More stories

  • in

    This baby turtle surprised scientists by swimming against the current

    In 2008, I had just begun volunteering at Equilibrio Azul — a non-profit marine-research and -conservation organization based in Quito — when colleagues discovered a hawksbill sea turtle (Eretmochelys imbricata) nesting at La Playita beach in Ecuador. The eastern Pacific population of hawksbill sea turtles is one of the most endangered in the world and was considered functionally extinct in the region before this turtle and others were observed.That discovery was a tipping point for hawksbill research in Ecuador and throughout the Pacific Ocean. Since 2008, we’ve found about 20 nests each year at La Playita, and one season, we documented 50.We have tagged 11 adult females with satellite transmitters. Previously, most of our understanding of these turtles had been based on observations in the Caribbean, where the reptiles are strictly coral-reef dwellers. But Ecuador’s reefs are mostly rocky, with patches of coral, and we were surprised to see females migrate south to mangroves, mainly for food.
    Women in science
    In this image, we have just attached a transmitter to a baby turtle — a first for hawksbill turtles this young and in the eastern Pacific region. We did not know much about hawksbills at this young age. It is tricky working with baby turtles, because they grow very fast, and the transmitters, which give us location data, can easily fall off. We’ve used cement to glue the devices to the shells of six newborns so far. The longest the transmitters have lasted is three months and the shortest period was only six weeks — but the devices provided our first insights into the ‘lost years’ of sea-turtle biology.Our findings have overturned assumptions that neonates were just carried along by currents. Instead, we found that one-day-old turtles can swim against the current. They aim for a specific direction — north by northwest — as they learn to dive and swim. We tracked one-year-old hawksbills to Costa Rican waters, a journey of roughly 2,000 kilometres, before we lost their signal.Cristina Miranda is a scientific coordinator at Equilibrio Azul in Quito, Ecuador. Interview by Virginia Gewin. More

  • in

    Nocardiopsis changdeensis sp. nov., an endophytic actinomycete isolated from the roots of Eucommia ulmoides Oliv

    Rainey FA, WardRainey N, Kroppenstedt RM, Stackebrandt E. The genus Nocardiopsis represents a phylogenetically coherent taxon and a distinct actinomycete lineage: proposal of Nocardiopsaceae fam. nov. Int J Syst Evol Microbiol. 1996;46:1088–92.CAS 

    Google Scholar 
    Goodfellow M, Order XV Streptosporangiales ord. nov. In: Goodfellow M, Kämpfer P, Busse HJ, Trujillo ME, Suzuki K, Ludwig W, Whitman WB (eds), Bergey’s Manual of Systematic Bacteriology vol. 5, 2nd edn., Springer, New York, 2012, p. 1805.Meyer J. Nocardiopsis, a new genus of the order Actinomycetales. Int J Sys Bacteriol. 1976;26:487–93.Article 

    Google Scholar 
    Chen YG, Cui XL, Kroppenstedt RM, Stackebrandt E, Wen ML, et al. Nocardiopsis quinghaiensis sp. nov. isolated from saline soil in China. Int J Syst Evol Microbiol. 2008;58:699–705.Article 
    CAS 

    Google Scholar 
    Chen YG, Zhang YQ, Tang SK, Liu ZX, Xu LH, et al. Nocardiopsis terrae sp. nov., a halophilic actinomycete isolated from saline soil. Antonie van Leeuwenhoek. 2010;98:31–8.Article 

    Google Scholar 
    Pan HQ, Zhang DF, Li L, Jiang Z, Li WJ. Nocardiopsis oceani sp. nov. and nocardiopsis nanhaiensis sp. nov. actinomycetes isolated from marine sediment of the south china sea. Int J Syst Evol Microbiol. 2015;65:3384–91.Article 
    CAS 

    Google Scholar 
    Akhwale JK, Göker M, Rohde M, Schumann P, Boga HI, et al. Nocardiopsis mwathae sp. nov., isolated from the haloalkaline Lake Elmenteita in the African Rift Valley. Antonie van Leeuwenhoek. 2016;109:421–30.Article 
    CAS 

    Google Scholar 
    Schippers A. Nocardiopsis metallicus sp. nov. a metal-leaching actinomycete isolated from an alkaline slag dump. Int J Syst Evol Microbiol. 2002;52:2291–5.CAS 

    Google Scholar 
    Devi AM, Nimaichand S, Hamidah I, Xiao-Tong Z, Bull AT, et al. Nocardiopsis deserti sp. nov. isolated from a high altitude atacama desert soil. Int J Syst Evol Microbiol. 2020;70:3210–8.Article 

    Google Scholar 
    Hamedi J, Mohammadipanah F, Von JM, Potter G, Schumann P, et al. Nocardiopsis sinuspersici sp. nov. isolated from sandy rhizospheric soil. Int J Syst Evol Microbiol. 2010;60:2346–52.Article 
    CAS 

    Google Scholar 
    Zhang YG, Lu XH, Ding YB, Zhou XK, Wan HF, et al. Nocardiopsis rhizosphaerae sp. nov., isolated from rhizosphere soil of Halocnermum strobilaceum (Pall.) Bieb. Int J Syst Evol Microbiol. 2016;66:5129–33.Article 
    CAS 

    Google Scholar 
    Muangham S, Suksaard P, Mingma R, Matsumoto A, Takahashi Y, et al. Nocardiopsis sediminis sp. nov., isolated from mangrove sediment Free. Int J Syst Evol Microbiol. 2016;66:3835–40.Article 
    CAS 

    Google Scholar 
    Qin S, Li J, Chen HH, Zhao GZ, Zhu WY, et al. Isolation, diversity, and antimicrobial activity of rare actinobacteria from medicinal plants of tropical rain forests in Xishuangbanna, China. Appl Environ Microbiol. 2009;75:6176–86.Article 
    CAS 

    Google Scholar 
    Sindhuphak W, Macdonald E. Head actinomycetoma caused by Nocardiopsis dassonvillei. Arch. Dermatol. 1985;121:1332–4.Article 
    CAS 

    Google Scholar 
    Mordarska H, Zakrzewska-Czerwiñska J, Paściak M, Szponar B, Rowiñski S. Rare, suppurative pulmonary infection caused by Nocardiopsis dassonvillei recognized by glycolipid markers. FEMS Immunol Med Microbiol. 1998;21:47–55.Article 
    CAS 

    Google Scholar 
    Bennur T, Kumar AR, Zinjarde SS, Javdekar V. Nocardiopsis species: a potential source of bioactive compounds. J Appl Microbiol. 2016;120:1–16.Article 
    CAS 

    Google Scholar 
    Mo P, Yu YZ, Zhao JR, Gao J. Streptomyces xiangtanensis sp. nov., isolated from a manganese-contaminated soil. Antonie van Leeuwenhoek. 2017;110:297–304.Article 
    CAS 

    Google Scholar 
    Atlas RM In: Parks LC (ed) Handbook of microbiological media. CRC Press, Boca Raton, 1993;pp: 666–72.Shirling EB, Gottlieb D. Methods for characterization of Streptomyces species. Int J Syst Bacteriol. 1966;16:313–40.Article 

    Google Scholar 
    Ridgway R Color standards and color nomenclature. Ridgway, Washington, DC, 1912;pp: 1–43.Ruan JS, Huang Y Rapid identification and systematics of Actinobacteria. Science Press, Beijing, China, 2011;pp: 72–7.Xu LH, Li WJ, Liu ZH, Jiang CL Actinomycetes systematics: principles, methods and practices. Science Press, Beijing, China. 2007;pp: 40–53.MIDI. Sherlock Microbial Identification System Operating Manual, Version 6.0. Newark DE: MIDI Inc. 2005;pp: 1–7.Hasegawa T, Takizawa M, Tanida S. A rapid analysis for chemical grouping of aerobic actinomycetes. J Gen Appl Microbiol. 1983;29:319–22.Article 
    CAS 

    Google Scholar 
    Lechevalier MP, Lechevalier H. Chemical composition as a criterion in the classification of aerobic actinomycetes. Int J Syst Bacteriol. 1970;20:435–43.Article 
    CAS 

    Google Scholar 
    Collins MD, Pirouz T, Goodfellow M, Minnikin DE. Distribution of menaquinones in actinomycetes and corynebacteria. J Gen Microbiol. 1977;100:221–30.Article 
    CAS 

    Google Scholar 
    Kroppenstedt RM Fatty acid and menaquinone analysis of actinomycetes and related organisms. In: Goodfellow M, Minnikin DE (eds) Chemical methods in bacterial systematics. Academic Press, London, England, pp, 1985: 173–99.Kates M Techniques of Lipidology, 2nd ed. Amsterdam: Elsevier, 1986.Komagata K, Suzuki KI. 4 lipid and cell-wall analysis in bacterial systematics. Method Microbiol. 1988;19:161–207.Article 

    Google Scholar 
    Lane, DJ 16S/23S rRNA sequencing. In: nucleic acid techniques in bacterial systematics. Stackebrandt E, Goodfellow M, eds., John Wiley and Sons, New York, NY, pp, 1991: 115–75.Yoon SH, Ha SM, Kwon S, Lim J, Kim Y, et al. Introducing EzBioCloud: a taxonomically united database of 16S rRNA gene sequences and whole-genome assemblies. Int J Syst Evol Microbiol. 2017;67:1613–7.Article 
    CAS 

    Google Scholar 
    Saitou N, Nei M. The Neighbor-joining Method: A New Method for Reconstructing Phylogenetic Trees. Mol Biol Evol. 1987;4:406–25.CAS 

    Google Scholar 
    Felsenstein J. Evolutionary trees from DNA sequences: a maximum likelihood approach. J Mol Evol. 1981;17:368–76.Article 
    CAS 

    Google Scholar 
    Fitch WM. Toward defining the course of evolution: minimum change for a specific tree topology. Syst Biol. 1971;20:406–16.Article 

    Google Scholar 
    Kumar S, Stecher G, Tamura K. MEGA 7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol Biol Evol. 2016;33:1870–4.Article 
    CAS 

    Google Scholar 
    Felsenstein J. Confidence limits on phylogenies: an approach using the bootstrap. Evolution. 1985;39:783–91.Article 

    Google Scholar 
    Brettin T, Davis JJ, Disz T, Edwards RA, Gerdes S, et al. RASTtk: a modular and extensible implementation of the RAST algorithm for building custom annotation pipelines and annotating batches of genomes. Sci Rep. 2015;5:8365.Article 

    Google Scholar 
    Overbeek R, Olson R, Pusch GD, Olsen GJ, Stevens R, et al. The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST). Nucleic Acids Res. 2014;42:D206–214.Article 
    CAS 

    Google Scholar 
    Meier-Kolthoff JP, Göker M. TYGS is an automated high-throughput platform for state-of-the-art genome-based taxonomy. Nat. Commun. 2019;10:2182.Article 

    Google Scholar 
    Richter M, Rosselló-Móra R, Ckner FOG, Peplies J. JSpeciesWS: a web server for prokaryotic species circumscription based on pairwise genome comparison. Bioinformatics. 2015;32:929–31.Article 

    Google Scholar 
    Meier-Kolthoff JP, Auch AF, Klenk HP, Göker M. Genome sequence-based species delimitation with confidence intervals and improved distance functions. BMC Bioinform. 2013;14:1–14.Article 

    Google Scholar 
    Rodriguez RL, Gunturu S, Harvey WT, Rossello-Mora R, Tiedje JM, et al. The Microbial Genomes Atlas (MiGA) webserver: taxonomicand gene diversity analysis of Archaea and Bacteria at the whole genome level. Nucleic Acids Res. 2018;46:W282–W288.Article 

    Google Scholar 
    Wayne LG, Brenner DJ, Colwell RR, Grimont PAD, Kandler O. International committee on systematic bacteriology. report of the ad hoc committee on the reconciliation of approaches to bacterial systematics. Int J Syst Bacteriol. 1987;37:463–4.Article 

    Google Scholar 
    Richter M, Rossello-Mora R. Shifting the genomic gold standard for the prokaryotic species definition. Proc Nat Acad Sci USA. 2009;106:19126–31.Article 
    CAS 

    Google Scholar 
    Vincent L, Richard D, Olivier G. FastME 2.0: a comprehensive, accurate, and fast distance-based phylogeny inference program. Mol Biol Evol. 2015;32:2798–800.Article 

    Google Scholar 
    Farris JS. Estimating phylogenetictrees from distance matrices. Am Nat. 1972;106:645–68.Article 

    Google Scholar 
    Fang CY, Zhang JL, Pang HC, Li YY, Xin YH, et al. Nocardiopsis flavescens sp. nov., an actinomycete isolated from marine sediment. Int J Syst Evol Microbiol. 2011;61:2640–5.Article 
    CAS 

    Google Scholar  More

  • in

    Publisher Correction: Future temperature extremes threaten land vertebrates

    Authors and AffiliationsJacob Blaustein Center for Scientific Cooperation, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Midreshet Ben-Gurion, IsraelGopal MuraliMitrani Department of Desert Ecology, The Swiss Institute for Dryland Environments and Energy Research, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Midreshet Ben-Gurion, IsraelGopal Murali & Uri RollDepartment F.-A. Forel for Aquatic and Environmental Sciences, Faculty of Science, University of Geneva, Geneva, SwitzerlandTakuya IwamuraDepartment of Forest Ecosystems and Society, College of Forestry, Oregon State University, Corvallis, OR, USATakuya IwamuraSchool of Zoology, Tel Aviv University, Tel Aviv, IsraelShai MeiriThe Steinhardt Museum of Natural History, Tel Aviv University, Tel Aviv, IsraelShai MeiriAuthorsGopal MuraliTakuya IwamuraShai MeiriUri RollCorresponding authorCorrespondence to
    Gopal Murali. More

  • in

    Rapid diversification underlying the global dominance of a cosmopolitan phytoplankton

    Genetic and morphological delineation between G. huxleyi strainsWe first assessed genetic variability through analysis of genomic polymorphism to determine whether distinct genetic lineages exist in G. huxleyi and to test whether these relate to morphotypes. We used 2,086,643 high-quality biallelic single nucleotide polymorphisms (SNPs) retrieved from the 47 clonal culture strains with the best genome sequence coverage ( >20×). A principal component analysis (PCA) and a discriminant analysis in principal component (DAPC) both delineate three well-defined genetic groups, with the distribution of strains being unequal and with no overlap on the principal components (Fig. 1a; Supplementary Fig. S3a,b). With regards to population structure, the DAPC analysis suggested that 3 clusters (K = 3) can be used to depict a genotype membership matrix for each strain (Fig. 1b; Supplementary Fig. S4). As such, it confirmed the three-lineage delineation proposed by the PCA, while illustrating no admixture between lineages.Fig. 1: Relationship between genetic structure and morphotypes in G. huxleyi.a Principal component analysis (PCA) based on 2,086,643 SNPs recovered from 47 G. huxleyi genomes; b Relationship between coalescent species phylogeny (ASTRAL tree based on 1000 supergenes) and DAPC clustering; c Correspondence between morphotypes and lineages within G. huxleyi, and sub-lineages within A1 (scale bar = 4 μm). Variable elements in relation to genotypes are highlighted in the schematics under the SEM pictures; d Distribution of coccolith length for 5 randomly chosen strains representing each clade and sub-clade, with a jittered box-plot on the left and a half-violin plot on the right for each group; e Matrix plot of Bonferroni corrected p-value corresponding to the Dunn-test for the comparison of coccolith length measurements between groups.Full size imagePhylogenetic inference based on alignments with higher mapping coverage only (47 strains) or including sequences with lower mapping coverage (59 strains) all supported segregation of strains into three main lineages, which we term clades A1, A2 and B, with A1 and A2 being more closely related to each other than to B (Fig. 1b; Supplementary Fig. S5a, b). This delineation is congruent with previous studies on the phylogeny of the Gephyrocapsa genus [17, 46, 65]. These clades also correspond to differences in morphotypes (Fig. 1b, c). All strains in clade A1 produce unambiguous A-group coccolith morphotypes (type A and type R). Similarly, all strains in clade B produce unambiguous B-group coccolith morphotypes (type B and type O). Clade A2 is less distinctive, with strains producing lightly calcified type A coccoliths. Some of these strains could be classified as type B/C [66] or C (both regarded as B-group morphotypes), but distinctive by the lower elevation of distal shield elements and by greater degree of calcification of the central area grid (which is reduced and sometimes absent in morphotypes B/C and C). At a finer level, clade A1 is composed of four sub-clades, which we term A1a, A1b, A1c, and A1d. Strains in sub-clades A1a, A1c and A1d all produce coccoliths with type A morphologies and distinctive degrees of calcification: strains in the sub-clade A1a form relatively lightly calcified coccoliths with regular elements, while strains in sub-clades A1c and A1d produce similar moderately calcified coccoliths, sometimes with conspicuous irregularities (inner tube elements overlapping into the central area). Strains in clade A1b produce distinct coccoliths exhibiting A-group morphology but with heavy calcification, including forms with heavily calcified shields which have been termed type R and also forms with heavily calcified central areas which have been referred to as “type A overcalcified”. Some clade A2 strains produce coccoliths with a similar morphology to strains in A1a, indicative of partially cryptic lineages (Supplementary Fig. S2; Supplementary Table S4).The congruence between morphotypes and clades is also supported by significant differences in the length of coccoliths measured between some of the clades (Fig. 1d, e). The morphogroups A and B differ significantly, and insignificant comparison relates to the comparison of sub-clades against the clade A2, which reinforces the closest relationship between A1 and A2. We denote also that the case of A1a and A2 demonstrating no significant difference in coccolith length concurs with the cryptic delineation mentioned above.Based on the clustering analyses and the phylogenetic reconstructions, we tested whether different groupings are distinct species with regards to the null hypothesis “G. huxleyi is a single species”, which correspond to the current state of taxonomy. Species delimitation based on comparison of Marginal Likelihood Estimators (MLE) with Bayes Factors (BF) supported the hypothesis that the three lineages depicted by ordination and phylogenetic reconstructions are distinct species as the best model (Table 1).Table 1 Species delimitation based on Bayes Factor Delimitation (BDF).Full size tableD-statistics calculated to estimate gene flow reveal a non-significant excess of alleles shared between the three lineages (Fig. 2a; Supplementary Table S5). Fbranch statistics, (fb) revealed significant signatures of gene-flow between sub-lineages within A1 associated with correlated estimates in relation to A1a, A2 and B (Fig. 2a) [60]. Signatures on the basal branch of diversification in A1 may correspond to genetic exchanges between A1 and B, with gene-flow signatures attributed to A2 corresponding to correlated estimates due to common ancestry. Recent signatures of gene-flow throughout the evolution of A1 are thus likely associated to the common ancestry between A1a, A2 and B during gene-flow events between the sub-lineages, as supported by the non-significant D statistics between the three lineages. Moreover, the phylogenetic network revealed similar convolutions between A1 sub-lineages but clear separation of the main lineages and longer branches in the A2 lineage (Fig. 2b).Fig. 2: Excess of allele sharing and differentiation in G. huxleyi.a f-branch (fb) statistics between lineages and sub-lineages. The gradient represents the fb score, grey blocks represents tests not consistent with the species tree (for each branch on the topology of the y axis, having itself or a sister taxon as donor on the topology of the x axis); asterisks denote block jack-knifing significance at p  More

  • in

    Aerial transport of bacteria by dust plumes in the Eastern Mediterranean revealed by complementary rRNA/rRNA-gene sequencing

    Katra, I. et al. Richness and diversity in dust stormborne biomes at the Southeast Mediterranean. Sci. Rep. 4, 5265 (2014).CAS 

    Google Scholar 
    Kellogg, C. A. & Griffin, D. W. Aerobiology and the global transport of desert dust. Trends Ecol. Evolution 21, 638–644 (2006).
    Google Scholar 
    Mazar, Y., Cytryn, E., Erel, Y. & Rudich, Y. Effect of dust storms on the atmospheric microbiome in the eastern Mediterranean. Environ. Sci. Technol. 50, 4194–4202 (2016).CAS 

    Google Scholar 
    Gat, D., Mazar, Y., Cytryn, E. & Rudich, Y. Origin-dependent variations in the atmospheric microbiome community in Eastern Mediterranean Dust Storms. Environ. Sci. Technol. 51, 6709–6718 (2017).CAS 

    Google Scholar 
    Lang-Yona, N. et al. Links between airborne microbiome, meteorology, and chemical composition in northwestern Turkey. Sci. Total Environ. 725, 138227 (2020).CAS 

    Google Scholar 
    Gat, D. et al. Size-resolved community structure of bacteria and fungi transported by dust in the Middle East. Front. Microbiol. 12 (2021) https://doi.org/10.3389/fmicb.2021.744117.Hill, T. C. J. et al. Sources of organic ice nucleating particles in soils. Atmos. Chem. Phys. 16, 7195–7211 (2016).CAS 

    Google Scholar 
    Pandey, R. et al. Ice-nucleating bacteria control the order and dynamics of interfacial water. Sci. Adv. 2, e1501630 (2016).
    Google Scholar 
    Fröhlich-Nowoisky, J. et al. Ice nucleation activity in the widespread soil fungus Mortierella alpina. Biogeosciences 12, 1057–1071 (2015).
    Google Scholar 
    Estillore, A. D., Trueblood, J. V. & Grassian, V. H. Atmospheric chemistry of bioaerosols: heterogeneous and multiphase reactions with atmospheric oxidants and other trace gases. Chem. Sci. 7, 6604–6616 (2016).CAS 

    Google Scholar 
    Brodie, E. L. et al. Urban aerosols harbor diverse and dynamic bacterial populations. Proc. Natl. Acad. Sci. 104, 299–304 (2007).CAS 

    Google Scholar 
    Šantl-Temkiv, T. et al. Characterization of airborne ice-nucleation-active bacteria and bacterial fragments. Atmos. Environ. 109, 105–117 (2015).
    Google Scholar 
    Rahav, E., Ovadia, G., Paytan, A. & Herut, B. Contribution of airborne microbes to bacterial production and N2 fixation in seawater upon aerosol deposition. Geophys. Res. Lett. 43, 719–727 (2016).CAS 

    Google Scholar 
    Failor, K. C., Schmale, D. G., Vinatzer, B. A. & Monteil, C. L. Ice nucleation active bacteria in precipitation are genetically diverse and nucleate ice by employing different mechanisms. ISME J. 11, 2740–2753 (2017).CAS 

    Google Scholar 
    de Araujo, G. G., Rodrigues, F., Gonçalves, F. L. T. & Galante, D. Survival and ice nucleation activity of Pseudomonas syringae strains exposed to simulated high-altitude atmospheric conditions. Sci. Rep. 9, 7768 (2019).
    Google Scholar 
    Lazaridis, M. Bacteria as Cloud Condensation Nuclei (CCN) in the Atmosphere. Atmosphere 10, 786 (2019).CAS 

    Google Scholar 
    Amato, P. et al. Active microorganisms thrive among extremely diverse communities in cloud water. PLOS ONE 12, e0182869 (2017).
    Google Scholar 
    Amato, P. et al. Metatranscriptomic exploration of microbial functioning in clouds. Sci. Rep. 9, 4383 (2019).
    Google Scholar 
    Vaïtilingom, M. et al. Potential impact of microbial activity on the oxidant capacity and organic carbon budget in clouds. Proc. Natl. Acad. Sci. 110, 559–564 (2013).
    Google Scholar 
    Triadó-Margarit, X., Cáliz, J. & Casamayor, E. O. A long-term atmospheric baseline for intercontinental exchange of airborne pathogens. Environ. Int. 158, 106916 (2022).
    Google Scholar 
    Brodie, E. L. et al. Urban aerosols harbor diverse and dynamic bacterial populations. Proc. Natl. Acad. Sci. 104, 299 (2007).CAS 

    Google Scholar 
    Archer, S. D. J. et al. Airborne microbial transport limitation to isolated Antarctic soil habitats. Nat. Microbiol 4, 925–932 (2019).CAS 

    Google Scholar 
    Mayol, E. et al. Long-range transport of airborne microbes over the global tropical and subtropical ocean. Nat. Commun. 8, 201 (2017).
    Google Scholar 
    Favet, J. et al. Microbial hitchhikers on intercontinental dust: catching a lift in Chad. ISME J. 7, 850–867 (2013).CAS 

    Google Scholar 
    Cáliz, J., Triadó-Margarit, X., Camarero, L. & Casamayor, E. O. A long-term survey unveils strong seasonal patterns in the airborne microbiome coupled to general and regional atmospheric circulations. Proc. Natl. Acad. Sci. 115, 12229–12234 (2018).
    Google Scholar 
    Du, P., Du, R., Ren, W., Lu, Z. & Fu, P. Seasonal variation characteristic of inhalable microbial communities in PM2.5 in Beijing city, China. Sci. Total Environ. 610-611, 308–315 (2018).CAS 

    Google Scholar 
    Lang-Yona, N. et al. Links between airborne microbiome, meteorology, and chemical composition in northwestern Turkey. Sci. Total Environ. 725, 138227 (2020).CAS 

    Google Scholar 
    Gong, J., Qi, J., E, B., Yin, Y. & Gao, D. Concentration, viability and size distribution of bacteria in atmospheric bioaerosols under different types of pollution. Environ. Pollut. 257, 113485 (2020).CAS 

    Google Scholar 
    Zhang, T., Li, X., Wang, M., Chen, H. & Yao, M. Time- and size-resolved bacterial aerosol dynamics in highly polluted air: new clues for haze formation mechanism. bioRxiv, 513093 (2019) https://doi.org/10.1101/513093.Wei, M. et al. Size distribution of bioaerosols from biomass burning emissions: Characteristics of bacterial and fungal communities in submicron (PM1.0) and fine (PM2.5) particles. Ecotoxicol. Environ. Saf. 171, 37–46 (2019).CAS 

    Google Scholar 
    Blazewicz, S. J., Barnard, R. L., Daly, R. A. & Firestone, M. K. Evaluating rRNA as an indicator of microbial activity in environmental communities: limitations and uses. Isme J. 7, 2061–2068 (2013).CAS 

    Google Scholar 
    Barnard, R. L., Osborne, C. A. & Firestone, M. K. Responses of soil bacterial and fungal communities to extreme desiccation and rewetting. ISME J. 7, 2229–2241 (2013).CAS 

    Google Scholar 
    Schostag, M. et al. Distinct summer and winter bacterial communities in the active layer of Svalbard permafrost revealed by DNA- and RNA-based analyses. Front. Microbiol. 6 (2015) https://doi.org/10.3389/fmicb.2015.00399.Campbell, B. J., Yu, L., Heidelberg, J. F. & Kirchman, D. L. Activity of abundant and rare bacteria in a coastal ocean. Proc. Natl Acad. Sci. 108, 12776–12781 (2011).CAS 

    Google Scholar 
    Denef, V. J., Fujimoto, M., Berry, M. A. & Schmidt, M. L. Seasonal succession leads to habitat-dependent differentiation in ribosomal RNA:DNA Ratios among freshwater lake bacteria. Front. Microbiol.7 (2016) https://doi.org/10.3389/fmicb.2016.00606.Zhang, Y., Zhao, Z., Dai, M., Jiao, N. & Herndl, G. J. Drivers shaping the diversity and biogeography of total and active bacterial communities in the South China Sea. Mol. Ecol. 23, 2260–2274 (2014).CAS 

    Google Scholar 
    Hospodsky, D., Yamamoto, N. & Peccia, J. Accuracy, precision, and method detection limits of quantitative PCR for airborne bacteria and fungi. Appl Environ. Microbiol 76, 7004–7012 (2010).CAS 

    Google Scholar 
    Nieto-Caballero, M., Savage, N., Keady, P. & Hernandez, M. High fidelity recovery of airborne microbial genetic materials by direct condensation capture into genomic preservatives. J. Microbiological Methods 157, 1–3 (2019).CAS 

    Google Scholar 
    Behzad, H., Gojobori, T. & Mineta, K. Challenges and opportunities of airborne metagenomics. Genome Biol. Evol. 7, 1216–1226 (2015).CAS 

    Google Scholar 
    Šantl-Temkiv, T., Gosewinkel, U., Starnawski, P., Lever, M. & Finster, K. Aeolian dispersal of bacteria in southwest Greenland: their sources, abundance, diversity and physiological states. FEMS Microbiol. Ecol. 94 (2018) https://doi.org/10.1093/femsec/fiy031.Klein, A. M., Bohannan, B. J. M., Jaffe, D. A., Levin, D. A. & Green, J. L. Molecular evidence for metabolically active bacteria in the atmosphere. Front. Microbiol. 7, 772–772 (2016).
    Google Scholar 
    Amato, P. et al. Active microorganisms thrive among extremely diverse communities in cloud water. PLoS One 12, e0182869 (2017).
    Google Scholar 
    Vellend, B. M. Conceptual synthesis in community ecology. Q. Rev. Biol. 85, 183–206 (2010).
    Google Scholar 
    Bodenheimer, S., Lensky, I. M. & Dayan, U. Characterization of Eastern Mediterranean dust storms by area of origin; North Africa vs. Arabian Peninsula. Atmos. Environ. 198, 158–165 (2019).CAS 

    Google Scholar 
    Kishcha, P., Volpov, E., Starobinets, B., Alpert, P. & Nickovic, S. Dust dry deposition over Israel. Atmosphere 11, 197 (2020).
    Google Scholar 
    Krasnov, H., Katra, I. & Friger, M. Increase in dust storm related PM10 concentrations: A time series analysis of 2001-2015. Environ. Pollut. 213, 36–42 (2016).CAS 

    Google Scholar 
    Zittis, G. et al. Climate change and weather extremes in the eastern Mediterranean and Middle East. Rev. Geophysics 60, e2021RG000762 (2022).
    Google Scholar 
    Griffin, D. W. Atmospheric movement of microorganisms in clouds of desert dust and implications for human health. Clin. Microbiol Rev. 20, 459–477 (2007).
    Google Scholar 
    Prospero, J. M. Long-range transport of mineral dust in the global atmosphere: Impact of African dust on the environment of the southeastern United States. Proc. Natl. Acad. Sci. 96, 3396–3403 (1999).CAS 

    Google Scholar 
    Klingmüller, K., Pozzer, A., Metzger, S., Stenchikov, G. L. & Lelieveld, J. Aerosol optical depth trend over the Middle East. Atmos. Chem. Phys. 16, 5063–5073 (2016).
    Google Scholar 
    Notaro, M., Alkolibi, F., Fadda, E. & Bakhrjy, F. Trajectory analysis of Saudi Arabian dust storms. J. Geophys. Res. Atmospheres 118, 6028–6043 (2013).
    Google Scholar 
    Tegen, I. & Schepanski, K. The global distribution of mineral dust. IOP Conf. Ser. Earth Environ. Sci. 7, 012001 (2009).
    Google Scholar 
    Klappenbach, J. A., Saxman, P. R., Cole, J. R. & Schmidt, T. M. rrndb: the Ribosomal RNA Operon Copy Number Database. Nucleic Acids Res. 29, 181–184 (2001).CAS 

    Google Scholar 
    Bremer, H. & Dennis, P. P. Modulation of chemical composition and other parameters of the cell at different exponential growth rates. EcoSal Plus 3 (2008) https://doi.org/10.1128/ecosal.5.2.3.Schneider, D. A., Ross, W. & Gourse, R. L. Control of rRNA expression in Escherichia coli. Curr. Opin. Microbiol 6, 151–156 (2003).CAS 

    Google Scholar 
    Gralla, J. D. Escherichia coli ribosomal RNA transcription: regulatory roles for ppGpp, NTPs, architectural proteins and a polymerase-binding protein. Mol. Microbiol 55, 973–977 (2005).CAS 

    Google Scholar 
    Oliveira, A. et al. Insight of genus corynebacterium: ascertaining the role of pathogenic and non-pathogenic species. Front. Microbiol. 8, 1937–1937 (2017).
    Google Scholar 
    Wexler, H. M. Bacteroides: the good, the bad, and the nitty-gritty. Clin. Microbiol Rev. 20, 593–621 (2007).CAS 

    Google Scholar 
    Duar, R. M. et al. Lifestyles in transition: evolution and natural history of the genus Lactobacillus. FEMS Microbiol. Rev. 41, S27–S48 (2017).
    Google Scholar 
    Magzal, F. et al. Increased physical activity improves gut microbiota composition and reduces short-chain fatty acid concentrations in older adults with insomnia. Sci. Rep. 12, 2265 (2022).CAS 

    Google Scholar 
    Wang, L. et al. Increased abundance of Sutterella spp. and Ruminococcus torques in feces of children with autism spectrum disorder. Mol. Autism 4, 42 (2013).CAS 

    Google Scholar 
    Tavella, T. et al. Elevated gut microbiome abundance of Christensenellaceae, Porphyromonadaceae and Rikenellaceae is associated with reduced visceral adipose tissue and healthier metabolic profile in Italian elderly. Gut microbes 13, 1–19 (2021).
    Google Scholar 
    Bennur, T., Kumar, A. R., Zinjarde, S. & Javdekar, V. Nocardiopsis species: Incidence, ecological roles and adaptations. Microbiological Res. 174, 33–47 (2015).
    Google Scholar 
    Jones, S. E. & Elliot, M. A. Streptomyces exploration: competition, volatile communication and new bacterial behaviours. Trends Microbiol. 25, 522–531 (2017).CAS 

    Google Scholar 
    Gtari, M. et al. Contrasted resistance of stone-dwelling Geodermatophilaceae species to stresses known to give rise to reactive oxygen species. FEMS Microbiol. Ecol. 80, 566–577 (2012).CAS 

    Google Scholar 
    Weon, H.-Y. et al. Adhaeribacter aerophilus sp. nov., Adhaeribacter aerolatus sp. nov. and Segetibacter aerophilus sp. nov., isolated from air samples. Int. J. Syst. Evolut. Microbiol. 60, 2424–2429 (2010).CAS 

    Google Scholar 
    Marín, I. et al.) 115-133 (Springer Berlin Heidelberg, 2014).Yoon, J.-H. et al.) 1099-1113 (Springer New York, 2006).Steinberg, J. P. & Burd, E. M. in Mandell, Douglas, and Bennett’s Principles and Practice of Infectious Diseases (Eighth Edition) (eds John E. Bennett, R. Dolin, & M. J. Blaser) 2667-2683.e2664 (W.B. Saunders, 2015).Kelly, D. P., et al.) 232-249 (Springer New York, 2006).Silby, M. W., Winstanley, C., Godfrey, S. A. C., Levy, S. B. & Jackson, R. W. Pseudomonas genomes: diverse and adaptable. FEMS Microbiol. Rev. 35, 652–680 (2011).CAS 

    Google Scholar 
    Hyeon, J. W. & Jeon, C. O. Roseomonas aerofrigidensis sp. nov., isolated from an air conditioner. Int. J. Syst. Evolut. Microbiol. 67, 4039–4044 (2017).CAS 

    Google Scholar 
    Battista, J. R. & Rainey, F. A. in Bergey’s Manual of Systematics of Archaea and Bacteria 1-13.Angly, F. E. et al. Marine microbial communities of the Great Barrier Reef lagoon are influenced by riverine floodwaters and seasonal weather events. PeerJ 4, e1511 (2016).
    Google Scholar 
    Cárdenas, A., Rodriguez-R, L. M., Pizarro, V., Cadavid, L. F. & Arévalo-Ferro, C. Shifts in bacterial communities of two caribbean reef-building coral species affected by white plague disease. ISME J. 6, 502–512 (2012).
    Google Scholar 
    Kämpfer, P., Lodders, N., Huber, B., Falsen, E. & Busse, H. J. Deinococcus aquatilis sp. nov., isolated from water. Int J. Syst. Evol. Microbiol 58, 2803–2806 (2008).
    Google Scholar 
    Gallego, V., Sánchez-Porro, C., García, M. T. & Ventosa, A. Roseomonas aquatica sp. nov., isolated from drinking water. Int J. Syst. Evol. Microbiol 56, 2291–2295 (2006).CAS 

    Google Scholar 
    Roskin, J., Katra, I. & Blumberg, D. G. Particle-size fractionation of eolian sand along the Sinai–Negev erg of Egypt and Israel. GSA Bull. 126, 47–65 (2014).
    Google Scholar 
    Ganor, E. & Foner, H. A. Mineral dust concentrations, deposition fluxes and deposition velocities in dust episodes over Israel. J. Geophys. Res.: Atmospheres 106, 18431–18437 (2001).CAS 

    Google Scholar 
    Amir, A., Ozel, E., Haberman, Y. & Shental, N. Achieving pan-microbiome biological insights via the dbBact knowledge base. bioRxiv, 2022.2002.2027.482174 (2022) https://doi.org/10.1101/2022.02.27.482174.Eisenhofer, R. et al. Contamination in low microbial biomass microbiome studies: issues and recommendations. Trends Microbiol. 27, 105–117 (2019).CAS 

    Google Scholar 
    Labeda, D. P. & Goodfellow, M. in Bergey’s Manual of Systematics of Archaea and Bacteria 1-7.Rickard, A. H. et al. Adhaeribacter aquaticus gen. nov., sp. nov., a Gram-negative isolate from a potable water biofilm. Int J. Syst. Evol. Microbiol 55, 821–829 (2005).CAS 

    Google Scholar 
    Guo, L. et al. Oligotrophic bacterium Hymenobacter latericoloratus CGMCC 16346 degrades the neonicotinoid imidacloprid in surface water. AMB Express 10, 7 (2020).CAS 

    Google Scholar 
    Philippon, T. et al. Denitrifying bio-cathodes developed from constructed wetland sediments exhibit electroactive nitrate reducing biofilms dominated by the genera Azoarcus and Pontibacter. Bioelectrochemistry 140, 107819 (2021).CAS 

    Google Scholar 
    Jurado, V., Miller, A. Z., Alias-Villegas, C., Laiz, L. & Saiz-Jimenez, C. Rubrobacter bracarensis sp. nov., a novel member of the genus Rubrobacter isolated from a biodeteriorated monument. Syst. Appl Microbiol 35, 306–309 (2012).CAS 

    Google Scholar 
    de Vries, H. J. et al. Isolation and characterization of Sphingomonadaceae from fouled membranes. npj Biofilms Microbiomes 5, 6 (2019).
    Google Scholar 
    Vacca, M. et al. The Controversial Role of Human Gut Lachnospiraceae. Microorganisms 8, 573 (2020).CAS 

    Google Scholar 
    Baldani, J. I. et al. in The Prokaryotes: Alphaproteobacteria and Betaproteobacteria (eds Eugene Rosenberg et al.) 919-974 (Springer Berlin Heidelberg, 2014).Dastager, S. G., et al.) 455-498 (Springer Berlin Heidelberg, 2014).Ivanova, N. et al. Complete genome sequence of Geodermatophilus obscurus type strain (G-20). Stand Genom. Sci. 2, 158–167 (2010).
    Google Scholar 
    Alonso-Reyes, D. et al. Genomic Insights of an Andean Multi-resistant Soil Actinobacterium of Biotechnological Interest. bioRxiv, 2020.2012.2021.423370 (2020) https://doi.org/10.1101/2020.12.21.423370.Kumar, C. G. & Sujitha, P. Kocuran, an exopolysaccharide isolated from Kocuria rosea strain BS-1 and evaluation of its in vitro immunosuppression activities. Enzym. Micro. Technol. 55, 113–120 (2014).CAS 

    Google Scholar 
    Raguénès, G. et al. A novel exopolymer-producing bacterium, Paracoccus zeaxanthinifaciens subsp. payriae, isolated from a “kopara” mat located in Rangiroa, an atoll of French Polynesia. Curr. Microbiol 49, 145–151 (2004).
    Google Scholar 
    Bailey, A. C. et al. Draft Genome Sequence of Massilia sp. Strain BSC265, Isolated from Biological Soil Crust of Moab, Utah. Genome Announc 2, e01199–01114 (2014).
    Google Scholar 
    Denef, V. J., Fujimoto, M., Berry, M. A. & Schmidt, M. L. Seasonal succession leads to habitat-dependent differentiation in ribosomal RNA:DNA Ratios among freshwater lake bacteria. Front Microbiol 7, 606 (2016).
    Google Scholar 
    Salazar, G. et al. Particle-association lifestyle is a phylogenetically conserved trait in bathypelagic prokaryotes. Mol. Ecol. 24, 5692–5706 (2015).
    Google Scholar 
    Schmidt, M. L., White, J. D. & Denef, V. J. Phylogenetic conservation of freshwater lake habitat preference varies between abundant bacterioplankton phyla. Environ. Microbiol. 18, 1212–1226 (2016).
    Google Scholar 
    Stepanauskas, R. et al. Improved genome recovery and integrated cell-size analyses of individual uncultured microbial cells and viral particles. Nat. Commun. 8, 84 (2017).
    Google Scholar 
    Doughari, H. J., Ndakidemi, P. A., Human, I. S. & Benade, S. The ecology, biology and pathogenesis of Acinetobacter spp.: an overview. Microbes Environ. 26, 101–112 (2011).
    Google Scholar 
    Bläckberg, A., Falk, L., Oldberg, K., Olaison, L. & Rasmussen, M. infective endocarditis due to corynebacterium species: clinical features and antibiotic resistance. Open Forum Infect. Dis. 8 (2021) https://doi.org/10.1093/ofid/ofab055.Zhang, Q. et al. Hymenobacter xinjiangensis sp. nov., a radiation-resistant bacterium isolated from the desert of Xinjiang, China. Int J. Syst. Evol. Microbiol 57, 1752–1756 (2007).CAS 

    Google Scholar 
    Lee, J.-J. et al. Hymenobacter aquaticus sp. nov., a radiation-resistant bacterium isolated from a river. Int. J. Syst. Evolut. Microbiol. 67, 1206–1211 (2017).CAS 

    Google Scholar 
    Alessa, O. et al. Comprehensive comparative genomics and phenotyping of methylobacterium species. Front. Microbiol. 12 (2021) https://doi.org/10.3389/fmicb.2021.740610.Titécat, M., Wallet, F., Vieillard, M. H., Courcol, R. J. & Loïez, C. Ruminococcus gnavus: an unusual pathogen in septic arthritis. Anaerobe 30, 159–160 (2014).
    Google Scholar 
    Weber, B. S., Harding, C. M. & Feldman, M. F. Pathogenic acinetobacter: from the cell surface to infinity and beyond. J. Bacteriol. 198, 880–887 (2015).
    Google Scholar 
    Hacker, E., Antunes, C. A., Mattos-Guaraldi, A. L., Burkovski, A. & Tauch, A. Corynebacterium ulcerans, an emerging human pathogen. Future Microbiol. 11, 1191–1208 (2016).CAS 

    Google Scholar 
    Smith, K. F. & Oram, D. M. in Encyclopedia of Microbiology (Third Edition) (ed Moselio Schaechter) 94-106 (Academic Press, 2009).Kovaleva, J., Degener, J. E. & van der Mei, H. C. Methylobacterium and its role in health care-associated infection. J. Clin. Microbiol 52, 1317–1321 (2014).
    Google Scholar 
    Dyer, J. & Harris, P. Paracoccus yeei—An emerging pathogen or incidental finding? Pathology 52, S123 (2020).
    Google Scholar 
    Moradali, M. F., Ghods, S. & Rehm, B. H. A. Pseudomonas aeruginosa lifestyle: a paradigm for adaptation, survival, and persistence. Front. Cellular Infect. Microbiol. 7 (2017) https://doi.org/10.3389/fcimb.2017.00039.Ryan, M. P. & Adley, C. C. Sphingomonas paucimobilis: a persistent Gram-negative nosocomial infectious organism. J. Hosp. Infect. 75, 153–157 (2010).CAS 

    Google Scholar 
    Souto, A., Guinda, M., Mera, A. & Pardo, F. Septic arthritis caused by Sphingomonas paucimobilis in an immunocompetent patient. Reumatol. Clin. 8, 378–379 (2012).
    Google Scholar 
    Lanoix, J. P. et al. Sphingomonas paucimobilis bacteremia related to intravenous human immunoglobulin injections. Med Mal. Infect. 42, 37–39 (2012).
    Google Scholar 
    van Bruggen, A. H., Brown, P. R. & Jochimsen, K. N. Corky root of lettuce caused by strains of a gram-negative bacterium from muck soils of Florida, new york, and wisconsin. Appl Environ. Microbiol 55, 2635–2640 (1989).
    Google Scholar 
    VAN BRUGGEN, A. H. C., JOCHIMSEN, K. N. & BROWN, P. R. Rhizomonas suberifaciens gen. nov., sp. nov., the Causal Agent of Corky Root of Lettuce. Int. J. Syst. Evolut. Microbiol. 40, 175–188 (1990).
    Google Scholar 
    Davis, J. H. & Williamson, J. R. Structure and dynamics of bacterial ribosome biogenesis. Philos. Trans. R Soc. Lond. B Biol. Sci. 372 (2017) https://doi.org/10.1098/rstb.2016.0181.Maitra, A. & Dill, K. A. Bacterial growth laws reflect the evolutionary importance of energy efficiency. Proc. Natl Acad. Sci. 112, 406–411 (2015).CAS 

    Google Scholar 
    Klumpp, S. & Hwa, T. Traffic patrol in the transcription of ribosomal RNA. RNA Biol. 6, 392–394 (2009).CAS 

    Google Scholar 
    Jia, Y. et al. Rare taxa exhibit disproportionate cell-level metabolic activity in enriched anaerobic digestion microbial communities. mSystems 4, e00208–e00218 (2019).CAS 

    Google Scholar 
    Zhou, Y. et al. Profiling airborne microbiota in mechanically ventilated buildings across seasons in hong kong reveals higher metabolic activity in low-abundance bacteria. Environ. Sci. Technol. 55, 249–259 (2021).CAS 

    Google Scholar 
    Fessler, M., Gummesson, B., Charbon, G., Svenningsen, S. L. & Sørensen, M. A. Short-term kinetics of rRNA degradation in Escherichia coli upon starvation for carbon, amino acid or phosphate. Mol. Microbiol. 113, 951–963 (2020).CAS 

    Google Scholar 
    Lahtinen, S. J. et al. Degradation of 16S rRNA and attributes of viability of viable but nonculturable probiotic bacteria. Lett. Appl Microbiol 46, 693–698 (2008).CAS 

    Google Scholar 
    Li, R. et al. Comparison of DNA-, PMA-, and RNA-based 16S rRNA Illumina sequencing for detection of live bacteria in water. Sci. Rep. 7, 5752 (2017).
    Google Scholar 
    McKillip, J. L., Jaykus, L. A. & Drake, M. rRNA stability in heat-killed and UV-irradiated enterotoxigenic Staphylococcus aureus and Escherichia coli O157:H7. Appl Environ. Microbiol 64, 4264–4268 (1998).CAS 

    Google Scholar 
    Sheridan, G. E., Masters, C. I., Shallcross, J. A. & MacKey, B. M. Detection of mRNA by reverse transcription-PCR as an indicator of viability in Escherichia coli cells. Appl. Environ. Microbiol. 64, 1313–1318 (1998).CAS 

    Google Scholar 
    Villarino, A., Bouvet, O. M., Regnault, B., Martin-Delautre, S. & Grimont, P. A. D. Exploring the frontier between life and death in Escherichia coli: evaluation of different viability markers in live and heat- or UV-killed cells. Res Microbiol 151, 755–768 (2000).CAS 

    Google Scholar 
    Schostag, M. D., Albers, C. N., Jacobsen, C. S. & Priemé, A. Low turnover of soil bacterial rRNA at low temperatures. Front. Microbiol. 11 (2020) https://doi.org/10.3389/fmicb.2020.00962.Emerson, J. B. et al. Schrödinger’s microbes: Tools for distinguishing the living from the dead in microbial ecosystems. Microbiome 5, 86 (2017).
    Google Scholar 
    Wang, Y. et al. Characterizing microbial community viability with RNA-based high-throughput sequencing. Microbiome Version 1, posted 22 Jul, 2022 (2022) https://doi.org/10.21203/rs.3.rs-1870950/v1.Mbareche, H., Veillette, M., Bilodeau, G. J., Duchaine, C. & Schaffner, D. W. Bioaerosol sampler choice should consider efficiency and ability of samplers to cover microbial diversity. Appl. Environ. Microbiol. 84, e01589–01518 (2018).CAS 

    Google Scholar 
    Pan, M., Lednicky, J. A. & Wu, C.-Y. Collection, particle sizing and detection of airborne viruses. J. Appl. Microbiol. 127, 1596–1611 (2019).CAS 

    Google Scholar 
    Nieto-Caballero, M., Savage, N., Keady, P. & Hernandez, M. High fidelity recovery of airborne microbial genetic materials by direct condensation capture into genomic preservatives. J. Microbiol Methods 157, 1–3 (2019).CAS 

    Google Scholar 
    Šantl-Temkiv, T., Gosewinkel, U., Starnawski, P., Lever, M. & Finster, K. Aeolian dispersal of bacteria in southwest Greenland: their sources, abundance, diversity and physiological states. FEMS Microbiol Ecol 94 (2018) https://doi.org/10.1093/femsec/fiy031.Maki, T. et al. Aeolian dispersal of bacteria associated with desert dust and anthropogenic particles over continental and oceanic surfaces. J. Geophys. Res.: Atmospheres 124, 5579–5588 (2019).
    Google Scholar 
    Gonzalez-Martin, C., Teigell-Perez, N., Valladares, B. & Griffin, D. W. in Advances in Agronomy Vol. 127 (ed Donald Sparks) 1-41 (Academic Press, 2014).Tisch Environmental, I. (2004).Krasnov, H., Katra, I. & Friger, M. Increase in dust storm related PM10 concentrations: A time series analysis of 2001–2015. Environ. Pollut. 213, 36–42 (2016).CAS 

    Google Scholar 
    Varga, G., Újvári, G. & Kovács, J. Spatiotemporal patterns of Saharan dust outbreaks in the Mediterranean Basin. Aeolian Res. 15, 151–160 (2014).
    Google Scholar 
    Dayan, U. & Levy, I. Relationship between synoptic-scale atmospheric circulation and ozone concentrations over Israel. J. Geophys. Res.: Atmospheres 107, ACL 31-31–ACL 31-12 (2002).
    Google Scholar 
    Klein, A. M., Bohannan, B. J. M., Jaffe, D. A., Levin, D. A. & Green, J. L. Molecular Evidence for Metabolically Active Bacteria in the Atmosphere. Front. Microbiol. 7 (2016) https://doi.org/10.3389/fmicb.2016.00772.Luhung, I. et al. Experimental parameters defining ultra-low biomass bioaerosol analysis. npj Biofilms Microbiomes 7, 37 (2021).CAS 

    Google Scholar 
    Stein, A. F. et al. Noaa’s Hysplit Atmospheric Transport and Dispersion Modeling System. Bull. Am. Meteorological Soc. 96, 2059–2077 (2015).
    Google Scholar 
    Rolph, G., Stein, A. & Stunder, B. Real-time Environmental Applications and Display sYstem: READY. Environ. Model. Softw. 95, 210–228 (2017).
    Google Scholar 
    Acker, J. G. & Leptoukh, G. Online analysis enhances use of NASA Earth science data. Eos, Trans. Am. Geophys. Union 88, 14–17 (2007).
    Google Scholar 
    Brauer, S. L. et al. Culturable Rhodobacter and Shewanella species are abundant in estuarine turbidity maxima of the Columbia River. Environ. Microbiol. 13, 589–603 (2011).CAS 

    Google Scholar 
    Caporaso, J. G. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. Isme J. 6, 1621–1624 (2012).CAS 

    Google Scholar 
    Soergel, D. A. W., Dey, N., Knight, R. & Brenner, S. E. Selection of primers for optimal taxonomic classification of environmental 16S rRNA gene sequences. ISME J. 6, 1440–1444 (2012).CAS 

    Google Scholar 
    Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581 (2016).CAS 

    Google Scholar 
    McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8, e61217 (2013).CAS 

    Google Scholar 
    Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).CAS 

    Google Scholar 
    Davis, N. M., Proctor, D. M., Holmes, S. P., Relman, D. A. & Callahan, B. J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6, 226 (2018).
    Google Scholar 
    Martin-Fernandez, J. A., Hron, K., Templ, M., Filzmoser, P. & Palarea-Albaladejo, J. Bayesian-multiplicative treatment of count zeros in compositional data sets. Stat. Model. 15 (2015) https://doi.org/10.1177/1471082×14535524.Palarea-Albaladejo, J. & Martin-Fernandez, J. A. zCompositions—R Package for multivariate imputation of left-censored data under a compositional approach. Chemometrics Intell. Lab. Syst. 143, 85–96 (2015).CAS 

    Google Scholar 
    van den Boogaart, K. G. & Tolosana-Delgado, R. “compositions”: A unified R package to analyze compositional data. Comput. Geosci. 34, 320–338 (2008).
    Google Scholar 
    Amato, P. et al. In Microbiology of Aerosols 1–21 (2017).Rao, A. K. & Whitby, K. T. Nonideal collection characteristics of single stage and cascade impactors. Am. Ind. Hyg. Assoc. J. 38, 174–179 (1977).CAS 

    Google Scholar 
    Jari Oksanen, F. G. B. et al. vegan: Community Ecology Package. (2020).Gilmour, S. G. In Wiley StatsRef: Statistics Reference Online.Margolin, B. H. In Wiley StatsRef: Statistics Reference Online.Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B (Methodol.) 57, 289–300 (1995).
    Google Scholar 
    Mallick, H. et al. Multivariable association discovery in population-scale meta-omics studies. PLOS Comput. Biol. 17, e1009442 (2021).CAS 

    Google Scholar  More

  • in

    Shifts in vegetation activity of terrestrial ecosystems attributable to climate trends

    Plant growth model without environmental forcingThe model without environmental forcing closely follows the original description of the Thornley transport resistance (TTR) model29. A summary of the model parameters is provided in Supplementary Table 2. The shoot and root mass pools (MS and MR, in kg structural dry matter) change as a function of growth and loss (equations (1) and (2)). The litter (kL) and maintenance respiration (r) loss rates (in kg kg−1 d−1) are treated as constants. In the original model description29 r = 0. The parameter KM (units kg) describes how loss varies with mass (MS or MR). Growth (Gs and Gr, in kg d−1) varies as a function of the carbon and nitrogen concentrations (equations (3) and (4)). CS, CR, NS and NR are the amounts (kg) of carbon and nitrogen in the roots and shoots. These assumptions yield the following equations for shoot and root dry matter,$${mathrm{MS}}[t+1]={mathrm{MS}}[t]+{G}_{{mathrm{S}}}[t]-frac{({k}_{{mathrm{L}}}+r){mathrm{MS}}[t]}{1+frac{{K}_{{{M}}}}{{mathrm{MS}}[t]}},$$
    (1)
    $${mathrm{MR}}[t+1]={mathrm{MR}}[t]+{G}_{{mathrm{R}}}[t]-frac{({k}_{{mathrm{L}}}+r){mathrm{MR}}[t]}{1+frac{{K}_{{{M}}}}{{mathrm{MR}}[t]}},$$
    (2)
    where GS and GR are$${G}_{{mathrm{S}}}=gfrac{{mathrm{CS}}times {mathrm{NS}}}{{mathrm{MS}}},$$
    (3)
    $${G}_{{mathrm{R}}}=gfrac{{mathrm{CR}}times {mathrm{NR}}}{{mathrm{MR}}},$$
    (4)
    and g is the growth coefficient (in kg kg−1 d−1).Carbon uptake UC is determined by the net photosynthetic rate (a, in kg kg−1 d−1) and the shoot mass (equation (5)). Similarly, nitrogen uptake (UN) is determined by the nitrogen uptake rate (b, in kg kg−1 d−1) and the root mass. The parameter KA (units kg) forces both photosynthesis and nitrogen uptake to be asymptotic with mass. The second terms in the denominators of equations (5) and (6) model product inhibitions of carbon and nitrogen uptake, respectively; that is, the parameters JC and JN (in kg kg−1) mimic the inhibition of source activity when substrate concentrations are high,$${U}_{{mathrm{C}}}=frac{a{mathrm{MS}}}{left(1+frac{{mathrm{MS}}}{{K}_{{mathrm{A}}}}right)left(1+frac{{mathrm{CS}}}{{mathrm{MS}}times {J}_{{mathrm{C}}}}right)},$$
    (5)
    $${U}_{{mathrm{N}}}=frac{b{mathrm{MR}}}{left(1+frac{{mathrm{MR}}}{{K}_{{mathrm{A}}}}right)left(1+frac{{mathrm{NR}}}{{mathrm{MR}}times {J}_{{mathrm{N}}}}right)}.$$
    (6)
    The substrate transport fluxes of C and N (τC and τN, in kg d−1) between roots and shoots are determined by the concentration gradients between root and shoot and by the resistances. In the original model description29, these resistances are defined flexibly, but we simplify and assume that they scale linearly with plant mass,$${tau }_{{mathrm{C}}}=frac{{mathrm{MS}}times {mathrm{MR}}}{{mathrm{MS}}+{mathrm{MR}}}left(frac{{mathrm{CS}}}{{mathrm{MS}}}-frac{{mathrm{CR}}}{{mathrm{MR}}}right)$$
    (7)
    $${tau }_{{mathrm{N}}}=frac{{mathrm{MS}}times {mathrm{MR}}}{{mathrm{MS}}+{mathrm{MR}}}left(frac{{mathrm{NR}}}{{mathrm{MR}}}-frac{{mathrm{NS}}}{{mathrm{MS}}}right)$$
    (8)
    The changes in mass of carbon and nitrogen in the roots and shoots are then$${mathrm{CS}}[t+1]={mathrm{CS}}[t]+{U}_{{mathrm{C}}}[t]-{f}_{{mathrm{C}}}{G}_{{mathrm{s}}}[t]-{tau }_{{mathrm{C}}}[t]$$
    (9)
    $${mathrm{CR}}[t+1]={mathrm{CR}}[t]+{tau }_{{mathrm{C}}}[t]-{f}_{{mathrm{C}}}{G}_{{mathrm{r}}}[t]$$
    (10)
    $${mathrm{NS}}[t+1]={mathrm{NS}}[t]+{tau }_{{mathrm{N}}}[t]-{f}_{{mathrm{N}}}{G}_{{mathrm{s}}}[t]$$
    (11)
    $${mathrm{NR}}[t+1]={mathrm{NR}}[t]+{U}_{{mathrm{N}}}[t]-{f}_{{mathrm{N}}}{G}_{{mathrm{r}}}[t]-{tau }_{{mathrm{N}}}[t]$$
    (12)
    where fC and fN (in kg kg−1) are the fractions of structural carbon and nitrogen in dry matter.Adding environmental forcing to the plant growth modelIn this section, we describe how the net photosynthetic rate (a), the nitrogen uptake rate (b), the growth rate (g) and the respiration rate (r) are influenced by environmental-forcing factors. These environmental-forcing effects are described in equations (13)–(17) and summarized graphically in Extended Data Fig. 1. All other model parameters are treated as constants. Previous work that implemented the TTR model as a species distribution model30 is used as a starting point for adding environmental forcing. As in this previous work30, we assume that parameters a, b and g are co-limited by environmental factors in a manner analogous to Liebig’s law of the minimum, which is a crude but pragmatic abstraction. The implementation here differs in some details.Unlike previous work30, we use the Farquhar model of photosynthesis47,48 to represent how solar radiation, atmospheric CO2 concentration and air temperature co-limit photosynthesis35. We assume that the Farquhar model parameters are universal and that all vegetation in our study uses the C3 photosynthetic pathway. The Farquhar model photosynthetic rates are rescaled to [0,amax] to yield afqr. The effects of soil moisture (Msoil) on photosynthesis are represented as an increasing step function ({{{{S}}}}(M_{mathrm{soil}},{beta }_{1},{beta }_{2})=max left{min left(frac{M_{mathrm{soil}}-{beta }_{1}}{{beta }_{2}-{beta }_{1}},1right),0right}). This allows us to redefine a as,$$a={a}_{{mathrm{fqr}}} {{{{S}}}}(M_{mathrm{soil}},{beta }_{1},{beta }_{2})$$
    (13)
    The processes influencing nitrogen availability are complex, and global data products on plant available nitrogen are uncertain. We therefore assume that nitrogen uptake will vary with soil temperature and soil moisture. That is, the nitrogen uptake rate b is assumed to have a maximum rate (bmax) that is co-limited by soil temperature Tsoil and soil moisture Msoil,$$b={b}_{{mathrm{max}}} {{{{S}}}}({T}_{soil},{beta }_{3},{beta }_{4}) {{{{Z}}}}(M_{mathrm{soil}},{beta }_{5},{beta }_{6},{beta }_{7},{beta }_{8}).$$
    (14)
    In equation (14), we have assumed that the nitrogen uptake rate is a simple increasing and saturating function of temperature. We have also assumed that the nitrogen uptake rate is a trapezoidal function of soil moisture with low uptake rates in dry soils, higher uptake rates at intermediate moisture levels and lower rates once soils are so moist as to be waterlogged. The trapezoidal function is ({{{{Z}}}}(M_{mathrm{soil}},{beta }_{5},{beta }_{6},{beta }_{7},{beta }_{8})=max left{min left(frac{M_{mathrm{soil}}-{{{{{beta }}}}}_{5}}{{{{{{beta }}}}}_{6}-{{{{{beta }}}}}_{5}},1,frac{{{{{{beta }}}}}_{8}-M_{mathrm{soil}}}{{beta }_{8}-{beta }_{7}}right),0right}).The previous sections describe how the assimilation of carbon and nitrogen by a plant are influenced by environmental factors. The TTR model describes how these assimilate concentrations influence growth (equations (3) and (4)). In our implementation, we additionally allow the growth rate to be co-limited by temperature (soil temperature, Tsoil) and soil moisture (Msoil),$$g={g}_{{mathrm{max}}} {{{{Z}}}}({T}_{{mathrm{soil}}},{beta }_{9},{beta }_{10},{beta }_{11},{beta }_{12}) {{{{S}}}}(M_{mathrm{soil}},{beta }_{13},{beta }_{14}).$$
    (15)
    We use Tsoil since we assume that growth is more closely linked to soil temperature, which varies slower than air temperature. The respiration rate (r, equations (1) and (2)) increases as a function of air temperature (Tair) to a maximum rmax,$$r={r}_{{mathrm{max}}}{{{{S}}}}({T}_{{mathrm{air}}},{beta }_{15},{beta }_{16}).$$
    (16)
    The parameter r is best interpreted as a maintenance respiration. Growth respiration is not explicitly considered; it is implicitly incorporated in the growth rate parameter (g, equation (15)), and any temperature dependence in growth respiration is therefore assumed to be accommodated by equation (15).Fire can reduce the structural shoot mass MS as follows,$${mathrm{MS}}[t+1]={mathrm{MS}}[t](1-{{{{S}}}}(F,{beta }_{17},{beta }_{18})).$$
    (17)
    where F is an indicator of fire severity at a point in time (for example, burnt area) and the function S(F, β17, β18) allows MS to decrease when the fire severity indicator F is high. If F = 0, this process plays no role. This fire impact equation was used in preliminary analyses, but the data on fire activity did not provide sufficient information to estimate β17 and β18; we therefore excluded this process from the final analyses.We further estimate two additional β parameters (βa and βb) so that each site can have unique maximum carbon and nitrogen uptake rates. Specifically, we redefine a as ({a}^{{prime} }={beta }_{a} a) and b as ({b}^{{prime} }={beta }_{b} b).Data sources and preparationTo describe vegetation activity, we use the GIMMS 3g v.1 NDVI data26,27 and the MODIS EVI28 data. The GIMMS data product has been derived from the AVHRR satellite programme and controls for atmospheric contamination, calibration loss, orbital drift and volcanic eruptions26,27. The data provide 24 NDVI raster grids for each year, starting in July 1981 and ending in December 2015. The spatial resolution is 1/12° (~9 × 9 km). The EVI data used are from the MODIS programme’s Terra satellite; it is a 1 km data product provided at a 16-day interval. We use data from the start of the record (February 2000) to December 2019. The MODIS data product (MOD13A2) uses a temporal compositing algorithm to produce an estimate every 16 days that filters out atmospheric contamination. The EVI is designed to reduce the effects of atmospheric, bare-ground and surface water on the vegetation index28.For environmental forcing, we use the ERA5-Land data31,32 (European Centre for Medium-Range Weather Forecasts Reanalysis v. 5; hereafter, ERA5). The ERA5 products are global reanalysis products based on hourly estimates of atmospheric variables and extend from present back to 1979. The data products are supplied at a variety of spatial and temporal resolutions. We used the monthly averages from 1981 to 2019 at a 0.1° spatial resolution (~11 km). The ERA5 data provide air temperature (2 m surface air temperature), soil temperature (0–7 cm soil depth), surface solar radiation and volumetric soil water (0–7 cm soil depth). Fire data were taken from the European Space Agency Fire Disturbance Climate Change Initiative’s AVHRR Long-Term Data Record Grid v.1.0 product49. This product provides gridded (0.25° resolution) data of monthly global (from 1982 to 2017) burned area derived from the AVHRR satellite programme. As mentioned, the fire data did not enrich our analysis, and the analyses we present here therefore exclude further consideration of the fire data.All data were resampled to the GIMMS grid. The mean pixel EVI was then calculated for each GIMMS cell for each time point in the MODIS EVI data. We used linear interpolation on the NDVI, EVI and ERA5 environmental-forcing data to estimate each variable on a weekly time step. This served to set the time step of the TTR difference equations to one week and to synchronize the different time series.Site selectionThe GIMMS grid cells define the spatial resolution of our sample points. GIMMS grid cells are large (1/12°, ~9 km), meaning that most grid cells contain multiple land-cover types. We focused on wilderness landscapes, and our aim was to find multiple grid cells for the major ecosystems of the world. We used the following classification of ecosystem types to guide the stratification of our grid-cell selection: tropical evergreen forest (RF), boreal forest (BF), temperate evergreen and temperate deciduous forest (TF), savannah (SA), shrubland (SH), grassland (GR), tundra (TU) and Mediterranean-type ecosystems (MT).We used the following criteria to select grid cells. (1) Selected grid cells should contain relatively homogeneous vegetation. Small-scale heterogeneity was allowed (for example, catenas, drainage lines, peatlands) as long as many of these elements are repeated in the scene (for example, rolling hills were accepted, but elevation gradients were rejected). (2) Sites should have no signs of transformative human activity (for example, tree harvesting, crop cultivation, paved surfaces). We used the Time Tool in Google Earth Pro, which provides annual satellite imagery of the Earth from 1984 onwards, to ensure that no such activity occurred during the observation period (note that the GIMMS record starts in July 1981; however, it is likely that evidence of transformative activity between July 1981 and 1984 would be visible in 1984). Grid cells with extensive livestock holding on non-improved pasture were included. In some cases, a small agricultural field or pasture was present, and such grid cells were used as long as the field or pasture was small and remained constant in size. (3) Grid cells should not include large water bodies, but small drainage lines or ponds were accepted as long as they did not violate the first criterion. (4) Grid cells should be independent (neighbouring grid cells were not selected) and cover the major ecosystems of the world. Using these criteria, we were able to include 100 sites in the study (Extended Data Figs. 2 and 3 and Supplementary Table 4).State-space modelWe used a Bayesian state-space approach. Conceptually, the analysis takes the form,$$M[t]=f(M[t-1],{boldsymbol{beta}},{boldsymbol{theta}}_{t-1},{epsilon }_{t-1})$$
    (18)
    $${mathrm{VI}}[t]=m M[t]+eta .$$
    (19)
    Here M[t] is the plant growth model’s prediction of biomass (M = MS + MR) at time t, and ϵt−1 is the process error associated with the state variables. In the model, each underlying state variable (MS, MR, CS, CR, NS and NR) has an associated process error term. The function f(M[t − 1], β, θt−1, ϵt−1) summarizes that the development of M is influenced by the state variables MS, MR, CS, CR, NS and NR, the environmental-forcing data θt−1 and the β parameters. The observation equation (equation (19)) uses the parameter m to link the VI (vegetation index, either NDVI or EVI) observations to the modelled state M. The parameter η is the observation error. Equation (19) assumes that there is a linear relationship between modelled biomass (M) and VI, which is a simplification of reality50,51,52. The observation error η is structured by our simplification of the data products quality scores (coded Q = 0, 1, 2, with 0 being good and 2 being poor; Supplementary Table 3) to allow the error to increase with each level of the quality score. Specifically, we define η = e0 + e1 × Q.The model was formulated using the R package LaplacesDemon53. All β parameters are given vague uniform priors. The parameter m is given a vague normal prior (truncated to be >0). The process error terms are modelled using normal distributions, and the variances of the error terms are given vague half-Cauchy priors. The ex parameters are given vague normal priors. The model also requires the parameterization of M[0], the initial vegetation biomass; M[0] is given a vague uniform prior. We used the twalk Markov chain Monte Carlo (MCMC) algorithm as implemented in LaplacesDemon53 and its default control parameters to estimate the posterior distributions of the model parameters. We further fitted the model using DEoptim54,55, which is a robust genetic algorithm that is known to perform stably on high-dimensional and multi-modal problems56, to verify that the MCMC algorithm had not missed important regions of the parameter space. The models estimated with MCMC had significantly lower log root-mean-square error than models estimated with DEoptim (paired t-test NDVI analysis: t = –2.9806, degrees of freedom (d.f.) = 99, P = 0.00362; EVI analysis: t = –4.6229, d.f. = 99, P = 1.144 × 10–5), suggesting that the MCMC algorithm performed well compared with the genetic algorithm.Anomaly extraction and trend estimationWe use the ‘seasonal and trend decomposition using Loess’ (STL57) as implemented in the R58 base function stl. STL extracts the seasonal component s of a time series using Loess smoothing. What remains after seasonal extraction (the anomaly or remainder, r) is the sum of any long-term trend and stochastic variation. We estimate the trend in two ways. First, we estimate the trend by fitting a quadratic polynomial (r = a + bx + cx2) to the remainder (r is the remainder, x is time and a, b and c are regression coefficients). The use of polynomials allows the data to specify whether a trend exists, whether the trend is linear, cup or hat shaped and whether the overall trend is increasing or decreasing. As a second method, we estimate the trend by fitting a bent-cable regression to the remainder. Bent-cable regression is a type of piecewise linear regression for estimating the point of transition between two linear phases in a time series59,60. The model takes the form r = b0 + b1x + b2 q(x, τ, γ)60. Here r is the remainder, x is time, b0 is the initial intercept, b1 is the slope in phase 1, the slope in phase 2 is b2 − b1 and q is a function that defines the change point: (q(x,tau ,gamma )=frac{{(x-tau +gamma )}^{2}}{4gamma }I(tau -gamma < tau +gamma )+(x-tau )I(x > tau +gamma )); τ represents the location of the change point and γ the span of the bent cable that joins the two linear phases; I(A) is an indicator function that returns 1 if A is true and 0 if A is false. The bent-cable model allows the data to specify whether a trend exists and whether there has been a switch in the trend, thereby allowing the identification of whether the trend is linear, cup or hat shaped and whether the overall trend is increasing or decreasing. Both the polynomial and bent-cable models were estimated using LaplacesDemon’s53 Adaptive Metropolis MCMC algorithm and vague priors, although for the bent-cable model we constrained τ to be in the middle 70% of the time series and γ to be at most two years.The STL extraction of the seasonal components in the air temperature, soil temperature, soil moisture and solar radiation data (there is no stochasticity or seasonal trend in the CO2 data we used) allows us to simulate detrended time series d of these forcing variables as (d=bar{y}+s+{{{{N}}}}(mu ,sigma )) where N(μ, σ) is a normally distributed random variable with mean and standard deviation estimated from the remainder r (we verified that r was well described by the normal distribution), (bar{y}) is the mean of the data over the time series and s is the seasonal component extracted by STL. For CO2, the detrended time series is simply the average CO2 over the time series. More

  • in

    Reconciling oil palm and ecosystems

    Oil palm plantations can supplant once biodiverse tropical forests. As planted areas expand, it is vital to plan landscapes to better balance biodiversity and oil palm production. Strategic ‘set-asides’ offer a key approach.In recent decades, oil palm has expanded spectacularly in some of the most biodiverse areas of the tropics, especially in Indonesia and Malaysia. This expansion has caused extensive deforestation (including loss of more than 2.1 million ha of primary forests in Borneo2, as well as other forests and agroforests), and management of plantations often relies heavily on clearing, herbicides and pesticides. This has generated many direct and indirect impacts on wildlife, ecosystems, climate and human communities3. Further expansion is ongoing, and global demand continues to rise4. More

  • in

    Ocean warming and acidification affect the transitional C:N:P ratio and macromolecular accumulation in the harmful raphidophyte Heterosigma akashiwo

    Pachauri, R. K. et al. Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC, 2014).Fu, F. X., Warner, M. E., Zhang, Y., Feng, Y. & Hutchins, D. A. Effects of Increased temperature and CO2 on photosynthesis, growth, and elemental ratios in marine Synechococcus and Prochlorococcus (cyanobacteria) 1. J. Phycol. 43, 485–496 (2007).
    Google Scholar 
    Schippers, P., Lürling, M. & Scheffer, M. Increase of atmospheric CO2 promotes phytoplankton productivity. Ecol. Lett. 7, 446–451 (2004).
    Google Scholar 
    Raven, J. A., Gobler, C. J. & Hansen, P. J. Dynamic CO2 and pH levels in coastal, estuarine, and inland waters: Theoretical and observed effects on harmful algal blooms. Harmful Algae 91, 101594 (2020).CAS 

    Google Scholar 
    Gobler, C. J. et al. Ocean warming since 1982 has expanded the niche of toxic algal blooms in the North Atlantic and North Pacific oceans. Proc. Natl Acad. Sci. 114, 4975–4980 (2017).CAS 

    Google Scholar 
    Rost, B., Richter, K. U., Riebesell, U. & Hansen, P. J. Inorganic carbon acquisition in red tide dinoflagellates. Plant, Cell Environ. I 29, 810–822 (2006).CAS 

    Google Scholar 
    Honjo, T. Harmful Red Tides of Heterosigma akashiwo. NOAA Technical Report NMFS. 111, 27–32 (1992).Rensel, J. J. & Haigh, N. Fraser river sockeye salmon marine survival decline and harmful blooms of Heterosigma akashiwo. Harmful Algae 10, 98–115 (2010).
    Google Scholar 
    Herndon, J. & Cochlan, W. P. Nitrogen utilization by the raphidophyte Heterosigma akashiwo: growth and uptake kinetics in laboratory cultures. Harmful Algae 6, 260–270 (2007).
    Google Scholar 
    Haley, S. T., Alexander, H., Juhl, A. R. & Dyhrman, S. T. Transcriptional response of the harmful raphidophyte Heterosigma akashiwo to nitrate and phosphate stress. Harmful Algae 68, 258–270 (2017).CAS 

    Google Scholar 
    Wang, Z.-h, Liang, Y. & Kang, W. Utilization of dissolved organic phosphorus by different groups of phytoplankton taxa. Harmful Algae 12, 113–118 (2011).CAS 

    Google Scholar 
    Ji, N. et al. Metatranscriptome analysis reveals environmental and diel regulation of a Heterosigma akashiwo (raphidophyceae) bloom. Environ. Microbiol. 20, 1078–1094 (2018).CAS 

    Google Scholar 
    Zhang, H. et al. Functional differences in the blooming phytoplankton Heterosigma akashiwo and Prorocentrum donghaiense revealed by comparative metaproteomics. Appl. Environ. Microbiol. 85, e01425–01419 (2019).CAS 

    Google Scholar 
    Redfield, A. C. The biological control of chemical factors in the environment. Am. Scientist 46, 230A–221 (1958).
    Google Scholar 
    Liefer, J. D. et al. The macromolecular basis of phytoplankton C: N: P under nitrogen starvation. Front. Microbiol. 10, 763 (2019).
    Google Scholar 
    Matsumoto, K., Tanioka, T. & Rickaby, R. Linkages between dynamic phytoplankton C: N: P and the ocean carbon cycle under climate change. Oceanography 33, 44–52 (2020).
    Google Scholar 
    Thrane, J. E., Hessen, D. O. & Andersen, T. Plasticity in algal stoichiometry: Experimental evidence of a temperature‐induced shift in optimal supply N: P ratio. Limnol. Oceanogr. 62, 1346–1354 (2017).CAS 

    Google Scholar 
    Toseland, A. et al. The impact of temperature on marine phytoplankton resource allocation and metabolism. Nat. Clim. Change 3, 979–984 (2013).CAS 

    Google Scholar 
    Mittler, R., Finka, A. & Goloubinoff, P. How do plants feel the heat? Trends Biochem. Sci. 37, 118–125 (2012).CAS 

    Google Scholar 
    Dingman, J. E. & Lawrence, J. E. Heat-stress-induced programmed cell death in Heterosigma akashiwo (Raphidophyceae). Harmful Algae 16, 108–116 (2012).
    Google Scholar 
    Whitten, S. T., García-Moreno E, B. & Hilser, V. J. Local conformational fluctuations can modulate the coupling between proton binding and global structural transitions in proteins. Proc. Natl Acad. Sci. 102, 4282–4287 (2005).CAS 

    Google Scholar 
    Casey, J. R., Grinstein, S. & Orlowski, J. Sensors and regulators of intracellular pH. Nat. Rev. Mol. Cell Biol. 11, 50–61 (2010).CAS 

    Google Scholar 
    Kim, H., Spivack, A. J. & Menden-Deuer, S. pH alters the swimming behaviors of the raphidophyte Heterosigma akashiwo: implications for bloom formation in an acidified ocean. Harmful Algae 26, 1–11 (2013).CAS 

    Google Scholar 
    Hennon, G. M., Williamson, O. M., Limón, M. D. H., Haley, S. T. & Dyhrman, S. T. Non-linear physiology and gene expression responses of harmful alga Heterosigma akashiwo to rising CO2. Protist 170, 38–51 (2019).CAS 

    Google Scholar 
    Xu, H., Jaynes, J. & Ding, X. Combining two-level and three-level orthogonal arrays for factor screening and response surface exploration. Statistica Sin. 24, 269–289 (2014).
    Google Scholar 
    Boyd, P. W. & Ellwood, M. J. The biogeochemical cycle of iron in the ocean. Nat. Geosci. 3, 675–682 (2010).CAS 

    Google Scholar 
    Sterner, R. W. & Elser, J. J. in Ecological Stoichiometry (Princeton university press, 2002).Liu, H. C., Liao, H. T. & Charng, Y. Y. The role of class A1 heat shock factors (HSFA1s) in response to heat and other stresses in Arabidopsis. Plant Cell Environ. 34, 738–751 (2011).CAS 

    Google Scholar 
    Geider, R. J. & La Roche, J. J. Redfield revisited: variability of C [ratio] N [ratio] P in marine microalgae and its biochemical basis. Eur. J. Phycol. 37, 1–17 (2002).
    Google Scholar 
    Loladze, I. & Elser, J. J. The origins of the Redfield nitrogen‐to‐phosphorus ratio are in a homoeostatic protein‐to‐rRNA ratio. Ecol. Lett. 14, 244–250 (2011).
    Google Scholar 
    Hennige, S. J., Coyne, K. J., MacIntyre, H., Liefer, J. & Warner, M. E. The photobiology of Heterosigma akashiwo. Photoacclimation, diurnal periodicity, and its ability to rapidly exploit exposure to high light. J. Phycol. 49, 349–360 (2013).CAS 

    Google Scholar 
    Collier, J. L. & Grossman, A. A small polypeptide triggers complete degradation of light‐harvesting phycobiliproteins in nutrient‐deprived cyanobacteria. EMBO J. 13, 1039–1047 (1994).CAS 

    Google Scholar 
    Gordillo, F. J., Jimenez, C., Figueroa, F. L. & Niell, F. X. Influence of elevated CO2 and nitrogen supply on the carbon assimilation performance and cell composition of the unicellular alga Dunaliella viridis. Physiologia Plant. 119, 513–518 (2003).CAS 

    Google Scholar 
    Satoh, E., Watanabe, M. M. & Fujii, T. Photoperiodic regulation of cell division and chloroplast replication in Heterosigma akashiwo. Plant Cell Physiol. 28, 1093–1099 (1987).
    Google Scholar 
    Ashworth, J. et al. Genome-wide diel growth state transitions in the diatom Thalassiosira pseudonana. Proc. Natl Acad. Sci. 110, 7518–7523 (2013).CAS 

    Google Scholar 
    Thangaraj, S. & Sun, J. J. E. M. Transcriptomic reprogramming of the oceanic diatom Skeletonema dohrnii under warming ocean and acidification. Environ. Microbiol. 23, 980–995 (2021).CAS 

    Google Scholar 
    Nakajima, K., Tanaka, A. & Matsuda, Y. SLC4 family transporters in a marine diatom directly pump bicarbonate from seawater. Proc. Natl Acad. Sci. 110, 1767–1772 (2013).CAS 

    Google Scholar 
    Kranz, S. A. et al. Low temperature reduces the energetic requirement for the CO2 concentrating mechanism in diatoms. N. Phytologist 205, 192–201 (2015).CAS 

    Google Scholar 
    Ralston, A. & Shaw, K. Gene expression regulates cell differentiation. Nat. Educ. 1, 127–131 (2008).
    Google Scholar 
    Lobo, I. Environmental influences on gene expression. Nat. Educ. 1, 39 (2008).
    Google Scholar 
    Suzuki, N. et al. Respiratory burst oxidases: the engines of ROS signaling. Curr. Opin. Plant Biol. 14, 691–699 (2011).CAS 

    Google Scholar 
    Saidi, Y., Finka, A. & Goloubinoff, P. Heat perception and signalling in plants: a tortuous path to thermotolerance. N. Phytologist 190, 556–565 (2011).CAS 

    Google Scholar 
    Saidi, Y. et al. The heat shock response in moss plants is regulated by specific calcium-permeable channels in the plasma membrane. Plant Cell 21, 2829–2843 (2009).CAS 

    Google Scholar 
    Zhang, W. et al. Molecular and genetic evidence for the key role of AtCaM3 in heat-shock signal transduction in Arabidopsis. Plant Physiol. 149, 1773–1784 (2009).CAS 

    Google Scholar 
    Li, S. et al. Functional characterization of Arabidopsis thaliana WRKY39 in heat stress. Mol. Cells 29, 475–483 (2010).CAS 

    Google Scholar 
    Sangwan, V., Örvar, B. L., Beyerly, J., Hirt, H. & Dhindsa, R. S. Opposite changes in membrane fluidity mimic cold and heat stress activation of distinct plant MAP kinase pathways. Plant J. 31, 629–638 (2002).CAS 

    Google Scholar 
    Reddy, A. S., Ali, G. S., Celesnik, H. & Day, I. S. Coping with stresses: roles of calcium-and calcium/calmodulin-regulated gene expression. Plant Cell 23, 2010–2032 (2011).CAS 

    Google Scholar 
    Meiri, D. & Breiman, A. J. Arabidopsis ROF1 (FKBP62) modulates thermotolerance by interacting with HSP90. 1 and affecting the accumulation of HsfA2‐regulated sHSPs. Plant J. 59, 387–399 (2009).CAS 

    Google Scholar 
    Mishkind, M., Vermeer, J. E., Darwish, E. & Munnik, T. J. Heat stress activates phospholipase D and triggers PIP2 accumulation at the plasma membrane and nucleus. Plant J. 60, 10–21 (2009).CAS 

    Google Scholar 
    Zheng, S. Z. et al. Phosphoinositide‐specific phospholipase C9 is involved in the thermotolerance of Arabidopsis. Plant J. 69, 689–700 (2012).CAS 

    Google Scholar 
    Pincus, D. et al. BiP binding to the ER-stress sensor Ire1 tunes the homeostatic behavior of the unfolded protein response. PLoS Biol. 8, e1000415 (2010).
    Google Scholar 
    Sugio, A., Dreos, R., Aparicio, F. & Maule, A. J. The cytosolic protein response as a subcomponent of the wider heat shock response in Arabidopsis. Plant Cell 21, 642–654 (2009).CAS 

    Google Scholar 
    Vasseur, F., Pantin, F. & Vile, D. J. P. Cell & Environment. Changes in light intensity reveal a major role for carbon balance in Arabidopsis responses to high temperature. Plant Cell Environ. 34, 1563–1576 (2011).CAS 

    Google Scholar 
    Paroutis, P., Touret, N. & Grinstein, S. The pH of the secretory pathway: measurement, determinants, and regulation. J. Physiol. 19, 207–215 (2004).CAS 

    Google Scholar 
    Forgac, M. Vacuolar ATPases: rotary proton pumps in physiology and pathophysiology. Nat. Rev. Mol. Cell Biol. 8, 917–929 (2007).CAS 

    Google Scholar 
    Cipriano, D. J. et al. Structure and regulation of the vacuolar ATPases. Biochim. et. Biophys. Acta -Bioenerg. 1777, 599–604 (2008).CAS 

    Google Scholar 
    Abad, M. F. C., Di Benedetto, G., Magalhães, P. J., Filippin, L. & Pozzan, T. Mitochondrial pH monitored by a new engineered green fluorescent protein mutant. J. Biol. Chem. 279, 11521–11529 (2004).CAS 

    Google Scholar 
    McCORMACK, J. G., Halestrap, A. P. & Denton, R. M. Role of calcium ions in regulation of mammalian intramitochondrial metabolism. Physiol. Rev. 70, 391–425 (1990).CAS 

    Google Scholar 
    Garlid, K. D., Sun, X., Paucek, P. & Woldegiorgis, G. in Methods in enzymology Vol. 260 331–348 (Elsevier, 1995).Yamada, E. W. & Huzel, N. J. J. B. Calcium-binding ATPase inhibitor protein of bovine heart mitochondria. Role in ATP synthesis and effect of calcium. Biochemistry 28, 9714–9718 (1989).CAS 

    Google Scholar 
    Moreno-Sánchez, R. Inhibition of oxidative phosphorylation by a Ca2+-induced diminution of the adenine nucleotide translocator. Biochim. et. Biophys. Acta -Bioenerg. 724, 278–285 (1983).
    Google Scholar 
    Matsuyama, S., Llopis, J., Deveraux, Q. L., Tsien, R. Y. & Reed, J. Changes in intramitochondrial and cytosolic pH: early events that modulate caspase activation during apoptosis. Nat. Cell Biol. 2, 318–325 (2000).CAS 

    Google Scholar 
    Sunda, W. G., Price, N. M. & Morel, F. M. Trace metal ion buffers and their use in culture studies. Algal Cultur. Tech. 4, 35–63 (2005).
    Google Scholar 
    Sun, J. et al. Effects of changing pCO2 and phosphate availability on domoic acid production and physiology of the marine harmful bloom diatom Pseudo‐nitzschia multiseries. Limnol. Oceanogr. 56, 829–840 (2011).CAS 

    Google Scholar 
    Pierrot, D., Lewis, E. & Wallace, D. J. MS Excel Program Developed for CO2 System Calculations ORNL/CDIAC‐105 (US Dept. of Energy, Oak Ridge, TN, 2006).Wilbur, K. M. & Anderson, N. G. Electrometric and colorimetric determination of carbonic anhydrase. J. Biol. Chem. 176, 147–154 (1948).CAS 

    Google Scholar 
    Solórzano, L. & Sharp, J. H. Determination of total dissolved phosphorus and particulate phosphorus in natural waters 1. Limnol. Oceanogr. 25, 754–758 (1980).
    Google Scholar 
    Myklestad, S. M., Skånøy, E. & Hestmann, S. J. Sensitive and rapid method for analysis of dissolved mono-and polysaccharides in seawater. Mar. Chem. 56, 279–286 (1997).CAS 

    Google Scholar 
    Pakulski, J. D. & Benner, R. J. An improved method for the hydrolysis and MBTH analysis of dissolved and particulate carbohydrates in seawater. Mar. Chem. 40, 143–160 (1992).CAS 

    Google Scholar 
    Folch, J. & Lees, M. & Sloane Stanley, G. H. A simple method for the isolation and purification of total lipids from animal tissues. J. Biol. Chem. 226, 497–509 (1957).CAS 

    Google Scholar 
    Pande, S., Khan, R. P. & Venkitasubramanian, T. Microdetermination of lipids and serum total fatty acids. Anal. Biochem. 6, 415–423 (1963).CAS 

    Google Scholar 
    Lowry, O., Rosebrough, N., Farr, A. L. & Randall, R. J. Protein measurement with the Folin phenol reagent. J. Biol. Chem. 193, 265–275 (1951).CAS 

    Google Scholar 
    Berdalet, E., Roldán, C., Olivar, M. P. & Lysnes, K. Quantifying RNA and DNA in planktonic organisms with SYBR Green II and nucleases. Part A. Optimisation of the assay. Sci. Mar. 69, 1–16 (2005).CAS 

    Google Scholar 
    Chomoczynski, P. Single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloro-form extraction. Anal. Biochem. 162, 156–159 (1987).
    Google Scholar 
    Sañudo-Wilhelmy, S. A. et al. The impact of surface-adsorbed phosphorus on phytoplankton Redfield stoichiometry. Phycol. Res. 432, 897–901 (2004).
    Google Scholar 
    Dyhrman, S. T. Nutrients and their acquisition: phosphorus physiology in microalgae. Physiol. Microalgae 155–183 (2016).Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 

    Google Scholar 
    Waterhouse, R. M. et al. BUSCO applications from quality assessments to gene prediction and phylogenomics. Mol. Biol. Evol. 35, 543–548 (2018).CAS 

    Google Scholar 
    Pruitt, K. D., Tatusova, T. & Maglott, D. R. J. N. A. R. NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 35, D61–D65 (2007).CAS 

    Google Scholar 
    Kanehisa, M. & Goto, S. J. N. A. R. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).CAS 

    Google Scholar 
    Mitchell, A. L. et al. InterPro in 2019: improving coverage, classification and access to protein sequence annotations. Nucleic Acids Res. 47, D351–D360 (2019).CAS 

    Google Scholar 
    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 1–21 (2014).
    Google Scholar 
    Wang, L., Feng, Z., Wang, X., Wang, X. & Zhang, X. J. B. DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics 26, 136–138 (2010).
    Google Scholar  More