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    Unique high Arctic methane metabolizing community revealed through in situ 13CH4-DNA-SIP enrichment in concert with genome binning

    1.Ferrari, B. C., Winsley, T., Gillings, M. & Binnerup, S. Cultivating previously uncultured soil bacteria using a soil substrate membrane system. Nat. Protoc. 3, 1261–1269 (2008).CAS 
    PubMed 

    Google Scholar 
    2.Nichols, D. et al. Use of ichip for high-throughput in situ cultivation of “uncultivable” microbial species. Appl. Environ. Microbiol. 76, 2445–2450 (2010).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    3.Torsvik, V., Øvreås, L. & Thingstad, T. F. Prokaryotic diversity–magnitude, dynamics, and controlling factors. Science 296, 1064–1066 (2002).ADS 
    CAS 
    PubMed 

    Google Scholar 
    4.Gies, E. A., Konwar, K. M., Beatty, J. T. & Hallam, S. J. Illuminating microbial dark matter in meromictic Sakinaw Lake. Appl. Environ. Microbiol. AEM. 80, 6807–6818 (2014).ADS 

    Google Scholar 
    5.Albertsen, M. et al. Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes. Nat. Biotechnol. 31, 533 (2013).CAS 
    PubMed 

    Google Scholar 
    6.Lazar, C. S., Baker, B. J., Seitz, K. W. & Teske, A. P. Genomic reconstruction of multiple lineages of uncultured benthic archaea suggests distinct biogeochemical roles and ecological niches. ISME J. 11, 1118–1129 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    7.Manefield, M., Whiteley, A. S., Griffiths, R. I. & Bailey, M. J. RNA stable isotope probing, a novel means of linking microbial community function to phylogeny. Appl. Environ. Microbiol. 68, 5367–5373 (2002).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    8.Dumont, M. G. & Murrell, J. C. Stable isotope probing—Linking microbial identity to function. Nat. Rev. Microbiol. 3, 499–504 (2005).CAS 
    PubMed 

    Google Scholar 
    9.Gadkari, P. S., McGuinness, L. R., Männistö, M. K., Kerkhof, L. J. & Häggblom, M. M. Arctic tundra soil bacterial communities active at subzero temperatures detected by stable isotope probing. FEMS Microbiol. Ecol. 96, fiz192 (2020).CAS 
    PubMed 

    Google Scholar 
    10.Achouak, W. & el Zahar Haichar, F. Stable isotope probing of microbiota structure and function in the plant rhizosphere. Methods Mol. Biol. 2046, 233–243 (2019).CAS 
    PubMed 

    Google Scholar 
    11.Graef, C., Hestnes, A. G., Svenning, M. M. & Frenzel, P. The active methanotrophic community in a wetland from the High Arctic. Environ. Microbiol. Rep. 3, 466–472 (2011).CAS 
    PubMed 

    Google Scholar 
    12.He, R. et al. Identification of functionally active aerobic methanotrophs in sediments from an arctic lake using stable isotope probing. Environ. Microbiol. 14, 1403–1419 (2012).CAS 
    PubMed 

    Google Scholar 
    13.Martineau, C., Whyte, L. G. & Greer, C. W. Stable isotope probing analysis of the diversity and activity of methanotrophic bacteria in soils from the Canadian High Arctic. Appl. Environ. Microbiol. 76, 5773–5784 (2010).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    14.Hanson, R. S. & Hanson, T. E. Methanotrophic bacteria. Microbiol. Rev. 60, 439–471 (1996).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    15.Conrad, R. Microbial ecology of methanogens and methanotrophs. Adv. Agron. 96, 1–63. https://doi.org/10.1016/s0065-2113(07)96005-8 (2007).CAS 
    Article 

    Google Scholar 
    16.Ettwig, K. F. et al. Nitrite-driven anaerobic methane oxidation by oxygenic bacteria. Nature 464, 543–548 (2010).ADS 
    CAS 
    PubMed 

    Google Scholar 
    17.Ricke, P. et al. First genome data from uncultured upland soil cluster alpha methanotrophs provide further evidence for a close phylogenetic relationship to Methylocapsa acidiphila B2 and for high-affinity methanotrophy involving particulate methane monooxygenase. Appl. Environ. Microbiol. 71, 7472–7482 (2005).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    18.Knief, C. Diversity and habitat preferences of cultivated and uncultivated aerobic methanotrophic bacteria evaluated based on pmoA as molecular marker. Front. Microbiol. 6, 1346 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    19.Jørgensen, C. J., Johansen, K. M. L., Westergaard-Nielsen, A. & Elberling, B. Net regional methane sink in High Arctic soils of northeast Greenland. Nat. Geosci. 8, 20–23 (2015).ADS 

    Google Scholar 
    20.Emmerton, C. A. et al. The net exchange of methane with high Arctic landscapes during the summer growing season. Biogeosciences 11, 3095–3106 (2014).ADS 

    Google Scholar 
    21.Martineau, C. et al. Atmospheric methane oxidizers are present and active in Canadian high Arctic soils. FEMS Microbiol. Ecol. 89, 257–269 (2014).CAS 
    PubMed 

    Google Scholar 
    22.Christiansen, J. R. et al. Methane fluxes and the functional groups of methanotrophs and methanogens in a young Arctic landscape on Disko Island, West Greenland. Biogeochemistry 122, 15–33 (2015).CAS 

    Google Scholar 
    23.Lau, M. et al. An active atmospheric methane sink in high Arctic mineral cryosols. ISME J. 9, 1880–1891 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    24.Altshuler, I. et al. Species interactions and distinct microbial communities in high Arctic permafrost affected cryosols are associated with the CH4 and CO2 gas fluxes. Environ. Microbiol. 21, 3711–3727 (2019).CAS 
    PubMed 

    Google Scholar 
    25.Holmes, A. J. et al. Characterization of methanotrophic bacterial populations in soils showing atmospheric methane uptake. Appl. Environ. Microbiol. 65, 3312–3318 (1999).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    26.Kolb, S., Knief, C., Dunfield, P. F. & Conrad, R. Abundance and activity of uncultured methanotrophic bacteria involved in the consumption of atmospheric methane in two forest soils. Environ. Microbiol. 7, 1150–1161 (2005).CAS 
    PubMed 

    Google Scholar 
    27.Pratscher, J., Vollmers, J., Wiegand, S., Dumont, M. G. & Kaster, A. K. Unravelling the identity, metabolic potential and global biogeography of the atmospheric methane-oxidizing upland soil cluster α. Environ. Microbiol. 20, 1016–1029 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    28.Tveit, A. T. et al. Widespread soil bacterium that oxidizes atmospheric methane. Proc. Natl. Acad. Sci. 116, 8515–8524 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    29.Cai, Y., Zheng, Y., Bodelier, P. L., Conrad, R. & Jia, Z. Conventional methanotrophs are responsible for atmospheric methane oxidation in paddy soils. Nat. Commun. 7, 11728 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    30.Curry, C. The consumption of atmospheric methane by soil in a simulated future climate. Biogeosciences 6, 2355–2367 (2009).ADS 
    CAS 

    Google Scholar 
    31.Stackhouse, B. T. et al. Effects of simulated spring thaw of permafrost from mineral cryosol on CO2 emissions and atmospheric CH4 uptake. J. Geophys. Res. Biogeosci. 120, 1764–1784 (2015).CAS 

    Google Scholar 
    32.Collier, S. M., Ruark, M. D., Oates, L. G., Jokela, W. E. & Dell, C. J. Measurement of greenhouse gas flux from agricultural soils using static chambers. J. Vis. Exp. JoVE 90, 52110 (2014).
    Google Scholar 
    33.Nadkarni, M. A., Martin, F. E., Jacques, N. A. & Hunter, N. Determination of bacterial load by real-time PCR using a broad-range (universal) probe and primers set. Microbiology 148, 257–266 (2002).CAS 
    PubMed 

    Google Scholar 
    34.Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods 25, 402–408 (2001).CAS 
    PubMed 

    Google Scholar 
    35.Martineau, C., Whyte, L. G. & Greer, C. W. Development of a SYBR safe™ technique for the sensitive detection of DNA in cesium chloride density gradients for stable isotope probing assays. J. Microbiol. Methods 73, 199–202 (2008).CAS 
    PubMed 

    Google Scholar 
    36.McDonald, I. R., Bodrossy, L., Chen, Y. & Murrell, J. C. Molecular ecology techniques for the study of aerobic methanotrophs. Appl. Environ. Microbiol. 74, 1305–1315 (2008).ADS 
    CAS 
    PubMed 

    Google Scholar 
    37.Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: Assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).CAS 
    PubMed 

    Google Scholar 
    38.Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    39.Bushnell, B. BBMap: A Fast, Accurate, Splice-Aware Aligner (Lawrence Berkeley National Lab. (LBNL), Berkeley, 2014).
    Google Scholar 
    40.Li, F., Zhu, R., Bao, T., Wang, Q. & Xu, H. Sunlight stimulates methane uptake and nitrous oxide emission from the High Arctic tundra. Sci. Total Environ. 572, 1150–1160 (2016).ADS 
    CAS 
    PubMed 

    Google Scholar 
    41.Kang, D. D., Froula, J., Egan, R. & Wang, Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 3, e1165 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    42.Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: Assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    43.Rodriguez-R, L. M. et al. The Microbial Genomes Atlas (MiGA) webserver: Taxonomic and gene diversity analysis of Archaea and Bacteria at the whole genome level. Nucleic Acids Res. 46, W282–W288 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    44.Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: A toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2020).CAS 

    Google Scholar 
    45.Zhu, W., Lomsadze, A. & Borodovsky, M. Ab initio gene identification in metagenomic sequences. Nucleic Acids Res. 38, e132–e132 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    46.Aziz, R. K. et al. The RAST Server: rapid annotations using subsystems technology. BMC Genom. 9, 75 (2008).
    Google Scholar 
    47.Huntemann, M. et al. The standard operating procedure of the DOE-JGI microbial genome annotation pipeline (MGAP v. 4). Stand. Genom. Sci. 10, 86 (2015).
    Google Scholar 
    48.Huntemann, M. et al. The standard operating procedure of the DOE-JGI Metagenome Annotation Pipeline (MAP v. 4). Stand. Genom. Sci. 11, 17 (2016).
    Google Scholar 
    49.Waterhouse, A. et al. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 46, W296–W303 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    50.Holm, L. Using Dali for Protein Structure Comparison. In Structural Bioinformatics. Methods in Molecular Biology. (ed. Gáspári Z.) vol 2112 (Humana, New York, 2020) https://doi.org/10.1007/978-1-0716-0270-6_3.51.Yang, J. et al. The I-TASSER Suite: Protein structure and function prediction. Nat. Methods 12, 7 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    52.Wen, X., Yang, S. & Liebner, S. Evaluation and update of cutoff values for methanotrophic pmoA gene sequences. Arch. Microbiol. 198, 629–636 (2016).CAS 
    PubMed 

    Google Scholar 
    53.Shrestha, P. M., Kammann, C., Lenhart, K., Dam, B. & Liesack, W. Linking activity, composition and seasonal dynamics of atmospheric methane oxidizers in a meadow soil. ISME J. 6, 1115–1126 (2012).CAS 
    PubMed 

    Google Scholar 
    54.Tremblay, J. & Yergeau, E. Systematic processing of ribosomal RNA gene amplicon sequencing data. GigaScience 8, giz146 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    55.Fish, J. A. et al. FunGene: The functional gene pipeline and repository. Front. Microbiol. 4, 291 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    56.Konstantinidis, K. T. & Tiedje, J. M. Towards a genome-based taxonomy for prokaryotes. J. Bacteriol. 187, 6258–6264 (2005).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    57.Doxey, A. C., Mansfield, M. J. & Montecucco, C. Discovery of novel bacterial toxins by genomics and computational biology. Toxicon 147, 2–12 (2018).CAS 
    PubMed 

    Google Scholar 
    58.Pearson, W. R. An introduction to sequence similarity (“homology”) searching. Curr. Protoc. Bioinform. 42, 1–8 (2013).
    Google Scholar 
    59.Sachs, T., Giebels, M., Boike, J. & Kutzbach, L. Environmental controls on CH4 emission from polygonal tundra on the microsite scale in the Lena river delta, Siberia. Glob. Change Biol. 16, 3096–3110 (2010).
    Google Scholar 
    60.Brummell, M. E., Farrell, R. E., Hardy, S. P. & Siciliano, S. D. Greenhouse gas production and consumption in High Arctic deserts. Soil Biol. Biochem. 68, 158–165 (2014).CAS 

    Google Scholar 
    61.Allan, J. et al. Methanogen community composition and rates of methane consumption in Canadian High Arctic permafrost soils. Environ. Microbiol. Rep. 6, 136–144 (2014).CAS 
    PubMed 

    Google Scholar 
    62.Natali, S. M. et al. Permafrost thaw and soil moisture driving CO2 and CH4 release from upland tundra. J. Geophys. Res. Biogeosci. 120, 525–537 (2015).CAS 

    Google Scholar 
    63.Qiu, Q., Noll, M., Abraham, W.-R., Lu, Y. & Conrad, R. Applying stable isotope probing of phospholipid fatty acids and rRNA in a Chinese rice field to study activity and composition of the methanotrophic bacterial communities in situ. ISME J. 2, 602–614 (2008).CAS 
    PubMed 

    Google Scholar 
    64.Esson, K. C. et al. Alpha-and gammaproteobacterial methanotrophs codominate the active methane-oxidizing communities in an acidic boreal peat bog. Appl. Environ. Microbiol. 82, 2363–2371 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    65.Kolb, S., Knief, C., Stubner, S. & Conrad, R. Quantitative detection of methanotrophs in soil by novel pmoA-targeted real-time PCR assays. Appl. Environ. Microbiol. 69, 2423–2429 (2003).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    66.Knief, C., Lipski, A. & Dunfield, P. F. Diversity and activity of methanotrophic bacteria in different upland soils. Appl. Environ. Microbiol. 69, 6703–6714 (2003).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    67.Belova, S. E., Danilova, O. V., Ivanova, A. A., Merkel, A. Y. & Dedysh, S. N. Methane-oxidizing communities in lichen-dominated forested tundra are composed exclusively of high-affinity USCα methanotrophs. Microorganisms 8, 2047 (2020).CAS 
    PubMed Central 

    Google Scholar 
    68.Hirayama, H. et al. Methylomarinovum caldicuralii gen. nov., sp. Nov., a moderately thermophilic methanotroph isolated from a shallow submarine hydrothermal system, and proposal of the family Methylothermaceae fam. nov. Int. J. Syst. Evol. Microbiol. 64, 989–999 (2014).CAS 
    PubMed 

    Google Scholar 
    69.Graham, D. W., Chaudhary, J. A., Hanson, R. S. & Arnold, R. G. Factors affecting competition between type I and type II methanotrophs in two-organism, continuous-flow reactors. Microb. Ecol. 25, 1–17 (1993).CAS 
    PubMed 

    Google Scholar 
    70.Macalady, J. L., McMillan, A. M., Dickens, A. F., Tyler, S. C. & Scow, K. M. Population dynamics of type I and II methanotrophic bacteria in rice soils. Environ. Microbiol. 4, 148–157 (2002).PubMed 

    Google Scholar 
    71.Morris, B. E., Henneberger, R., Huber, H. & Moissl-Eichinger, C. Microbial syntrophy: Interaction for the common good. FEMS Microbiol. Rev. 37, 384–406 (2013).CAS 
    PubMed 

    Google Scholar 
    72.Zarzycki, J. & Fuchs, G. Coassimilation of organic substrates via the autotrophic 3-hydroxypropionate bi-cycle in Chloroflexus aurantiacus. Appl. Environ. Microbiol. 77, 6181–6188 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    73.Ward, L. M. et al. Genomic evidence for phototrophic oxidation of small alkanes in a member of the chloroflexi phylum. bioRxiv 531582 (2019) https://doi.org/10.1101/531582.74.Khadem, A. F. et al. Autotrophic methanotrophy in Verrucomicrobia: Methylacidiphilum fumariolicum SolV uses the Calvin-Benson-Bassham cycle for carbon dioxide fixation. J. Bacteriol. 193, 4438–4446 (2011).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    75.Rasigraf, O., Kool, D. M., Jetten, M. S., Damsté, J. S. S. & Ettwig, K. F. Autotrophic carbon dioxide fixation via the Calvin-Benson-Bassham cycle by the denitrifying methanotroph “Candidatus Methylomirabilis oxyfera”. Appl. Environ. Microbiol. 80, 2451–2460 (2014).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    76.Kalyuzhnaya, M. G., Gomez, O. A., Murrell J. C. The Methane-Oxidizing Bacteria (Methanotrophs). In: McGenity T. (eds) Taxonomy, Genomics and Ecophysiology of Hydrocarbon-Degrading Microbes. Handbook of Hydrocarbon and Lipid Microbiology. Springer, Cham. https://doi.org/10.1007/978-3-030-14796-9_10 (2019).77.Contin, M., Rizzardini, C. B., Catalano, L. & De Nobili, M. Contamination by mercury affects methane oxidation capacity of aerobic arable soils. Geoderma 189, 250–256 (2012).ADS 

    Google Scholar 
    78.Boden, R. & Murrell, J. C. Response to mercury (II) ions in Methylococcus capsulatus (Bath). FEMS Microbiol. Lett. 324, 106–110 (2011).CAS 
    PubMed 

    Google Scholar 
    79.Yan, X., Chu, F., Puri, A. W., Fu, Y. & Lidstrom, M. E. Electroporation-based genetic manipulation in type I methanotrophs. Appl. Environ. Microbiol. AEM 82, 2062–2069 (2016).ADS 
    CAS 

    Google Scholar 
    80.Strong, P. J. et al. The opportunity for high-performance biomaterials from methane. Microorganisms 4, 11 (2016).PubMed Central 

    Google Scholar 
    81.Cai, C. et al. A methanotrophic archaeon couples anaerobic oxidation of methane to Fe (III) reduction. ISME J. 12, 1929–1939 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    82.Iguchi, H., Yurimoto, H. & Sakai, Y. Soluble and particulate methane monooxygenase gene clusters of the type I methanotroph Methylovulum miyakonense HT12. FEMS Microbiol. Lett. 312, 71–76 (2010).CAS 
    PubMed 

    Google Scholar 
    83.Semrau, J. D. et al. Methanobactin and MmoD work in concert to act as the ‘copper-switch’in methanotrophs. Environ. Microbiol. 15, 3077–3086 (2013).CAS 
    PubMed 

    Google Scholar 
    84.Csaki, R., Bodrossy, L., Klem, J., Murrell, J. C. & Kovacs, K. L. Genes involved in the copper-dependent regulation of soluble methane monooxygenase of Methylococcus capsulatus (Bath): Cloning, sequencing and mutational analysis. Microbiology 149, 1785–1795 (2003).CAS 
    PubMed 

    Google Scholar 
    85.Lund, J., Woodland, M. P. & Dalton, H. Electron transfer reactions in the soluble methane monooxygenase of Methylococcus capsulatus (Bath). Eur. J. Biochem. 147, 297–305 (1985).CAS 
    PubMed 

    Google Scholar 
    86.Lund, J. & Howard, D. Further characterisation of the FAD and Fe2S2 redox centres of component C, the NADH: Acceptor reductase of the soluble methane monooxygenase of Methylococcus capsulatus (Bath). Eur. J. Biochem. 147, 291–296 (1985).CAS 
    PubMed 

    Google Scholar 
    87.Teufel, R. et al. Bacterial phenylalanine and phenylacetate catabolic pathway revealed. Proc. Natl. Acad. Sci. 107, 14390–14395 (2010).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    88.Katayama, T. et al. Phylogenetic analysis of bacteria preserved in a permafrost ice wedge for 25,000 years. Appl. Environ. Microbiol. 73, 2360–2363 (2007).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    89.Scanlan, J., Dumont, M. G. & Murrell, J. C. Involvement of MmoR and MmoG in the transcriptional activation of soluble methane monooxygenase genes in Methylosinus trichosporium OB3b. FEMS Microbiol. Lett. 301, 181–187 (2009).CAS 
    PubMed 

    Google Scholar 
    90.Ali, H. & Murrell, J. C. Development and validation of promoter-probe vectors for the study of methane monooxygenase gene expression in Methylococcus capsulatus Bath. Microbiology 155, 761–771 (2009).CAS 
    PubMed 

    Google Scholar 
    91.Yu, Z., Groom, J., Zheng, Y., Chistoserdova, L. & Huang, J. Synthetic methane-consuming communities from a natural lake sediment. MBio 10, e01072-01019 (2019).
    Google Scholar 
    92.Tarlera, S. & Denner, E. B. Sterolibacterium denitrificans gen. nov., sp. nov., a novel cholesterol-oxidizing, denitrifying member of the β-Proteobacteria. Int. J. Syst. Evol. Microbiol. 53, 1085–1091 (2003).CAS 
    PubMed 

    Google Scholar 
    93.Beller, H. R. et al. The genome sequence of the obligately chemolithoautotrophic, facultatively anaerobic bacterium Thiobacillus denitrificans. J. Bacteriol. 188, 1473–1488 (2006).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    94.Kojima, H. & Fukui, M. Sulfuricella denitrificans gen. nov., sp. nov., a sulfur-oxidizing autotroph isolated from a freshwater lake. Int. J. Syst. Evol. Microbiol. 60, 2862–2866 (2010).CAS 
    PubMed 

    Google Scholar 
    95.Boden, R., Hutt, L. P. & Rae, A. W. Reclassification of Thiobacillus aquaesulis (Wood & Kelly, 1995) as Annwoodia aquaesulis gen. nov., comb. nov. Transfer of Thiobacillus (Beijerinck, 1904) from the Hydrogenophilales to the Nitrosomonadales, proposal of Hydrogenophilalia class. nov. within the ‘Proteobacteria’, and 4 new families within the orders Nitrosomonadales and Rhodocyclales. Int. J. Syst. Evol. Microbiol. 67, 1191–1205 (2017).CAS 
    PubMed 

    Google Scholar 
    96.Yoon, S., Cruz-García, C., Sanford, R., Ritalahti, K. M. & Löffler, F. E. Denitrification versus respiratory ammonification: Environmental controls of two competing dissimilatory NO3−/NO2− reduction pathways in Shewanella loihica strain PV-4. ISME J. 9, 1093 (2015).CAS 
    PubMed 

    Google Scholar 
    97.Bonin, P. Anaerobic nitrate reduction to ammonium in two strains isolated from coastal marine sediment: A dissimilatory pathway. FEMS Microbiol. Ecol. 19, 27–38 (1996).CAS 

    Google Scholar 
    98.Tiedje, J. M. Ecology of denitrification and dissimilatory nitrate reduction to ammonium. Biol. Anaerob. Microorg. 717, 179–244 (1988).
    Google Scholar 
    99.Stein, L. Y. & Klotz, M. G. Nitrifying and denitrifying pathways of methanotrophic bacteria. Biochem. Soc. Trans. 39, 1826–1831 (2011).CAS 
    PubMed 

    Google Scholar 
    100.Tays, C., Guarnieri, M. T., Sauvageau, D. & Stein, L. Y. Combined effects of carbon and nitrogen source to optimize growth of proteobacterial methanotrophs. Front. Microbiol. 9, 2239 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    101.van den Berg, E. M., Boleij, M., Kuenen, J. G., Kleerebezem, R. & van Loosdrecht, M. DNRA and denitrification coexist over a broad range of acetate/N-NO3− ratios, in a chemostat enrichment culture. Front. Microbiol. 7, 1842 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    102.Edwards, C. R. et al. Draft genome sequence of uncultured upland soil cluster Gammaproteobacteria gives molecular insights into high-affinity methanotrophy. Genome Announc. 5, e00047-e117 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    103.Singleton, C. M. et al. Methanotrophy across a natural permafrost thaw environment. ISME J. 12, 2544 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    104.Gupta, V., Smemo, K. A., Yavitt, J. B. & Basiliko, N. Active methanotrophs in two contrasting North American peatland ecosystems revealed using DNA-SIP. Microb. Ecol. 63, 438–445 (2012).CAS 
    PubMed 

    Google Scholar  More

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    The choice of the white clover population alters overyielding of mixtures with perennial ryegrass and chicory and underlying processes

    1.Tilman, D., Wedin, D. & Knops, J. Productivity and sustainability influenced by biodiversity in grassland ecosystems. Nature 379, 718–720. https://doi.org/10.1038/379718a0 (1996).ADS 
    CAS 
    Article 

    Google Scholar 
    2.Hector, A. et al. Plant diversity and productivity experiments in European grasslands. Science 286, 1123–1127. https://doi.org/10.1126/science.286.5442.1123 (1999).CAS 
    Article 
    PubMed 

    Google Scholar 
    3.Kirwan, L. et al. Evenness drives consistent diversity effects in intensive grassland systems across 28 European sites. J. Ecol. 95, 530–539. https://doi.org/10.1111/j.1365-2745.2007.01225.x (2007).Article 

    Google Scholar 
    4.Sturludóttir, E. et al. Benefits of mixing grasses and legumes for herbage yield and nutritive value in Northern Europe and Canada. Grass Forage Sci. 69, 229–240. https://doi.org/10.1111/gfs.12037 (2014).CAS 
    Article 

    Google Scholar 
    5.Roscher, C. et al. Overyielding in experimental grassland communities – irrespective of species pool or spatial scale. Ecol. Lett. 8, 419–429. https://doi.org/10.1111/j.1461-0248.2005.00736.x (2005).Article 

    Google Scholar 
    6.Prieto, I. et al. Complementary effects of species and genetic diversity on productivity and stability of sown grasslands. Nat. Plants 1, 1–5. https://doi.org/10.1038/nplants.2015.33 (2015).ADS 
    CAS 
    Article 

    Google Scholar 
    7.Fridley, J. D. Resource availability dominates and alters the relationship between species diversity and ecosystem productivity in experimental plant communities. Oecologia 132, 271–277. https://doi.org/10.1007/s00442-002-0965-x (2002).ADS 
    Article 
    PubMed 

    Google Scholar 
    8.Sanderson, M. A. et al. Plant species diversity and management of temperate forage and grazing land ecosystems. Crop Sci. 44, 1132–1144. https://doi.org/10.2135/cropsci2004.1132 (2004).Article 

    Google Scholar 
    9.Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol. Monogr. 75, 3–35. https://doi.org/10.1890/04-0922 (2005).Article 

    Google Scholar 
    10.Spehn, E. M. et al. Ecosystem effects of biodiversity manipulations in European grasslands. Ecol. Monogr. 75, 37–63. https://doi.org/10.1890/03-4101 (2005).Article 

    Google Scholar 
    11.Wright, A. J., Wardle, D. A., Callaway, R. & Gaxiola, A. The overlooked role of facilitation in biodiversity experiments. Trends Ecol. Evol. 32, 383–390. https://doi.org/10.1016/j.tree.2017.02.011 (2017).Article 
    PubMed 

    Google Scholar 
    12.Brophy, C. et al. Major shifts in species’ relative abundance in grassland mixtures alongside positive effects of species diversity in yield: a continental-scale experiment. J. Ecol. 105, 1210–1222. https://doi.org/10.1111/1365-2745.12754 (2017).CAS 
    Article 

    Google Scholar 
    13.Nyfeler, D. et al. Strong mixture effects among four species in fertilized agricultural grassland led to persistent and consistent transgressive overyielding. J. Appl. Ecol. 46, 683–691. https://doi.org/10.1111/j.1365-2664.2009.01653.x (2009).Article 

    Google Scholar 
    14.Søegaard, K., Gierus, M., Hopkins, A. & Halling, M. Temporary grassland – challenges in the future. Grassl. Sci. Eur. 12, 27–38. https://www.europeangrassland.org/fileadmin/documents/Infos/Printed_Matter/Proceedings/EGF2007_GSE_vol12.pdf (2007).15.Høgh-Jensen, H., Nielsen, B. & Thamsborg, S. M. Productivity and quality, competition and facilitation of chicory in ryegrass/legume-based pastures under various nitrogen supply levels. Eur. J. Agron. 24, 247–256. https://doi.org/10.1016/j.eja.2005.10.007 (2006).CAS 
    Article 

    Google Scholar 
    16.Cong, W. F., Jing, J., Rasmussen, J., Søegaard, K. & Eriksen, J. Forbs enhance productivity of unfertilised grass-clover leys and support low-carbon bioenergy. Sci. Rep. 7, 1–10. https://doi.org/10.1038/s41598-017-01632-4 (2017).CAS 
    Article 

    Google Scholar 
    17.Loreau, M. & Hector, A. Partitioning selection and complementarity in biodiversity experiments. Nature 412, 72–76. https://doi.org/10.1038/35083573 (2001).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    18.Loreau, M., Sapijanskas, J., Isbell, F. & Hector, A. Niche and fitness differences relate the maintenance of diversity to ecosystem function: comment. Ecology 93, 1482–1487. https://doi.org/10.1890/11-0792.1 (2012).Article 
    PubMed 

    Google Scholar 
    19.Montazeaud, G. et al. Crop mixtures: does niche complementarity hold for belowground resources? An experimental test using rice genotypic pairs. Plant Soil 424, 187–202. https://doi.org/10.1007/s11104-017-3496-2 (2018).CAS 
    Article 

    Google Scholar 
    20.Vermeulen, P. J., Van Ruijven, J., Anten, N. P. & Van der Werf, W. An evolutionary game theoretical model shows the limitations of the additive partitioning method for interpreting biodiversity experiments. J. Ecol. 105, 345–353. https://doi.org/10.1111/1365-2745.12706 (2017).Article 

    Google Scholar 
    21.Hille Ris Lambers, J. H. R., Harpole, W. S., Tilman, D., Knops, J. & Reich, P. B. Mechanisms responsible for the positive diversity–productivity relationship in Minnesota grasslands. Ecol. Lett. 7, 661–668. https://doi.org/10.1111/j.1461-0248.2004.00623.x (2004).22.Marquard, E. et al. Plant species richness and functional composition drive overyielding in a six-year grassland experiment. Ecology 90, 3290–3302. https://doi.org/10.1890/09-0069.1 (2009).Article 
    PubMed 

    Google Scholar 
    23.Lüscher, A., Suter, M., Finn, J., Collins, R. & Gastal, F. Quantification of the effect of legume proportion in the sward on yield advantage and options to keep stable legume proportions (over climatic zones relevant for livestock production). HAL 7, 1–35. https://hal.archives-ouvertes.fr/hal-01611404 (2014).24.Roscher, C., Schumacher, J., Weisser, W. W., Schmid, B. & Schulze, E. D. Detecting the role of individual species for overyielding in experimental grassland communities composed of potentially dominant species. Oecologia 154, 535–549. https://doi.org/10.1007/s00442-007-0846-4 (2007).ADS 
    Article 
    PubMed 

    Google Scholar 
    25.Finn, J. A. et al. Ecosystem function enhanced by combining four functional types of plant species in intensively managed grassland mixtures: a 3-year continental-scale field experiment. J. Appl. Ecol. 50, 365–375. https://doi.org/10.1111/1365-2664.12041 (2013).Article 

    Google Scholar 
    26.Grange, G., Finn, J. A. & Brophy, C. Plant diversity enhanced yield and mitigated drought impacts in intensively managed grassland communities. J. Appl. Ecol. 58, 1864–1875. https://doi.org/10.1111/1365-2664.13894 (2021).CAS 
    Article 

    Google Scholar 
    27.Cardinale, B. J. et al. Impacts of plant diversity on biomass production increase through time because of species complementarity. Proc. Natl. Acad. Sci. U.S.A. 104, 18123–18128. https://doi.org/10.1073/pnas.0709069104 (2007).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    28.Marquard, E. et al. Changes in the abundance of grassland species in monocultures versus mixtures and their relation to biodiversity effects. PLoS ONE 8, e75599. https://doi.org/10.1371/journal.pone.0075599 (2013).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    29.Guerrero-Ramírez, N. R. et al. Diversity-dependent temporal divergence of ecosystem functioning in experimental ecosystems. Nat. Ecol. Evol. 1, 1639–1642. https://doi.org/10.1038/s41559-017-0325-1 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    30.Fargione, J. et al. From selection to complementarity: shifts in the causes of biodiversity-productivity relationships in a long-term biodiversity experiment. Proc. Royal Soc. B 274, 871–876. https://doi.org/10.1098/rspb.2006.0351 (2007).Article 

    Google Scholar 
    31.Grant, K., Kreyling, J., Heilmeier, H., Beierkuhnlein, C. & Jentsch, A. Extreme weather events and plant-plant interactions: shifts between competition and facilitation among grassland species in the face of drought and heavy rainfall. Ecol. Res. 29, 991–1001. https://doi.org/10.1007/s11284-014-1187-5 (2014).Article 

    Google Scholar 
    32.Reich, P. B. et al. Impacts of biodiversity loss escalate through time as redundancy fades. Science 336, 589–592. https://doi.org/10.1126/science.1217909 (2012).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    33.Roscher, C., Schmid, B., Kolle, O. & Schulze, E. D. Complementarity among four highly productive grassland species depends on resource availability. Oecologia 181, 571–582. https://doi.org/10.1007/s00442-016-3587-4 (2016).ADS 
    Article 
    PubMed 

    Google Scholar 
    34.Frankow-Lindberg, B. E., Brophy, C., Collins, R. P. & Connolly, J. Biodiversity effects on yield and unsown species invasion in a temperate forage ecosystem. Ann. Bot. 103, 913–921. https://doi.org/10.1093/aob/mcp008 (2009).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    35.Caradus, J. R. & Woodfield, D. R. World checklist of white clover varieties II. New Zealand J. Agric. Res. 40, 115–206. https://doi.org/10.1080/00288233.1997.9513239 (1997).Article 

    Google Scholar 
    36.Annicchiarico, P., Barrett, B., Brummer, E. C., Julier, B. & Marshall, A. H. Achievements and challenges in improving temperate perennial forage legumes. Crit. Rev. Plant Sci. 34, 327–380. https://doi.org/10.1080/07352689.2014.898462 (2015).CAS 
    Article 

    Google Scholar 
    37.Abberton, M. T. & Marshall, A. H. Progress in breeding perennial clovers for temperate agriculture. J. Agric. Sci. 143, 117–135. https://doi.org/10.1017/S0021859605005101 (2005).Article 

    Google Scholar 
    38.Evans, D. R., Williams, T. A. & Mason, S. A. Contribution of white clover varieties to total sward production under typical farm management. Grass Forage Sci. 45, 129–134. https://doi.org/10.1111/j.1365-2494.1990.tb02193.x (1990).Article 

    Google Scholar 
    39.Hooper, D. U. & Dukes, J. S. Overyielding among plant functional groups in a long-term experiment. Ecol. Lett. 7, 95–105. https://doi.org/10.1046/j.1461-0248.2003.00555.x (2004).Article 

    Google Scholar 
    40.Ergon, Å. et al. Species interactions in a grassland mixture under low nitrogen fertilization and two cutting frequencies: 1. dry-matter yield and dynamics of species composition. Grass Forage Sci. 71, 667–682. https://doi.org/10.1111/gfs.12250 (2016).CAS 
    Article 

    Google Scholar 
    41.Van Ruijven, J. & Berendse, F. Diversity–productivity relationships: Initial effects, long-term patterns, and underlying mechanisms. Proc. Natl. Acad. Sci. U.S.A. 102, 695–700. https://doi.org/10.1073/pnas.0407524102 (2005).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    42.Barry, K. E. et al. The future of complementarity: disentangling causes from consequences. Trends Ecol. Evol. 34, 167–180. https://doi.org/10.1016/j.tree.2018.10.013 (2019).Article 
    PubMed 

    Google Scholar 
    43.Annicchiarico, P. Breeding white clover for increased ability to compete with associated grasses. J. Agric. Sci. 140, 255–266. https://doi.org/10.1017/S0021859603003198 (2003).Article 

    Google Scholar 
    44.Zuppinger-Dingley, D. et al. Selection for niche differentiation in plant communities increases biodiversity effects. Nature 515, 108–111. https://doi.org/10.1038/nature13869 (2014).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    45.Schöb, C. et al. Intraspecific genetic diversity and composition modify species-level diversity–productivity relationships. New Phytol. 205, 720–730. https://doi.org/10.1111/nph.13043 (2015).Article 
    PubMed 

    Google Scholar 
    46.Barot, S. et al. Designing mixtures of varieties for multifunctional agriculture with the help of ecology A review. Agron. Sustain. Dev. 37, 13. https://doi.org/10.1007/s13593-017-0418-x (2017).Article 

    Google Scholar 
    47.Van Ruijven, J. & Berendse, F. Long-term persistence of a positive plant diversity–productivity relationship in the absence of legumes. Oikos 118, 101–106. https://doi.org/10.1111/j.1600-0706.2008.17119.x (2009).Article 

    Google Scholar 
    48.Roscher, C. et al. A functional trait-based approach to understand community assembly and diversity–productivity relationships over 7 years in experimental grasslands. Perspect. Plant Ecol. Evol. Syst. 15, 139–149. https://doi.org/10.1016/j.ppees.2013.02.004 (2013).Article 

    Google Scholar 
    49.Roscher, C., Thein, S., Schmid, B. & Scherer-Lorenzen, M. Complementary nitrogen use among potentially dominant species in a biodiversity experiment varies between two years. J. Ecol. 96, 477–488. https://doi.org/10.1111/j.1365-2745.2008.01353.x (2008).Article 

    Google Scholar 
    50.Kayser, M., Müller, J. & Isselstein, J. Grassland renovation has important consequences for C and N cycling and losses. Food Energy Secur. 7, e00146. https://doi.org/10.1002/fes3.146 (2018).Article 

    Google Scholar 
    51.Bundessortenamt. Beschreibende Sortenliste Futtergräser Esparsette, Klee, Luzerne 2020. https://www.bundessortenamt.de/bsa/media/Files/BSL/bsl_futtergraeser_2020.pdf (2020).52.Rognli, O. A., Pecetti, L., Kovi, M. R. & Annicchiarico, P. Grass and legume breeding matching the future needs of European grassland farming. Grass Forage Sci. 76, 175–185. https://doi.org/10.1111/gfs.12535 (2021).Article 

    Google Scholar 
    53.Annicchiarico, P. et al. Do we need specific breeding for legume-based mixtures?. Adv. Agron. 157, 141–215. https://doi.org/10.1016/bs.agron.2019.04.001 (2019).Article 

    Google Scholar 
    54.Sampoux, J. P., Giraud, H. & Litrico, I. Which recurrent selection scheme to improve mixtures of crop species? Theoretical expectations. G3-Genes Genom. Genet. 10, 89–107. https://doi.org/10.1534/g3.120.401092 (2020).55.Hofer, D. et al. Yield of temperate forage grassland species is either largely resistant or resilient to experimental summer drought. J. Appl. Ecol. 53, 1023–1034. https://doi.org/10.1111/1365-2664.12694 (2016).Article 

    Google Scholar 
    56.Pinheiro, J. et al. Nlme: Linear and nonlinear mixed effects models. R package version 3.1–137. https://CRAN.R-project.org/package=nlme (2018).57.Bartón, K. MuMIn: Multi-model inference. R package version 1.43.6. https://CRAN.R-project.org/package=MuMIn (2019). More

  • in

    Heat sensitivity of first host and cercariae may restrict parasite transmission in a warming sea

    1.Harley, C. D. G. et al. The impacts of climate change in coastal marine systems. Ecol. Lett. 9, 228–241 (2006).ADS 
    PubMed 

    Google Scholar 
    2.Poloczanska, E. S. et al. Global imprint of climate change on marine life. Nat. Clim. Change 3, 919–925 (2013).ADS 

    Google Scholar 
    3.Hoegh-Guldberg, O. et al. Impacts of 1.5 °C global warming on natural and human systems. In: […]. in Global Warming of 1.5 °C. An IPCC Special Report on the Impacts of Global Warming of 1.5 °C Above Preindustrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change (eds. Masson-Delmotte, V. et al.) 175–311 (2018).4.Gunderson, A. R., Armstrong, E. J. & Stillman, J. H. Multiple stressors in a changing world: The need for an improved perspective on physiological responses to the dynamic marine environment. Ann. Rev. Mar. Sci. 8, 357–378 (2016).PubMed 

    Google Scholar 
    5.Ban, S. S., Graham, N. A. J. & Connolly, S. R. Evidence for multiple stressor interactions and effects on coral reefs. Glob. Change Biol. 20, 681–697 (2014).ADS 

    Google Scholar 
    6.Marcogliese, D. J. The impact of climate change on the parasites and infectious diseases of aquatic animals. OIE Rev. Sci. Tech. 27, 467–484 (2008).CAS 

    Google Scholar 
    7.Urban, M. C., Tewksbury, J. J. & Sheldon, K. S. On a collision course: Competition and dispersal differences create no-analogue communities and cause extinctions during climate change. Proc. R. Soc. B Biol. Sci. 279, 2072–2080 (2012).
    Google Scholar 
    8.Mouritsen, K. N. & Poulin, R. Parasitism, climate oscillations and the structure of natural communities. Oikos 97, 462–468 (2002).
    Google Scholar 
    9.Poulin, R. & Mouritsen, K. N. Climate change, parasitism and the structure of intertidal ecosystems. J. Helminthol. 80, 183–191 (2006).CAS 
    PubMed 

    Google Scholar 
    10.Mouritsen, K. N., Sørensen, M. M., Poulin, R. & Fredensborg, B. L. Coastal ecosystems on a tipping point: Global warming and parasitism combine to alter community structure and function. Glob. Change Biol. 24, 4340–4356 (2018).ADS 

    Google Scholar 
    11.James, C. C. et al. Marine host–pathogen dynamics: Influences of global climate change. Oceanography 31, 182–193 (2018).
    Google Scholar 
    12.Friesen, O. C., Poulin, R. & Lagrue, C. Temperature and multiple parasites combine to alter host community structure. Oikos 130(9), 1500–1511 (2021).

    Google Scholar 
    13.Poulin, R. Parasite biodiversity revisited: Frontiers and constraints. Int. J. Parasitol. 44, 581–589 (2014).PubMed 

    Google Scholar 
    14.Kuris, A. M. et al. Ecosystem energetic implications of parasite and free-living biomass in three estuaries. Nature 454, 515–518 (2008).ADS 
    CAS 
    PubMed 

    Google Scholar 
    15.Galaktionov, K. V. & Dobrovolskij, A. A. The Biology and Evolution of Trematodes: An Essay on the Biology, Morphology, Life Cycles, Transmissions, and Evolution of Digenetic Trematodes. The American Journal of Semiotics vol. 4 (Springer-Science+Business Media Dordrecht, 2003).16.Thieltges, D. W., Jensen, K. T. & Poulin, R. The role of biotic factors in the transmission of free-living endohelminth stages. Parasitology 135, 407–426 (2008).CAS 
    PubMed 

    Google Scholar 
    17.Pietrock, M. & Marcogliese, D. J. Free-living endohelminth stages: At the mercy of environmental conditions. Trends Parasitol. 19, 293–299 (2003).PubMed 

    Google Scholar 
    18.Morley, N. J. Thermodynamics of cercarial survival and metabolism in a changing climate. Parasitology 138, 1442–1452 (2011).CAS 
    PubMed 

    Google Scholar 
    19.Poulin, R. Global warming and temperature-mediated increases in cercarial emergence in trematode parasites. Parasitology 132, 143–151 (2006).CAS 
    PubMed 

    Google Scholar 
    20.Thieltges, D. W. & Rick, J. Effect of temperature on emergence, survival and infectivity of cercariae of the marine trematode Renicola roscovita (Digenea: Renicolidae). Dis. Aquat. Organ. 73, 63–68 (2006).PubMed 

    Google Scholar 
    21.Selbach, C. & Poulin, R. Some like it hotter: Trematode transmission under changing temperature conditions. Oecologia 194, 745–755 (2020).ADS 
    PubMed 

    Google Scholar 
    22.Morley, N. J. Inbred laboratory cultures and natural trematode transmission under climate change. Trends Parasitol. 27, 286–287 (2011).PubMed 

    Google Scholar 
    23.Paull, S. H. & Johnson, P. T. J. Experimental warming drives a seasonal shift in the timing of host–parasite dynamics with consequences for disease risk. Ecol. Lett. 17, 445–453 (2014).PubMed 

    Google Scholar 
    24.Paull, S. H., Lafonte, B. E. & Johnson, P. T. J. Temperature-driven shifts in a host–parasite interaction drive nonlinear changes in disease risk. Glob. Change Biol. 18, 3558–3567 (2012).ADS 

    Google Scholar 
    25.Studer, A., Poulin, R. & Tompkins, D. M. Local effects of a global problem: Modelling the risk of parasite-induced mortality in an intertidal trematode-amphipod system. Oecologia 172, 1213–1222 (2013).ADS 
    CAS 
    PubMed 

    Google Scholar 
    26.Studer, A., Thieltges, D. W. & Poulin, R. Parasites and global warming: Net effects of temperature on an intertidal host–parasite system. Mar. Ecol. Prog. Ser. 415, 11–22 (2010).ADS 

    Google Scholar 
    27.Mouritsen, K. N., Tompkins, D. M. & Poulin, R. Climate warming may cause a parasite-induced collapse in coastal amphipod populations. Oecologia 146, 476–483 (2005).ADS 
    PubMed 

    Google Scholar 
    28.Selbach, C., Barsøe, M., Vogensen, T. K., Samsing, A. B. & Mouritsen, K. N. Temperature–parasite interaction: Do trematode infections protect against heat stress?. Int. J. Parasitol. 50, 1189–1194 (2020).PubMed 

    Google Scholar 
    29.Werding, B. Morphologie, Entwicklung und Ökologie digener Trematoden-Larven der Strandschnecke Littorina littorea. Mar. Biol. 3, 306–333 (1969).
    Google Scholar 
    30.Somero, G. N. The physiology of climate change: How potentials for acclimatization and genetic adaptation will determine ‘winners’ and ‘losers’. J. Exp. Biol. 213, 912–920 (2010).CAS 
    PubMed 

    Google Scholar 
    31.Fredensborg, B. L., Mouritsen, K. N. & Poulin, R. Impact of trematodes on host survival and population density in the intertidal gastropod Zeacumantus subcarcinatus. Mar. Ecol. Prog. Ser. 290, 109–117 (2005).ADS 

    Google Scholar 
    32.Morón Lugo, S. C. et al. Warming and temperature variability determine the performance of two invertebrate predators. Sci. Rep. 10, 1–14 (2020).ADS 

    Google Scholar 
    33.Wolf, F. et al. High resolution water temperature data between January 1997 and December 2018 at the GEOMAR pier surface. Bremen PANGAEA. https://doi.org/10.1594/PANGAEA.919186 (2020).34.Franz, M., Lieberum, C., Bock, G. & Karez, R. Environmental parameters of shallow water habitats in the SW Baltic Sea. Earth Syst. Sci. Data 11, 947–957 (2019).ADS 

    Google Scholar 
    35.Lajeunesse, M. J. Bias and correction for the log response ratio in ecological meta-analysis. Ecology 96, 2056–2063 (2015).PubMed 

    Google Scholar 
    36.Gräwe, U., Friedland, R. & Burchard, H. The future of the western Baltic Sea: Two possible scenarios. Ocean Dyn. 63, 901–921 (2013).ADS 

    Google Scholar 
    37.Pansch, C. et al. Heat waves and their significance for a temperate benthic community: A near-natural experimental approach. Glob. Change Biol. 24, 4357–4367 (2018).ADS 

    Google Scholar 
    38.Sokolova, I. M., Frederich, M., Bagwe, R., Lannig, G. & Sukhotin, A. A. Energy homeostasis as an integrative tool for assessing limits of environmental stress tolerance in aquatic invertebrates. Mar. Environ. Res. 79, 1–15 (2012).CAS 
    PubMed 

    Google Scholar 
    39.Clarke, A. P., Mill, P. J. & Grahame, J. The nature of heat coma in Littorina littorea (Mollusca: Gastropoda). Mar. Biol. 137, 447–451 (2000).
    Google Scholar 
    40.McDaniel, S. J. Littorina littorea: Lowered heat tolerance due to Cryptocotyle lingua. Exp. Parasitol. 25, 13–15 (1969).CAS 
    PubMed 

    Google Scholar 
    41.Ataev, G. Temperature influence on the development and biology of rediae and cercariae of Philophthalmus rhionica (Trematoda). Parazitologiâ 25, 349–359 (1991).
    Google Scholar 
    42.Paull, S. H. & Johnson, P. T. J. High temperature enhances host pathology in a snail-trematode system: Possible consequences of climate change for the emergence of disease. Freshw. Biol. 56, 767–778 (2011).
    Google Scholar 
    43.Paull, S. H., Raffel, T. R., Lafonte, B. E. & Johnson, P. T. J. How temperature shifts affect parasite production: Testing the roles of thermal stress and acclimation. Funct. Ecol. 29, 941–950 (2015).
    Google Scholar 
    44.Kuris, A. M. Effect of exposure to Echinostoma liei miracidia on growth and survival of young Biomphalaria glabrata snails. Int. J. Parasitol. 10, 303–308 (1980).CAS 
    PubMed 

    Google Scholar 
    45.Mouritsen, K. N. & Haun, S. C. B. Community regulation by herbivore parasitism and density: Trait-mediated indirect interactions in the intertidal. J. Exp. Mar. Biol. Ecol. 367, 236–246 (2008).
    Google Scholar 
    46.Bommarito, C. et al. Effects of first intermediate host density, host size and salinity on trematode infections in mussels of the south-western Baltic Sea. Parasitology 148, 486–494 (2021).CAS 
    PubMed 

    Google Scholar 
    47.McCarthy, A. M. The influence of temperature on the survival and infectivity of the cercariae of Echinoparyphium recurvatum (Digenea: Echinostomatidae). Parasitology 118, 383–388 (1999).PubMed 

    Google Scholar 
    48.Morley, N. J. & Lewis, J. W. Thermodynamics of trematode infectivity. Parasitology 142, 585–597 (2015).CAS 
    PubMed 

    Google Scholar 
    49.Mouritsen, K. N. & Jensen, K. T. Parasite transmission between soft-bottom invertebrates: Temperature mediated infection rates and mortality in Corophium volutator. Mar. Ecol. Prog. Ser. 151, 123–134 (1997).ADS 

    Google Scholar 
    50.de Montaudouin, X., Wegeberg, A. M., Jensen, K. T. & Sauriau, P. G. Infection characteristics of Himasthla elongata cercariae in cockles as a function of water current. Dis. Aquat. Organ. 34, 63–70 (1998).
    Google Scholar 
    51.Vajedsamiei, J. et al. Simultaneous recording of filtration and respiration in marine organisms in response to short-term environmental variability. Limnol. Oceanogr. Methods https://doi.org/10.1002/lom3.10414 (2021).Article 

    Google Scholar 
    52.Koehler, A. V., Brown, B., Poulin, R., Thieltges, D. W. & Fredensborg, B. L. Disentangling phylogenetic constraints from selective forces in the evolution of trematode transmission stages. Evol. Ecol. 26, 1497–1512 (2012).
    Google Scholar 
    53.Stunkard, H. W. The morphology and life history of the digenetic trematode, Himasthla littorinae sp. n. (Echinostomatidae). J. Parasitol. 52, 367–372 (2014).
    Google Scholar 
    54.Selbach, C. & Poulin, R. Parasites in space and time: A novel method to assess and illustrate host-searching behaviour of trematode cercariae. Parasitology 145, 1469–1474 (2018).PubMed 

    Google Scholar 
    55.Gorbushin, A. M. & Levakin, I. A. Encystment in vitro of the cercariae Himasthla elongata (Trematoda: Echinostomatidae). J. Evol. Biochem. Physiol. 41, 428–436 (2005).
    Google Scholar 
    56.Gorbushin, A. M. & Shaposhnikova, T. G. In vitro culture of the avian echinostome Himasthla elongata: From redia to marita. Exp. Parasitol. 101, 234–239 (2002).CAS 
    PubMed 

    Google Scholar 
    57.Levakin, I. A., Losev, E. A., Nikolaev, K. E. & Galaktionov, K. V. In vitro encystment of Himasthla elongata cercariae (Digenea, Echinostomatidae) in the haemolymph of blue mussels Mytilus edulis as a tool for assessing cercarial infectivity and molluscan susceptibility. J. Helminthol. 87, 180–188 (2013).CAS 
    PubMed 

    Google Scholar 
    58.Choisy, M., Brown, S. P., Lafferty, K. D. & Thomas, F. Evolution of trophic transmission in parasites: Why add intermediate hosts?. Am. Nat. 162, 172–181 (2003).PubMed 

    Google Scholar 
    59.Pechenik, J. & Fried, B. Effect of temperature on survival and infectivity of Echinostoma trivolvis cercariae: A test of the energy limitation hypothesis. Parasitology 111, 373–378 (1995).
    Google Scholar 
    60.Fried, B. & Ponder, E. L. Effects of temperature on survival, infectivity and in vitro encystment of the cercariae of Echinostoma caproni. J. Helminthol. 77, 235–238 (2003).CAS 
    PubMed 

    Google Scholar 
    61.Bommarito, C. et al. Freshening rather than warming drives trematode transmission from periwinkles to mussels. Mar. Biol. 167, 1–12 (2020).
    Google Scholar 
    62.Morley, N. J. & Lewis, J. W. Thermodynamics of cercarial development and emergence in trematodes. Parasitology 140, 121–1214 (2013).
    Google Scholar 
    63.Büttger, H. et al. Community dynamics of intertidal soft-bottom mussel beds over two decades. Helgol. Mar. Res. 62, 23–36 (2008).ADS 

    Google Scholar 
    64.Jaatinen, K., Westerbom, M., Norkko, A., Mustonen, O. & Koons, D. N. Detrimental impacts of climate change may be exacerbated by density-dependent population regulation in blue mussels. J. Anim. Ecol. 90, 562–573 (2021).PubMed 

    Google Scholar 
    65.Studer, A. & Poulin, R. Analysis of trait mean and variability versus temperature in trematode cercariae: Is there scope for adaptation to global warming?. Int. J. Parasitol. 44, 403–413 (2014).CAS 
    PubMed 

    Google Scholar 
    66.Berkhout, B. W., Lloyd, M. M., Poulin, R. & Studer, A. Variation among genotypes in responses to increasing temperature in a marine parasite: Evolutionary potential in the face of global warming?. Int. J. Parasitol. 44, 1019–1027 (2014).PubMed 

    Google Scholar 
    67.Vanoverschelde, R. Studies on the life-cycle of Himasthla militaris (Trematoda: Echinostomatidae): Influence of salinity and temperature on egg development and miracidial emergence. Parasitology 82, 459–465 (1981).
    Google Scholar 
    68.Vanoverschelde, R. Studies on the life-cycle of Himasthla militaris (Trematoda: Echinostomatidae): Influence of temperature and salinity on the life-span of the miracidium and the infection of the first intermediate host, Hydrobia ventrosa. Parasitology 84, 131–135 (1982).
    Google Scholar 
    69.de Montaudouin, X. et al. Digenean trematode species in the cockle Cerastoderma edule: Identification key and distribution along the North-Eastern Atlantic Shoreline. J. Mar. Biol. Assoc. U.K. 89, 543–556 (2009).
    Google Scholar 
    70.Richard, A., de Montaudouin, X., Rubiello, A. & Maire, O. Cockle as second intermediate host of trematode parasites: Consequences for sediment bioturbation and nutrient fluxes across the benthic interface. J. Mar. Sci. Eng. 9, 749 (2021).
    Google Scholar 
    71.Magalhães, L., Freitas, R. & de Montaudouin, X. How costly are metacercarial infections in a bivalve host? Effects of two trematode species on biochemical performance of cockles. J. Invertebr. Pathol. 177, 107479 (2020).PubMed 

    Google Scholar 
    72.Magalhães, L., de Montaudouin, X., Figueira, E. & Freitas, R. Trematode infection modulates cockles biochemical response to climate change. Sci. Total Environ. 637–638, 30–40 (2018).ADS 
    PubMed 

    Google Scholar 
    73.Magalhães, L. et al. Seasonal variation of transcriptomic and biochemical parameters of cockles (Cerastoderma edule) related to their infection by trematode parasites. J. Invertebr. Pathol. 148, 73–80 (2017).PubMed 

    Google Scholar 
    74.Bakhmet, I., Nikolaev, K. & Levakin, I. Effect of infection with Metacercariae of Himasthla elongata (Trematoda: Echinostomatidae) on cardiac activity and growth rate in blue mussels (Mytilus edulis) in situ. J. Sea Res. 123, 51–54 (2017).ADS 

    Google Scholar 
    75.Stier, T., Drent, J. & Thieltges, D. W. Trematode infections reduce clearance rates and condition in blue mussels Mytilus edulis. Mar. Ecol. Prog. Ser. 529, 137–144 (2015).ADS 

    Google Scholar 
    76.de Montaudouin, X., Bazairi, H. & Culloty, S. Effect of trematode parasites on cockle Cerastoderma edule growth and condition index: A transplant experiment. Mar. Ecol. Prog. Ser. 471, 111–121 (2012).ADS 

    Google Scholar 
    77.Seuront, L., Nicastro, K. R., Zardi, G. I. & Goberville, E. Decreased thermal tolerance under recurrent heat stress conditions explains summer mass mortality of the blue mussel Mytilus edulis. Sci. Rep. 9, 1–14 (2019).ADS 

    Google Scholar 
    78.Österblom, H. et al. Human-induced trophic cascades and ecological regime shifts in the baltic sea. Ecosystems 10, 877–889 (2007).
    Google Scholar 
    79.Zander, C. D. & Reimer, L. W. Parasitism at the ecosystem level in the Baltic Sea. Parasitology 124, 119–135 (2002).
    Google Scholar 
    80.Johnson, P. T. J. et al. Aquatic eutrophication promotes pathogenic infection in amphibians. Proc. Natl. Acad. Sci. U. S. A. 104, 15781–15786 (2007).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    81.Budria, A. & Candolin, U. How does human-induced environmental change influence host–parasite interactions?. Parasitology 141, 462–474 (2014).PubMed 

    Google Scholar 
    82.Aalto, S. L., Decaestecker, E. & Pulkkinen, K. A three-way perspective of stoichiometric changes on host–parasite interactions. Trends Parasitol. 31, 333–340 (2015).PubMed 

    Google Scholar 
    83.Vajedsamiei, J., Melzner, F., Raatz, M., Moron, S. & Pansch, C. Cyclic thermal fluctuations can be burden or relief for an ectotherm depending on fluctuations’ average and amplitude. Funct. Ecol. 35, 2483–2496 (2021).
    Google Scholar 
    84.Moisez, E., Spilmont, N. & Seuront, L. Microhabitats choice in intertidal gastropods is species-, temperature- and habitat-specific. J. Therm. Biol. 94, 102785 (2020).PubMed 

    Google Scholar 
    85.Bates, A. E., Leiterer, F., Wiedeback, M. L. & Poulin, R. Parasitized snails take the heat: A case of host manipulation?. Oecologia 167, 613–621 (2011).ADS 
    CAS 
    PubMed 

    Google Scholar 
    86.Shinagawa, K., Urabe, M. & Nagoshi, M. Relationships between trematode infection and habitat depth in a freshwater snail, Semisulcospira libertina (Gould). Hydrobiologia 397, 171–178 (1999).
    Google Scholar 
    87.Friesen, O. C., Poulin, R. & Lagrue, C. Parasite-mediated microhabitat segregation between congeneric hosts. Biol. Lett. 14, 20170671 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    88.Welsh, J. E., Van Der Meer, J., Brussaard, C. P. D. & Thieltges, D. W. Inventory of organisms interfering with transmission of a marine trematode. J. Mar. Biol. Assoc. U.K. 94, 697–702 (2014).
    Google Scholar 
    89.Soldánová, M., Selbach, C. & Sures, B. The early worm catches the bird? Productivity and patterns of Trichobilharzia szidati cercarial emission from Lymnaea stagnalis. PLoS One 11, 1–21 (2016).
    Google Scholar 
    90.Solovyeva, A. et al. Reduced infectivity in Himasthla elongata (Trematoda, Himasthlidae) cercariae with deviant photoreaction. J. Helminthol. 94, 1–5 (2020).
    Google Scholar 
    91.de Montaudouin, X., Blanchet, H., Desclaux-Marchand, C., Lavesque, N. & Bachelet, G. Cockle infection by Himasthla quissetensis—I. From cercariae emergence to metacercariae infection. J. Sea Res. 113, 99–107 (2016).
    Google Scholar 
    92.Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R. Statistics for Biology and Health vol. 36 (Springer, 2009).93.Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).
    Google Scholar 
    94.Hartig, F. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level/Mixed) Regression Models. R package version 0.2.0. (2018).
    https://CRAN.R-project.org/package=DHARMa 1–36 https://cran.r-project.org/web/packages/DHARMa/DHARMa.pdf. Accessed 26 Feb 2021.95.Wood, S. N. Generalized Additive Models: An Introduction with R 2nd edn. (CRC, 2017).MATH 

    Google Scholar 
    96.Wood, S. N., Pya, N. & Säfken, B. Smoothing parameter and model selection for general smooth models. J. Am. Stat. Assoc. 111, 1548–1563 (2016).MathSciNet 
    CAS 

    Google Scholar 
    97.Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142 (2013).
    Google Scholar 
    98.Nakagawa, S., Johnson, P. C. D. & Schielzeth, H. The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. J. R. Soc. Interface 14, 1–11 (2017).
    Google Scholar 
    99.Lüdecke, D., Makowski, D., Waggoner, P. & Patil, I. Assessment of Regression Models Performance. CRAN. CRAN https://easystats.github.io/performance/ (2020) https://doi.org/10.1098/rsif.2017.0213. Accessed 1 Sept 2021.100.Fox, J. & Weisberg, S. An R Companion to Applied Regression. Robust Regression in R (Sage, 2019).
    Google Scholar 
    101.Hedges, L. V., Gurevitch, J. & Curtis, P. S. The meta-analysis of response ratios in experimental ecology. Ecology 80, 1150–1156 (1999).
    Google Scholar  More

  • in

    Risk assessment of microplastic particles

    1.Science Advice for Policy by European Academies. A scientific perspective on microplastics in nature and society (SAPEA, 2019). Expert group report summarizing the state of the science regarding microplastics in nature and society.2.Koelmans, A. A. et al. Risks of plastic debris: Unravelling fact, opinion, perception and belief. Environ. Sci. Technol. 51, 11513–11519 (2017).CAS 

    Google Scholar 
    3.Henderson, L. & Green, C. Making sense of microplastics? Public understandings of plastic pollution. Mar. Pollut. Bull. 152, 110908 (2020).CAS 

    Google Scholar 
    4.Group of Experts on the Scientific Aspects of Marine Environmental Protection (GESAMP). Sources, fate and effects of microplastics in the marine environment. Part two of a global assessment (eds Kershaw, P. J. & Rochman, C. M.) (IMO/FAO/UNESCO-IOC/UNIDO/WMO/IAEA/UN/UNEP/UNDP, 2016).5.Arthur, C., Baker, J. & Bamford, H. (eds) NOAA technical memorandum NOS-OR&R-30. In Proc. Int. Res. Worksh. Occurrence, Effects and Fate of Microplastic Marine Debris (NOAA, 2009).6.European Chemicals Agency. Annex XV restriction report proposal for a restriction: intentionally added microplastics. Version 1.2. Proposal 1.2. ECA https://echa.europa.eu/documents/10162/05bd96e3-b969-0a7c-c6d0-441182893720 (2019).7.Coffin, S. Proposed definition of ‘microplastics in drinking water’. California Water Boards https://www.waterboards.ca.gov/drinking_water/certlic/drinkingwater/docs/stffrprt_jun3.pdf (2020).8.Andrady, A. L. Microplastics in the marine environment. Mar. Pollut. Bull. 62, 1596–1605 (2011).CAS 

    Google Scholar 
    9.Kooi, M. & Koelmans, A. A. Simplifying microplastic via continuous probability distributions for size, shape and density. Environ. Sci. Technol. Lett. 6, 551–557 (2019). This paper introduces the concept of describing microplastic characteristics through continuous PDFs, allowing us to capture the diversity of microplastics as a single contaminant in transport, exposure and risk assessment, rather than across many separate categories.CAS 

    Google Scholar 
    10.Rochman, C. M. et al. Rethinking microplastics as a diverse contaminant suite. Environ. Toxicol. Chem. 38, 703–711 (2019).CAS 

    Google Scholar 
    11.Kooi, M., Besseling, E., Kroeze, C., van Wezel, A. P. & Koelmans, A. A. Modelling the fate and transport of plastic debris in fresh waters. Review and guidance. In Freshwater Microplastics. The Handbook of Environmental Chemistry Vol. 58 (eds Wagner M. & Lambert S.) 125–152 (Springer, 2017).12.Hardesty, B. D. et al. Using numerical model simulations to improve the understanding of micro-plastic distribution and pathways in the marine environment. Front. Mar. Sci. 4, 1–30 (2017).
    Google Scholar 
    13.Redondo-Hasselerharm, P. E., Falahudin, D., Peeters, E. T. H. M. & Koelmans, A. A. Microplastic effect thresholds for freshwater benthic macroinvertebrates. Environ. Sci. Technol. 52, 2278–2286 (2018).CAS 

    Google Scholar 
    14.Adam, V., Yang, T. & Nowack, B. Toward an ecotoxicological risk assessment of microplastics: comparison of available hazard and exposure data in freshwaters. Environ. Toxicol. Chem. 38, 436–447 (2019). This paper introduces probabilistic SSDs for microplastic particles.CAS 

    Google Scholar 
    15.Koelmans, A. A., Diepens N. J. & Mohamed Nor, N. H. Weight of evidence for the microplastic vector effect in the context of chemical risk assessment. In Microplastic in the Environment: Pattern and Process (ed. Bank, M. S.) (Springer, 2021).16.Besseling, E., Redondo-Hasselerharm, P. E., Foekema, E. M. & Koelmans, A. A. Quantifying ecological risks of aquatic micro- and nanoplastic. Crit. Rev. Environ. Sci. Technol. 49, 32–80 (2019).
    Google Scholar 
    17.Burns, E. E. & Boxall, A. B. A. Microplastics in the aquatic environment: evidence for or against adverse impacts and major knowledge gaps. Environ. Toxicol. Chem. 37, 2776–2796 (2018).CAS 

    Google Scholar 
    18.Wright, S. L. & Kelly, F. J. Plastic and human health: a micro issue? Environ. Sci. Technol. 51, 6634–6647 (2017). This is a thorough review and outlook on the implications of plastic for human health.CAS 

    Google Scholar 
    19.Mohamed Nor, N. H., Kooi, M., Diepens, N. J. & Koelmans, A. A. Lifetime accumulation of nano- and microplastic in children and adults. Environ. Sci. Technol. 55, 5084–5096 (2021). This paper is the first probabilistic and aligned microplastic exposure assessment for humans, using PDFs.CAS 

    Google Scholar 
    20.Noventa, S. et al. Paradigms to assess the human health risks of nano- and microplastics. Micropl. Nanopl. 1, 9 (2021).
    Google Scholar 
    21.Ramsperger, A. F. R. M. et al. Environmental exposure enhances the internalization of microplastic particles into cells. Sci. Adv. 6, eabd1211 (2020).CAS 

    Google Scholar 
    22.Rejman, J., Oberle, V., Zuhorn, I. S. & Hoekstra, D. Size-dependent internalization of particles via the pathways of clathrin- and caveolae-mediated endocytosis. Biochem. J. 377, 159–169 (2004).CAS 

    Google Scholar 
    23.Ragusa, A. et al. Plasticenta: first evidence of microplastics in human placenta. Environ. Int. 146, 106274 (2021).CAS 

    Google Scholar 
    24.Schwabl, P. et al. Detection of various microplastics in human stool: a prospective case series. Ann. Intern. Med. 171, 453–457 (2019).
    Google Scholar 
    25.Connors, K. A., Dyer, S. D. & Belanger, S. E. Advancing the quality of environmental microplastic research. Environ. Toxicol. Chem. 36, 1697–1703 (2017). This paper highlights the need for better quality in microplastic research.CAS 

    Google Scholar 
    26.Wesch, C., Bredimus, K., Paulus, M. & Klein, R. Towards the suitable monitoring of ingestion of microplastics by marine biota: a review. Environ. Pollut. 218, 1200–1208 (2016).CAS 

    Google Scholar 
    27.O’Connor, J. et al. Microplastics in freshwater biota: a critical review of isolation, characterization and assessment methods. Glob. Challeng. https://doi.org/10.1002/gch2.201800118 (2019).28.de Ruijter, V. N., Redondo-Hasselerharm, P. E., Gouin, T. & Koelmans, A. A. Quality criteria for microplastic effect studies in the context of risk assessment: a critical review. Environ. Sci. Technol. 54, 11692–11705 (2020).
    Google Scholar 
    29.Hartmann, N. B. et al. Are we speaking the same language? Recommendations for a definition and categorization framework for plastic debris. Environ. Sci. Technol. 53, 1039–1047 (2019).CAS 

    Google Scholar 
    30.Kögel, T., Bjorøy, Ø., Toto, B., Bienfait, A. M. & Sanden, M. Micro- and nanoplastic toxicity on aquatic life: determining factors. Sci. Total. Environ. 709, 136050 (2020).
    Google Scholar 
    31.Bond, T., Ferrandiz-Mas, V., Felipe-Sotelo, M. & van Sebille, E. The occurrence and degradation of aquatic plastic litter based on polymer physicochemical properties: a review. Crit. Rev. Environ. Sci. Technol. 48, 685 (2018).
    Google Scholar 
    32.Riediker, M. et al. Particle toxicology and health — where are we? Part. Fibre Toxicol. 16, 1–33 (2019).
    Google Scholar 
    33.Kooi, M. et al. Characterizing the multidimensionality of microplastics across environmental compartments. Water Res. 202, 117429 (2021).CAS 

    Google Scholar 
    34.Wiesinger, H., Wang, Z. & Hellweg, S. Deep dive into plastic monomers, additives, and processing aids. Environ. Sci. Technol. 55, 9339–9351 (2021).CAS 

    Google Scholar 
    35.Gouin, T. Addressing the importance of microplastic particles as vectors for long-range transport of chemical contaminants: perspective in relation to prioritizing research and regulatory actions. Micropl. Nanopl. 1, 14 (2021).
    Google Scholar 
    36.Hermabessiere, L. et al. Occurrence and effects of plastic additives on marine environments and organisms: a review. Chemosphere 182, 781–793 (2017).CAS 

    Google Scholar 
    37.Gouin, T., Roche, N., Lohmann, R. & Hodges, G. A thermodynamic approach for assessing the environmental exposure of chemicals absorbed to microplastic. Environ. Sci. Technol. 45, 1466–1472 (2011).CAS 

    Google Scholar 
    38.Lohmann, R. Microplastics are not important for the cycling and bioaccumulation of organic pollutants in the oceans — but should microplastics be considered POPs themselves? Int. Environ. Assess. Manag. 13, 460–465 (2017).CAS 

    Google Scholar 
    39.Takada, H. & Karapanagioti, H. K. (eds) Hazardous Chemicals Associated with Plastics in the Marine Environment (Springer International Publishing, 2016).40.Hong, S. H., Shim, W. J. & Hong, K. Methods of analysing chemicals associated with microplastics: a review. Anal. Methods 9, 1361–1368 (2017).
    Google Scholar 
    41.Koelmans, A. A., Bakir, A., Burton, G. A. & Janssen, C. R. Microplastic as a vector for chemicals in the aquatic environment. critical review and model-supported re-interpretation of empirical studies. Environ. Sci. Technol. 50, 3315–3326 (2016).CAS 

    Google Scholar 
    42.Jahnke, A. et al. Reducing uncertainty and confronting ignorance about the possible impacts of weathering plastic in the marine environment. Environ. Sci. Technol. Lett. 4, 85–90 (2017).CAS 

    Google Scholar 
    43.Boucher, J. and Friot D. Primary Microplastics in the Oceans: A Global Evaluation of Sources 43 (IUCN, 2017).44.Koelmans, A. A., Kooi, M., Lavender-Law, K. & Van Sebille, E. All is not lost: deriving a top-down mass budget of plastic at sea. Environ. Res. Lett. 12, 114028 (2017).
    Google Scholar 
    45.Kawecki, D. & Nowack, D. Polymer-specific modeling of the environmental emissions of seven commodity plastics as macro- and microplastics. Environ. Sci. Technol. 53, 9664–9676 (2019).CAS 

    Google Scholar 
    46.Kooi, M., Van Nes, E. H., Scheffer, M. & Koelmans, A. A. Ups and downs in the ocean: effects of biofouling on vertical transport of microplastics. Environ. Sci. Technol. 51, 7963–7971 (2017).CAS 

    Google Scholar 
    47.Mateos-Cárdenas, A., O’Halloran, J., van Pelt, F. N. A. M. & Jansen, M. A. K. Rapid fragmentation of microplastics by the freshwater amphipod Gammarus duebeni (Lillj.). Sci. Rep. 10, 12799 (2020).
    Google Scholar 
    48.Julienne, F., Delorme, N. & Lagarde, F. From macroplastics to microplastics: role of water in the fragmentation of polyethylene. Chemosphere 236, 124409 (2019).CAS 

    Google Scholar 
    49.Koelmans, A. A., Redondo-Hasselerharm, P. E., Mohamed Nor, N. H. & Kooi, M. Solving the non-alignment of methods and approaches used in microplastic research in order to consistently characterize risk. Environ. Sci. Technol. 54, 12307–12315 (2020).CAS 

    Google Scholar 
    50.Cózar, A. et al. Plastic debris in the open ocean. Proc. Natl Acad. Sci. USA 111, 10239–10244 (2014).
    Google Scholar 
    51.Mattsson, K., Björkroth, F., Karlsson, T. & Hassellöv, M. Nanofragmentation of expanded polystyrene under simulated environmental weathering (thermooxidative degradation and hydrodynamic turbulence). Front. Mar. Sci., 7, 1–9 (2021). This paper demonstrates log linear particle size distributions extending to the nanoparticle scale.
    Google Scholar 
    52.Kaandorp, M. L. A., Dijkstra, H. A. & van Sebille, E. Modelling size distributions of marine plastics under the influence of continuous cascading fragmentation. Environ. Res. Lett. 16, 054075 (2021).
    Google Scholar 
    53.Koelmans, A. A., Besseling, E. & Shim, W. J. Nanoplastics in the aquatic environment. Critical review. In Marine Anthropogenic Litter (eds Bergmann, M., Gutow, L. & Klages, M.) 325–340 (Springer, 2015).54.Koelmans, A. A. et al. Microplastics in freshwaters and drinking water: critical review and assessment of data quality. Water Res. 155, 410–422 (2019).CAS 

    Google Scholar 
    55.Chamas, A. et al. Degradation rates of plastics in the environment. ACS Sust. Chem. Engin. 8, 3494–3511 (2020). This paper provides a rare estimate of degradation rates for plastic items in the environment.CAS 

    Google Scholar 
    56.Unice, K. M. et al. Characterizing export of land-based microplastics to the estuary — Part II: Sensitivity analysis of an integrated geospatial microplastic transport modeling assessment of tire and road wear particles. Sci. Total. Environ. 646, 1650–1659 (2019).CAS 

    Google Scholar 
    57.Buffle, J. & van Leeuwen, H. P. Environmental Particles Vol. 1 76 (CRC Press, 1992).58.Chamley, H., Clay formation through weathering. In Clay Sedimentology (Springer, 1989).59.Blott, S. J. & Pye, K. Particle size scales and classification of sediment types based on particle size distributions: review and recommended procedures. Sedimentology 59, 2071–2096 (2012).
    Google Scholar 
    60.Boyd, C. E. Suspended solids, color, turbidity, and light. In Water Quality 119–133 (Springer, 2020).61.Konrad, K. et al. Chemical composition and complex refractive index of Saharan mineral dust at Izaña, Tenerife (Spain) derived by electron microscopy. Atmos. Env. 41, 8058–8074 (2007).
    Google Scholar 
    62.Mahowald, N. et al. The size distribution of desert dust aerosols and its impact on the Earth system. Aeolian Res. 15, 53–71 (2014).
    Google Scholar 
    63.De Wit, C. T. & Arens, P. L. Moisture content and density of some clay minerals and some remarks on the hydration pattern of clay. Trans. Int. Congr. Soil Science 2, 59–62 (1951).
    Google Scholar 
    64.Utembe, W., Potgieter, K., Stefaniak, A. B. & Gulumian, M. Dissolution and biodurability: important parameters needed for risk assessment of nanomaterials. Part. Fiber Toxicol. 12, 11 (2015).
    Google Scholar 
    65.Köhler, S. J., Bosbach, D. & Oelkers, E. H. Do clay mineral dissolution rates reach steady state? Geochim. Cosmochim. Acta 69, 1997–2006 (2005).
    Google Scholar 
    66.Torrey, M. L. S. T. Chemistry of Lake Michigan (Argonne National Laboratory, 1976).67.Prestigiacomo, A. R. et al. Turbidity and suspended solids levels and loads in a sediment enriched stream: implications for impacted lotic and lentic ecosystems. Lake Res. Manag. 23, 231–244 (2007).
    Google Scholar 
    68.Baran, A. et al. The influence of the quantity and quality of sediment organic matter on the potential mobility and toxicity of trace elements in bottom sediment. Environ. Geochem. Health 41, 2893–2910 (2019).CAS 

    Google Scholar 
    69.Schwarzenbach, R. P., Gschwend, P. M. & Imboden, D. M. Environmental Organic Chemistr 3rd edn 1024 (Wiley, 2016).70.Van Valkenburg, S. D., Jones, J. K. & Heinle, D. R. A comparison by size class and volume of detritus versus phytoplankton in Chesapeake Bay. Estuar. Coast. Mar. Sci. 6, 569–582 (1978).
    Google Scholar 
    71.Hamilton, S. K., Sippel, S. J. & Bunn, S. E. Separation of algae from detritus for stable isotope or ecological stoichiometry studies using density fractionation in colloidal silica. Limnol. Oceanogr. Methods 3, 149–157 (2005).CAS 

    Google Scholar 
    72.Zimmer, M. Detritus. Encyclopedia of Ecology 903–911 (Elsevier, 2008).73.Zhao, H.-C., Wang, S.-R., Jiao, L.-X., Yang, S.-W. & Cui, C.-N. Characteristics of composition and spatial distribution of organic matter in the sediment of Erhai Lake. Res. Environ. Sci. 26, 243–249 (2013).CAS 

    Google Scholar 
    74.Duan, H., Feng, L., Ma, R., Zhang, Y. & Loiselle, S. A. Variability of particulate organic carbon in inland waters observed from MODIS Aqua imagery. Environ. Res. Lett. 9, 084011 (2014).CAS 

    Google Scholar 
    75.Suaria, G. et al. Microfibers in oceanic surface waters: a global characterization. Sci. Adv. 6, eaay8493 (2020). This paper identifies the relative proportion of microplastic fibres in the oceans.
    Google Scholar 
    76.Le Guen, C. et al. Microplastic study reveals the presence of natural and synthetic fibres in the diet of King penguins (Aptenodytes patagonicus) foraging from South Georgia. Environ. Intern. 134, 105303 (2020).
    Google Scholar 
    77.Stanton, T., Johnson, M., Nathanail, P., MacNaughtan, W. & Gomes, R. L. Sci. Total. Environ. 666, 377–389 (2019).CAS 

    Google Scholar 
    78.Comnea-Stancu, L. R., Wieland, H., Ramer, G., Schwaighofer, A. & Lendl, B. On the identification of rayon/viscose as a major fraction of microplastics in the marine environment: discrimination between natural and manmade cellulosic fibers using Fourier transform infrared spectroscopy. Appl. Spectrosc. 71, 939–950 (2017).CAS 

    Google Scholar 
    79.Seiler, W. & Crutzen, P. J. Estimates of gross and net fluxes of carbon between the biosphere and the atmosphere from biomass burning. Clim. Change 2, 207–247 (1980).CAS 

    Google Scholar 
    80.Cornelissen, G. et al. Critical review. Extensive sorption of organic compounds to black carbon, coal, and kerogen in sediments and soils: mechanisms and consequences for distribution, bioaccumulation and biodegradation. Environ. Sci. Technol. 39, 6881–6895 (2005).CAS 

    Google Scholar 
    81.Jonker, M. T. O., Hawthorne, S. B. & Koelmans, A. A. Extremely slow desorption and limited bioaccumulation of BC-associated PAHs. ACS Div. Environ. Chem. 45, 381–384 (2005).CAS 

    Google Scholar 
    82.Shrestha, G., Traina, S. J., Swanson & C., W. Black carbons properties and role in the environment: a comprehensive review. Sustainability 2, 294–320 (2010).CAS 

    Google Scholar 
    83.Bisiaux, M. M. et al. Stormwater and fire as sources of black carbon nanoparticles to Lake Tahoe. Environ. Sci. Technol. 45, 2065–2071 (2011). This paper identifies black carbon abundance in surface waters.CAS 

    Google Scholar 
    84.World Health Organization. Health effects of black carbon. (WHO, 2012).85.Jonker, M. T. O. & Koelmans, A. A. Sorption of polycyclic aromatic hydrocarbons and polychlorinated biphenyls to soot and soot-like materials in the aqueous environment. Mechanistic considerations. Environ. Sci. Technol. 36, 3725–3734 (2002).CAS 

    Google Scholar 
    86.Liu, H. et al. Mixing characteristics of refractory black carbon aerosols at an urban site in Beijing. Atmos. Chem. Phys. 20, 5771–5785 (2020).CAS 

    Google Scholar 
    87.Ouf, F.-X. et al. True density of soot particles: a comparison of results highlighting the influence of the organic contents. J. Aerosol Sci. 134, 1–13 (2019).CAS 

    Google Scholar 
    88.Wu, Y. et al. A study of the morphology and effective density of externally mixed black carbon aerosols in ambient air using a size-resolved single-particle soot photometer (SP2). Atmos. Meas. Tech. 12, 4347–4359 (2019).CAS 

    Google Scholar 
    89.Kuzyakov, Y., Subbotina, I., Chen, H., Bogomolova, I. & Xu, X. Black carbon decomposition and incorporation into soil microbial biomass estimated by 14C labeling. Soil. Biol. Biochem. 41, 210–219 (2009).CAS 

    Google Scholar 
    90.Middelburg, J. J., Nieuwenhuize, J. & Van Breugel, P. Black carbon in marine sediments. Mar. Chem. 65, 245–252 (1999).CAS 

    Google Scholar 
    91.Murr, L. E., Bang, J. J., Esquivel, E. V., Guerrero, P. A. & Lopez, D. A. Carbon nanotubes, nanocrystal forms and complex nanoparticle aggregates in common fuel gas combustion streams. J. Nanopart. Res. 6, 241–251 (2004).CAS 

    Google Scholar 
    92.Koelmans, A. A., Nowack, B. & Wiesner, M. Comparison of manufactured and black carbon nanoparticle concentrations in aquatic sediments. Environ. Pollut. 157, 1110–1116 (2009).CAS 

    Google Scholar 
    93.Dickens, A. F., Gelinas, Y., Masiello, C. A., Wakeham, S. & Hedges, J. I. Reburial of fossil organic carbon in marine sediments. Nature 427, 336–339 (2004).CAS 

    Google Scholar 
    94.Kharbush, J. J. et al. Particulate organic carbon deconstructed: molecular and chemical composition of particulate organic carbon in the ocean. Front. Mar. Sci. 7, 518 (2020).
    Google Scholar 
    95.Redondo-Hasselerharm, P. E. Effect assessment of nano- and microplastics in freshwater ecosystems. Thesis, Wageningen Univ. (2020).96.Allen, S. et al. Atmospheric transport and deposition of microplastics in a remote mountain catchment. Nat. Geosci. 12, 339–344 (2019).CAS 

    Google Scholar 
    97.Evangeliou, N. et al. Atmospheric transport is a major pathway of microplastics to remote regions. Nat. Commun. 11, 3381 (2020).CAS 

    Google Scholar 
    98.Velzeboer, I., Kwadijk, C. J. A. F. & Koelmans, A. A. Strong sorption of PCBs to nanoplastics, microplastics, carbon nanotubes and fullerenes. Environ. Sci. Technol. 48, 4869–4876 (2014).CAS 

    Google Scholar 
    99.Beckingham, B. & Ghosh, U. Differential bioavailability of polychlorinated biphenyls associated with environmental particles: microplastic in comparison to wood, coal and biochar. Environ. Pollut. 220, 150–158 (2017).CAS 

    Google Scholar 
    100.Liping, L. et al. Mechanism of and relation between the sorption and desorption of nonylphenol on black carbon-inclusive sediment. Environ. Pollut. 190, 101–108 (2014).
    Google Scholar 
    101.Voparil, I. M. et al. Digestive bioavailability to a deposit feeder (Arenicola marina) of polycyclic aromatic hydrocarbons associated with anthropogenic particles. Environ. Toxicol. Chem. 23, 2618–2626 (2004).CAS 

    Google Scholar 
    102.Birdwell, J., Cook, R. L. & Thibodeaux, L. J. Desorption kinetics of hydrophobic organic chemicals from sediment to water: a review of data and models. Environ. Toxicol. Chem. 26, 424–434 (2007).CAS 

    Google Scholar 
    103.Koelmans, A. A., Besseling, E. & Foekema, E. M. Leaching of plastic additives to marine organisms. Environ. Pollut. 187, 49–54 (2014).CAS 

    Google Scholar 
    104.Bundschuh, M. et al. Nanoparticles in the environment: where do we come from, where do we go to? Environ. Sci. Eur. 30, 6 (2018).
    Google Scholar 
    105.Peijnenburg, W. J. G. M. et al. A review of the properties and processes determining the fate of engineered nanomaterials in the aquatic environment. Crit. Rev. Environ. Sci. Technol. 45, 2084–2134 (2015).CAS 

    Google Scholar 
    106.Gigault, J. et al. Nanoplastics are neither microplastics nor engineered nanoparticles. Nat. Nanotechnol. 16, 501–507 (2021).CAS 

    Google Scholar 
    107.Ter Halle, A. et al. Nanoplastic in the North Atlantic Subtropical Gyre. Environ. Sci. Technol. 51, 13689–13697 (2017).
    Google Scholar 
    108.Sengul, A. B. & Asmatulu, E. Toxicity of metal and metal oxide nanoparticles: a review. Environ. Chem. Lett. 18, 1659–1683 (2020).CAS 

    Google Scholar 
    109.Botterell, Z. L. R. et al. Bioavailability and effects of microplastics on marine zooplankton: a review. Environ. Pollut. 245, 98–110 (2019).CAS 

    Google Scholar 
    110.Ribeiro, F., O’Brien, J. W., Galloway, T. & Thomas, K. V. Accumulation and fate of nano- and micro-plastics and associated contaminants in organisms. TrAC 111, 139–147 (2019).CAS 

    Google Scholar 
    111.da Costa Araújo, A. P. et al. How much are microplastics harmful to the health of amphibians? A study with pristine polyethylene microplastics and Physalaemus cuvieri. J. Hazard. Mater. 382, 121066 (2020).
    Google Scholar 
    112.Jovanović, B. Ingestion of microplastics by fish and its potential consequences from a physical perspective. Integr. Environ. Assess. Manag. 13, 510–515 (2017).
    Google Scholar 
    113.Windsor, F. M., Tilley, R. M., Tyler, C. R. & Ormerod, S. J. Microplastic ingestion by riverine macroinvertebrates. Sci. Total. Environ. 646, 68–74 (2018).
    Google Scholar 
    114.Hu, L., Chernick, M., Hinton, D. E. & Shi, H. Microplastics in small waterbodies and tadpoles from Yangtze River Delta, China. Environ. Sci. Technol. 52, 8885–8893 (2018).CAS 

    Google Scholar 
    115.McNeish, R. E. et al. Microplastic in riverine fish is connected to species traits. Sci. Rep. 8, 11639 (2018).CAS 

    Google Scholar 
    116.Duncan, E. M. et al. Microplastic ingestion ubiquitous in marine turtles. Glob. Chang. Biol. 25, 744–752 (2019).
    Google Scholar 
    117.Kühn, S., Bravo Rebolledo, E. L. & Van Franeker, J. A. Deleterious effects of litter on marine life. In Marine Anthropogenic Litter (eds Bergmann, M., Gutow, L. & Klages, M.) 75–116 (Springer International Publishing, 2015).118.Nelms, S. E. et al. Microplastics in marine mammals stranded around the British coast: ubiquitous but transitory? Sci. Rep. 9, 1–9 (2019).CAS 

    Google Scholar 
    119.O’Connor, J. D. et al. Microplastics in freshwater biota: a critical review of isolation, characterization, and assessment methods. Glob. Challen. 4, 1800118 (2019).
    Google Scholar 
    120.Vroom, R. J. E., Koelmans, A. A., Besseling, E. & Halsband, C. Aging of microplastics promotes their ingestion by marine zooplankton. Environ. Pollut. 231, 987–996 (2017).CAS 

    Google Scholar 
    121.Bour, A., Haarr, A., Keiter, S. & Hylland, K. Environmentally relevant microplastic exposure affects sediment-dwelling bivalves. Environ. Pollut. 236, 652–660 (2018).CAS 

    Google Scholar 
    122.Kaposi, K. L., Mos, B., Kelaher, B. P. & Dworjanyn, S. A. Ingestion of microplastic has limited impact on a marine larva. Environ. Sci. Technol. 48, 1638–1645 (2014).CAS 

    Google Scholar 
    123.Lu, Y. et al. Uptake and accumulation of polystyrene microplastics in zebrafish (Danio rerio) and toxic effects in liver. Environ. Sci. Technol. 50, 4054–4060 (2016).CAS 

    Google Scholar 
    124.Ribeiro, F. et al. Microplastics effects in Scrobicularia plana. Mar. Pollut. Bull. 122, 379–391 (2017).CAS 

    Google Scholar 
    125.Von Moos, N., Burkhardt-Holm, P. & Köhler, A. Uptake and effects of microplastics on cells and tissue of the blue mussel Mytilus edulis L. after an experimental exposure. Environ. Sci. Technol. 46, 11327–11335 (2012).
    Google Scholar 
    126.Browne, M. A., Dissanayake, A., Galloway, T. S., Lowe, D. M. & Thompson, R. C. Ingested microscopic plastic translocates to the circulatory system of the mussel, Mytilus edulis (L.). Environ. Sci. Technol. 42, 5026–5031 (2008).CAS 

    Google Scholar 
    127.Dawson, A. L. et al. Turning microplastics into nanoplastics through digestive fragmentation by Antarctic krill. Nat. Commun. 9, 1001 (2018).
    Google Scholar 
    128.Zhang, C., Chen, X., Wang, J. & Tan, L. Toxic effects of microplastic on marine microalgae Skeletonema costatum: interactions between microplastic and algae. Environ. Pollut. 220, 1282–1288 (2017).CAS 

    Google Scholar 
    129.Mateos-Cárdenas, A. et al. Polyethylene microplastics adhere to Lemna minor (L.), yet have no effects on plant growth or feeding by Gammarus duebeni (Lillj.). Sci. Total. Environ. 689, 413–421 (2019).
    Google Scholar 
    130.Murphy, F. & Quinn, B. The effects of microplastic on freshwater Hydra attenuata feeding, morphology and reproduction. Environ. Pollut. 234, 487–494 (2018).CAS 

    Google Scholar 
    131.Cole, M. et al. Microplastic ingestion by zooplankton. Environ. Sci. Technol. 47, 6646–6655 (2013).CAS 

    Google Scholar 
    132.Green, D. S., Boots, B., O’Connor, N. E. & Thompson, R. Microplastics affect the ecological functioning of an important biogenic habitat. Environ. Sci. Technol. 51, 68–77 (2017).CAS 

    Google Scholar 
    133.Senga Green, D. Effects of microplastics on European flat oysters, Ostrea edulis and their associated benthic communities. Environ. Pollut. 216, 95–103 (2016).
    Google Scholar 
    134.Ziajahromi, S., Kumar, A., Neale, P. A. & Leusch, F. D. L. Environmentally relevant concentrations of polyethylene microplastics negatively impact the survival, growth and emergence of sediment-dwelling invertebrates. Environ. Pollut. 236, 425–431 (2018).CAS 

    Google Scholar 
    135.Ogonowski, M., Schür, C., Jarsén, Å. & Gorokhova, E. The effects of natural and anthropogenic microparticles on individual fitness in daphnia magna. PLoS ONE 11, e0155063 (2016). This paper systematically addresses the differences between the biological effects of microplastic and natural particles.
    Google Scholar 
    136.Mazurais, D. et al. Evaluation of the impact of polyethylene microbeads ingestion in European sea bass (Dicentrarchus labrax) larvae. Mar. Environ. Res. 112, 78–85 (2015).CAS 

    Google Scholar 
    137.Lee, K.-W., Shim, W. J., Kwon, O. Y. & Kang, J.-H. Size-dependent effects of micro polystyrene particles in the marine copepod Tigriopus japonicus. Env. Sci. Technol. 47, 11278–11283 (2013).CAS 

    Google Scholar 
    138.Au, S. Y., Bruce, T. F., Bridges, W. C. & Klaine, S. J. Responses of Hyalella azteca to acute and chronic microplastic exposures. Environ. Toxicol. Chem. 34, 2564–2572 (2015).CAS 

    Google Scholar 
    139.Cole, M., Lindeque, P., Fileman, E., Halsband, C. & Galloway, T. S. The impact of polystyrene microplastics on feeding, function and fecundity in the marine copepod Calanus helgolandicus. Environ. Sci. Technol. 49, 1130–1137 (2015).CAS 

    Google Scholar 
    140.Sussarellu, R. et al. Oyster reproduction is affected by exposure to polystyrene microplastics. Proc. Natl Acad. Sci. USA 113, 2430–2435 (2016).CAS 

    Google Scholar 
    141.Jeong, C. B. et al. Microplastic size-dependent toxicity, oxidative stress induction, and p-JNK and p-p38 activation in the monogonont rotifer (Brachionus koreanus). Environ. Sci. Technol. 50, 8849–8857 (2016).CAS 

    Google Scholar 
    142.Blarer, P. & Burkhardt-Holm, P. Microplastics affect assimilation efficiency in the freshwater amphipod Gammarus fossarum. Environ. Sci. Pollut. Res. 23, 23522–23532 (2016).CAS 

    Google Scholar 
    143.Wright, S. L., Rowe, D., Thompson, R. C. & Galloway, T. S. Microplastic ingestion decreases energy reserves in marine worms. Curr. Biol. 23, R1031–R1033 (2013).CAS 

    Google Scholar 
    144.Straub, S., Hirsch, P. E. & Burkhardt-Holm, P. Biodegradable and petroleum-based microplastics do not differ in their ingestion and excretion but in their biological effects in a freshwater invertebrate Gammarus fossarum. Int. J. Environ. Res. Public Health 14, 774 (2017).
    Google Scholar 
    145.Green, D. S., Boots, B., Sigwart, J., Jiang, S. & Rocha, C. Effects of conventional and biodegradable microplastics on a marine ecosystem engineer (Arenicola marina) and sediment nutrient cycling. Environ. Pollut. 208, 426–434 (2016).CAS 

    Google Scholar 
    146.Ziajahromi, S., Kumar, A., Neale, P. A. & Leusch, F. D. L. Impact of microplastic beads and fibers on waterflea (Ceriodaphnia dubia) survival, growth, and reproduction: implications of single and mixture exposures. Environ. Sci. Technol. 51, 13397–13406 (2017).CAS 

    Google Scholar 
    147.Nobre, C. R. et al. Assessment of microplastic toxicity to embryonic development of the sea urchin Lytechinus variegatus (Echinodermata: Echinoidea). Mar. Pollut. Bull. 92, 99–104 (2015).CAS 

    Google Scholar 
    148.Rehse, S., Kloas, W. & Zarfl, C. Short-term exposure with high concentrations of pristine microplastic particles leads to immobilisation of Daphnia magna. Chemosphere 153, 91–99 (2016).CAS 

    Google Scholar 
    149.Gambardella, C. et al. Effects of polystyrene microbeads in marine planktonic crustaceans. Ecotoxicol. Environ. Saf. 145, 250–257 (2017).CAS 

    Google Scholar 
    150.Watts, A. J. R. et al. Effect of microplastic on the gills of the shore crab Carcinus maenas. Environ. Sci. Technol. 50, 5364–5369 (2016).CAS 

    Google Scholar 
    151.Espinosa, C., Cuesta, A. & Esteban, M. Á. Effects of dietary polyvinylchloride microparticles on general health, immune status and expression of several genes related to stress in gilthead seabream (Sparus aurata L.). Fish. Shellfish. Immunol. 68, 251–259 (2017).CAS 

    Google Scholar 
    152.Jin, Y., Lu, L., Tu, W., Luo, T. & Fu, Z. Impacts of polystyrene microplastic on the gut barrier, microbiota and metabolism of mice. Sci. Total. Environ. 649, 308–317 (2019).CAS 

    Google Scholar 
    153.Jin, Y. et al. Polystyrene microplastics induce microbiota dysbiosis and inflammation in the gut of adult zebrafish. Environ. Pollut. 235, 322–329 (2018).CAS 

    Google Scholar 
    154.Bucci, K., Tulio, M. & Rochman, C. M. What is known and unknown about the effects of plastic pollution: a meta-analysis and systematic review. Ecol. Appl. 30, e02044 (2020). This paper reviews the evidence for effects of plastic pollution across endpoints, organisms and levels of biological organization.CAS 

    Google Scholar 
    155.Kjelland, M. E., Woodley, C. M., Swannack, T. M. & Smith, D. L. A review of the potential effects of suspended sediment on fishes: potential dredging-related physiological, behavioral, and transgenerational implications. Environ. Syst. Decis. 35, 334–350 (2015).
    Google Scholar 
    156.Michel, C., Herzog, S., de Capitani, C., Burkhardt-Holm, P. & Pietsch, C. Natural mineral particles are cytotoxic to rainbow trout gill epithelial cells in vitro. PLoS ONE 9, e100856 (2014).
    Google Scholar 
    157.Gordon, A. K. & Palmer, C. G. Defining an exposure-response relationship for suspended kaolin clay particulates and aquatic organisms: work toward defining a water quality guideline for suspended solids. Environ. Toxicol. Chem. 34, 907–912 (2015).CAS 

    Google Scholar 
    158.Lu, C., Kania, P. W. & Buchmann, K. Particle effects on fish gills: an immunogenetic approach for rainbow trout and zebrafish. Aquaculture 484, 98–104 (2018).CAS 

    Google Scholar 
    159.Ogonowski, M., Gerdes, Z. & Gorokhova, E. What we know and what we think we know about microplastic effects — a critical perspective. Curr. Opin. Environ. Sci. Health 1, 41–46 (2018).
    Google Scholar 
    160.Albarano, L. et al. Comparison of in situ sediment remediation amendments: risk perspectives from species sensitivity distribution. Environ. Pollut. 272, 115995 (2021).CAS 

    Google Scholar 
    161.Newcombe, C. P. & Macdonald, D. D. Effects of suspended sediments on aquatic ecosystems. North. Am. J. Fish. Manag. 11, 72–82 (1991).
    Google Scholar 
    162.Yap, V. H. et al. A comparison with natural particles reveals a small specific effect of PVC microplastics on mussel performance. Mar. Pollut. Bull. 160, 111703 (2020).CAS 

    Google Scholar 
    163.Schür, C., Zipp, S., Thalau, T. & Wagner, M. Microplastics but not natural particles induce multigenerational effects in Daphnia magna. Environ. Pollut. 260, 113904 (2020).
    Google Scholar 
    164.Gerdes, Z., Hermann, M., Ogonowski, M. & Gorokhova, E. A novel method for assessing microplastic effect in suspension through mixing test and reference materials. Sci. Rep. 9, 1–9 (2019).CAS 

    Google Scholar 
    165.Niranjan, R. & Thakur, A. K. The toxicological mechanisms of environmental soot (black carbon) and carbon black: focus on oxidative stress and inflammatory pathways. Front. Immunol. 8, 763 (2017).
    Google Scholar 
    166.Tsuji, J. S. et al. Research strategies for safety evaluation of nanomaterials. Part IV: Risk assessment of nanoparticles. Toxicol. Sci. 89, 42–50 (2006).CAS 

    Google Scholar 
    167.Schwarze, P. E. et al. Importance of size and composition of particles for effects on cells in vitro. Inhal. Toxicol. 19, 17–22 (2007).CAS 

    Google Scholar 
    168.Schmid, O. & Stoeger, T. Surface area is the biologically most effective dose metric for acute nanoparticle toxicity in the lung. J. Aerosol Sci. 99, 133–143 (2016). This paper identifies the toxicologically relevant dose metric for particle effects.CAS 

    Google Scholar 
    169.Fubini, B. Surface reactivity in the pathogenic response to particulates. Environ. Health Perspect. 105, 1013–1020 (1997).
    Google Scholar 
    170.Poland, C. A., Duffin, R. & Donaldson, K. High aspect ratio nanoparticles and the fibre pathogenicity paradigm. In Nanotoxicity Vivo and In Vitro Models to Health Risks 61–80 (John Wiley and Sons, 2009).171.Gualtieri, A. F. Bridging the gap between toxicity and carcinogenicity of mineral fibres by connecting the fibre crystal-chemical and physical parameters to the key characteristics of cancer. Curr. Res. Toxicol. 2, 42–52 (2021).
    Google Scholar 
    172.Shao, X. R. et al. Independent effect of polymeric nanoparticle zeta potential/surface charge, on their cytotoxicity and affinity to cells. Cell Prolif. 48, 465–474 (2015).CAS 

    Google Scholar 
    173.Motskin, M. et al. Hydroxyapatite nano and microparticles: correlation of particle properties with cytotoxicity and biostability. Biomaterials 30, 3307–3317 (2009).CAS 

    Google Scholar 
    174.Cox, K. D. et al. Human consumption of microplastics. Environ. Sci. Technol. 53, 7068–7074 (2019).CAS 

    Google Scholar 
    175.Zhang, Q. et al. A review of microplastics in table salt, drinking water, and air: direct human exposure. Environ. Sci. Technol. 54, 3740–3751 (2020).CAS 

    Google Scholar 
    176.Everaert, G. et al. Risk assessment of microplastics in the ocean: modelling approach and first conclusions. Environ. Pollut. 242, 1930–1938 (2018).CAS 

    Google Scholar 
    177.Everaert, G. et al. Risks of floating microplastic in the global ocean. Environ. Pollut. 267, 115499 (2020).CAS 

    Google Scholar 
    178.Zhang, X., Leng, Y., Liu, X., Huang, K. & Wang, J. Microplastics’ pollution and risk assessment in an urban river: a case study in the Yongjiang River, Nanning City, South China. Exposure Health 12, 141–151 (2020).CAS 

    Google Scholar 
    179.Skåre, J. U. et al. Microplastics, occurrence, levels and implications for environment and human health related to food. Opinion of the steering committee of the Norwegian Scientific Committee for Food and Environment (VKM, 2019).180.Adam, V., von Wyl, A. & Nowack, B. Probabilistic environmental risk assessment of microplastics in marine habitats. Aq. Toxicol. 230, 105689 (2021).CAS 

    Google Scholar 
    181.Jung, J.-W. et al. Ecological risk assessment of microplastics in coastal, shelf, and deep sea waters with a consideration of environmentally relevant size and shape. Environ. Pollut. 270, 116217 (2021).CAS 

    Google Scholar 
    182.Posthuma, L., Suter, G. W. & Traas, T. P. Species Sensitivity Distributions In Ecotoxicology (Lewis, 2002).183.Gouin, T. et al. Toward the development and application of an environmental risk assessment framework for microplastic. Environ. Toxicol. Chem. 38, 2087–2100 (2019).CAS 

    Google Scholar 
    184.Kong, X. & Koelmans, A. A. Effects of microplastic on shallow lake food webs. Environ. Sci. Technol. 53, 13822–13831 (2019).CAS 

    Google Scholar 
    185.Zimmermann, L., Göttlich, S., Oehlmann, J., Wagner, M. & Völker, C. What are the drivers of microplastic toxicity? Comparing the toxicity of plastic chemicals and particles to Daphnia magna. Environ. Pollut. 267, 115392 (2020).CAS 

    Google Scholar 
    186.Tian, Z. et al. A ubiquitous tire rubber-derived chemical induces acute mortality in coho salmon. Science 371, 185–189 (2021).CAS 

    Google Scholar 
    187.Bakir, A., O’Connor, I. A., Rowland, S. J., Hendriks, A. J. & Thompson, R. C. Relative importance of microplastics as a pathway for the transfer of hydrophobic organic chemicals to marine life. Environ. Pollut. 219, 56–65 (2016).CAS 

    Google Scholar 
    188.Capolupo, M., Sørensen, L., Jayasena, K., Booth, A. M. & Fabbri, E. Chemical composition and ecotoxicity of plastic and car tire rubber leachates to aquatic organisms. Water Res. 169, 115270 (2020).CAS 

    Google Scholar 
    189.Zimmermann, L. et al. Plastic products leach chemicals that induce in vitro toxicity under realistic use conditions. Environ. Sci. Technol. 55, 11814–11823 (2021).CAS 

    Google Scholar 
    190.Bucci, K. & Rochman, C. M. A proposed framework for microplastics risk assessment [abstract 07.05.02]. Society of Environmental Toxicology and Chemistry North America 42nd Annual Meeting – SETAC SciCon4 https://scicon4.setac.org/wp-content/uploads/2021/11/SciCon4-abstract-book.pdf (2021).191.Primpke, S., Lorenz, C., Rascher-Friesenhausen, R. & Gerdts, G. An automated approach for microplastics analysis using focal plane array (FPA) FTIR microscopy and image analysis. Anal. Methods 9, 1499–1511 (2017).CAS 

    Google Scholar 
    192.Rauchschwalbe, M.-T., Fueser, H., Traunspurger, W. & Höss, S. Bacterial consumption by nematodes is disturbed by the presence of polystyrene beads: the roles of food dilution and pharyngeal pumping. Environ. Pollut. 273, 116471 (2021).CAS 

    Google Scholar 
    193.Donaldson, K. & Seaton, A. A short history of the toxicology of inhaled particles. Part. Fibre Toxicol. 9, 13 (2012).
    Google Scholar 
    194.Primpke, S., Dias, A. P. & Gerdts, G. Automated identification and quantification of microfibers and microplastics. Anal. Methods 11, 2138–2147 (2019).CAS 

    Google Scholar  More

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    Changes in the sediment microbial community structure of coastal and inland sinkholes of a karst ecosystem from the Yucatan peninsula

    Our results show that differences in environmental conditions between inland and coastal sinkholes, caused mainly by the inflow of seawater in the latter, influence the microbial community structure of their sediments. Furthermore, the microbial community structure also varied within the sinkholes and according to the sediment zone sampled, suggesting that a connection between the atmosphere in the outermost location of the sediments and sunlight creates an environment distinct from that found in deeper caves. Together with the different environmental factors that were measured (in situ physicochemical composition of water and sediment) these characteristics could drive niche-specific microbial community structures associated with the sediment zones. Additionally, beta-diversity analysis showed separate clustering of the sediment microbial communities from the coastal and inland sinkholes, and of the WM zone from cavern and cave zones at both sinkholes. Microbial community structure associated with karst environments have shown to be significantly influenced by environmental factors as seen in the Bahamian blue holes19, a coastal sinkhole13, a Floridan anchialine sinkhole20, and sediments from Chinese karst caves21.Microbial communities from karst sediments can be limited by nutrients such as carbon, phosphorus, and nitrogen21, therefore, influencing their structure. Differences in the microbial community composition associated with multiple environmental factors (moisture, type of niche, nitrogen) were also reported in karst cave sediments from China21. Previous studies had shown there was no effect on the alpha diversity of water column assemblages in the Yucatan groundwater associated with the type of sinkhole (inland or coastal)6. However, this observation may be limited due to the low sample number used in the study6. For other karst sinkholes, the microbial community dynamics differ between the water column and the sediments6,22,23,24.The karst caves and sinkholes of the underground river in Yucatan are characterized by low phosphorus concentrations and high levels of nitrate, mostly related to Anthropocentric activities (urban developments, farms and agriculture)3. The inland sinkhole at Noh Mozón showed the highest concentration of nitrate detected in the study and, not surprisingly, the area is surrounded by agricultural fields. The presence of organic matter in the sinkholes from the Yucatan peninsula are highly dependent on the connection between the cave systems, on the levels of exposure to light, and on their morphology3. High concentrations of organic carbon (661 ± 132 μM) and methane (6466 ± 659 nM) have been reported in the top layer of the water masses in coastal sinkholes before13. In this study, the highest concentration of organic carbon was observed in the sediments from the coastal sinkhole, likely originating from the surrounding vegetation and from seawater intrusion. We hypothesize that the differences in nutrients found at these two types of sinkholes influence the structure of their microbial communities.Other environmental factors such as pH and dissolved oxygen (DO), may also contribute significantly to the composition and structure of microbial communities, as seen in freshwater lake sediments22. Davis and Garey20 reported distinct microbial communities with unique functions for each water layer from an anchialine sinkhole from the Florida karst aquifer and suggested that this occurred as a result of the influence of the hydrochemistry, including differences in the concentration carbon and other nutrients from the environment20. Analyses of the sinkhole caves from the Yucatan underwater river support observations that physical and chemical parameters create distinct ecological niches which host unique microbes, as a high abundance of exclusive (not shared) ASVs were observed in the three sediment zones at both locations.The taxonomic diversity from the coastal and inland sinkholes included Chloroflexi, Crenarchaeota, Desulfobacterota, Proteobacteria, Nitrospirota, Bacteroidota, and Firmicutes as the most abundant phyla in the sediment samples, however, there were differences in the relative abundance associated with the type of sinkhole and sediment zone. Some of these phyla (Chloroflexi, Proteobacteria, and Bacteroidetes) have been reported in sediments from freshwater karst sinkholes from Lake Huron25 in water and sediments from other sinkholes in the Yucatan peninsula6, and in the karst caves bacteriome from southwest China21. A study that included coastal marine sediments from two sites in the Yucatan peninsula, showed high abundances of Spirochaeta, Desulfococcus, Clostridium, Psychrobacter26, four genera that were abundant in the coastal sinkhole. However, Desulfococcus, Synechococcus were also abundant in the inland sinkhole. Of the most abundant genera reported for sediments from different marine environments in the Yucatan coast23, Acinetobacter, Desulfotignum, Desulfovibrio, Pseudomonas, Sedimenticola, and Sulfurimonas were also present in the coastal sinkhole while only Pseudomonas and Sedimenticola were also present in the inland sinkhole23. The high number of families shared between the coastal sinkhole and marine sediments from the Yucatan coast, together with the salinity levels registered at the bottom layer of the water column in the coastal sinkhole, suggest an interconnection between these two environments which shapes the microbial communities present in the sediments of caverns and caves of this sinkhole. The genus Nitrospira was abundant in the WM from the coastal sinkhole and in all sediment zones from the inland sinkhole. This genus has been reported as one of the most abundant in the surface of speleothems from El Zapote coastal sinkhole2, and is considered a complete ammonia oxidizer (comammox), meaning it converts ammonia to nitrate through nitrite. A negative correlation between abundance of this genus and salinity has been reported before, which could explain the low concentration of Nitrospira in the cavern and cave from the coastal sinkhole, where the highest salinity was observed27. Connectivity between coastal sinkholes and the ocean, as well as the terrestrial input of soil organic matter (OM) has been reported for the underground karst aquifer in the Yucatan peninsula13. As in other sediments, degradation of OM is carried out by several MFGs including acetogenic bacteria, methanogens, and sulfate reducers13,20,28. When these MFGs were analyzed in coastal and inland sinkholes, differences in their relative abundances were clearly marked by the type of sinkhole and by the sediment zone analyzed, supporting the hypothesis that environmental differences drive microbial community distributions in these niches. The high abundance of sulfate-reducing bacteria (SRB) in the three sediment zones from the coastal sinkhole suggests that sulfate reduction is a predominant function. SRB degrade organic matter using sulfate with sulfide as waste or end-product19,30, originating hydrogen sulfide (H2S)29, which could explain the low concentration of sulfate the hydrogen sulfide (H2S) cloud observed and previously reported in the WM zone of El Zapote coastal sinkhole30. In this study, high levels of sulfate (SO−4) were measured in the water samples from the cavern and cave zones from El Zapote sinkhole, which could be associated with sulfate-rich deposits, such as gypsum beds, which have been reported in other sinkholes from the Yucatan peninsula (up to 2400 mg/L of sulfate concentration)3. However, we do not disregard other possible sources of sulfate, associated with seawater intrusion or as a product of sulfide or sulfur oxidation29,31 by sulfur-oxidizing bacteria detected in this study.The inland sinkhole had a low concentration of sulfate and low abundances of SRB. The high abundance of methanogenic bacteria in the WM zone from the coastal sinkhole detected in the MFG analysis supports the previous hypothesis of acetoclastic methanogenesis due to high inputs of organic matter13. Methylotrophic bacteria were most abundant at the inland sinkhole in the WM zone, suggesting the presence of methyl compounds, such as methane or methanol which can be used as a source of carbon and energy32. High methane concentrations have been quantified in shallow water masses from the Yucatan aquifer system13, consistent with observations from this study. ‘Candidatus Methylomirabilis’ was identified in the sediment of the WM zone from Noh-Mozón and has been previously described as being able to perform nitrite-dependent anaerobic methane oxidation, using methane as electron donor and nitrate and nitrite as electron acceptors33, which would be possible in these sediments considering the low levels of oxygen (average of 2.3 mg/L) detected in the water column above them and assuming this would lead to lower levels of oxygen in the sediments. We hypothesize that bacteria from this genus could be using the nitrite produced by ammonia oxidizing bacteria and archaea observed in this zone (Nitrosomonadaceae, Nitrospiraceae, and Nitrosococcaceae). The low abundance of methanotrophic microbes in the coastal sinkhole (mainly the cavern and cave zones) could be derived from the high concentrations of hydrogen sulfide previously reported at this location, which have been suggested to be toxic to methane-oxidizing microbes34. Therefore, a decrease in the anaerobic oxidation of methane, and a poor methane removal capacity is hypothesized in the sediments from this coastal sinkhole. Further research could focus on the influence of the saline intrusion on methanotrophic microbes and methane levels in El Zapote sinkhole sediments. As expected, photosynthetic bacterial abundances differed with the type of sinkhole and sediment zone. Both sinkholes are so the presence of daylight can start the photosynthetic process which would occur most in the WM zone. However, only the inland sinkhole showed a high abundance of photosynthetic bacteria within its WM zone. The coastal sinkhole water column and sediments would be deprived of photosynthetic bacteria since the water source is the underground aquifer, lacks photosynthesizers. Ammonia oxidizing bacteria (AOB) and archaea (AOA), and nitrite-oxidizing bacteria (NOB) were relatively abundant in the three sediment zones from the inland sinkhole and in the WM from the coastal sinkhole, these observation at the WM from El Zapote agree with previous observations2. Anaerobic ammonium oxidation (anammox) uses nitrite (as a product of nitrate reduction), as electron acceptor35. The high levels of nitrate concentration in the water column from the three sediment zones at the inland sinkhole and at the WM from the coastal sinkhole may influence the abundance of AOB, AOA and NOB in the sediments from these zones, while NH4+ and nitrite values were below detection limit ( More

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    Personality, density and habitat drive the dispersal of invasive crayfish

    1.Clobert, J., Danchin, E., Dhondt, A. A. & Nichols, J. D. Dispersal (Oxford University Press, 2001).
    Google Scholar 
    2.Ronce, O. How does it feel to be like a rolling stone? Ten questions about dispersal evolution. Annu. Rev. Ecol. Evol. Syst. 38, 231–253 (2007).
    Google Scholar 
    3.Clobert, J., Baguette, M., Benton, T. G. & Bullock, J. M. Dispersal Ecology and Evolution (Oxford University Press, 2012).
    Google Scholar 
    4.Cote, J., Fogarty, S., Brodin, T., Weinersmith, K. & Sih, A. Personality-dependent dispersal in the invasive mosquitofish: Group composition matters. Proc. R. Soc. B Biol. Sci. 278, 1670–1678 (2011).
    Google Scholar 
    5.Quinn, J. L., Cole, E. F., Patrick, S. C. & Sheldon, B. C. Scale and state dependence of the relationship between personality and dispersal in a great tit population. J. Anim. Ecol. 80, 918–928 (2011).PubMed 

    Google Scholar 
    6.Brodin, T., Lind, M. I., Wiberg, M. K. & Johansson, F. Personality trait differences between mainland and island populations in the common frog (Rana temporaria). Behav. Ecol. Sociobiol. 67, 135–143 (2013).
    Google Scholar 
    7.Wilson, D. S. Adaptive individual differences within single populations. Philos. Trans. R. Soc. London. Ser. B Biol. Sci. 353, 199–205 (1998).
    Google Scholar 
    8.Sih, A., Bell, A. & Johnson, J. C. Behavioral syndromes: An ecological and evolutionary overview. Trends Ecol. Evol. 19, 372–378 (2004).PubMed 

    Google Scholar 
    9.Sih, A., Bell, A. M., Johnson, J. C. & Ziemba, R. E. Behavioral syndromes: An integrative overview. Q. Rev. Biol. 79, 241–277 (2004).PubMed 

    Google Scholar 
    10.Réale, D., Reader, S. M., Sol, D., McDougall, P. T. & Dingemanse, N. J. Integrating animal temperament within ecology and evolution. Biol. Rev. 82, 291–318 (2007).PubMed 

    Google Scholar 
    11.Wolf, M. & Weissing, F. J. Animal personalities: Consequences for ecology and evolution. Trends Ecol. Evol. 27, 452–461 (2012).PubMed 

    Google Scholar 
    12.Juette, T., Cucherousset, J. & Cote, J. Animal personality and the ecological impacts of freshwater non-native species. Curr. Zool. 60, 417–427 (2014).
    Google Scholar 
    13.Duckworth, R. A. & Badyaev, A. V. Coupling of dispersal and aggression facilitates the rapid range expansion of a passerine bird. Proc. Natl. Acad. Sci. 104, 15017–15022 (2007).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    14.Conrad, J. L., Weinersmith, K. L., Brodin, T., Saltz, J. B. & Sih, A. Behavioural syndromes in fishes: A review with implications for ecology and fisheries management. J. Fish Biol. 78, 395–435 (2011).CAS 
    PubMed 

    Google Scholar 
    15.Cote, J., Fogarty, S., Weinersmith, K., Brodin, T. & Sih, A. Personality traits and dispersal tendency in the invasive mosquitofish (Gambusia affinis). Proc. R. Soc. B Biol. Sci. 277, 1571–1579 (2010).
    Google Scholar 
    16.Malange, J., Izar, P. & Japyassú, H. Personality and behavioural syndrome in Necromys lasiurus (Rodentia: Cricetidae): Notes on dispersal and invasion processes. Acta Ethol. 19, 189–195 (2016).
    Google Scholar 
    17.Rees, E. M. A. et al. Socio-economic drivers of specialist anglers targeting the non-native European catfish (Silurus glanis) in the UK. PLoS ONE 12, e0178805 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    18.Bowler, D. E. & Benton, T. G. Causes and consequences of animal dispersal strategies: Relating individual behaviour to spatial dynamics. Biol. Rev. 80, 205–225 (2005).PubMed 

    Google Scholar 
    19.Clobert, J., Le Galliard, J.-F., Cote, J., Meylan, S. & Massot, M. Informed dispersal, heterogeneity in animal dispersal syndromes and the dynamics of spatially structured populations. Ecol. Lett. 12, 197–209 (2009).PubMed 

    Google Scholar 
    20.Dukes, J. S. & Mooney, H. A. Does global change increase the success of biological invaders?. Trends Ecol. Evol. 14, 135–139 (1999).CAS 
    PubMed 

    Google Scholar 
    21.Gozlan, R. E., Britton, J. R., Cowx, I. & Copp, G. H. Current knowledge on non-native freshwater fish introductions. J. Fish Biol. 76, 751–786 (2010).
    Google Scholar 
    22.Pimentel, D. et al. Economic and environmental threats of alien plant, animal, and microbe invasions. Agric. Ecosyst. Environ. 84, 1–20 (2001).
    Google Scholar 
    23.Dingemanse, N. J., Kazem, A. J. N., Réale, D. & Wright, J. Behavioural reaction norms: Animal personality meets individual plasticity. Trends Ecol. Evol. 25, 81–89 (2010).PubMed 

    Google Scholar 
    24.Dochtermann, N. A., Schwab, T. & Sih, A. The contribution of additive genetic variation to personality variation: Heritability of personality. Proc. R. Soc. B Biol. Sci. 282, 20142201 (2015).
    Google Scholar 
    25.Duckworth, R. A. Evolution of personality: Developmental constraints on behavioral flexibility. Auk 127, 752–758 (2010).
    Google Scholar 
    26.Trillmich, F., Müller, T. & Müller, C. Understanding the evolution of personality requires the study of mechanisms behind the development and life history of personality traits. Biol. Lett. 14, 20170740 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    27.Dingemanse, N. J. & Réale, D. Natural selection and animal personality. Behaviour 142, 1159–1184 (2005).
    Google Scholar 
    28.Sih, A., Cote, J., Evans, M., Fogarty, S. & Pruitt, J. Ecological implications of behavioural syndromes. Ecol. Lett. 15, 278–289 (2012).PubMed 

    Google Scholar 
    29.Stamps, J. A. Growth-mortality tradeoffs and ‘personality traits’ in animals. Ecol. Lett. 10, 355–363 (2007).PubMed 

    Google Scholar 
    30.Chapple, D. G., Simmonds, S. M. & Wong, B. B. M. Can behavioral and personality traits influence the success of unintentional species introductions?. Trends Ecol. Evol. 27, 57–64 (2012).PubMed 

    Google Scholar 
    31.Hirsch, P. E., Thorlacius, M., Brodin, T. & Burkhardt-Holm, P. An approach to incorporate individual personality in modeling fish dispersal across in-stream barriers. Ecol. Evol. 7, 720–732 (2017).PubMed 

    Google Scholar 
    32.Groen, M. et al. Is there a role for aggression in round goby invasion fronts?. Behaviour 149, 685–703 (2012).
    Google Scholar 
    33.Urban, M. C., Phillips, B. L., Skelly, D. K. & Shine, R. A toad more traveled: The heterogeneous invasion dynamics of cane toads in Australia. Am. Nat. 171, E134–E148 (2008).PubMed 

    Google Scholar 
    34.Lopez, D. P., Jungman, A. A. & Rehage, J. S. Nonnative African jewelfish are more fit but not bolder at the invasion front: A trait comparison across an Everglades range expansion. Biol. Invasions 14, 2159–2174 (2012).
    Google Scholar 
    35.Dingemanse, N. J. & Wolf, M. Recent models for adaptive personality differences: A review. Philos. Trans. R. Soc. B Biol. Sci. 365, 3947–3958 (2010).
    Google Scholar 
    36.Dingemanse, N. J. & Réale, D. What is the evidence that natural selection maintains variation in animal personalities? In Animal Personalities: Behavior, Physiology, and Evolution (eds Carere, C. & Maestripieri, D.) 201–220 (University of Chicago Press, 2013).
    Google Scholar 
    37.Weiss, A. Personality traits: A view from the animal kingdom. J. Pers. 86, 12–22 (2018).PubMed 

    Google Scholar 
    38.Archard, G. A. & Braithwaite, V. A. The importance of wild populations in studies of animal temperament. J. Zool. 281, 149–160 (2010).
    Google Scholar 
    39.Holt, R. D., Keitt, T. H., Lewis, M. A., Maurer, B. A. & Taper, M. L. Theoretical models of species’ borders: Single species approaches. Oikos 108, 18–27 (2005).
    Google Scholar 
    40.Liedvogel, M., Chapman, B. B., Muheim, R. & Åkesson, S. The behavioural ecology of animal movement: Reflections upon potential synergies. Anim. Migr. 1, 39–46 (2013).
    Google Scholar 
    41.Campos-Candela, A., Palmer, M., Balle, S., Álvarez, A. & Alós, J. A mechanistic theory of personality-dependent movement behaviour based on dynamic energy budgets. Ecol. Lett. 22, 213–232 (2019).PubMed 

    Google Scholar 
    42.Bubb, D. H., Thom, T. J. & Lucas, M. C. Movement, dispersal and refuge use of co-occurring introduced and native crayfish. Freshw. Biol. 51, 1359–1368 (2006).
    Google Scholar 
    43.Luque, G. M. et al. The 100th of the world’s worst invasive alien species. Biol. Invasions 16, 981–985 (2014).
    Google Scholar 
    44.Galib, S. M., Findlay, J. S. & Lucas, M. C. Strong impacts of signal crayfish invasion on upland stream fish and invertebrate communities. Freshw. Biol. 66, 223–240 (2021).
    Google Scholar 
    45.Lindstrom, T., Brown, G. P., Sisson, S. A., Phillips, B. L. & Shine, R. Rapid shifts in dispersal behavior on an expanding range edge. Proc. Natl. Acad. Sci. 110, 13452–13456 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    46.Bubb, D. H., Thom, T. J. & Lucas, M. C. The within-catchment invasion of the non-indigenous signal crayfish Pacifastacus leniusculus (Dana), in upland rivers. Bull. Fr. Pêche Piscic. 376–377, 665–673 (2005).
    Google Scholar 
    47.Závorka, L., Lassus, R., Britton, J. R. & Cucherousset, J. Phenotypic responses of invasive species to removals affect ecosystem functioning and restoration. Glob. Chang. Biol. https://doi.org/10.1111/gcb.15271 (2020).Article 
    PubMed 

    Google Scholar 
    48.Sbragaglia, V. & Breithaupt, T. Daily activity rhythms, chronotypes, and risk-taking behavior in the signal crayfish. Curr. Zool. https://doi.org/10.1093/cz/zoab023 (2021).Article 

    Google Scholar 
    49.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.r-project.org/ (2020).50.Pintor, L. M., Sih, A. & Bauer, M. L. Differences in aggression, activity and boldness between native and introduced populations of an invasive crayfish. Oikos 117, 1629–1636 (2008).
    Google Scholar 
    51.Rupia, E. J., Binning, S. A., Roche, D. G. & Lu, W. Fight-flight or freeze-hide? Personality and metabolic phenotype mediate physiological defence responses in flatfish. J. Anim. Ecol. 85, 927–937 (2016).PubMed 

    Google Scholar 
    52.Karavanich, C. & Atema, J. Individual recognition and memory in lobster dominance. Anim. Behav. 56, 1553–1560 (1998).CAS 
    PubMed 

    Google Scholar 
    53.Houlihan, D., Govind, C. & El Haj, A. Energetics of swimming in Callinectes sapidus and walking in Homarus americanus. Comp. Biochem. Physiol. Part A Physiol. 82, 267–279 (1985).
    Google Scholar 
    54.Vogt, G. Functional anatomy. In Biology of Freshwater Crayfish (ed. Holdich, D. M.) 53–151 (Blackwell Science Ltd., 2002).
    Google Scholar 
    55.Southwood, T. R. E. & Henderson, P. A. Ecological Methods (Blackwell Science Ltd., 2000).
    Google Scholar 
    56.Clark, J. & Kershner, M. Size-dependent effects of visible implant elastomer marking on crayfish (Orconectes obscurus) growth, mortality, and tag retention. Crustaceana 79, 275–284 (2006).
    Google Scholar 
    57.Streissl, F. & Hödl, W. Habitat and shelter requirements of the stone crayfish, Austropotamobius torrentium Schrank. Hydrobiologia 477, 195–199 (2002).
    Google Scholar 
    58.Chadwick, D. D. A. et al. A novel ‘triple drawdown’ method highlights deficiencies in invasive alien crayfish survey and control techniques. J. Appl. Ecol. 58, 316–326 (2021).
    Google Scholar 
    59.Stoffel, M. A., Nakagawa, S. & Schielzeth, H. rptR: repeatability estimation and variance decomposition by generalized linear mixed-effects models. Methods Ecol. Evol. 8, 1639–1644 (2017).
    Google Scholar 
    60.Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70 (1979).MathSciNet 
    MATH 

    Google Scholar 
    61.Quinn, G. P. & Keough, M. J. Experimental Design and Data Analysis for Biologists (Cambridge University Press, 2002).
    Google Scholar 
    62.Jackson, D. A. Stopping rules in principal components analysis: A comparison of heuristical and statistical approaches. Ecology 74, 2204–2214 (1993).
    Google Scholar 
    63.Budaev, S. V. Using principal components and factor analysis in animal behaviour research: Caveats and guidelines. Ethology 116, 472–480 (2010).
    Google Scholar 
    64.Robinson, C. A., Thom, T. J. & Lucas, M. C. Ranging behaviour of a large freshwater invertebrate, the white-clawed crayfish Austropotamobius pallipes. Freshw. Biol. 44, 509–521 (2000).
    Google Scholar 
    65.Bubb, D. H., O’Malley, O. J., Gooderham, A. C. & Lucas, M. C. Relative impacts of native and non-native crayfish on shelter use by an indigenous benthic fish. Aquat. Conserv. Mar. Freshw. Ecosyst. 19, 448–455 (2009).
    Google Scholar 
    66.Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage, 2011).
    Google Scholar 
    67.Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inferences: A Practical Information-Theoretic Approach (Springer, 2002).MATH 

    Google Scholar 
    68.Bartoń, K. MuMIn: Multi-Model Inference. R Package version 1.43.6. (2019).69.Kleiber, C. & Zeileis, A. Applied Econometrics with R (Springer, 2008).MATH 

    Google Scholar 
    70.Edwards, D. D., Rapin, K. E. & Moore, P. A. Linking phenotypic correlations from a diverse set of laboratory tests to field behaviors in the crayfish, Orconectes virilis. Ethology 124, 311–330 (2018).
    Google Scholar 
    71.Teknomo, K. Similarity Measurements. https://people.revoledu.com/kardi/tutorial/Similarity (2015).72.Bell, A. M., Hankison, S. J. & Laskowski, K. L. The repeatability of behaviour: A meta-analysis. Anim. Behav. 77, 771–783 (2009).PubMed 
    PubMed Central 

    Google Scholar 
    73.Vainikka, A., Rantala, M. J., Niemelä, P., Hirvonen, H. & Kortet, R. Boldness as a consistent personality trait in the noble crayfish, Astacus astacus. Acta Ethol. 14, 17–25 (2011).
    Google Scholar 
    74.Fraser, D. F., Gilliam, J. F., Daley, M. J., Le, A. N. & Skalski, G. T. Explaining leptokurtic movement distributions: Intrapopulation variation in boldness and exploration. Am. Nat. 158, 124–135 (2001).CAS 
    PubMed 

    Google Scholar 
    75.Dingemanse, N. J., Both, C., van Noordwijk, A. J., Rutten, A. L. & Drent, P. J. Natal dispersal and personalities in great tits (Parus major). Proc. R. Soc. London. Ser. B Biol. Sci. 270, 741–747 (2003).
    Google Scholar 
    76.McMahon, T. E. & Tash, J. C. Experimental analysis of the role of emigration in population regulation of desert pupfish. Ecology 69, 1871–1883 (1988).
    Google Scholar 
    77.Porter, J. H. & Dooley, J. L. Animal dispersal patterns: A reassessment of simple mathematical models. Ecology 74, 2436–2443 (1993).
    Google Scholar 
    78.Einum, S., Sundt-Hansen, L. & Nislow, K. H. The partitioning of density-dependent dispersal, growth and survival throughout ontogeny in a highly fecund organism. Oikos 113, 489–496 (2006).
    Google Scholar 
    79.Lodge, D. M. & Hill, A. M. Factors governing species composition, population size and productivity of coolwater crayfishes. Nord. J. Freshw. Res. 69, 111–136 (1994).
    Google Scholar 
    80.Berthouly-Salazar, C., van Rensburg, B. J., Le Roux, J. J., van Vuuren, B. J. & Hui, C. Spatial sorting drives morphological variation in the invasive bird, Acridotheris tristis. PLoS ONE 7, e38145 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    81.Juanes, F. & Smith, L. D. The ecological consequences of limb damage and loss in decapod crustaceans: A review and prospectus. J. Exp. Mar. Biol. Ecol. 193, 197–223 (1995).
    Google Scholar 
    82.Wilshin, S. et al. Limping following limb loss increases locomotor stability. J. Exp. Biol. 221, jeb174268 (2018).PubMed 

    Google Scholar 
    83.Podgorniak, T., Blanchet, S., De Oliveira, E., Daverat, F. & Pierron, F. To boldly climb: Behavioural and cognitive differences in migrating European glass eels. R. Soc. Open Sci. 3, 150665 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    84.Bubb, D. H., Thom, T. J. & Lucas, M. C. Movement patterns of the invasive signal crayfish determined by PIT telemetry. Can. J. Zool. 84, 1202–1209 (2006).
    Google Scholar 
    85.Bilton, D. T., Freeland, J. R. & Okamura, B. Dispersal in freshwater invertebrates. Annu. Rev. Ecol. Syst. 32, 159–181 (2001).
    Google Scholar 
    86.Bubb, D. H., Thom, T. J. & Lucas, M. C. Movement and dispersal of the invasive signal crayfish Pacifastacus leniusculus in upland rivers. Freshw. Biol. 49, 357–368 (2004).
    Google Scholar 
    87.Hudina, S., Kutleša, P., Trgovčić, K. & Duplić, A. Dynamics of range expansion of the signal crayfish (Pacifastacus leniusculus) in a recently invaded region in Croatia. Aquat. Invasions 12, 67–75 (2017).
    Google Scholar 
    88.Wutz, S. & Geist, J. Sex- and size-specific migration patterns and habitat preferences of invasive signal crayfish (Pacifastacus leniusculus Dana). Limnologica 43, 59–66 (2013).
    Google Scholar 
    89.Fraser, H., Barnett, A., Parker, T. H. & Fidler, F. The role of replication studies in ecology. Ecol. Evol. 10, 5197–5207 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    90.Linzmaier, S. M., Goebel, L. S., Ruland, F. & Jeschke, J. M. Behavioral differences in an over-invasion scenario: marbled vs. spiny-cheek crayfish. Ecosphere 9, e02385 (2018).
    Google Scholar 
    91.Wang, X. et al. Anthropogenic habitat loss accelerates the range expansion of a global invader. Divers. Distrib. https://doi.org/10.1111/ddi.13359 (2021).Article 

    Google Scholar  More

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    Hydrological properties predict the composition of microbial communities cycling methane and nitrogen in rivers

    Relationships between microbial diversity and base flow indexThe number of reads obtained per sample and total number of OTUs obtained after rarefaction for each gene dataset are summarised in Table S2. According to taxonomic analyses of our 16S rRNA gene dataset, archaeal communities in our river sediment samples consisted largely of OTUs assigned to the Woesarchaeota (20.8% of OTUs and 24.7% of reads) and Methanomicrobia (16.9% of OTUs and 31.8% of reads). Of the functional groups analysed here, ten OTUs were assigned to AOA, Nitrososphaera (n = 8) and Nitrosopumilus (n = 2), that together formed 4.8% of all archaeal 16S rRNA reads. A total of 137 OTUs were assigned to orders of methanogenic archaea, with 15.3% and 16% of archaeal reads assigned to the orders Methanomicrobiales and Methanosarcinales, respectively, with other methanogen orders constituting a further 6.7% of reads.Bacterial communities were more diverse and OTUs assigned to taxa within the functional groups analysed here formed a relatively small proportion of our bacterial 16S rRNA gene dataset. Ammonia oxidising bacteria were represented by only five OTUs (all assigned to Nitrosospira) that together constituted 0.02% of the total bacterial community across our sediments. A further 84 OTUs were assigned to methanotrophic genera, and these OTUs contributed a total of 0.88% of all bacterial 16S rRNA sequences. These were Methylobacter (30 OTUs, 0.7% of bacterial sequences), Methylophilus (15 OTUs, 0.1% of bacterial sequences), Methylosoma (7 OTUs, 0.004% of bacterial sequences), Methylomonas and Methylotenera (6 OTUs each, 0.02 and 0.002% of bacterial sequences, respectively), and Methylosarcina (5 OTUs, 0.002% of bacterial sequences), with a further eight genera represented by a total of 15 OTUs. As reported previously, no OTUs were assigned to known anammox genera, which were likely below the limit of detection in our study [8].The OTU richness of archaeal communities (based on 16S rRNA amplicons) was negatively, albeit weakly, related to BFI (coef = 0.52, z = −2.95, adj-D2 = 0.12, P  More

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    Effectiveness of protection areas in safeguarding biodiversity and ecosystem services in Tibet Autonomous Region

    1.Cao, S. & Zhang, J. Political risks arising from the impacts of large-scale afforestation on water resources of the Tibetan Plateau. Gondwana Res. 28, 898–903 (2015).ADS 

    Google Scholar 
    2.Kinzig, A. P. et al. Response—Ecosystem services: Free lunch no more. Science 335, 656 (2012).ADS 
    CAS 

    Google Scholar 
    3.Zhang, J. et al. Natural recovery and restoration in giant panda habitat after the Wenchuan earthquake. For. Ecol. Manage. 319, 1–9 (2014).
    Google Scholar 
    4.Chen, Z. et al. Land-use change from arable lands to orchards reduced soil erosion and increased nutrient loss in a small catchment. Sci. Total Environ. 648, 1097–1104 (2019).ADS 
    CAS 
    PubMed 

    Google Scholar 
    5.Boerema, A., Van Passel, S. & Meire, P. Cost-effectiveness analysis of ecosystem management with ecosystem services: From theory to practice. Ecol. Econ. 152, 207–218 (2018).
    Google Scholar 
    6.Bouwma, I. et al. Adoption of the ecosystem services concept in EU policies. Ecosyst. Serv. 29, 213–222 (2018).
    Google Scholar 
    7.Carpenter, S. R. et al. Millennium ecosystem assessment: Research needs. Science 314, 257 (2006).CAS 
    PubMed 

    Google Scholar 
    8.Xiao, Q., Tao, J., Xiao, Y. & Qian, F. Monitoring vegetation cover in Chongqing between 2001 and 2010 using remote sensing data. Environ. Monit. Assess. 189, 493 (2017).PubMed 

    Google Scholar 
    9.Harrison, X. A. et al. A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ 6, e4794 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    10.Zhang, J. et al. Modeling activity patterns of wildlife using time-series analysis. Ecol. Evol. 7, 2575–2584 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    11.Fu, B. et al. Hydrogeomorphic ecosystem responses to natural and anthropogenic changes in the Loess Plateau of China. Annu. Rev. Earth Planet. Sci. 45, 223–243 (2017).ADS 
    CAS 

    Google Scholar 
    12.Ouyang, W. et al. Combined impacts of land use and soil property changes on soil erosion in a mollisol area under long-term agricultural development. Sci. Total Environ. 613–614, 798–809 (2018).ADS 
    PubMed 

    Google Scholar 
    13.Arneth, A. et al. Post-2020 biodiversity targets need to embrace climate change. Proc. Natl. Acad. Sci. 117, 30882 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    14.Keyes, A. A., McLaughlin, J. P., Barner, A. K. & Dee, L. E. An ecological network approach to predict ecosystem service vulnerability to species losses. Nat. Commun. 12, 1586 (2021).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    15.Feng, X. et al. Human cystic and alveolar echinococcosis in the Tibet Autonomous Region (TAR), China. J. Helminthol. 89, 671–679 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    16.Hallquist, M. et al. Photochemical smog in China: Scientific challenges and implications for air-quality policies. Natl. Sci. Rev. 3, 401–403 (2016).CAS 

    Google Scholar 
    17.Zhang, G. G. et al. Abundance and conservation of waterbirds breeding on the Changtang Plateau, Tibet Autonomous Region, China. Waterbirds 38, 19–29 (2015).CAS 

    Google Scholar 
    18.Sun, D. et al. Soil erosion and water retention varies with plantation type and age. For. Ecol. Manage. 422, 1–10 (2018).
    Google Scholar 
    19.Wangdwei, M., Steele, B. & Harris, R. B. Demographic responses of plateau pikas to vegetation cover and land use in the Tibet Autonomous Region, China. J. Mammal. 94, 1077–1086 (2013).
    Google Scholar 
    20.Zhuo, G., La, B., Pubu, C. & Luo, B. Study on daily surface evapotranspiration with SEBS in Tibet Autonomous Region. J. Geogr. Sci. 24, 113–128 (2014).ADS 

    Google Scholar 
    21.Butarbutar, T., Soedirman, S., Neupane, P. R. & Köhl, M. Carbon recovery following selective logging in tropical rainforests in Kalimantan, Indonesia. For. Ecosyst. https://doi.org/10.1186/s40663-019-0195-x (2019).Article 

    Google Scholar 
    22.Yu, W. J. & Zhou, W. Q. Spatial pattern of urban change in two Chinese megaregions: Contrasting responses to national policy and economic mode. Sci. Total Environ. 634, 1362–1371 (2018).ADS 
    CAS 
    PubMed 

    Google Scholar 
    23.Storch, F., Kändler, G. & Bauhus, J. Assessing the influence of harvesting intensities on structural diversity of forests in south-west Germany. For. Ecosyst. https://doi.org/10.1186/s40663-019-0199-6 (2019).Article 

    Google Scholar 
    24.Xiao, Y. & Xiao, Q. Identifying key areas of ecosystem services potential to improve ecological management in Chongqing City, southwest China. Environ. Monit. Assess 190, 258 (2018).PubMed 

    Google Scholar 
    25.Ge, J. et al. Modeling alpine grassland cover based on MODIS data and support vector machine regression in the headwater region of the Huanghe River, China. Remote Sens. Environ. 218, 162–173 (2018).ADS 

    Google Scholar 
    26.Symes, W. S., Edwards, D. P., Miettinen, J., Rheindt, F. E. & Carrasco, L. R. Combined impacts of deforestation and wildlife trade on tropical biodiversity are severely underestimated. Nat. Commun. 9, 4052 (2018).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    27.Xin, S. et al. Forestland-cover changes in China’s tropical area: Historical patterns, implications, and policy options-a case study from Xishuangbanna. J. Sustain. For. 36, 18–31 (2017).
    Google Scholar 
    28.Rao, Y. et al. Integrating ecosystem services value for sustainable land-use management in semi-arid region. J. Clean. Prod. 186, 662–672 (2018).
    Google Scholar 
    29.Ricketts, T. H. et al. Disaggregating the evidence linking biodiversity and ecosystem services. Nat. Commun. 7, 13106 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    30.Nguyen, M. D., Ancev, T. & Randall, A. Forest governance and economic values of forest ecosystem services in Vietnam. Land Use Policy 97, 103297 (2018).
    Google Scholar 
    31.Xu, W. et al. Strengthening protected areas for biodiversity and ecosystem services in China. Proc. Natl. Acad. Sci. U.S.A. 114, 1601–1606 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    32.Ouyang, Z. et al. Using gross ecosystem product (GEP) to value nature in decision making. Proc. Natl. Acad. Sci. U.S.A. 117, 14593–14601 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    33.Anne, B. et al. Towards an operational methodology to optimize ecosystem services provided by urban soils. Landsc. Urban Plan. 176, 1–9 (2018).
    Google Scholar 
    34.Karlen, D. L., Peterson, G. A. & Westfall, D. G. Soil and water conservation: Our history and future challenges. Soil Sci. Soc. Am. J. 78, 1493–1499 (2014).ADS 

    Google Scholar 
    35.Tuo, D., Xu, M. & Gao, G. Relative contributions of wind and water erosion to total soil loss and its effect on soil properties in sloping croplands of the Chinese Loess Plateau. Sci. Total Environ. 633, 1032–1040 (2018).ADS 
    CAS 
    PubMed 

    Google Scholar 
    36.Rubio-Delgado, J., Schnabel, S., Gómez-Gutiérrez, Á. & Sánchez-Fernández, M. Estimation of soil erosion rates in dehesas using the inflection point of holm oaks. CATENA 166, 56–67 (2018).
    Google Scholar 
    37.Abouabdillah, A. et al. Evaluation of soil and water conservation measures in a semi-arid river basin in Tunisia using SWAT. Soil Use Manage. 30, 539–549 (2014).
    Google Scholar 
    38.Dominati, E. J., Mackay, A., Lynch, B., Heath, N. & Millner, I. An ecosystem services approach to the quantification of shallow mass movement erosion and the value of soil conservation practices. Ecosyst. Serv. 9, 204–215 (2014).
    Google Scholar 
    39.Engdawork, A. & Bork, H.-R. Long-term indigenous soil conservation technology in the Chencha Area, Southern Ethiopia: Origin, characteristics, and sustainability. Ambio 43, 932–942 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    40.Sverdrup, H. U. & Olafsdottir, A. H. Considerations on the future biomass production potential of Iceland, and what role that could have in future fuel supply and carbon balances. J. Sustain. For. 36, 647–665 (2017).
    Google Scholar 
    41.Ofoegbu, C. & Speranza, C. I. Assessing rural peoples’ intention to adopt sustainable forest use and management practices in South Africa. J. Sustain. For. 36, 729–746 (2017).
    Google Scholar 
    42.Munyati, C. & Sinthumule, N. I. Cover gradients and the forest-community frontier: Indigenous forests under communal management at Vondo and Xanthia, South Africa. J. Sustain. For. 33, 757–775 (2014).
    Google Scholar 
    43.Xiao, Q., Gao, Y., Hu, D., Tan, H. & Wang, T. Assessment of the interactions between economic growth and industrial wastewater discharges using co-integration analysis: A case study for China’s Hunan Province. Int. J. Environ. Res. Public Health 8, 2937–2950 (2011).PubMed 
    PubMed Central 

    Google Scholar 
    44.Sun, Q., Miao, C., Qiao, Y. & Duan, Q. The nonstationary impact of local temperature changes and ENSO on extreme precipitation at the global scale. Clim. Dyn. 49, 4281–4292 (2017).
    Google Scholar 
    45.Cao, S., Chen, L., Xu, C. & Liu, Z. Impact of three soil types on afforestation in China’s Loess Plateau: Growth and survival of six tree species and their effects on soil properties. Landsc. Urban Plan. 83, 208–217 (2007).
    Google Scholar 
    46.Setten, G. & Brown, K. M. Ecosystem services as an integrative framework: What is the potential? Land Use Policy 75, 549–556 (2018).
    Google Scholar 
    47.Arroyo-Vargas, P., Fuentes-Ramírez, A., Muys, B. & Pauchard, A. Impacts of fire severity and cattle grazing on early plant dynamics in old-growth Araucaria-Nothofagus forests. For. Ecosyst. https://doi.org/10.1186/s40663-019-0202-2 (2019).Article 

    Google Scholar 
    48.Paudel, S. & Sah, J. P. Effects of different management practices on stand composition and species diversity in subtropical forests in Nepal: Implications of community participation in biodiversity conservation. J. Sustain. For. 34, 738–760 (2015).
    Google Scholar 
    49.Su, L., Miao, C., Borthwick, A. G. L. & Duan, Q. Wavelet-based variability of Yellow River discharge at 500-, 100-, and 50-year timescales. Gondwana Res. 49, 94–105 (2017).ADS 

    Google Scholar 
    50.Enquist, B. J., Abraham, A. J., Harfoot, M. B. J., Malhi, Y. & Doughty, C. E. The megabiota are disproportionately important for biosphere functioning. Nat. Commun. 11, 699 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    51.Schuldt, A. et al. Multiple plant diversity components drive consumer communities across ecosystems. Nat. Commun. 10, 1460 (2019).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    52.Miao, C., Sun, Q., Duan, Q. & Wang, Y. Joint analysis of changes in temperature and precipitation on the Loess Plateau during the period 1961–2011. Clim. Dyn. 47, 3221–3234 (2016).
    Google Scholar 
    53.Zhang, J. et al. Divergent responses of sympatric species to livestock encroachment at fine spatiotemporal scales. Biol. Conserv. 209, 119–129 (2017).
    Google Scholar 
    54.Cao, S., Liu, Y., Su, W., Zheng, X. & Yu, Z. The net ecosystem services value in mainland China. Sci. China Earth Sci. 61, 595–603 (2018).ADS 

    Google Scholar 
    55.Waiswa, D., Stern, M. J. & Prisley, S. P. Drivers of deforestation in the Lake Victoria crescent, Uganda. J. Sustain. For. 34, 259–275 (2015).
    Google Scholar 
    56.Xiao, Q. & Hu, D. Dynamic characteristics of a water resource structure in an urban ecological system: Structure modelling based on input–occupancy–output technology. J. Clean. Prod. 153, 548–557 (2017).
    Google Scholar  More