More stories

  • in

    Automated design of synthetic microbial communities

    1.
    Pantoja-Hernández, L. & Martínez-García, J. C. Retroactivity in the context of modularly structured biomolecular systems. Front. Bioeng. Biotechnol. 3, 85 (2015).
    PubMed  PubMed Central  Article  Google Scholar 
    2.
    Jayanthi, S. & Del Vecchio, D. Retroactivity attenuation in bio-molecular systems based on timescale separation. IEEE Trans. Autom. Control 56, 748–761 (2011).
    MathSciNet  Article  Google Scholar 

    3.
    Gyorgy, A. et al. Isocost lines describe the cellular economy of genetic circuits. Biophys. J. 109, 639–646 (2015).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    4.
    Summers, D. The kinetics of plasmid loss. Trends Biotechnol 9, 273–278 (1991).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    5.
    Mishra, D., Rivera, P. M., Lin, A., Del Vecchio, D. & Weiss, R. A load driver device for engineering modularity in biological networks. Nat. Biotechnol. 32, 1268–1275 (2014).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    6.
    Weiße, A. Y., Oyarzún, D. A., Danos, V. & Swain, P. S. Mechanistic links between cellular trade-offs, gene expression, and growth. Proc. Natl. Acad. Sci. USA 112, E1038–E1047 (2015).
    ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

    7.
    Brenner, K., You, L. & Arnold, F. H. Engineering microbial consortia: a new frontier in synthetic biology. Trends Biotechnol 26, 483–489 (2008).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    8.
    Kennedy, T. A. et al. Biodiversity as a barrier to ecological invasion. Nature 417, 636–638 (2002).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    9.
    Beyter, D. et al. Diversity, productivity, and stability of an industrial microbial ecosystem. Appl. Environ. Microbiol. 82, 2494–2505 (2016).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    10.
    Butler, G. J. & Wolkowicz, G. S. K. A mathematical model of the chemostat with a general class of functions describing nutrient uptake. SIAM J. Appl. Math. 45, 138–151 (1985).
    MathSciNet  Article  Google Scholar 

    11.
    Foster, K. R. & Bell, T. Competition, not cooperation, dominates interactions among culturable microbial species. Curr. Biol. 22, 1845–1850 (2012).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    12.
    Hibbing, M. E., Fuqua, C., Parsek, M. R. & Peterson, S. B. Bacterial competition: surviving and thriving in the microbial jungle. Nat. Rev. Microb. 8, 15–25 (2010).
    CAS  Article  Google Scholar 

    13.
    Freilich, S. et al. Competitive and cooperative metabolic interactions in bacterial communities. Nat. Commun. 2, 589 (2011).
    ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

    14.
    Zelezniak, A. et al. Metabolic dependencies drive species co-occurrence in diverse microbial communities. Proc. Natl. Acad. Sci. USA 112, 6449–6454 (2015).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    15.
    May, A. et al. Kombucha: a novel model system for cooperation and conflict in a complex multi-species microbial ecosystem. PeerJ 7, e7565 (2019).
    PubMed  PubMed Central  Article  Google Scholar 

    16.
    Czaran, T. L., Hoekstra, R. F. & Pagie, L. Chemical warfare between microbes promotes biodiversity. Proc. Natl. Acad. Sci. USA 99, 786–790 (2002).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    17.
    Dinh, C. V., Chen, X. & Prather, K. L. J. Development of a quorum-sensing based circuit for control of coculture population composition in a naringenin production system. ACS Synth. Biol. 9, 590–597 (2020).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    18.
    Stephens, K., Pozo, M., Tsao, C.-Y., Hauk, P. & Bentley, W. E. Bacterial coculture with cell signaling translator and growth controller modules for autonomously regulated culture composition. Nat. Commun. 10, 4129 (2019).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    19.
    Liu, F., Mao, J., Lu, T. & Hua, Q. Synthetic, context-dependent microbial consortium of predator and prey. ACS Synth. Biol. 8, 1713–1722 (2019).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    20.
    Gupta, A., Reizman, I. M. B., Reisch, C. R. & Prather, K. L. J. Dynamic regulation of metabolic flux in engineered bacteria using a pathwayindependent quorum-sensing circuit. Nat. Biotechnol. 35, 273–279 (2017).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    21.
    Scott, S. R. & Hasty, J. Quorum sensing communication modules for microbial consortia. ACS Synth. Biol. 5, 969–977 (2016).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    22.
    Balagaddé, F. K. et al. A synthetic Escherichia coli predator–prey ecosystem. Mol. Syst. Biol. 4, 187 (2008).
    PubMed  PubMed Central  Article  Google Scholar 

    23.
    Kong, W., Meldgin, D. R., Collins, J. J. & Lu, T. Designing microbial consortia with defined social interactions. Nat. Chem. Biol. 14, 821–829 (2018).
    CAS  PubMed  Article  Google Scholar 

    24.
    Rebuffat S. M. (ed. Kastin, A. J.) In Handbook of Biologically Active Peptides 129–137 (Elsevier, 2013).

    25.
    Geldart, K., Forkus, B., McChesney, E., McCue, M. & Kaznessis, Y. pMPES: a modular peptide expression system for the delivery of antimicrobial peptides to the site of gastrointestinal infections using probiotics. Pharmaceuticals 9, 60 (2016).
    PubMed Central  Article  CAS  PubMed  Google Scholar 

    26.
    Fedorec, A. J. H. et al. Two new plasmid post-segregational killing mechanisms for the implementation of synthetic gene networks in Escherichia coli. iScience 14, 323–334 (2019).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    27.
    MacDonald, J. T., Barnes, C., Kitney, R. I., Freemont, P. S. & Stan, G.-B. V. Computational design approaches and tools for synthetic biology. Integr. Biol. 3, 97 (2011).
    Article  Google Scholar 

    28.
    Kirk, P., Thorne, T. & Stumpf, M. P. H. Model selection in systems and synthetic biology. Curr. Opin. Biotechnol. 24, 767–774 (2013).
    CAS  PubMed  Article  Google Scholar 

    29.
    Barnes, C. P., Silk, D., Sheng, X. & Stumpf, M. P. H. Bayesian design of synthetic biological systems. Proc. Natl. Acad. Sci. USA 108, 15190–15195 (2011).
    ADS  CAS  PubMed  Article  Google Scholar 

    30.
    Woods, M. L., Leon, M., Perez-Carrasco, R. & Barnes, C. P. A Statistical approach reveals designs for the most robust stochastic gene oscillators. ACS Synth. Biol. 5, 459–470 (2016).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    31.
    Leon, M., Woods, M. L., Fedorec, A. J. H. & Barnes, C. P. A computational method for the investigation of multistable systems and its application to genetic switches. BMC Syst. Biol. 10, 130 (2016).
    PubMed  PubMed Central  Article  Google Scholar 

    32.
    Yeoh, J. W. et al. An automated biomodel selection system (BMSS) for gene circuit designs. ACS Synth. Biol. 8, 1484–1497 (2019).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    33.
    Beal, J. et al. An end-to-end workflow for engineering of biological networks from high-level specifications. ACS Synth. Biol. 1, 317–331 (2012).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    34.
    Rodrigo, G. & Jaramillo, A. AutoBioCAD: full biodesign automation of genetic circuits. ACS Synth. Biol. 2, 230–236 (2013).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    35.
    Friedman, J. & Gore, J. Ecological systems biology: the dynamics of interacting populations. Current Opinion in Systems Biology 1, 114–121 (2017).
    Article  Google Scholar 

    36.
    Toni, T., Welch, D., Strelkowa, N., Ipsen, A. & Stumpf, M. P. H. Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems. J. R. Soc. Interface 6, 187–202 (2009).
    PubMed  Article  PubMed Central  Google Scholar 

    37.
    Kass, R. E. & Raftery, A. E. Bayes factors. J. Am. Stat. Assoc. 90, 773–795 (1995).
    MathSciNet  Article  Google Scholar 

    38.
    Salis, H. M., Mirsky, E. A. & Christopher, C. Automated design of synthetic ribosome binding sites to control protein expression. Nat. Biotechnol. 27, 946–950 (2009).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    39.
    Marisch, K. et al. A Comparative analysis of industrial Escherichia coli K-12 and B strains in high-glucose batch cultivations on process-, transcriptomeand proteome level. PLoS ONE 8, e70516 (2013).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    40.
    Treloar, N. J., Fedorec, A. J. H., Ingalls, B. & Barnes, C. P. Deep reinforcement learning for the control of microbial co-cultures in bioreactors. PLOS Comput. Biol. 16, e1007783 (2020).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    41.
    Lee, D. D. & Seung, H. S. Learning the parts of objects by non-negative matrix factorization. Nature 401, 788–791 (1999).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    42.
    Kerner, A., Park, J., Williams, A. & Lin, X. N. A programmable Escherichia coli consortium via tunable symbiosis. PLoS ONE 7, e34032 (2012).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    43.
    Zhou, K., Qiao, K., Edgar, S. & Stephanopoulos, G. Distributing a metabolic pathway among a microbial consortium enhances production of natural products. Nat. Biotechnol. 33, 377–383 (2015).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    44.
    Shou, W., Ram, S. & Vilar, J. M. G. Synthetic cooperation in engineered yeast populations. Proc. Natl. Acad. Sci. USA 104, 1877–1882 (2007).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    45.
    Pande, S. et al. Fitness and stability of obligate cross-feeding interactions that emerge upon gene loss in bacteria. ISME J 8, 953–962 (2014).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    46.
    Yurtsev, E. A., Conwill, A. & Gore, J. Oscillatory dynamics in a bacterial crossprotection mutualism. Proc. Natl. Acad. Sci. USA 113, 6236–6241 (2016).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    47.
    Hosoda, K. et al. Cooperative adaptation to establishment of a synthetic bacterial mutualism. PLoS ONE 6, e17105 (2011).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    48.
    Zhang, X. & Reed, J. L. Adaptive evolution of synthetic cooperating communities improves growth performance. PLoS ONE 9, e108297 (2014).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    49.
    Chen, Y., Kim, J. K., Hirning, A. J., Josi, K. & Bennett, M. R. Emergent genetic oscillations in a synthetic microbial consortium. Science 349, 986–989 (2015).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    50.
    Bernstein, H. C., Paulson, S. D. & Carlson, R. P. Synthetic Escherichia coli consortia engineered for syntrophy demonstrate enhanced biomass productivity. J. Biotechnol. 157, 159–166 (2012).
    CAS  PubMed  Article  Google Scholar 

    51.
    Scott, S. R. et al. A stabilized microbial ecosystem of self-limiting bacteria using synthetic quorum-regulated lysis. Nat. Microbiol. 2, 17083 (2017).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    52.
    Ziesack, M. et al. Engineered Interspecies amino acid cross-feeding increases population evenness in a synthetic bacterial consortium. mSystems 4, e00352–19 (2019).
    PubMed  PubMed Central  Article  Google Scholar 

    53.
    Liao, M. J., Din, M. O., Tsimring, L. & Hasty, J. Rock-paper-scissors: engineered population dynamics increase genetic stability. Science 365, 1045–1049 (2019).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    54.
    Ahn, J. et al. Human gut microbiome and risk for colorectal cancer. J. Natl Cancer Inst 105, 1907–1911 (2013).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    55.
    Stokell, J. R. et al. Analysis of changes in diversity and abundance of the microbial community in a cystic fibrosis patient over a multiyear period. J. Clin. Microbiol. 53, 237–247 (2015).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    56.
    Louca, S. et al. Function and functional redundancy in microbial systems. Nat. Ecol. Evol. 2, 936–943 (2018).
    PubMed  Article  PubMed Central  Google Scholar 

    57.
    Tyson, G. W. et al. Community structure and metabolism through reconstruction of microbial genomes from the environment. Nature 428, 37–43 (2004).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    58.
    Wang, X., Policarpio, L., Prajapati, D., Li, Z. & Zhang, H. Developing E. coli– E. coli co-cultures to overcome barriers of heterologous tryptamine biosynthesis. Metab. Eng. Commun. 10, e00110 (2020).
    PubMed  Article  PubMed Central  Google Scholar 

    59.
    Yuan, S. F., Yi, X., Johnston, T. G. & Alper, H. S. De novo resveratrol production through modular engineering of an Escherichia coli–Saccharomyces cerevisiae co-culture. Microb. Cell Factor 19, 143 (2020).
    CAS  Article  Google Scholar 

    60.
    Friedman, J., Higgins, L. M. & Gore, J. Community structure follows simple assembly rules in microbial microcosms. Nat. Ecol. Evol 1, 109 (2017).
    PubMed  Article  Google Scholar 

    61.
    Carmona-Fontaine, C. & Xavier, J. B. Altruistic cell death and collective drug resistance. Molecular Systems Biology 8, 627 (2012).
    PubMed  PubMed Central  Article  Google Scholar 

    62.
    Tanouchi, Y., Pai, A., Buchler, N. E. & You, L. Programming stress-induced altruistic death in engineered bacteria. Mol. Syst. Biol. 8, 626 (2012).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    63.
    Ackermann, M. et al. Self-destructive cooperation mediated by phenotypic noise. Nature 454, 987–990 (2008).
    ADS  CAS  PubMed  Article  Google Scholar 

    64.
    Williams, G. T. Programmed cell death: a fundamental protective response to pathogens. Trends Microbiol 2, 463–464 (1994).
    CAS  PubMed  Article  Google Scholar 

    65.
    Calles, B., Goñi-Moreno, Á. & Lorenzo, V. Digitalizing heterologous gene expression in Gram-negative bacteria with a portable ON/OFF module. Mol. Syst. Biol. 15, e8777 (2019).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    66.
    Fedorec, A., Karkaria, B., Sulu, M. & Barnes, C. Single strain control of microbial consortia. bioRxiv, https://doi.org/10.1101/2019.12.23.887331 (2019).

    67.
    Bell, T., Newman, J. A., Silverman, B. W., Turner, S. L. & Lilley, A. K. The contribution of species richness and composition to bacterial services. Nature 436, 1157–1160 (2005).
    ADS  CAS  PubMed  Article  Google Scholar 

    68.
    Hsu, R. H. et al. Venturelli. Microbial interaction network inference in microfluidic droplets. Cell Syst 9, 229–242.e4 (2019).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    69.
    Doekes, H. M., De Boer, R. J. & Hermsen, R. Toxin production spontaneously becomes regulated by local cell density in evolving bacterial populations. PLoS Comput. Biol. 15, e1007333 (2019).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    70.
    McNaughton, S. J. Stability and diversity of ecological communities. Nature 274, 251–253 (1978).
    ADS  Article  Google Scholar 

    71.
    Sterner, R. W., Bajpai, A. & Adams, T. The enigma of food chain length: absence of theoretical evidence for dynamic constraints. Ecology 78, 2258–2262 (1997).
    Article  Google Scholar 

    72.
    Barabás, G., Michalska-Smith, M. J. & Allesina, S. Self-regulation and the stability of large ecological networks. Nat. Ecol. Evol. 1, 1870–1875 (2017).
    PubMed  Article  PubMed Central  Google Scholar 

    73.
    Thébault, E. & Fontaine, C. Stability of ecological communities and the architecture of mutualistic and trophic networks. Science 329, 853–856 (2010).
    ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

    74.
    Tang, S., Pawar, S. & Allesina, S. Correlation between interaction strengths drives stability in large ecological networks. Ecol. Lett. 17, 1094–1100 (2014).
    PubMed  Article  PubMed Central  Google Scholar 

    75.
    Harris, C. R. et al. Array programming with NumPy. Nature 585, 357–362 (2020).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    76.
    Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    77.
    Siek, J. G., Lee, L.-Q., Lumsdaine, A. The Boost Graph Library, 243 (Addison-Wesley, 2002).

    78.
    Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
    MathSciNet  Google Scholar 

    79.
    Harper, M., et al. python-ternary: ternary plots in python. Zenodo https://doi.org/10.5281/zenodo.594435 (2019).

    80.
    Wickham, H. ggplot2-Positioning Elegant Graphics for Data Analysis (Springer-Verlag New York, 2016).

    81.
    Kylilis, N., Tuza, Z. A., Stan, G. B. & Polizzi, K. M. Tools for engineering coordinated system behaviour in synthetic microbial consortia. Nat. Commun. 9, 2677 (2018).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    82.
    Senn, H., Lendenmann, U., Snozzi, M., Hamer, G. & Egli, T. The growth of Escherichia coli in glucose-limited chemostat cultures: a re-examination of the kinetics. BBA—Gen. Subj. 1201, 424–436 (1994).
    Article  Google Scholar 

    83.
    Destoumieux-Garzón, D. The iron-siderophore transporter FhuA is the receptor for the antimicrobial peptide microcin J25: role of the microcin Val11-Pro16 β-hairpin region in the recognition mechanism. Biochem. J. 389, 869–876 (2005).
    PubMed  PubMed Central  Article  Google Scholar 

    84.
    Kaur, K. et al. Characterization of a highly potent antimicrobial peptide microcin N from uropathogenic Escherichia coli. FEMS Microbiology Letters 363, fnw095 (2016).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    85.
    Andersen, K. B. & Meyenburg, K. V. Are growth rates of Escherichia coli in batch cultures limited by respiration? J. Bacteriol. 144, 114–123 (1980).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    86.
    Marenda, M., Zanardo, M., Trovato, A., Seno, F. & Squartini, A. Modeling quorum sensing trade-offs between bacterial cell density and system extension from open boundaries. Sci. Rep. 6, 39142 (2016).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    87.
    Destoumieux-Garzón, D. et al. Microcin E492 antibacterial activity: evidence for a TonB-dependent inner membrane permeabilization on Escherichia coli. Mol. Microbiol. 49, 1031–1041 (2003).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    88.
    Karkaria, B. D., Fedorec, A. J. H. & Barnes, C. P. Automated design of synthetic microbial communities. Zenodo https://doi.org/10.5281/zenodo.4266261 (2020). More

  • in

    3D morphology of nematode encapsulation in snail shells, revealed by micro-CT imaging

    1.
    Frank, S. A. Immunology and Evolution of Infectious Diseases (Princeton, Princeton University Press, 2002).
    Google Scholar 
    2.
    Barker, G. M. Natural Enemies of Terrestrial Molluscs (CABI Publishing, Wallingford, 2004).
    Google Scholar 

    3.
    Grewal, P. S., Grewal, S. K., Tan, L. & Adams, B. J. Parasitism of molluscs by nematodes: types of associations and evolutionary trends. J. Nematol. 35, 146–156 (2003).
    CAS  PubMed  PubMed Central  Google Scholar 

    4.
    Blaxter, M. L. et al. A molecular evolutionary framework for the phylum Nematoda. Nature 392, 71–75 (1998).
    ADS  CAS  Article  Google Scholar 

    5.
    Pieterse, A., Malan, A. P. & Ross, J. L. Nematodes that associate with terrestrial molluscs as definitive hosts, including Phasmarhabditis hermaphrodita (Rhabditida: Rhabditidae) and its development as a biological molluscicide. J. Helminthol. 91, 517–527 (2017).
    CAS  Article  Google Scholar 

    6.
    Tillier, S., Masselot, M. & Tillier, A. Phylogenic relationships of the pulmonate gastropods from rRNA sequences, and tempo and age of the Stylommatophoran radiation. In Origin and Evolutionary Radiation of the Mollusca (ed. Taylor, J.D.) 267–284 (Oxford, Oxford University Press, 1996).

    7.
    Félix, M-A. & Braendle, C. The natural history of Caenorhabditis elegans. Curr. Biol. 20, R965-R969 (2010).

    8.
    Bolt, G., Monrad, J., Koch, J. & Jensen, A. L. Canine angiostrongylosis: a review. Vet. Rec. 135, 447–452 (1994).
    CAS  Article  Google Scholar 

    9.
    Loker E.S. Gastropod immunobiology in Invertebrate Immunity (ed. Soderhall, K.) 17–43 (Springer, 2010).

    10.
    South, A. Terrestrial Slugs: Biology, Ecology and Control (Chapman & Hall, London, 1992).
    Google Scholar 

    11.
    Wilson, M. J., Glen, D. M. & George, S. K. The rhabditid nematode Phasmarhabditis hermaphrodita as a potential biological control agent for slugs. Biocontrol Sci. Technol. 3, 503–511 (1993).
    Article  Google Scholar 

    12.
    Williams, A. J. & Rae, R. Susceptibility of the Giant African Snail (Achatina fulica) exposed to the gastropod parasitic nematode Phasmarhabditis hermaphrodita. J. Invertebr. Pathol. 127, 122–126 (2015).
    CAS  Article  Google Scholar 

    13.
    Williams, A. & Rae, R. Cepaea nemoralis uses its shell as a defence mechanism to trap and kill parasitic nematodes. J. Mollus. Stud. 12, 1–2 (2016).
    Google Scholar 

    14.
    Rae, R. The gastropod shell has been co-opted to kill parasitic nematodes. Sci. Rep. 7, 4745. https://doi.org/10.1038/s41598-017-04695-5 (2017).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    15.
    Rae, R., 2018. Shell encapsulation of parasitic nematodes by Arianta arbustorum (Linnaeus, 1758) in the laboratory and in field collections. J. Molluscan Stud. 84, 92–95 (2018).

    16.
    Cowlishaw, R. M., Andrus, P. & Rae, R. An investigation into nematodes encapsulated in shells of wild, farmed and museum specimens of Cornu aspersum and Helix pomatia. J. Conchol. 43, 1–8 (2020).
    Google Scholar 

    17.
    Lowenstam, H. A. & Weiner, S. On Biomineralization (Oxford University Press, Oxford, 1989).
    Google Scholar 

    18.
    Rae, R. G., Robertson, J. F. & Wilson, M. J. Susceptibility and immune response of Deroceras reticulatum, Milax gagates and Limax pseudoflavus exposed to the slug parasitic nematode Phasmarhabditis hermaphrodita. J. Invertebr. Pathol. 97, 61–69 (2008).
    Article  Google Scholar 

    19.
    Littlewood, D. T. J. & Donovan, S. K. Fossil parasites: a case of identity. Geol. Today. 19, 136–142 (2003).
    Article  Google Scholar 

    20.
    Poinar, G. O. Jr. The geological record of parasitic nematode evolution. Adv. Parasitol. 90, 53–92 (2015).
    Article  Google Scholar 

    21.
    Garwood, R., Dunlop, J.A. & Sutton, M.D. High-fidelity X-ray micro-tomography reconstruction of siderite-hosted Carboniferous arachnids. Biol. Lett. 5, 6 https://doi.org/10.1098/rsbl.2009.0464 (2009).

    22.
    Inoue, S. & Kondo, S. Structure pattern formation in ammonites and the unknown rear mantle structure. Sci. Rep. 6, 33689; https://doi.org/10.1038/srep33689 (2016).

    23.
    Shapiro, B. Ancient DNA. In Princeton Guide to Evolution (ed. Losos, J.) 475–481 (Princeton, Princeton University Press, 2013).

    24.
    Slon, V. et al. The genome of the offspring of a Neanderthal mother and a Denisovan father. Nature 561, 113–116 (2018).
    ADS  CAS  Article  Google Scholar 

    25.
    Swarts, K. et al. Genomic estimation of complex traits reveals ancient maize adaptation to temperate North America. Science 357, 512–515 (2017).
    ADS  CAS  Article  Google Scholar 

    26.
    Spyrou, M. A. et al. Analysis of 3800-year-old Yersinia pestis genomes suggests Bronze Age origin for bubonic plague. Nat. Commun. 9, 2234. https://doi.org/10.1038/s41467-018-04550-9 (2018).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    27.
    Loreille, O., Roumat, E., Verneau, O., Bouchet, F. & Hänni, C. Ancient DNA from Ascaris: extraction amplification and sequences from eggs collected from coprolites. Int. J. Parasitol. 31, 1101–1106 (2001).
    CAS  Article  Google Scholar 

    28.
    Søe, M. J., Nejsum, P., Fredensborg, B. L. & Kapel, C. M. O. DNA typing of ancient parasite eggs from environmental samples identifies human and animal worm infections in Viking-age settlement. J. Parasitol. 101, 57–63 (2015).
    Article  Google Scholar 

    29.
    Lubell, D. Prehistoric edible land snails in the cicum-Mediterranean: the archaeological evidence. In Petits Animaux et Societes Humaines. Du Complement Alimentaire Aux Resources Utiliaires. XXIVe rencontres internationals d’archeologie et d’histoire d’Antibes (eds. Brugal, J-J & Dess, J.) 77–98 (Editions APDCA, 2004).

    30.
    Eamsobhana, P. Eosinophilic meningitis caused by Angiostrongylus cantonenses – a neglected disease with escalating importance. Trop. Biomed. 31, 569–578 (2014).
    CAS  PubMed  Google Scholar  More

  • in

    Utilization of the zebrafish model to unravel the harmful effects of biomass burning during Amazonian wildfires

    In vivo study: embryotoxicity test
    Zebrafish embryos exposed to tested compounds developed lethal and sub-lethal alterations including different abnormalities and unhatching events. LC50 (for mortality rate) and EC50 (for abnormality and unhatching rate) values were extrapolated from concentration–response curves shown in Fig. 1. The rate of dead, abnormal, and/or unhatched specimens was concentration-dependent for all tested compounds (Fig. 1a–c). The lethality of the negative control group was less than 5%. Compounds 4NC and CAT showed the highest toxicity with LC50 values of 8.16 and 10.95 mg/L, respectively, followed by 4,6DNG  > 5NG  > GUA. Experimental LC50/EC50 values and the predicted ones obtained by ECOlogical Structure Activity Relationship (ECOSAR) v2.0 software (https://www.epa.gov/tsca-screening-tools/ecological-structure-activity-relationships-ecosar-predictive-model) based on Quantitative Structure Activity Relationships (QSAR) models showed 4NC and CAT as the most toxic chemicals (Table 2). However, it is important to notice that experimental values for both compounds were approximately two times lower than the predicted ones. This led to the classification of 4NC into the group of molecules toxic to fish (1  GUA).
    Figure 3

    Recorded sublethal morphological effects in D. rerio embryos/larvae after 48, 72, and 96 h of exposure to CAT, 4NC, GUA, 5NG, and 4,6DNG. Negative control: normally developed embryo at (a) 48, (b) 72, and (c) 96 hpf. During exposure period alterations were manifested as: (d) yolk sac edema (arrow); (e) pericardial edema (asterisk), undeveloped tail region (arrow); (f) hatched fish with malformed spine (arrow); (g) underdeveloped tail and necrosis of its apical part (dashed arrow), rare pigments; (h) pericardial edema (asterisk), scoliosis (arrow), necrosis of the apical part of the tail (dashed arrow), rare pigments, not hatched; (i) scoliosis (arrows), blood accumulation in the brain region (dashed arrow); (j) pericardial edema (asterisk), yolk sac edema (arrow), scoliosis (dashed arrow); (k, l) pericardial edema (asterisk); (m) underdeveloped embryo: underdeveloped head (arrow), tail not detached (asterisk), delay or anomaly in the absorption of the yolk sac; (n) pericardial edema (asterisk), blood accumulation (arrow), not hatched; (o) pericardial edema (asterisk), blood clotting (arrow), not hatched; (p) blood accumulation at the yolk sac (arrow); (r) hatched fish with malformed spine; (s) pericardial edema (black asterisk), blood accumulation above the yolk sac (arrow), swelling of the yolk sac (white asterisk), yolk sac edema (dashed arrow), mild scoliosis. Developmental abnormalities were recorded using LAS EZ 3.2.0 digitizing software (https://www.leica-microsystems.com/products/microscope-software/p/leica-las-ez/).

    Full size image

    The morphometric measurements (Fig. 4) showed that all tested samples significantly affected sensorial (eye area), skeletal (head height), and physiological (yolk and pericardial sac area) parameters in zebrafish. Significant differences among all treatments with exact p values are presented in Table S2.
    Figure 4

    Morphometric measurements of D. rerio larvae after 96-h exposure to tested compounds (CAT, 4NC, GUA, 5NG, and 4,6DNG) and control (C). (a) Lateral view showing eye area (EA), head height (HH), yolk sac area (YSA), and pericardial sac area (PSA). Scale bar = 1000 µm. Morphometric parameters are presented by their mean value (b–e; n = 15). The symbol * indicates a significant difference between tested samples and negative control (*p  More

  • in

    Recovery of freshwater microbial communities after extreme rain events is mediated by cyclic succession

    1.
    Battin, T. J. et al. Biophysical controls on organic carbon fluxes in fluvial networks. Nat. Geosci. 1, 95–100 (2008).
    CAS  Article  Google Scholar 
    2.
    Tranvik, L. J. et al. Lakes and reservoirs as regulators of carbon cycling and climate. Limnol. Oceanogr. 54, 2298–2314 (2009).
    CAS  Article  Google Scholar 

    3.
    Raymond, P. A. et al. Global carbon dioxide emissions from inland waters. Nature 503, 355–359 (2013).
    CAS  PubMed  Article  Google Scholar 

    4.
    Downing, J. A. Emerging global role of small lakes and ponds: little things mean a lot. Limnetica 29, 9–24 (2010).
    Google Scholar 

    5.
    Bastviken, D., Tranvik, L. J., Downing, J. A., Crill, P. M. & Enrich-Prast, A. Freshwater methane emissions offset the continental carbon sink. Science 331, 50–50 (2011).
    CAS  PubMed  Article  Google Scholar 

    6.
    Fairchild, G. W. & Velinsky, D. J. Effects of small ponds on stream water chemistry. Lake Reserv. Manag. 22, 321–330 (2006).
    CAS  Article  Google Scholar 

    7.
    Yin, C. & Shan, B. Multipond systems: a sustainable way to control diffuse phosphorus pollution. AMBIO 30, 369–375 (2001).
    CAS  PubMed  Article  Google Scholar 

    8.
    Stanley, E. H. & Doyle, M. W. A geomorphic perspective on nutrient retention following dam removal: geomorphic models provide a means of predicting ecosystem responses to dam removal. BioScience 52, 693–701 (2002).
    Article  Google Scholar 

    9.
    Downing, J. A., Cherrier, C. T. & Fulweiler, R. W. Low ratios of silica to dissolved nitrogen supplied to rivers arise from agriculture not reservoirs. Ecol. Lett. 19, 1414–1418 (2016).
    PubMed  Article  Google Scholar 

    10.
    Dickman, M. Some effects of lake renewal on phytoplankton productivity and species composition. Limnol. Oceanogr. 14, 660–666 (1969).
    Article  Google Scholar 

    11.
    Madsen, H., Lawrence, D., Lang, M., Martinkova, M. & Kjeldsen, T. R. Review of trend analysis and climate change projections of extreme precipitation and floods in Europe. J. Hydrol. 519, 3634–3650 (2014).
    Article  Google Scholar 

    12.
    Clark, J. M. et al. The importance of the relationship between scale and process in understanding long-term DOC dynamics. Sci. Total Environ. 408, 2768–2775 (2010).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    13.
    Vystavna, Y., Hejzlar, J. & Kopáček, J. Long-term trends of phosphorus concentrations in an artificial lake: socio-economic and climate drivers. PLoS ONE 12, e0186917 (2017).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    14.
    Reynolds, C. S. Phytoplankton assemblages and their periodicity in stratifying lake systems. Ecography 3, 141–159 (1980).
    Article  Google Scholar 

    15.
    Sommer, U., Gliwicz, Z. M., Lampert, W. & Duncan, A. The PEG-model of seasonal succession of planktonic events in fresh waters. Arch. Hydrobiol. 106, 433–471 (1986).
    Google Scholar 

    16.
    Kundzewicz, Z. W. et al. Differences in flood hazard projections in Europe—their causes and consequences for decision making. Hydrol. Sci. J. 62, 1–14 (2017).
    Google Scholar 

    17.
    Arnell, N. W. & Gosling, S. N. The impacts of climate change on river flood risk at the global scale. Clim. Change 134, 387–401 (2016).
    Article  Google Scholar 

    18.
    Hirabayashi, Y. et al. Global flood risk under climate change. Nat. Clim. Change 3, 816–821 (2013).
    Article  Google Scholar 

    19.
    Lynch, L. M. et al. River channel connectivity shifts metabolite composition and dissolved organic matter chemistry. Nat. Commun. 10, 459 (2019).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    20.
    Pimm, S. L. The complexity and stability of ecosystems. Nature 307, 321–326 (1984).
    Article  Google Scholar 

    21.
    Shade, A. et al. Lake microbial communities are resilient after a whole-ecosystem disturbance. ISME J. 6, 2153–2167 (2012).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    22.
    Holling, C. S. Resilience and stability of ecological systems. Annu. Rev. Ecol. Evol. Syst. 4, 1–23 (1973).
    Article  Google Scholar 

    23.
    Holling, C. S. & Gunderson, L. H. in Panarchy Synopsis: Understanding Transformations in Human and Natural Systems (eds Gunderson, L. H. & Holling, C. S.) 25–62 (Island Press, 2002).

    24.
    Gabaldón, C. et al. Repeated flood disturbance enhances rotifer dominance and diversity in a zooplankton community of a small dammed mountain pond. J. Limnol. 76, 13 (2016).
    Google Scholar 

    25.
    Porcal, P. & Kopáček, J. Photochemical degradation of dissolved organic matter reduces the availability of phosphorus for aquatic primary producers. Chemosphere 193, 1018–1026 (2018).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    26.
    Macarthur, R. & Levins, R. The limiting similarity, convergence, and divergence of coexisting species. Am. Nat. 101, 377–385 (1967).
    Article  Google Scholar 

    27.
    Newton, R. J., Kent, A. D., Triplett, E. W. & McMahon, K. D. Microbial community dynamics in a humic lake: differential persistence of common freshwater phylotypes. Environ. Microbiol. 8, 956–970 (2006).
    PubMed  Article  PubMed Central  Google Scholar 

    28.
    Neuenschwander, S. M., Ghai, R., Pernthaler, J. & Salcher, M. M. Microdiversification in genome-streamlined ubiquitous freshwater Actinobacteria. ISME J. 12, 185–198 (2018).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    29.
    Cabello-Yeves, P. J. et al. Reconstruction of diverse verrucomicrobial genomes from metagenome datasets of freshwater reservoirs. Front. Microbiol. 8, 2131 (2017).
    PubMed  PubMed Central  Article  Google Scholar 

    30.
    Reznick, D., Bryant, M. J. & Bashey, F. r- and K-selection revisited: the role of population regulation in life-history evolution. Ecology 83, 1509–1520 (2002).
    Article  Google Scholar 

    31.
    Mac Arthur, R. H. & Wilson, E. O. The Theory of Island Biogeography (Princeton Univ. Press, 1967).

    32.
    Šimek, K. et al. A finely tuned symphony of factors modulates the microbial food web of a freshwater reservoir in spring. Limnol. Oceanogr. 59, 1477–1492 (2014).
    Article  CAS  Google Scholar 

    33.
    Logue, J. B., Mouquet, N., Peter, H. & Hillebrand, H. Empirical approaches to metacommunities: a review and comparison with theory. Trends Ecol. Evol. 26, 482–491 (2011).
    PubMed  Article  Google Scholar 

    34.
    Shabarova, T. et al. Bacterial community structure and dissolved organic matter in repeatedly flooded subsurface karst water pools. FEMS Microbiol. Ecol. 89, 111–126 (2014).
    CAS  PubMed  Article  Google Scholar 

    35.
    Shabarova, T., Widmer, F. & Pernthaler, J. Mass effects meet species sorting: transformations of microbial assemblages in epiphreatic subsurface karst water pools. Environ. Microbiol. 15, 2476–2488 (2013).
    CAS  PubMed  Article  Google Scholar 

    36.
    Jones, S. E. et al. Typhoons initiate predictable change in aquatic bacterial communities. Limnol. Oceanogr. 53, 1319–1326 (2008).
    Article  Google Scholar 

    37.
    Shade, A. et al. Fundamentals of microbial community resistance and resilience. Front. Microbiol. 3, 417 (2012).
    PubMed  PubMed Central  Article  Google Scholar 

    38.
    Hahn, M. W. Isolation of strains belonging to the cosmopolitan Polynucleobacter necessarius cluster from freshwater habitats located in three climatic zones. Appl. Environ. Microb. 69, 5248–5254 (2003).
    CAS  Article  Google Scholar 

    39.
    Salcher, M. M., Neuenschwander, S. M., Posch, T. & Pernthaler, J. The ecology of pelagic freshwater methylotrophs assessed by a high-resolution monitoring and isolation campaign. ISME J. 9, 2442–2453 (2015).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    40.
    Vuono, D. C. et al. Disturbance and temporal partitioning of the activated sludge metacommunity. ISME J. 9, 425–435 (2014).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    41.
    Shabarova, T. et al. Distribution and ecological preferences of the freshwater lineage LimA (genus Limnohabitans) revealed by a new double hybridization approach. Environ. Microbiol. 19, 1296–1309 (2017).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    42.
    Hahn, M. W., Lang, E., Tarao, M. & Brandt, U. Polynucleobacter rarus sp. nov., a free-living planktonic bacterium isolated from an acidic lake. Int. J. Syst. Evol. Microbiol. 61, 781–787 (2011).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    43.
    Hahn, M. W. et al. The passive yet successful way of planktonic life: genomic and experimental analysis of the ecology of a free-living Polynucleobacter population. PLoS ONE 7, e32772 (2012).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    44.
    Pernthaler, J. Predation on prokaryotes in the water column and its ecological implications. Nat. Rev. Microbiol. 3, 537–546 (2005).
    CAS  PubMed  Article  Google Scholar 

    45.
    Sommer, U. et al. Beyond the plankton ecology group (Peg) model: mechanisms driving plankton succession. Annu. Rev. Ecol. Evol. Syst. 43, 429–448 (2012).
    Article  Google Scholar 

    46.
    Šimek, K. et al. Bacterial prey food characteristics modulate community growth response of freshwater bacterivorous flagellates. Limnol. Oceanogr. 63, 484–502 (2018).
    Article  Google Scholar 

    47.
    Posch, T. et al. Network of interactions between ciliates and phytoplankton during spring. Front. Microbiol. 6, 1289 (2015).
    PubMed  PubMed Central  Article  Google Scholar 

    48.
    Geraldes, A. M. & Boavida, M.-J. Zooplankton assemblages in two reservoirs: one subjected to accentuated water level fluctuations, the other with more stable water levels. Aquat. Ecol. 41, 273–284 (2007).
    CAS  Article  Google Scholar 

    49.
    Nilssen, J. P. & Wærvågen, S. B. Superficial ecosystem similarities vs autecological stripping: the ‘twin species’ Mesocyclops leuckarti (Claus) and Thermocyclops oithonoides (Sars)—seasonal habitat utilisation and life history traits. J. Limnol. 59, 79–102 (2000).
    Article  Google Scholar 

    50.
    Cole, T. M. & Wells, S. A. CE-QUAL-W2: A Two-Dimensional, Laterally Averaged, Hydrodynamic and Water Quality Model, Version 4.1 (Department of Civil and Environmental Engineering, 2018).

    51.
    Brussaard, C. P. D. Optimization of procedures for counting viruses by flow cytometry. Appl. Environ. Microb. 70, 1506–1513 (2004).
    CAS  Article  Google Scholar 

    52.
    Porter, K. G. & Feig, Y. S. The use of DAPI for identifying and counting aquatic microflora. Limnol. Oceanogr. 25, 943–948 (1980).
    Article  Google Scholar 

    53.
    Sherr, E. B. & Sherr, B. F. in Handbook of Methods in Aquatic Microbial Ecology (eds Kemp, P. F. et al.) 207–212 (Lewis Publishers, 1993).

    54.
    Sherr, E. B. & Sherr, B. F. in Handbook of Methods in Aquatic Microbial Ecology (eds Kemp, P. F. et al.) 695–701 (Lewis Publishers, 1993).

    55.
    Kasalický, V., Jezbera, J., Hahn, M. W. & Šimek, K. The diversity of the Limnohabitans genus, an important group of freshwater bacterioplankton, by characterization of 35 isolated strains. PLoS ONE 8, e58209 (2013).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    56.
    Šimek, K. et al. Microbial food webs in hypertrophic fishponds: omnivorous ciliate taxa are major protistan bacterivores. Limnol. Oceanogr. 64, 2295–2309 (2019).
    Article  CAS  Google Scholar 

    57.
    Lund, J. W. G., Kipling, C. & Le Cren, E. D. The inverted microscope method of estimating algal numbers and the statistical basis of estimations by counting. Hydrobiologia 11, 143–170 (1958).
    Article  Google Scholar 

    58.
    Hillebrand, H., Dürselen, C. D., Kirschtel, D., Pollingher, U. & Zohary, T. Biovolume calculation for pelagic and benthic microalgae. J. Phycol. 35, 403–424 (1999).
    Article  Google Scholar 

    59.
    Straškraba, M. & Hrbáček, J. Net-plankton cycle in slapy reservoir during 1958–1960. Hydrobiol. Stud. 1, 113–153 (1966).
    Google Scholar 

    60.
    Nercessian, O., Noyes, E., Kalyuzhnaya, M. G., Lidstrom, M. E. & Chistoserdova, L. Bacterial populations active in metabolism of C1 compounds in the sediment of Lake Washington, a freshwater lake. Appl. Environ. Microb. 71, 6885–6899 (2005).
    CAS  Article  Google Scholar 

    61.
    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 

    62.
    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).

    63.
    Yilmaz, P. et al. The SILVA and ‘all-species living tree project (LTP)’ taxonomic frameworks. Nucleic Acids Res. 42, D643–D648 (2014).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    64.
    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).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    65.
    Pruesse, E. et al. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 35, 7188–7196 (2007).
    CAS  PubMed  PubMed Central  Google Scholar 

    66.
    Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    67.
    Schöfl, G. reutils: talk to the NCBI EUtils. R version 0.2.3 https://CRAN.R-project.org/package=reutils (2016).

    68.
    Pruesse, E., Peplies, J. & Glöckner, F. O. SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics 28, 1823–1829 (2012).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    69.
    Ludwig, W. et al. ARB: a software environment for sequence data. Nucleic Acids Res. 32, 1363–1371 (2004).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    70.
    Fuchs, B. M., Glöckner, F. O., Wulf, J. & Amann, R. Unlabeled helper oligonucleotides increase the in situ accessibility to 16S rRNA of fluorescently labeled oligonucleotide probes. Appl. Environ. Microb. 66, 3603–3607 (2000).
    CAS  Article  Google Scholar 

    71.
    Buckley, D. H. & Schmidt, T. M. Environmental factors influencing the distribution of rRNA from verrucomicrobia in soil. FEMS Microbiol. Ecol. 35, 105–112 (2001).
    CAS  PubMed  Article  Google Scholar 

    72.
    Yilmaz, L. S., Parnerkar, S. & Noguera, D. R. mathFISH, a web tool that uses thermodynamics-based mathematical models for in silico evaluation of oligonucleotide probes for fluorescence in situ hybridization. Appl. Environ. Microb. 77, 1118–1122 (2011).
    CAS  Article  Google Scholar 

    73.
    Sekar, R. et al. An improved protocol for quantification of freshwater Actinobacteria by fluorescence in situ hybridization. Appl. Environ. Microb. 69, 2928–2935 (2003).
    CAS  Article  Google Scholar 

    74.
    Lorenzen, C. J. Determination of chlorophyll and pheo-pigments: spectrophotometric equations 1. Limnol. Oceanogr. 12, 343–346 (1967).
    CAS  Article  Google Scholar 

    75.
    Golterman, H. L. Methods for Chemical Analysis of Fresh Waters (F. A. Davis Company, 1969).

    76.
    Murphy, J. & Riley, J. P. A modified single solution method for the determination of phosphate in natural waters. Anal. Chim. Acta 27, 31–36 (1962).
    CAS  Article  Google Scholar 

    77.
    Kopáček, J. & Hejzlar, J. Semi-micro determination of total phosphorus in fresh waters with perchloric acid digestion. Int. J. Environ. Anal. Chem. 53, 173–183 (1993).
    Article  Google Scholar 

    78.
    Oksanen, J. et al. vegan: community ecology package. R version 2.5–6 (2019); https://CRAN.R-project.org/package=vegan More

  • in

    Phenological shifts of abiotic events, producers and consumers across a continent

    Department of Ecology, Swedish University of Agricultural Sciences, Uppsala, Sweden
    Tomas Roslin

    University of Helsinki, Helsinki, Finland
    Tomas Roslin, Laura Antão, Maria Hällfors, Coong Lo, Juri Kurhinen & Otso Ovaskainen

    EarthCape OY, Helsinki, Finland
    Evgeniy Meyke

    Department of Computer Science, Aalto University, Espoo, Finland
    Gleb Tikhonov

    Research Unit of Biodiversity (UMIB, UO-CSIC-PA), Oviedo University, Mieres, Spain
    Maria del Mar Delgado

    3237 Biology-Psychology Building, University of Maryland, College Park, MD, USA
    Eliezer Gurarie

    National Park Orlovskoe Polesie, Oryol, Russian Federation
    Marina Abadonova

    Institute of Botany, Academy of Sciences of the Republic of Uzbekistan, Tashkent, Uzbekistan
    Ozodbek Abduraimov, Azizbek Mahmudov & Mirabdulla Turgunov

    Kostomuksha Nature Reserve, Kostomuksha, Russian Federation
    Olga Adrianova, Irina Gaydysh & Natalia Sikkila

    Altai State Nature Biosphere Reserve, Gorno-Altaysk, Russian Federation
    Tatiana Akimova, Svetlana Chuhontseva, Elena Gorbunova, Yury Kalinkin, Helen Korolyova, Oleg Mitrofanov, Miroslava Sahnevich, Vladimir Yakovlev & Tatyana Zubina

    Kabardino-Balkarski Nature Reserve, Kashkhatau, Russian Federation
    Muzhigit Akkiev

    FSE Zapovednoe Podlemorye, Ust-Bargizin, Russian Federation
    Aleksandr Ananin, Evgeniya Bukharova & Natalia Luzhkova

    Institute of General and Experimental Biology, Siberian Branch, Russian Academy of Sciences, Ulan-Ude, Russian Federation
    Aleksandr Ananin

    State Nature Reserve Stolby, Krasnoyarsk, Russian Federation
    Elena Andreeva, Nadezhda Goncharova, Alexander Hritankov, Anastasia Knorre, Vladimir Kozsheechkin & Vladislav Timoshkin

    Carpathian Biosphere Reserve, Rakhiv, Ukraine
    Natalia Andriychuk, Alla Kozurak & Anatoliy Vekliuk

    Nizhne-Svirsky State Nature Reserve, Lodeinoe Pole, Russian Federation
    Maxim Antipin

    State Nature Reserve Prisursky, Cheboksary, Russian Federation
    Konstantin Arzamascev

    Zapovednoe Pribajkalje (Bajkalo-Lensky State Nature Reserve, Pribajkalsky National Park), Irkutsk, Russian Federation
    Svetlana Babina

    Darwin Nature Biosphere Reserve, Borok, Russian Federation
    Miroslav Babushkin, Andrey Kuznetsov, Natalia Nemtseva, Irina Rybnikova & Nicolay Zelenetskiy

    Volzhsko-Kamsky National Nature Biosphere Rezerve, Sadovy, Russian Federation
    Oleg Bakin, Elena Chakhireva & Alexey Pavlov

    FGBU National Park Shushenskiy Bor, Shushenskoe, Russian Federation
    Anna Barabancova & Andrej Tolmachev

    Voronezhsky Nature Biosphere Reserve, Voronezh, Russian Federation
    Inna Basilskaja & Inna Sapelnikova

    Baikalsky State Nature Biosphere Reserve, Tankhoy, Russian Federation
    Nina Belova, Olga Ermakova, Irina Kozyr, Aleksandra Krasnopevtseva & Nikolay Volodchenkov

    Visimsky Nature Biosphere Reserve, Kirovgrad, Russian Federation
    Natalia Belyaeva & Rustam Sibgatullin

    Kondinskie Lakes National Park named after L. F. Stashkevich, Sovietsky, Russian Federation
    Tatjana Bespalova, Alena Butunina, Aleksandra Esengeldenova, Natalia Korotkikh & Evgeniy Larin

    FSBI United Administration of the Kedrovaya Pad’ State Biosphere Nature Reserve and Leopard’s Land National Park, Vladivostok, Russian Federation
    Evgeniya Bisikalova

    Pechoro-Ilych State Nature Reserve, Yaksha, Russian Federation
    Anatoly Bobretsov, Murad Kurbanbagamaev, Irina Megalinskaja, Viktor Teplov, Valentina Teplova & Tatiana Tertitsa

    A. N. Severtsov Institute of Ecology and Evolution, Moscow, Russian Federation
    Vladimir Bobrov & Igor Pospelov

    Komsomolskiy Department, FGBU Zapovednoye Priamurye, Komsomolsk-on-Amur, Russian Federation
    Vadim Bobrovskyi, Olga Kuberskaya, Polina Van & Vladimir Van

    Tigirek State Nature Reserve, Barnaul, Russian Federation
    Elena Bochkareva & Evgeniy A. Davydov

    Institute of Systematics and Ecology of Animals, Siberian Branch, Russian Academy of Sciences, Novosibirsk, Russian Federation
    Elena Bochkareva

    State Nature Reserve Bolshaya Kokshaga, Yoshkar-Ola, Russian Federation
    Gennady Bogdanov

    Institute of Plant and Animal Ecology, Ural Branch, Russian Academy of Sciences, Ekaterinburg, Russian Federation
    Vladimir Bolshakov

    Sikhote-Alin State Nature Biosphere Reserve named after K. G. Abramov, Terney, Russian Federation
    Svetlana Bondarchuk, Sergey Elsukov, Ludmila Gromyko, Irina Nesterova & Elena Smirnova

    FSBI Prioksko-Terrasniy State Reserve, Danky, Russian Federation
    Yuri Buyvolov & Galina Sokolova

    Lomonosov Moscow State University, Moscow, Russian Federation
    Anna Buyvolova & Ilya Prokhorov

    National Park Meshchera, Gus-Hrustalnyi, Russian Federation
    Yuri Bykov, Zoya Drozdova & Svetlana Mayorova

    South Urals Federal Research Center of Mineralogy and Geoecology, Ilmeny State Reserve, Ural Branch, Russian Academy of Sciences, Miass, Russian Federation
    Olga Chashchina, Nadezhda Kuyantseva & Valery Zakharov

    FGBU National Park Kenozersky, Arkhangelsk, Russian Federation
    Nadezhda Cherenkova, Svetlana Drovnina & Alexander Samoylov

    FGBU GPZ Kologrivskij les im. M.G. Sinicina, Kologriv, Russian Federation
    Sergej Chistjakov

    Altai State University, Barnaul, Russian Federation
    Evgeniy A. Davydov

    Pryazovskyi National Nature Park, Melitopol’, Ukraine
    Viktor Demchenko, Elena Diadicheva & Valeri Sanko

    State Nature Reserve Privolzhskaya Lesostep, Penza, Russian Federation
    Aleksandr Dobrolyubov & Aleksey Kudryavtsev

    Komarov Botanical Institute, Russian Academy of Sciences, Saint Petersburg, Russian Federation
    Ludmila Dostoyevskaya, Violetta Fedotova & Pavel Lebedev

    Sary-Chelek State Nature Reserve, Aksu, Kyrgyzstan
    Akynaly Dubanaev

    Institute for Evolutionary Ecology NAS Ukraine, Kiev, Ukraine
    Yuriy Dubrovsky

    FGBU State Nature Reserve Kuznetsk Alatau, Mezhdurechensk, Russian Federation
    Lidia Epova

    Kerzhenskiy State Nature Biosphere Reserve, Nizhny Novgorod, Russian Federation
    Olga S. Ermakova

    FSBI United Administration of the Mordovia State Nature Reserve and National Park Smolny, Republic of Mordovia, Saransk, Russian Federation
    Elena Ershkova

    Ogarev Mordovia State University, Saransk, Russian Federation
    Elena Ershkova

    Bryansk Forest Nature Reserve, Nerussa, Russian Federation
    Oleg Evstigneev, Evgeniya Kaygorodova, Sergey Kossenko, Sergey Kruglikov & Elena Sitnikova

    Pinezhsky State Nature Reserve, Pinega, Russian Federation
    Irina Fedchenko, Lyudmila Puchnina, Svetlana Rykova & Andrei Sivkov

    The Central Chernozem State Biosphere Nature Reserve named after Professor V.V. Alyokhin, Kurskiy, Russian Federation
    Tatiana Filatova

    Tyumen State University, Tyumen, Russian Federation
    Sergey Gashev

    Reserves of Taimyr, Norilsk, Russian Federation
    Anatoliy Gavrilov, Leonid Kolpashikov, Elena Pospelova & Violetta Strekalovskaya

    Chatkalski National Park, Toshkent, Uzbekistan
    Dmitrij Golovcov

    National Park Ugra, Kaluga, Russian Federation
    Tatyana Gordeeva & Viktorija Teleganova

    Kaniv Nature Reserve, Kaniv, Ukraine
    Vitaly Grishchenko, Yuliia Kulsha, Vasyl Shevchyk & Eugenia Yablonovska-Grishchenko

    Smolenskoe Poozerje National Park, Przhevalskoe, Russian Federation
    Vladimir Hohryakov, Gennadiy Kosenkov & Ksenia Shalaeva

    FSBI Zeya State Nature Reserve, Zeya, Russian Federation
    Elena Ignatenko, Klara Pavlova & Sergei Podolski

    Polistovsky State Nature Reserve, Pskov, Russian Federation
    Svetlana Igosheva & Tatiana Novikova

    Ural State Pedagogical University, Yekaterinburg, Russian Federation
    Uliya Ivanova, Margarita Kupriyanova, Tamara Nezdoliy, Nataliya Skok & Oksana Yantser

    Institute of Mathematical Problems of Biology RAS—the Branch of the Keldysh Institute of Applied Mathematics, Russian Academy of Sciences, Pushchino, Russian Federation
    Natalya Ivanova & Maksim Shashkov

    Kronotsky Federal Nature Biosphere Reserve, Yelizovo, Russian Federation
    Fedor Kazansky & Darya Panicheva

    Zhiguli Nature Reserve, P. Bakhilova Polyana, Russian Federation
    Darya Kiseleva

    Institute for Ecology and Geography, Siberian Federal University, Krasnoyarsk, Russian Federation
    Anastasia Knorre

    Central Forest State Nature Biosphere Reserve, Tver, Russian Federation
    Evgenii Korobov, Elena Shujskaja, Sergei Stepanov & Anatolii Zheltukhin

    National Park Bashkirija, Nurgush, Russian Federation
    Elvira Kotlugalyamova & Lilija Sultangareeva

    State Nature Reserve Kurilsky, Juzhno-Kurilsk, Russian Federation
    Evgeny Kozlovsky

    Vodlozersky National Park, Karelia, Petrozavodsk, Russian Federation
    Elena Kulebyakina & Viktor Mamontov

    State Nature Reserve Kivach, Kondopoga, Russian Federation
    Anatoliy Kutenkov, Nadezhda Kutenkova, Anatoliy Shcherbakov, Svetlana Skorokhodova, Alexander Sukhov & Marina Yakovleva

    South-Ural Federal University, Miass, Russian Federation
    Nadezhda Kuyantseva

    Saint-Petersburg State Forest Technical University, St. Petersburg, Russian Federation
    Pavel Lebedev

    Astrakhan Biosphere Reserve, Astrakhan, Russian Federation
    Kirill Litvinov

    FSBI United Administration of the Lazovsky State Reserve and National Park Zov Tigra, Lazo, Russian Federation
    Lidiya Makovkina, Aleksandr Myslenkov & Inna Voloshina

    State Nature Reserve Tungusskiy, Krasnoyarsk, Russian Federation
    Artur Meydus, Julia Raiskaya & Vladimir Sopin

    Krasnoyarsk State Pedagogical University named after V.P. Astafyev, Krasnoyarsk, Russian Federation
    Artur Meydus

    Institute of Geography, Russian Academy of Sciences, Moscow, Russian Federation
    Aleksandr Minin

    Koltzov Institute of Developmental Biology, Russian Academy of Sciences, Moscow, Russian Federation
    Aleksandr Minin

    Carpathian National Nature Park, Yaremche, Ukraine
    Mykhailo Motruk

    State Environmental Institution National Park Braslav lakes, Braslav, Belarus
    Nina Nasonova

    National Park Synevyr, Synevyr-Ostriki, Ukraine
    Tatyana Niroda, Ivan Putrashyk, Yurij Tyukh & Yurij Yarema

    Pasvik State Nature Reserve, Nikel, Russian Federation
    Natalja Polikarpova

    Mari Chodra National Park, Krasnogorsky, Russian Federation
    Tatiana Polyanskaya

    State Nature Reserve Vishersky, Krasnovishersk, Russian Federation
    Irina Prokosheva

    State Nature Reserve Olekminsky, Olekminsk, Russian Federation
    Yuri Rozhkov, Olga Rozhkova & Dmitry Tirski

    Crimea Nature Reserve, Alushta, Republic of Crimea
    Marina Rudenko

    Forest Research Institute Karelian Research Centre, Russian Academy of Sciences, Petrozavodsk, Russian Federation
    Sergei Sazonov, Lidia Vetchinnikova & Juri Kurhinen

    Black Sea Biosphere Reserve, Hola Prystan’, Ukraine
    Zoya Selyunina

    Institute of Physicochemical and Biological Problems in Soil Sciences, Russian Academy of Sciences, Pushchino, Russian Federation
    Maksim Shashkov

    State Nature Reserve Nurgush, Kirov, Russian Federation
    Sergej Shubin & Ludmila Tselishcheva

    Caucasian State Biosphere Reserve of the Ministry of Natural Resources, Maykop, Russian Federation
    Yurii Spasovski

    National Nature Park Vyzhnytskiy, Berehomet, Ukraine
    Vitalіy Stratiy

    National Park Khvalynsky, Khvalynsk, Russian Federation
    Guzalya Suleymanova

    State Research Center Arctic and Antarctic Research Institute, Saint Petersburg, Russian Federation
    Aleksey Tomilin

    Information-Analytical Centre for Protected Areas, Moscow, Russian Federation
    Aleksey Tomilin

    State Nature Reserve Malaya Sosva, Sovetskiy, Russian Federation
    Aleksander Vasin & Aleksandra Vasina

    Krasnoyarsk State Medical University named after Prof. V.F.Voino-Yasenetsky, Krasnoyarsk, Russian Federation
    Vladislav Vinogradov

    Surhanskiy State Nature Reserve, Sherabad, Uzbekistan
    Tura Xoliqov

    Mordovia State Nature Reserve, Pushta, Russian Federation
    Andrey Zahvatov

    Centre for Biodiversity Dynamics, Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
    Otso Ovaskainen

    The data were collected by the 195 authors starting from M.A. and ending with T.Z. in the author list. J.K., E.M., C.L., G.T. and E.G. contributed to the establishment and coordination of the collaborative network and to the compilation and curation of the resulting dataset. T.R., O.O., L.A., M.H. and M.d.M.D. conceived the idea behind the current study and wrote the first draft of the paper, with O.O. conducting the analyses. All authors provided useful comments on earlier drafts. More