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    Microbial community shifts induced by plastic and zinc as substitutes of tire abrasion

    Hirai, H. et al. Organic micropollutants in marine plastics debris from the open ocean and remote and urban beaches. Mar. Pollut. Bull. 62(8), 1683–1692. https://doi.org/10.1016/j.marpolbul.2011.06.004 (2011).Article 
    CAS 
    PubMed 

    Google Scholar 
    Masó, M., Garcés, E., Pagès, F. & Camp, J. Drifting plastic debris as a potential vector for dispersing harmful algal bloom (HAB) species. Sci. Mar. 67(1), 107–111. https://doi.org/10.3989/scimar.2003.67n1107 (2003).Article 

    Google Scholar 
    Pandey, D., Singh, A., Ramanathan, A. & Kumar, M. The combined exposure of microplastics and toxic contaminants in the floodplains of North India: A review. J. Environ. Manag. 279, 111557. https://doi.org/10.1016/j.jenvman.2020.111557 (2021).Article 
    CAS 

    Google Scholar 
    Peng, L. et al. Micro- and nano-plastics in marine environment: Source, distribution and threats—A review. Sci. Total Environ. 698, 134254. https://doi.org/10.1016/j.scitotenv.2019.134254 (2020).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Rillig, M. C. & Lehmann, A. Microplastic in terrestrial ecosystems and the soil?. Environ. Sci. Technol. 46(12), 6453–6454. https://doi.org/10.1021/es302011r (2012).Article 
    CAS 
    PubMed 

    Google Scholar 
    Rochman, C. M. & Hoellein, T. The global odyssey of plastic pollution. Science 368(6496), 1184–1185. https://doi.org/10.1126/science.abc4428 (2020).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Jan Kole, P., Löhr, A. J., van Belleghem, F. G. A. J. & Ragas, A. M. J. Wear and tear of tyres: A stealthy source of microplastics in the environment. Int. J. Environ. Res. Public Health https://doi.org/10.3390/ijerph14101265 (2017).Article 

    Google Scholar 
    Sommer, F. et al. Tire abrasion as a major source of microplastics in the environment. Aerosol Air Qual. Res. 18(8), 2014–2028. https://doi.org/10.4209/aaqr.2018.03.0099 (2018).Article 
    CAS 

    Google Scholar 
    Beita-Sandí, W., Selbes, M., Ersan, M. S. & Karanfil, T. Release of nitrosamines and nitrosamine precursors from scrap tires. Environ. Sci. Technol. Lett. 6(4), 251–256. https://doi.org/10.1021/acs.estlett.9b00172 (2019).Article 
    CAS 

    Google Scholar 
    Kaminsky, W. & Mennerich, C. Pyrolysis of synthetic tire rubber in a fluidised-bed reactor to yield 1,3-butadiene, styrene and carbon black. J. Anal. Appl. Pyrolysis 58–59, 803–811. https://doi.org/10.1016/S0165-2370(00)00129-7 (2001).Article 

    Google Scholar 
    Sundt, P., Schulze, P. E. & Syversen, F. Sources of microplastic- pollution to the marine environment. Mepex Nor. Environ. Agency 86, 20 (2014).
    Google Scholar 
    White, W. C. Butadiene production process overview. Chem. Biol. Interact. 166(1–3), 10–14. https://doi.org/10.1016/j.cbi.2007.01.009 (2007).Article 
    CAS 
    PubMed 

    Google Scholar 
    Alimi, O. S., Farner Budarz, J., Hernandez, L. M. & Tufenkji, N. Microplastics and nanoplastics in aquatic environments: Aggregation, deposition, and enhanced contaminant transport. Environ. Sci. Technol. 52(4), 1704–1724. https://doi.org/10.1021/acs.est.7b05559 (2018).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Cooper, D. A. & Corcoran, P. L. Effects of mechanical and chemical processes on the degradation of plastic beach debris on the island of Kauai, Hawaii. Mar. Pollut. Bull. 60(5), 650–654. https://doi.org/10.1016/j.marpolbul.2009.12.026 (2010).Article 
    CAS 
    PubMed 

    Google Scholar 
    O’Brine, T. & Thompson, R. C. Degradation of plastic carrier bags in the marine environment. Mar. Pollut. Bull. 60(12), 2279–2283. https://doi.org/10.1016/j.marpolbul.2010.08.005 (2010).Article 
    CAS 
    PubMed 

    Google Scholar 
    Song, Y. K. et al. Combined effects of UV exposure duration and mechanical abrasion on microplastic fragmentation by polymer type. Environ. Sci Technol. 51(8), 4368–4376. https://doi.org/10.1021/acs.est.6b06155 (2017).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Adobe Inc. (2019). Adobe illustrator. Retrieved from https://www.adobe.com/Products/Illustrator.Chamas, A. et al. Degradation rates of plastics in the environment. ACS Sustain. Chem. Eng. 8(9), 3494–3511. https://doi.org/10.1021/acssuschemeng.9b06635 (2020).Article 
    CAS 

    Google Scholar 
    Councell, T. B., Duckenfield, K. U., Landa, E. R. & Callender, E. Tire-wear particles as a source of zinc to the environment. Environ. Sci. Technol. 38(15), 4206–4214. https://doi.org/10.1021/es034631f (2004).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Awet, T. T. et al. Effects of polystyrene nanoparticles on the microbiota and functional diversity of enzymes in soil. Environ. Sci. Eur. https://doi.org/10.1186/s12302-018-0140-6 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Chung, H., Son, Y., Yoon, T. K., Kim, S. & Kim, W. The effect of multi-walled carbon nanotubes on soil microbial activity. Ecotoxicol. Environ. Saf. 74(4), 569–575. https://doi.org/10.1016/j.ecoenv.2011.01.004 (2011).Article 
    CAS 
    PubMed 

    Google Scholar 
    Huber, M., Welker, A. & Helmreich, B. Critical review of heavy metal pollution of traffic area runoff: Occurrence, influencing factors, and partitioning. Sci. Total Environ. 541, 895–919. https://doi.org/10.1016/j.scitotenv.2015.09.033 (2016).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Miandad, R., Barakat, M. A., Aburiazaiza, A. S., Rehan, M. & Nizami, A. S. Catalytic pyrolysis of plastic waste: A review. Process Saf. Environ. Prot. 102, 822–838. https://doi.org/10.1016/j.psep.2016.06.022 (2016).Article 
    CAS 

    Google Scholar 
    Zhang, X., Li, H., Cao, Q., Jin, L. & Wang, F. Upgrading pyrolytic residue from waste tires to commercial carbon black. Waste Manag. Res. 36(5), 436–444. https://doi.org/10.1177/0734242X18764292 (2018).Article 
    CAS 
    PubMed 

    Google Scholar 
    Zhu, D., Li, G., Wang, H. T. & Duan, G. L. Effects of nano- or microplastic exposure combined with arsenic on soil bacterial, fungal, and protistan communities. Chemosphere 281, 130998. https://doi.org/10.1016/j.chemosphere.2021.130998 (2021).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Pathan, S. I. et al. Soil Pollution from micro-and nanoplastic debris: A hidden and unknown biohazard. Sustainability 12(18), 1–31. https://doi.org/10.3390/su12187255 (2020).Article 
    CAS 

    Google Scholar 
    Rillig, M. C. & Bonkowski, M. Microplastic and soil protists: A call for research. Environ. Pollut. 241, 1128–1131. https://doi.org/10.1016/j.envpol.2018.04.147 (2018).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zettler, E. R., Mincer, T. J. & Amaral-Zettler, L. A. Life in the “Plastisphere”: Microbial communities on plastic marine debris. Environ. Sci. Technol. 47(13), 7137–7146. https://doi.org/10.1021/es401288x (2013).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    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(11), 2776–2796. https://doi.org/10.1002/etc.4268 (2018).Article 
    CAS 
    PubMed 

    Google Scholar 
    Bradney, L. et al. Particulate plastics as a vector for toxic trace-element uptake by aquatic and terrestrial organisms and human health risk. Environ. Int. 2019(131), 104937. https://doi.org/10.1016/j.envint.2019.104937 (2018).Article 
    CAS 

    Google Scholar 
    Duis, K. & Coors, A. Microplastics in the Aquatic and Terrestrial Environment: Sources (with a Specific Focus on Personal Care Products), fate and effects. Environ. Sci. Eur. 28(1), 1–25. https://doi.org/10.1186/s12302-015-0069-y (2016).Article 
    CAS 

    Google Scholar 
    Geyer, R., Jambeck, J. R. & Law, K. L. Production, use, and fate of all plastics ever made. Sci. Adv. 3(7), 25–29. https://doi.org/10.1126/sciadv.1700782 (2017).Article 
    CAS 

    Google Scholar 
    Jayasiri, H. B., Purushothaman, C. S. & Vennila, A. Quantitative analysis of plastic debris on recreational beaches in Mumbai, India. Mar. Pollut. Bull. 77(1–2), 107–112. https://doi.org/10.1016/j.marpolbul.2013.10.024 (2013).Article 
    CAS 
    PubMed 

    Google Scholar 
    Lassen, C., Hansen, S. F., Magnusson, K., Hartmann, N. B., Rehne Jensen, P., Nielsen, T. G. & Brinch, A. Microplastics occurrence, effects and sources of releases (2015).Weithmann, N. et al. Organic fertilizer as a vehicle for the entry of microplastic into the environment. Sci. Adv. https://doi.org/10.1126/sciadv.aap8060 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hidalgo-Ruz, V., Gutow, L., Thompson, R. C. & Thiel, M. Microplastics in the marine environment: A review of the methods used for identification and quantification. Environ. Sci. Technol. 46(6), 3060–3075. https://doi.org/10.1021/es2031505 (2012).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Boenigk, J., Matz, C., Jürgens, K. & Arndt, H. Confusing selective feeding with differential digestion in bacterivorous nanoflagellates. J. Eukaryot. Microbiol. 48(4), 425–432. https://doi.org/10.1111/j.1550-7408.2001.tb00175.x (2001).Article 
    CAS 
    PubMed 

    Google Scholar 
    Boenigk, J., Matz, C., Jürgens, K. & Arndt, H. Food concentration-dependent regulation of food selectivity of interception-feeding bacterivorous nanoflagellates. Aquat. Microb. Ecol. 27(2), 195–202. https://doi.org/10.3354/ame027195 (2002).Article 

    Google Scholar 
    Wright, S. L., Thompson, R. C. & Galloway, T. S. The physical impacts of microplastics on marine organisms: A review. Environ. Pollut. 178, 483–492. https://doi.org/10.1016/j.envpol.2013.02.031 (2013).Article 
    CAS 
    PubMed 

    Google Scholar 
    Moore, C. J. Synthetic polymers in the marine environment: A rapidly increasing, long-term threat. Environ. Res. 108(2), 131–139. https://doi.org/10.1016/j.envres.2008.07.025 (2008).Article 
    CAS 
    PubMed 

    Google Scholar 
    Fu, S. F. et al. Exposure to polystyrene nanoplastic leads to inhibition of anaerobic digestion system. Sci. Total Environ. 625, 64–70. https://doi.org/10.1016/j.scitotenv.2017.12.158 (2018).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Bock, C. et al. Factors shaping community patterns of protists and bacteria on a European scale. Environ. Microbiol. 22(6), 2243–2260. https://doi.org/10.1111/1462-2920.14992 (2020).Article 
    PubMed 

    Google Scholar 
    Besseling, E., Wang, B., Lürling, M. & Koelmans, A. A. Nanoplastic affects growth of S. obliquus and reproduction of D. magna. Environ. Sci. Technol. 48(20), 12336–12343. https://doi.org/10.1021/es503001d (2014).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Brown, D. M., Wilson, M. R., MacNee, W., Stone, V. & Donaldson, K. Size-dependent proinflammatory effects of ultrafine polystyrene particles: A role for surface area and oxidative stress in the enhanced activity of ultrafines. Toxicol. Appl. Pharmacol. 175(3), 191–199. https://doi.org/10.1006/taap.2001.9240 (2001).Article 
    CAS 
    PubMed 

    Google Scholar 
    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(16), 8849–8857. https://doi.org/10.1021/acs.est.6b01441 (2016).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Kang, H. C., Jeong, H. J., Jang, S. H. & Lee, K. H. Feeding by common heterotrophic protists on the phototrophic dinoflagellate Biecheleriopsis adriatica (Suessiaceae) compared to that of other suessioid dinoflagellates. Algae 34(2), 127–140. https://doi.org/10.4490/algae.2019.34.5.29 (2019).Article 
    CAS 

    Google Scholar 
    Sjollema, S. B., Redondo-Hasselerharm, P., Leslie, H. A., Kraak, M. H. S. & Vethaak, A. D. Do plastic particles affect microalgal photosynthesis and growth?. Aquat. Toxicol. 170, 259–261. https://doi.org/10.1016/j.aquatox.2015.12.002 (2016).Article 
    CAS 
    PubMed 

    Google Scholar 
    Rossi, G., Barnoud, J. & Monticelli, L. Polystyrene nanoparticles perturb lipid membranes. J. Phys. Chem. Lett. 5(1), 241–246. https://doi.org/10.1021/jz402234c (2014).Article 
    CAS 
    PubMed 

    Google Scholar 
    Brandts, I. et al. Effects of nanoplastics on mytilus galloprovincialis after individual and combined exposure with carbamazepine. Sci. Total Environ. 643, 775–784. https://doi.org/10.1016/j.scitotenv.2018.06.257 (2018).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Ciacci, C. et al. Nanoparticle-biological interactions in a marine benthic foraminifer. Sci. Rep. https://doi.org/10.1038/s41598-019-56037-2 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kim, J. A. et al. Low dose of amino-modified nanoparticles induces cell cycle arrest. ACS Nano 7(9), 7483–7494 (2013).Article 
    CAS 
    PubMed 

    Google Scholar 
    Mao, Y. et al. Phytoplankton response to polystyrene microplastics: Perspective from an entire growth period. Chemosphere https://doi.org/10.1016/j.chemosphere.2018.05.170 (2018).Article 
    PubMed 

    Google Scholar 
    Wang, F. et al. Time resolved study of cell death mechanisms induced by amine-modified polystyrene nanoparticles. Nanoscale 5(22), 10868–10876. https://doi.org/10.1039/c3nr03249c (2013).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Xia, T. et al. Comparison of the abilities of ambient and manufactured nanoparticles to induce cellular toxicity according to an oxidative stress paradigm. Nano Lett. 6(8), 1794–1807. https://doi.org/10.1021/nl061025k (2006).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Lagarde, F. et al. Microplastic interactions with freshwater microalgae: Hetero-aggregation and changes in plastic density appear strongly dependent on polymer type. Environ. Pollut. 215, 331–339. https://doi.org/10.1016/j.envpol.2016.05.006 (2016).Article 
    CAS 
    PubMed 

    Google Scholar 
    Bhattacharya, P., Lin, S., Turner, J. P. & Ke, P. C. Physical adsorption of charged plastic nanoparticles affects algal photosynthesis. J. Phys. Chem. C 114(39), 16556–16561. https://doi.org/10.1021/jp1054759 (2010).Article 
    CAS 

    Google Scholar 
    Johansen, J. L., Rønn, R. & Ekelund, F. Toxicity of cadmium and zinc to small soil protists. Environ. Pollut. 242, 1510–1517. https://doi.org/10.1016/j.envpol.2018.08.034 (2018).Article 
    CAS 
    PubMed 

    Google Scholar 
    Díaz, S., Martín-González, A. & Carlos Gutiérrez, J. Evaluation of heavy metal acute toxicity and bioaccumulation in soil ciliated protozoa. Environ. Int. 32(6), 711–717. https://doi.org/10.1016/j.envint.2006.03.004 (2006).Article 
    CAS 
    PubMed 

    Google Scholar 
    Subba, P. et al. Zinc stress induces physiological, ultra-structural and biochemical changes in mandarin orange (Citrus Reticulata Blanco) seedlings. Physiol. Mol. Biol. Plants 20(4), 461–473. https://doi.org/10.1007/s12298-014-0254-2 (2014).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Corcoll, N. et al. The effect of metals on photosynthesis processes and diatom metrics of biofilm from a metal-contaminated river: A translocation experiment. Ecol. Indic. 18, 620–631. https://doi.org/10.1016/j.ecolind.2012.01.026 (2012).Article 
    CAS 

    Google Scholar 
    Moffett, B. F. et al. Zinc contamination decreases the bacterial diversity of agricultural soil. FEMS Microbiol. Ecol. 43(1), 13–19. https://doi.org/10.1016/S0168-6496(02)00448-8 (2003).Article 
    CAS 
    PubMed 

    Google Scholar 
    Kuperman, R. G. & Carreiro, M. M. Soil heavy metal concentrations, microbial biomass and enzyme activities in a contaminated grassland ecosystem. Soil Biol. Biochem. 29(2), 179–190. https://doi.org/10.1016/S0038-0717(96)00297-0 (1997).Article 
    CAS 

    Google Scholar 
    Masmoudi, S. et al. Cadmium, copper, sodium and zinc effects on diatoms: From heaven to hell-a review. Cryptogam Algol 34(2), 185–225. https://doi.org/10.7872/crya.v34.iss2.2013.185 (2013).Article 

    Google Scholar 
    Gadd, G. M. & de Rome, L. Biosorption of copper by fungal melanin. Appl. Microbiol. Biotechnol. 29(6), 610–617. https://doi.org/10.1007/BF00260993 (1988).Article 
    CAS 

    Google Scholar 
    Khan, M. & Scullion, J. Effects of metal (Cd, Cu, Ni, Pb or Zn) enrichment of sewage-sludge on soil micro-organisms and their activities. Appl. Soil. Ecol. 20(2), 145–155. https://doi.org/10.1016/S0929-1393(02)00018-5 (2002).Article 

    Google Scholar 
    Guillard, R. R. L. & Lorenzen, C. J. Yellow-green algae with chlorophyllide C12. J. Phycol. 8(1), 10–14. https://doi.org/10.1111/j.1529-8817.1972.tb03995.x (1972).Article 
    CAS 

    Google Scholar 
    Zagata, P., Kopańska, M., Greczek-Stachura, M. & Burnecki, T. Acute toxicity of metals: Nickel and zinc to Paramecium bursaria and its endosymbionts. J. Microbiol. Biotechnol. Food Sci. 04, 128–131. https://doi.org/10.15414/jmbfs.2015.4.special2.128-131 (2015).Article 
    CAS 

    Google Scholar 
    Lenz, R., Enders, K. & Nielsen, T. G. Microplastic exposure studies should be environmentally realistic. Proc. Natl. Acad. Sci. U. S. A. 113(29), E4121–E4122. https://doi.org/10.1073/pnas.1606615113 (2016).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Schertzinger, G., Ruchter, N. & Sures, B. Metal accumulation in sediments and amphipods downstream of combined sewer overflows. Sci. Total Environ. 616–617, 1199–1207. https://doi.org/10.1016/j.scitotenv.2017.10.199 (2018).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Erasmus, J. H. et al. Metal accumulation in riverine macroinvertebrates from a platinum mining region. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2019.134738 (2020).Article 
    PubMed 

    Google Scholar 
    Pradhan, S., Hedberg, J., Blomberg, E., Wold, S. & Odnevall Wallinder, I. Effect of sonication on particle dispersion, administered dose and metal release of non-functionalized, non-inert metal nanoparticles. J. Nanopart. Res. 18(9), 1–14. https://doi.org/10.1007/s11051-016-3597-5 (2016).Article 
    CAS 

    Google Scholar 
    Taurozzi, J. S., Hackley, V. A. & Wiesner, M. R. Preparation of nanoparticle dispersions from powdered material using ultrasonic disruption. NIST Spec. Publ. 1200–2, 1–15 (2012).
    Google Scholar 
    Graupner, N. et al. Effects of short-term flooding on aquatic and terrestrial microeukaryotic communities: A mesocosm approach. Aquat. Microb. Ecol. 80(3), 257–272. https://doi.org/10.3354/ame01853 (2017).Article 

    Google Scholar 
    Strasser, R., Srivastava, A. & Tsimilli-Michael, M. The fluorescence transient as a tool to characterize and screen photosynthetic samples. In Probing Photosynthesis Mechanisms, Regulation and Adaption (eds Yanus, M. et al.) (Taylor and Francis, 2020).
    Google Scholar 
    Thwe, A. & Kasemsap, P. Quantification of OJIP fluorescence transient in tomato plants under acute ozone stress (2015).Amaral-Zettler, L. A., McCliment, E. A., Ducklow, H. W. & Huse, S. M. A method for studying protistan diversity using massively parallel sequencing of V9 hypervariable regions of small-subunit ribosomal RNA genes. PLoS ONE 4(7), 1–9. https://doi.org/10.1371/journal.pone.0006372 (2009).Article 
    CAS 

    Google Scholar 
    Medlin, L., Elwood, H. J., Stickel, S. & Sogin, M. L. The characterization of enzymatically amplified eukaryotic 16S-like RRNA-coding regions. Gene 71(2), 491–499. https://doi.org/10.1016/0378-1119(88)90066-2 (1988).Article 
    CAS 
    PubMed 

    Google Scholar 
    Andrews, S. FastQC: A quality control tool for high throughput sequence data (2015).Lange, A. et al. AmpliconDuo: A split-sample filtering protocol for high-throughput amplicon sequencing of microbial communities. PLoS ONE 10(11), 1–22. https://doi.org/10.1371/journal.pone.0141590 (2015).Article 
    CAS 

    Google Scholar 
    Schmieder, R. & Edwards, R. Quality control and preprocessing of metagenomic datasets. Bioinformatics 27(6), 863–864. https://doi.org/10.1093/bioinformatics/btr026 (2011).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Masella, P. A., Bartram, A. K., Truszkowski, J. M., Brow, D. G. & Neufeld, J. D. PANDAseq: Paired-end assembler for illumina sequences. BMC Bioinform. https://doi.org/10.1186/1471-2105-13-31 (2012).Article 

    Google Scholar 
    Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27(16), 2194–2200. https://doi.org/10.1093/bioinformatics/btr381 (2011).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Mahé, F., Rognes, T., Quince, C., de Vargas, C. & Dunthorn, M. Swarm: Robust and fast clustering method for amplicon-based studies. PeerJ 2014(1), 1–13. https://doi.org/10.7717/peerj.593 (2014).Article 

    Google Scholar 
    Callahan, B. J. et al. DADA2: High-resolution sample inference from illumina amplicon data. Nat. Methods 13(7), 581–583. https://doi.org/10.1038/nmeth.3869 (2016).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Welzel, M. et al. Natrix: A snakemake-based workflow for processing, clustering, and taxonomically assigning amplicon sequencing reads. BMC Bioinform. 21(1), 1–14. https://doi.org/10.1186/s12859-020-03852-4 (2020).Article 
    CAS 

    Google Scholar 
    Oksanen, J. Package “vegan” Title Community Ecology Package (2022).R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.r-project.org/.Chen, W., Simpson, J. & Leveque, C. RAM: R for amplicon-sequencing-based microbial-ecology (2018).Yarza, P. et al. Uniting the classification of cultured and uncultured bacteria and archaea using 16S RRNA gene sequences. Nat. Rev. Microbiol. 12(9), 635–645. https://doi.org/10.1038/nrmicro3330 (2014).Article 
    CAS 
    PubMed 

    Google Scholar 
    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15(12), 1–21. https://doi.org/10.1186/s13059-014-0550-8 (2014).Article 
    CAS 

    Google Scholar 
    Palarea-Albaladejo, J. & Martín-Fernández, J. A. ZCompositions—R package for multivariate imputation of left-censored data under a compositional approach. Chemom. Intell. Lab. Syst. 143, 85–96. https://doi.org/10.1016/j.chemolab.2015.02.019 (2015).Article 
    CAS 

    Google Scholar 
    Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: And this is not optional. Front. Microbiol. 8, 1–6. https://doi.org/10.3389/fmicb.2017.02224 (2017).Article 

    Google Scholar 
    Dusaucy, J., Gateuille, D., Perrette, Y. & Naffrechoux, E. Microplastic pollution of worldwide lakes. Environ. Pollut. 284, 117075. https://doi.org/10.1016/j.envpol.2021.117075 (2021).Article 
    CAS 
    PubMed 

    Google Scholar 
    Vardhan, K. H., Kumar, P. S. & Panda, R. C. A review on heavy metal pollution, toxicity and remedial measures: Current trends and future perspectives. J. Mol. Liq. 290, 111197. https://doi.org/10.1016/j.molliq.2019.111197 (2019).Article 
    CAS 

    Google Scholar 
    Damare, V. S. Diversity of thraustochytrid protists isolated from brown alga, Sargassum cinereum using 18S RDNA sequencing and their morphological response to heavy metals. J. Mar. Biol. Assoc. 95(2), 265–276. https://doi.org/10.1017/S0025315414001696 (2015).Article 
    CAS 

    Google Scholar 
    Giongo, A. et al. Adaption of Microbial communities to the hostile environment in the Doce river after the collapse of two iron ore tailing dams. Heliyon https://doi.org/10.1016/j.heliyon.2020.e04778 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kelly, J. J., Häggblom, M. M. & Tate, R. L. Effects of heavy metal contamination and remediation on soil microbial communities in the vicinity of a zinc smelter as indicated by analysis of microbial community phospholipid fatty acid profiles. Biol. Fertil. Soils 38(2), 65–71. https://doi.org/10.1007/s00374-003-0642-1 (2003).Article 
    CAS 

    Google Scholar 
    Baddar, Z. E., Peck, E. & Xu, X. Temporal deposition of copper and zinc in the sediments of metal removal constructed wetlands. PLoS ONE 16, 1–14. https://doi.org/10.1371/journal.pone.0255527 (2021).Article 
    CAS 

    Google Scholar 
    Li, X., Shen, Z., Wai, O. W. H. & Li, Y. S. Chemical partitioning of heavy metal contaminants in sediments of the Pearl River Estuary. Chem. Speciat. Bioavailab. 12(1), 17–25. https://doi.org/10.3184/095422900782775607 (2000).Article 
    CAS 

    Google Scholar 
    Müller, B. & Sigg, L. Interaction of trace metals with natural particle surfaces: Comparison between adsorption experiments and field measurements—Dedicated to Werner Stumm for his 65th birthday. Aquat. Sci. 52(1), 75–92. https://doi.org/10.1007/BF00878242 (1990).Article 

    Google Scholar 
    Bradl, H. B. Adsorption of heavy metal ions on soils and soils constituents. J. Colloid Interface Sci. 277(1), 1–18. https://doi.org/10.1016/j.jcis.2004.04.005 (2004).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Siegel, F. R. Environmental Geochemistry of Potentially Toxic Heavy Metals (Springer-Verlag, 2002).Book 

    Google Scholar 
    Vig, K., Megharaj, M., Sethunathan, N. & Naidu, R. Bioavailability and toxicity of cadmium to microorganisms and their activities in soil: A review. Adv. Environ. Res. 8(1), 121–135. https://doi.org/10.1016/S1093-0191(02)00135-1 (2003).Article 
    CAS 

    Google Scholar 
    Nicolau, A., Mota, M. & Lima, N. Physiological responses of tetrahymena pyriformis to copper, zinc, cycloheximide and triton X-100. FEMS Microbiol. Ecol. 30(3), 209–216. https://doi.org/10.1016/S0168-6496(99)00057-4 (1999).Article 
    CAS 
    PubMed 

    Google Scholar 
    Admiraal, W. et al. Short-term toxicity of zinc to microbenthic algae and bacteria in a metal polluted stream. Water Res. 33(9), 1989–1996. https://doi.org/10.1016/S0043-1354(98)00426-6 (1999).Article 
    CAS 

    Google Scholar 
    Bradac, P., Navarro, E., Odzak, N., Behra, R. & Sigg, L. Kinetics of cadmium accumulation in periphyton under freshwater conditions. Environ. Toxicol. Chem. 28(10), 2108–2116. https://doi.org/10.1897/08-511R1.1 (2009).Article 
    CAS 
    PubMed 

    Google Scholar 
    Collard, J. & Matagne, R. F. Cd2+ resistance in wild-type and mutant strains of Chlamydomonas reinhardtii. Environ. Exp. Bot. 34(2), 235–244 (1994).Article 
    CAS 

    Google Scholar 
    Wright, R. J., Gibson, M. I. & Christie-Oleza, J. A. Understanding microbial community dynamics to improve optimal microbiome selection. Microbiome 7(1), 1–14. https://doi.org/10.1186/s40168-019-0702-x (2019).Article 

    Google Scholar 
    Buffle, J. The key role of environmental colloids/nanoparticles for the sustainability of life. Environ. Chem. 3(3), 155–158. https://doi.org/10.1071/ENv3n3_ES (2006).Article 
    CAS 

    Google Scholar 
    Nowack, B. & Bucheli, T. D. Occurrence, behavior and effects of nanoparticles in the environment. Environ. Pollut. 150(1), 5–22. https://doi.org/10.1016/j.envpol.2007.06.006 (2007).Article 
    CAS 
    PubMed 

    Google Scholar 
    Fetzer, I. et al. The extent of functional redundancy changes as species’ roles shift in different environments. Proc. Natl. Acad. Sci. U. S. A. 112(48), 14888–14893. https://doi.org/10.1073/pnas.1505587112 (2015).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Biggs, C. R. et al. Does functional redundancy affect ecological stability and resilience? A review and meta-analysis. Ecosphere https://doi.org/10.1002/ecs2.3184 (2020).Article 

    Google Scholar 
    Fleeger, J. W. How do indirect effects of contaminants inform ecotoxicology? A review. Processes https://doi.org/10.3390/pr8121659 (2020).Article 

    Google Scholar 
    Oriekhova, O. & Stoll, S. Heteroaggregation of nanoplastic particles in the presence of inorganic colloids and natural organic matter. Environ. Sci. Nano. 5(3), 792–799. https://doi.org/10.1039/c7en01119a (2018).Article 
    CAS 

    Google Scholar 
    Rowenczyk, L. et al. Heteroaggregates of polystyrene nanospheres and organic matter: Preparation, characterization and evaluation of their toxicity to algae in environmentally relevant conditions. Nanomaterials 11(2), 1–15. https://doi.org/10.3390/nano11020482 (2021).Article 
    CAS 

    Google Scholar 
    Saavedra, J., Stoll, S. & Slaveykova, V. I. Influence of nanoplastic surface charge on eco-corona formation, aggregation and toxicity to freshwater zooplankton. Environ. Pollut. 252, 715–722. https://doi.org/10.1016/j.envpol.2019.05.135 (2019).Article 
    CAS 
    PubMed 

    Google Scholar 
    Bižic-Ionescu, M., Ionescu, D. & Grossart, H. P. Organic particles: Heterogeneous hubs for microbial interactions in aquatic ecosystems. Front. Microbiol. 9, 1–15. https://doi.org/10.3389/fmicb.2018.02569 (2018).Article 

    Google Scholar 
    Lespes, G., Faucher, S. & Slaveykova, V. I. natural nanoparticles, anthropogenic nanoparticles, where is the Frontier?. Front. Environ. Sci. 8, 1–5. https://doi.org/10.3389/fenvs.2020.00071 (2020).Article 

    Google Scholar 
    Stabnikova, O. et al. Microbial life on the surface of microplastics in natural waters. Appl. Sci. 11(24), 1–19. https://doi.org/10.3390/app112411692 (2021).Article 
    CAS 

    Google Scholar 
    Suominen, S., Doorenspleet, K., Sinninghe Damsté, J. S. & Villanueva, L. Microbial community development on model particles in the deep sulfidic waters of the Black Sea. Environ. Microbiol. 23(6), 2729–2746. https://doi.org/10.1111/1462-2920.15024 (2021).Article 
    CAS 
    PubMed 

    Google Scholar 
    Wagner, S., Gondikas, A., Neubauer, E., Hofmann, T. & von der Kammer, F. Spot the difference: Engineered and natural nanoparticles in the environment-release, behavior, and fate. Angew. Chem. Int. Ed. 53(46), 12398–12419. https://doi.org/10.1002/anie.201405050 (2014).Article 
    CAS 

    Google Scholar 
    Amelia, T. S. et al. Marine microplastics as vectors of major ocean pollutants and its hazards to the marine ecosystem and humans. Prog. Earth Planet. Sci. https://doi.org/10.1186/s40645-020-00405-4 (2021).Article 

    Google Scholar 
    Liu, J., Huang, J. & Che, F. Microalgae as feedstocks for biodiesel production. In Biodiesel—Feedstocks and Processing Technologies (ed. Stoytcheva, M.) (InTech, 2011). https://doi.org/10.5772/25600.Chapter 

    Google Scholar 
    Takamura, N., Kasai, F. & Watanabe, M. M. Effects of Cu, Cd and Zn on photosynthesis of freshwater benthic algae. J. Appl. Phycol. 1(1), 39–52. https://doi.org/10.1007/BF00003534 (1989).Article 
    CAS 

    Google Scholar 
    Brembu, T., Jørstad, M., Winge, P., Valle, K. C. & Bones, A. M. Genome-wide profiling of responses to cadmium in the diatom Phaeodactylum tricornutum. Environ. Sci. Technol. 45(18), 7640–7647. https://doi.org/10.1021/es2002259 (2011).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Fernandez, J. C. & Henriques, F. S. Biochemical, physiological and structural effects of excess copper in plants. Bot. Rev. 57(3), 246–273 (1991).Article 

    Google Scholar 
    Haq, R. U., Rehman, A. & Shakoori, A. R. Effect of dichromate on population and growth of various protozoa isolated from industrial effluents. Folia Microbiol. 45(3), 275–278. https://doi.org/10.1007/bf02908959 (2000).Article 
    CAS 

    Google Scholar 
    Rehman, A., Shakoori, F. R. & Shakoori, A. R. Heavy metal resistant freshwater ciliate, Euplotes mutabilis, isolated from industrial effluents has potential to decontaminate wastewater of toxic metals. Bioresour. Technol. 99(9), 3890–3895. https://doi.org/10.1016/j.biortech.2007.08.007 (2008).Article 
    CAS 
    PubMed 

    Google Scholar 
    Rehman, A., Ashraf, S., Qazi, J. I. & Shakoori, A. R. Uptake of lead by a ciliate, stylonychia mytilus, isolated from industrial effluents: Potential use in bioremediation of wastewater. Bull. Environ. Contam. Toxicol. 75(2), 290–296. https://doi.org/10.1007/s00128-005-0751-7 (2005).Article 
    CAS 
    PubMed 

    Google Scholar 
    Shakoori, A. R., Rehman, A. & ul-Haq, R. Multiple metal resistance in the ciliate protozoan, vorticella microstoma, isolated from industrial effluents and its potential in bioremediation of toxic wastes. Bull. Environ. Contam. Toxicol. 72(5), 1046–1051. https://doi.org/10.1007/s00128-004-0349-5 (2004).Article 
    CAS 
    PubMed 

    Google Scholar 
    Falasco, E. et al. Morphological abnormalities of diatom silica walls in relation to heavy metal contamination and artificial growth conditions. Water SA 35(5), 595–606. https://doi.org/10.4314/wsa.v35i5.49185 (2009).Article 
    CAS 

    Google Scholar 
    Tadros, M. G., Mbuthia, P. & Smith, W. Differential response of marine diatoms to trace metals. Bull. Environ. Contam. Toxicol. 44(6), 826–831. https://doi.org/10.1007/BF01702170 (1990).Article 
    CAS 
    PubMed 

    Google Scholar 
    Wanner, M. et al. Soil testate amoebae and diatoms as bioindicators of an old heavy metal contaminated floodplain in Japan. Microb. Ecol. 79(1), 123–133. https://doi.org/10.1007/s00248-019-01383-x (2020).Article 
    CAS 
    PubMed 

    Google Scholar 
    Shi, J., Podola, B. & Melkonian, M. Application of a prototype-scale twin-layer photobioreactor for effective N and P removal from different process stages of municipal wastewater by immobilized microalgae. Bioresour. Technol. 154, 260–266. https://doi.org/10.1016/j.biortech.2013.11.100 (2014).Article 
    CAS 
    PubMed 

    Google Scholar 
    Li, T., Lin, G., Podola, B. & Melkonian, M. Continuous removal of zinc from wastewater and mine dump leachate by a microalgal biofilm PSBR. J. Hazard. Mater. 297, 112–118. https://doi.org/10.1016/j.jhazmat.2015.04.080 (2015).Article 
    CAS 
    PubMed 

    Google Scholar 
    Bruning, K. Infection of the diatom Asterionella by a chytrid. I. Effects of light on reproduction and infectivity of the parasite. J. Plankton Res. 13(1), 103–117. https://doi.org/10.1093/plankt/13.1.103 (1991).Article 

    Google Scholar 
    Carney, L. T. & Lane, T. W. Parasites in algae mass culture. Front. Microbiol. 5, 1–8. https://doi.org/10.3389/fmicb.2014.00278 (2014).Article 

    Google Scholar 
    Hanic, L. A., Sekimoto, S. & Bates, S. S. Oomycete and chytrid infections of the marine diatom Pseudo-nitzschia pungens (Bacillariophyceae) from Prince Edward Island. Botany 87(11), 1096–1105. https://doi.org/10.1139/B09-070 (2009).Article 
    CAS 

    Google Scholar 
    Sun, A. et al. Fertilization alters protistan consumers and parasites in crop-associated microbiomes. Environ. Microbiol. 23(4), 2169–2183. https://doi.org/10.1111/1462-2920.15385 (2021).Article 
    CAS 
    PubMed 

    Google Scholar 
    Scholz, B., Guillou, L., Marano, A. V., Neuhauser, S. & Brooke, K. Europe PMC funders group zoosporic parasites infecting marine diatoms—A black box that needs to be opened. Fungal Ecol. https://doi.org/10.1016/j.funeco.2015.09.002.Zoosporic (2017).Article 

    Google Scholar 
    Peacock, E. E., Olson, R. J. & Sosik, H. M. Parasitic infection of the diatom Guinardia delicatula, a recurrent and ecologically important phenomenon on the New England Shelf. Mar. Ecol. Prog. Ser. 503, 1–10. https://doi.org/10.3354/meps10784 (2014).Article 
    ADS 

    Google Scholar 
    Duarte, S., Pascoal, C. & Cássio, F. Effects of zinc on leaf decomposition by fungi in streams: Studies in microcosms. Microb. Ecol. 48(3), 366–374. https://doi.org/10.1007/s00248-003-2032-5 (2004).Article 
    CAS 
    PubMed 

    Google Scholar 
    Kammerlander, B. et al. High diversity of protistan plankton communities in remote high mountain lakes in the European Alps and the Himalayan Mountains. FEMS Microbiol. Ecol. 91(4), 1–10. https://doi.org/10.1093/femsec/fiv010 (2015).Article 
    CAS 

    Google Scholar 
    Sieber, G., Beisser, D., Bock, C. & Boenigk, J. Protistan and fungal diversity in soils and freshwater lakes are substantially different. Sci. Rep. 10(1), 1–11. https://doi.org/10.1038/s41598-020-77045-7 (2020).Article 
    CAS 

    Google Scholar 
    Gunaalan, K., Fabbri, E. & Capolupo, M. The hidden threat of plastic leachates: A critical review on their impacts on aquatic organisms. Water Res. https://doi.org/10.1016/j.watres.2020.116170 (2020).Article 
    PubMed 

    Google Scholar 
    Tetu, S. G., Sarker, I., Schrameyer, V., Pickford, R., Elbourne, L. D., Moore, L.R. & Paulsen, I.T. Plastic leachates impair growth and oxygen production in Prochlorococcus, the ocean’s most abundant photosynthetic bacteria. Commun. Biol. 2(1), 1–9. https://doi.org/10.1038/s42003-019-0410-x (2019).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(4), 1466–1472. https://doi.org/10.1021/es1032025 (2011).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Lohmann, R. Microplastics are not important for the cycling and bioaccumulation of organic pollutants in the oceans—But should microplastics be considered POPs themselves?. Integr. Environ. Assess. Manag. 13(3), 460–465. https://doi.org/10.1002/ieam.1914 (2017).Article 
    CAS 
    PubMed 

    Google Scholar 
    Sukkasem, C. & Laehlah, S. An economical upflow bio-filter circuit (UBFC): A biocatalyst microbial fuel cell for sulfate-sulfide rich wastewater treatment. Environ. Sci. 1(2), 161–168. https://doi.org/10.1039/c4ew00028e (2015).Article 
    CAS 

    Google Scholar 
    Abatenh, E., Gizaw, B., Tsegaye, Z. & Wassie, M. The role of microorganisms in bioremediation-A review. Open J. Environ. Biol. 2(1), 38–46. https://doi.org/10.17352/ojeb (2017).Article 

    Google Scholar 
    Zrimec, J., Kokina, M., Jonasson, S., Zorrilla, F. & Zelezniak, A. Plastic-degrading potential across the global microbiome correlates with recent pollution trends. MBio https://doi.org/10.1128/mBio (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Siver, P. A. Synurophyte algae. In Freshwater Algae of North America. Ecology and classification (eds Wehr, J. D. & Sheath, R. G.) 523–558 (Elsevier, 2003).Chapter 

    Google Scholar 
    Andersen, R. A. Molecular systematics of the chrysophyceae and synurophyceae. In Unravelling the Algae: The Past, Present, and Future of Algal Systematics (eds Brodie, J. & Lewis, J.) 285–314 (CRC Press, Boca Raton, 2007).Chapter 

    Google Scholar 
    Engin, I. K., Cekmecelioglu, D., Yücel, A. M. & Oktem, H. A. Evaluation of heterotrophic and mixotrophic cultivation of novel Micractinium Sp. ME05 on vinasse and its scale up for biodiesel production. Bioresour. Technol. 251, 128–134. https://doi.org/10.1016/j.biortech.2017.12.023 (2018).Article 
    CAS 
    PubMed 

    Google Scholar 
    Patrick, R. Ecology of freshwater diatoms and diatom communities. In The Biology of Diatoms (ed. Werner, D.) 284–332 (University of California Press, 1977).
    Google Scholar 
    Findenig, B. M., Chatzinotas, A. & Boenigk, J. Taxonomic and ecological characterization of stomatocysts of spumella-like flagellates (Chrysophyceae). J. Phycol. 46(5), 868–881. https://doi.org/10.1111/j.1529-8817.2010.00892.x (2010).Article 

    Google Scholar 
    Perez-Garcia, O., Escalante, F. M. E., de-Bashan, L. E. & Bashan, Y. Heterotrophic cultures of microalgae: Metabolism and potential products. Water Res. 45(1), 11–36. https://doi.org/10.1016/j.watres.2010.08.037 (2011).Article 
    CAS 
    PubMed 

    Google Scholar 
    Preisig, H. R. & Hibberd, D. J. Ultrastructure and taxonomy of Paraphysomonas (Chrysophyceae) and related genera 3. Nord. J. Bot. 3(6), 695–723. https://doi.org/10.1111/j.1756-1051.1983.tb01481.x (1983).Article 

    Google Scholar 
    Atkins, M. S. et al. Tolerance of flagellated protists to high sulfide and metal concentrations potentially encountered at deep-sea hydrothermal vents. Mar. Ecol. Prog. Ser. 226, 63–75. https://doi.org/10.3354/meps226063 (2002).Article 
    ADS 
    CAS 

    Google Scholar 
    Manru, G., Weisong, F. & Yunfen, S. Ecological study on protozoa in the sediment of the three-gorges area of the Changjiang River. Chin. J. Oceanol. Limnol. 6(3), 272–280. https://doi.org/10.1007/BF02846505 (1988).Article 

    Google Scholar 
    Tomilina, I. I., Gremyachikh, V. A., Myl’Nikov, A. P. & Komov, V. T. The effect of metal oxide nanoparticles (CeO2, TiO2, and ZnO) on biological parameters of freshwater nanoflagellates and crustaceans. Dokl. Biol. Sci. 436(1), 53–55. https://doi.org/10.1134/S0012496611010169 (2011).Article 
    CAS 
    PubMed 

    Google Scholar 
    Schampera, C. et al. Exposure to nanoplastics affects the outcome of infectious disease in phytoplankton. Environ. Pollut. https://doi.org/10.1016/j.envpol.2021.116781 (2021).Article 
    PubMed 

    Google Scholar 
    Gonçalves, J. M., Sousa, V. S., Teixeira, M. R. & Bebianno, M. J. Chronic toxicity of polystyrene nanoparticles in the marine mussel Mytilus galloprovincialis. Chemosphere https://doi.org/10.1016/j.chemosphere.2021.132356 (2021).Article 
    PubMed 

    Google Scholar 
    Kelpsiene, E., Torstensson, O., Ekvall, M. T., Hansson, L. A. & Cedervall, T. Long-term exposure to nanoplastics reduces life-time in Daphnia magna. Sci. Rep. 10(1), 1–7. https://doi.org/10.1038/s41598-020-63028-1 (2020).Article 
    CAS 

    Google Scholar 
    Amin, N. M. Techniques for assessment of heavy metal toxicity using Acanthamoeba Sp, a small, naked and free-living amoeba. Funct. Ecosyst. https://doi.org/10.5772/36008 (2012).Article 

    Google Scholar 
    Amin, N. M., Azhar, N. & Shazili, M. Cytotoxic effects of mercury, cadmium, lead and zinc on Acanthamoeba Castellanii (2006).Gnecco, I., Berretta, C., Lanza, L. G. & la Barbera, P. Storm water pollution in the urban environment of Genoa, Italy. Atmos. Res. 77, 60–73. https://doi.org/10.1016/j.atmosres.2004.10.017 (2005).Article 
    CAS 

    Google Scholar 
    Heim, R. R. An overview of weather and climate extremes—Products and trends. Weather Clim. Extrem. 10, 1–9. https://doi.org/10.1016/j.wace.2015.11.001 (2015).Article 

    Google Scholar 
    Saiki, M. K., Castleberry, D. T., May, T. W., Martin, B. A. & Bullard, F. N. Copper, cadmium, and zinc concentrations in aquatic food chains from the upper Sacramento River (California) and selected tributaries. Arch. Environ. Contam. Toxicol. 29(4), 484–491. https://doi.org/10.1007/BF00208378 (1995).Article 
    CAS 

    Google Scholar 
    Wagner, S. et al. Tire wear particles in the aquatic environment—A review on generation, analysis, occurrence, fate and effects. Water Res. 139, 83–100. https://doi.org/10.1016/j.watres.2018.03.051 (2018).Article 
    CAS 
    PubMed 

    Google Scholar 
    Zhang, L., Zhao, B., Xu, G. & Guan, Y. Characterizing fluvial heavy metal pollutions under different rainfall conditions: Implication for aquatic environment protection. Sci. Total Environ. 635, 1495–1506. https://doi.org/10.1016/j.scitotenv.2018.04.211 (2018).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Zhao, B. et al. Characterization of nitrosamines and nitrosamine precursors as non-point source pollutants during heavy rainfall events in an urban water environment. J. Hazard. Mater. 424, 127552. https://doi.org/10.1016/j.jhazmat.2021.127552 (2022).Article 
    CAS 
    PubMed 

    Google Scholar 
    Hüffer, T., Wagner, S., Reemtsma, T. & Hofmann, T. Sorption of organic substances to tire wear materials: Similarities and differences with other types of microplastic. TrAC Trends Anal. Chem. 113, 392–401. https://doi.org/10.1016/j.trac.2018.11.029 (2019).Article 
    CAS 

    Google Scholar 
    Tamis, J. E. et al. Environmental risks of car tire microplastic particles and other road runoff pollutants. Microplastics Nanoplastics 1(1), 1–17. https://doi.org/10.1186/s43591-021-00008-w (2021).Article 

    Google Scholar 
    Chèvre, N. et al. Substance flow analysis as a tool for urban water management. Water Sci. Technol. 63(7), 1341–1348. https://doi.org/10.2166/wst.2011.132 (2011).Article 
    PubMed 

    Google Scholar 
    Šourková, M., Adamcová, D. & Vaverková, M. D. The influence of microplastics from ground tyres on the acute, subchronical toxicity and microbial respiration of soil. Environ. MDPI 8(11), 1–14. https://doi.org/10.3390/environments8110128 (2021).Article 

    Google Scholar 
    Ye, G., Zhang, X., Yan, C., Lin, Y. & Huang, Q. Polystyrene microplastics induce microbial dysbiosis and dysfunction in surrounding seawater. Environ. Int. 156, 106724. https://doi.org/10.1016/j.envint.2021.106724 (2021).Article 
    CAS 
    PubMed 

    Google Scholar  More

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    A database of seed plants on taxonomy, geography and ecology in the Qinling-Daba Mountains and adjacent areas

    Each of the 23 key variables can be used for analysis. To validate the dataset, we used five plant-related variables (diversity of order, family, genus, species and species endemic to China) to demonstrate the process of using the dataset for analysis as follows:(1) For the four variables of plant taxa “order”, “family”, “genus” and “species”, the similarity and difference in spatial distribution pattern of diversity of different taxa in the Qinling-Daba Mountains climate transition zone were analyzed. The spatial distribution pattern of the diversity of the four taxa is shown in Fig. 3, which is increasingly lower from south (low latitude) to north (high latitude). This result is consistent with the classical latitudinal gradient model of plant diversity. The boundary between higher diversity in the south and lower diversity in the north is roughly located in the area of Funiu Mountains in the eastern Qinling-Daba Mountains, Taibai Mountains in the central Qinling-Daba Mountains and Baishui River in the western Qinling-Daba Mountains. However, with the reduction in taxon scale, the spatial distribution pattern of diversity tends to be complex. Orders (Fig. 3a) and families (Fig. 3b) can be divided by lines, while genera (Fig. 3c) need thicker lines, and species (Fig. 3d) can only be divided by polygons. Figure 3 shows that the taxonomic groups of families are more clearly divided, while species can only be divided by staggered bands. Therefore, when dividing the north–south boundary, the family taxon scale is appropriate, whereas the species scale is more appropriate when studying the north–south transition zone.Fig. 3Spatial distribution of diversity of orders, families, genera and species. (a) The blue dotted line is basically the dividing line of the order diversity of 50 species. The order diversity to the north of the blue dotted line is lower than 50 species, and the order diversity to the south of the blue dotted line is higher than 50 species. (b) The blue dotted line is basically the dividing line of the family diversity of 150 species. The family diversity to the north of the blue dotted line is lower than 150 species, and the family diversity to the south of the blue dotted line is higher than 150 species. (c) The thicker blue dotted line is basically the dividing line of genus diversity of 578–681 species. The genus diversity to the north of the blue dotted line is lower than 578 species, and the genus diversity to the south of the blue dotted line is higher than 681 species. (d) The blue area is basically the dividing line of species diversity of 1385–1618 species. The species diversity to the north of the blue dotted line is lower than 1385 species, and the species diversity to the south of the blue dotted line is higher than 1618 species.Full size imageThe dataset can also count the orders, families and genera that appear in 58 nature reserves, indicating that these orders, families and genera are widely distributed in this area, while the orders, families and genera that only appear in a single nature reserve indicate that these taxa are unique to this nature reserve in this area, reflecting their locality and uniqueness, which is helpful to understanding the specific distribution of plants in detail. The relevant statistics are as follows:
    There are 28 orders present in every nature reserve:
    Liliales, Dipsacales, Lamiales, Fabales, Ericales, Poales, Saxifragales, Malpighiales, Malvales, Asterales, Fagales, Gentianales, Geraniales, Ranunculales, Rosales, Solanales, Apiales, Cornales, Brassicales, Caryophyllales, Dioscoreales, Santalales, Myrtales, Asparagales, Celastrales, Sapindales, Alismatales, and Boraginales.The order that only appears in one nature reserve is Petrosaviales, which appears in the Dabashan Nature Reserve in Chongqing.
    There are 51 families present in every nature reserve:
    Liliaceae, Primulaceae, Plantaginaceae, Lamiaceae, Euphorbiaceae, Cannabaceae, Juncaceae, Fabaceae, Poaceae, Elaeagnaceae, Betulaceae, Apocynaceae, Violaceae, Malvaceae, Crassulaceae, Campanulaceae, Asteraceae, Orchidaceae, Polygonaceae, Orobanchaceae, Onagraceae, Gentianaceae, Geraniaceae, Ranunculaceae, Rubiaceae, Rosaceae, Caprifoliaceae, Thymelaeaceae, Apiaceae, Cyperaceae, Cornaceae, Paeoniaceae, Brassicaceae, Amaryllidaceae, Caryophyllaceae, Rhamnaceae, Santalaceae, Asparagaceae, Celastraceae, Sapindaceae, Adoxaceae, Araliaceae, Berberidaceae, Hydrangeaceae, Scrophulariaceae, Convolvulaceae, Urticaceae, Salicaceae, Papaveraceae, Iridaceae, and Boraginaceae.There are 15 families that only appear in one nature reserve, as shown in Table 2.Table 2 Endemic families of the nature reserves in the Qinling-Daba Mountains and surrounding areas.Full size table
    There are 54 genera present in every nature reserve:
    Patrinia, Polygonum, Sanicula, Plantago, Allium, Delphinium, Euphorbia, Juncus, Cynanchum, Trigonotis, Artemisia, Sorbus, Polygonatum, Scutellaria, Cirsium, Viburnum, Ajuga, Viola, Galium, Geranium, Salix, Epilobium, Gentiana, Ranunculus, Malus, Acer, Rubia, Rosa, Torilis, Lonicera, Adenophora, Philadelphus, Cornus, Paeonia, Rhamnus, Rumex, Carex, Thalictrum, Asparagus, Carpesium, Clematis, Potentilla, Euonymus, Eleutherococcus, Berberis, Spiraea, Rubus, Populus, Vicia, Silene, Iris, Poa, Aster, and Buddleja.There were 225 genera that only appeared in one nature reserve, as shown in Figshare file 269.(2) For the “species endemic to China” variable of plants, we can see from the diversity distribution pattern of species endemic to China in this region (Fig. 4) that the number of endemic species in the Qinling-Daba Mountains is higher than that of species outside of the region, which reflects the strong transition zone in the Qinling-Daba Mountains. The variables of species endemic to China obtained from the Qinling-Daba Mountains and their surroundings were clustered by the Bray–Curtis dissimilarity measure70 and Ward’s minimum variance (the clustering method recommended for plant cluster analysis). The clustering results are shown in Fig. 5a. At the same time, the clustering results are displayed in space. Figure 5b shows that category 3 extends from the east outside the Qinling-Daba Mountains to the Baishuijiang Nature Reserve inside the western Qinling-Daba Mountains, which is consistent with the fact that the Qinling-Daba Mountains are an important ecogeographical “corridor” connecting the east and the west.Fig. 4Spatial distribution of diversity of species endemic to China in the Qinling-Daba Mountains and adjacent areas.Full size imageFig. 5(a) Clustering results of Ward’s connection aggregation of species endemic to China in 58 nature reserves. (b) Spatial distribution of clustering results of species endemic to China; the larger the dot and the darker the color, the earlier it is merged into this category, and the smaller the dot and the lighter the color, the later it is merged into this category.Full size image More

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    Longitudinal analysis of the Five Sisters hot springs in Yellowstone National Park reveals a dynamic thermoalkaline environment

    Mueller, R. C. et al. An emerging view of the diversity, ecology, and function of Archaea in alkaline hydrothermal environments. FEMS Microbiol. Ecol. 97, fiaa246 (2020).
    Google Scholar 
    López-López, O., Cerdán, M.-E. & González-Siso, M.-I. Thermus thermophilus as a source of thermostable lipolytic enzymes. Microorganisms 3, 792–808 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Sahay, H. et al. Hot springs of Indian Himalayas: Potential sources of microbial diversity and thermostable hydrolytic enzymes. 3 Biotech 7, 118 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Patel, A. K., Singhania, R. R., Sim, S. J. & Pandey, A. Thermostable cellulases: Current status and perspectives. Bioresour Technol 279, 385–392 (2019).CAS 
    PubMed 

    Google Scholar 
    Decastro, M.-E., Rodríguez-Belmonte, E. & González-Siso, M.-I. Metagenomics of thermophiles with a focus on discovery of novel thermozymes. Front. Microbiol. 7, 1521–1521 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Meslé, M. M. et al. Isolation and characterization of lignocellulose-degrading geobacillus thermoleovorans from Yellowstone National Park. Appl. Environ. Microbiol. 88, e0095821 (2022).PubMed 

    Google Scholar 
    Verma, P., Yadav, A. N., Shukla, L., Saxena, A. K. & Suman, A. Hydrolytic enzymes production by thermotolerant Bacillus altitudinis IARI-MB-9 and Gulbenkiania mobilis IARI-MB-18 isolated from Manikaran hot springs. Int. J. Adv. Res. 3, 1241–1250 (2015).CAS 

    Google Scholar 
    Wu, B. et al. Microbial sulfur metabolism and environmental implications. Sci. Total Environ. 778, 146085 (2021).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Lavrentyeva, E. V. et al. Bacterial diversity and functional activity of microbial communities in hot springs of the Baikal Rift Zone. Microbiology 87, 272–281 (2018).CAS 

    Google Scholar 
    Miller Scott, R., Strong Aaron, L., Jones Kenneth, L. & Ungerer Mark, C. Bar-Coded pyrosequencing reveals shared bacterial community properties along the temperature gradients of two alkaline hot springs in Yellowstone National Park. Appl. Environ. Microbiol. 75, 4565–4572 (2009).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sharp, C. E. et al. Humboldt’s spa: Microbial diversity is controlled by temperature in geothermal environments. ISME J. 8, 1166–1174 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Stefanova, K. et al. Archaeal and bacterial diversity in two hot springs from geothermal regions in Bulgaria as demostrated by 16S rRNA and GH-57 genes. Int. Microbiol. 18, 217–223 (2015).CAS 
    PubMed 

    Google Scholar 
    Hou, W. et al. A comprehensive census of microbial diversity in hot springs of Tengchong, Yunnan Province China using 16S rRNA gene pyrosequencing. PLoS ONE 8, e53350 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sahm, K. et al. High abundance of heterotrophic prokaryotes in hydrothermal springs of the Azores as revealed by a network of 16S rRNA gene-based methods. Extremophiles 17, 649–662 (2013).CAS 
    PubMed 

    Google Scholar 
    Purcell, D. et al. The effects of temperature, pH and sulphide on the community structure of hyperthermophilic streamers in hot springs of northern Thailand. FEMS Microbiol. Ecol. 60, 456–466 (2007).CAS 
    PubMed 

    Google Scholar 
    Meyer-Dombard, D. R. & Amend, J. P. Geochemistry and microbial ecology in alkaline hot springs of Ambitle Island, Papua New Guinea. Extremophiles 18, 763–778 (2014).CAS 
    PubMed 

    Google Scholar 
    de Leon, K. B., Gerlach, R., Peyton, B. M. & Fields, M. W. Archaeal and bacterial communities in three alkaline hot springs in Heart Lake Geyser Basin, Yellowstone National Park. Front. Microbiol. 4, 10 (2013).
    Google Scholar 
    Boomer, S. M., Noll, K. L., Geesey, G. G. & Dutton, B. E. Formation of multilayered photosynthetic biofilms in an alkaline thermal spring in Yellowstone National Park, Wyoming. Appl. Environ. Microbiol. 75, 2464–2475 (2009).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wang, S. et al. Greater temporal changes of sediment microbial community than its waterborne counterpart in Tengchong hot springs, Yunnan Province, China. Sci. Rep. 4, 7479 (2014).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sun, Y., Liu, Y., Pan, J., Wang, F. & Li, M. Perspectives on cultivation strategies of archaea. Microb. Ecol. 79, 770–784 (2020).PubMed 

    Google Scholar 
    Brock, T. D. Life at high temperatures. Science 158, 1012 (1967).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Christiansen, R. L. The Quaternary and Pliocene Yellowstone Plateau volcanic field of Wyoming, Idaho, and Montana. Professional Paper (2001).Rowe, J. J., Fournier, R. & Morey, G. Chemical analysis of thermal waters in Yellowstone National Park, Wyoming, 1960–65. USGS https://doi.org/10.3133/b1303 (1973).Article 

    Google Scholar 
    Fournier, R., Thompson, M. J. & Hutchinson, R. A. The geochemistry of hot spring waters at Norris Geyser Basin, Yellowstone National Park. International symposium on water-rock interactions (1992).Podar, P. T., Yang, Z., Björnsdóttir, S. H. & Podar, M. Comparative analysis of microbial diversity across temperature gradients in hot springs from Yellowstone and Iceland. Front. Microbiol. 11, 1625 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Pala, C. et al. Environmental drivers controlling bacterial and archaeal abundance in the sediments of a Mediterranean lagoon ecosystem. Curr. Microbiol. 75, 1147–1155 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Foyer, C. H., Noctor, G. & Hodges, M. Respiration and nitrogen assimilation: Targeting mitochondria-associated metabolism as a means to enhance nitrogen use efficiency. J. Exp. Bot. 62, 1467–1482 (2011).CAS 
    PubMed 

    Google Scholar 
    Ershanovich, V. N. et al. Nitrogen assimilation enzymes in Bacillus subtilis mutants with hyperproduction of riboflavin. Mol. Gen. Mikrobiol. Virusol. 2005(3), 29–34 (2005).
    Google Scholar 
    Offre, P., Spang, A. & Schleper, C. Archaea in biogeochemical cycles. Annu Rev Microbiol 67, 437–457 (2013).CAS 
    PubMed 

    Google Scholar 
    Cabello, P., Roldán, M. D. & Moreno-Vivián, C. Nitrate reduction and the nitrogen cycle in archaea. Microbiology 150, 3527–3546 (2004).CAS 
    PubMed 

    Google Scholar 
    Graupner, M., Xu, H. & White, R. H. The pyrimidine nucleotide reductase step in riboflavin and F(420) biosynthesis in archaea proceeds by the eukaryotic route to riboflavin. J. Bacteriol. 184, 1952–1957 (2002).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Chernyh, N. A. et al. Dissimilatory sulfate reduction in the archaeon “Candidatus Vulcanisaeta moutnovskia” sheds light on the evolution of sulfur metabolism. Nat. Microbiol. 5, 1428–1438 (2020).CAS 
    PubMed 

    Google Scholar 
    Castelle, C. J. & Banfield, J. F. Major new microbial groups expand diversity and alter our understanding of the tree of life. Cell 172, 1181–1197 (2018).CAS 
    PubMed 

    Google Scholar 
    Williams, T. A. et al. Integrative modeling of gene and genome evolution roots the archaeal tree of life. Proc. Natl. Acad. Sci. U.S.A. 114, E4602–E4611 (2017).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Guy, L. & Ettema, T. J. G. The archaeal ‘TACK’ superphylum and the origin of eukaryotes. Trends Microbiol. 19, 580–587 (2011).CAS 
    PubMed 

    Google Scholar 
    Wang, Y., Wegener, G., Hou, J., Wang, F. & Xiao, X. Expanding anaerobic alkane metabolism in the domain of Archaea. Nat. Microbiol. 4, 595–602 (2019).CAS 
    PubMed 

    Google Scholar 
    Hedlund, B. P. et al. Uncultivated thermophiles: Current status and spotlight on ‘Aigarchaeota’. Curr. Opin. Microbiol. 25, 136–145 (2015).CAS 
    PubMed 

    Google Scholar 
    Reichart, N. J. et al. Activity-based cell sorting reveals responses of uncultured archaea and bacteria to substrate amendment. ISME J. 14, 2851–2861 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hua, Z.-S. et al. Genomic inference of the metabolism and evolution of the archaeal phylum Aigarchaeota. Nat. Commun. 9, 2832 (2018).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Beam, J. P. et al. Ecophysiology of an uncultivated lineage of Aigarchaeota from an oxic, hot spring filamentous “streamer” community. ISME J. 10, 210–224 (2016).CAS 
    PubMed 

    Google Scholar 
    Gonsior, M. et al. Yellowstone hot springs are organic chemodiversity hot spots. Sci. Rep. 8, 14155 (2018).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gibson, M. L. & Hinman, N. W. Mixing of hydrothermal water and groundwater near hot springs, Yellowstone National Park (USA): Hydrology and geochemistry. Hydrogeol. J. 21, 919–933 (2013).ADS 
    CAS 

    Google Scholar 
    Campbell, K. M. et al. Sulfolobus islandicus meta-populations in Yellowstone National Park hot springs. Environ. Microbiol. 19, 2334–2347 (2017).PubMed 

    Google Scholar 
    Thiel, V. et al. The dark side of the mushroom spring microbial mat: Life in the shadow of chlorophototrophs. I. Microbial diversity based on 16S rRNA gene amplicons and metagenomic sequencing. Front. Microbiol. 7, 919 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. U.S.A. 108, 4516–4522 (2011).ADS 
    CAS 
    PubMed 

    Google Scholar 
    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 
    Apprill, A., McNally, S., Parsons, R. & Weber, L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat. Microb. Ecol. 75, 129–137 (2015).
    Google Scholar 
    Thompson, L. R. et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 555, 457–463 (2017).ADS 

    Google Scholar 
    Eloe-Fadrosh, E. A., Ivanova, N. N., Woyke, T. & Kyrpides, N. C. Metagenomics uncovers gaps in amplicon-based detection of microbial diversity. Nat. Microbiol. 1, 15032 (2016).CAS 
    PubMed 

    Google Scholar 
    Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 
    PubMed 

    Google Scholar 
    Edgar, R. C. UNOISE2: Improved error-correction for Illumina 16S and ITS amplicon sequencing. BioRxiv https://doi.org/10.1101/081257 (2016).Article 

    Google Scholar 
    Murali, A., Bhargava, A. & Wright, E. S. IDTAXA: A novel approach for accurate taxonomic classification of microbiome sequences. Microbiome 6, 140 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Parks, D. H. et al. A complete domain-to-species taxonomy for Bacteria and Archaea. Nat. Biotechnol. 38, 1079–1086 (2020).CAS 
    PubMed 

    Google Scholar 
    Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).CAS 
    PubMed 
    PubMed Central 

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

    Google Scholar 
    Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v5: An online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 49, W293–W296 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Matsen, F. A., Kodner, R. B. & Armbrust, E. V. pplacer: Linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinform. 11, 538 (2010).
    Google Scholar 
    Chong, J., Liu, P., Zhou, G. & Xia, J. Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data. Nat. Protoc. 15, 799–821 (2020).CAS 
    PubMed 

    Google Scholar 
    Chambers, M. C. et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 30, 918–920 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pluskal, T., Castillo, S., Villar-Briones, A. & Oresic, M. MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinform. 11, 395 (2010).
    Google Scholar 
    Patiny, L. & Borel, A. ChemCalc: A building block for tomorrow’s chemical infrastructure. J. Chem. Inf. Model. 53, 1223–1228 (2013).CAS 
    PubMed 

    Google Scholar 
    Chong, J., Wishart, D. S. & Xia, J. Using MetaboAnalyst 4.0 for comprehensive and integrative metabolomics data analysis. Curr. Protoc. Bioinform. 68, e86 (2019).
    Google Scholar 
    Liu, G., Lee, D. P., Schmidt, E. & Prasad, G. L. Pathway analysis of global metabolomic profiles identified enrichment of caffeine, energy, and arginine metabolism in smokers but not moist snuff consumers. Bioinform. Biol. Insights 13, 1177932219882961–1177932219882961 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Xia, J. & Wishart, D. S. MetPA: A web-based metabolomics tool for pathway analysis and visualization. Bioinformatics 26, 2342–2344 (2010).CAS 
    PubMed 

    Google Scholar 
    Huber, W. et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat. Methods 12, 115–121 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rohart, F., Gautier, B., Singh, A. & Lé Cao, K.-A. mixOmics: An R package for ’omics feature selection and multiple data integration. PLoS Comput. Biol. 13, e1005752–e1005752 (2017).ADS 
    PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Some hope and many concerns on the future of the vaquita

    Davies EK, Peters AD, Keightley PD (1999) High frequency of cryptic deleterious mutations in Caenorhabditis elegans. Science 285:1748–1751Article 
    CAS 
    PubMed 

    Google Scholar 
    Eyre-Walker A, Keightley PD (2007) The distribution of fitness effects of new mutations. Nat Rev Genet 8:610–618Article 
    CAS 
    PubMed 

    Google Scholar 
    Eyre-Walker A, Keightley PD (2013) A comparison of models to infer the distribution of fitness effects of new mutations. Genetics 193:1197–1208Article 

    Google Scholar 
    Fry JD, Keightley PD, Heinsohn SL, Nuzhdi SV (1999) New estimates of the rates and effects of mildly deleterious mutation in Drosophila melanogaster. Proc Natl Acad Sci 96:574–579Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    García-Dorado A (2007) Shortcut predictions for fitness properties at the mutation-selection-drift balance and for its buildup after size reduction under different management strategies. Genetics 176:983–997Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    García-Dorado A (2012) Understanding and predicting the fitness decline of shrunk populations: inbreeding, purging, mutation, and standard selection. Genetics 190:1461–1476Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    García-Dorado A (2015) On the consequences of ignoring purging on genetic recommendations for minimum viable population rules. Heredity 115:185–187Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    García-Dorado A, Caballero A (2021) Neutral genetic diversity as a useful tool for conservation biology. Conserv Genet 22:541–545Article 

    Google Scholar 
    Garner BA, Hoban S, Luikart G (2020) IUCN Red List and the value of integrating genetics. Conserv Genet 21:795–801Article 

    Google Scholar 
    Hedrick PW, García-Dorado A (2016) Understanding inbreeding depression, purging, and genetic rescue. Trends Ecol Evol 31:940–952Article 
    PubMed 

    Google Scholar 
    Kardos M, Armstrong EE, Fitzpatrick SW, Hauser S, Hedrick PW, Miller J et al. (2021) The crucial role of genome-wide genetic variation in conservation. Proc Natl Acad Sci USA 118:e2104642118Khan A, Patel A, Shukla H, Viswanathan A, van der Valk T, Borthakur U, … & Ramakrishnan U (2021) Genomic evidence for inbreeding depression and purging of deleterious genetic variation in Indian tigers. Proc. Natl. Acad. Sci. 118Kimura M, Maruyama T, Crow JF (1963) The mutation load in small populations. Genetics 48:1303–1312Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kimura M (1980) Average time until fixation of a mutant allele in a finite population under continued mutation pressure: Studies by analytical, numerical, and pseudo-sampling methods. Proc Natl Acad Sci 77:522–526Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Morin PA, Archer FI, Avila CD, Balacco JR, Bukhman YV, Chow, W, … & Jarvis ED (2021) Reference genome and demographic history of the most endangered marine mammal, the vaquita. Mol Ecol Resour 21:1008–1020Mukai T (1964) The genetic structure of natural populations of Drosophila melanogaster. I. Spontaneous mutation rate of polygenes controlling viability. Genetics 50:1–19Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Nietlisbach P, Muff S, Reid JM, Whitlock MC, Keller LF (2019) Nonequivalent lethal equivalents: Models and inbreeding metrics for unbiased estimation of inbreeding load. Evol Applic 12:266–279Article 

    Google Scholar 
    O’Grady JJ, Brook BW, Reed DH, Ballou JD, Tonkyn DW, Frankham R (2006) Realistic levels of inbreeding depression strongly affect extinction risk in wild populations. Biol Conserv 133:42–51Article 

    Google Scholar 
    Pérez-Pereira N, Caballero A, García-Dorado A (2021) Reviewing the consequences of genetic purging on the success of rescue programs. Conserv Gen 23:1–17Article 

    Google Scholar 
    Pérez-Pereira N, Wang J, Quesada H, Caballero A (2022). Prediction of the minimum effective size of a population viable in the long term. Biodivers Conserv https://doi.org/10.1007/s10531-022-02456-zRobinson JA, Kyriazis CC, Nigenda-Morales SF, Beichman AC, Rojas-Bracho L, Robertson KM et al. (2022) The critically endangered vaquita is not doomed to extinction by inbreeding depression. Science 376:635–639Article 
    CAS 
    PubMed 

    Google Scholar 
    Teixeira JC, Huber CD (2021) The inflated significance of neutral genetic diversity in conservation genetics. Proc Natl Acad Sci USA 118:e2015096118Wade EE, Kyriazis C, Cavassim MIA, Lohmueller KE (2022) Quantifying the fraction of new mutations that are recessive lethal. bioRxiv 1–24, https://www.biorxiv.org/content/10.1101/2022.04.22.489225v1 More

  • in

    Effects of different pioneer and exotic species on the changes of degraded soils

    Sacristán, D., Peñarroya, B., Recatalá, L. Increasing the Knowledge on the Management of Cu-Contaminated Agricultural Soils by Cropping Tomato (Solanum Lycopersicum L.). Land Degrad. Dev. 26, 587–595 (2015).FAO. Land Degradation Assessment in Drylands. Manual for Local Level Assessment of Land Degradation and Sustainable Land Management. Part 1: Planning and Methodological Approach, Analysis and Reporting. https://www.fao.org/3/i6362e/i6362e.pdf (Food and Agriculture Organization of the United Nations, 2011).Vlachodimos, K., Papatheodorou, E. M., Diamantopoulos, J. & Monokrousos, N. Assessment of Robinia pseudoacacia cultivations as a restoration strategy for reclaimed mine spoil heaps. Environ Monit. Assess. 185, 6921–6932 (2013).Article 
    CAS 
    PubMed 

    Google Scholar 
    Misano, G. & Di Pietro, R. Habitat 9250 “Quercus trojana woods” in Italy. Fitosociologia 44, 235–238 (2007).
    Google Scholar 
    Biondi, E. et al. A contribution towards the knowledge of semideciduous and evergreen woods of Apulia (south-eastern Italy). Fitosociologia 41(1), 3–28 (2004).MathSciNet 

    Google Scholar 
    Brunetti, G. et al. Remediation of a heavy metals contaminated soil using mycorrhized and non-mycorrhized Helichrysum italicum (Roth) Don. Land Degrad. Dev. 29, 91–104 (2017).Article 

    Google Scholar 
    Poblador, S. et al. The influence of the invasive alien nitrogen-fixing Robinia pseudoacacia L. on soil nitrogen availability in a mixed Mediterranean riparian forest. Eur. J. For. Res. 138, 1083–1093 (2019).Article 
    CAS 

    Google Scholar 
    Vítková, M., Müllerová, J., Sádlo, J., Pergl, J. & Pyšek, P. Black locust (Robinia pseudoacacia) beloved and despised: A story of an invasive tree in Central Europe. For. Ecol. Manag. 384, 287–302 (2017).Article 

    Google Scholar 
    Doran, J.W., Parkin, T.B. Quantitative indicators of soil quality: a minimum data set. in Methods for Assessing Soil Quality (eds. Doran, J.W., Jones, A.J.). 25–37 (Soil Science Society of America, 1996).Gil-Sotres, F., Trasar-Cepeda, C., Leirós, M. C. & Seoane, S. Different approaches to evaluating soil quality using biochemical properties. Soil Biol. Biochem. 37, 877–887 (2005).Article 
    CAS 

    Google Scholar 
    Andriani, G. F. & Walsh, N. An example of the effects of anthropogenic changes on natural environment in the Apulian karst (southern Italy). Environ. Geol. 58, 313–325 (2009).Article 
    ADS 

    Google Scholar 
    Bisantino, T., Pizzo, V., Polemio, M. & Gentile, F. Analysis of the flooding event of October 22–23, 2005 in a small basin in the province of Bari (Southern Italy). J. Agric. Eng. 531, 197–204 (2016).Article 

    Google Scholar 
    Soil Survey Staff. Keys to Soil Taxonomy 12th edn. (USDA-Natural Resources Conservation Service, 2014).
    Google Scholar 
    Tartarino, P. Inventario dei Boschi Spontanei e dei Rimboschimenti delle Provincie BAT e Bari e Stima del Loro Volume Legnoso e della sua Frazione Prelevabile nel Prossimo Ventennio. (Rapporto Tecnico Scientifico, 2011).Ismail, A. et al. Chemical composition and biological activities of Tunisian Cupressus arizonica Greene essential oils. Chem. Biodivers. 11, 150–160 (2014).Article 
    CAS 
    PubMed 

    Google Scholar 
    Navarro, A. et al. Feasibility of SRC Species for growing in Mediterranean conditions. Bioenerg. Res. 9, 208–223 (2015).Article 

    Google Scholar 
    Perrino, E. V., Brunetti, G. & Farrag, K. Plant communities in multi-metal contaminated soils: A case study in the National Park of Alta Murgia (Apulia Region-Southern Italy). Int. J. Phytoremediat. 16, 871–888 (2014).Article 
    CAS 

    Google Scholar 
    VV AA Perizia Studi per il Riequilibrio Socio-Economico dell’area Interessata dall’invaso sul Torrente Locone. Consorzio Di Bonifica Apulo Lucano (1986).Lavarra, P. et al. Il Sistema Carta della Natura della Regione Puglia. (ISPRA, Serie Rapporti 204, 2014).Sparks, D. L. et al. Method of Soil Analysis: Part 3 (American Society of Agronomy Inc, 1996).Book 

    Google Scholar 
    Brink, R. H. Jr., Dubach, P. & Lynch, D. L. Measurement of carbohydrates in soil hydrolyzates with anthrone. Soil Sci. 89, 157–166 (1960).Article 
    ADS 
    CAS 

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

    Google Scholar 
    García, C., Hernandez, T. & Costa, F. Potential use of dehydrogenase activity as an index of microbial activity in degraded soils. Commun. Soil Sci. Plant Anal. 28, 123–134 (1997).Article 

    Google Scholar 
    Vance, E. D., Brookes, P. C. & Jenkinson, D. S. An extraction method for measuring soil microbial biomass C. Soil Biol. Biochem. 19, 703–707 (1987).Article 
    CAS 

    Google Scholar 
    Gregorich, E. G., Wen, G., Voroney, R. P. & Kachanoski, R. G. Calibration of a rapid direct chloroform extraction method for measuring soil microbial biomass C. Soil Biol. Biochem. 22, 1009–1011 (1990).Article 
    CAS 

    Google Scholar 
    Nannipieri, P., Ceccanti, B., Cervelli, S. & Matarese, E. Extraction of phosphatase, urease, protease, organic carbon and nitrogen from soil. Soil Sci. Soc. Am. J. 44, 1011–1016 (1980).Article 
    ADS 
    CAS 

    Google Scholar 
    Tabatabai, M.A. (1994) Soil enzymes. in Methods of Soil Analysis. Part 2. Microbiological and Biochemical Properties (eds. Weaver, R.W. et al.). 775–833 (Soil Science Society of America, Inc., 1996)Traversa, A., Said-Pullicino, D., D’Orazio, V., Gigliotti, G., & Senesi, N. Properties of humic acids in Mediterranean forest soils (Southern Italy): Influence of different plant covering. Eur. J. For. Res. 130, 1045–1054 (2011)De Marco, A. et al. Decomposition of black locust and black pine leaf litter in two coeval forest stands on Mount Vesuvius and dynamics of organic components assessed through proximate analysis and NMR spectroscopy. Soil Biol. Biochem. 51, 1–15 (2012).Article 
    CAS 

    Google Scholar 
    Wei, G. et al. Invasive Robinia pseudoacacia in China is nodulated by Mesorhizobium and Sinorhizobium species that share similar nodulation genes with native American symbionts. FEMS Microbiol. Ecol. 68, 320–328 (2009).Article 
    CAS 
    PubMed 

    Google Scholar 
    Schulze, E. D., Gebauer, G., Ziegler, H. & Lange, O. L. Estimates of nitrogen fixation by trees on an aridity gradient in Namibia. Oecologia 88, 451–455 (1991).Article 
    ADS 
    PubMed 

    Google Scholar 
    Zahran, H. H. Rhizobium-legume symbiosis and nitrogen fixation under severe conditions and in an arid climate. Microbiol. Mol. Biol. Rev. 63, 968–989 (1999).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Veste, M. & Kriebitzsch, W. U. Influence of drought stress on photosynthesis, transpiration, and growth of juvenile black locust (Robinia pseudoacacia L.). Forstarchiv 84, 35–42 (2013).
    Google Scholar 
    Nicolescu, V. N. et al. Ecology, growth and management of black locust (Robinia pseudoacacia L.), a non-native species integrated into European forests. J. For. Res. 31, 1081–1101 (2020).Article 
    CAS 

    Google Scholar 
    Sposito, G. The Chemistry of Soil (Oxford University Press, 2008).
    Google Scholar 
    Margalef, O. et al. Global patterns of phosphatase activity in natural soils. Sci. Rep. 7, 1337. https://doi.org/10.1038/s41598-017-01418-8 (2017).Prescott, C. E. & Grayston, S. J. Tree species influence on microbial communities in litter and soil: Current knowledge and research needs. For. Ecol. Manag. 309, 19–27 (2013).Article 

    Google Scholar 
    Frankenberger, W. T. & Dick, W. A. Relationships between enzyme, activities and microbial growth and activity indices in soil. Soil Sci. Soc. Am. J. 47, 945–951 (1983).Article 
    ADS 
    CAS 

    Google Scholar 
    Frankenberger, W.T., Tabatabai, M.A. Amidase activity in soils III. Stability and distribution. Soil Sci. Soc. Am. J. 45, 333–338 (1981).Nannipieri, P., Trasar-Cepeda, C. & Dick, R. P. Soil enzyme activity: A brief history and biochemistry as a basis for appropriate interpretations and meta-analysis. Biol. Fertil. Soils 54, 11–19 (2018).Article 
    CAS 

    Google Scholar 
    Pascual, J. A., Garcia, C., Hernandez, T., Moreno, J. L. & Ros, M. Soil microbial activity as a biomarker of degradation and remediation processes. Soil Biol. Biochem. 32, 1877–1883 (2000).Article 
    CAS 

    Google Scholar 
    García-Gil, J. C., Plaza, C., Solker-Rovira, P. & Polo, A. Long-term effects of municipal solid waste compost application on soil enzyme activities and microbial biomass. Soil Biol. Biochem. 32, 1907–1913 (2000).Article 

    Google Scholar 
    Insam, H. & Domsch, K. H. Relationship between soil organic carbon and microbial biomass on chronosequences of reclamation sites. Microb. Ecol. 15, 177–188 (1988).Article 
    CAS 
    PubMed 

    Google Scholar 
    Acosta-Martinez, V. & Tabatabai, M. Enzyme activities in a limed agricultural soil. Biol. Fertil. Soils 31, 85–91 (2000).Article 
    CAS 

    Google Scholar 
    Uselman, S. M., Qualls, R. G. & Thomas, R. B. A test of a potential short cut in the nitrogen cycle: the role of exudation of symbiotically fixed nitrogen from the roots of a N-fixing tree and the effects of increased atmospheric CO2 and temperature. Plant Soil 210, 21–32 (1999).Article 
    CAS 

    Google Scholar 
    De Marco, A., Esposito, F., Berg, B., Zarrelli, A. & Virzo De Santo, A. Litter inhibitory effects on soil microbial biomass activity, and catabolic diversity in two paired stands of Robinia pseudoacacia L. and Pinus nigra Arn. Forest 9, 766. https://doi.org/10.3390/f9120766 (2018).Article 

    Google Scholar 
    Haghverdi, K. & Kooch, Y. Effects of diversity of tree species on nutrient cycling and soil-related processes. CATENA 178, 335–344 (2019).Article 
    CAS 

    Google Scholar 
    Anderson, H. T. Microbial eco-physiological indicators to assess soil quality. Agric. Ecosyst. Environ. 98, 285–293 (2003).Article 

    Google Scholar 
    Jenkinson, D.S., Ladd, J.N. Microbial biomass in soil: Measurement and turnover. in Soil Biochemistry (eds. Paul, E.A., Ladd, J.N.). 415–471 (Marcel Dekker Inc., 1981) More

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    Zebras of all stripes repel biting flies at close range

    The evolutionary origins of zebra stripes have been investigated—and debated—for centuries. The trait is rare, conspicuous, and intensely expressed, and thus appears to beg an adaptationist explanation. However, the utility of a complete coat of densely packed, starkly contrasting black-and-white stripes is not immediately apparent. Unlike many conspicuous visual traits, striped pelage is expressed with comparable intensity in both sexes and is thus unlikely to have arisen through sexual selection alone (although in plains zebras, Equus quagga, males have stripes closer to true black than females). Stripes are clearly not aposematic warning signals, nor do they provide camouflage in either the woodland or savannah habitats common across zebra ranges1,2. So, striping presents an ideal evolutionary puzzle: a trait so refined it seems it must be “for” something, but one that confers no clear advantage upon its bearers and imposes apparent costs (conspicuousness) that cannot be explained in Zahavian terms.Scientists have proposed and investigated several possible explanations for the evolution of zebra stripes (reviewed in3). The hypotheses suggest various ways in which stripes may provide a social function (species or individual recognition or social cohesion1,4), a temperature-regulation benefit5,6, an anti-predator effect7,8, or an anti-parasite effect9,10. There is continued debate over both the merits of individual hypotheses and the likelihood of stripes having arisen via a single driver vs. a confluence or alternation of multiple selective pressures6,11.The present study addresses the hypothesis that has thus far received the most empirical support: the anti-parasite hypothesis (also known as the ectoparasite hypothesis12). Zebras, like most ungulates, are harassed by tabanid, glossinid and Stomoxys species of biting flies, which can inflict significant blood loss, transmit disease, and weaken hosts when fly-avoidance behaviors reduce the host’s feeding rate9,13,14. Yet zebras are attacked far less than sympatric ungulates across their African range15,16, and also less than other equids9,17. Zebras also produce odors that may augment their anti-fly defenses18, but so do other sympatric ungulate species18,19, and a host of observations and experiments have demonstrated that black-and-white stripes alone are unattractive, or actively repellent to tabanid, glossinid, and Stomoxys flies17,20,21,22,23.Though the effect of stripes on flies is well-established, the source of the effect remains unexplained. Since Waage’s foundational studies in the 1970s and 1980s9,24 most hypotheses have suggested ways that stripes might interfere with the visual and navigational systems of flies, making it harder for them to locate, identify, or successfully land on striped targets. These hypothetical mechanisms can be roughly grouped by the distance (and the attendant phase of a fly’s orientation and landing behavior) at which they would likely operate:

    From afar: stripes might make it harder for flies to locate and distinguish zebras from background vegetation, perhaps by breaking up their outline9 or varying the way they polarize or reflect light17,31 especially from distances at which composite eyes support only low-resolution vision and cannot resolve zebra stripes as clear bands of alternating color on a single host (estimated at  > 2.0 m22,  > 4.4 m24, and even  > 20 m25).

    At close range (estimates range from 0.5 to 4.0 m26): stripes might interfere with orientation or landing behavior via any of several disruptive or ‘dazzle’-related visual effects27. For example, stripes might affect ‘optic flow’, or the fly’s perceived relative motion to its target as it approaches, by creating an illusion of false direction or speed of motion (e.g., via variants of the ‘barber pole’ or ‘wagon wheel’ effects28). Alternatively, relative motion to a striped pattern within the visual field may create the perception of self-rotation, inducing the fly’s involuntary ‘optomotor response’ and resulting in an avoidance turn in an effort to stay on a straight course29.

    Finally, stripes might cause confusion in the transition between long- and short-distance orientation. If zebras appear as blurred gray from a distance and then, at closer range, suddenly resolve into a sequence of floating black and white bars, this abrupt ‘visual transformation’26 might disrupt the behavioral sequence that facilitates landing.

    Within these categories, hypotheses have proliferated faster than experimental tests of many of the proposed mechanisms. The very active literature on this question has grown in somewhat haphazard fashion, as curious researchers test new possibilities without eliminating old ones6. Importantly, few experiments have controlled the distance from which flies are first able to view potential landing sites (but see23). While growing evidence supports a mechanism operating at close range22,26, failing to restrict the starting distance of the fly means that the full set of possible mechanisms outlined above all remain plausible contributors to most previous results.Additionally, while many studies have, appropriately, used artificial stimuli to isolate basic effects of color, pattern, brightness, and light polarization of (usually flat) test surfaces, possible contributions of several aspects of natural zebra pelage remain untested. Controlled experiments have used various landing substrates, including striped and solid oil tray traps, sticky plastic, smooth plastic17, cloth (Experiment 2 in22), horse blankets or sheets26, and paint on live animals30. These have all clearly demonstrated a broadly replicable visual effect: stripes, and some other juxtapositions of black and white (e.g., checkerboard patterns26), repel flies. However, insofar as specific features of zebra pelage factor into proposed mechanisms of fly repellence—the reflective properties of “smooth, shiny” coats31; the orientation of the stripes17,32; the light-polarizing effects of black and white hair vs. background vegetation25; and the complex structure of hair25—there is a need for more experiments that present natural targets to wild flies (but see22,33). Similarly, most experiments have compared landing preferences between black-and-white striped, solid black, solid white, and occasionally solid grey substrates, which have served as important controls for determining that light polarization, rather than a combination of polarization and brightness, is sufficient to induce the effect of stripe avoidance17. However, it is now time to refocus on the original question by presenting flies with more realistic choices. Since biting flies seeking a bloodmeal on the African savannah seldom encounter solid black hosts, and even more rarely solid white hosts, landing choices should be compared between zebra stripes and common coat colors of sympatric mammals, namely various shades of brown. Further, tabanid, glossinid, and Stomoxys flies all avoid landing on stripes that are the same width or narrower than the widest zebra stripes 17,23, and there is some evidence that narrower stripes are even more repellent to tabanids17. This pattern is potentially significant in the application of the anti-parasite hypothesis to an adaptive explanation for the striking variation in stripe width across zebra species and between the different areas of the body on individual zebras22, but must first be confirmed with experiments using real zebra pelage.Here, we present a simple experiment designed to address each of these gaps in the literature on the anti-fly benefits of zebra stripes. In this field experiment, the landing choices of flies were tested entirely within the range at which all estimates agree flies should be able to perceive the presented stripes ( More

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    Improving quantitative synthesis to achieve generality in ecology

    Houlahan, J. E., McKinney, S. T., Anderson, T. M. & McGill, B. J. The priority of prediction in ecological understanding. Oikos 126, 1–7 (2017).Article 

    Google Scholar 
    Lawton, J. H. Are there general laws in ecology? Oikos 84, 177–192 (1999).Article 

    Google Scholar 
    Elliott-Graves, A. Generality and causal interdependence in ecology. Philos. Sci. 85, 1102–1114 (2018).Article 

    Google Scholar 
    Fox, J. W. The many roads to generality in ecology. Philos. Top. 9, 83–104 (2019).Article 

    Google Scholar 
    McGill, B. J. et al. Species abundance distributions: moving beyond single prediction theories to integration within an ecological framework. Ecol. Lett. 10, 995–1015 (2007).Article 
    PubMed 

    Google Scholar 
    MacArthur, R. H. & Wilson, E. O. An equilibrium theory of insular zoogeography. Evolution 17, 373–387 (1963).Article 

    Google Scholar 
    Gurevitch, J., Fox, G. A., Wardle, G. M., Inderjit & Taub, D. Emergent insights from the synthesis of conceptual frameworks for biological invasions. Ecol. Lett. 14, 407–418 (2011).Article 
    PubMed 
    CAS 

    Google Scholar 
    Borer, E. T. et al. Finding generality in ecology: a model for globally distributed experiments. Methods Ecol. Evol. 5, 65–73 (2014).Article 

    Google Scholar 
    Gurevitch, J., Koricheva, J., Nakagawa, S. & Stewart, G. Meta-analysis and the science of research synthesis. Nature 555, 175–182 (2018).Article 
    PubMed 
    CAS 

    Google Scholar 
    Anderson, S. C. et al. Trends in ecology and conservation over eight decades. Front. Ecol. Environ. 19, 274–282 (2021).Article 

    Google Scholar 
    Kneale, D., Thomas, J., O’Mara-Eves, A. & Wiggins, R. How can additional secondary data analysis of observational data enhance the generalisability of meta-analytic evidence for local public health decision making? Res. Synth. Methods 10, 44–56 (2019).Article 
    PubMed 

    Google Scholar 
    Aguinis, H., Pierce, C. A., Bosco, F. A., Dalton, D. R. & Dalton, C. M. Debunking myths and urban legends about meta-analysis. Organ. Res. Methods 14, 306–331 (2011).Article 

    Google Scholar 
    Polit, D. F. & Beck, C. T. Generalization in quantitative and qualitative research: myths and strategies. Int. J. Nurs. Stud. 47, 1451–1458 (2010).Article 
    PubMed 

    Google Scholar 
    Cardinale, B. J., Gonzalez, A., Allington, G. R. H. & Loreau, M. Is local biodiversity declining or not? A summary of the debate over analysis of species richness time trends. Biol. Conserv. 219, 175–183 (2018).Article 

    Google Scholar 
    Lundberg, I., Johnson, R. & Stewart, B. M. What is your estimand? Defining the target quantity connects statistical evidence to theory. Am. Sociol. Rev. 86, 532–565 (2021).Article 

    Google Scholar 
    Lawrance, R. et al. What is an estimand & how does it relate to quantifying the effect of treatment on patient-reported quality of life outcomes in clinical trials? J. Patient-Rep. Outcomes 4, 68 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Findley, M. G., Kikuta, K. & Denly, M. External validity. Annu. Rev. Polit. Sci. 24, 365–393 (2021).Article 

    Google Scholar 
    Pearl, J. & Bareinboim, E. External validity: from do-calculus to transportability across populations. Stat. Sci. 29, 579–595 (2014).Article 

    Google Scholar 
    Westreich, D., Edwards, J. K., Lesko, C. R., Cole, S. R. & Stuart, E. A. Target validity and the hierarchy of study designs. Am. J. Epidemiol. 188, 438–443 (2019).Article 
    PubMed 

    Google Scholar 
    Carpenter, C. J. Meta-analyzing apples and oranges: how to make applesauce instead of fruit salad. Hum. Commun. Res. 46, 322–333 (2020).Article 

    Google Scholar 
    Rohrer, J. M. & Arslan, R. C. Precise answers to vague questions: issues with interactions. Adv. Methods Pract. Psychol. Sci. 4, 1–19 (2021).
    Google Scholar 
    Breslow, N. E. & Clayton, D. G. Approximate inference in generalized linear mixed models. J. Am. Stat. Assoc. 88, 9–25 (1993).
    Google Scholar 
    Koricheva, J. & Gurevitch, J. Uses and misuses of meta-analysis in plant ecology. J. Ecol. 102, 828–844 (2014).Article 

    Google Scholar 
    Gonzalez, A. et al. Estimating local biodiversity change: a critique of papers claiming no net loss of local diversity. Ecology 97, 1949–1960 (2016).Article 
    PubMed 

    Google Scholar 
    Konno, K. et al. Ignoring non-English-language studies may bias ecological meta-analyses. Ecol. Evol. 10, 6373–6384 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Nakagawa, S. et al. Methods for testing publication bias in ecological and evolutionary meta-analyses. Methods Ecol. Evol. 13, 4–21 (2022).Article 

    Google Scholar 
    Rosenthal, R. The file drawer problem and tolerance for null results. Psychol. Bull. 86, 638–641 (1979).Article 

    Google Scholar 
    Leung, B. et al. Clustered versus catastrophic global vertebrate declines. Nature 588, 267–271 (2020).Article 
    PubMed 
    CAS 

    Google Scholar 
    Rothman, K. J., Gallacher, J. E. J. & Hatch, E. E. Why representativeness should be avoided. Int. J. Epidemiol. 42, 1012–1014 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Spake, R. et al. Implications of scale dependence for cross-study syntheses of biodiversity differences. Ecol. Lett. 24, 374–390 (2021).Article 
    PubMed 

    Google Scholar 
    Spake, R. & Doncaster, C. P. Use of meta-analysis in forest biodiversity research: key challenges and considerations. For. Ecol. Manag. 400, 429–437 (2017).Article 

    Google Scholar 
    Christie, A. P. et al. Simple study designs in ecology produce inaccurate estimates of biodiversity responses. J. Appl. Ecol. 56, 2742–2754 (2019).Article 

    Google Scholar 
    Nakagawa, S., Noble, D. W. A., Senior, A. M. & Lagisz, M. Meta-evaluation of meta-analysis: ten appraisal questions for biologists. BMC Biol. 15, 18 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Higgins, J. P. T. & Thompson, S. G. Quantifying heterogeneity in a meta-analysis. Stat. Med. 21, 1539–1558 (2002).Article 
    PubMed 

    Google Scholar 
    Schielzeth, H. & Nakagawa, S. Conditional repeatability and the variance explained by reaction norm variation in random slope models. Methods Ecol. Evol. 13, 1214–1223 (2022).Article 

    Google Scholar 
    Nakagawa, S. et al. The orchard plot: cultivating a forest plot for use in ecology, evolution, and beyond. Res. Synth. Methods 12, 4–12 (2021).Article 
    PubMed 

    Google Scholar 
    Lorah, J. Effect size measures for multilevel models: definition, interpretation, and TIMSS example. Large-Scale Assess. Educ. 6, 8 (2018).Article 

    Google Scholar 
    O’Connor, M. I. et al. A general biodiversity–function relationship is mediated by trophic level. Oikos 126, 18–31 (2017).Article 

    Google Scholar 
    Ojha, M., Naidu, D. G. T. & Bagchi, S. Meta-analysis of induced anti-herbivore defence traits in plants from 647 manipulative experiments with natural and simulated herbivory. J. Ecol. 110, 799–816 (2022).Dodds, K. C. et al. Material type influences the abundance but not richness of colonising organisms on marine structures. J. Environ. Manag. 307, 114549 (2022).Article 

    Google Scholar 
    Dornelas, M. et al. Assemblage time series reveal biodiversity change but not systematic loss. Science 344, 296–299 (2014).Article 
    PubMed 
    CAS 

    Google Scholar 
    Senior, A. M. et al. Heterogeneity in ecological and evolutionary meta- analyses: its magnitude and implications. Ecology 97, 3293–3299 (2016).Article 
    PubMed 

    Google Scholar 
    Blowes, S. A. et al. The geography of biodiversity change in marine and terrestrial assemblages. Science 366, 339–345 (2019).Article 
    PubMed 
    CAS 

    Google Scholar 
    Nakagawa, S. & Cuthill, I. C. Effect size, confidence interval and statistical significance: a practical guide for biologists. Biol. Rev. 82, 591–605 (2007).Article 
    PubMed 

    Google Scholar 
    Glass, G. V. Primary, secondary, and meta-analysis of research. Educ. Res. 5, 3–8 (1976).Article 

    Google Scholar 
    Glass, G. V. Meta‐analysis at 25: a personal history. Education in Two Worlds https://ed2worlds.blogspot.com/2022/07/meta-analysis-at-25-personal-history.html (2000).Cooper, H. M. Organizing knowledge syntheses: a taxonomy of literature reviews. Knowl. Soc. 1, 104–126 (1988).
    Google Scholar 
    Soranno, P. A. et al. Cross-scale interactions: quantifying multi-scaled cause-effect relationships in macrosystems. Front. Ecol. Environ. 12, 65–73 (2014).Article 

    Google Scholar 
    Gerstner, K. et al. Will your paper be used in a meta-analysis? Make the reach of your research broader and longer lasting. Methods Ecol. Evol. 8, 777–784 (2017).Article 

    Google Scholar 
    Hortal, J. et al. Seven shortfalls that beset large-scale knowledge of biodiversity. Annu. Rev. Ecol. Evol. Syst. 46, 523–549 (2015).Article 

    Google Scholar 
    Simons, D. J., Shoda, Y. & Lindsay, D. S. Constraints on Generality (CoG): a proposed addition to all empirical papers. Perspect. Psychol. Sci. 12, 1123–1128 (2017).Article 
    PubMed 

    Google Scholar 
    Yarkoni, T. The generalizability crisis. Behav. Brain Sci. https://doi.org/10.1017/S0140525X20001685 (2020).Lopez, P. M., Subramanian, S. V. & Schooling, C. M. Effect measure modification conceptualized using selection diagrams as mediation by mechanisms of varying population-level relevance. J. Clin. Epidemiol. 113, 123–128 (2019).Article 
    PubMed 

    Google Scholar 
    Campbell, D. T. in Advances in QuasiExperimental Design and Analysis (ed. Trochim, W.) 67–77 (Jossey-Bass, 1986).Spake, R. et al. Meta‐analysis of management effects on biodiversity in plantation and secondary forests of Japan. Conserv. Sci. Pract. 1, e14 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Forest Ecosystem Diversity Basic Survey (in Japanese) (Forestry Agency of Japan, 2019); https://www.rinya.maff.go.jp/j/keikaku/tayouseichousa/index.htmlIto, S., Ishigamia, S., Mizoue, N. & Buckley, G. P. Maintaining plant species composition and diversity of understory vegetation under strip-clearcutting forestry in conifer plantations in Kyushu, southern Japan. For. Ecol. Manag. 231, 234–241 (2006).Article 

    Google Scholar 
    Utsugi, E. et al. Hardwood recruitment into conifer plantations in Japan: effects of thinning and distance from neighboring hardwood forests. For. Ecol. Manag. 237, 15–28 (2006).Article 

    Google Scholar 
    Kominami, Y. et al. Classification of bird-dispersed plants by fruiting phenology, fruit size, and growth form in a primary lucidophyllous forest: an analysis, with implications for the conservation of fruit–bird interactions. Ornthological Sci. 2, 3–23 (2003).Article 

    Google Scholar 
    Tsujino, R. & Matsui, K. Forest regeneration inhibition in a mixed broadleaf-conifer forest under sika deer pressure. J. For. Res. 27, 230–235 (2021).Article 

    Google Scholar 
    Spake, R., Soga, M., Catford, J. A. & Eigenbrod, F. Applying the stress-gradient hypothesis to curb the spread of invasive bamboo. J. Appl. Ecol. 58, 1993–2003 (2021).Article 

    Google Scholar 
    Mize, T. D. Best practices for estimating, interpreting, and presenting nonlinear interaction effects. Sociol. Sci. 6, 81–117 (2019).Article 

    Google Scholar 
    Karaca-Mandic, P., Norton, E. C. & Dowd, B. Interaction terms in nonlinear models. Health Serv. Res. 47, 255–274 (2012).Article 
    PubMed 

    Google Scholar 
    Spake, R. et al. Forest damage by deer depends on cross-scale interactions between climate, deer density and landscape structure. J. Appl. Ecol. 57, 1376–1390 (2020).McCabe, C. J., Kim, D. S. & King, K. M. Improving present practices in the visual display of interactions. Adv. Methods Pract. Psychol. Sci. 1, 147–165 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Shackelford, G. E. et al. Dynamic meta-analysis: a method of using global evidence for local decision making. BMC Biol. 19, 33 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Christie, A. P. et al. Innovation and forward‐thinking are needed to improve traditional synthesis methods: a response to Pescott and Stewart. J. Appl. Ecol. 59, 1191–1197 (2022).Article 

    Google Scholar 
    Haddaway, N. R. et al. EviAtlas: a tool for visualising evidence synthesis databases. Environ. Evid. 8, 22 (2019).Delory, B. M., Li, M., Topp, C. N. & Lobet, G. archiDART v3.0: a new data analysis pipeline allowing the topological analysis of plant root systems. F1000Research 7, 22 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Perkel, J. M. The future of scientific figures. Nature 554, 133–134 (2018).Article 
    PubMed 
    CAS 

    Google Scholar 
    Weaver, S. & Gleeson, M. P. The importance of the domain of applicability in QSAR modeling. J. Mol. Graph. Model. 26, 1315–1326 (2008).Article 
    PubMed 
    CAS 

    Google Scholar 
    Sutton, C. et al. Identifying domains of applicability of machine learning models for materials science. Nat. Commun. 11, 4428 (2020).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Meyer, H. & Pebesma, E. Predicting into unknown space? Estimating the area of applicability of spatial prediction models. Methods Ecol. Evol. 12, 1620–1633 (2021).Article 

    Google Scholar 
    Pearl, J. & Bareinboim, E. Transportability of causal and statistical relations: a formal approach. In 2011 IEEE 11th International Conference on Data Mining Workshops https://doi.org/10.1109/ICDMW.2011.169 (IEEE, 2011).Munthe-Kaas, H., Nøkleby, H. & Nguyen, L. Systematic mapping of checklists for assessing transferability. Syst. Rev. 8, 22 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Dekkers, O. M., von Elm, E., Algra, A., Romijn, J. A. & Vandenbroucke, J. P. How to assess the external validity of therapeutic trials: a conceptual approach. Int. J. Epidemiol. 39, 89–94 (2010).Article 
    PubMed 
    CAS 

    Google Scholar 
    Schloemer, T. & Schröder-Bäck, P. Criteria for evaluating transferability of health interventions: a systematic review and thematic synthesis. Implement. Sci. 13, 88 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Fernandez-Hermida, J. R., Calafat, A., Becoña, E., Tsertsvadze, A. & Foxcroft, D. R. Assessment of generalizability, applicability and predictability (GAP) for evaluating external validity in studies of universal family-based prevention of alcohol misuse in young people: systematic methodological review of randomized controlled trials. Addiction 107, 1570–1579 (2012).Article 
    PubMed 

    Google Scholar 
    Avellar, S. A. et al. External validity: the next step for systematic reviews? Eval. Rev. 41, 283–325 (2017).Article 
    PubMed 

    Google Scholar 
    Bareinboim, E. & Pearl, J. A general algorithm for deciding transportability of experimental results. J. Causal Inference 1, 107–134 (2013).Article 

    Google Scholar 
    Degtiar, I. & Rose, S. A review of generalizability and transportability. Preprint at https://doi.org/10.48550/arXiv.2102.11904 (2021).Bareinboim, E. & Pearl, J. Meta-transportability of causal effects: a formal approach. J. Mach. Learn. Res. 31, 135–143 (2013).
    Google Scholar 
    Jamieson, D. Scientific uncertainty: how do we know when to communicate research findings to the public? Sci. Total Environ. 184, 103–107 (1996).Article 
    CAS 

    Google Scholar 
    Burchett, H. E. D., Mayhew, S. H., Lavis, J. N. & Dobrow, M. J. When can research from one setting be useful in another? Understanding perceptions of the applicability and transferability of research. Health Promot. Int. 28, 418–430 (2013).Article 
    PubMed 

    Google Scholar 
    Forscher, P. et al. Build up big-team science. Nature 601, 505–507 (2022).Article 

    Google Scholar 
    Whalen, M. A. et al. Climate drives the geography of marine consumption by changing predator communities. Proc. Natl Acad. Sci. USA 117, 28160–28166 (2020).Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Moshontz, H. et al. The Psychological Science Accelerator: advancing psychology through a distributed collaborative network. Adv. Methods Pract. Psychol. Sci. 1, 501–515 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Marschner, I. C. A general framework for the analysis of adaptive experiments. Stat. Sci. 36, 465–492 (2021).Article 

    Google Scholar 
    Clark, M. Shrinkage in Mixed Effects Models https://m-clark.github.io/posts/2019-05-14-shrinkage-in-mixed-models/ (2019).Gurevitch, J. & Hedges, L. V. Statistical issues in ecological meta-analyses. Ecology 80, 1142–1149 (1999).Article 

    Google Scholar 
    Mengersen, K., Gurevitch, J. & Schmid, C. H. in Handbook of Meta-analysis in Ecology and Evolution (eds Koricheva, U. et al.) 300–312 (Princeton Univ. Press, 2013).Hudson, L. N. et al. The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project. Ecol. Evol. 7, 145–188 (2017).Article 
    PubMed 

    Google Scholar 
    Dornelas, M. et al. BioTIME: a database of biodiversity time series for the Anthropocene. Glob. Ecol. Biogeogr. 27, 760–786 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Salguero-Gómez, R. et al. The COMPADRE Plant Matrix Database: an open online repository for plant demography. J. Ecol. 103, 202–218 (2015).Article 

    Google Scholar 
    Salguero-Gómez, R. et al. COMADRE: a global data base of animal demography. J. Anim. Ecol. 85, 371–384 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pastor, D. A. & Lazowski, R. A. On the multilevel nature of meta-analysis: a tutorial, comparison of software programs, and discussion of analytic choices. Multivar. Behav. Res. 53, 74–89 (2018).Article 

    Google Scholar  More

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    Low levels of sibship encourage use of larvae in western Atlantic bluefin tuna abundance estimation by close-kin mark-recapture

    Our results show that GoM BFT larval survey samples can provide the crucial mark events for eventual CKMR estimates of adult abundance. The adult parents marked by larval samples can be directly recaptured in the fishery many years later as POPs, and indirectly through their progeny in future samples of larvae, as evidenced by the two cross-cohort HSPs (XHSPs) recovered in this study, which imply that a parent survived and spawned in the GoM in consecutive years. As more cohorts are sampled in future, the growing number of XHSPs could be used to estimate average adult survival rates, in addition to helping with the estimation of adult abundance31, as is now done for southern blue tuna40.There is a modest level of sibship within our 2016 samples, and a high level (involving over half the samples) in 2017, but it turns out not to be high enough to cause serious problems for POP-based CKMR. High sibship per se does not lead to bias in CKMR by virtue of the statistical construction of the estimate, but it does increase variance, which can be summarized through a reduction in effective sample size. In a POP-based CKMR model, our effective sample size would be about 75% of nominal for the two years combined, or 66% of nominal for the targeted sampling of 2017. Since it is actually the product of adult and juvenile sample sizes which drives precision in CKMR14, one way to think about the 75% is that we will need about 33% more adult samples to achieve a given precision on abundance estimates than if we had somehow been able to collect the same number of “independent” juvenile samples (i.e. without oversampling siblings). That increase is appreciable but entirely achievable; for WBFT, it is logistically much easier to collect more feeding-ground adult samples than to collect more larvae, and at present there is no known practical way to collect large numbers of older, more dispersed, and thus more independent, juvenile western origin bluefin tuna (WBFT).This study was motivated by the concern that sibship might be a serious impediment to use of WBFT larvae for CKMR. High levels of sibship have been found in larval collections for other taxa despite a pelagic larval phase, suggesting that abiotic factors can impede random mixing of larvae after a spawning event41. Our larval samples were only a few days old (4–11) and thus had little time to disperse since fertilization; our concern beforehand was that each tow might sample the offspring of a very small number of adults (one spawning group in one night), and in 2017 that repeatedly towing the same water mass might simply be resampling the same “family”. In practice, though, the cumulative effect was limited. Samples were not dominated by progeny from just a few adults; the maximum DPG size (i.e., number of offspring from any one adult) was 5, which is under 2% of the larval sample size. There are several possible reasons for this finding. First, plankton sample tows are typically standardized to a ten-minute duration, covering on average about 0.3 nautical miles. Based on continuous plankton cameras42, each tow is likely to tow through multiple patches of zooplankton, and therefore potentially multiple patches of BFT larvae. Second, spawning aggregations of BFT may contain many adults. For example, on the spawning grounds near the Balearic Islands in the Mediterranean, purse seine fisheries target spawning fish and individual net sets routinely capture upwards of 500 mature individuals43. These numbers suggest that BFT spawner aggregations can be quite large, although the number of individuals that contribute gametes to a single spawning event may be lower. The results of this study pose intriguing scenarios for understanding BFT larval ecology and spawning behavior, which could be explored with larger sample sizes paired with data on oceanographic conditions, direct observation of spawning aggregations, and modeling to compare observed and predicted dispersal. The results of this study are based on just two years of sampling, and numerous practical and theoretical challenges remain to fully understand BFT reproduction in the GoM.Our sibship impact calculations assume use of an unmodified adult-size-based CKMR POP model, where each juvenile is compared to each adult taking into account the latter’s size (e.g.,14). That will give unbiased estimates, which we regard as essential in a CKMR model. However, for WBFT the estimates are not fully statistically efficient, in that some adults receive more statistical weight than others because they are marked more often (by having a large DPG), and thus variance might not be the lowest achievable. Modifying the model to fix that would be simple in a “cartoon” CKMR setting where all adults are identical (e.g., Fig. 1 of14), simply by first condensing each DPG to a single representative, then only using those representatives (rather than all the larvae) in POP comparisons. Each marked parent then receives the same weight, giving maximum efficiency. For the cartoon, this condensed-DPG model still gives an unbiased estimate of abundance, because each DPG has one parent of given sex, and the chance of any sampled cartoon adult of that sex being that parent is 1/N. The DPG-condensed effective sample size is simply half the number of distinct parents, which would be a little larger than the effective sample sizes for the unmodified model shown in Table 3; e.g., in 2017, 504/2 = 252 versus 209. However, no such straightforward improvement is available for an adult-size-based CKMR model such as is needed for WABFT. Using condensed DPGs directly would bias the juvenile sampling against larger more-fecund adults, whose DPGs will tend on average to be larger and thus to experience disproportionate condensation. Those adults would be marked less often by the DPG-condensed juveniles than the model assumes, violating the basic requirements for unbiased CKMR in14. A more sophisticated model might be able to combine unbiasedness with higher efficiency but, since the unmodified adult-size-based POP model that we expect to use is unbiased and only mildly inefficient (at worst 209/252 = 83% efficient, in 2017) there seems no particular need for extra complications at present. However, that may not hold true if we eventually move to a POP + XHSP model, where the impact on unmodified CKMR variance is worse (though there is still no bias, for the same reason as with POPs). Intuitively, the biggest impact that a DPG of size 5 can have in a POP model is to suddenly raise the number of POPs by 5 if its parent happens to be sampled; within a useful total of, say, 75 POPs, the influence is not that large. But if two DPGs both of size 5 in different cohorts happen to share a parent, then the total of XHSPs suddenly jumps by 25— likely a substantial proportion of total XHSPs. Supplementary Material B also includes effective sample size formulae for a simplified XHSP-only model, which demonstrate the increased impact of within-cohort sibship; for our WBFT samples, it turns out that the XHSP-effective size is slightly lower for the targeted 2017 samples (110) than for the 2016 samples (130), unlike the POP-only effective size. Dropping from a maximum theoretical effective sample size of 252 (half the number of DPGs) down to 110 would be rather inefficient and would increase the number of years of sampling required to yield a useful XHSP dataset. This motivates developing a modified POP + XHSP model that retains unbiasedness without sacrificing too much efficiency. In principle, that can be done by condensing each DPG but then conditioning its comparison probabilities on the DPG’s original size, in accordance with the framework in14. This is a topic for subsequent research, and the results will inform future sampling strategy decisions for WBFT.One potential difficulty for western BFT CKMR might occur if a substantial proportion of animals reaching maturity are the offspring of “Western” (in genetic terms) adults who persistently spawn in the western North Atlantic but outside the GoM. However, as long as the adults marked by GoM larvae are well mixed at the time of sampling with any western adults that do spawn outside of the GoM, the total POP-based population estimate of genetically-western BFT from CKMR will remain unbiased. Given evidence from tagging of widespread adult movements within the western North Atlantic2, good mixing in the sampled feeding grounds seems likely; so, even if successful non-GoM western BFT spawning really is commonplace, there should not be a problem with relying on GoM larvae for at least the POP component of CKMR14.Studies of fish early life history have long been considered to have great potential to provide novel insight into the unique population dynamics of fishes44,45,46. Sampling efforts aimed at estimating fish recruitment dynamics have spawned a diversity of larval survey programs. Examples of these long-term programs include the California Cooperative Oceanic Fisheries Investigations, International Council for the Exploration of the Sea (ICES) surveys in the North Atlantic and adjacent areas, Southeast Monitoring and Assessment Program (SEAMAP) in the GoM, Ecosystem Monitoring (EcoMon) in the Northeast U.S., and numerous others, many of which provide indices of larval abundance widely used in fisheries and ecosystem assessments. Yet, as a result of the inherent patchiness of larvae42, sampling variability, and highly variable density dependent mortality45, fisheries scientists have often struggled to determine how larval surveys relate to the adult fish populations. Inclusion of estimates of sibship among larvae collected in surveys could refine estimates of adult spawning stock biomass estimated from these surveys.The results of this study also represent products of decades of work and coordination in obtaining high-quality DNA from larval specimens. Key steps to successful genotyping of larvae include ensuring that larvae are preserved, sorted, and handled in 95% non-denatured ethanol. In addition, strict instrument cleaning protocols must be followed, and stomachs should be removed or avoided (this study used larval tails and, when possible, eyes to avoid cross contamination of prey contents, including possible congeners and other BFT individuals). Exposure to hot lamps during the sorting and dissection processes should also be minimized to ensure that DNA quality is sufficiently high for genotyping-by-sequencing. Although the tissues available for genetic analysis were limited by the needs of other experiments that required BFT tissues, otoliths, gut contents, and other information from the same larvae, we were able to successfully genotype most larvae greater than 6 mm SL and identify thousands of informative SNPs. The lower size limit of larvae could likely be decreased if whole specimens were available for genotyping, although the use of younger larvae could increase the incidence of sibship.In summary, while we observed both FSPs and HSPs in larval collections, with elevated sibship overall and with siblings being more prevalent within tows and in nearby tows, the level of sibship was sufficiently low that collections of GoM BFT larvae can still provide the critical genetic mark of parental genotypes required for CKMR. Our results demonstrate a crucial proof of concept and are the first step towards an operational CKMR modelling estimate of spawning stock abundance for western BFT. More