lazo, M. Competition influence in the segregation of the trophic niche of otariids: a case study using isotopic bayesian mixing models in Galapagos pinnipeds. Rapid Commun. Mass Spectrom. 28, 2550–2558 (2014).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
52.Jeglinski, J. W. E., Wolf, J. B., Werner, C., Costa, D. & Trillmich, F. Differences in foraging ecology align with genetically divergent ecotypes of a highly mobile marine top predator. Oecologia 179, 1041–1052 (2015).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
53.Páez-Rosas, D. & Aurioles-Gamboa, D. Spatial variation in the foraging behaviour of the Galapagos sea lions (Zalophus wollebaeki) assessed using scat collections and stable isotope analysis. J. Mar. Biol. Assoc. U.K. 94, 1099–1107 (2014).54.Drago, M. et al. Stable Isotopes Reveal Long-Term Fidelity to Foraging Grounds in the Galapagos Sea Lion (Zalophus wollebaeki). PLoS ONE 11(1), e0147857. https://doi.org/10.1371/journal.pone.0147857 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
55.Páez-Rosas, D., Villegas-Amtmann, S. & Costa, D. P. Intraspecific variation in feeding strategies of Galapagos sea lions: A case of trophic specialization. PLoS ONE 12(10), e0185165. https://doi.org/10.1371/journal.pone.0185165 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
56.Villegas-Amtmann, S., McDonald, B., Páez-Rosas, D., Aurioles-Gamboa, D. & Costa, D. P. Adapted to change: Low energy requirements in a low and unpredictable productivity environment, the case of the Galapagos sea lion. Deep Sea Res. Part II Top. Stud. Oceanogr. 140, 94–104 (2017).57.Heller, E. Mammals of the Galapagos Archipelago: Exclusive of the Cetacea (University of Harvard Press, 1904).58.Franco-Trecu, V., Aurioles-Gamboa, D., Arim, M. & Lima, M. Pre-partum and postpartum trophic segregation between sympatrically breeding female Arctocephalus australis and Otaria flavenscens. J. Mammal. 93(2), 514–521 (2012).Article 

Google Scholar 
59.Trillmich, F. & Kooyman, G. L. Field metabolic rate of lactating female Galapagos fur seals (Arctocephalus galapagoensis): the influence of offspring age and environment. Comp. Biochem. Phys. A. 129(4), 741–749 (2001).CAS 
Article 

Google Scholar 
60.Lopes, F. et al. Fine-scale matrilineal population structure in the Galapagos fur seal and its implications for conservation management. Conserv. Genet. 16, 1099–1113 (2015).Article 

Google Scholar 
61.Jeglinski, J. W. E., Goetz, K. T., Werner, C., Costa, D. P. & Trillmich, F. Same size–same niche? Foraging niche separation between sympatric juvenile Galapagos sea lions and adult Galapagos fur seals. J. Anim. Ecol. 82, 694–706 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
62.Lowry, L. F., Frost, K. J., Davis, R., DeMaster, D. P. & Suydam, R. S. Movements and behavior of satellite-tagged spotted seals (Phoca largha) in the Bering and Chukchi Seas. Polar Biol. 19, 221–230 (1998).Article 

Google Scholar 
63.O’Corry-Crowe, G., Gelatt, T., Rea, L., Bonin, C. & Rehberg, M. Crossing to safety: dispersal, colonization and mate choice in evolutionarily distinct populations of Steller sea lions. Eumetopias jubatus. Mol. Ecol. 23, 5415–5434 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
64.Forcada, J., Trathan, P. N. & Murphy, E. J. Life history buffering in Antarctic mammals and birds against changing patterns of climate and environmental variation. Glob. Chang. Biol. 14, 2473–2488 (2008).ADS 
Article 

Google Scholar 
65.Pacifici, M. et al. Generation length for mammals. Nat. Conserv. 5, 87–94 (2013).
Google Scholar 
66.Dellinger, T. & Trillmich, F. Fish prey of the sympatric Galapagos fur seals and sea lions: seasonal variation and niche separation. Can. J. Zool. 77, 1204–1216 (1999).Article 

Google Scholar 
67.Trillmich, F. Attendance behavior of Galapagos sea lions. In: Fur Seals: Maternal Strategies on Land and at Sea. (eds. Gentry, R. L. & Kooyman, G. L.) 196–208 (Princeton University Press, 1986).68.Trillmich, F. Maternal investment and sex-allocation in the Galapagos fur seal. Arctocephalus galapagoensis. Behav. Ecol. Sociobiol. 19(3), 157–164 (1986).
Google Scholar 
69.Riofrío-Lazo, M. & Páez-Rosas, D. Galapagos sea lions and fur seals: adaptations to environmental variability. In: Ethology and Behavioral Ecology of Otariids and the Odobenid (eds. Campagna, C. & Harcourt, R.) (Springer Science & Business Media, Germany, 2021).70.Adame, F., Elorriaga-Verplancken, F., Beier, E., Acevedo-Whitehouse, K. & Pardo, M. The demographic decline of a sea lion population followed multi-decadal sea surface warming. Sci. Rep. 10, 10499. https://doi.org/10.1038/s41598-020-67534-0 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
71.García-Aguilar, M. C., Turrent, C., Elorriaga-Verplancken, F. R., Arias-Del-Razo, A. & Schramm, Y. Climate change and the northern elephant seal (Mirounga angustirostris) population in Baja California. Mexico. PLoS ONE. 13(2), e0193211. https://doi.org/10.1371/journal.pone.0193211 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
72.Mueller, B., Porschmann, U., Wolf, J. B. W. & Trillmich, F. Growth under uncertainty: the influence of marine variability on early development of Galapagos sea lions. Mar. Mammal. Sci. 27, 350–365 (2011).Article 

Google Scholar 
73.Doney, S. C. et al. Climate change impacts on marine ecosystems. Ann. Rev. Mar. Sci. 4, 11–37 (2011).Article 

Google Scholar 
74.Wernberg, T. et al. An extreme climatic event alters marine ecosystem structure in a global biodiversity hotspot. Nat. Clim. Change. 3, 78–82 (2013).ADS 
Article 

Google Scholar 
75.Páez-Rosas, D., Moreno-Sánchez, X., Tripp-Valdez, A., Elorriaga-Verplancken, F. & Carranco-Narváez, S. Changes in the Galapagos sea lion diet as a response to El Niño-Southern Oscillation. Reg. Stud. Mar. Sci. 40, 101485. https://doi.org/10.1016/j.rsma.2020.101485 (2020).Article 

Google Scholar 
76.Urquía, D. & Páez-Rosas, D. δ13C and δ15N values in pup whiskers as a proxy for the trophic behavior of Galapagos sea lion females. Mamm. Biol. 96, 28–36 (2019).Article 

Google Scholar 
77.Martin, J. H. et al. Testing the iron hypothesis in ecosystems of the equatorial Pacific-Ocean. Nature 371(6493), 123–129 (1994).ADS 
CAS 
Article 

Google Scholar 
78.Sakamoto, C.M., et al. Surface seawater distributions of inorganic carbon and nutrients around the Galapagos Islands: results from the PlumEx experiment using automated chemical mapping. Deep Sea Res. Part 2 Top Stud. Oceanogr. 45(6), 1055–1071 (1998).79.Fritz, L. W. & Hinckley, S. A. Critical review of the regime shift “junk food” nutritional stress hypothesis for the decline of the western stock of Steller sea lion. Mar. Mammal. Sci. 21, 476–518 (2005).Article 

Google Scholar 
80.McClatchie, S. et al. Food limitation of sea lion pups and the decline of forage off central and southern California. R. Soc. Open Sci. 3, 150628. https://doi.org/10.1098/rsos.150628 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
81.Villegas-Amtmann, S., Costa, D., Tremblay, Y., Aurioles-Gamboa, D. & Salazar, S. Multiple foraging strategies in a marine apex predator, the Galapagos Sea Lion. Mar. Ecol. Prog. Ser. 363, 299–309 (2008).ADS 
Article 

Google Scholar 
82.Kraus, C. et al. Mama’s boy: sex differences in juvenile survival in a highly dimorphic large mammal, the Galapagos sea lion. Oecologia 171, 893–903 (2013).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
83.Gentry, R. L. & Kooyman, G. L. Fur seals: Maternal Strategies on Land and at Sea (Princeton University Press, 1987). More

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript. More

1.Savci, S. An agricultural pollutant: chemical fertilizer. Int. J. Environ. Sci. Dev. 3, 77–80 (2012).CAS 

Google Scholar 
2.Guo, J. H. et al. Significant acidification in major Chinese croplands. Science 327, 1008–1010 (2010).ADS 
CAS 
PubMed 
Article 

Google Scholar 
3.Raza, S. et al. Dramatic loss of inorganic carbon by nitrogen‐induced soil acidification in Chinese croplands. Glob. Change Biol. 26, 3738–3751 (2020).ADS 
Article 

Google Scholar 
4.Jez, J. M., Lee, S. G. & Sherp, A. M. The next green movement: plant biology for the environment and sustainability. Science 353, 1241–1244 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
5.Cordovez, V., Dini-Andreote, F., Carrion, V. J. & Raaijmakers, J. M. Ecology and evolution of plant microbiomes. Annu. Rev. Microbiol. 73, 69–88 (2019).CAS 
PubMed 
Article 

Google Scholar 
6.Duran, P. et al. Microbial interkingdom interactions in roots promote Arabidopsis survival. Cell 175, 973–983.e914 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
7.Dini-Andreote, F. & Raaijmakers, J. M. Embracing community ecology in plant microbiome research. Trends Plant Sci. 23, 467–469 (2018).CAS 
PubMed 
Article 

Google Scholar 
8.de Vries, F. T., Griffiths, R. I., Knight, C. G., Nicolitch, O. & Williams, A. Harnessing rhizosphere microbiomes for drought-resilient crop production. Science 368, 270–274 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
9.Hubbard, C. J. et al. The effect of rhizosphere microbes outweighs host plant genetics in reducing insect herbivory. Mol. Ecol. 28, 1801–1811 (2019).CAS 
PubMed 
Article 

Google Scholar 
10.Oldroyd, G. E. D. & Leyser, O. A plant’s diet, surviving in a variable nutrient environment. Science 368, eaba0196 (2020).CAS 
PubMed 
Article 

Google Scholar 
11.Tedersoo, L., Bahram, M. & Zobel, M. How mycorrhizal associations drive plant population and community biology. Science 367, eaba1223 (2020).CAS 
PubMed 
Article 

Google Scholar 
12.Martín‐Robles, N. et al. Impacts of domestication on the arbuscular mycorrhizal symbiosis of 27 crop species. New Phytol. 218, 322–334 (2018).PubMed 
Article 

Google Scholar 
13.Genre, A., Lanfranco, L., Perotto, S. & Bonfante, P. Unique and common traits in mycorrhizal symbioses. Nat. Rev. Microbiol. 18, 649–660 (2020).CAS 
PubMed 
Article 

Google Scholar 
14.Liu, X. et al. Partitioning of soil phosphorus among arbuscular and ectomycorrhizal trees in tropical and subtropical forests. Ecol. Lett. 21, 713–723 (2018).PubMed 
Article 

Google Scholar 
15.Varoquaux, N. et al. Transcriptomic analysis of field-droughted sorghum from seedling to maturity reveals biotic and metabolic responses. Proc. Natl Acad. Sci. USA 116, 27124–27132 (2019).CAS 
Article 

Google Scholar 
16.Lazcano, C., Barrios-Masias, F. H. & Jackson, L. E. Arbuscular mycorrhizal effects on plant water relations and soil greenhouse gas emissions under changing moisture regimes. Soil Biol. Biochem. 74, 184–192 (2014).CAS 
Article 

Google Scholar 
17.Sprent, J. I. Evolving ideas of legume evolution and diversity: a taxonomic perspective on the occurrence of nodulation. New Phytol. 174, 11–25 (2007).CAS 
PubMed 
Article 

Google Scholar 
18.Soltis, D. E. et al. Chloroplast gene sequence data suggest a single origin of the predisposition for symbiotic nitrogen fixation in angiosperms. Proc. Natl Acad. Sci. USA 92, 2647–2651 (1995).ADS 
CAS 
PubMed 
Article 

Google Scholar 
19.Young, N. D. et al. The Medicago genome provides insight into the evolution of rhizobial symbioses. Nature 480, 520–524 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.van Velzen, R. et al. Comparative genomics of the nonlegume Parasponia reveals insights into evolution of nitrogen-fixing Rhizobium symbioses. Proc. Natl Acad. Sci. USA 115, E4700–E4709 (2018).PubMed 
Article 
CAS 

Google Scholar 
21.Smil, V. Nitrogen in crop production: an account of global flows. Glob. Biogeochem. Cycles 13, 647–662 (1999).ADS 
CAS 
Article 

Google Scholar 
22.O’Hara, G. W. The role of nitrogen fixation in crop production. J. Crop Prod. 1, 115–138 (1998).Article 

Google Scholar 
23.Remigi, P., Zhu, J., Young, J. P. W. & Masson-Boivin, C. Symbiosis within symbiosis: evolving nitrogen-fixing legume symbionts. Trends Microbiol. 24, 63–75 (2016).CAS 
PubMed 
Article 

Google Scholar 
24.Garcia, K., Delaux, P. M., Cope, K. R. & Ané, J. M. Molecular signals required for the establishment and maintenance of ectomycorrhizal symbioses. New Phytol. 208, 79–87 (2015).PubMed 
Article 

Google Scholar 
25.Fisher, R. F. & Long, S. R. Rhizobium–plant signal exchange. Nature 357, 655–660 (1992).ADS 
CAS 
PubMed 
Article 

Google Scholar 
26.Cao, Y., Halane, M. K., Gassmann, W. & Stacey, G. The role of plant innate immunity in the legume–Rhizobium symbiosis. Annu. Rev. Plant Biol. 68, 535–561 (2017).CAS 
PubMed 
Article 

Google Scholar 
27.Ferguson, B. J. et al. Legume nodulation: the host controls the party. Plant Cell Environ. 42, 41–51 (2019).CAS 
PubMed 
Article 

Google Scholar 
28.Remans, R. et al. Effect of Rhizobium–Azospirillum coinoculation on nitrogen fixation and yield of two contrasting Phaseolus vulgaris L. genotypes cultivated across different environments in Cuba. Plant Soil 312, 25–37 (2008).CAS 
Article 

Google Scholar 
29.Cassán, F. & Diaz-Zorita, M. Azospirillum sp. in current agriculture: from the laboratory to the field. Soil Biol. Biochem. 103, 117–130 (2016).Article 
CAS 

Google Scholar 
30.Han, Q. et al. Variation in rhizosphere microbial communities and its association with the symbiotic efficiency of rhizobia in soybean. ISME J. 14, 1915–1928 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Saharan, B. S. & Nehra, V. Plant growth promoting rhizobacteria: a critical review. Life Sci. Med. Res. 21, 30 (2011).
Google Scholar 
32.Cheng, Y. T., Zhang, L. & He, S. Y. Plant–microbe interactions facing environmental challenge. Cell Host Microbe 26, 183–192 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
33.Dini-Andreote, F. Endophytes: the second layer of plant defense. Trends Plant Sci. 25, 319–322 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
34.Carrión, V. J. et al. Pathogen-induced activation of disease-suppressive functions in the endophytic root microbiome. Science 366, 606–612 (2019).ADS 
Article 
CAS 

Google Scholar 
35.Sieber, M. et al. Neutrality in the metaorganism. PLoS Biol. 17, e3000298 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
36.Trivedi, P., Leach, J. E., Tringe, S. G., Sa, T. & Singh, B. K. Plant–microbiome interactions: from community assembly to plant health. Nat. Rev. Microbiol. 18, 607–621 (2020).CAS 
PubMed 
Article 

Google Scholar 
37.Burns, A. R. et al. Contribution of neutral processes to the assembly of gut microbial communities in the zebrafish over host development. ISME J. 10, 655–664 (2016).CAS 
PubMed 
Article 

Google Scholar 
38.Sloan, W. T. et al. Quantifying the roles of immigration and chance in shaping prokaryote community structure. Environ. Microbiol. 8, 732–740 (2006).PubMed 
Article 

Google Scholar 
39.Ning, D., Deng, Y., Tiedje, J. M. & Zhou, J. A general framework for quantitatively assessing ecological stochasticity. Proc. Natl Acad. Sci. USA 116, 16892–16898 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
40.Carlström, C. I. et al. Synthetic microbiota reveal priority effects and keystone strains in the Arabidopsis phyllosphere. Nat. Ecol. Evol. 3, 1445–1454 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
41.Purugganan, M. D. & Fuller, D. Q. The nature of selection during plant domestication. Nature 457, 843–848 (2009).ADS 
CAS 
PubMed 
Article 

Google Scholar 
42.Chen, Y. H., Gols, R. & Benrey, B. Crop domestication and its impact on naturally selected trophic interactions. Annu. Rev. Entomol. 60, 35–58 (2015).CAS 
PubMed 
Article 

Google Scholar 
43.Szoboszlay, M. et al. Comparison of root system architecture and rhizosphere microbial communities of Balsas teosinte and domesticated corn cultivars. Soil Biol. Biochem. 80, 34–44 (2015).CAS 
Article 

Google Scholar 
44.Perez-Jaramillo, J. E., Mendes, R. & Raaijmakers, J. M. Impact of plant domestication on rhizosphere microbiome assembly and functions. Plant Mol. Biol. 90, 635–644 (2016).CAS 
PubMed 
Article 

Google Scholar 
45.Perez-Jaramillo, J. E., Carrion, V. J., de Hollander, M. & Raaijmakers, J. M. The wild side of plant microbiomes. Microbiome 6, 143 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
46.Emmett, B. D., Buckley, D. H., Smith, M. E. & Drinkwater, L. E. Eighty years of maize breeding alters plant nitrogen acquisition but not rhizosphere bacterial community composition. Plant Soil 431, 53–69 (2018).CAS 
Article 

Google Scholar 
47.Mutch, L. A. & Young, J. P. W. Diversity and specificity of Rhizobium leguminosarum biovar viciae on wild and cultivated legumes. Mol. Ecol. 13, 2435–2444 (2004).CAS 
PubMed 
Article 

Google Scholar 
48.Kiers, E. T., Hutton, M. G. & Denison, R. F. Human selection and the relaxation of legume defences against ineffective rhizobia. Proc. R. Soc. B 274, 3119–3126 (2007).CAS 
PubMed 
Article 

Google Scholar 
49.Pérez-Jaramillo, J. E. et al. Linking rhizosphere microbiome composition of wild and domesticated Phaseolus vulgaris to genotypic and root phenotypic traits. ISME J. 11, 2244–2257 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
50.Zachow, C., Müller, H., Tilcher, R. & Berg, G. Differences between the rhizosphere microbiome of Beta vulgaris ssp. maritima—ancestor of all beet crops—and modern sugar beets. Front. Microbiol. 5, 415 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
51.Coleman‐Derr, D. et al. Plant compartment and biogeography affect microbiome composition in cultivated and native Agave species. New Phytol. 209, 798–811 (2016).PubMed 
Article 
CAS 

Google Scholar 
52.Warschefsky, E., Penmetsa, R. V., Cook, D. R. & von Wettberg, E. J. Back to the wilds: tapping evolutionary adaptations for resilient crops through systematic hybridization with crop wild relatives. Am. J. Bot. 101, 1791–1800 (2014).PubMed 
Article 

Google Scholar 
53.Brozynska, M., Furtado, A. & Henry, R. J. Genomics of crop wild relatives: expanding the gene pool for crop improvement. Plant Biotechnol. J. 14, 1070–1085 (2016).CAS 
PubMed 
Article 

Google Scholar 
54.Zhang, H., Mittal, N., Leamy, L. J., Barazani, O. & Song, B. H. Back into the wild—apply untapped genetic diversity of wild relatives for crop improvement. Evol. Appl. 10, 5–24 (2017).PubMed 
Article 

Google Scholar 
55.Maxted, N. & Kell, S. P. Establishment of a Global Network for the In Situ Conservation of Crop Wild Relatives: Status and Needs (FAO Commission on Genetic Resources for Food and Agriculture, 2009).56.Stenberg, J. A., Heil, M., Åhman, I. & Björkman, C. Optimizing crops for biocontrol of pests and disease. Trends Plant Sci. 20, 698–712 (2015).CAS 
PubMed 
Article 

Google Scholar 
57.Heil, M. & Baldwin, I. T. Fitness costs of induced resistance: emerging experimental support for a slippery concept. Trends Plant Sci. 7, 61–67 (2002).CAS 
PubMed 
Article 

Google Scholar 
58.Liu, H. & Brettell, L. E. Plant defense by VOC-induced microbial priming. Trends Plant Sci. 24, 187–189 (2019).CAS 
PubMed 
Article 

Google Scholar 
59.Schulz-Bohm, K. et al. Calling from distance: attraction of soil bacteria by plant root volatiles. ISME J. 12, 1252–1262 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Ehlers, B. K. et al. Plant secondary compounds in soil and their role in belowground species interactions. Trends Ecol. Evol. 35, 716–730 (2020).PubMed 
Article 

Google Scholar 
61.Preece, C. & Penuelas, J. A return to the wild: root exudates and food security. Trends Plant Sci. 25, 14–21 (2020).CAS 
PubMed 
Article 

Google Scholar 
62.Rasmann, S. et al. Recruitment of entomopathogenic nematodes by insect-damaged maize roots. Nature 434, 732–737 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
63.Köllner, T. G. et al. A maize (E)-β-caryophyllene synthase implicated in indirect defense responses against herbivores is not expressed in most American maize varieties. Plant Cell 20, 482–494 (2008).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
64.Lebeis, S. L. et al. Salicylic acid modulates colonization of the root microbiome by specific bacterial taxa. Science 349, 860–864 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
65.Vorholt, J. A., Vogel, C., Carlstrom, C. I. & Muller, D. B. Establishing causality: opportunities of synthetic communities for plant microbiome research. Cell Host Microbe 22, 142–155 (2017).CAS 
PubMed 
Article 

Google Scholar 
66.Zhang, J. et al. NRT1.1B is associated with root microbiota composition and nitrogen use in field-grown rice. Nat. Biotechnol. 37, 676–684 (2019).CAS 
PubMed 
Article 

Google Scholar 
67.Hatzenpichler, R., Krukenberg, V., Spietz, R. L. & Jay, Z. J. Next-generation physiology approaches to study microbiome function at single cell level. Nat. Rev. Microbiol. 18, 241–256 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
68.Cui, L., Zhang, D., Yang, K., Zhang, X. & Zhu, Y. G. Perspective on surface-enhanced Raman spectroscopic investigation of microbial world. Anal. Chem. 91, 15345–15354 (2019).CAS 
PubMed 
Article 

Google Scholar 
69.Wang, Y., Huang, W. E., Cui, L. & Wagner, M. Single cell stable isotope probing in microbiology using Raman microspectroscopy. Curr. Opin. Biotechnol. 41, 34–42 (2016).CAS 
PubMed 
Article 

Google Scholar 
70.Cui, L. et al. Functional single-cell approach to probing nitrogen-fixing bacteria in soil communities by resonance Raman spectroscopy with 15N2 labeling. Anal. Chem. 90, 5082–5089 (2018).CAS 
PubMed 
Article 

Google Scholar 
71.Yang, K. et al. Rapid antibiotic susceptibility testing of pathogenic bacteria using heavy-water-labeled single-cell Raman spectroscopy in clinical samples. Anal. Chem. 91, 6296–6303 (2019).CAS 
PubMed 
Article 

Google Scholar 
72.Li, H. Z. et al. D2O-isotope-labeling approach to probing phosphate-solubilizing bacteria in complex soil communities by single-cell Raman spectroscopy. Anal. Chem. 91, 2239–2246 (2019).CAS 
PubMed 
Article 

Google Scholar 
73.Moutia, J.-F. Y., Saumtally, S., Spaepen, S. & Vanderleyden, J. Plant growth promotion by Azospirillum sp. in sugarcane is influenced by genotype and drought stress. Plant Soil 337, 233–242 (2010).CAS 
Article 

Google Scholar 
74.Bashan, Y. & De-Bashan, L. E. How the plant growth-promoting bacterium Azospirillum promotes plant growth—a critical assessment. Adv. Agron. 108, 77–136 (2010).CAS 
Article 

Google Scholar 
75.Figueiredo, M. V. B., Burity, H. A., Martínez, C. R. & Chanway, C. P. Alleviation of drought stress in the common bean (Phaseolus vulgaris L.) by co-inoculation with Paenibacillus polymyxa and Rhizobium tropici. Appl. Soil Ecol. 40, 182–188 (2008).Article 

Google Scholar 
76.Uma, C., Sivagurunathan, P. & Sangeetha, D. Performance of bradyrhizobial isolates under drought conditions. Int. J. Curr. Microbiol. App. Sci. 2, 228–232 (2013).
Google Scholar 
77.Tank, N. & Saraf, M. Salinity-resistant plant growth promoting rhizobacteria ameliorates sodium chloride stress on tomato plants. J. Plant Interact. 5, 51–58 (2010).CAS 
Article 

Google Scholar 
78.Tahir, H. A. et al. Plant growth promotion by volatile organic compounds produced by Bacillus subtilis SYST2. Front. Microbiol. 8, 171 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
79.Vardharajula, S., Zulfikar Ali, S., Grover, M., Reddy, G. & Bandi, V. Drought-tolerant plant growth promoting Bacillus spp.: effect on growth, osmolytes, and antioxidant status of maize under drought stress. J. Plant Interact. 6, 1–14 (2011).CAS 
Article 

Google Scholar 
80.Santoyo, G., Orozco-Mosqueda, M. D. C. & Govindappa, M. Mechanisms of biocontrol and plant growth-promoting activity in soil bacterial species of Bacillus and Pseudomonas: a review. Biocontrol Sci. Technol. 22, 855–872 (2012).Article 

Google Scholar 
81.Leclere, V. et al. Mycosubtilin overproduction by Bacillus subtilis BBG100 enhances the organism’s antagonistic and biocontrol activities. Appl. Environ. Microbiol. 71, 4577–4584 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
82.Hu, J. et al. Probiotic Pseudomonas communities enhance plant growth and nutrient assimilation via diversity-mediated ecosystem functioning. Soil Biol. Biochem. 113, 122–129 (2017).CAS 
Article 

Google Scholar 
83.Kohler, J., Hernández, J. A., Caravaca, F. & Roldán, A. Plant-growth-promoting rhizobacteria and arbuscular mycorrhizal fungi modify alleviation biochemical mechanisms in water-stressed plants. Funct. Plant Biol. 35, 141–151 (2008).CAS 
PubMed 
Article 

Google Scholar 
84.Nassar, A. H., El-Tarabily, K. A. & Sivasithamparam, K. Growth promotion of bean (Phaseolus vulgaris L.) by a polyamine-producing isolate of Streptomyces griseoluteus. Plant Growth Reg. 40, 97–106 (2003).CAS 
Article 

Google Scholar 
85.Gopalakrishnan, S. et al. Evaluation of Streptomyces strains isolated from herbal vermicompost for their plant growth-promotion traits in rice. Microbiol. Res. 169, 40–48 (2014).CAS 
PubMed 
Article 

Google Scholar 
86.Kwak, M.-J. et al. Rhizosphere microbiome structure alters to enable wilt resistance in tomato. Nat. Biotechnol. 36, 1100–1109 (2018).CAS 
Article 

Google Scholar 
87.Sang, M. K. & Kim, K. D. The volatile‐producing Flavobacterium johnsoniae strain GSE09 shows biocontrol activity against Phytophthora capsici in pepper. J. Appl. Microbiol. 113, 383–398 (2012).CAS 
PubMed 
Article 

Google Scholar 
88.Naznin, H. A. et al. Systemic resistance induced by volatile organic compounds emitted by plant growth-promoting fungi in Arabidopsis thaliana. PLoS ONE 9, e86882 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
89.Kiss, L., Russell, J. C., Szentiványi, O., Xu, X. & Jeffries, P. Biology and biocontrol potential of Ampelomyces mycoparasites, natural antagonists of powdery mildew fungi. Biocontrol Sci. Technol. 14, 635–651 (2004).Article 

Google Scholar 
90.Lee, S., Yap, M., Behringer, G., Hung, R. & Bennett, J. W. Volatile organic compounds emitted by Trichoderma species mediate plant growth. Fungal Biol. Biotechnol. 3, 1–14 (2016).CAS 
Article 

Google Scholar 
91.Zhang, S., Gan, Y. & Xu, B. Application of plant-growth-promoting fungi Trichoderma longibrachiatum t6 enhances tolerance of wheat to salt stress through improvement of antioxidative defense system and gene expression. Front. Plant Sci. 7, 1405 (2016).PubMed 
PubMed Central 

Google Scholar 
92.van der Meij, A., Worsley, S. F., Hutchings, M. I. & van Wezel, G. P. Chemical ecology of antibiotic production by Actinomycetes. FEMS Microbiol. Rev. 41, 392–416 (2017).PubMed 
Article 
CAS 

Google Scholar 
93.Bhatti, A. A., Haq, S. & Bhat, R. A. Actinomycetes benefaction role in soil and plant health. Microb. Pathog. 111, 458–467 (2017).CAS 
PubMed 
Article 

Google Scholar 
94.Chaurasia, A. et al. Actinomycetes: an unexplored microorganisms for plant growth promotion and biocontrol in vegetable crops. World J. Microbiol. Biotechnol. 34, 1–16 (2018).Article 

Google Scholar 
95.Ercoli, L., Schüßler, A., Arduini, I. & Pellegrino, E. Strong increase of durum wheat iron and zinc content by field-inoculation with arbuscular mycorrhizal fungi at different soil nitrogen availabilities. Plant Soil 419, 153–167 (2017).CAS 
Article 

Google Scholar 
96.Xu, L. et al. Arbuscular mycorrhiza enhances drought tolerance of tomato plants by regulating the 14-3-3 genes in the ABA signaling pathway. Appl. Soil Ecol. 125, 213–221 (2018).Article 

Google Scholar 
97.Ghorchiani, M., Etesami, H. & Alikhani, H. A. Improvement of growth and yield of maize under water stress by co-inoculating an arbuscular mycorrhizal fungus and a plant growth promoting rhizobacterium together with phosphate fertilizers. Agric. Ecosyst. Environ. 258, 59–70 (2018).CAS 
Article 

Google Scholar 
98.Meeds, J. A. et al. Phosphorus deficiencies invoke optimal allocation of exoenzymes by ectomycorrhizas. ISME J. https://doi.org/10.1038/s41396-020-00864-z (2021). More

Description of Suinzona borowieci sp. nov. (Figs. 1, 2 and 3)Figure 1Morphology of Suinzona borowieci sp. nov. and related species: (a,b) Holotype of S. borowieci sp. nov. (a) Dorsal habitus, (b) lateral habitus; (c–e) exposed hind wing, (c) S. borowieci sp. nov., (d) S. cyrtonoides, (e) Potaninia assamensis; (f–g) aedeagus with everted internal sac (left) and flagellum (right); (f) S. borowieci sp. nov., (g) S. cyrtonoides.Full size imageFigure 2Genitalia of Suinzona borowieci sp. nov. and related species: (a–d) S. borowieci sp. nov. (a) Aedeagus, dorsal view; (b) aedeagus, lateral view; (c) aedeagus, apical view; (d) spermatheca. (e) Aedeagus of Suinzona cyrtonoides, apical view.Full size imageFigure 3Distribution map of Suinzona and sampling sites: (a) Distribution of Suinzona species in China, South Korea and Japan, (b) type locality and collection sites of Suinzona borowieci sp. nov. in South Korea. Records of distribution are taken from Ge et al.3, Suzuki et al.21 and the results of this work. The map is redrawn and modified from National Geographic Information Institute of Korea (https://www.ngii.go.kr).Full size imageFamily Chrysomelidae Latreille, 1802Subfamily Chrysomelinae Latreille, 1802Genus Suinzona Chen, 1931Type localitySouth Korea: Gyeongbuk Province, Yeongyang County, Irwolsan Mountain, 36° 48′ 30.42″ N, 129° 5′ 23.56″ E, ca. 1135 m.Type materialHolotype: male (NMPC), South Korea: Gyeongbuk Prov., Yeongyang, Mt. Irwolsan, 36° 48′ 30.42″ N, 129° 5′ 23.56″ E, ca. 1135 m, 12.VI.2011, H.W. Cho // HOLOTYPUS Suinzona borowieci sp. n. Cho & Kim 2020. Paratype: SOUTH KOREA – Gyeongbuk Prov.: 1 female (NMPC), same data as holotype plus PARATYPUS Suinzona borowieci sp. n. Cho & Kim 2020; 1 female (HCC), same data as holotype except 31.VII.2004; 1 female (HCC), same data as holotype except 31.VII.2004; 4 males 2 females (HCC), same data as holotype except 22.V.2009; 8 males 2 females (HCC), same data as holotype except 25.VI.2010; 4 males 2 females (HCC), same data as holotype except 10.VI.2017; 1 male 1 female (HCC), same data as holotype except 17.VI.2017; 1 male (HCC), same data as holotype except 36° 48′ 11.74″ N, 129° 6′ 10.01″ E, ca. 1190 m, 17.V.2020; 3 males 1 female (HCC), same data as holotype except 7.VI.2020; 2 males (KNAE), Yeongyang, Irwol-myeon, Mt. Irwolsan, 7.VI.2014, J.K. Park // I14_KNAE483613 // I14_KNAE483649; 1 male 1 female (HCC), Bongwha, Myeongho-myeon, Bukgok-ri, Mt. Cheongnyangsan, 36° 47′ 47″ N, 128° 54′ 30″ E, 21–22.V.2015, J.S. Lee; 1 female (HCC), Daegu, Dong-gu, Mt. Palgongsan, 21.V.1998; 2 males 1 female (HCC), Gunwi, Bugye-myeon, Dongsan-ri, Mt. Palgongsan, 9.V.2009, S.S. Jung; 1 male 1 female (HCC), Yecheon, Bomun-myeon, Urae-ri, Mt. Hakgasan, 26.V.2010, Y.J. You; 1 male (HCC), Yecheon, Bomun-myeon, Mt. Hakgasan, 36° 40′ 32.16″ N, 128° 35′ 38.24″ E, ca. 330 m, 3.VI.2020, H.W. Cho; 1 female (HCC), Cheongsong, Hyeonseo-myeon, Galcheon-ri, 26.V.2004, H.W. Cho; Gangwon Prov.: 2 females (HCC), Taebaek, Hwangji-dong, Mt. Hambaeksan, 37° 9′ 53.22″ N, 128° 55′ 1.35″ E, ca. 1470 m, 6.VI.2005, H.W. Cho; 2 males 3 females (HCC), same data as preceding one except 6.VI.2006; 1 female (HCC), same data as preceding one except 29.V.2009; 1 female (HCC), same data as preceding one except 10.VI.2017; 1 female (HCC), same data as preceding one except 5.VI.2020; Chungnam Prov.: 1 male (HCC), Buyeo, Gyuam-myeon, Sumok-ri, 1–15.VI.2005, J.W. Lee.Other materialSix mature larvae (HCC), same data as holotype except 29.VI.2017; 5 mature larvae (HCC), Gangwon Prov., Taebaek, Hwangji-dong, Mt. Hambaeksan, 19.VI.2006, H.W. Cho; 8 mature larvae (HCC), Gyeongbuk Prov., Yecheon, Bomun-myeon, Mt. Hakgasan, 31.V.2020, H.W. Cho; 7 mature larvae (HCC), same data as preceding one except 3.VI.2020.DiagnosisSuinzona borowieci sp. nov. is almost identical to S. cyrtonoides in the shape of the flagellum of the aedeagus. However, it can be distinguished by its larger body size (5.5–7.0 mm vs. 4.8–6.0 mm), denser punctures on elytra (less dense punctures in S. cyrtonoides), larger and broader aedeagus with the distal tips of the flagellum quadrifurcated and slightly curved, arising from two sclerotized tubes (with a smaller and narrower aedeagus with distal tips of the flagellum quadrifurcated and almost straight, arising from a sclerotized tube in S. cyrtonoides).DescriptionMeasurements in mm (n = 5): length of body: 5.50–7.00 (mean 6.18); width of body: 3.50–4.50 (mean 3.97); height of body: 2.60–3.40 (mean 2.94); width of head: 1.65–1.95 (mean 1.81); interocular distance: 1.15–1.50 (mean 1.33); width of apex of pronotum: 1.90–2.20 (mean 2.02); width of base of pronotum: 2.70–3.25 (mean 2.94); length of pronotum along midline: 1.75–2.05 (mean 1.90); length of elytra along suture: 3.75–5.20 (mean 4.41). Body: oval and strongly convex (Fig. 1a,b). Body dark bluish-black with weak metallic lustre, rarely with a dark brass dorsum. Antenna, mouthparts and tarsus partially dark reddish-brown. Head. Vertex weakly convex, covered with sparse punctures, becoming coarser and denser towards sides, with convex area above antennal insertion. Eyes strongly transverse-oblong and protuberant. Frontal suture V-shaped, forming obtuse angle, arms bent at middle, reaching anterior margin. Frons flat, strongly depressed at anterior margin, covered with dense punctures. Clypeus narrow and trapezoidal. Anterior margin of labrum weakly concave. Mandibles with 2 blunt apical teeth and dense punctures bearing setae on outer side. Maxillary palp 4-segmented with apical palpomere fusiform, truncate apically. Antennae in males much longer than half the length of the body; antennomere 1 robust; antennomere 2 shorter than 3; antennomere 3 longer than 4; antennomeres 7–10 each moderately widened, much longer than wide; antennomere 11 longest, approximately 2.4 times as long as wide. Antennae in females less than half the length of the body. Pronotum. 1.50–1.63 times as wide as long. Lateral sides widest at or near base, roundly narrowed anteriorly, anterior angles strongly produced. Anterior and lateral margins bordered, lateral margins barely visible in dorsal view. Trichobothria present on posterior angles. Disc glabrous, covered with moderately dense punctures, becoming coarser along basal margin; interspaces covered with fine and moderately dense punctures. Scutellum much wider than long, widely rounded apically, with a few fine punctures. Elytra. 1.07–1.16 times as long as wide. Lateral sides widest near middle, roundly narrowed posteriorly. Humeral calli not developed. Disc glabrous and finely rugose, covered with rather irregular punctures arranged in longitudinal rows near suture and lateral margin, more irregular in median region; interspaces covered with fine and sparse punctures. Epipleura wholly visible in lateral view. Hind wings steno- and brachypterous (Fig. 1c). Venter. Hypomera weakly rugose, with a few punctures near anterolateral corners of prosternum. Prosternum covered with coarse and dense punctures bearing long setae; prosternal process broad and strongly expanded apicolaterally, closing procoxal cavities posteriorly. Metasternum covered with punctures bearing long setae, dense medially, sparse laterally. Abdominal ventrites covered with moderately dense punctures bearing long or short setae; apex of last visible abdominal ventrite deeply emarginate in males while rounded in females. Legs. Moderately robust. Tibiae simple without preapical tooth. Tarsomere 1 subequal in width to tarsomere 3 in males but distinctly narrower than tarsomere 3 in females. Tarsal claws simple. Genitalia. Aedeagus broad, lateral margins shallowly concave, with apex moderately produced and truncate in dorsal view (Fig. 2a,c); regularly curved, tapering from middle to apex, with apex pointed and slightly bent upward in lateral view (Fig. 2b); flagellum club-shaped with sharp, sclerotized and quadrifid tips (Fig. 1f). Spermatheca U-shaped, long and rounded at apex (Fig. 2d).EtymologyDedicated to the first author’s mentor Prof. dr hab. Lech Borowiec (University of Wrocław, Poland), the world’s leading specialist in tortoise beetles.DistributionSouth Korea: Chungnam, Gangwon, Gyeongbuk, Daegu (Fig. 3a,b).RemarksThe shape of the apical part of the male genitalia exhibits a certain degree of variation even within the same population. It is difficult to recognize a significant difference in the shape of the male genitalia between populations, but individuals from Yeongyang have a relatively large aedeagus. All specimens that we examined had a dark bluish-black dorsum with a weak metallic lustre, but a single specimen with a dark brass dorsum was found in Yecheon.Mature larva and biology of Suinzona borowieci sp. nov. (Figs. 4, 5 and 6)DiagnosisThe fourth (last) instar larva of S. borowieci sp. nov. is very similar to that of S. cyrtonoides comb. nov. in body shape, colouration and tubercular pattern. However, this species can be distinguished by the 4–5 small secondary tubercles between Dae and DLai on the meso- and metathorax and more densely setose bodies (1 large tubercle between Dae and DLai on the meso- and metathorax and less densely setose body in S. cyrtonoides).Figure 4Mature larva of Suinzona borowieci sp. nov.: (a) Dorsal habitus, (b) lateral habitus, (c) ventral habitus.Full size imageFigure 5Larval morphology of Suinzona borowieci sp. nov.: (a) Head, (b) maxillae and labium, (c) tibiotarsus and pretarsus, (d) mandible, (e) labrum and epipharynx, (f) Schematic presentation of tubercular patterns (top: prothorax; middle: mesothorax; bottom: 2nd abdominal segment).Full size imageFigure 6Host plants of Suinzona borowieci sp. nov.: (a) Arabis pendula L. from Yeongyang, (b) Urtica angustifolia Fisch. ex Hornem. from Yeongyang, (c) Aconitum pseudolaeve Nakai from Taebaek, (d) Isodon inflexus (Thunb.) Kudo from Yecheon; (e–f) A. pseudolaeve Nakai and U. angustifolia Fisch. ex Hornem. for laboratory tests (e) Adult from Yeongyang feeding on leaves, (f) larvae from Yecheon feeding on leaves.Full size imageDescriptionBody length 8.1–8.8 mm, width 2.9–3.2 mm, head width 1.75–1.80 mm (n = 3). Body elongate, rather broad, widest at abdominal segments III–IV, thence moderately narrowed posteriorly and slightly convex dorsally (Fig. 4a). Head pale yellowish-brown, densely setose, with a blackish-brown V-shaped mark along frontal arms; lateral regions of epicrania largely blackish-brown; posterior half of clypeus brown to dark brown; apex of labrum and mandibles blackish-brown. General colouration of integument yellowish-white, but dorsal integument densely covered with minute brown spinules (Fig. 4b); dorsal tubercles dark brown and ventral ones unpigmented (Fig. 4c), both densely setose; spiracles blackish-brown. Legs pale yellow with apex of tibiotarsus and pretarsus brown. Eversible glands absent. Pseudopods present on abdominal segments VI–VII. Head. Hypognathous, rounded, strongly sclerotized (Fig. 5a). Epicranium with 72–77 pairs of setae of varying length; epicranial stem distinct; frontal arms V-shaped, slightly sinuate, not extending to antennal insertions; median endocarina distinct, extending to frontoclypeal suture. Frons slightly depressed medially with 25–29 pairs of setae of varying length. Clypeus almost straight at anterior margin with 3 pairs of setae. Labrum deeply concave anteriorly with 2 pairs of setae and 2 pairs of campaniform sensilla (Fig. 5e, left); epipharynx with 6–7 pairs of setae at anterior margin (Fig. 5e, right). Mandible robust, palmate and 5-toothed, with 4–5 setae and 3 campaniform sensilla; penicillus present (Fig. 5d). Maxillary palp 3-segmented; palpomere I rectangular with 2 setae and 2 campaniform sensilla; II swollen cylindrical with 3 setae and 1 campaniform sensillum; III subconical with 1 seta, 1 digitiform sensillum and 1 campaniform sensillum on sides and a group of peg-like sensilla at the apex; palpifer well developed with 2 setae (Fig. 5b). Mala rounded with 13–14 setae and 1 campaniform sensillum; stipes distinctly longer than wide with 12–14 setae; cardo with 2–3 setae. Labial palp 2-segmented; palpomere I rectangular with 1 campaniform sensillum; II subconical with 1 seta, 1 campaniform sensillum and a group of peg-like sensilla at the apex. Hypopharynx bilobed, densely covered with minute spinules; prementum with four pairs of setae and three pairs of campaniform sensilla; postmentum basolaterally covered with minute spinules, with 8–9 pairs of setae. Six stemmata present on each side, 4 of them located above the antenna and 2 behind the antenna. Antenna 3-segmented; antenomere I wider than long with 2 campaniform sensilla; II approximately as wide as long, with a conical sensorium and 3–4 min setae; III subconical with 5–6 min setae. Thorax. Prothorax with D-DL-EP (dorsal, dorsolateral and epipleural tubercles fused together, 164–179) largest; P (pleural tubercle, 9–11) and ES-SS (eusternal and sternellar tubercles fused, 6–7) unpigmented (Fig. 5f). Meso- and metathorax with dorsal tubercles more or less arranged in 3 transverse rows; Dai (dorsal anterior interior, 6–10) on both sides separated, smaller than Dae (dorsal anterior exterior, 11–15); DLai (dorsolateral anterior interior, 4–5); Dpi (dorsal posterior interior, 12–15); Dpe (dorsal posterior exterior, 10–13) smaller than Dpi; DLpi (dorsolateral posterior interior, 17–19); DLe (dorsolateral exterior, 40–47) large; dorsal region with 8–9 secondary tubercles, 3 of them located anterior to Dai and Dae, 4–5 between Dae and DLai and 1 anterior to DLe; EPa (epipleural anterior, 17–22) larger than EPp (epipleural posterior, 8–11), both unpigmented; P (9–13), SS (1) and ES (3–4) unpigmented; sternal region with 4–5 additional setae arising from weakly sclerotized base. Mesothoracic spiracles annuliform and largest. Legs moderately long, 5-segmented; tibiotarsus with 23–25 setae; pretarsus large, strongly curved, basal tooth well developed, with 1 short seta (Fig. 5c). Abdomen. Segments I–VI with dorsal tubercles arranged in 3 transverse rows; Dai (5–8) on both sides separated, smaller than Dae (13–14); DLae (12–14) larger than DLai (7); Dpi (16–19), Dpe (15–19) and DLp (24–29) transverse, subequal in size; dorsal region with 5–10 small secondary tubercles; EP (23–27), P (12–13), PS-SS (parasternal and sternellar tubercles fused, 5–7) and ES (5–7) unpigmented; as1 (secondary tubercle on antero-exterior part of ES, 1) and as2 (secondary tubercle between P and PS, 1); sternal region with 3–4 additional setae arising from weakly sclerotized base. Segment VII with Dai and Dae fused and Dpi and Dpe fused. Segments VIII with dorsal and dorso-lateral tubercles completely fused (30–37). Segment IX with dorsal to epipleural tubercles completely fused (34–36). Segment X not visible from above, with paired pygopods. Spiracles annuliform, present on segments I–VIII.Host plantsBrassicaceae: Arabis pendula L.; Lamiaceae: Isodon inflexus (Thunb.) Kudo; Ranunculaceae: Aconitum pseudolaeve Nakai; Urticaceae: Urtica angustifolia Fisch. ex Hornem.Biological notesSuinzona borowieci sp. nov. is univoltine. Overwintered adults appear in late May. They mate and lay 15–18 eggs per cluster on the leaves of host plants in early June. Eggs are pale yellow to yellowish-orange and hatch after 8–9 days. The larvae are solitary during the instar stages and feed on the leaves. There are four larval instars, and pupation occurs in soil. The larvae take 14–16 days to pupate and then take 7–8 days to emerge as adults. Newly emerged adults are found during July. We observed larvae or adults of this species in nearby localities (~ 62 km), feeding on A. pendula L. (Fig. 6a) and U. angustifolia Fisch. ex Hornem. (Fig. 6b) from Yeongyang (at 1135 ~ 1190 m a.s.l.), A. pseudolaeve Nakai (Fig. 6c) from Taebaek (at 1,470 m a.s.l.), and I. inflexus (Thunb.) Kudo (Fig. 6d) from Yecheon (at 330 m a.s.l.). Each population showed a preference for its natural host plant but fed on other host plants and completed its life cycle in laboratory tests (Fig. 6e,f).
Suinzona cyrtonoides (Jacoby, 1885) comb. nov. (Figs. 1, 2 and 3)Type localityJapan: Kyushu, Kumamoto Prefecture, Konose.Type materialSyntypes: 1 female (BMNH), Lectotype [mislabelled, not lectotype] // Type // DATA under card // Japan, G. Lewis, 1910–320. // Chrysomela crytonoides Jac. // Lectotype, Chrysomela crytonoides Jacoby, Designated. S. GE 2004 // Potaninia cyrtonoides Jacoby, Det. S. GE 2004 // Suinzona cyrtonoides (Jacoby, 1885) det. H.W. Cho 2014; 1 female (BMNH), Japan, G. Lewis, 1910–320. // Paralectotype // Paralectotype, Chrysomela crytonoides Jacoby, Designated. S. GE 2004 // Potaninia cyrtonoides Jacoby, Det. S. GE 2004 // Suinzona cyrtonoides (Jacoby, 1885) det. H.W. Cho 2014; 1 male (MCZC), Japan Lewis // 1st Jacoby Coll. // cyrtonoides Jac. // Type 17,474; 1 female (MCZC), Japan Lewis // 1st Jacoby Coll.Other materialJAPAN – Kyushu: 1 male (BMNH), Yuyama 1883 // Japan, G. Lewis, 1910–320. // Paralectotype [mislabelled, not type series] // Paralectotype, Chrysomela crytonoides Jacoby, Designated. S. GE 2004 // Potaninia cyrtonoides Jacoby, Det. S. GE 2004 // Suinzona cyrtonoides (Jacoby, 1885) det. H.W. Cho 2014; Honshu: 3 males 2 females (KMNH), Nippara, Okutama, Tokyo, 5.VI.1955, Y. Tominaga; 2 males 3 females (BMNH), Mt. Mitake, Ome-shi, Tokyo, 15.VII.2005, Y. Komiya; 1 male (HSC), Chichibu, Saitama Pref., 18.VI.1984, M. Minami; 1 male (HSC), Tochigi, Sano-shi, Tanuma, 4.VI.2008, H. Ohkawa; 1 male (HSC), Gumma, Fujioka-shi, Mikabo-yama rindo, 8.VI.2009, H. Ohkawa; 1 male 2 females (HSC), same data as preceding one except 21.VII.2009; 1 male 1 female (HSC), same data as preceding one except 1.V.2010; Shikoku: 1 female (HSC), Tokushima, Yoshinokawa-shi, Mt. Kotsu-zan, 18.V.1987, S. Mano; 2 females (EUMJ), Tokushima, Mt. Tsurugi, 15.VII.1984, M. Miyatake; 1 male 1 female (EUMJ), Ehime: Omogo-Sibukusa, Kamiukena-gun, 5.VI.2005, Y. Satoh; 7 males (HCC), Ehime, Kamiukena, Kumakogen, Wakayama, 33° 43′ 59.4″ N, 133° 08′ 06.5″ E, 5.VI.2019, H.W. Cho & Y. Hiroyuki; 1 male (HSC), Ehime, Saijo-shi, Mt. Ishizuchizan, 30.V.2009, H. Suenaga; 2 males (HSC), Ehime, Saijo-shi, Nishinokawa, 16.V.2010, H. Suenaga; 1 male 1 female (HSC), Ehime, Saijo-shi, Nishinokawa, 5.VI.2010, H. Suenaga; 1 female (EUMJ), Jiyoshi-toge, Ehime Pref., 26.IV.1976, A. Oda; 1 male (EUMJ), Mt. Ishizuchi, Ehime pref., 1.VI.1975, H. Kan; 1 female (EUMJ), Iwayaji, Ehime Pref., 1.VI.1969, M. Miyatake; 1 male (EUMJ), Ehime: Yokono, 750 m alt. Yanadani-mura, 7.V.1994, M. Sakai; 1 male (EUMJ), Ehime: Yokono, 660 m alt. Yanadani-mura, 6.V.1994, M. Sakai; 1 female (EUMJ), Ehime: Yokono, 700 m alt. Yanadani-mura, 15.VII.1994, M. Sakai.DistributionJapan: Honshu, Shikoku, Kyushu (Fig. 3a).Host plantsUrticaceae: Boehmeria spicata (Thunb.) Thunb., Boehmeria tricuspis (Hance) Makino.Biological notesDetailed descriptions of larvae and pupae and the life cycle have been published by Kimoto16 and Kimoto and Takizawa11. Its life cycle is similar to that of S. borowieci sp. nov., but they feed on different host plants.RemarksThe apical part of the aedeagus is highly variable, narrow to broad, apex narrowly to widely rounded or weakly truncate, mainly with two weak or strong denticles on the apicolateral margin. The aedeagus of the type specimen is narrowly rounded without apicolateral denticles (Fig. 2e). However, we were not able to find an obvious tendency in the morphological variation of the aedeagus at the intrapopulation or interpopulation level. Chrysomela cyrtonoides Jacoby, 1885 was described from Japan. Later, it was transferred to the genus Potaninia by Chûjô and Kimoto17 and then accepted by various authors until now. However, we found that all materials of P. cyrtonoides have reduced hind wings (Fig. 1d), which are the key diagnostic features of the genus Suinzona, and molecular analysis also suggests its placement in Suinzona. Therefore, Suinzona cyrtonoides (Jacoby, 1885) comb. nov. is proposed. Jacoby18 gave ‘Konose’ as the type locality and used at least two specimens collected by G. Lewis for the description. A male specimen (BMNH) from ‘Yuyama’, designated by Ge et al.3 as a lectotype, did not belong to the type series of S. cyrtonoides and thus lost its lectotype status (ICZN: Article 74.2). Indeed, a female specimen (BMNH) was mislabelled as a lectotype. We were able to find four specimens collected from Japan that might belong to the type series of S. cyrtonoides in the G. Lewis collection (BMNH, MCZC), but more precise locality data were not available. Therefore, we regard them as syntypes and defer selection of a lectotype.Molecular phylogenetic analysesIt is evident from the clarified phylogenetic inference based on mitogenomes that the genus Suinzona differs from the genus Potaninia, S. borowieci sp. nov. as the sister species of S. cyrtonoides (Fig. 7a). The phylogenetic inferences included a total of 20 mitogenomes of Chrysomelinae and outgroups of Galerucinae (Supplementary Table S1). The complete mitogenomes of the four Suinzona species and one Potaninia species (incomplete) were newly sequenced in the present study. Each mitogenome contains a typical set of mitochondrial genes (13 PCGs, 22 tRNAs and two rRNAs) and a control region. Phylogenetic trees based on ML and BI inferences revealed the presence of two well-supported clades (Chrysomelini and Doryphorini + Entomoscelini + Gonioctenini), placing the genus Suinzona as the sister group of the genus Potaninia. This result matched the morphological character of the hind wing. The COI haplotype network of the genus Suinzona complex (Fig. 7b) confirms the previous results and shows that the currently known single species is well distinguished as a species. Two independent networks were completely separated without any connection due to the existence of the mutation (62 steps) exceeding the 95% parsimony limits between them.Figure 7Phylogenetic tree and parsimonious network: (a) Bayesian consensus tree inferred from the combined mitochondrial 13 PCGs + 2 rRNA gene. Bayesian inference (left) and maximum likelihood (right) support values are shown on the branch nodes. Only the values over 70% are reported, (b) Parsimonious network of COI haplotypes. Circles correspond to haplotypes, the frequency and geographic origin of which are indicated by circle size. The geographical origins of the haplotypes are noted at the bottom right of the figure.Full size imageKey to Suinzona borowieci sp. nov. and related species1. Hind wings well developed (Fig. 1e); humeral calli present; trichobothria present on anterior angles of pronotum; lateral margins of pronotum distinctly visible from above. China, India, Laos, Myanmar, Thailand and Vietnam……………………………………………………………… Potaninia assamensis (Baly, 1879)– Hind wings reduced (Fig. 1c,d); humeral calli absent; trichobothria absent on anterior angles of pronotum; lateral margins of pronotum not or barely visible from above. China, Korea and Japan……………………… 22. Aedeagus with apex of flagellum quadrifid (Fig. 1f,g). South Korea, Japan……………. 3– Aedeagus with apex of flagellum varied in shape, but not quadrifid (see Ge et al.3 for key to 23 species). China (Sichuan, Yunnan)……………………………………………… Suinzona spp.3. Larger, body length 5.5–7.0 mm; elytra more densely punctate (Fig. 1a); aedeagus larger and broader (Fig. 2c). South Korea…………………………………. Suinzona borowieci sp. nov.– Smaller, body length 4.8–6.0 mm; elytra less densely punctate (Fig. 1d); aedeagus smaller and narrower (Fig. 2e). Japan…………………………….. Suinzona cyrtonoides (Jacoby, 1885) More

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript. More