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    Combining multi-marker metabarcoding and digital holography to describe eukaryotic plankton across the Newfoundland Shelf

    Lombard, F. et al. Consistent quantitative observations of planktonic ecosystems. Front. Mar. Sci. 6, 196. https://doi.org/10.3389/fmars.2019.00196 (2019).Article 

    Google Scholar 
    Sieracki, M. E., et al. Optical plankton imaging and analysis systems for ocean observation. Proceedings of OceanObs’09: Sustained Ocean Observations and Information for Society, 878–885 (2010). https://doi.org/10.5270/OceanObs09.cwp.81.Irisson, J.-O., Ayata, S.-D., Lindsay, D. J., Karp-Boss, L. & Stemmann, L. Machine learning for the study of plankton and marine snow from images. Ann. Rev. Mar. Sci. 14(1), 277. https://doi.org/10.1146/annurev-marine-041921-013023 (2022).Article 
    PubMed 

    Google Scholar 
    Mars Brisbin, M., Brunner, O. D., Grossmann, M. M. & Mitarai, S. Paired high-throughput, in situ imaging and high-throughput sequencing illuminate acantharian abundance and vertical distribution. Limnol. Oceanogr. 65(12), 2953–2965. https://doi.org/10.1002/lno.11567 (2020).ADS 
    CAS 
    Article 

    Google Scholar 
    Benfield, M. et al. RAPID: Research on automated plankton identification. Oceanography 20(2), 172–187. https://doi.org/10.5670/oceanog.2007.63 (2007).Article 

    Google Scholar 
    Colin, S. et al. Quantitative 3D-imaging for cell biology and ecology of environmental microbial eukaryotes. Elife 6, e26066. https://doi.org/10.7554/eLife.26066 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kim, M. K. Principles and techniques of digital holographic microscopy. J. Photonics Energy. 1, 018005. https://doi.org/10.1117/6.0000006 (2010).Article 

    Google Scholar 
    Tahara, T., Quan, X., Otani, R., Takaki, Y. & Matoba, O. Digital holography and its multidimensional imaging applications: A review. Microscopy 67(2), 55–67. https://doi.org/10.1093/jmicro/dfy007 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jericho, S. K., Garcia-Sucerquia, J. F. W., Jericho, M. H. & Kreuzer, H. J. Submersible digital in-line holographic microscope. Rev. Sci. Instrum. 77(4), 043706. https://doi.org/10.1063/1.2193827 (2006).ADS 
    CAS 
    Article 

    Google Scholar 
    Bochdansky, A. B., Jericho, M. H. & Herndl, G. J. Development and deployment of a point-source digital inline holographic microscope for the study of plankton and particlesto a depth of 6000 m. Limnol. Oceanogr: Methods 11, 28–40 (2013).Article 

    Google Scholar 
    Yourassowsky, C. & Dubois, F. High throughput holographic imaging-in-flow for the analysis of a wide plankton size range. Opt. Express 22(6), 6661. https://doi.org/10.1364/OE.22.006661 (2014).ADS 
    Article 
    PubMed 

    Google Scholar 
    Jericho, M. H. & Kreuzer, H. J. Point source digital in-line holographic microscopy. In Coherent Light Microscopy (eds Ferraro, P. et al.) 3–30 (Springer, 2011).Chapter 

    Google Scholar 
    Kanka, M., Riesenberg, R. & Kreuzer, H. J. Reconstruction of high-resolution holographic microscopic images. Opt. Lett. 34(8), 1162. https://doi.org/10.1364/OL.34.001162 (2009).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Jericho, M. H., Kreuzer, H. J., Kanka, M. & Riesenberg, R. Quantitative phase and refractive index measurements with point-source digital in-line holographic microscopy. Appl. Opt. 51(10), 1503. https://doi.org/10.1364/AO.51.001503 (2012).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Wu, Y. & Ozcan, A. Lensless digital holographic microscopy and its applications in biomedicine and environmental monitoring. Methods 136, 4–16 (2018).CAS 
    Article 

    Google Scholar 
    Sun, H. et al. digital holography for studies of marine plankton. Philos. Trans. R. Soc. A. 366, 1789–1806 (2008).ADS 
    CAS 
    Article 

    Google Scholar 
    Bianco, V. et al. microplastic identification via holographic imaging and machine learning. Adv. Intell. Syst. 2(2), 1900153. https://doi.org/10.1002/aisy.201900153 (2020).Article 

    Google Scholar 
    Guo, B. et al. Automated plankton classification from holographic imagery with deep convolutional neural networks. Limnol. Oceanogr. 19(1), 21–36. https://doi.org/10.1002/lom3.10402 (2021).Article 

    Google Scholar 
    Nayak, A. R., Malkiel, E., McFarland, M. N., Twardowski, M. S. & Sullivan, J. M. A Review of holography in the aquatic sciences: In situ characterization of particles, plankton, and small scale biophysical interactions. Front. Mar. Sci. 7, 572147. https://doi.org/10.3389/fmars.2020.572147 (2021).Article 

    Google Scholar 
    Di Bella, J. M., Bao, Y., Gloor, G. B., Burton, J. P. & Reid, G. High throughput sequencing methods and analysis for microbiome research. J. Microbiol. Methods 95(3), 401–414. https://doi.org/10.1016/j.mimet.2013.08.011 (2013).CAS 
    Article 
    PubMed 

    Google Scholar 
    Stoeck, T. et al. Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. Mol. Ecol. 19, 21–31. https://doi.org/10.1111/j.1365-294X.2009.04480.x (2010).CAS 
    Article 
    PubMed 

    Google Scholar 
    de Vargas, C. et al. Eukaryotic plankton diversity in the sunlit ocean. Science 348(6237), 1261605–1261605. https://doi.org/10.1126/science.1261605 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    Lima-Mendez, G. et al. Determinants of community structure in the global plankton interactome. Science 348(6237), 1262073–1262073. https://doi.org/10.1126/science.1262073 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    Santoferrara, L. et al. Perspectives from ten years of protist studies by high-throughput metabarcoding. J. Eukaryot. Microbiol. 67(5), 612–622. https://doi.org/10.1111/jeu.12813 (2020).Article 
    PubMed 

    Google Scholar 
    Eickbush, T. H. & Eickbush, D. G. Finely orchestrated movements: evolution of the ribosomal RNA genes. Genetics 175(2), 477–485. https://doi.org/10.1534/genetics.107.071399 (2007).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kirkham, A. R. et al. Basin-scale distribution patterns of photosynthetic picoeukaryotes along an Atlantic Meridional Transect: Marine photosynthetic picoeukaryote community structure. Environ. Microbiol. 13(4), 975–990. https://doi.org/10.1111/j.1462-2920.2010.02403.x (2011).CAS 
    Article 
    PubMed 

    Google Scholar 
    Decelle, J. et al. PhytoREF: A reference database of the plastidial 16S rRNA gene of photosynthetic eukaryotes with curated taxonomy. Mol. Ecol. Resour. 15(6), 1435–1445. https://doi.org/10.1111/1755-0998.12401 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    Leray, M. & Knowlton, N. Censusing marine eukaryotic diversity in the twenty-first century. Phil. Trans. R. Soc. B. 371(1702), 20150331. https://doi.org/10.1098/rstb.2015.0331 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cowart, D. A. et al. Metabarcoding is powerful yet still blind: A comparative analysis of morphological and molecular surveys of seagrass communities. PLoS ONE 10(2), e0117562. https://doi.org/10.1371/journal.pone.0117562 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Stefanni, S. et al. Multi-marker metabarcoding approach to study mesozooplankton at basin scale. Sci. Rep. 8(1), 12085. https://doi.org/10.1038/s41598-018-30157-7 (2018).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pappalardo, P. et al. The role of taxonomic expertise in interpretation of metabarcoding studies. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsab082 (2021).Article 

    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, 2224. https://doi.org/10.3389/fmicb.2017.02224 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zhu, F., Massana, R., Not, F., Marie, D. & Vaulot, D. Mapping of picoeucaryotes in marine ecosystems with quantitative PCR of the 18S rRNA gene. FEMS Microbiol. Ecol. 52(1), 79–92. https://doi.org/10.1016/j.femsec.2004.10.006 (2005).CAS 
    Article 
    PubMed 

    Google Scholar 
    Sargent, E. C. et al. Evidence for polyploidy in the globally important diazotroph Trichodesmium. FEMS Microbiol. Lett. 363(21), 244. https://doi.org/10.1093/femsle/fnw244 (2016).CAS 
    Article 

    Google Scholar 
    Gong, W. & Marchetti, A. Estimation of 18S gene copy number in marine eukaryotic plankton using a next-generation sequencing approach. Front. Mar. Sci. 6, 219. https://doi.org/10.3389/fmars.2019.00219 (2019).Article 

    Google Scholar 
    Biard, T. et al. Biogeography and diversity of collodaria (radiolaria) in the global ocean. ISME J. 11, 1331–1344 (2017).Article 

    Google Scholar 
    Callahan, B. J., McMurdie, P. J. & Holmes, S. P. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 11(12), 2639–2643. https://doi.org/10.1038/ismej.2017.119 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Behrenfeld, M. J. et al. The North Atlantic aerosol and marine ecosystem study (NAAMES): Science motive and mission overview. Front. Mar. Sci. 6, 122. https://doi.org/10.3389/fmars.2019.00122 (2019).Article 

    Google Scholar 
    Bolaños, L. M. et al. Seasonality of the microbial community composition in the North Atlantic. Front. Mar. Sci. 8, 624164. https://doi.org/10.3389/fmars.2021.624164 (2021).Article 

    Google Scholar 
    Aitchison, J. The statistical analysis of compositional data. J. R. Stat. Soc. B 44(2), 139–160. https://doi.org/10.1111/j.2517-6161.1982.tb01195.x (1982).MathSciNet 
    Article 
    MATH 

    Google Scholar 
    Decelle, J. & Not, F. Acantharia. ELS, 1–10 (2015). https://doi.org/10.1002/9780470015902.a0002102.pub2.Yu, L., An, Y. & Cai, L. Numerical reconstruction of digital holograms with variable viewing angles. Opt. Express 10(22), 1250. https://doi.org/10.1364/OE.10.001250 (2002).ADS 
    Article 
    PubMed 

    Google Scholar 
    Della Penna, A. & Gaube, P. Overview of (sub)mesoscale Ocean dynamics for the NAAMES field program. Front. Mar. Sci. 6, 384. https://doi.org/10.3389/fmars.2019.00384 (2019).Article 

    Google Scholar 
    Sverdrup, H. U. Oceanography for Meteorologists (Prentice Hall, 1942).Book 

    Google Scholar 
    Mahadevan, A. The impact of submesoscale physics on primary productivity of plankton. Annu. Rev. Mar. Sci. 8(1), 161–184. https://doi.org/10.1146/annurev-marine-010814-015912 (2016).ADS 
    Article 

    Google Scholar 
    Fratantoni, P. S. & Pickart, R. S. The Western North Atlantic shelfbreak current system in summer. J. Phys. Oceanogr. 37(10), 2509–2533. https://doi.org/10.1175/JPO3123.1 (2007).ADS 
    Article 

    Google Scholar 
    Bolaños, L. M. et al. Small phytoplankton dominate western North Atlantic biomass. ISME J. 14(7), 1663–1674. https://doi.org/10.1038/s41396-020-0636-0 (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kramer, S. J., Siegel, D. A. & Graff, J. R. Phytoplankton community composition determined from co-variability among phytoplankton pigments from the NAAMES field campaign. Front. Mar. Sci. 7, 215. https://doi.org/10.3389/fmars.2020.00215 (2020).Article 

    Google Scholar 
    Faure, E. et al. Mixotrophic protists display contrasted biogeographies in the global ocean. ISME J. 13(4), 1072–1083. https://doi.org/10.1038/s41396-018-0340-5 (2019).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Fratantoni, P. S. & McCartney, M. S. Freshwater export from the labrador current to the North Atlantic Current at the tail of the grand banks of Newfoundland. Deep Sea Res. I. 57(2), 258–283. https://doi.org/10.1016/j.dsr.2009.11.006 (2010).Article 

    Google Scholar 
    Torti, A., Lever, M. A. & Jørgensen, B. B. Origin, dynamics, and implications of extracellular DNA pools in marine sediments. Mar. Genom. 24, 185–196. https://doi.org/10.1016/j.margen.2015.08.007 (2015).Article 

    Google Scholar 
    Jian, C., Salonen, A. & Korpela, K. Commentary: How to count our microbes? The effect of different quantitative microbiome profiling approaches. Front. Cell. Infect. Microbiol. 11, 627910. https://doi.org/10.3389/fcimb.2021.627910 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Djurhuus, A. et al. Evaluation of marine zooplankton community structure through environmental DNA metabarcoding: Metabarcoding zooplankton from eDNA. Limnol. Oceanogr. Methods 16(4), 209–221. https://doi.org/10.1002/lom3.10237 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    del Campo, J. et al. The others: Our biased perspective of eukaryotic genomes. Trends Ecol. Evol. 29(5), 252–259. https://doi.org/10.1016/j.tree.2014.03.006 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Karst, S. M. et al. Retrieval of a million high-quality, full-length microbial 16S and 18S rRNA gene sequences without primer bias. Nat. Biotech. 36(2), 190–195. https://doi.org/10.1038/nbt.4045 (2018).CAS 
    Article 

    Google Scholar 
    Johnson, J. S. et al. Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis. Nat. Commun. 10(1), 5029. https://doi.org/10.1038/s41467-019-13036-1 (2019).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Callahan, B. J. et al. High-throughput amplicon sequencing of the full-length 16S rRNA gene with single-nucleotide resolution. Nucleic Acids Res. 47(18), e103–e103. https://doi.org/10.1093/nar/gkz569 (2019).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lin, Y., Gifford, S., Ducklow, H., Schofield, O. & Cassar, N. Towards quantitative microbiome community profiling using internal standards. Appl. Environ. Microbiol. 85(5), 18. https://doi.org/10.1128/AEM.02634-18 (2019).Article 

    Google Scholar 
    Vogt, M. et al. Global marine plankton functional type biomass distributions: Phaeocystis spp. Earth Syst. Sci. Data 5, 405–443. https://doi.org/10.5194/essdd-5-405-2012 (2012).ADS 
    Article 

    Google Scholar 
    MacNeil, L., Missan, S., Luo, J., Trappenberg, T. & LaRoche, J. Plankton classification with high-throughput submersible holographic microscopy and transfer learning. BMC Ecol. Evol. 21(1), 123. https://doi.org/10.1186/s12862-021-01839-0 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pan, J., del Campo, J. & Keeling, P. J. Reference tree and environmental sequence diversity of labyrinthulomycetes. J. Eukary. Microbiol. 64(1), 88–96. https://doi.org/10.1111/jeu.12342 (2017).Article 

    Google Scholar 
    Bochdansky, A. B., Clouse, M. A. & Herndl, G. J. Eukaryotic microbes, principally fungi and labyrinthulomycetes, dominate biomass on bathypelagic marine snow. ISME J. 11(2), 362–373. https://doi.org/10.1038/ismej.2016.113 (2017).Article 
    PubMed 

    Google Scholar 
    Xie, N., Hunt, D. E., Johnson, Z. I., He, Y. & Wang, G. Annual partitioning patterns of Labyrinthulomycetes protists reveal their multifaceted role in marine microbial food webs. Appl. Environ. Microbiol. https://doi.org/10.1128/AEM.01652-20 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Walcutt, N. L. et al. Assessment of holographic microscopy for quantifying marine particle size and concentration. Limnol. Oceanogr. Methods 3, 10379. https://doi.org/10.1002/lom3.10379 (2020).Article 

    Google Scholar 
    Axler, K. et al. Fine-scale larval fish distributions and predator-prey dynamics in a coastal river-dominated ecosystem. Mar. Ecol. Prog. Ser. 650, 37–61. https://doi.org/10.3354/meps13397 (2020).ADS 
    Article 

    Google Scholar 
    Trudnowska, E. et al. Marine snow morphology illuminates the evolution of phytoplankton blooms and determines their subsequent vertical export. Nat. Commun. 12(1), 2816. https://doi.org/10.1038/s41467-021-22994-4 (2021).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    González, P. et al. Automatic plankton quantification using deep features. J. Plankton Res. 41(4), 449–463. https://doi.org/10.1093/plankt/fbz023 (2019).Article 

    Google Scholar 
    Briseño-Avena, C. et al. Three-dimensional cross-shelf zooplankton distributions off the Central Oregon Coast during anomalous oceanographic conditions. Prog. Oceanogr. 188, 102436. https://doi.org/10.1016/j.pocean.2020.102436 (2020).Article 

    Google Scholar 
    Biard, T. et al. In situ imaging reveals the biomass of giant protists in the global ocean. Nature 532, 504–507 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    Orenstein, E. C. et al. The scripps plankton camera system: A framework and platform for in situ microscopy. Limnol. Oceanogr. Methods 18(11), 681–695. https://doi.org/10.1002/lom3.10394 (2020).Article 

    Google Scholar 
    Fowler, B. L. et al. Dynamics and functional diversity of the smallest phytoplankton on the Northeast US Shelf. PNAS 117(22), 12215–12221. https://doi.org/10.1073/pnas.1918439117 (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Tréguer, P. et al. Influence of diatom diversity on the ocean biological carbon pump. Nat. Geosci. 11(1), 27–37. https://doi.org/10.1038/s41561-017-0028-x (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    Ryabov, A. et al. Shape matters: The relationship between cell geometry and diversity in phytoplankton. Ecol. Lett. 24(4), 847–861. https://doi.org/10.1111/ele.13680 (2021).MathSciNet 
    Article 
    PubMed 

    Google Scholar 
    Keeling, P. J. & del Campo, J. marine protists are not just big bacteria. Curr. Biol. 27(11), R541–R549. https://doi.org/10.1016/j.cub.2017.03.075 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    Sgubin, G., Swingedouw, D., Drijfhout, S., Mary, Y. & Bennabi, A. Abrupt cooling over the North Atlantic in modern climate models. Nat. Commun. 8(1), 14375. https://doi.org/10.1038/ncomms14375 (2017).CAS 
    Article 
    PubMed Central 

    Google Scholar 
    Desbruyères, D., Chafik, L. & Maze, G. A shift in the ocean circulation has warmed the subpolar North Atlantic Ocean since 2016. Commun. Earth Environ. 2(1), 48. https://doi.org/10.1038/s43247-021-00120-y (2021).ADS 
    Article 

    Google Scholar 
    Mitchell, M. R. et al. Atlantic zone monitoring program protocol. Can. Tech. Rep. Hydrogr. Ocean Sci. 223, 1–23 (2002).
    Google Scholar 
    Li, W. K. W., Glen Harrison, W. & Head, E. J. H. Coherent assembly of phytoplankton communities in diverse temperate ocean ecosystems. Proc. R. Soc. B. 273(1596), 1953–1960. https://doi.org/10.1098/rspb.2006.3529 (2006).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Richardson, P. L. Florida current, gulf stream, and labrador current. In Encyclopedia of Ocean Sciences (ed. Steele, J. H.) 1054–1064 (Academic Press, 2001). https://doi.org/10.1006/rwos.2001.0357.Chapter 

    Google Scholar 
    Henson, S. A., Dunne, J. P. & Sarmiento, J. L. Decadal variability in North Atlantic phytoplankton blooms. J. Geophys. Res. 114(C4), C04013. https://doi.org/10.1029/2008JC005139 (2009).ADS 
    CAS 
    Article 

    Google Scholar 
    Han, G., Lu, Z., Wang, Z., Helbig, J. & Chen, N. Seasonal variability of the labrador current and shelf circulation off Newfoundland. J. Geophys. Res. 113, 10. https://doi.org/10.1029/2007JC004376 (2008).Article 

    Google Scholar 
    Pante, E. & Simon-Bouhet, B. marmap: A package for importing, plotting and analyzing bathymetric and topographic data in R. PLoS ONE 8(9), e73051. https://doi.org/10.1371/journal.pone.0073051 (2013).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kelley, D. “The Oce Package” In Oceanographic Analysis with R 91–101 (Springer, 2018).Book 

    Google Scholar 
    Oksanen, J., et al. vegan: Community Ecology Package. R package version 2.5-7 (2020). https://CRAN.R-project.org/package=vegan.Tomas, C. R. Identifying Marine Phytoplankton (Academic Press Inc, 1997).
    Google Scholar 
    Comeau, A. M., Li, W. K. W., Tremblay, J. -É., Carmack, E. C. & Lovejoy, C. Arctic ocean microbial community structure before and after the 2007 record sea ice minimum. PLoS ONE 6(11), e27492. https://doi.org/10.1371/journal.pone.0027492 (2011).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    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: Primers for marine microbiome studies. Environ. Microbiol. 18(5), 1403–1414. https://doi.org/10.1111/1462-2920.13023 (2016).CAS 
    Article 
    PubMed 

    Google Scholar 
    Walters, W. et al. Improved bacterial 16S rRNA gene (V4 and V4–5) and fungal internal transcribed spacer marker gene primers for microbial community surveys. MSystems https://doi.org/10.1128/mSystems.00009-15 (2016).Article 
    PubMed 

    Google Scholar 
    Comeau, A. M., Douglas, G. M. & Langille, M. G. I. Microbiome helper: A custom and streamlined workflow for microbiome research. MSystems 2(1), e00127-e216. https://doi.org/10.1128/mSystems.00127-16 (2017).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotech. 37(8), 852–857. https://doi.org/10.1038/s41587-019-0209-9 (2019).CAS 
    Article 

    Google Scholar 
    Amir, A. et al. Deblur rapidly resolves single-nucleotide community sequence patterns. MSystems 2(2), e00191-e216. https://doi.org/10.1128/mSystems.00191-16 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Guillou, L. et al. The protist ribosomal reference database (PR2): A catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 41(D1), D597–D604. https://doi.org/10.1093/nar/gks1160 (2013).CAS 
    Article 
    PubMed 

    Google Scholar 
    Mohsen, A., Park, J., Chen, Y.-A., Kawashima, H. & Mizuguchi, K. Impact of quality trimming on the efficiency of reads joining and diversity analysis of Illumina paired-end reads in the context of QIIME1 and QIIME2 microbiome analysis frameworks. BMC Bioinform. 20(1), 581. https://doi.org/10.1186/s12859-019-3187-5 (2019).Article 

    Google Scholar 
    Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6(1), 90. https://doi.org/10.1186/s40168-018-0470-z (2018).MathSciNet 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41(D1), D590–D596. https://doi.org/10.1093/nar/gks1219 (2012).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2021). https://www.R-project.org/.McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8(4), e61217. https://doi.org/10.1371/journal.pone.0061217 (2013).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Willis, A. & Bunge, J. Estimating diversity via frequency ratios: estimating diversity via ratios. Biometrics 71(4), 1042–1049. https://doi.org/10.1111/biom.12332 (2015).MathSciNet 
    Article 
    PubMed 
    MATH 

    Google Scholar 
    Willis, A. D. Rarefaction, alpha diversity, and statistics. Front. Microbiol. 10, 2407. https://doi.org/10.3389/fmicb.2019.02407 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Quinn, T. P. et al. A field guide for the compositional analysis of any-omics data. GigaScience 8(9), 107. https://doi.org/10.1093/gigascience/giz107 (2019).CAS 
    Article 

    Google Scholar 
    Silverman, J. D., Roche, K., Mukherjee, S. & David, L. A. Naught all zeros in sequence count data are the same. Comput. Struct. Biotech. J. 18, 2789–2798. https://doi.org/10.1016/j.csbj.2020.09.014 (2020).CAS 
    Article 

    Google Scholar 
    Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral. Ecol. 26, 32–46 (2001).
    Google Scholar  More

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    Socio-psychological determinants of Iranian rural households' adoption of water consumption curtailment behaviors

    Sun, C., Zhang, J., Ma, Q., Chen, Y. & Ju, H. Polycyclic aromatic hydrocarbons (PAHs) in water and sediment from a river basin: Sediment–water partitioning, source identification and environmental health risk assessment. Environ. Geochem. Health 39, 63–74 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Savari, M. & Shokati Amghani, M. Factors influencing farmers’ adaptation strategies in confronting the drought in Iran. Environ. Dev. Sustain. 2020 234 23, 4949–4972 (2020).Article 

    Google Scholar 
    Kumar Singh, P., Dey, P., Kumar Jain, S. & Mujumdar, P. P. Hydrology and water resources management in ancient India. Hydrol. Earth Syst. Sci. 24, 4691–4707 (2020).ADS 
    Article 

    Google Scholar 
    Warner, L. A. & Diaz, J. M. Amplifying the Theory of Planned behavior with connectedness to water to inform impactful water conservation program planning and evaluation. J. Agric. Educ. Ext. 27, 229–253 (2021).Article 

    Google Scholar 
    Warner, L. A. Who conserves and who approves? Predicting water conservation intentions in urban landscapes with referent groups beyond the traditional ‘important others’. Urban For. Urban Green. 60, 127070 (2021).Article 

    Google Scholar 
    Savari, M., Eskandari Damaneh, H. & Eskandari Damaneh, H. Drought vulnerability assessment: Solution for risk alleviation and drought management among Iranian farmers. Int. J. Disaster Risk Reduct. 67, 102654 (2022).Article 

    Google Scholar 
    Eskandari Damaneh, H. et al. Testing possible scenario-based responses of vegetation under expected climatic changes in Khuzestan Province. Air Soil Water Res. https://doi.org/10.1177/1178622121101333214 (2021).Article 

    Google Scholar 
    Eskandari Damaneh, H., Khosravi, H., Habashi, K., Eskandari Damaneh, H. & Tiefenbacher, J. P. The impact of land use and land cover changes on soil erosion in western Iran. Nat. Hazards 110, 2185–2205 (2022).Article 

    Google Scholar 
    Savari, M., Abdeshahi, A., Gharechaee, H. & Nasrollahian, O. Explaining farmers’ response to water crisis through theory of the norm activation model: Evidence from Iran. Int. J. Disaster Risk Reduct. 60, 102284 (2021).Article 

    Google Scholar 
    Liu, J., Scanlon, B. R., Zhuang, J. & Varis, O. Food-energy-water nexus for multi-scale sustainable development. Resour. Conserv. Recycl. 154, 104565 (2020).Article 

    Google Scholar 
    Araya, F., Osman, K. & Faust, K. M. Perceptions versus reality: Assessing residential water conservation efforts in the household. Resour. Conserv. Recycl. 162, 105020 (2020).Article 

    Google Scholar 
    Omer, A., Elagib, N. A., Zhuguo, M., Saleem, F. & Mohammed, A. Water scarcity in the Yellow River Basin under future climate change and human activities. Sci. Total Environ. 749, 141446 (2020).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Aslam, S. et al. Sustainable model: Recommendations for water conservation strategies in a developing country through a psychosocial wellness program. Water (Switzerland) 13, 1–20 (2021).
    Google Scholar 
    Diaz, J., Odera, E. & Warner, L. Delving deeper: Exploring the influence of psycho-social wellness on water conservation behavior. J. Environ. Manag. 264, 110404 (2020).Article 

    Google Scholar 
    Fader, M., Shi, S., Von Bloh, W., Bondeau, A. & Cramer, W. Mediterranean irrigation under climate change: More efficient irrigation needed to compensate for increases in irrigation water requirements. Hydrol. Earth Syst. Sci. 20, 953–973 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    Brown, T. C., Mahat, V. & Ramirez, J. A. Adaptation to future water shortages in the United States caused by population growth and climate change. Earth’s Future 7, 219–234 (2019).ADS 
    Article 

    Google Scholar 
    Lall, U., Josset, L. & Russo, T. A snapshot of the world’s groundwater challenges. Annu. Rev. Environ. Resour. 45, 171–194 (2020).Article 

    Google Scholar 
    Jin, J. et al. Impacts of climate change on hydrology in the Yellow River Source Region, China. J. Water Clim. Change 11, 916–930 (2020).Article 

    Google Scholar 
    Cochand, F., Brunner, P., Hunkeler, D., Rössler, O. & Holzkämper, A. Cross-sphere modelling to evaluate impacts of climate and land management changes on groundwater resources. Sci. Total Environ. 798, 148759 (2021).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Waha, K. et al. Climate change impacts in the Middle East and Northern Africa (MENA) region and their implications for vulnerable population groups. Reg. Environ. Change 17, 1623–1638 (2017).Article 

    Google Scholar 
    Boretti, A. & Rosa, L. Reassessing the projections of the World Water Development Report. npj Clean Water 2, 1–6 (2019).Article 

    Google Scholar 
    Fragaszy, S. R. et al. Drought monitoring in the Middle East and North Africa (MENA) region. Bull. Am. Meteorol. Soc. 101, 1148–1173 (2020).Article 

    Google Scholar 
    Tajeri moghadam, M., Raheli, H., Zarifian, S. & Yazdanpanah, M. The power of the health belief model (HBM) to predict water demand management: A case study of farmers’ water conservation in Iran. J. Environ. Manag. 263, 110388 (2020).Article 

    Google Scholar 
    Marston, L., Ao, Y., Konar, M., Mekonnen, M. M. & Hoekstra, A. Y. High-resolution water footprints of production of the United States. Water Resour. Res. 54, 2288–2316 (2018).ADS 
    Article 

    Google Scholar 
    Savari, M. & Shokati Amghani, M. SWOT-FAHP-TOWS analysis for adaptation strategies development among small-scale farmers in drought conditions. Int. J. Disaster Risk Reduct. 67, 102695 (2022).Article 

    Google Scholar 
    Savari, M. & Moradi, M. The effectiveness of drought adaptation strategies in explaining the livability of Iranian rural households. Habitat Int. 124, 102560 (2022).Article 

    Google Scholar 
    Warner, L., Chaudhary, A. K., Rumble, J., Lamm, A. & Momol, E. Using audience segmentation to tailor residential irrigation water conservation programs. J. Agric. Educ. 58, 313–333 (2017).Article 

    Google Scholar 
    Tapsuwan, S., Cook, S. & Moglia, M. Willingness to pay for rainwater tank features: A post-drought analysis of Sydney water users. Water (Switzerland) 10, 1199 (2018).
    Google Scholar 
    Chubaka, C. E., Whiley, H., Edwards, J. W. & Ross, K. E. A review of roof harvested rainwater in Australia. J. Environ. Public Health 2018, 6471324 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Smith, H. M., Brouwer, S., Jeffrey, P. & Frijns, J. Public responses to water reuse—Understanding the evidence. J. Environ. Manag. 207, 43–50 (2018).CAS 
    Article 

    Google Scholar 
    Addo, I. B., Thoms, M. C. & Parsons, M. Barriers and drivers of household water-conservation behavior: A profiling approach. Water (Switzerland) 10, 1794 (2018).
    Google Scholar 
    Jarrett, W. B. A survey of the influences on water conservation behavior in Pickens and Oconee counties (2015).Yazdanpanah, M., Forouzani, M., Abdeshahi, A. & Jafari, A. Investigating the effect of moral norm and self-identity on the intention toward water conservation among Iranian young adults. Water Policy 18, 73–90 (2016).Article 

    Google Scholar 
    Sabzali Parikhani, R., Sadighi, H. & Bijani, M. Ecological consequences of nanotechnology in agriculture: Researchers’ perspective. J. Agric. Sci. Technol. 20, 205–219 (2018).
    Google Scholar 
    Moglia, M., Cook, S. & Tapsuwan, S. Promoting water conservation: Where to from here?. Water (Switzerland) 10, 1510 (2018).
    Google Scholar 
    Savari, M. & Zhoolideh, M. The role of climate change adaptation of small-scale farmers on the households food security level in the west of Iran. Dev. Pract. 31, 650–664 (2021).Article 

    Google Scholar 
    Bennett, N. J. et al. Conservation social science: Understanding and integrating human dimensions to improve conservation. Biol. Conserv. 205, 93–108 (2017).Article 

    Google Scholar 
    Kumar Chaudhary, A., Lamm, A. & Warner, L. Using cognitive dissonance to theoretically explain water conservation intentions. J. Agric. Educ. 59, 194–210 (2018).Article 

    Google Scholar 
    Russell, S. V. & Knoeri, C. Exploring the psychosocial and behavioural determinants of household water conservation and intention. Int. J. Water Resour. Dev. 36, 940–955 (2020).Article 

    Google Scholar 
    Savari, M., Yazdanpanah, M. & Rouzaneh, D. Factors affecting the implementation of soil conservation practices among Iranian farmers. Sci. Rep. 12, 1–13 (2022).Article 
    CAS 

    Google Scholar 
    Savari, M., Zhoolideh, M. & Khosravipour, B. Explaining pro-environmental behavior of farmers: A case of rural Iran. Curr. Psychol. https://doi.org/10.1007/S12144-021-02093-9 (2021).Article 

    Google Scholar 
    Lee, M. & Tansel, B. Water conservation quantities vs customer opinion and satisfaction with water efficient appliances in Miami, Florida. J. Environ. Manag. 128, 683–689 (2013).Article 

    Google Scholar 
    Yazdanpanah, M., Klein, K., Zobeidi, T., Sieber, S. & Löhr, K. Why have economic incentives failed to convince farmers to adopt drip irrigation in southwestern Iran?. Sustainability 14, 1–15 (2022).Article 

    Google Scholar 
    Zobeidi, T., Yaghoubi, J. & Yazdanpanah, M. Developing a paradigm model for the analysis of farmers’ adaptation to water scarcity. Environ. Dev. Sustain. 24, 5400–5425 (2022).Article 

    Google Scholar 
    Russell, S. & Fielding, K. Water demand management research: A psychological perspective. Water Resour. Res. 46, 1–12 (2010).Article 

    Google Scholar 
    Shahangian, S. A., Tabesh, M., Yazdanpanah, M., Zobeidi, T. & Raoof, M. A. Promoting the adoption of residential water conservation behaviors as a preventive policy to sustainable urban water management. J. Environ. Manag. 313, 115005 (2022).Article 

    Google Scholar 
    Onwezen, M. C., Antonides, G. & Bartels, J. The Norm Activation Model: An exploration of the functions of anticipated pride and guilt in pro-environmental behaviour. J. Econ. Psychol. 39, 141–153 (2013).Article 

    Google Scholar 
    Shahangian, S. A., Tabesh, M. & Yazdanpanah, M. Psychosocial determinants of household adoption of water-efficiency behaviors in Tehran capital, Iran: Application of the social cognitive theory. Urban Clim. 39, 100935 (2021).Article 

    Google Scholar 
    Yazdanpanah, M., Feyzabad, F. R., Forouzani, M., Mohammadzadeh, S. & Burton, R. J. F. Predicting farmers’ water conservation goals and behavior in Iran: A test of social cognitive theory. Land Use Policy 47, 401–407 (2015).Article 

    Google Scholar 
    Valizadeh, N., Bijani, M., Hayati, D. & Fallah Haghighi, N. Social-cognitive conceptualization of Iranian farmers’ water conservation behavior. Hydrogeol. J. 27, 1131–1142 (2019).ADS 
    Article 

    Google Scholar 
    Greaves, M., Zibarras, L. D. & Stride, C. Using the theory of planned behavior to explore environmental behavioral intentions in the workplace. J. Environ. Psychol. 34, 109–120 (2013).Article 

    Google Scholar 
    Wang, Y. et al. Analysis of the environmental behavior of farmers for non-point source pollution control and management: An integration of the theory of planned behavior and the protection motivation theory. J. Environ. Manag. 237, 15–23 (2019).Article 

    Google Scholar 
    Savari, M. & Gharechaee, H. Application of the extended theory of planned behavior to predict Iranian farmers’ intention for safe use of chemical fertilizers. J. Clean. Prod. 263, 121512 (2020).CAS 
    Article 

    Google Scholar 
    Strydom, W. F. Applying the theory of planned behavior to recycling behavior in South Africa. Recycling 3, 43 (2018).Article 

    Google Scholar 
    Lam, S. P. Predicting intention to save water: Theory of planned behavior, response efficacy, vulnerability, and perceived efficiency of alternative solutions. J. Appl. Soc. Psychol. 36, 2803–2824 (2006).Article 

    Google Scholar 
    Abdulkarim, B., Yacob, M. R., Abdullahi, A. M. & Radam, A. Farmers’ perceptions and attitudes toward forest watershed conservation of the North Selangor Peat Swamp Forest. J. Sustain. For. 36, 309–323 (2017).
    Google Scholar 
    Yuriev, A., Dahmen, M., Paillé, P., Boiral, O. & Guillaumie, L. Pro-environmental behaviors through the lens of the theory of planned behavior: A scoping review. Resour. Conserv. Recycl. 155, 104660 (2020).Article 

    Google Scholar 
    Bosnjak, M., Ajzen, I. & Schmidt, P. Editorial the theory of planned behavior: Selected recent advances and applications (1841).Ajzen, I. Consumer attitudes and behavior: The theory of planned behavior applied to food consumption decisions. Ital. Rev. Agric. Econ. 70(2), 121–138. https://doi.org/10.13128/REA-18003 (2015).Article 

    Google Scholar 
    Soorani, F. & Ahmadvand, M. Determinants of consumers’ food management behavior: Applying and extending the theory of planned behavior. Waste Manag. 98, 151–159 (2019).PubMed 
    Article 

    Google Scholar 
    Popa, B., Niță, M. D. & Hălălișan, A. F. Intentions to engage in forest law enforcement in Romania: An application of the theory of planned behavior. For. Policy Econ. 100, 33–43 (2019).Article 

    Google Scholar 
    Tam, K. P. Understanding the psychology X politics interaction behind environmental activism: The roles of governmental trust, density of environmental NGOs, and democracy. J. Environ. Psychol. 71, 101330 (2020).Article 

    Google Scholar 
    Ajzen, I. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50, 179–211 (1991).Article 

    Google Scholar 
    Icek, A. From intentions to actions: A theory of planned behavior. in Action Control 11–39 (1985).Empidi, A. V. A. & Emang, D. Understanding public intentions to participate in protection initiatives for forested watershed areas using the theory of planned behavior: A case study of Cameron highlands in Pahang, Malaysia. Sustainability 13, 4399 (2021).Article 

    Google Scholar 
    Holt, J. R. et al. Using the theory of planned behavior to understand family forest owners’ intended responses to invasive forest insects. Soc. Nat. Resour. 34, 1001–1018 (2021).Article 

    Google Scholar 
    Marcos, K. J., Moersidik, S. S. & Soesilo, T. E. B. Extended theory of planned behavior on utilizing domestic rainwater harvesting in Bekasi, West Java, Indonesia. IOP Conf. Ser. Earth Environ. Sci. 716, 012054 (2021).Article 

    Google Scholar 
    Sánchez, M., López-Mosquera, N., Lera-López, F. & Faulin, J. An extended planned behavior model to explain the willingness to pay to reduce noise pollution in road transportation. J. Clean. Prod. 177, 144–154 (2018).Article 

    Google Scholar 
    Fernandez, M. E., Ruiter, R. A. C., Markham, C. M. & Kok, G. Intervention mapping: Theory-and evidence-based health promotion program planning: Perspective and examples. Front. Public Health 7, 209 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zhong, F. et al. Quantifying the influence path of water conservation awareness on water-saving irrigation behavior based on the theory of planned behavior and structural equation modeling: A case study from Northwest China. Sustainability 11, 1–16 (2019).
    Google Scholar 
    Ullah, S. et al. Predicting behavioral intention of rural inhabitants toward economic incentive for deforestation in Gilgit-Baltistan, Pakistan. Sustainability 13, 1–17 (2021).
    Google Scholar 
    Koop, S. H. A., Van Dorssen, A. J. & Brouwer, S. Enhancing domestic water conservation behaviour: A review of empirical studies on influencing tactics. J. Environ. Manag. 247, 867–876 (2019).CAS 
    Article 

    Google Scholar 
    Goh, E., Ritchie, B. & Wang, J. Non-compliance in national parks: An extension of the theory of planned behaviour model with pro-environmental values. Tour. Manag. 59, 123–127 (2017).Article 

    Google Scholar 
    Liang, Y., Kee, K. F. & Henderson, L. K. Towards an integrated model of strategic environmental communication: Advancing theories of reactance and planned behavior in a water conservation context. J. Appl. Commun. Res. 46, 135–154 (2018).CAS 
    Article 

    Google Scholar 
    Gkargkavouzi, A., Halkos, G. & Matsiori, S. Environmental behavior in a private-sphere context: Integrating theories of planned behavior and value belief norm, self-identity and habit. Resour. Conserv. Recycl. 148, 145–156 (2019).Article 

    Google Scholar 
    Vaske, J. J., Landon, A. C. & Miller, C. A. Normative influences on farmers’ intentions to practice conservation without compensation. Environ. Manag. 66, 191–201 (2020).Article 

    Google Scholar 
    Nguru, W. M., Gachene, C. K., Onyango, C. M., Ng’ang’a, S. K. & Girvetz, E. H. Factors constraining the adoption of soil organic carbon enhancing technologies among small-scale farmers in Ethiopia. Heliyon 7, e08497 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Møller, M., Haustein, S. & Bohlbro, M. S. Adolescents’ associations between travel behaviour and environmental impact: A qualitative study based on the Norm-Activation Model. Travel Behav. Soc. 11, 69–77 (2018).Article 

    Google Scholar 
    Savari, M., Naghibeiranvand, F. & Asadi, Z. Modeling environmentally responsible behaviors among rural women in the forested regions in Iran. Glob. Ecol. Conserv. 35, e02102 (2022).Article 

    Google Scholar 
    van Valkengoed, A. M. & Steg, L. Meta-analyses of factors motivating climate change adaptation behaviour. Nat. Clim. Chang. 9, 158–163 (2019).ADS 
    Article 

    Google Scholar 
    Maduku, D. K. Water conservation campaigns in an emerging economy: How effective are they?. Int. J. Advert. 40, 452–472 (2021).Article 

    Google Scholar 
    Thøgersen, J. & Grønhøj, A. Electricity saving in households—A social cognitive approach. Energy Policy 38, 7732–7743 (2010).Article 

    Google Scholar 
    Ouellette, J. A. & Wood, W. Habit and intention in everyday life: The multiple processes by which past behavior predicts future behavior. Psychol. Bull. 124, 54–74 (1998).Article 

    Google Scholar 
    Ajzen, I. The theory of planned behavior: Frequently asked questions. Hum. Behav. Emerg. Technol. 2, 314–324 (2020).Article 

    Google Scholar 
    Hofmann, W., Gschwendner, T., Friese, M., Wiers, R. W. & Schmitt, M. Working memory capacity and self-regulatory behavior: toward an individual differences perspective on behavior determination by automatic versus controlled processes. J. Pers. Soc. Psychol. 95, 962–977 (2008).PubMed 
    Article 

    Google Scholar 
    Jorgensen, B. S., Martin, J. F., Pearce, M. W. & Willis, E. M. Aligning theory and measurement in behavioral models of water conservation. Water Policy 17, 762–776 (2015).Article 

    Google Scholar 
    Barr, S. & Gilg, A. W. A conceptual framework for understanding and analyzing attitudes towards environmental behaviour. Geogr. Ann. Ser. B Hum. Geogr. 89 B, 361–379 (2007).Article 

    Google Scholar 
    Hansmann, R., Bernasconi, P., Smieszek, T., Loukopoulos, P. & Scholz, R. W. Justifications and self-organization as determinants of recycling behavior: The case of used batteries. Resour. Conserv. Recycl. 47, 133–159 (2006).Article 

    Google Scholar 
    Tang, Z., Chen, X. & Luo, J. Determining socio-psychological drivers for rural household recycling behavior in developing countries: A case study from Wugan, Hunan, China. Environ. Behav. 43, 848–877 (2011).Article 

    Google Scholar 
    Krejcie, R. V. & Morgan, W. D. (1970) “Determining sample size for research activities”, educational and psychological measurement. Int. J. Employ. Stud. 18, 89–123 (1996).
    Google Scholar 
    Gregory, G. D. & Di Leo, M. Repeated behavior and environmental psychology: The role of personal involvement and habit formation in explaining water consumption. J. Appl. Soc. Psychol. 33, 1261–1296 (2003).Article 

    Google Scholar 
    Keramitsoglou, K. M. & Tsagarakis, K. P. Raising effective awareness for domestic water saving: Evidence from an environmental educational programme in Greece. Water Policy 13, 828–844 (2011).Article 

    Google Scholar 
    Chaudhary, A. K. et al. Using the theory of planned behavior to encourage water conservation among extension clients. J. Agric. Educ. 58, 185–202 (2017).Article 

    Google Scholar 
    Pradhananga, A. K., Davenport, M. A., Fulton, D. C., Maruyama, G. M. & Current, D. An integrated moral obligation model for landowner conservation norms. Soc. Nat. Resour. 30, 212–227 (2017).Article 

    Google Scholar 
    Heath, Y. & Gifford, R. Extending the theory of planned behavior: Predicting the use of public transportation. J. Appl. Soc. Psychol. 32, 2154–2189 (2002).Article 

    Google Scholar 
    Bodimeade, H. et al. Testing the direct, indirect, and interactive roles of referent group injunctive and descriptive norms for sun protection in relation to the theory of planned behavior. J. Appl. Soc. Psychol. 44, 739–750 (2014).Article 

    Google Scholar 
    Veisi, K., Bijani, M. & Abbasi, E. A human ecological analysis of water conflict in rural areas: Evidence from Iran. Glob. Ecol. Conserv. 23, e01050 (2020).Article 

    Google Scholar 
    Botetzagias, I., Dima, A. F. & Malesios, C. Extending the Theory of Planned Behavior in the context of recycling: The role of moral norms and of demographic predictors. Resour. Conserv. Recycl. 95, 58–67 (2015).Article 

    Google Scholar 
    Martínez-Espiñeira, R., García-Valiñas, M. A. & Nauges, C. Households’ pro-environmental habits and investments in water and energy consumption: Determinants and relationships. J. Environ. Manag. 133, 174–183 (2014).Article 

    Google Scholar 
    Dolnicar, S., Hurlimann, A. & Grün, B. Water conservation behavior in Australia. J. Environ. Manag. 105, 44–52 (2012).Article 

    Google Scholar 
    Untaru, E. N., Ispas, A., Candrea, A. N., Luca, M. & Epuran, G. Predictors of individuals’ intention to conserve water in a lodging context: The application of an extended Theory of Reasoned Action. Int. J. Hosp. Manag. 59, 50–59 (2016).Article 

    Google Scholar 
    Khoshmaram, M., Shiri, N., Shinnar, R. S. & Savari, M. Environmental support and entrepreneurial behavior among Iranian farmers: The mediating roles of social and human capital. J. Small Bus. Manag. https://doi.org/10.1111/jsbm.1250158,1064-1088 (2020).Article 

    Google Scholar 
    Benitez, J., Henseler, J., Castillo, A. & Schuberth, F. How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research. Inf. Manag. 57, 103168 (2020).Article 

    Google Scholar 
    Sarstedt, M., Ringle, C. M. & Hair, J. F. Partial least squares structural equation modeling. in Handbook of Market Research 1–47. https://doi.org/10.1007/978-3-319-05542-8_15-2 (2021).Clark, W. A. & Finley, J. C. Determinants of water conservation intention in Blagoevgrad, Bulgaria. Soc. Nat. Resour. 20, 613–627 (2007).Article 

    Google Scholar 
    De Dominicis, S., Sokoloski, R., Jaeger, C. M. & Schultz, P. W. Making the smart meter social promotes long-term energy conservation. Palgrave Commun. 5, 1–8 (2019).Article 

    Google Scholar 
    Wang, S., Hung, K. & Huang, W.-J. Motivations for entrepreneurship in the tourism and hospitality sector: A social cognitive theory perspective. Int. J. Hosp. Manag. https://doi.org/10.1016/j.ijhm.2018.11.018 (2018).Article 

    Google Scholar 
    Ramirez, E., Kulinna, P. H. & Cothran, D. Constructs of physical activity behaviour in children: The usefulness of Social Cognitive Theory. Psychol. Sport Exerc. 13, 303–310 (2012).Article 

    Google Scholar 
    Glanz, K., Rimer, B. K. & Viswanath, K. Health and Health (2002). More

  • in

    Microbiota succession throughout life from the cradle to the grave

    Chu, D. M. et al. Maturation of the infant microbiome community structure and function across multiple body sites and in relation to mode of delivery. Nat. Med. 23, 314–326 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ward, T. L. et al. Development of the human mycobiome over the first month of life and across body sites. mSystems 3, e00140–17 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Oh, J. et al. Biogeography and individuality shape function in the human skin metagenome. Nature 514, 59–64 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Abeles, S. R. et al. Human oral viruses are personal, persistent and gender-consistent. ISME J. 8, 1753–1767 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Grice, E. A. & Segre, J. A. The human microbiome: our second genome. Annu. Rev. Genomics Hum. Genet. 13, 151–170 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

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

    Google Scholar 
    Zengler, K. & Zaramela, L. S. The social network of microorganisms – how auxotrophies shape complex communities. Nat. Rev. Microbiol. 16, 383–390 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Smits, S. A. et al. Seasonal cycling in the gut microbiome of the Hadza hunter-gatherers of Tanzania. Science 357, 802–806 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Rasko, D. A. Changes in microbiome during and after travellers’ diarrhea: what we know and what we do not. J. Travel. Med. 24, S52–S56 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zheng, D., Liwinski, T. & Elinav, E. Interaction between microbiota and immunity in health and disease. Cell Res. 30, 492–506 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zaneveld, J. R., McMinds, R. & Vega Thurber, R. Stress and stability: applying the Anna Karenina principle to animal microbiomes. Nat. Microbiol. 2, 17121 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Dini-Andreote, F., Stegen, J. C., van Elsas, J. D. & Salles, J. F. Disentangling mechanisms that mediate the balance between stochastic and deterministic processes in microbial succession. Proc. Natl Acad. Sci. USA 112, E1326–E1332 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Dominguez-Bello, M. G. et al. Delivery mode shapes the acquisition and structure of the initial microbiota across multiple body habitats in newborns. Proc. Natl Acad. Sci. USA 107, 11971–11975 (2010).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Yassour, M. et al. Natural history of the infant gut microbiome and impact of antibiotic treatment on bacterial strain diversity and stability. Sci. Transl. Med. 8, 343ra81 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Bokulich, N. A. et al. Antibiotics, birth mode, and diet shape microbiome maturation during early life. Sci. Transl. Med. 8, 343ra82 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    David, L. A. et al. Host lifestyle affects human microbiota on daily timescales. Genome Biol. 15, R89 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Vangay, P. et al. US immigration westernizes the human gut microbiome. Cell 175, 962–972.e10 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Yatsunenko, T. et al. Human gut microbiome viewed across age and geography. Nature 486, 222–227 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Gregory, A. C. et al. The gut virome database reveals age-dependent patterns of virome diversity in the human gut. Cell Host Microbe 28, 724–740.e8 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Faith, J. J. et al. The long-term stability of the human gut microbiota. Science 341, 1237439 (2013).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Thaiss, C. A. et al. Microbiota diurnal rhythmicity programs host transcriptome oscillations. Cell 167, 1495–1510.e12 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Zaura, E. et al. Same exposure but two radically different responses to antibiotics: resilience of the salivary microbiome versus long-term microbial shifts in feces. mBio 6, e01693–15 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Dethlefsen, L. & Relman, D. A. Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proc. Natl Acad. Sci. USA 108 (Suppl. 1), 4554–4561 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    Hsiao, A. et al. Members of the human gut microbiota involved in recovery from Vibrio cholerae infection. Nature 515, 423–426 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Chng, K. R. et al. Metagenome-wide association analysis identifies microbial determinants of post-antibiotic ecological recovery in the gut. Nat. Ecol. Evol. 4, 1256–1267 (2020).PubMed 
    Article 

    Google Scholar 
    Gibbons, S. M. Keystone taxa indispensable for microbiome recovery. Nat. Microbiol. 5, 1067–1068 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Rizzatti, G., Lopetuso, L. R., Gibiino, G., Binda, C. & Gasbarrini, A. Proteobacteria: a common factor in human diseases. Biomed. Res. Int. 2017, 9351507 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Biagi, E. et al. Gut microbiota and extreme longevity. Curr. Biol. 26, 1480–1485 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Lim, A. I. et al. Prenatal maternal infection promotes tissue-specific immunity and inflammation in offspring. Science 373, eabf3002 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Al Nabhani, Z. & Eberl, G. Imprinting of the immune system by the microbiota early in life. Mucosal Immunol. 13, 183–189 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Lynn, M. A. et al. Early-life antibiotic-driven dysbiosis leads to dysregulated vaccine immune responses in mice. Cell Host Microbe 23, 653–660.e5 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Blaser, M. J. The theory of disappearing microbiota and the epidemics of chronic diseases. Nat. Rev. Immunol. 17, 461–463 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Thorburn, A. N. et al. Evidence that asthma is a developmental origin disease influenced by maternal diet and bacterial metabolites. Nat. Commun. 6, 7320 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Gomez de Agüero, M. et al. The maternal microbiota drives early postnatal innate immune development. Science 351, 1296–1302 (2016).PubMed 
    Article 
    CAS 

    Google Scholar 
    Macpherson, A. J., de Agüero, M. G. & Ganal-Vonarburg, S. C. How nutrition and the maternal microbiota shape the neonatal immune system. Nat. Rev. Immunol. 17, 508–517 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Nakajima, A. et al. Maternal high fiber diet during pregnancy and lactation influences regulatory T cell differentiation in offspring in mice. J. Immunol. 199, 3516–3524 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Jamalkandi, S. A. et al. Oral and nasal probiotic administration for the prevention and alleviation of allergic diseases, asthma and chronic obstructive pulmonary disease. Nutr. Res. Rev. 34, 1–16 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Örtqvist, A. K., Lundholm, C., Halfvarson, J., Ludvigsson, J. F. & Almqvist, C. Fetal and early life antibiotics exposure and very early onset inflammatory bowel disease: a population-based study. Gut 68, 218–225 (2019).PubMed 
    Article 
    CAS 

    Google Scholar 
    Munyaka, P. M., Eissa, N., Bernstein, C. N., Khafipour, E. & Ghia, J.-E. Antepartum antibiotic treatment increases offspring susceptibility to experimental colitis: a role of the gut microbiota. PLoS ONE 10, e0142536 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Kiss, E. A. et al. Natural aryl hydrocarbon receptor ligands control organogenesis of intestinal lymphoid follicles. Science 334, 1561–1565 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    Lee, J. S. et al. AHR drives the development of gut ILC22 cells and postnatal lymphoid tissues via pathways dependent on and independent of Notch. Nat. Immunol. 13, 144–151 (2011).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Qiu, J. et al. The aryl hydrocarbon receptor regulates gut immunity through modulation of innate lymphoid cells. Immunity 36, 92–104 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Schulfer, A. F. et al. Intergenerational transfer of antibiotic-perturbed microbiota enhances colitis in susceptible mice. Nat. Microbiol. 3, 234–242 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Ma, J. et al. High-fat maternal diet during pregnancy persistently alters the offspring microbiome in a primate model. Nat. Commun. 5, 3889 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Torres, J. et al. Infants born to mothers with IBD present with altered gut microbiome that transfers abnormalities of the adaptive immune system to germ-free mice. Gut 69, 42–51 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Milliken, S., Allen, R. M. & Lamont, R. F. The role of antimicrobial treatment during pregnancy on the neonatal gut microbiome and the development of atopy, asthma, allergy and obesity in childhood. Expert. Opin. Drug. Saf. 18, 173–185 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Santacruz, A. et al. Gut microbiota composition is associated with body weight, weight gain and biochemical parameters in pregnant women. Br. J. Nutr. 104, 83–92 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    Trevisanuto, D. et al. Fetal placental inflammation is associated with poor neonatal growth of preterm infants: a case-control study. J. Matern. Fetal Neonatal Med. 26, 1484–1490 (2013).PubMed 
    Article 

    Google Scholar 
    Song, S. J. et al. Naturalization of the microbiota developmental trajectory of Cesarean-born neonates after vaginal seeding. Med 2, 951–964.e5 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Abu-Raya, B., Michalski, C., Sadarangani, M. & Lavoie, P. M. Maternal immunological adaptation during normal pregnancy. Front. Immunol. 11, 575197 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hanson, L. A. et al. The transfer of immunity from mother to child. Ann. NY. Acad. Sci. 987, 199–206 (2003).CAS 
    PubMed 
    Article 

    Google Scholar 
    Dominguez-Bello, M. G. et al. Partial restoration of the microbiota of cesarean-born infants via vaginal microbial transfer. Nat. Med. 22, 250–253 (2016). This study demonstrates that ‘seeding’ infants born by caesarean delivery with the vaginal microbiota of the mother at birth partially naturalizes development of the microbial community.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ferretti, P. et al. Mother-to-infant microbial transmission from different body sites shapes the developing infant gut microbiome. Cell Host Microbe 24, 133–145.e5 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Helve, O. et al. 2843. Maternal fecal transplantation to infants born by cesarean section: safety and feasibility. Open. Forum Infect. Dis. 6, S68 (2019).PubMed Central 
    Article 

    Google Scholar 
    Subramanian, S. et al. Persistent gut microbiota immaturity in malnourished Bangladeshi children. Nature 510, 417–421 (2014). This study shows that severe acute malnutrition leads to immature microbial development and introduces a metric for the measure of microbiota maturity.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Palmer, C., Bik, E. M., DiGiulio, D. B., Relman, D. A. & Brown, P. O. Development of the human infant intestinal microbiota. PLoS Biol. 5, e177 (2007).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Groer, M. W. et al. Development of the preterm infant gut microbiome: a research priority. Microbiome 2, 38 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Henrick, B. M. et al. Bifidobacteria-mediated immune system imprinting early in life. Cell 184, 3884–3898.e11 (2021). This report describes the immune development driven by microbial interactions and the negative impact of lack of HMO-utilizing microorganisms on the immune system.CAS 
    PubMed 
    Article 

    Google Scholar 
    Sela, D. A. & Mills, D. A. Nursing our microbiota: molecular linkages between bifidobacteria and milk oligosaccharides. Trends Microbiol. 18, 298–307 (2010).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Seppo, A. E. et al. Infant gut microbiome is enriched with Bifidobacterium longum ssp. infantis in old order mennonites with traditional farming lifestyle. Allergy 76, 3489–3503 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Triantis, V., Bode, L. & van Neerven, R. J. J. Immunological effects of human milk oligosaccharides. Front. Pediatr. 6, 190 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Yu, Z.-T., Chen, C. & Newburg, D. S. Utilization of major fucosylated and sialylated human milk oligosaccharides by isolated human gut microbes. Glycobiology 23, 1281–1292 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).Article 
    CAS 

    Google Scholar 
    McDonald, D. et al. American gut: an open platform for citizen science microbiome research. mSystems 3, e00031–18 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Odamaki, T. et al. Age-related changes in gut microbiota composition from newborn to centenarian: a cross-sectional study. BMC Microbiol. 16, 90 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Schei, K. et al. Early gut mycobiota and mother-offspring transfer. Microbiome 5, 107 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Alonso, R., Pisa, D., Fernández-Fernández, A. M. & Carrasco, L. Infection of fungi and bacteria in brain tissue from elderly persons and patients with Alzheimer’s disease. Front. Aging Neurosci. 10, 159 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Nagpal, R. et al. Gut mycobiome and its interaction with diet, gut bacteria and Alzheimer’s disease markers in subjects with mild cognitive impairment: a pilot study. EBioMedicine 59, 102950 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ahmad, H. F. et al. Gut mycobiome dysbiosis is linked to hypertriglyceridemia among home dwelling elderly Danes. Preprint at bioRxiv https://doi.org/10.1101/2020.04.16.044693 (2020).Article 

    Google Scholar 
    Wampach, L. et al. Colonization and succession within the human gut microbiome by archaea, bacteria, and microeukaryotes during the first year of life. Front. Microbiol. 8, 738 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Breitbart, M. et al. Metagenomic analyses of an uncultured viral community from human feces. J. Bacteriol. 185, 6220–6223 (2003).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Liang, G. et al. The stepwise assembly of the neonatal virome is modulated by breastfeeding. Nature 581, 470–474 (2020). This study describes the assembly of the human virome during development.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lim, E. S. et al. Early life dynamics of the human gut virome and bacterial microbiome in infants. Nat. Med. 21, 1228–1234 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Liang, G. et al. Dynamics of the stool virome in very early-onset inflammatory bowel disease. J. Crohns. Colitis 14, 1600–1610 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Koren, O. & Rautava, S. The Human Microbiome in Early Life: Implications to Health and Disease (Academic, 2020).Reyes, A. et al. Gut DNA viromes of Malawian twins discordant for severe acute malnutrition. Proc. Natl Acad. Sci. USA 112, 11941–11946 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Liang, G. & Bushman, F. D. The human virome: assembly, composition and host interactions. Nat. Rev. Microbiol. 19, 514–527 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Oude Munnink, B. B. & van der Hoek, L. Viruses causing gastroenteritis: the known, the new and those beyond. Viruses 8, 42 (2016).PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Woolhouse, M., Scott, F., Hudson, Z., Howey, R. & Chase-Topping, M. Human viruses: discovery and emergence. Phil. Trans. R. Soc. B 367, 2864–2871 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Rascovan, N., Duraisamy, R. & Desnues, C. Metagenomics and the human virome in asymptomatic individuals. Annu. Rev. Microbiol. 70, 125–141 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Mason, M. R., Chambers, S., Dabdoub, S. M., Thikkurissy, S. & Kumar, P. S. Characterizing oral microbial communities across dentition states and colonization niches. Microbiome 6, 67 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Dzidic, M. et al. Oral microbiome development during childhood: an ecological succession influenced by postnatal factors and associated with tooth decay. ISME J. 12, 2292–2306 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Merglova, V. & Polenik, P. Early colonization of the oral cavity in 6- and 12-month-old infants by cariogenic and periodontal pathogens: a case-control study. Folia Microbiol. 61, 423–429 (2016).CAS 
    Article 

    Google Scholar 
    Gomez, A. & Nelson, K. E. The oral microbiome of children: development, disease, and implications beyond oral health. Microb. Ecol. 73, 492–503 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Cephas, K. D. et al. Comparative analysis of salivary bacterial microbiome diversity in edentulous infants and their mothers or primary care givers using pyrosequencing. PLoS ONE 6, e23503 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Crielaard, W. et al. Exploring the oral microbiota of children at various developmental stages of their dentition in the relation to their oral health. BMC Med. Genomics 4, 22 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Darwazeh, A. M. & al-Bashir, A. Oral candidal flora in healthy infants. J. Oral. Pathol. Med. 24, 361–364 (1995).CAS 
    PubMed 
    Article 

    Google Scholar 
    Stecksén-Blicks, C., Granström, E., Silfverdal, S. A. & West, C. E. Prevalence of oral Candida in the first year of life. Mycoses 58, 550–556 (2015).PubMed 
    Article 
    CAS 

    Google Scholar 
    Ghannoum, M. A. et al. Characterization of the oral fungal microbiome (mycobiome) in healthy individuals. PLoS Pathog. 6, e1000713 (2010).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Brusa, T., Conca, R., Ferrara, A., Ferrari, A. & Pecchioni, A. The presence of methanobacteria in human subgingival plaque. J. Clin. Periodontol. 14, 470–471 (1987).CAS 
    PubMed 
    Article 

    Google Scholar 
    Ferrari, A., Brusa, T., Rutili, A., Canzi, E. & Biavati, B. Isolation and characterization ofMethanobrevibacter oralis sp. nov. Curr. Microbiol. 29, 7–12 (1994).CAS 
    Article 

    Google Scholar 
    Nguyen-Hieu, T., Khelaifia, S., Aboudharam, G. & Drancourt, M. Methanogenic archaea in subgingival sites: a review. APMIS 121, 467–477 (2013).PubMed 
    Article 

    Google Scholar 
    Abeles, S. R., Ly, M., Santiago-Rodriguez, T. M. & Pride, D. T. Effects of long term antibiotic therapy on human oral and fecal viromes. PLoS ONE 10, e0134941 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Pérez-Brocal, V. & Moya, A. The analysis of the oral DNA virome reveals which viruses are widespread and rare among healthy young adults in Valencia (Spain). PLoS ONE 13, e0191867 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Dye, B. A., Li, X. & Thornton-Evans, G. Oral health disparities as determined by selected healthy people 2020 oral health objectives for the United States, 2009–2010. NCHS Data Brief. 104, 1–8 (2012).
    Google Scholar 
    Baker, J. L., Bor, B., Agnello, M., Shi, W. & He, X. Ecology of the oral microbiome: beyond bacteria. Trends Microbiol. 25, 362–374 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Gaitanis, G. et al. Variation of cultured skin microbiota in mothers and their infants during the first year postpartum. Pediatr. Dermatol. 36, 460–465 (2019).PubMed 

    Google Scholar 
    Lee, Y. W., Yim, S. M., Lim, S. H., Choe, Y. B. & Ahn, K. J. Quantitative investigation on the distribution of Malassezia species on healthy human skin in Korea. Mycoses 49, 405–410 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    Byrd, A. L., Belkaid, Y. & Segre, J. A. The human skin microbiome. Nat. Rev. Microbiol. 16, 143–155 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Sugita, T. et al. Quantitative analysis of the cutaneous Malassezia microbiota in 770 healthy Japanese by age and gender using a real-time PCR assay. Med. Mycol. 48, 229–233 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    Probst, A. J., Auerbach, A. K. & Moissl-Eichinger, C. Archaea on human skin. PLoS ONE 8, e65388 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hulcr, J. et al. A jungle in there: bacteria in belly buttons are highly diverse, but predictable. PLoS ONE 7, e47712 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Caporaso, J. G. et al. Moving pictures of the human microbiome. Genome Biol. 12, R50 (2011).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Moya, A. & Brocal, V. P. The Human Virome: Methods and Protocols (Springer, 2018).Foulongne, V. et al. Human skin microbiota: high diversity of DNA viruses identified on the human skin by high throughput sequencing. PLoS ONE 7, e38499 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Turnbaugh, P. J. et al. Organismal, genetic, and transcriptional variation in the deeply sequenced gut microbiomes of identical twins. Proc. Natl Acad. Sci. USA 107, 7503–7508 (2010). This study shows that cohabitating identical twins result in different microbial communities, highlighting the many unknown processes that lead to the unique human microbiota.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Shao, Y. et al. Stunted microbiota and opportunistic pathogen colonization in caesarean-section birth. Nature 574, 117–121 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Stewart, C. J. et al. Temporal development of the gut microbiome in early childhood from the TEDDY study. Nature 562, 583–588 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ainonen, S. et al. Antibiotics at birth and later antibiotic courses: effects on gut microbiota. Pediatr. Res. 91, 154–162 (2022).CAS 
    PubMed 
    Article 

    Google Scholar 
    Chen, X., Lu, Y., Chen, T. & Li, R. The female vaginal microbiome in health and bacterial vaginosis. Front. Cell. Infect. Microbiol. 11, 631972 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Wells, J. S., Chandler, R., Dunn, A. & Brewster, G. The vaginal microbiome in U.S. black women: a systematic review. J. Womens Health 29, 362–375 (2020).Article 

    Google Scholar 
    Martino, C. et al. Context-aware dimensionality reduction deconvolutes gut microbial community dynamics. Nat. Biotechnol. 39, 165–168 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Furman, O. et al. Stochasticity constrained by deterministic effects of diet and age drive rumen microbiome assembly dynamics. Nat. Commun. 11, 1904 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Henderickx, J. G. E., Zwittink, R. D., van Lingen, R. A., Knol, J. & Belzer, C. The preterm gut microbiota: an inconspicuous challenge in nutritional neonatal care. Front. Cell. Infect. Microbiol. 9, 85 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Malamitsi-Puchner, A. et al. The influence of the mode of delivery on circulating cytokine concentrations in the perinatal period. Early Hum. Dev. 81, 387–392 (2005).CAS 
    PubMed 
    Article 

    Google Scholar 
    Stokholm, J. et al. Maturation of the gut microbiome and risk of asthma in childhood. Nat. Commun. 9, 141 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Andersen, V., Möller, S., Jensen, P. B., Møller, F. T. & Green, A. Caesarean delivery and risk of chronic inflammatory diseases (inflammatory bowel disease, rheumatoid arthritis, coeliac disease, and diabetes mellitus): a population based registry study of 2,699,479 births in Denmark during 1973–2016. Clin. Epidemiol. 12, 287–293 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Blustein, J. et al. Association of caesarean delivery with child adiposity from age 6 weeks to 15 years. Int. J. Obes. 37, 900–906 (2013).CAS 
    Article 

    Google Scholar 
    Ardic, C., Usta, O., Omar, E., Yıldız, C. & Memis, E. Caesarean delivery increases the risk of overweight or obesity in 2-year-old children. J. Obstet. Gynaecol. 41, 374–379 (2021).PubMed 
    Article 

    Google Scholar 
    Cox, L. M. et al. Altering the intestinal microbiota during a critical developmental window has lasting metabolic consequences. Cell 158, 705–721 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Martinez, K. A. 2nd et al. Increased weight gain by C-section: functional significance of the primordial microbiome. Sci. Adv. 3, eaao1874 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Olszak, T. et al. Microbial exposure during early life has persistent effects on natural killer T cell function. Science 336, 489–493 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Livanos, A. E. et al. Antibiotic-mediated gut microbiome perturbation accelerates development of type 1 diabetes in mice. Nat. Microbiol. 1, 16140 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Moya-Pérez, A. et al. Intervention strategies for cesarean section–induced alterations in the microbiota-gut-brain axis. Nutr. Rev. 75, 225–240 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Braniste, V. et al. The gut microbiota influences blood-brain barrier permeability in mice. Sci. Transl. Med. 6, 263ra158 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Forbes, J. D. et al. Association of exposure to formula in the hospital and subsequent infant feeding practices with gut microbiota and risk of overweight in the first year of life. JAMA Pediatr. 172, e181161 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Shenhav, L. & Azad, M. B. Using community ecology theory and computational microbiome methods to study human milk as a biological system. mSystems 7, e01132–21 (2022).PubMed Central 
    Article 

    Google Scholar 
    Kaetzel, C. S. Cooperativity among secretory IgA, the polymeric immunoglobulin receptor, and the gut microbiota promotes host-microbial mutualism. Immunol. Lett. 162, 10–21 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Munblit, D., Verhasselt, V. & Warner, J. O. Human Milk Composition and Health Outcomes in Children (Frontiers Media, 2019).Mastromarino, P. et al. Correlation between lactoferrin and beneficial microbiota in breast milk and infant’s feces. Biometals 27, 1077–1086 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Agus, A., Planchais, J. & Sokol, H. Gut microbiota regulation of tryptophan metabolism in health and disease. Cell Host Microbe 23, 716–724 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Coats, S. R., Pham, T.-T. T., Bainbridge, B. W., Reife, R. A. & Darveau, R. P. MD-2 mediates the ability of tetra-acylated and penta-acylated lipopolysaccharides to antagonize Escherichia coli lipopolysaccharide at the TLR4 signaling complex. J. Immunol. 175, 4490–4498 (2005).CAS 
    PubMed 
    Article 

    Google Scholar 
    Denou, E. et al. Defective NOD 2 peptidoglycan sensing promotes diet‐induced inflammation, dysbiosis, and insulin resistance. EMBO Mol. Med. 7, 259–274 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Quinn, R. A. et al. Global chemical effects of the microbiome include new bile-acid conjugations. Nature 579, 123–129 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Rooks, M. G. & Garrett, W. S. Gut microbiota, metabolites and host immunity. Nat. Rev. Immunol. 16, 341–352 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Vatanen, T. et al. Variation in microbiome LPS immunogenicity contributes to autoimmunity in humans. Cell 165, 1551 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Fan, Y. & Pedersen, O. Gut microbiota in human metabolic health and disease. Nat. Rev. Microbiol. 19, 55–71 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Xiao, J., Fiscella, K. A. & Gill, S. R. Oral microbiome: possible harbinger for children’s health. Int. J. Oral. Sci. 12, 12 (2020).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Zhao, S. et al. Adaptive evolution within gut microbiomes of healthy people. Cell Host Microbe 25, 656–667.e8 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zhang, R., Lahens, N. F., Ballance, H. I., Hughes, M. E. & Hogenesch, J. B. A circadian gene expression atlas in mammals: implications for biology and medicine. Proc. Natl Acad. Sci. USA 111, 16219–16224 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Allaband, C. et al. Intermittent hypoxia and hypercapnia alter diurnal rhythms of luminal gut microbiome and metabolome. mSystems 6, e00116–e00121 (2021).CAS 
    PubMed Central 
    Article 

    Google Scholar 
    Marotz, C. et al. Quantifying live microbial load in human saliva samples over time reveals stable composition and dynamic load. mSystems 6, e01182–20 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bouslimani, A. et al. The impact of skin care products on skin chemistry and microbiome dynamics. BMC Biol. 17, 47 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Costello, E. K. et al. Bacterial community variation in human body habitats across space and time. Science 326, 1694–1697 (2009). This study demonstrates the important variability between body habitats and between individuals across the same body habitat.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kolodziejczyk, A. A., Zheng, D. & Elinav, E. Diet–microbiota interactions and personalized nutrition. Nat. Rev. Microbiol. 17, 742–753 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Zaramela, L. S. et al. Gut bacteria responding to dietary change encode sialidases that exhibit preference for red meat-associated carbohydrates. Nat. Microbiol. 4, 2082–2089 (2019).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Zmora, N., Suez, J. & Elinav, E. You are what you eat: diet, health and the gut microbiota. Nat. Rev. Gastroenterol. Hepatol. 16, 35–56 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Etemadi, A. et al. Mortality from different causes associated with meat, heme iron, nitrates, and nitrites in the NIH-AARP Diet and Health Study: population based cohort study. BMJ 357, j1957 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Koeth, R. A. et al. Intestinal microbiota metabolism of L-carnitine, a nutrient in red meat, promotes atherosclerosis. Nat. Med. 19, 576–585 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Gilbert, J. A. et al. Current understanding of the human microbiome. Nat. Med. 24, 392–400 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Durack, J. & Lynch, S. V. The gut microbiome: relationships with disease and opportunities for therapy. J. Exp. Med. 216, 20–40 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lai, Y. et al. Commensal bacteria regulate Toll-like receptor 3–dependent inflammation after skin injury. Nat. Med. 15, 1377–1382 (2009).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Chng, K. R. et al. Whole metagenome profiling reveals skin microbiome-dependent susceptibility to atopic dermatitis flare. Nat. Microbiol. 1, 16106 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Li, H. et al. Skin commensal Malassezia globosa secreted protease attenuates Staphylococcus aureus biofilm formation. J. Invest. Dermatol. 138, 1137–1145 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Shirtliff, M. E., Peters, B. M. & Jabra-Rizk, M. A. Cross-kingdom interactions: Candida albicans and bacteria. FEMS Microbiol. Lett. 299, 1–8 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    Santus, W., Devlin, J. R. & Behnsen, J. Crossing kingdoms: how the mycobiota and fungal-bacterial interactions impact host health and disease. Infect. Immun. 89, e00648–20 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Taur, Y. et al. Reconstitution of the gut microbiota of antibiotic-treated patients by autologous fecal microbiota transplant. Sci. Transl. Med. 10, eaap9489 (2018). This study shows that autologous faecal microbiota transplantation helps to restore the microbiota of patients who underwent antibiotic treatment.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    van Nood, E., Dijkgraaf, M. G. W. & Keller, J. J. Duodenal infusion of feces for recurrent Clostridium difficile. N. Engl. J. Med. 368, 2145 (2013).PubMed 
    Article 
    CAS 

    Google Scholar 
    Tariq, R., Pardi, D. S., Bartlett, M. G. & Khanna, S. Low cure rates in controlled trials of fecal microbiota transplantation for recurrent Clostridium difficile infection: a systematic review and meta-analysis. Clin. Infect. Dis. 68, 1351–1358 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Panigrahi, P. et al. Corrigendum: a randomized synbiotic trial to prevent sepsis among infants in rural India. Nature 553, 238 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Halkjær, S. I. et al. Faecal microbiota transplantation alters gut microbiota in patients with irritable bowel syndrome: results from a randomised, double-blind placebo-controlled study. Gut 67, 2107–2115 (2018).PubMed 
    Article 
    CAS 

    Google Scholar 
    Korpela, K. et al. Maternal fecal microbiota transplantation in cesarean-born infants rapidly restores normal gut microbial development: a proof-of-concept study. Cell 183, 324–334.e5 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Morton, J. T. et al. Learning representations of microbe–metabolite interactions. Nat. Methods 16, 1306–1314 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kehe, J. et al. Positive interactions are common among culturable bacteria. Sci. Adv. 7, eabi7159 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Strandwitz, P. et al. GABA-modulating bacteria of the human gut microbiota. Nat. Microbiol. 4, 396–403 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Rubin, B. E. et al. Species- and site-specific genome editing in complex bacterial communities. Nat. Microbiol. 7, 34–47 (2022).CAS 
    PubMed 
    Article 

    Google Scholar 
    Zmora, N. et al. Personalized gut mucosal colonization resistance to empiric probiotics is associated with unique host and microbiome features. Cell 174, 1388–1405.e21 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Zeevi, D. et al. Personalized nutrition by prediction of glycemic responses. Cell 163, 1079–1094 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Schooley, R. T. et al. Development and use of personalized bacteriophage-based therapeutic cocktails to treat a patient with a disseminated resistant Acinetobacter baumannii infection. Antimicrob. Agents Chemother. 61, e00954–17 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Mu, A. et al. Effects on the microbiome during treatment of a staphylococcal device infection. Preprint at Research Square https://doi.org/10.21203/rs.3.rs-969336/v1 (2021).Article 

    Google Scholar 
    Claesson, M. J. et al. Gut microbiota composition correlates with diet and health in the elderly. Nature 488, 178–184 (2012). This study reports microbial community alterations between older individuals (aged 65 years and older) dependent on whether they live in the company of others or alone, the latter of which was correlated to worse outcomes (that is, frailty and co-morbidity).CAS 
    PubMed 
    Article 

    Google Scholar 
    Wu, L. et al. A cross-sectional study of compositional and functional profiles of gut microbiota in Sardinian centenarians. mSystems 4, e00325–19 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Kong, F. et al. Gut microbiota signatures of longevity. Curr. Biol. 26, R832–R833 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Claesson, M. J. et al. Composition, variability, and temporal stability of the intestinal microbiota of the elderly. Proc. Natl Acad. Sci. USA 108 (Suppl. 1), 4586–4591 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    O’Toole, P. W. & Jeffery, I. B. Gut microbiota and aging. Science 350, 1214–1215 (2015).PubMed 
    Article 
    CAS 

    Google Scholar 
    Shibagaki, N. et al. Aging-related changes in the diversity of women’s skin microbiomes associated with oral bacteria. Sci. Rep. 7, 10567 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Liu, S., Wang, Y., Zhao, L., Sun, X. & Feng, Q. Microbiome succession with increasing age in three oral sites. Aging 12, 7874–7907 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Schwartz, J. L. et al. Old age and other factors associated with salivary microbiome variation. BMC Oral. Health 21, 490 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Strati, F. et al. Age and gender affect the composition of fungal population of the human gastrointestinal tract. Front. Microbiol. 7, 01227 (2016).Article 

    Google Scholar 
    Wu, L. et al. Age-related variation of bacterial and fungal communities in different body habitats across the young, elderly, and centenarians in Sardinia. mSphere 5, e00558–19 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Nagpal, R. et al. Gut microbiome and aging: physiological and mechanistic insights. Nutr. Healthy Aging 4, 267–285 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Wilmanski, T. et al. Gut microbiome pattern reflects healthy ageing and predicts survival in humans. Nat. Metab. 3, 274–286 (2021).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Sato, Y. et al. Novel bile acid biosynthetic pathways are enriched in the microbiome of centenarians. Nature 599, 458–464 (2021). This study finds that centenarians often had high abundances of microorganisms that produced unique secondary bile acids, namely various isoforms of lithocholic acid.CAS 
    PubMed 
    Article 

    Google Scholar 
    Gill-King, H. in Forensic Taphonomy: the Postmortem Fate of Human Remains 93–108 (CRC, 1997).Janaway, R. C., Percival, S. L. & Wilson, A. S. in Microbiology and Aging (ed. Percival, S. L) 313–334 (Humana, 2009).Forbes, S. L., Perrault, K. A. & Comstock, J. L. in Taphonomy of Human Remains: Forensic Analysis of the Dead and the Depositional Environment (eds Schotsmans, E. M. J., Márquez-Grant, N. & Forbes, S. L.) 26–38 (Wiley, 2017).Heimesaat, M. M. et al. Comprehensive postmortem analyses of intestinal microbiota changes and bacterial translocation in human flora associated mice. PLoS ONE 7, e40758 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Parkinson, R. A. et al. in Criminal and Environmental Soil Forensics (eds Ritz, K., Dawson, L. & Miller, D.) 379–394 (Springer, 2009).Metcalf, J. L. et al. Microbial community assembly and metabolic function during mammalian corpse decomposition. Science 351, 158–162 (2016). This study finds that the time since death was predictable through the microbial community composition independent of the soil type and season.CAS 
    PubMed 
    Article 

    Google Scholar 
    DeBruyn, J. M. & Hauther, K. A. Postmortem succession of gut microbial communities in deceased human subjects. PeerJ 5, e3437 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Pechal, J. L., Schmidt, C. J., Jordan, H. R. & Benbow, M. E. A large-scale survey of the postmortem human microbiome, and its potential to provide insight into the living health condition. Sci. Rep. 8, 5724 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Kodama, W. A. et al. Trace evidence potential in postmortem skin microbiomes: from death scene to morgue. J. Forensic Sci. 64, 791–798 (2019).PubMed 
    Article 

    Google Scholar 
    Hauther, K. A., Cobaugh, K. L., Jantz, L. M., Sparer, T. E. & DeBruyn, J. M. Estimating time since death from postmortem human gut microbial communities. J. Forensic Sci. 60, 1234–1240 (2015).PubMed 
    Article 

    Google Scholar 
    Burcham, Z. M. et al. Fluorescently labeled bacteria provide insight on post-mortem microbial transmigration. Forensic Sci. Int. 264, 63–69 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Burcham, Z. M. et al. Bacterial community succession, transmigration, and differential gene transcription in a controlled vertebrate decomposition model. Front. Microbiol. 10, 745 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Balzan, S., de Almeida Quadros, C., de Cleva, R., Zilberstein, B. & Cecconello, I. Bacterial translocation: overview of mechanisms and clinical impact. J. Gastroenterol. Hepatol. 22, 464–471 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    Metcalf, J. L. et al. A microbial clock provides an accurate estimate of the postmortem interval in a mouse model system. eLife 2, e01104 (2013).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Hyde, E. R., Haarmann, D. P., Petrosino, J. F., Lynne, A. M. & Bucheli, S. R. Initial insights into bacterial succession during human decomposition. Int. J. Leg. Med. 129, 661–671 (2015).Article 

    Google Scholar 
    Javan, G. T., Finley, S. J., Smith, T., Miller, J. & Wilkinson, J. E. Cadaver thanatomicrobiome signatures: the ubiquitous nature of Clostridium species in human decomposition. Front. Microbiol. 8, 2096 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Johnson, H. R. et al. A machine learning approach for using the postmortem skin microbiome to estimate the postmortem interval. PLoS ONE 11, e0167370 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Belk, A. et al. Microbiome data accurately predicts the postmortem interval using random forest regression models. Genes 9, 104 (2018).PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Metcalf, J. L. Estimating the postmortem interval using microbes: knowledge gaps and a path to technology adoption. Forensic Sci. Int. Genet. 38, 211–218 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Deel, H. et al. A pilot study of microbial succession in human rib skeletal remains during terrestrial decomposition. mSphere 6, e0045521 (2021).PubMed 
    Article 

    Google Scholar 
    Metcalf, J. L. et al. Microbiome tools for forensic science. Trends Biotechnol. 35, 814–823 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Nguyen, T. T., Hathaway, H., Kosciolek, T., Knight, R. & Jeste, D. V. Gut microbiome in serious mental illnesses: a systematic review and critical evaluation. Schizophr. Res. 234, 24–40 (2021).PubMed 
    Article 

    Google Scholar 
    Jeste, D. V., Koh, S. & Pender, V. B. Perspective: social determinants of mental health for the new decade of healthy aging. Am. J. Geriatr. Psychiatry 30, 733–736 (2022).PubMed 
    Article 

    Google Scholar 
    Matijašić, M. et al. Gut microbiota beyond bacteria-mycobiome, virome, archaeome, and eukaryotic parasites in IBD. Int. J. Mol. Sci. 21, 2668 (2020).PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Morton, J. T. et al. Establishing microbial composition measurement standards with reference frames. Nat. Commun. 10, 2719 (2019).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Gerber, G. K. The dynamic microbiome. FEBS Lett. 588, 4131–4139 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Zarrinpar, A., Chaix, A., Yooseph, S. & Panda, S. Diet and feeding pattern affect the diurnal dynamics of the gut microbiome. Cell Metab. 20, 1006–1017 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Vázquez-Baeza, Y. et al. Guiding longitudinal sampling in IBD cohorts. Gut 67, 1743–1745 (2018).PubMed 
    Article 

    Google Scholar 
    Kane, P. B., Bittlinger, M. & Kimmelman, J. Individualized therapy trials: navigating patient care, research goals and ethics. Nat. Med. 27, 1679–1686 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Huang, S. et al. Human skin, oral, and gut microbiomes predict chronological age. mSystems 5, e00630–19 (2020).PubMed 
    PubMed Central 
    Article 

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

    Google Scholar 
    Franzosa, E. A. et al. Identifying personal microbiomes using metagenomic codes. Proc. Nat. Acad. Sci. USA 112, E2930–E2938 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Vangay, P. et al. Microbiome metadata standards: report of the national microbiome data collaborative’s workshop and follow-on activities. mSystems 6, e01194–20 (2021).PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Biogeographic implication of temperature-induced plant cell wall lignification

    Körner, C. The cold range limit of trees. Trends Ecol. Evo. 36, 979–989 (2021).Article 

    Google Scholar 
    Körner, C. Alpine Treelines (Springer, 2012).Miehe, G., Miehe, S., Vogel, J., Co, S. & Duo, L. Highest treeline in the northern hemisphere found in southern Tibet. Mt. Res. Dev. 27, 169–173 (2007).Article 

    Google Scholar 
    Hoch, G. & Körner, C. Growth, demography and carbon relations of Polylepis trees at the world’s highest treeline. Funct. Ecol. 19, 941–951 (2005).Article 

    Google Scholar 
    von Humboldt, A. & Bonpland, A. Ideen zu einer Geographie der Pflanzen nebst einem Naturgemälde der Tropenländer: auf Beobachtungen und Messungen gegründet, welche vom 10ten Grade nördlicher bis zum 10ten Grade südlicher Breite, in den Jahren 1799, 1800, 1801, 1802 und 1803 angestellt worden sind. Tübingen, Bey F.G. Cotta (1807).Körner, C. Climatic treelines: conventions, global patterns, causes. Erdkunde 61, 315–324 (2007).Article 

    Google Scholar 
    Piermattei, A., Crivellaro, A., Carrer, M. & Urbinati, C. The “blue ring”: anatomy and formation hypothesis of a new tree-ring anomaly in conifers. Trees Struct. Funct. 29, 613–620 (2015).CAS 
    Article 

    Google Scholar 
    Körner, C. et al. Life at 0 °C: the biology of the alpine snowbed plant Soldanella pulsatilla. Alp. Bot. 129, 63–80 (2019).Article 

    Google Scholar 
    Crivellaro, A. & Büntgen, U. New evidence of thermally-constraint plant cell wall lignification. Trends Plant Sci. 24, 322–324 (2020).Article 
    CAS 

    Google Scholar 
    Büntgen, U. et al. Temperature-induced recruitment pulses of Arctic dwarf shrub communities. J. Ecol. 103, 489–501 (2015).Article 

    Google Scholar 
    Dolezal, J. et al. Vegetation dynamics at the upper elevational limit of vascular plants in Himalaya. Sci. Rep. 6, 24881 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ryan, M. G. & Yoder, B. J. Hydraulic limits to tree height and tree growth. Biosci 47, 235–242 (1997).Article 

    Google Scholar 
    Koch, G. W., Sillett, S. C., Jennings, G. M. & Davis, S. D. The limits to tree height. Nature 428, 851–854 (2004).CAS 
    PubMed 
    Article 

    Google Scholar 
    Körner, C. Alpine Plant Life: Functional Plant Ecology of High Mountain Ecosystems (Springer, 2003).Scherrer, D. & Körner, C. Infra-red thermometry of alpine landscapes challenges climatic warming projections. Glob. Change Biol. 16, 2602–2613 (2010).
    Google Scholar 
    Begum, S., Nakaba, S., Yamagishi, Y., Oribe, Y. & Funada, R. Regulation of cambial activity in relation to environmental conditions: understanding the role of temperature in wood formation of trees. Physiol. Planta 147, 46–54 (2013).CAS 
    Article 

    Google Scholar 
    Plomion, C., Leprovost, G. & Stokes, A. Wood formation in trees. Plant Physiol. 127, 1513–1523 (2001).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Rossi, S., Deslauriers, A., Anfodillo, T. & Carraro, V. Evidence of threshold temperatures for xylogenesis in conifers at high altitudes. Oecologia 152, 1–12 (2007).PubMed 
    Article 

    Google Scholar 
    Moura, J. C. M. S., Bonine, C. A. V., Viana, J. O. F., Dornelas, M. C. & Mazzafera, P. Abiotic and biotic stresses and changes in the lignin content and composition in plants. J. Integr. Plant Biol. 52, 360–376 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    Weng, J. K. & Chapple, C. The origin and evolution of lignin biosynthesis. N. Phytol. 187, 273–285 (2010).CAS 
    Article 

    Google Scholar 
    Niklas, K. J., Cobb, E. D. & Matas, A. J. The evolution of hydrophobic cell wall biopolymers: from algae to angiosperms. J. Exp. 68, 5261–5269 (2017).CAS 

    Google Scholar 
    Popper, Z. A. et al. Evolution and diversity of plant cell walls: from algae to flowering plants. Annu. Rev. Plant Biol. 62, 567–590 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    Piquemal, J. et al. Down regulation of cinnamoyl CoA reductase induces significant changes of lignin profiles in transgenic tobacco plants. Plant J. 13, 71–83 (1998).CAS 
    Article 

    Google Scholar 
    Renault, H., Werck-Reichhart, D. & Weng, J.-K. Harnessing lignin evolution for biotechnological applications. Curr. Opin. Biotechnol. 56, 105–111 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Schenk, H. J., Espino, S., Rich-Cavazos, S. M. & Jansen, S. From the sap’s perspective: The nature of vessel surfaces in angiosperm xylem. Am. J. Bot. 105, 172–185 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Polo, C. C. et al. Correlations between lignin content and structural robustness in plants revealed by X-ray ptychography. Sci. Rep. 10, 6023 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Meents, M. J., Watanabe, Y. & Samuels, A. L. The cell biology of secondary cell wall biosynthesis. Ann. Bot. 121, 1107–1125 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Campbell, M. M. & Sederoff, R. R. Variation in lignin content and composition (mechanisms of control and implications for the genetic improvement of plants). Plant Physiol. 110, 3–13 (1996).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Schweingruber, F. H. & Büntgen, U. What is ‘wood’ – An anatomical re-definition. Dendrochronologia 31, 187–191 (2013).Article 

    Google Scholar 
    Ellenberg, H. & Mueller-Dombois, D. A key to Raunkiaer plant life forms with revised subdivisions. Ber. Geobot. Inst. ETH Z.ürich. 37, 56–73 (1967).
    Google Scholar 
    Kim, W. J., Campbell, A. G. & Koch, P. Chemical variation in Lodgepole pine with latitude, elevation, and diameter class. Prod. J. 39, 7–12 (1989).CAS 

    Google Scholar 
    Gindl, W., Grabner, M. & Wimmer, R. The influence of temperature on latewood lignin content in treeline Norway spruce compared with maximum density and ring width. Trees, Struct. Funct. 14, 409–414 (2000).Article 

    Google Scholar 
    Schenker, G., Lens, A., Körner, C. & Hoch, G. Physiological minimum temperatures for root growth in seven common European broad-leaved tree species. Tree Physiol. 34, 302–313 (2014).PubMed 
    Article 

    Google Scholar 
    Nagelmüller, S., Hiltbrunner, E. & Körner, C. Low temperature limits for root growth in alpine species are set by cell differentiation. AoB Plants 9, plx054 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ji, H. et al. The Arabidopsis RCC1 family protein TCF1 regulates freezing tolerance and cold acclimation through modulating lignin biosynthesis. PLoS Gen. 11, e1005471 (2015).Article 
    CAS 

    Google Scholar 
    Büntgen, U. Re-thinking the boundaries of dendrochronology. Dendrochronologia 53, 1–4 (2019).Article 

    Google Scholar 
    Piermattei, A. et al. A millennium-long ‘Blue-Ring’ chronology from the Spanish Pyrenees reveals sever ephemeral summer cooling after volcanic eruptions. Environ. Res. Lett. 15, 124016 (2020).Article 

    Google Scholar 
    Montwé, D., Isaac-Rentin, M., Hamman, A. & Spiecker, H. Cold adaptation recorded in tree rings highlights risks associated with climate change and assisted migration. Nat. Comm. 9, 1574 (2018).Article 
    CAS 

    Google Scholar 
    Barros, J., Serk, H., Granlund, I. & Pesquet, E. The cell biology of lignification in higher plants. Ann. Bot. 115, 1053–1074 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hao, Z. & Mohnen, D. A review of xylan and lignin biosynthesis: Foundation for studying Arabidopsis irregular xylem mutants with pleiotropic phenotypes. Cri. Rev. Biochem. Mol. Biol. 49, 212–241 (2014).CAS 
    Article 

    Google Scholar 
    Liu, Q., Luo, L. & Zheng, L. Lignins: biosynthesis and biological functions in plants. Int. J. Mol. Sci. 19, 335 (2018).PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Kumar, M., Campbell, L. & Turner, S. Secondary cell walls: biosynthesis and manipulation. J. Exp. Bot. 67, 515–531 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Mellerowicz, E. J., Baucher, M., Sundberg, B. & Boerjan, W. Unravelling cell wall formation in the woody dicot stem. Plant Mol. Biol. 47, 239–274 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    Petit, G., Anfodillo, T., Carraro, V., Grani, F. & Carrer, M. Hydraulic constraints limit height growth in trees at high altitude. N. Phytol. 189, 241–252 (2010).Article 

    Google Scholar 
    Li, L. et al. Combinatorial modification of multiple lignin traits in trees through multigene co-transformation. Proc. Natl Acad. Sci. USA 100, 4939–4944 (2003).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Baldacci-Cresp, F. et al. A rapid and quantitative safranin-based fluorescent microscopy method to evaluate cell wall lignification. Plant J. 102, 1074–1089 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Körner, C. A re-assessment of high elevation treeline positions and their explanation. Oecologia 115, 445–459 (1998).PubMed 
    Article 

    Google Scholar 
    Landolt, E. et al. Flora indicativa: Okologische Zeigerwerte und biologische Kennzeichen zur Flora der Schweiz und der Alpen (Haupt, 2010).Büntgen, U., Psomas, A. & Schweingruber, F. H. Introducing wood anatomical and dendrochronological aspects of herbaceous plants: applications of the Xylem Database to vegetation science. J. Veg. Sci. 25, 967–977 (2014).Article 

    Google Scholar 
    Körner, C. Coldest places on earth with angiosperm plant life. Alp. Bot. 121, 11–22 (2011).Article 

    Google Scholar 
    GBIF.org. GBIF Occurrence Download. https://doi.org/10.15468/dl.ms4hjt (2018).Chamberlain, S., Ram, K. & Hart, T. Spocc: Interface to Specie Occurrence Data Sources, R package v.0.9.0. http://CRAN.R-project.org/package=spocc (2018).Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high-resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).Article 

    Google Scholar 
    Hijmans, R. J. Raster: geographic data analysis and modelling, R package v.2.2-12. http://CRAN.R-project.org/package=raster (2014).Gärtner, H. et al. A technical perspective in modern tree-ring research – How to overcome dendroecological and wood anatomical challenges. J. Vis. Exp. 97, e52337 (2015).
    Google Scholar 
    Gärtner, H. & Schweingruber, F. H. Microscopic Preparation Techniques for Plant Stem Analysis (Verlag Kessel, 2013).Ghislan, B., Engel, J. & Clair, B. Diversity of anatomical structure of tension wood among 242 tropical tree species. IAWA J. 40, 1–20 (2019).Article 

    Google Scholar 
    Schweingruber, F. H., Börner, A. & Schulze, E. D. Atlas of Stem Anatomy in Herbs, Shrubs and Trees Vol. 1 (Springer, 2011).Schweingruber, F. H., Börner, A. & Schulze, E. D. Atlas of Stem Anatomy in Herbs, Shrubs and Trees Vol. 2 (Springer, 2013).Dolezal, J., Dvorsky, M., Börner, A., Wild, J. & Schweingruber, F. H. Anatomy, Age and Ecology of High Mountain Plants in Ladakh, the Western Himalaya (Springer International Publishing, 2018).Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH image to imageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ter Braak, C. J. F. & Šmilauer, P. Canoco Reference Manual and User’s Guide: Software 559 for Ordination, Version 5.0 (Cambridge Univ. Press, 2012).Šmilauer, P. & Lepš, J. Multivariate Analysis of Ecological Data Using Canoco 5 (Cambridge Univ. Press, 2014). More

  • in

    Modeling the spatial distribution of Culicoides species (Diptera: Ceratopogonidae) as vectors of animal diseases in Ethiopia

    MacLachlan, N. J. & Guthrie, A. J. Re-emergence of bluetongue, African horse sickness, and other Orbivirus diseases. Vet. Res. https://doi.org/10.1051/vetres/2010007 (2010).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Koenraadt, C. J. M. et al. Bluetongue, Schmallenberg—What is next? Culicoides-borne viral diseases in the 21st Century. BMC Res. Notes 10, 77 (2014).
    Google Scholar 
    Dennis, S. J., Meyers, A. E., Hitzeroth, I. I. & Rybicki, E. P. African horse sickness: A review of current understanding and vaccine development in the. Viruses 11, 844 (2019).CAS 
    PubMed Central 
    Article 

    Google Scholar 
    Collins, Á. B., Doherty, M. L., Barrett, D. J. & Mee, J. F. Schmallenberg virus: A systematic international literature review (2011–2019) from an Irish perspective. Ir. Vet. J. 72, 1–22 (2019).Article 

    Google Scholar 
    Tkuwet, G. & Firesbhat, A. A review on African horse sickness. Eur. J. Appl. Sci. 7, 213–219 (2015).CAS 

    Google Scholar 
    Mellor, P. S. & Hamblin, C. African horse sickness. Vet. Res. 35, 445–466 (2004).PubMed 
    Article 

    Google Scholar 
    Coetzee, P., Stokstad, M., Venter, E. H., Myrmel, M. & Van Vuuren, M. Bluetongue: A historical and epidemiological perspective with the emphasis on South Africa. Virol. J. 9, 198 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Cagienard, A., Griot, C., Mellor, P. S., Denison, E. & Stärk, K. D. Bluetongue vector species of Culicoides in Switzerland. Med. Vet. Entomol. 20, 239–247 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    Oluwayelu, D., Adebiyi, A. & Tomori, O. Endemic and emerging arboviral diseases of livestock in Nigeria: A review. Parasit. Vectors 11, 1–12 (2018).Article 

    Google Scholar 
    Sibhat, B., Ayelet, G., Gebremedhin, E. Z., Skjerve, E. & Asmare, K. Seroprevalence of Schmallenberg virus in dairy cattle in Ethiopia. Acta Trop. 178, 61–67 (2018).PubMed 
    Article 

    Google Scholar 
    Aklilu, N. et al. African horse sickness outbreaks caused by multiple virus types in Ethiopia. Transbound. Emerg. Dis. 61, 185–192 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Rojas, J. M., Rodríguez-Martín, D., Martín, V. & Sevilla, N. Diagnosing bluetongue virus in domestic ruminants: Current perspectives. Vet. Med. Res. Rep. 10, 17 (2019).
    Google Scholar 
    Gizaw, D., Sibhat, D., Ayalew, B. & Sehal, M. Sero-prevalence study of bluetongue infection in sheep and goats in selected areas of Ethiopia. Ethiop. Vet. J. 20, 105 (2016).Article 

    Google Scholar 
    Abera, T. et al. Bluetongue disease in small ruminants in south western Ethiopia: Cross-sectional sero-epidemiological study. BMC Res. Notes 11, 112 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Mellor, P. S., Boorman, J. & Baylis, M. Culicoides biting midges: Their role as arbovirus vectors. Annu. Rev. Entomol. 45, 307–340 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    Carpenter, S., Groschup, M. H., Garros, C., Felippe-Bauer, M. L. & Purse, B. V. Culicoides biting midges, arboviruses and public health in Europe. Antivir. Res. 100, 102–113 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    Sick, F., Beer, M., Kampen, H. & Wernike, K. Culicoides biting midges—Underestimated vectors for arboviruses of public health and veterinary importance. Viruses 11, 376 (2019).CAS 
    PubMed Central 
    Article 

    Google Scholar 
    Blanda, V. et al. Geo-statistical analysis of Culicoides spp. distribution and abundance in Sicily, Italy. Parasit. Vectors 11, 78 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Vasić, A. et al. Species diversity, host preference and arbovirus detection of Culicoides (Diptera: Ceratopogonidae) in south-eastern Serbia. Parasit. Vectors 12, 1–9 (2019).Article 

    Google Scholar 
    Martin, E. et al. Culicoides species community composition and infection status with parasites in an urban environment of east central Texas, USA. Parasit. Vectors 12, 1–10 (2019).Article 

    Google Scholar 
    Gusmão, G. M. C., Brito, G. A., Moraes, L. S., Bandeira, M. D. C. A. & Rebêlo, J. M. M. Temporal variation in species abundance and richness of Culicoides (Diptera: Ceratopogonidae) in a tropical equatorial area. J. Med. Entomol. https://doi.org/10.1093/jme/tjz015 (2019).Article 
    PubMed 

    Google Scholar 
    Sghaier, S. et al. New species of the genus Culicoides (Diptera Ceratopogonidae) for Tunisia, with detection of Bluetongue viruses in vectors. Vet. Ital. 53, 357–366 (2017).PubMed 

    Google Scholar 
    Gordon, S. J. G. et al. The occurrence of Culicoides species, the vectors of arboviruses, at selected trap sites in Zimbabwe. Onderstepoort J. Vet. Res. 82, e1–e8 (2015).PubMed 
    Article 
    CAS 

    Google Scholar 
    Villard, P. et al. Modeling Culicoides abundance in mainland France: Implications for surveillance. Parasit. Vectors 12, 1–10 (2019).Article 

    Google Scholar 
    Diarra, M. et al. Spatial distribution modelling of Culicoides (Diptera: Ceratopogonidae) biting midges, potential vectors of African horse sickness and bluetongue viruses in Senegal. Parasit. Vectors 11, 341 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Calvete, C. et al. Spatial distribution of Culicoides imicola, the main vector of bluetongue virus, Spain. Vet. Rec. 158, 130–131 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    Purse, B. V. et al. Modelling the distributions of Culicoides bluetongue virus vectors in Sicily in relation to satellite-derived climate variables. Med. Vet. Entomol. 18, 90–101 (2004).CAS 
    PubMed 
    Article 

    Google Scholar 
    Purse, B. V. et al. Spatial and temporal distribution of bluetongue and its Culicoides vectors in Bulgaria. Med. Vet. Entomol. 20, 335–344 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    Leta, S. et al. Modeling the global distribution of Culicoides imicola: An ensemble approach. Sci. Rep. 9, 1–9 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    Mulatu, T. & Hailu, A. The occurrence and identification of Culicoides species in the Western Ethiopia. Acad. J. Entomol. 12, 40–43 (2019).
    Google Scholar 
    Khamala, C. P. M. & Kettle, D. S. The Culicoides Latreille (Diptera: Ceratopogonidae) of East Africa. Trans. R. Entomol. Soc. Lond. 123, 1–95 (1971).Article 

    Google Scholar 
    Venter, G. J. Specie di Culicoides (Diptera: Ceratopogonidae) vettori del virus della Bluetongue in Sud Africa. Vet. Ital. 51, 325–333 (2015).PubMed 

    Google Scholar 
    Mathieu, B. et al. Development and validation of IIKC: An interactive identification key for Culicoides (Diptera: Ceratopogonidae) females from the Western Palaearctic region. Parasit. Vectors 5, 137 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Thuiller, W., Lafourcade, B., Engler, R. & Araújo, M. B. BIOMOD—A platform for ensemble forecasting of species distributions. Ecography (Cop.) 32, 369–373 (2009).Article 

    Google Scholar 
    Baylis, M., Bouayoune, H., Touti, J. & El Hasnaoui, H. Use of climatic data and satellite imagery to model the abundance of Culicoides imicola, the vector of African horse sickness virus, in Morocco. Med. Vet. Entomol. 12, 255–266 (1998).CAS 
    PubMed 
    Article 

    Google Scholar 
    Diarra, M. et al. Modelling the abundances of two major culicoides (Diptera: Ceratopogonidae) species in the Niayes area of Senegal. PLoS One 10, e0131021 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Ramilo, D. W., Nunes, T., Madeira, S., Boinas, F. & da Fonseca, I. P. Geographical distribution of Culicoides (DIPTERA: CERATOPOGONIDAE) in mainland Portugal: Presence/absence modelling of vector and potential vector species. PLoS One 12, e0180606 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Ben Rais Lasram, F. et al. The Mediterranean Sea as a ‘cul-de-sac’ for endemic fishes facing climate change. Glob. Chang. Biol. 16, 3233–3245 (2010).ADS 
    Article 

    Google Scholar 
    Tiffin, P. & Ross-Ibarra, J. Goal-oriented evaluation of species distribution models accuracy and precision: True Skill Statistic profile and uncertainty maps. PeerJ PrePints https://doi.org/10.7287/peerj.preprints.488v1 (2014).Article 

    Google Scholar 
    Graham, M. H. Confronting multicollinearity in ecological multiple regression. Ecology 84, 2809–2815 (2003).Article 

    Google Scholar 
    Demissie, G. H. Seroepidemiological study of African horse sickness in southern Ethiopia. Open Sci. Repos. Vet. Med. 10, e70081919 (2013).
    Google Scholar 
    Zeleke, A., Sori, T., Powel, K., Gebre-Ab, F. & Endebu, B. Isolation and identification of circulating serotypes of African horse sickness virus in Ethiopia. J. Appl. Res. Vet. Med. 3, 40–43 (2005).
    Google Scholar 
    Ayelet, G. et al. Outbreak investigation and molecular characterization of African horse sickness virus circulating in selected areas of Ethiopia. Acta Trop. 127, 91–96 (2013).PubMed 
    Article 

    Google Scholar 
    Gulima, D. Seroepidemiological study of bluetongue in indigenous sheep in selected districts of Amhara National Regional State, north western Ethiopia. Ethiop. Vet. J. 13, 1–15 (2009).
    Google Scholar 
    Borkent, A. & Dominiak, P. Catalog of the biting midges of the world (Diptera: Ceratopogonidae). Zootaxa 4787, 1–377 (2020).Article 

    Google Scholar 
    Borkent, A. & Wirth, W. W. World species of biting midges (Diptera: Ceratopogonidae). Bull. Am. Museum Nat. Hist. 233, 5–195 (1997).
    Google Scholar 
    Guichard, S. et al. Worldwide niche and future potential distribution of Culicoides imicola, a major vector of bluetongue and African horse sickness viruses. PLoS One 9, e112491 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Becker, E. E. E., Venter, G. J., Labuschagne, K., Greyling, T. & van Hamburg, H. Occurrence of Culicoides species Diptera: Ceratopogonidae) in the Khomas region of Namibia during the winter months. Vet. Ital. 48, 45–54 (2012).PubMed 

    Google Scholar 
    Capela, R. et al. Spatial distribution of Culicoides species in Portugal in relation to the transmission of African horse sickness and bluetongue viruses. Med. Vet. Entomol. 17, 165–177 (2003).CAS 
    PubMed 
    Article 

    Google Scholar 
    Calvete, C. et al. Modelling the distributions and spatial coincidence of bluetongue vectors Culicoides imicola and the Culicoides obsoletus group throughout the Iberian peninsula. Med. Vet. Entomol. 22, 124–134 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    Riddin, M. A., Venter, G. J., Labuschagne, K. & Villet, M. H. Culicoides species as potential vectors of African horse sickness virus in the southern regions of South Africa. Med. Vet. Entomol. 33, 498–511 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Foxi, C. et al. Role of different Culicoides vectors (Diptera: Ceratopogonidae) in bluetongue virus transmission and overwintering in Sardinia (Italy). Parasit. Vectors 9, 440 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Musuka, G. N., Mellor, P. S., Meiswinkel, R., Baylis, M. & Kelly, P. J. Prevalence of Culicoides imicola and other species (Diptera: Ceratopogonidae) ateight sites in Zimbabwe: To the editor. J. S. Afr. Vet. Assoc. 72, 62–63 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    Meiswinkel, R. The 1996 outbreak of African horse sickness in South Africa—the entomological perspective. Arch. Virol. Suppl. 14, 69–83 (1998).CAS 
    PubMed 

    Google Scholar 
    Jean Pierre, T. et al. Characteristics, classification and genesis of vertisols under seasonally contrasted climate in the Lake Chad Basin, Central Africa. J. Afr. Earth Sci. 150, 176–193 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    Elias, E. Characteristics of Nitisol profiles as affected by land use type and slope class in some Ethiopian highlands. Environ. Syst. Res. 6, 1–15 (2017).Article 

    Google Scholar 
    Nachtergaele, F. The classification of leptosols in the world reference base for soil resources.Veronesi, E., Venter, G. J., Labuschagne, K., Mellor, P. S. & Carpenter, S. Life-history parameters of Culicoides (Avaritia) imicola Kieffer in the laboratory at different rearing temperatures. Vet. Parasitol. 163, 370–373 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    Verhoef, F. A. A., Venter, G. J. & Weldon, C. W. Thermal limits of two biting midges, Culicoides imicola Kieffer and C. bolitinos Meiswinkel (Diptera: Ceratopogonidae). Parasites Vectors 7, 384 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Conte, A., Goffredo, M., Ippoliti, C. & Meiswinkel, R. Influence of biotic and abiotic factors on the distribution and abundance of Culicoides imicola and the Obsoletus Complex in Italy. Vet. Parasitol. 150, 333–344 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    Martinez-de la Puente, J., Navarro, J., Ferraguti, M., Soriguer, R. & Figuerola, J. First molecular identification of the vertebrate hosts of Culicoides imicola in Europe and a review of its blood-feeding patterns worldwide: Implications for the transmission of bluetongue disease and African horse sickness. Med. Vet. Entomol. 31, 333–339 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Purse, B. V. et al. Impacts of climate, host and landscape factors on Culicoides species in Scotland. Med. Vet. Entomol. 26, 168–177 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Leta, S. et al. Updating the global occurrence of Culicoides imicola, a vector for emerging viral diseases. Sci. Data 6, 1–8 (2019).CAS 
    Article 

    Google Scholar  More

  • in

    The role of plant functional groups mediating climate impacts on carbon and biodiversity of alpine grasslands

    Data management and workflowsWe adopt best-practice approaches for open and reproducible research planning, execution, reporting, and management throughout the project (see e.g.29,30,31,32). Specifically, we use community-approved standards for experimental design and data collection29, and clean and manage the data using a fully scripted and reproducible data workflow, with data and code deposited at open repositories (Fig. 2).Fig. 2The data collection and management workflow of the FunCaB project. Reproducibility throughout the research process is assured as follows: Experimental design and data collection was based on best-practice community methods and protocols, adapted for the projects’ needs. Measurements were digitalized and the raw data stored in the project Open Science Foundation (OSF) repository before the raw data were cleaned and managed through code-based data curation, with version control secured via GitHub. The clean data are stored at the OSF repository, and a time-stamped version of the code to retrieve and clean data is provided through Zenodo. This data paper describes and documents the data collection and workflow, and describes how to access and use clean data, raw data, and code.Full size imageResearch site selection and basic information, and general study setupSite selectionOur study is conducted across the twelve calcareous grassland experimental sites in the Vestland Climate Grid (VCG), in south-western Norway (Fig. 1a). The VCG sites were chosen to fit within a climate grid reflecting a fully factorial design encompassing the major bioclimatic variation in Norway. Potential sites were identified using a combination of topographic maps, geological maps (NGU) and interpolated maps of summer temperature and annual precipitation using the 1960–1990 climate normal (100 m resolution gridded data, met.no; see33 and references therein). The three temperature levels (alpine, sub-alpine, boreal) and four levels of precipitation in the climate grid (Fig. 1b) were selected to reflect a difference in mean growing season temperature of ca. 2 °C between three temperature levels (alpine = 6.5 °C, sub-alpine = 8.5 °C, boreal = 10.5 °C mean temperature of the four warmest months of the year) and a difference in mean annual precipitation of 700 mm between four precipitation levels (precipitation levels 1 – 4 representing 700 mm, 1400 mm, 2100 mm, and 2800 mm, respectively). Climate data for the site selection was based on 100-m resolution downscaled data using the 1960–1990 climate normal from met.no. The final sites were selected from approximately 200 potential sites visited and surveyed in the summer of 2008, with selection criteria set to ensure that other factors such as grazing regime and history, bedrock, vegetation type and structure, slope and exposure were kept as constant as possible among the selected sites34. Geographical distance between sites is on average 15 km and ranges from 175 km to 650 m.Study system and experimental area selection within sitesAt each site, we selected an experimental area of ca. 75 –200 m2, targeting a homogeneous and representative part of the target grassland vegetation at large at that site. The experimental areas were placed on southerly-facing slopes, avoiding depressions and concave areas in the landscape and other features such as big rocks or formations that may affect light conditions, hydrology and/or snowdrift. The target vegetation type was forb-rich semi-natural upland grassland vegetation34,35, within the plant sociological association Potentillo-Festucetum ovinae tending towards Potentillo-Poligonium vivipara in the alpine sites and Nardo-Agrostion tenuis in some lowland sites36. The most common vascular plants across sites, based on sum of covers, are the graminoids Agrostis capillaris, Festuca rubra, Avenella flexuosa, Anthoxanthum odoratum, and Nardus stricta and the forbs Leucantemum vulgare, Hypericum maculatum, Silene acaulis, Alchemilla alpina, and Lotus corniculatus. Common bryophytes are Pleurotium schreberi, Hylocomium splendens, Polythricum spp, Racomitrium lanuginosum, R. fasciculare, and Dicranum spp. All sites were moderately grazed prior to the study by sheep, cattle, goats, reindeer, deer, moose, and/or horses; and the experimental areas were fenced for the duration of the study to prevent animal and human disturbance of the experimental infrastructure. The fenced area was lightly mowed at the end of each growing season to mimic past grazing pressure and minimize fence effects. For further description of the sites, see34 and for access to and further description of site-level data, see35.Block and experimental plot setupWithin these study areas we established four blocks, with a distance between the blocks ranging from one up to (in rare cases) 50 meters. Blocks were selectively placed in homogenous grassland vegetation, avoiding rocks, depressions, and other features as described above. Each block accommodates eight 25 × 25 cm plots, with at least 25 cm between adjacent plots. If a plot contained more than 10% bare rock, shrubs, or other non-grassland features, they were rejected or moved slightly to avoid these features. The plots were permanently marked with four aluminium 10-cm long pipes in the soil in the outer corners of all the 25 × 25 cm treatment plots, ensuring the pipes to fit the corners of a standardized vegetation analysis frame (aluminium frame demarking a 25 × 25 cm inner area, with poles fixed in the corners that fit into the aluminium tubes used for plot demarcation in the field). The upslope left corner tube was marked with a colour-coded waterproof tape. Note that in 31 out of 48 cases (12 sites × 4 blocks), the blocks were located within larger experimental blocks in the VCG sites, and control plots and various block-level data are then shared with other experiments in these larger blocks. Linking keys are described in the FunCaB data dictionaries below (see Fig. 3 and data records iii-vii below). For some datasets, additional plots within blocks were needed. These are described as needed below.Fig. 3Data structure for the FunCaB functional group removal experiment and associated Vestland Climate Grid (VCG) and FUNDER project data. Within each of the three projects, boxes represent data tables. The FunCaB project data tables include biomass of functional groups removed and forb species-level biomass (datasets i, ii), soil temperature and moisture (datasets iii, iv) plant community composition and the associated taxon table (dataset v), seedling recruitment (dataset vi), ecosystem carbon fluxes (dataset vii) and reflectance (dataset viii). Names of individual data tables are given in the coloured title area, and a selection of the main variables available within tables in the internal lists. For full sets of variables for each FunCaB dataset, see Tables 3–9. The lines linking three of the boxes exemplify links using species as keys across tables, note that all bold variables are shared between several tables and can be used as keys to join them. Keys can also be used to link to/from data from other projects in the VCG (for general VCG project keys, see top right hatched outline box, for keys between the FunCaB and FUNDER projects see the bottom right hatched outline box (both including an example value for each variable on the right). The (other) datasets* boxes refer to extensive datasets on plant community composition, cover, biomass, fitness, and reproduction available from previous projects in the VCG27 and upcoming datasets in the FUNDER project.Full size imageBackground abiotic and biotic data from the Vestland Climate GridThe Vestland Climate Grid field sites were established in 2008, and from a series of research projects within the grid over the years we have collected a broad range of datasets on the climate and environment, soils, land-use and environment, vegetation, and ecosystems, along with basic descriptive data of the 12 sites, as described in34. All these datasets are available from the previous projects through the VCG OSF (Open Science Framework) repository35, and the results are presented in associated papers, see for example34,37,38,39,40,41,42,43,44,45. The overall data structure, and the most relevant datasets from the VCG for the FunCaB project is laid out in Fig. 3, and briefly described below. Code to download and link these data to the FunCaB experimental data and sites are provided in the FunCaB github repository28 (see R/download_VCG_data).A new research project, ‘FUNDER – Direct and indirect climate impacts on the biodiversity and FUNctioning of the UNDERground ecosystem’ funded by the Norwegian Research Council KLIMAFORSK programme (project number 315249, 2021 – 2025) will augment the FunCaB experiment with data on the belowground components of the plant-soil ecosystem, including roots, mesofauna, fungi and microbes. These upcoming data will all link with the FunCaB and VCG project based on the given experimental, site and organismal keys, as indicated in Fig. 3.VCG Basic site-level attributesBasic descriptive data on the 12 sites include latitude, longitude, elevation, geology, land-use, soils, and their position within the climate grid (precipitation and temperature levels). These data are described in34,40, provided in35, and can be downloaded using28 (see R/download_VCG_data). For convenience, the climate grid information is also provided in the biomass dataset (see below).VCG Site-level climate dataTemperature was measured continuously at each of the 12 VCG sites at four heights (2 m and 30 cm above ground, at ground level, and 5 cm below ground), soil moisture was measured continuously with two replicate loggers ca. 5 cm below ground, and precipitation was measured at each site during the snow-free season. For these measurements, we used Delta T GP1 loggers (Delta T devices, Cambridge, UK) equipped with two temperature probes, two SM200 moisture sensors which were later replaced as necessary with SM300 and SM150T loggers, and an ARG 100 tipping bucket (EML LTD, North Shields, UK) from 2009 onwards. UTL-3 version 3.0 temperature loggers (GEOTEST AG, Zollikofen, Switzerland) were used for measuring the 2 m and 30 cm temperatures. Soil moisture was measured as the mean of four measurements taken along each side of the turf, several times during the growing season using a Delta T HH2 version 2.3 Moisture Meter with the same probes as for the GP1 logger (SM200, SM150T). These data are described in34,40, provided in35, and can be downloaded using28 (see folder R/download_VCG_data).VCG Soil chemical and structural dataOver the years, various soil chemical variables have been measured at the block level within each of the 12 VCG sites, including soil pH (2009) and % Loss-On-Ignition (2009, 2013), and available N, as sum of N available as NH4-N and NO3-N (available N per deployment period, 2010 & 2012). Soil pH was measured after adding 50 ml distilled water to 25 g soil and mixing for two hours. Loss-on-ignition (LOI), was measured by weighing dry soil (105 °C for 24, one hour in a desiccator), and burnt soil (six hours at 550 °C, one hour in the desiccator) and calculating LOI as the (burnt soil mass/dry soil mass) × 100. NH4-N and NO3-N were measured using in-situ ion exchange resin bags (IERBs) were used to measure the amount of plant-available nutrients in the soil. These data are partially described in34,40, and the full data are provided in35.VCG Litter decomposition dataDecomposition has been assessed at each of the 12 VCG sites using local plant litter and the Tea Bag Index method (Keuskamp et al., 2013). Local litter (dead leaves detached from live plants) was collected at each site in 2013 or 2014, with the specific timing of the collections at each site tuned to ensure that litter was present, not covered by snow, and not decomposed. In practice, this necessitated litter collection after snowmelt in spring in many sites. The litter was washed, dried, and stored in dark, dry, cool conditions. In 2016, five replicate litter bags containing 1 g of graminoid litter were buried at each site, and collected at four points in time after burial (1, 2, 3 and 12 months). Harvested litter bags were cleaned (soil and roots removed), dried for 48 h at 60 °C and weighed. The Tea Bag Index method46 was used in 2014, 2015 and 2016 to measure decomposition at all sites of the climate grid. At each site, 10 replicates of each tea type were buried pair-wise, 8 cm below ground and with at least 10 cm between the tea bags. For a couple of sites, the number of replicate tea bag pairs was higher in 2015 (12 replicates at the site Gudmedalen and 16 replicates at Låvisdalen). After collection, adhering soil particles and roots were removed and the tea bags were dried (48 h at 60 °C) and weighed. These data are partially described in47, and the full data are provided in35 and can be downloaded using28 (see folder R/download_VCG_data).VCG Species-level cover, biomass, and performance dataA variety of plant species and community composition, cover, biomass, fitness, and reproductive data exists for the sites and blocks in the VCG from 2008 to 2021. These data are described in e.g34,37,38,41,43,44,45,48,49,50, and provided in35.VCG Site-level plant functional traitsIn 2016 and 2017, we measured 11 leaf functional traits that are related to potential physiological growth rates and environmental tolerance of plants, following the standardized protocols in Pérez-Harguindeguy et al.51: leaf area (LA, cm2), leaf thickness (LT, mm), leaf dry matter content (LDMC, g/g), specific leaf area (SLA, cm2/g), carbon (C, %), nitrogen (N, %), phosphorus (P, %), carbon nitrogen ratio (C:N), nitrogen phosphorus ratio (N:P), carbon13 isotope ratio (δ13C, ‰), and nitrogen15 isotope ratio (δ15N, ‰). Trait data are available at the site level for species making up at least 80% of the vegetation cover in the control plots at each of the 12 VCG sites. The plants were collected outside of the experimental plots and within a 50 m perimeter from the blocks, and we aimed to collect up to five individuals from each species in each site. To avoid repeated sampling from a single clone, we selected individuals that were visibly separated from other ramets of that species. The sampled plant individuals were labelled, put in plastic bags with moist paper towels, and stored in darkness at 4 °C until processing, which was done as soon as possible and always within 4 days. These data are described in52, provided in35, and can be downloaded using28 (see folder R/download_VCG_data).Experimental designThe functional group removal experiment was designed to examine the impact of aboveground interactions among the major plant functional groups – graminoids, forbs and bryophytes – on the performance and functioning of other components of the vegetation and ecosystem. The experiment consists of eight 25 × 25 cm plots per site and block, with a fully factorial combination of removals of three plant functional groups, with treatments randomized within each block. The general experimental design, with the different removal treatments detailed, are provided as an insert to the timeline in Fig. 1c. The functional groups are delineated and abbreviated in the various datasets as follows: G = graminoids (including grasses, sedges and rushes), F = forbs (including herbaceous forbs, pteridophytes, dwarf-shrubs, and small individuals of trees and shrubs), B = Bryophytes (including mosses, liverworts, and hornworts). Note that all species are also coded by their respective functional group into which they were classified in the FunCaB taxon table. The experimental treatments are coded by functional group removed so that FGB = bare-ground gaps with all plants removed, FB = only graminoids remaining, GB = only forbs remaining, GF = only bryophytes remaining, B = graminoids and forbs remaining, F = bryophytes and graminoids remaining, G = bryophytes and forbs remaining, and C = intact vegetation controls with no vegetation removed. In 2016, four extra control (XC) plots were marked per site for aboveground biomass harvest and ecosystem carbon flux measurements. This sampling regime gave a total of 384 plots in the core FunCaB experiment, plus the additional 48 controls in 2016.Functional group removals were done once in 2015 (at peak growing season due to late snowmelt), twice per year in 2016 and 2017 (after the spring growth and at peak growing season) and annually from 2018 to 2021 (at peak growing season) as regrowth had declined (see below) and biannual removals were no longer necessary. At each sampling, all above-ground biomass of the relevant plant functional group was removed from each plot as follows: for each plot, all the above-ground parts of the relevant functional group(s) were removed using scissors and tweezers to cut the plants at the ground layer (i.e., the soil-vegetation interface). Roots and other below-ground parts were not removed, and non-target plant functional groups and litter were left intact.Species identification, taxonomy, and floraAll vascular plant species were identified to the species level in the field, with nomenclature following Lid and Lid53. Exceptions are sterile specimens of species that are not possible to identify without reproductive parts, and where flowers are either too rare or individuals too short-lived for comparisons of the position of individuals within the plots over years to be used to ascertain identifications (For example, Alchemilla spp. excluding A. alpina, and the annual Euphrasia spp.). Species identifications were confirmed by comparing records over time as described below. All unidentified specimens are included and flagged in the dataset, as described in Data Records below. The full taxon names are provided in the taxon table on OSF (Fig. 3).Dataset collection methodsDatasets (i–ii): Biomass and functional group removalAs described above, functional group removals were done once in 2015 at peak growing season, and twice per year in 2016 and 2017 (after the spring growth; at peak growing season) and annually at peak growing season from 2018 to 2021. For each removal plot and occasion, a picture was taken of the plot pre-removal, the biomass to be removed was collected in separate pre-marked paper bags for each functional group (graminoids, forbs and bryophytes), and a picture was taken post-removal. The collected biomass was then dried at 60 °C for 48 hours and weighed to the nearest 0.01 g (Model LPG-1002, VWR). From the four extra control (XC) plots in 2016, total above-ground biomass as well as litter (defined as dead biomass detached from live plants, see28) was collected at peak growing season. From these plots, biomass was sorted into functional groups as described above, except the forb functional group, which was sorted into species. The graminoid and bryophyte functional groups, each forb species, and litter were individually dried and weighed as described above. The data is available as (i) a biomass dataset, consisting of the removed biomass per plot, date, removal treatment, and functional group for all treatment plots, and the total biomass per functional group plus litter for the extra control plots in 2016, and (ii) a species-level forb biomass dataset from the extra control plots in 2016 (Fig. 3, Table 1).Datasets (iii-iv) – Soil microclimateWe measured soil temperature 3–5 cm below the soil surface for each plot using iButton temperature sensors (DS1922L, Manufacturer reports temperature accuracy of ±0.5 °C, Maxim Integrated INC., San Jose, CA, USA). The data are reported with a resolution of 0.0625 at 140 min intervals from June 2015 to July 2016. We measured soil moisture as volumetric soil moisture; expressed as % water volume per soil volume ((m3 water /m3 soil) × 100). These measurements were done c. 3–5 times during the growing seasons from 2015–2019, usually in connection with the flux and vegetation measurements, by taking the average of four measurements, one at each side of each plot (SM300, Manufacturer reports accuracy ±2.5% vol over 0 to 50% vol and 0–60 °C, Delta-T Devices, Cambridge, UK). The data is available as (iii) temperature and (iv) volumetric soil moisture % per plot and time point (temperature) or date (moisture) (Fig. 3, Table 1).Dataset (v): Vascular plant community composition and vegetation structureWe recorded the full vascular plant species composition of all experimental plots in 2015 (pre treatment), and the control plots plus the extra control plots in 2016. In 2017, 2018, and 2019, we recorded the community composition in controls and in the functional groups that remained in the experimental plots according to the plot’s treatment. At each analysis, each plot was sub-divided into 25 5 × 5 cm subplots, using a subplot overlay. We first recorded all species of vascular plants in the central five subplots, (i.e., the central + shaped area of each plot, Fig. 1c) noting the subplot cover of each species present in each of the five subplots (1 – 25% = 1, 26 – 50% = 2, 51 – 75% = 3, >76% = 4). Additionally, we noted if the individual was fertile (records circled if buds, flowers, or fruits were present). The five subplots were recorded and numbered (1-5) by row, and from left to right, starting from the top up-slope subplot. For the entire 25 × 25 cm plot, any additional species not present in one of the central subplots were recorded and their fertility noted. We then visually estimated the percentage cover of each vascular plant species in the whole plot to the nearest 1% and measured vegetation height in mm at four points within the plot. Note that the total coverage in each plot can exceed 100% due to layering of the vegetation. The vascular plant vegetation data is available as percentage cover and fertility status (sterile or fertile) per species per subplot and plot per sampling date, and vegetation height in mm per plot per sampling date (Fig. 3, Table 1).Other variables that were measured were percentage cover of bryophytes, litter, bare ground, and rock (measured per plot and per subplot) and moss layer depth in mm (mean of 4 measurements/plot), date of analysis, recorder/scribe (if any), and free-text comments. These data are available as % cover, depth in mm, date (year.month.day) and text strings per subplot and /or plot per sampling date (Fig. 3, Table 1).Dataset (vi): Seedling recruitmentThe total number of forb seedlings that emerged in the plots was recorded in 2018 and 2019. At peak growing season in 2018 (round 1, July-August, depending on site), all dicotyledonous seedlings were marked with wooden toothpicks and their x and y coordinates in the plot (mm, recorded from the bottom left hand-corner of the plot, Fig. 1c) and tentative species identity noted. Toward the end of the growing season (round 2, August-September, depending on site), each plot was revisited, seedling survival established, and any further seedlings marked. Survival (recorded when a seedling was present in subsequent surveys; recorded as mortality if absent) and new seedling emergence were followed up in the same manner in 2019 (rounds 3 and 4, respectively). Species identification was (re)assessed at all censuses and corrected if needed as the seedlings grew and identification uncertainty decreased. New seedlings were differentiated from emergent clonal ramets by looking for cotyledons or signs of above- or below-ground ramet connections. These data are available as talleys of seedlings, each with a status (dead or alive) and species identity (or NA when not identifiable), per subplot and /or plot per sampling round (Fig. 3, Table 1).Dataset (vii): Ecosystem carbon flux data and flux calculationsCarbon flux measurementsEcosystem CO2 fluxes were measured to estimate net ecosystem exchange (NEE), ecosystem respiration (Reco) and gross primary production (GPP). The dataset covers the years 2015, 2016 and 2017, and individual plots have multiple measurements for ecosystem carbon flux per year as detailed below. At peak growing season in 2015, a median of 2 sets of paired carbon flux measurements were measured pre-removal for all plots, where a paired set consist of a light and a dark flux measurement of an individual plot. In 2016, a median of 8 sets of paired measurements were made for all control plots, and a median of 7 for the 4 extra controls (see experimental design above). In the data files, some additional measurements exist for other experiments in the VCG sites (a median of 7 paired sets of measurements for controls (TTC) and graminoid removal plots (RTC), see42 for a presentation of this experiment and35 for technical details). In 2017, a median of 5 paired sets of measurements were made for all treated plots in nine of the sites, excluding the second wettest precipitation level (sites Gudmedalen, Rambera, and Arhelleren). These measurements were made ca. 1 week after the first round of plant functional group removals in that season.At each sampling occasion, a clear chamber (25 × 25 × 40 cm) equipped with two fans for air circulation and connected to an infrared gas analyzer (Li-840; Manufacturer reports accuracy within 1.5% of the reading value; LI-COR Biosciences, Lincoln, NE, USA) was used to measure CO2 fluxes at all plots. To prevent cutting of roots and disruption of water flow in the plots by installing collars, we instead attached a windshield to the bottom of the chamber and weighed it down on the ground by a heavy chain to prevent wind-air mixing. At each sampling occasion we made paired measurements of fluxes under light and dark conditions, covering the chamber with a fitted light-excluding cover for the dark measurements.NEE was estimated from measurements of CO2 flux under ambient light and dark conditions: NEElight = GPP – Reco, NEEdark = (-) Reco. We define NEE such that negative values reflect CO2 uptake in the ecosystem, and positive values reflect CO2 release from the ecosystem to the atmosphere. For each measurement, CO2 concentration was recorded at 5 s intervals over a period of 90–120 s. NEE was calculated from the temporal change of CO2 concentration within the closed chamber according to the following formula:$$NEE=frac{delta C{O}_{2}}{delta t}times frac{PV}{Rtimes Atimes (T+273.15)}$$where (delta frac{C{O}_{2}}{delta t}) is the slope of the CO2 concentration against time (µmol mol−1 s−1), P is the atmospheric pressure (kPa), R is the gasconstante (8.314 kPa m3 K−1 mol−1), T is the air temperature inside the chamber (°C), V is the chamber volume (m3) and A is the surface area (m2).Light intensity was measured as photosynthetically active radiation (PAR, µmol m−2 s−1) using a quantum sensor (Li-190; Manufacturer reports absolute calibration accuracy of ±5%; LI-COR Biosciences, Lincoln, NE, USA) placed inside the chamber. Temperature inside the chamber was measured using an iButton temperature logger (DS1922L, Manufacturer reports temperature accuracy of ±0.5 °C, Maxim Integrated, San Jose, CA, USA). Volumetric soil moisture content (m3 water/m3 soil) × 100 was measured by calculating the average of four measurements with a soil moisture sensor (SM300, Manufacturer reports moisture accuracy of ±2.5%, Delta-T Devices, Cambridge, UK), taken at each side of a plot.Data management and calculationsData from the LiCOR data logger and iButton was downloaded in the field and stored. The information from the field data sheets (metadata of CO2 measurements and plot soil moisture) was manually entered into digital worksheets, manually proof-read and stored. Data from the data logger (PAR and CO2) and the iButton temperature logger were linked based on information from the field data sheets. All measurements were first visually evaluated for quality and only measurements that showed a consistent linear relationship between CO2 over a time for a period of at least 60 s were used for NEE calculations. A second inclusion criterion was that this relationship had R2 ≤ 0.2 or R2 ≥ 0.8 for NEE measurement in light conditions and R2 ≥ 0.8 for NEE dark measurements (Reco). Measurements of NEE in light conditions with R2 ≤ 0.2 ensures representation of measurements with equal rates for Reco and GPP. Third, paired measurements that were more than 2 h apart were excluded. These data are available as raw fluxes and as GPP and Reco per plot per measurement (Fig. 3, Table 1).Dataset (vi): ReflectanceReflectance measures of Normalized Difference Vegetation Index (NDVI) were taken for each plot during the 2019 (post functional group removal) and 2021 (pre and post removal) field seasons (July-August), using a Trimble Greenseeker Handheld Crop Sensor (Trimble Inc., Sunnydale, CA, USA). As the sensor measures an elliptical plane, two measures perpendicular to each other were taken for each subplot (25 × 25 cm plot), with the centre of each ellipse being the centre of the subplot. Care was taken to ensure that sampling quadrat frames were not within the sensor range when conducting measurements (see methods Dataset ii). Measures of NDVI were taken at 60 cm above the surface where possible. Height was measured perpendicular to the sampled ground surface. These data are available as reflectance per plot per sampling date (Fig. 3, Table 1). More

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    Post-foraging in-colony behaviour of a central-place foraging seabird

    Naef-Daenzer, B. Patch time allocation and patch sampling by foraging great and blue tits. Anim. Behav. 59, 989–999 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    Kotler, B. P., Brown, J. S. & Bouskila, A. Apprehension and time allocation in gerbils: The effects of predatory risk and energetic state. Ecology 85, 917–922 (2004).Article 

    Google Scholar 
    Wajnberg, E., Bernhard, P., Hamelin, F. & Boivin, G. Optimal patch time allocation for time-limited foragers. Behav. Ecol. Sociobiol. 60, 1–10 (2006).Article 

    Google Scholar 
    Embar, K., Kotler, B. P. & Mukherjee, S. Risk management in optimal foragers: The effect of sightlines and predator type on patch use, time allocation, and vigilance in gerbils. Oikos 120, 1657–1666 (2011).Article 

    Google Scholar 
    Lima, S. L. & Bednekoff, P. A. Temporal variation in danger drives antipredator behavior: The predation risk allocation hypothesis. Am. Nat. 153, 649–659 (1999).PubMed 
    Article 

    Google Scholar 
    Beauchamp, G. & Ruxton, G. D. A reassessment of the predation risk allocation hypothesis: A comment on Lima and Bednekoff. Am. Nat. 177, 143–146 (2011).PubMed 
    Article 

    Google Scholar 
    Ferrari, M. C. O., Sih, A. & Chivers, D. P. The paradox of risk allocation: A review and prospectus. Anim. Behav. 78, 579–585 (2009).Article 

    Google Scholar 
    Wolf, L. L. & Hainsworth, F. R. Foraging efficiencies and time budgets in nectar-feeding birds. Ecology 56, 117–128 (1975).Article 

    Google Scholar 
    Litzow, M. A. & Piatt, J. F. Variance in prey abundance influences time budgets of breeding seabirds: Evidence from pigeon guillemots Cepphus columba. J. Avian Biol. 34, 54–64 (2003).Article 

    Google Scholar 
    Rishworth, G. M., Tremblay, Y. & Green, D. B. Drivers of time-activity budget variability during breeding in a pelagic seabird. PLoS One 9, e116544 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Stephens, D. W., Brown, J. S. & Ydenberg, R. C. Foraging: Behavior and Ecology. (The University of Chicago Press, 2007).Orians, G. & Pearson, N. On the theory of central place foraging. In Analysis of Ecological Systems (eds. Horn, D., Mitchell, R. & Stairs, G.) 154–177 (The Ohio State University Press, 1979).Chaurand, T. & Weimerskirch, H. The regular alternation of short and long foraging trips in the blue petrel Halobaena caerulea: A previously undescribed strategy of food provisioning in a pelagic seabird. J. Anim. Ecol. 63, 275–282 (1994).Article 

    Google Scholar 
    Weimerskirch, H. et al. Alternate long and short foraging trips in pelagic seabird parents. Anim. Behav. 47, 472–476 (1994).Article 

    Google Scholar 
    Welcker, J., Beiersdorf, A., Varpe, Ø. & Steen, H. Mass fluctuations suggest different functions of bimodal foraging trips in a central-place forager. Behav. Ecol. 23, 1372–1378 (2012).Article 

    Google Scholar 
    Welcker, J. et al. Flexibility in the bimodal foraging strategy of a high Arctic alcid, the little auk Alle alle. J. Avian Biol. 40, 388–399 (2009).Article 

    Google Scholar 
    Jakubas, D., Wojczulanis-Jakubas, K., Iliszko, L. M. & Kidawa, D. Flexibility of little auks foraging in various oceanographic features in a changing Arctic. Sci. Rep. https://doi.org/10.1038/s41598-020-65210-x (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Shoji, A. et al. Dual foraging and pair coordination during chick provisioning by Manx shearwaters: Empirical evidence supported by a simple model. J. Exp. Biol. 218, 2116–2123 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Phillips, R. A., Wakefield, E. D., Croxall, J. P., Fukuda, A. & Higuchi, H. Albatross foraging behaviour: No evidence for dual foraging, and limited support for anticipatory regulation of provisioning at South Georgia. Mar. Ecol. Prog. Ser. 391, 279–292 (2009).ADS 
    Article 

    Google Scholar 
    Brown, Z. W., Welcker, J., Harding, A. M. A., Walkusz, W. & Karnovsky, N. J. Divergent diving behavior during short and long trips of a bimodal forager, the little auk Alle alle. J. Avian Biol. 43, 215–226 (2012).Article 

    Google Scholar 
    Baduini, C. L. & Hyrenbach, K. D. Biogeography of procellariiform foraging strategies: Does ocean productivity influence provisioning?. Mar. Ornithol. 31, 101–112 (2003).
    Google Scholar 
    Navarro, J. & González-Solís, J. Environmental determinants of foraging strategies in Cory’s shearwaters Calonectris diomedea. Mar. Ecol. Prog. Ser. 378, 259–267 (2009).ADS 
    CAS 
    Article 

    Google Scholar 
    Ochi, D., Oka, N. & Watanuki, Y. Foraging trip decisions by the streaked shearwater Calonectris leucomelas depend on both parental and chick state. J. Ethol. 28, 313–321 (2010).Article 

    Google Scholar 
    Congdon, B. C., Krockenberger, A. K. & Smithers, B. V. Dual-foraging and co-ordinated provisioning in a tropical Procellariiform, the wedge-tailed shearwater. Mar. Ecol. Prog. Ser. 301, 293–301 (2005).ADS 
    Article 

    Google Scholar 
    Peck, D. R. & Congdon, B. C. Colony-specific foraging behaviour and co-ordinated divergence of chick development in the wedge-tailed shearwater Puffinus pacificus. Mar. Ecol. Prog. Ser. 299, 289–296 (2005).ADS 
    Article 

    Google Scholar 
    Weimerskirch, H. How can a pelagic seabird provision its chick when relying on a distant food resource? Cyclic attendance at the colony, foraging decision and body condition in sooty shearwaters. J. Anim. Ecol. 67, 99–109 (1998).Article 

    Google Scholar 
    Stempniewicz, L. BWP update. Little Auk (Alle alle). J. Birds West. Palearct. 3, 175–201 (2001).
    Google Scholar 
    Wojczulanis-Jakubas, K. & Jakubas, D. When and why does my mother leave me? The question of brood desertion in the Dovekie (Alle Alle). Auk 129, 632–637 (2012).Article 

    Google Scholar 
    Harding, A. M. A., Van Pelt, T. I., Lifjeld, J. T. & Mehlum, F. Sex differences in little auk Alle alle parental care: Transition from biparental to paternal-only care. Ibis (Lond. 1859). 146, 642–651 (2004).Article 

    Google Scholar 
    Wojczulanis-Jakubas, K. et al. Duration of female parental care and their survival in the little auk Alle alle—Are these two traits linked ?. Behav. Ecol. Sociobiol. 74, 1–11 (2020).Article 

    Google Scholar 
    Wojczulanis, K., Dariusz, J. & Lech, S. The Little Auk Alle alle: An ecological indicator of a changing Arctic and a model organism. Polar Biol. https://doi.org/10.1007/s00300-021-02981-7 (2021).Article 

    Google Scholar 
    Steen, H., Vogedes, D., Broms, F., Falk-Petersen, S. & Berge, J. Little auks (Alle alle) breeding in a High Arctic fjord system: Bimodal foraging strategies as a response to poor food quality?. Polar Res. 26, 118–125 (2007).Article 

    Google Scholar 
    Wojczulanis-Jakubas, K., Jakubas, D., Karnovsky, N. J. & Walkusz, W. Foraging strategy of little auks under divergent conditions on feeding grounds. Polar Res. 29, 22–29 (2010).Article 

    Google Scholar 
    Jakubas, D., Wojczulanis-Jakubas, K., Iliszko, L., Darecki, M. & Stempniewicz, L. Foraging strategy of the little auk Alle alle throughout breeding season—switch from unimodal to bimodal pattern. J. Avian Biol. 45, 551–560 (2014).Article 

    Google Scholar 
    Jakubas, D., Iliszko, L., Wojczulanis-Jakubas, K. & Stempniewicz, L. Foraging by little auks in the distant marginal sea ice zone during the chick-rearing period. Polar Biol. 35, 73–81 (2012).Article 

    Google Scholar 
    Jakubas, D. et al. Intra-seasonal variation in zooplankton availability, chick diet and breeding performance of a high Arctic planktivorous seabird. Polar Biol. 391, 1547–1561 (2016).Article 

    Google Scholar 
    Jakubas, D. et al. Foraging closer to the colony leads to faster growth in little auks. Mar. Ecol. Prog. Ser. 489, 263–278 (2013).ADS 
    Article 

    Google Scholar 
    Stempniewicz, L. Predator-prey interactions between Glaucous Gull Larus hyperboreus and Little Auk Alle alle in Spitsbergen. Acta Ornithol. 29, 155–170 (1995).
    Google Scholar 
    Wojczulanis-Jakubas, K., Jakubas, D. & Stempniewicz, L. Changes in the glaucous gull predatory pressure on little auks in Southwest Spitsbergen. Waterbirds 28, 430–435 (2005).Article 

    Google Scholar 
    Kharitonov, S. Methods and Theoretical Aspects of Seabird Studies. (Proc 5 All-Russian Mar Biol School, Marine Biological Institute, 2007).Wojczulanis-Jakubas, K., Jakubas, D. & Stempniewicz, L. Avifauna of Hornsund area, SW Spitsbergen: Present state and recent changes. Polish Polar Res. 29, 187–197 (2008).
    Google Scholar 
    Keslinka, K. L., Wojczulanis-Jakubas, K., Jakubas, D. & Neubauer, G. Determinants of the little auk (Alle alle) breeding colony location and size in W and NW coast of Spitsbergen. PLoS One 14, 1–20 (2019).
    Google Scholar 
    Kidawa, D., Barcikowski, M. & Palme, R. Parent-offspring interactions in a long-lived seabird, the Little Auk (Alle alle): Begging and provisioning under simulated stress. J. Ornithol. 158, 145–157 (2017).Article 

    Google Scholar 
    Welcker, J., Beiersdorf, A., Varpe, Ø. & Steen, H. Mass fluctuations suggest different functions of bimodal foraging trips in a central-place forager. Behav. Ecol. https://doi.org/10.1093/beheco/ars131 (2012).Article 

    Google Scholar 
    Jakubas, D. & Wojczulanis, K. Predicting the sex of Dovekies by discriminant analysis. Waterbirds 30, 92–96 (2007).Article 

    Google Scholar 
    Grissot, A. et al. Parental coordination of chick provisioning in a planktivorous arctic seabird under divergent conditions on foraging grounds. Front. Ecol. Evol. 7, 349 (2019).Article 

    Google Scholar 
    Stoffel, M. A., Nakagawa, S. & Schielzeth, H. rptR: Repeatability estimation and variance decomposition by generalized linear mixed-effects models. Methods Ecol. Evol. 8, 1639–1644 (2017).Article 

    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing. R. (2019).Wojczulanis-Jakubas, K., Jakubas, D. & Stempniewicz, L. Sex-specific parental care by incubating Little Auks (Alle alle). Ornis Fenn. 86, 140–148 (2009).
    Google Scholar 
    Welcker, J., Steen, H., Harding, A. M. A. & Gabrielsen, G. W. Sex-specific provisioning behaviour in a monomorphic seabird with a bimodal foraging strategy. Ibis (Lond. 1859). 151, 502–513 (2009).Article 

    Google Scholar 
    Kidawa, D. et al. Parental efforts of an Arctic seabird, the little auk Alle alle under variable foraging conditions. Mar. Biol. Res. 11, 349–360 (2015).Article 

    Google Scholar 
    Wickham, H. Hadley Wickham. Media 35, 211 (2009).
    Google Scholar 
    Karnovsky, N. J. et al. Inter-colony comparison of diving behavior of an Arctic top predator: Implications for warming in the Greenland Sea. Mar. Ecol. Prog. Ser. 440, 229–240 (2011).ADS 
    Article 

    Google Scholar 
    Karnovsky, N. et al. Foraging distributions of little auks Alle alle across the Greenland Sea: Implications of present and future Arctic climate change. Mar. Ecol. Prog. Ser. 415, 283–293 (2010).ADS 
    Article 

    Google Scholar 
    Gremillet, D. et al. Little auks buffer the impact of current Arctic climate change. Mar. Ecol. Prog. Ser. 454, 197–206 (2012).ADS 
    Article 

    Google Scholar 
    Harding, A. M. A. et al. Flexibility in the parental effort of an Arctic-breeding seabird. Funct. Ecol. 23, 348–358 (2009).Article 

    Google Scholar 
    Jakubas, D. et al. Foraging effort does not influence body condition and stress level in little auks. Mar. Ecol. Prog. Ser. 432, 277–290 (2011).ADS 
    Article 

    Google Scholar 
    Jakubas, D., Wojczulanis-Jakubas, K., Iliszko, L. M., Strøm, H. & Stempniewicz, L. Habitat foraging niche of a High Arctic zooplanktivorous seabird in a changing environment. Sci. Rep. 7, 1–14 (2017).CAS 
    Article 

    Google Scholar  More

  • in

    Ordering and topological defects in social wasps’ nests

    Camazine, S. et al. Self-organization in Biological Systems (Princeton University Press, Princeton, 2001).
    Google Scholar 
    Tschinkel, W. R. The nest architecture of the Florida harvester ant, Pogonomyrmex badius. J. Insect Sci. 4(1), 21 (2004).MathSciNet 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Reid, C. R. et al. Army ants dynamically adjust living bridges in response to a cost-benefit trade-off. Proc. Natl. Acad. Sci. 112(49), 15113–15118 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Grassé, P. P. Termitology. Termite anatomy-physiology-biology-systematics. Vol. II. Colony foundation-construction. Termitology. Termite anatomy-physiology-biology-systematics. Vol. II. Colony foundation-construction. Masson, Paris, (1984).Theraulaz, G., Bonabeau, E. & Deneubourg, J. L. The mechanisms and rules of coordinated building in social insects (In Information Processing in Social Insects, Birkhäuser, Basel, 1999).Hansell, M. & Hansell, M. H. Animal Architecture (Oxford University Press, Oxford, 2005).Book 

    Google Scholar 
    Peters, J. M., Peleg, O. & Mahadevan, L. Collective ventilation in honeybee nests. J. R. Soc. Interface 16(150), 20180561 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Grassé, P. P. La reconstruction du nid et les coordinations interindividuelles chezBellicositermes natalensis etCubitermes sp. la théorie de la stigmergie: Essai d’interprétation du comportement des termites constructeurs. Insectes Sociaux, 6(1):41–80 (1959).Theraulaz, G. & Bonabeau, E. Coordination in Distributed Building. Science 269(5224), 686–688 (1995).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Bonabeau, E., Theraulaz, G., Deneubourg, J. L. & Camazine, S. Self-organization in social insects. Trends Ecol. Evol. 12(5), 188–193 (1997).CAS 
    PubMed 
    Article 

    Google Scholar 
    Khuong, A. et al. Stigmergic construction and topochemical information shape ant nest architecture. Proc. Natl. Acad. Sci. 113(5), 1303–1308 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Pénzes, Z. & Karsai, I. Round shape combs produced by Stigmergic scripts in social wasp. Proc. Eur. Conf. Artif. Life 93, 896–905 (1993).
    Google Scholar 
    Karsai, I. Decentralized control of construction behavior in paper wasps: an overview of the Stigmergy Approach. Artif. Life 5(2), 117–136 (1999).CAS 
    PubMed 
    Article 

    Google Scholar 
    Perna, A. & Theraulaz, G. When social behaviour is moulded in clay: On growth and form of social insect nests. J. Exp. Biol. 220(1), 83–91 (2017).PubMed 
    Article 

    Google Scholar 
    Gallo, V. & Chittka, L. Cognitive Aspects of Comb-Building in the Honeybee?. Front. Psychol. 9, 900 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hales, T. C. The Honeycomb Conjecture. Discrete Comput. Geom. 25(1), 1–22 (2001).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    Tóth, L. F. What the bees know and what they do not know. Bull. Am. Math. Soc. 70(4), 468–481 (1964).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    Jeanne, R. L. The Adaptiveness of Social Wasp Nest Architecture. Q. Rev. Biol. 50(3), 267–287 (1975).Article 

    Google Scholar 
    Karsai, I. & Pénzes, Z. Optimality of cell arrangement and rules of thumb of cell initiation in Polistes dominulus: A modeling approach. Behav. Ecol. 11(4), 387–395 (1999).Article 

    Google Scholar 
    Pirk, C., Hepburn, H., Radloff, S. & Tautz, J. Honeybee combs: construction through a liquid equilibrium process? Naturwissenschaften, 91(7) (2004).Karihaloo, B. L., Zhang, K. & Wang, J. Honeybee combs: How the circular cells transform into rounded hexagons. J. R. Soc. Interface 10(86), 20130299 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Bauer, D. & Bienefeld, K. Hexagonal comb cells of honeybees are not produced via a liquid equilibrium process. Naturwissenschaften 100(1), 45–49 (2013).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Mermin, N. D. The topological theory of defects in ordered media. Rev. Mod. Phys. 51(3), 591–648 (1979).ADS 
    MathSciNet 
    CAS 
    Article 

    Google Scholar 
    Bhattacharjee, S. M. Use of Topology in physical problems. In Topology and Condensed Matter Physics (eds Bhattacharjee, S. M. et al.) 171–216 (Springer, Singapore, 2017).MATH 
    Chapter 

    Google Scholar 
    Griffin, S. M. & Spaldin, N. A. On the relationship between topological and geometric defects. J. Phys.: Condens. Matter 29(34), 343001 (2017).
    Google Scholar 
    Harris, W. F. Disclinations. Sci. Am. 237(6), 130–145 (1977).MathSciNet 
    Article 

    Google Scholar 
    de Gennes, P.-G. The Physics of liquid crystals (Clarendon Press, Oxford, 1979).
    Google Scholar 
    Iorio, A. & Sen, S. Virus Structure: From Crick and Watson to a New Conjecture. In arXiv 0707, 3690 (2007).Lee, K. C., Yu, Q. & Erb, U. Mesostructure of Ordered Corneal Nano-nipple Arrays: The Role of 5–7 Coordination Defects. Sci. Rep. 6(1), 28342 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Stone, A. J. & Wales, D. J. Theoretical studies of icosahedral C60 and some related species. Chem. Phys. Lett. 128(5), 501–503 (1986).ADS 
    CAS 
    Article 

    Google Scholar 
    Ma, J., Alfè, D., Michaelides, A. & Wang, E. Stone-Wales defects in graphene and other planar sp2 -bonded materials. Phys. Rev. B 80(3), 033407 (2009).ADS 
    Article 
    CAS 

    Google Scholar 
    Heggie, M. I., Haffenden, G. L., Latham, C. D. & Trevethan, T. The Stone-Wales transformation: From fullerenes to graphite, from radiation damage to heat capacity. Philos.Trans. Royal Soc. A Math. Phys. Eng. Sci. 374(2076), 20150317 (2016).ADS 
    Article 
    CAS 

    Google Scholar 
    Eberhard, M. J. W. The Social Biology of Polistine Wasps. Misc. Publ. Museum Zoology Univ. Michigan 140, 110 (1969).
    Google Scholar 
    Jeanne, R. L. A latitudinal gradient in rates of ant predation. Ecology 60(6), 1211–1224 (1979).Article 

    Google Scholar 
    Seeley, T. & Heinrich, B. (1981). Regulation of temperature in the nests of social insects. John Wiley and Sons, Inc, pp. 224–234.Wenzel, J. W. Evolution of nest architecture. In The Social Biology Wasps (eds Ross, K. G. & Matthews, R. W.) 480–519 (Cornell University Press, Ithaca, New York, 1991).
    Google Scholar 
    Karsai, I. & Pénzes, Z. (1998). Nest shapes in paper wasps: Can the variability of forms be deduced from the same construction algorithm? Proceedings of the Royal Society of London. Series B: Biological Sciences, 265(1402):1261–1268.Carpenter, J. M. Phylogeny and biogeography of Polistes. In Natural History and Evolution of Paper-Wasps (eds Turillazzi, S. & Eberhard, M. J. W.) 18–57 (Oxford University Press, Oxford, Newyork, 1996).
    Google Scholar 
    Ceccolini, F. New records and distribution update of Polistes (Gyrostoma) wattii Cameron, 1900 (Hymenoptera: Vespidae: Polistinae). Caucasian Entomol. Bull. 15(2), 323–326 (2019).Article 

    Google Scholar 
    Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Baddeley, A., Rubak, E. & Turner, R. Spatial Point Patterns: Methodology and Applications with R (Chapman and Hall/CRC Press, London, 2015).MATH 
    Book 

    Google Scholar 
    Steinhardt, P. J., Nelson, D. R. & Ronchetti, M. Bond-orientational order in liquids and glasses. Phys. Rev. B 28(2), 784–805 (1983).ADS 
    CAS 
    Article 

    Google Scholar 
    Schilling, T., Pronk, S., Mulder, B. & Frenkel, D. Monte Carlo study of hard pentagons. Phys. Rev. E 71(3), 036138 (2005).ADS 
    Article 
    CAS 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. https://www.R-project.org/ (2020).Bishop, M. & Bruin, C. The pair correlation function: A probe of molecular order. Am. J. Phys. 52(12), 1106–1108 (1984).ADS 
    CAS 
    Article 

    Google Scholar 
    Fleury, P. A. Phase Transitions, Critical Phenomena, and Instabilities. Science 211, 125–131 (1981).ADS 
    MathSciNet 
    CAS 
    PubMed 
    MATH 
    Article 

    Google Scholar 
    Wenzel, J. W. Endogenous factors, external cues, and eccentric construction in Polistes annularis (Hymenoptera: Vespidae). J. Insect Behavior 2(5), 679–699 (1989).Article 

    Google Scholar 
    Zsoldos. Effect of topological defects on graphene geometry and stability. Nanotechnol. Sci. Appl., p. 101 (2010).Ophus, C., Shekhawat, A., Rasool, H. & Zettl, A. Large-scale experimental and theoretical study of graphene grain boundary structures. Phys. Rev. B 92(20), 205402 (2015).ADS 
    Article 
    CAS 

    Google Scholar 
    Kosterlitz, J. M. (2016). Commentary on ‘Ordering, metastability and phase transitions in two-dimensional systems’ J M Kosterlitz and D J Thouless (1973 J. Phys. C: Solid State Phys. 6 1181-203)-the early basis of the successful Kosterlitz-Thouless theory. Journal of Physics: Condensed Matter28:481001.Hepburn, H. R. & Whiffler, L. A. Construction defects define pattern and method in comb building by honeybees. Apidologie 22(4), 381–388 (1991).Article 

    Google Scholar 
    Smith, M. L., Napp, N. & Petersen, K. H. Imperfect comb construction reveals the architectural abilities of honeybees. Proc. Natl. Acad. Sci. 118(31), e2103605118 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Nazzi, F. The hexagonal shape of the honeycomb cells depends on the construction behavior of bees. Sci. Rep. 6(1), 28341 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Tarnai, T. Buckling patterns of shells and spherical honeycomb structures. Symmetry, pp. 639–652 (1989).Downing, H. & Jeanne, R. The regulation of complex building behaviour in the paper wasp, Polistes fuscatus (Insecta, Hymenoptera, Vespidae). Anim. Behav. 39(1), 105–124 (1990).Article 

    Google Scholar  More