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    Climate-trait relationships exhibit strong habitat specificity in plant communities across Europe

    Bjorkman, A. D. et al. Plant functional trait change across a warming tundra biome. Nature 562, 57–62 (2018).ADS 
    CAS 

    Google Scholar 
    Sabatini, F. M. et al. Global patterns of vascular plant alpha diversity. Nat. Commun. 13, 4683 (2022).ADS 
    CAS 

    Google Scholar 
    Lavorel, S. & Garnier, E. Predicting changes in community composition and ecosystem functioning from plant traits: revisiting the Holy Grail. Funct. Ecol. 16, 545–556 (2002).
    Google Scholar 
    Chapin, F. S. III et al. Consequences of changing biodiversity. Nature 405, 234–242 (2000).CAS 

    Google Scholar 
    Garnier, E., Navas, M.-L. & Grigulis, K. Plant functional diversity. Organism traits, community structure, and ecosystem properties (Oxford University Press, Oxford, New York, NY, 2016).Funk, J. L. et al. Revisiting the Holy Grail: using plant functional traits to understand ecological processes. Biol. Rev. Camb. Philos. Soc. 92, 1156–1173 (2017).
    Google Scholar 
    Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).ADS 

    Google Scholar 
    Adler, P. B. et al. Functional traits explain variation in plant life history strategies. Proc. Natl. Acad. Sci. U.S.A. 111, 740–745 (2014).ADS 
    CAS 

    Google Scholar 
    Wright, I. J. et al. The worldwide leaf economics spectrum. Nature 428, 821–827 (2004).ADS 
    CAS 

    Google Scholar 
    Salguero-Gómez, R. et al. Fast-slow continuum and reproductive strategies structure plant life-history variation worldwide. Proc. Natl. Acad. Sci. U. S. A. 113, 230–235 (2016).ADS 

    Google Scholar 
    Bergmann, J. et al. The fungal collaboration gradient dominates the root economics space in plants. Sci. Adv. 6, eaba3756 (2020).ADS 
    CAS 

    Google Scholar 
    Shipley, B. et al. Reinforcing loose foundation stones in trait-based plant ecology. Oecologia 180, 923–931 (2016).ADS 

    Google Scholar 
    Bruelheide, H. et al. Global trait-environment relationships of plant communities. Nat. Ecol. Evol. 2, 1906–1917 (2018).
    Google Scholar 
    McGill, B. J., Enquist, B. J., Weiher, E. & Westoby, M. Rebuilding community ecology from functional traits. Trends Ecol. Evol. 21, 178–185 (2006).
    Google Scholar 
    Miller, J. E. D., Damschen, E. I. & Ives, A. R. Functional traits and community composition: A comparison among community‐weighted means, weighted correlations, and multilevel models. Methods Ecol. Evol. 10, 415–425 (2019).
    Google Scholar 
    Guerin, G. R. et al. Environmental associations of abundance-weighted functional traits in Australian plant communities. Basic Appl. Ecol. 58, 98–109 (2021).
    Google Scholar 
    Walter, H. Vegetation of the earth and ecological systems of the geo-biosphere (Springer-Verlag, Berlin, Germany, 1985).Ordoñez, J. C. et al. A global study of relationships between leaf traits, climate and soil measures of nutrient fertility. Glob. Ecol. Biogeogr. 18, 137–149 (2009).
    Google Scholar 
    Simpson, A. H., Richardson, S. J. & Laughlin, D. C. Soil-climate interactions explain variation in foliar, stem, root and reproductive traits across temperate forests. Glob. Ecol. Biogeogr. 25, 964–978 (2016).
    Google Scholar 
    Cubino, J. P. et al. The leaf economic and plant size spectra of European forest understory vegetation. Ecography 44, 1311–1324 (2021).
    Google Scholar 
    Garnier, E. et al. Assessing the effects of land-use change on plant traits, communities and ecosystem functioning in grasslands: a standardized methodology and lessons from an application to 11 European sites. Ann. Bot. 99, 967–985 (2007).
    Google Scholar 
    Herben, T., Klimešová, J. & Chytrý, M. Effects of disturbance frequency and severity on plant traits: An assessment across a temperate flora. Funct. Ecol. 32, 799–808 (2018).
    Google Scholar 
    Linder, H. P. et al. Biotic modifiers, environmental modulation and species distribution models. J. Biogeogr. 39, 2179–2190 (2012).
    Google Scholar 
    Gross, N. et al. Linking individual response to biotic interactions with community structure: a trait-based framework. Funct. Ecol. 23, 1167–1178 (2009).
    Google Scholar 
    Ordonez, A. & Svenning, J.-C. Consistent role of Quaternary climate change in shaping current plant functional diversity patterns across European plant orders. Sci. Rep. 7, 42988 (2017).ADS 
    CAS 

    Google Scholar 
    Kemppinen, J. et al. Consistent trait–environment relationships within and across tundra plant communities. Nat. Ecol. Evol. 5, 458–467 (2021).
    Google Scholar 
    Chytrý, M. et al. European Vegetation Archive (EVA): an integrated database of European vegetation plots. Appl. Veg. Sci. 19, 173–180 (2016).
    Google Scholar 
    Karger, D. N. et al. Data from: Climatologies at high resolution for the earth’s land surface areas. EnviDat, https://doi.org/10.16904/envidat.228 (2018).Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).
    Google Scholar 
    Kattge, J. et al. TRY plant trait database – enhanced coverage and open access. Glob. Change. Biol. 26, 119–188 (2020).ADS 

    Google Scholar 
    Laughlin, D. C., Leppert, J. J., Moore, M. M. & Sieg, C. H. A multi-trait test of the leaf-height-seed plant strategy scheme with 133 species from a pine forest flora. Funct. Ecol. 24, 493–501 (2010).
    Google Scholar 
    Davies, C. E., Moss, D. & Hill, M. O. EUNIS Habitat Classification Revised 2004. Report to: European Environment Agency, European Topic Centre on Nature Protection and Biodiversity, 2004.Chytrý, M. et al. EUNIS Habitat Classification: Expert system, characteristic species combinations and distribution maps of European habitats. Appl. Veg. Sci. 23, 648–675 (2020).
    Google Scholar 
    Pausas, J. G. & Bond, W. J. Humboldt and the reinvention of nature. J. Ecol. 107, 1031–1037 (2019).
    Google Scholar 
    Meng, T.-T. et al. Responses of leaf traits to climatic gradients: adaptive variation versus compositional shifts. Biogeosciences 12, 5339–5352 (2015).ADS 

    Google Scholar 
    Fang, J. et al. Precipitation patterns alter growth of temperate vegetation. Geophys. Res. Lett. 32, 81 (2005).
    Google Scholar 
    Butler, E. E. et al. Mapping local and global variability in plant trait distributions. Proc. Natl. Acad. Sci. U.S.A. 114, E10937–E10946 (2017).ADS 
    CAS 

    Google Scholar 
    Gong, H. & Gao, J. Soil and climatic drivers of plant SLA (specific leaf area). Glob. Ecol. Conserv. 20, e00696 (2019).
    Google Scholar 
    Laughlin, D. C. et al. Root traits explain plant species distributions along climatic gradients yet challenge the nature of ecological trade-offs. Nat. Ecol. Evol. 5, 1–12 (2021).
    Google Scholar 
    Carmona, C. P. et al. Fine-root traits in the global spectrum of plant form and function. Nature 597, 683–687 (2021).ADS 
    CAS 

    Google Scholar 
    Ding, J., Travers, S. K. & Eldridge, D. J. Occurrence of Australian woody species is driven by soil moisture and available phosphorus across a climatic gradient. J. Veg. Sci. 32, e13095 (2021).
    Google Scholar 
    Falster, D. S. & Westoby, M. Plant height and evolutionary games. Trends Ecol. Evol. 18, 337–343 (2003).
    Google Scholar 
    Kunstler, G. et al. Plant functional traits have globally consistent effects on competition. Nature 529, 204–207 (2016).ADS 
    CAS 

    Google Scholar 
    McLachlan, A. & Brown, A. C. Coastal Dune Ecosystems and Dune/Beach Interactions. In The Ecology of Sandy Shores (Elsevier), 251–271 (2006).Cui, E., Weng, E., Yan, E. & Xia, J. Robust leaf trait relationships across species under global environmental changes. Nat. Commun. 11, 1–9 (2020).ADS 

    Google Scholar 
    Cain, S. A. Life-Forms and Phytoclimate. Bot. Rev. 16, 1–32 (1950).
    Google Scholar 
    Yu, S. et al. Shift of seed mass and fruit type spectra along longitudinal gradient: high water availability and growth allometry. Biogeosciences 18, 655–667 (2021).ADS 

    Google Scholar 
    Murray, B. R., Brown, A. H. D., Dickman, C. R. & Crowther, M. S. Geographical gradients in seed mass in relation to climate. J. Biogeogr. 31, 379–388 (2004).
    Google Scholar 
    Metz, J. et al. Plant survival in relation to seed size along environmental gradients: a long-term study from semi-arid and Mediterranean annual plant communities. J. Ecol. 98, 697–704 (2010).
    Google Scholar 
    Tao, S., Guo, Q., Li, C., Wang, Z. & Fang, J. Global patterns and determinants of forest canopy height. Ecology 97, 3265–3270 (2016).
    Google Scholar 
    Gonzalez, P., Neilson, R. P., Lenihan, J. M. & Drapek, R. J. Global patterns in the vulnerability of ecosystems to vegetation shifts due to climate change. Glob. Ecol. Biogeogr. 19, 755–768 (2010).
    Google Scholar 
    Feeley, K. J., Bravo-Avila, C., Fadrique, B., Perez, T. M. & Zuleta, D. Climate-driven changes in the composition of New World plant communities. Nat. Clim. Chang. 10, 965–970 (2020).ADS 
    CAS 

    Google Scholar 
    Bruelheide, H. et al. sPlot—A new tool for global vegetation analyses. J. Veg. Sci. 30, 161–186 (2019).
    Google Scholar 
    Schrodt, F. et al. BHPMF—a hierarchical Bayesian approach to gap-filling and trait prediction for macroecology and functional biogeography. Glob. Ecol. Biogeogr. 24, 1510–1521 (2015).
    Google Scholar 
    Shan, H. et al. Gap filling in the plant kingdom—trait prediction using hierarchical probabilistic matrix factorization (Proceedings of the 29 th International Conference on Machine Learning, Edinburgh, Scotland, UK, 2012).Chytrý, M. et al. EUNIS-ESy, version 2021-06-01, https://doi.org/10.5281/zenodo.4812736 (2021).Wood, S. N., Pya, N. & Säfken, B. Smoothing Parameter and Model Selection for General Smooth Models. J. Am. Stat. Assoc. 111, 1548–1563 (2016).MathSciNet 
    CAS 

    Google Scholar 
    Wood, S. N. Generalized Additive Models. An Introduction with R, Second Edition (CRC Press, Portland, Oregon, USA, 2017).Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142 (2013).
    Google Scholar 
    Johnson, P. C. Extension of Nakagawa & Schielzeth’s R2GLMM to random slopes models. Methods Ecol. Evol. 5, 944–946 (2014).
    Google Scholar 
    R. Core Team. R: a language and environment for statistical computing (R Foundation for Statistical Computing, 2022).Lenth, R. V. et al. emmeans: estimated marginal means, aka least-squares means; R package version 1.6.2-1 (2021).Lüdecke, D. ggeffects: tidy data frames of marginal effects from regression models. J. Open Source Softw. 3, 772 (2018).ADS 

    Google Scholar 
    Hijmans, R.J., Phillips, S., Leathwick, J. & Elith, J. dismo: species distribution modelling; R package version 1.3-3 (2020).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag New York, 2016).Kambach, S. Habitat-specificity of climate-trait relationships in plant communities across Europe. github.com/StephanKambach, version 1.0; https://doi.org/10.5281/zenodo.7404176 (2022).Moles, A. T. et al. Global patterns in plant height. J. Ecol. 97, 923–932 (2009).
    Google Scholar 
    Moles, A. T. et al. Global patterns in seed size. Glob. Ecol. Biogeogr. 16, 109–116 (2007).
    Google Scholar 
    Zheng, J., Guo, Z. & Wang, X. Seed mass of angiosperm woody plants better explained by life history traits than climate across China. Sci. Rep. 7, 2741 (2017).ADS 

    Google Scholar 
    Saatkamp, A. et al. A research agenda for seed-trait functional ecology. N. Phytol. 221, 1764–1775 (2019).
    Google Scholar 
    Freschet, G. T. et al. Climate, soil and plant functional types as drivers of global fine‐root trait variation. J. Ecol. 105, 1182–1196 (2017).
    Google Scholar 
    Weigelt, A. et al. An integrated framework of plant form and function: The belowground perspective. N. Phytol. 232, 42–59 (2021).
    Google Scholar  More

  • in

    Trait biases in microbial reference genomes

    Overmann, J., Abt, B. & Sikorski, J. Present and future of culturing bacteria. Annual Review of Microbiology 71, 711–730 (2017).CAS 

    Google Scholar 
    O’Leary, N. A. et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Research 44, D733 (2016).
    Google Scholar 
    Bobay, L. M. & Ochman, H. Biological species are universal across life’s domains. Genome Biology and Evolution 9, 491–501 (2017).
    Google Scholar 
    Magnabosco, C., Moore, K., Wolfe, J. & Fournier, G. Dating phototrophic microbial lineages with reticulate gene histories. Geobiology 16, 179–189 (2018).CAS 

    Google Scholar 
    Louca, S. et al. Function and functional redundancy in microbial systems. Nature Ecology & Evolution 2, 936–943 (2018).ADS 

    Google Scholar 
    Jain, C., Rodriguez-R, L. M., Phillippy, A. M., Konstantinidis, K. T. & Aluru, S. High throughput ANI analysis of 90 K prokaryotic genomes reveals clear species boundaries. Nature Communications 9, 5114 (2018).ADS 

    Google Scholar 
    Zhu, Q. et al. Phylogenomics of 10,575 genomes reveals evolutionary proximity between domains bacteria and archaea. Nature Communications 10, 5477 (2019).ADS 
    CAS 

    Google Scholar 
    Royalty, T.M. & Steen, A.D. Quantitatively partitioning microbial genomic traits among taxonomic ranks across the microbial tree of life. mSphere 4 (2019).Murray, C. S., Gao, Y. & Wu, M. Re-evaluating the evidence for a universal genetic boundary among microbial species. Nature Communications 12, 4059 (2021).ADS 
    CAS 

    Google Scholar 
    Powell, S. et al. eggNOG v4.0: nested orthology inference across 3686 organisms. Nucleic Acids Research 42, D231–D239 (2014).CAS 

    Google Scholar 
    Stoddard, S. F., Smith, B. J., Hein, R., Roller, B. R. & Schmidt, T. M. rrnDB: improved tools for interpreting rRNA gene abundance in bacteria and archaea and a new foundation for future development. Nucleic Acids Research 43, D593–D598 (2014).
    Google Scholar 
    Douglas, G. M. et al. Picrust2 for prediction of metagenome functions. Nature Biotechnology 38, 685–688 (2020).CAS 

    Google Scholar 
    Wemheuer, F. et al. Tax4Fun2: prediction of habitat-specific functional profiles and functional redundancy based on 16S rRNA gene sequences. Environmental Microbiome 15, 1–12 (2020).
    Google Scholar 
    Louca, S., Parfrey, L. W. & Doebeli, M. Decoupling function and taxonomy in the global ocean microbiome. Science 353, 1272–1277 (2016).ADS 
    CAS 

    Google Scholar 
    Wu, D. et al. A phylogeny-driven genomic encyclopaedia of bacteria and archaea. Nature 462, 1056–1060 (2009).ADS 
    CAS 

    Google Scholar 
    Louca, S. & Pennell, M. W. A general and efficient algorithm for the likelihood of diversification and discrete-trait evolutionary models. Systematic Biology 69, 545–556 (2020).
    Google Scholar 
    Tyson, G. W. et al. Community structure and metabolism through reconstruction of microbial genomes from the environment. Nature 428, 37–43 (2004).ADS 
    CAS 

    Google Scholar 
    Sharon, I. & Banfield, J. F. Genomes from metagenomics. Science 342, 1057–1058 (2013).ADS 
    CAS 

    Google Scholar 
    Parks, D. H. et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nature Microbiology 2, 1533–1542 (2017).CAS 

    Google Scholar 
    Chen, L. X., Anantharaman, K., Shaiber, A., Eren, A. M. & Banfield, J. F. Accurate and complete genomes from metagenomes. Genome Research 30, 315–333 (2020).CAS 

    Google Scholar 
    Konstantinidis, K. T. & Tiedje, J. M. Genomic insights that advance the species definition for prokaryotes. Proceedings of the National Academy of Sciences 102, 2567–2572 (2005).ADS 
    CAS 

    Google Scholar 
    Kim, M., Oh, H. S., Park, S. C. & Chun, J. Towards a taxonomic coherence between average nucleotide identity and 16S rRNA gene sequence similarity for species demarcation of prokaryotes. Journal of Systematic and Evolutionary Microbiology 64, 346–351 (2014).CAS 

    Google Scholar 
    Shapiro, B.J. What microbial population genomics has taught us about speciation. In Polz, M.F. & Rajora, O.P. (eds.) Population Genomics: Microorganisms, 31–47 (Springer International Publishing, Cham, Switzerland, 2019).Olm, M. R. et al. Consistent metagenome-derived metrics verify and delineate bacterial species boundaries. mSystems 5, e00731–19 (2020).CAS 

    Google Scholar 
    Lagkouvardos, I., Overmann, J. & Clavel, T. Cultured microbes represent a substantial fraction of the human and mouse gut microbiota. Gut Microbes 8, 493–503 (2017).
    Google Scholar 
    Zhang, Z., Wang, J., Wang, J., Wang, J. & Li, Y. Estimate of the sequenced proportion of the global prokaryotic genome. Microbiome 8, 1–9 (2020).
    Google Scholar 
    Aramaki, T. et al. KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics 36, 2251–2252 (2019).
    Google Scholar 
    Mira, A., Ochman, H. & Moran, N. A. Deletional bias and the evolution of bacterial genomes. Trends in Genetics 17, 589–596 (2001).CAS 

    Google Scholar 
    Morris, J. J., Lenski, R. E. & Zinser, E. R. The Black Queen Hypothesis: evolution of dependencies through adaptive gene loss. MBio 3, e00036–12 (2012).
    Google Scholar 
    Giovannoni, S. J., Cameron Thrash, J. & Temperton, B. Implications of streamlining theory for microbial ecology. ISME Journal 8, 1553–1565 (2014).
    Google Scholar 
    Nayfach, S., Shi, Z. J., Seshadri, R., Pollard, K. S. & Kyrpides, N. C. New insights from uncultivated genomes of the global human gut microbiome. Nature 568, 505–510 (2019).ADS 
    CAS 

    Google Scholar 
    Gary, P.R. Adjusting for nonresponse in surveys. In Smart, J.C. (ed.) Higher Education: Handbook of Theory and Research, chap. 8, 411–449 (Springer, Dordrecht, Netherlands, 2007).Maguire, F. et al. Metagenome-assembled genome binning methods with short reads disproportionately fail for plasmids and genomic islands. Microbial Genomics 6, mgen000436 (2020).
    Google Scholar 
    Huerta-Cepas, J. et al. eggnog 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Research 47, D309–D314 (2019).CAS 

    Google Scholar 
    Abdel-Hamid, A.M., Solbiati, J.O., Cann, I.K.O., Sariaslani, S. & Gadd, G.M. Insights into lignin degradation and its potential industrial applications, vol. 82, chap. 1, 1–28 (Academic Press, 2013).El-Bondkly, A.M. Sequence analysis of industrially important genes from trichoderma. In Biotechnology and biology of Trichoderma, chap. 28, 377–392 (Elsevier, 2014).Dawood, A. & Ma, K. Applications of microbial β-mannanases. Frontiers in Bioengineering and Biotechnology 8 (2020).Khelaifia, S., Raoult, D. & Drancourt, M. A versatile medium for cultivating methanogenic archaea. PLOS ONE 8, e61563 (2013).ADS 
    CAS 

    Google Scholar 
    Khelaifia, S. et al. Aerobic culture of methanogenic archaea without an external source of hydrogen. European Journal of Clinical Microbiology & Infectious Diseases 35, 985–991 (2016).CAS 

    Google Scholar 
    Michał, B. et al. Phymet2: a database and toolkit for phylogenetic and metabolic analyses of methanogens. Environmental Microbiology Reports 10, 378–382 (2018).
    Google Scholar 
    Albright, S. & Louca, S. Trait biases in microbial reference genomes, figshare., https://doi.org/10.6084/m9.figshare.c.6055004.v1 (2022).Castelle, C. J. & Banfield, J. F. Major new microbial groups expand diversity and alter our understanding of the tree of life. Cell 172, 1181–1197 (2018).CAS 

    Google Scholar 
    Murray, A. E. et al. Roadmap for naming uncultivated archaea and bacteria. Nature Microbiology 5, 987–994 (2020).CAS 

    Google Scholar 
    Palleroni, N. J. Prokaryotic diversity and the importance of culturing. Antonie van Leeuwenhoek 72, 3–19 (1997).CAS 

    Google Scholar 
    Langille, M. G. et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nature Biotechnology 31, 814–821 (2013).CAS 

    Google Scholar 
    Tran, P. Q. et al. Depth-discrete metagenomics reveals the roles of microbes in biogeochemical cycling in the tropical freshwater Lake Tanganyika. The ISME Journal 15, 1971–1986 (2021).CAS 

    Google Scholar 
    Kroeger, M. E. et al. New biological insights into how deforestation in amazonia affects soil microbial communities using metagenomics and metagenome-assembled genomes. Frontiers in Microbiology 9, 1635 (2018).
    Google Scholar 
    Nathani, N. M. et al. 309 metagenome assembled microbial genomes from deep sediment samples in the Gulfs of Kathiawar Peninsula. Scientific Data 8, 194 (2021).
    Google Scholar 
    Irazoqui, J. M., Eberhardt, M. F., Adjad, M. M., Amadio, A. F. & Collado, M. C. Identification of key microorganisms in facultative stabilization ponds from dairy industries, using metagenomics. PeerJ 10, e12772 (2022).
    Google Scholar 
    Hwang, Y. et al. Leave no stone unturned: individually adapted xerotolerant Thaumarchaeota sheltered below the boulders of the Atacama Desert hyperarid core. Microbiome 9, 234 (2021).CAS 

    Google Scholar 
    Tully, B., Wheat, C. G., Glazer, B. T. & Huber, J. A dynamic microbial community with high functional redundancy inhabits the cold, oxic subseafloor aquifer. ISME Journal 12, 1–16 (2018).CAS 

    Google Scholar 
    Vanwonterghem, I., Jensen, P. D., Rabaey, K. & Tyson, G. W. Genome-centric resolution of microbial diversity, metabolism and interactions in anaerobic digestion. Environmental Microbiology 18, 3144–3158 (2016).CAS 

    Google Scholar 
    Glasl, B. et al. Comparative genome-centric analysis reveals seasonal variation in the function of coral reef microbiomes. The ISME Journal 14, 1435–1450 (2020).
    Google Scholar 
    Robbins, S. J. et al. A genomic view of the reef-building coral Porites lutea and its microbial symbionts. Nature Microbiology 4, 2090–2100 (2019).
    Google Scholar 
    Engelberts, J. P. et al. Characterization of a sponge microbiome using an integrative genome-centric approach. The ISME Journal 14, 1100–1110 (2020).CAS 

    Google Scholar 
    Bowerman, K. L. et al. Disease-associated gut microbiome and metabolome changes in patients with chronic obstructive pulmonary disease. Nature Communications 11, 5886 (2020).ADS 
    CAS 

    Google Scholar 
    Chen, Y. J. et al. Hydrodynamic disturbance controls microbial community assembly and biogeochemical processes in coastal sediments. The ISME Journal 16, 750–763 (2022).CAS 

    Google Scholar 
    Hugerth, L. W. et al. Metagenome-assembled genomes uncover a global brackish microbiome. Genome Biology 16, 279 (2015).
    Google Scholar 
    Alneberg, J. et al. Ecosystem-wide metagenomic binning enables prediction of ecological niches from genomes. Communications Biology 3, 119 (2020).
    Google Scholar 
    Di Cesare, A. et al. Genomic comparison and spatial distribution of different Synechococcus phylotypes in the Black Sea. Frontiers in Microbiology 11, 1979 (2020).
    Google Scholar 
    van Vliet, D. M. et al. The bacterial sulfur cycle in expanding dysoxic and euxinic marine waters. Environmental Microbiology 23, 2834–2857 (2021).
    Google Scholar 
    Dalcin Martins, P. et al. Enrichment of novel Verrucomicrobia, Bacteroidetes, and Krumholzibacteria in an oxygen-limited methane- and iron-fed bioreactor inoculated with Bothnian Sea sediments. MicrobiologyOpen 10, e1175 (2021).CAS 

    Google Scholar 
    Stewart, R. D. et al. Compendium of 4,941 rumen metagenome-assembled genomes for rumen microbiome biology and enzyme discovery. Nature Biotechnology 37, 953–961 (2019).CAS 

    Google Scholar 
    Segura-Wang, M., Grabner, N., Koestelbauer, A., Klose, V. & Ghanbari, M. Genome-resolved metagenomics of the chicken gut microbiome. Frontiers in Microbiology 12, 726923 (2021).
    Google Scholar 
    Ruuskanen, M. O. et al. Microbial genomes retrieved from High Arctic lake sediments encode for adaptation to cold and oligotrophic environments. Limnology and Oceanography 65, S233–S247 (2020).CAS 

    Google Scholar 
    Haas, S., Desai, D. K., LaRoche, J., Pawlowicz, R. & Wallace, D. W. R. Geomicrobiology of the carbon, nitrogen and sulphur cycles in Powell Lake: a permanently stratified water column containing ancient seawater. Environmental Microbiology 21, 3927–3952 (2019).CAS 

    Google Scholar 
    Spasov, E. et al. High functional diversity among Nitrospira populations that dominate rotating biological contactor microbial communities in a municipal wastewater treatment plant. The ISME Journal 14, 1857–1872 (2020).CAS 

    Google Scholar 
    Vigneron, A. et al. Genomic evidence for sulfur intermediates as new biogeochemical hubs in a model aquatic microbial ecosystem. Microbiome 9, 46 (2021).CAS 

    Google Scholar 
    Galambos, D., Anderson, R. E., Reveillaud, J. & Huber, J. A. Genome-resolved metagenomics and metatranscriptomics reveal niche differentiation in functionally redundant microbial communities at deep-sea hydrothermal vents. Environmental Microbiology 21, 4395–4410 (2019).CAS 

    Google Scholar 
    Stewart, R. D. et al. Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen. Nature Communications 9, 870 (2018).ADS 

    Google Scholar 
    Xing, P. et al. Stratification of microbiomes during the holomictic period of Lake Fuxian, an alpine monomictic lake. Limnology and Oceanography 65, S134–S148 (2020).
    Google Scholar 
    Zhang, S., Hu, Z. & Wang, H. Metagenomic analysis exhibited the co-metabolism of polycyclic aromatic hydrocarbons by bacterial community from estuarine sediment. Environment International 129, 308–319 (2019).CAS 

    Google Scholar 
    Lin, Y., Wang, L., Xu, K., Li, K. & Ren, H. Revealing taxon-specific heavy metal-resistance mechanisms in denitrifying phosphorus removal sludge using genome-centric metaproteomics. Microbiome 9, 67 (2021).CAS 

    Google Scholar 
    Liu, L. et al. High-quality bacterial genomes of a partial-nitritation/anammox system by an iterative hybrid assembly method. Microbiome 8, 155 (2020).CAS 

    Google Scholar 
    Kantor, R. S. et al. Bioreactor microbial ecosystems for thiocyanate and cyanide degradation unravelled with genome-resolved metagenomics. Environmental Microbiology 17, 4929–4941 (2015).CAS 

    Google Scholar 
    Zhou, Z. et al. Gammaproteobacteria mediating utilization of methyl-, sulfur- and petroleum organic compounds in deep ocean hydrothermal plumes. The ISME Journal 14, 3136–3148 (2020).CAS 

    Google Scholar 
    Reysenbach, A. L. et al. Complex subsurface hydrothermal fluid mixing at a submarine arc volcano supports distinct and highly diverse microbial communities. Proceedings of the National Academy of Sciences 117, 32627–32638 (2020).ADS 
    CAS 

    Google Scholar 
    Hou, J. et al. Microbial succession during the transition from active to inactive stages of deep-sea hydrothermal vent sulfide chimneys. Microbiome 8, 102 (2020).CAS 

    Google Scholar 
    Campanaro, S. et al. Metagenomic analysis and functional characterization of the biogas microbiome using high throughput shotgun sequencing and a novel binning strategy. Biotechnology for Biofuels 9, 26 (2016).
    Google Scholar 
    Singleton, C. M. et al. Connecting structure to function with the recovery of over 1000 high-quality metagenome-assembled genomes from activated sludge using long-read sequencing. Nature Communications 12, 2009 (2021).CAS 

    Google Scholar 
    Diamond, S. et al. Mediterranean grassland soil C–N compound turnover is dependent on rainfall and depth, and is mediated by genomically divergent microorganisms. Nature Microbiology 4, 1356–1367 (2019).CAS 

    Google Scholar 
    Rasigraf, O. et al. Microbial community composition and functional potential in Bothnian Sea sediments is linked to Fe and S dynamics and the quality of organic matter. Limnology and Oceanography 65, S113–S133 (2020).CAS 

    Google Scholar 
    Rissanen, A. J. et al. Vertical stratification patterns of methanotrophs and their genetic controllers in water columns of oxygen-stratified boreal lakes. FEMS Microbiology Ecology 97, fiaa252 (2021).CAS 

    Google Scholar 
    Campanaro, S. et al. New insights from the biogas microbiome by comprehensive genome-resolved metagenomics of nearly 1600 species originating from multiple anaerobic digesters. Biotechnology for Biofuels 13, 25 (2020).CAS 

    Google Scholar 
    Almeida, A. et al. A unified catalog of 204,938 reference genomes from the human gut microbiome. Nature Biotechnology 39, 105–114 (2021).CAS 

    Google Scholar 
    Zhou, Z. et al. Genome- and community-level interaction insights into carbon utilization and element cycling functions of hydrothermarchaeota in hydrothermal sediment. mSystems 5 (2020).Pachiadaki, M. G. et al. Charting the complexity of the marine microbiome through single-cell genomics. Cell 179, 1623–1635.e11 (2019).CAS 

    Google Scholar 
    Martijn, J., Vosseberg, J., Guy, L., Offre, P. & Ettema, T. J. G. Deep mitochondrial origin outside the sampled alphaproteobacteria. Nature 557, 101–105 (2018).ADS 
    CAS 

    Google Scholar 
    Greenlon, A. et al. Global-level population genomics reveals differential effects of geography and phylogeny on horizontal gene transfer in soil bacteria. Proceedings of the National Academy of Sciences 116, 15200–15209 (2019).ADS 
    CAS 

    Google Scholar 
    Hervé, V. et al. Phylogenomic analysis of 589 metagenome-assembled genomes encompassing all major prokaryotic lineages from the gut of higher termites. PeerJ 8, e8614 (2020).
    Google Scholar 
    von Appen, W.J. The expedition PS114 of the research vessel POLARSTERN to the Fram Strait in 2018. Tech. Rep., Alfred Wegener Institute for Polar and Marine Research (2018).Dombrowski, N., Seitz, K. W., Teske, A. P. & Baker, B. J. Genomic insights into potential interdependencies in microbial hydrocarbon and nutrient cycling in hydrothermal sediments. Microbiome 5, 106 (2017).
    Google Scholar 
    Yu, J. et al. Dna-stable isotope probing shotgun metagenomics reveals the resilience of active microbial communities to biochar amendment in oxisol soil. Frontiers in Microbiology 11, 587972 (2020).
    Google Scholar 
    Forster, S. C. et al. A human gut bacterial genome and culture collection for improved metagenomic analyses. Nature Biotechnology 37, 186–192 (2019).CAS 

    Google Scholar 
    Gharechahi, J. et al. Metagenomic analysis reveals a dynamic microbiome with diversified adaptive functions to utilize high lignocellulosic forages in the cattle rumen. The ISME Journal 15, 1108–1120 (2021).CAS 

    Google Scholar 
    Meier, D. V., Imminger, S., Gillor, O. & Woebken, D. Distribution of mixotrophy and desiccation survival mechanisms across microbial genomes in an arid biological soil crust community. mSystems 6, e00786–20 (2021).CAS 

    Google Scholar 
    Haro-Moreno, J. M. et al. Dysbiosis in marine aquaculture revealed through microbiome analysis: reverse ecology for environmental sustainability. FEMS Microbiology Ecology 96, fiaa218 (2020).CAS 

    Google Scholar 
    Haro-Moreno, J. M. et al. Fine metagenomic profile of the Mediterranean stratified and mixed water columns revealed by assembly and recruitment. Microbiome 6, 128 (2018).
    Google Scholar 
    Dong, X. et al. Metabolic potential of uncultured bacteria and archaea associated with petroleum seepage in deep-sea sediments. Nature Communications 10, 1816 (2019).ADS 

    Google Scholar 
    Poghosyan, L. et al. Metagenomic profiling of ammonia- and methane-oxidizing microorganisms in two sequential rapid sand filters. Water Research 185, 116288 (2020).CAS 

    Google Scholar 
    Paula, D. M., Jeroen, F., Hugh, M. & Meng, M. L. & J., W.M. Wetland sediments host diverse microbial taxa capable of cycling alcohols. Applied and Environmental Microbiology 85, 00189–19 (2019).
    Google Scholar 
    Aromokeye, D. A. et al. Crystalline iron oxides stimulate methanogenic benzoate degradation in marine sediment-derived enrichment cultures. The ISME Journal 15, 965–980 (2021).CAS 

    Google Scholar 
    Borchert, E. et al. Deciphering a marine bone-degrading microbiome reveals a complex community effort. mSystems 6, e01218–20 (2021).CAS 

    Google Scholar 
    Osvatic, J. T. et al. Global biogeography of chemosynthetic symbionts reveals both localized and globally distributed symbiont groups. Proceedings of the National Academy of Sciences 118, e2104378118 (2021).CAS 

    Google Scholar 
    Boeuf, D. et al. Biological composition and microbial dynamics of sinking particulate organic matter at abyssal depths in the oligotrophic open ocean. Proceedings of the National Academy of Sciences 116, 11824–11832 (2019).ADS 
    CAS 

    Google Scholar 
    Woodcroft, B. J. et al. Genome-centric view of carbon processing in thawing permafrost. Nature 560, 49–54 (2018).ADS 
    CAS 

    Google Scholar 
    Alqahtani, M. F. et al. Enrichment of Marinobacter sp. and halophilic homoacetogens at the biocathode of microbial electrosynthesis system inoculated with Red Sea brine pool. Frontiers in Microbiology 10, 2563 (2019).
    Google Scholar 
    Haroon, M. F., Thompson, L. R., Parks, D. H., Hugenholtz, P. & Stingl, U. A catalogue of 136 microbial draft genomes from Red Sea metagenomes. Scientific Data 3, 160050 (2016).CAS 

    Google Scholar 
    Vavourakis, C. D. et al. A metagenomics roadmap to the uncultured genome diversity in hypersaline soda lake sediments. Microbiome 6, 1–18 (2018).
    Google Scholar 
    Cabello-Yeves, P. J. et al. Microbiome of the deep Lake Baikal, a unique oxic bathypelagic habitat. Limnology and Oceanography 65, 1471–1488 (2020).ADS 
    CAS 

    Google Scholar 
    Vavourakis, C. D. et al. Metagenomes and metatranscriptomes shed new light on the microbial-mediated sulfur cycle in a siberian soda lake. BMC Biology 17, 69 (2019).
    Google Scholar 
    Waterworth, S. C., Isemonger, E. W., Rees, E. R., Dorrington, R. A. & Kwan, J. C. Conserved bacterial genomes from two geographically isolated peritidal stromatolite formations shed light on potential functional guilds. Environmental Microbiology Reports 13, 126–137 (2021).CAS 

    Google Scholar 
    Huddy, R. J. et al. Thiocyanate and organic carbon inputs drive convergent selection for specific autotrophic Afipia and Thiobacillus strains within complex microbiomes. Frontiers in Microbiology 12, 643368 (2021).
    Google Scholar 
    Emerson, J. B. et al. Diverse sediment microbiota shape methane emission temperature sensitivity in Arctic lakes. Nature Communications 12, 5815 (2021).ADS 
    CAS 

    Google Scholar 
    Chiri, E. et al. Termite gas emissions select for hydrogenotrophic microbial communities in termite mounds. Proceedings of the National Academy of Sciences 118, e2102625118 (2021).CAS 

    Google Scholar 
    Gong, G., Zhou, S., Luo, R., Gesang, Z. & Suolang, S. Metagenomic insights into the diversity of carbohydrate-degrading enzymes in the yak fecal microbial community. BMC Microbiology 20, 302 (2020).
    Google Scholar 
    Zhou, S. et al. Characterization of metagenome-assembled genomes and carbohydrate-degrading genes in the gut microbiota of Tibetan pig. Frontiers in Microbiology 11, 595066 (2020).
    Google Scholar 
    Tully, B. J., Graham, E. D. & Heidelberg, J. F. The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans. Scientific Data 5, 170203 (2018).CAS 

    Google Scholar 
    Lavrinienko, A. et al. Two hundred and fifty-four metagenome-assembled bacterial genomes from the bank vole gut microbiota. Scientific Data 7, 312 (2020).CAS 

    Google Scholar 
    Peng, X. et al. Genomic and functional analyses of fungal and bacterial consortia that enable lignocellulose breakdown in goat gut microbiomes. Nature Microbiology 6, 499–511 (2021).CAS 

    Google Scholar 
    Dudek, N. K. et al. Novel microbial diversity and functional potential in the marine mammal oral microbiome. Current Biology 27, 3752–3762.e6 (2017).CAS 

    Google Scholar 
    Pinto, A. J. et al. Metagenomic evidence for the presence of comammox nitrospira-like bacteria in a drinking water system. mSphere 1, e00054–15 (2015).
    Google Scholar 
    Zaremba-Niedzwiedzka, K. et al. Asgard archaea illuminate the origin of eukaryotic cellular complexity. Nature 541, 353–358 (2017).ADS 
    CAS 

    Google Scholar 
    Nobu, M. K. et al. Catabolism and interactions of uncultured organisms shaped by eco-thermodynamics in methanogenic bioprocesses. Microbiome 8, 111 (2020).CAS 

    Google Scholar 
    Butterfield, C. N. et al. Proteogenomic analyses indicate bacterial methylotrophy and archaeal heterotrophy are prevalent below the grass root zone. PeerJ 4, e2687 (2016).
    Google Scholar 
    Castelle, C. J. et al. Protein family content uncovers lineage relationships and bacterial pathway maintenance mechanisms in DPANN Archaea. Frontiers in Microbiology 12, 660052 (2021).
    Google Scholar 
    Alteio, L. V. et al. Complementary metagenomic approaches improve reconstruction of microbial diversity in a forest soil. mSystems 5, e00768–19 (2020).
    Google Scholar 
    Shaiber, A. et al. Functional and genetic markers of niche partitioning among enigmatic members of the human oral microbiome. Genome Biology 21, 292 (2020).
    Google Scholar 
    Jungbluth, S. P., Amend, J. P. & Rappé, M. S. Metagenome sequencing and 98 microbial genomes from Juan de Fuca Ridge flank subsurface fluids. Scientific Data 4, 170037 (2017).CAS 

    Google Scholar 
    Sheik, C. S. et al. Dolichospermum blooms in Lake Superior: DNA-based approach provides insight to the past, present and future of blooms. Journal of Great Lakes Research 48, 1191–1205 (2022).CAS 

    Google Scholar 
    Barnum, T. P. et al. Genome-resolved metagenomics identifies genetic mobility, metabolic interactions, and unexpected diversity in perchlorate-reducing communities. The ISME Journal 12, 1568–1581 (2018).CAS 

    Google Scholar 
    Julian, D. et al. Coastal ocean metagenomes and curated metagenome-assembled genomes from Marsh Landing, Sapelo Island (Georgia, USA). Microbiology Resource Announcements 8, e00934–19 (2019).
    Google Scholar 
    Breister, A. M. et al. Soil microbiomes mediate degradation of vinyl ester-based polymer composites. Communications Materials 1, 101 (2020).ADS 

    Google Scholar 
    Fu, H., Uchimiya, M., Gore, J. & Moran, M. A. Ecological drivers of bacterial community assembly in synthetic phycospheres. Proceedings of the National Academy of Sciences 117, 3656–3662 (2020).ADS 
    CAS 

    Google Scholar 
    Nobu, M. K. et al. Thermodynamically diverse syntrophic aromatic compound catabolism. Environmental Microbiology 19, 4576–4586 (2017).CAS 

    Google Scholar 
    Pasolli, E. et al. Extensive unexplored human microbiome diversity revealed by over 150,000 genomes from metagenomes spanning age, geography, and lifestyle. Cell 176, 649–662 (2019).CAS 

    Google Scholar 
    Nayfach, S. et al. A genomic catalog of Earth’s microbiomes. Nature Biotechnology 39, 499–509 (2021).CAS 

    Google Scholar 
    Li, Z. et al. Deep sea sediments associated with cold seeps are a subsurface reservoir of viral diversity. The ISME Journal 15, 2366–2378 (2021).CAS 

    Google Scholar 
    Bay, S. K. et al. Trace gas oxidizers are widespread and active members of soil microbial communities. Nature Microbiology 6, 246–256 (2021).CAS 

    Google Scholar 
    Seyler, L. M., Trembath-Reichert, E., Tully, B. J. & Huber, J. A. Time-series transcriptomics from cold, oxic subseafloor crustal fluids reveals a motile, mixotrophic microbial community. The ISME Journal 15, 1192–1206 (2021).CAS 

    Google Scholar 
    Herold, M. et al. Integration of time-series meta-omics data reveals how microbial ecosystems respond to disturbance. Nature Communications 11, 5281 (2020).ADS 
    CAS 

    Google Scholar 
    Dong, X. et al. Thermogenic hydrocarbon biodegradation by diverse depth-stratified microbial populations at a Scotian Basin cold seep. Nature Communications 11, 5825 (2020).ADS 
    CAS 

    Google Scholar 
    Thompson, L. R. et al. Metagenomic covariation along densely sampled environmental gradients in the Red Sea. The ISME Journal 11, 138–151 (2017).CAS 

    Google Scholar 
    Dominik, S., Daniela, Z., Anja, P., Katharina, R. & Rolf, D. Metagenome-assembled genome sequences from different wastewater treatment stages in Germany. Microbiology Resource Announcements 10, e00504–21 (2021).
    Google Scholar 
    Langwig, M. V. et al. Large-scale protein level comparison of Deltaproteobacteria reveals cohesive metabolic groups. The ISME Journal 16, 307–320 (2022).CAS 

    Google Scholar 
    Rezaei Somee, M. et al. Distinct microbial community along the chronic oil pollution continuum of the Persian Gulf converge with oil spill accidents. Scientific Reports 11, 11316 (2021).ADS 
    CAS 

    Google Scholar 
    Gilroy, R. et al. Metagenomic investigation of the equine faecal microbiome reveals extensive taxonomic diversity. PeerJ 10, e13084 (2022).
    Google Scholar 
    Bhattarai, B., Bhattacharjee, A. S., Coutinho, F. H. & Goel, R. K. Viruses and their interactions with bacteria and archaea of hypersaline Great Salt Lake. Frontiers in Microbiology 12, 701414 (2021).
    Google Scholar 
    Liu, L. et al. Microbial diversity and adaptive strategies in the Mars-like Qaidam Basin, North Tibetan Plateau, China. Environmental Microbiology Reports (2022).Lin, H. et al. Mercury methylation by metabolically versatile and cosmopolitan marine bacteria. The ISME Journal 15, 1810–1825 (2021).CAS 

    Google Scholar 
    Martnez-Pérez, C. et al. Lifting the lid: nitrifying archaea sustain diverse microbial communities below the Ross Ice Shelf. SSRN (2020).Zhang, L. et al. Metagenomic insights into the effect of thermal hydrolysis pre-treatment on microbial community of an anaerobic digestion system. Science of The Total Environment 791, 148096 (2021).ADS 
    CAS 

    Google Scholar 
    Starr, E. P. et al. Stable-isotope-informed, genome-resolved metagenomics uncovers potential cross-kingdom interactions in rhizosphere soil. mSphere 6, e00085–21 (2021).CAS 

    Google Scholar 
    Matthew, C. et al. Archaeal and bacterial metagenome-assembled genome sequences derived from pig feces. Microbiology Resource Announcements 11, 01142–21 (2022).
    Google Scholar 
    Wang, Y., Zhao, R., Liu, L., Li, B. & Zhang, T. Selective enrichment of comammox from activated sludge using antibiotics. Water Research 197, 117087 (2021).CAS 

    Google Scholar 
    Gilroy, R. et al. Extensive microbial diversity within the chicken gut microbiome revealed by metagenomics and culture. PeerJ 9, e10941 (2021).
    Google Scholar 
    Chen, Y. H. et al. Salvaging high-quality genomes of microbial species from a meromictic lake using a hybrid sequencing approach. Communications Biology 4, 996 (2021).CAS 

    Google Scholar 
    Beach, N. K., Myers, K. S., Donohue, T. J. & Noguera, D. R. Metagenomes from 25 low-abundance microbes in a partial nitritation anammox microbiome. Microbiology Resource Announcements 11, 00212–22 (2022).CAS 

    Google Scholar 
    Solanki, V. et al. Glycoside hydrolase from the GH76 family indicates that marine Salegentibacter sp. Hel_I_6 consumes alpha-mannan from fungi. The ISME Journal 16, 1818–1830 (2022).CAS 

    Google Scholar 
    Hiraoka, S. et al. Diverse DNA modification in marine prokaryotic and viral communities. Nucleic Acids Research 50, 1531–1550 (2022).CAS 

    Google Scholar 
    Haryono, M.A.S. et al. Recovery of high quality metagenome-assembled genomes from full-scale activated sludge microbial communities in a tropical climate using longitudinal metagenome sampling. Frontiers in Microbiology 13 (2022).Rodrguez-Ramos, J.A. et al. Microbial genome-resolved metaproteomic analyses frame intertwined carbon and nitrogen cycles in river hyporheic sediments. Research Square (2021).Kim, M., Cho, H. & Lee, W. Y. Distinct gut microbiotas between southern elephant seals and Weddell seals of Antarctica. Journal of Microbiology 58, 1018–1026 (2020).CAS 

    Google Scholar 
    Voorhies, A. A. et al. Cyanobacterial life at low O2: community genomics and function reveal metabolic versatility and extremely low diversity in a Great Lakes sinkhole mat. Geobiology 10, 250–267 (2012).CAS 

    Google Scholar 
    McDaniel, E. A. et al. Tbasco: trait-based comparative ‘omics identifies ecosystem-level and niche-differentiating adaptations of an engineered microbiome. ISME Communications 2, 111 (2022).
    Google Scholar 
    Wang, W. et al. Contrasting bacterial and archaeal distributions reflecting different geochemical processes in a sediment core from the Pearl River Estuary. AMB Express 10, 16 (2020).
    Google Scholar 
    Mandakovic, D. et al. Genome-scale metabolic models of Microbacterium species isolated from a high altitude desert environment. Scientific Reports 10, 5560 (2020).ADS 
    CAS 

    Google Scholar 
    Wang, Y. et al. Seasonal prevalence of ammonia-oxidizing archaea in a full-scale municipal wastewater treatment plant treating saline wastewater revealed by a 6-year time-series analysis. Environmental Science & Technology 55, 2662–2673 (2021).ADS 
    CAS 

    Google Scholar 
    Bulzu, P. A. et al. Casting light on Asgardarchaeota metabolism in a sunlit microoxic niche. Nature Microbiology 4, 1129–1137 (2019).CAS 

    Google Scholar 
    Karen, J. et al. Hydrogen-oxidizing bacteria are abundant in desert soils and strongly stimulated by hydration. mSystems 5, e01131–20 (2020).
    Google Scholar 
    Rust, M. et al. A multiproducer microbiome generates chemical diversity in the marine sponge Mycale hentscheli. Proceedings of the National Academy of Sciences 117, 9508–9518 (2020).ADS 
    CAS 

    Google Scholar 
    Podowski, J. C., Paver, S. F., Newton, R. J. & Coleman, M. L. Genome streamlining, proteorhodopsin, and organic nitrogen metabolism in freshwater nitrifiers. mBio 13, e02379–21 (2022).
    Google Scholar 
    Coutinho, F. H. et al. New viral biogeochemical roles revealed through metagenomic analysis of Lake Baikal. Microbiome 8, 163 (2020).CAS 

    Google Scholar 
    Philippi, M. et al. Purple sulfur bacteria fix N2 via molybdenum-nitrogenase in a low molybdenum Proterozoic ocean analogue. Nature Communications 12, 4774 (2021).ADS 
    CAS 

    Google Scholar 
    Katie, S. et al. Eight metagenome-assembled genomes provide evidence for microbial adaptation in 20,000- to 1,000,000-year-old Siberian permafrost. Applied and Environmental Microbiology 87, e00972–21 (2021).
    Google Scholar 
    Mert, K. et al. Unexpected abundance and diversity of phototrophs in mats from morphologically variable microbialites in Great Salt Lake, Utah. Applied and Environmental Microbiology 86, e00165–20 (2020).
    Google Scholar 
    Patin, N. V. et al. Gulf of Mexico blue hole harbors high levels of novel microbial lineages. The ISME Journal 15, 2206–2232 (2021).CAS 

    Google Scholar 
    Wang, J., Tang, X., Mo, Z. & Mao, Y. Metagenome-assembled genomes from Pyropia haitanensis microbiome provide insights into the potential metabolic functions to the seaweed. Frontiers in Microbiology 13, 857901 (2022).
    Google Scholar 
    Burgsdorf, I. et al. Lineage-specific energy and carbon metabolism of sponge symbionts and contributions to the host carbon pool. The ISME Journal 16, 1163–1175 (2022).CAS 

    Google Scholar 
    Suarez, C. et al. Disturbance-based management of ecosystem services and disservices in partial nitritation-anammox biofilms. npj Biofilms and Microbiomes 8, 47 (2022).CAS 

    Google Scholar 
    Kumar, D. et al. Textile industry wastewaters from Jetpur, Gujarat, India, are dominated by Shewanellaceae, Bacteroidaceae, and Pseudomonadaceae harboring genes encoding catalytic enzymes for textile dye degradation. Frontiers in Environmental Science 9, 720707 (2021).ADS 

    Google Scholar 
    Seitz, V. A. et al. Variation in root exudate composition influences soil microbiome membership and function. Applied and Environmental Microbiology 88, e00226–22 (2022).
    Google Scholar 
    Lindner, B. G. et al. Toward shotgun metagenomic approaches for microbial source tracking sewage spills based on laboratory mesocosms. Water Research 210, 117993 (2022).CAS 

    Google Scholar 
    Yancey, C. E. et al. Metagenomic and metatranscriptomic insights into population diversity of microcystis blooms: Spatial and temporal dynamics of mcy genotypes, including a partial operon that can be abundant and expressed. Applied and Environmental Microbiology 88, e02464–21 (2022).
    Google Scholar 
    Liu, L. et al. Charting the complexity of the activated sludge microbiome through a hybrid sequencing strategy. Microbiome 9, 205 (2021).CAS 

    Google Scholar 
    Speth, D. R. et al. Microbial communities of Auka hydrothermal sediments shed light on vent biogeography and the evolutionary history of thermophily. The ISME Journal 16, 1750–1764 (2022).CAS 

    Google Scholar 
    Blyton, M. D. J., Soo, R. M., Hugenholtz, P. & Moore, B. D. Maternal inheritance of the koala gut microbiome and its compositional and functional maturation during juvenile development. Environmental Microbiology 24, 475–493 (2022).CAS 

    Google Scholar 
    Nuccio, E. E. et al. Niche differentiation is spatially and temporally regulated in the rhizosphere. The ISME Journal 14, 999–1014 (2020).CAS 

    Google Scholar 
    Jaffe, A. L. et al. Long-term incubation of lake water enables genomic sampling of consortia involving planctomycetes and candidate phyla radiation bacteria. mSystems 7, e00223–22 (2022).
    Google Scholar 
    Cabral, L. et al. Gut microbiome of the largest living rodent harbors unprecedented enzymatic systems to degrade plant polysaccharides. Nature Communications 13, 629 (2022).ADS 
    CAS 

    Google Scholar 
    Blyton, M. D. J., Soo, R. M., Hugenholtz, P. & Moore, B. D. Characterization of the juvenile koala gut microbiome across wild populations. Environmental Microbiology 24, 4209–4219 (2022).CAS 

    Google Scholar 
    Xu, B. et al. A holistic genome dataset of bacteria, archaea and viruses of the Pearl River estuary. Scientific Data 9, 49 (2022).MathSciNet 
    CAS 

    Google Scholar 
    Royo-Llonch, M. et al. Compendium of 530 metagenome-assembled bacterial and archaeal genomes from the polar Arctic Ocean. Nature Microbiology 6, 1561–1574 (2021).CAS 

    Google Scholar 
    Sun, J., Prabhu, A., Aroney, S. T. N. & Rinke, C. Insights into plastic biodegradation: community composition and functional capabilities of the superworm (Zophobas morio) microbiome in styrofoam feeding trials. Microbial Genomics 8, 000842 (2022).CAS 

    Google Scholar 
    Kim, M. et al. Higher pathogen load in children from Mozambique vs. USA revealed by comparative fecal microbiome profiling. ISME Communications 2, 74 (2022).ADS 

    Google Scholar 
    Kelly, J. B., Carlson, D. E., Low, J. S. & Thacker, R. W. Novel trends of genome evolution in highly complex tropical sponge microbiomes. Microbiome 10, 164 (2022).CAS 

    Google Scholar 
    Bray, M. S. et al. Phylogenetic and structural diversity of aromatically dense pili from environmental metagenomes. Environmental Microbiology Reports 12, 49–57 (2020).CAS 

    Google Scholar 
    Cabello-Yeves, P. J. et al. α-cyanobacteria possessing form IA RuBisCO globally dominate aquatic habitats. The ISME Journal 16, 2421–2432 (2022).CAS 

    Google Scholar 
    Berben, T. et al. The Polar Fox Lagoon in Siberia harbours a community of Bathyarchaeota possessing the potential for peptide fermentation and acetogenesis. Antonie van Leeuwenhoek 115, 1229–1244 (2022).CAS 

    Google Scholar 
    Tamburini, F. B. et al. Short- and long-read metagenomics of urban and rural South African gut microbiomes reveal a transitional composition and undescribed taxa. Nature Communications 13, 926 (2022).ADS 
    CAS 

    Google Scholar 
    Kantor, R. S., Miller, S. E. & Nelson, K. L. The water microbiome through a pilot scale advanced treatment facility for direct potable reuse. Frontiers in Microbiology 10, 993 (2019).
    Google Scholar 
    Muratore, D. et al. Complex marine microbial communities partition metabolism of scarce resources over the diel cycle. Nature Ecology & Evolution 6, 218–229 (2022).
    Google Scholar 
    Zhou, Y. L., Mara, P., Cui, G. J., Edgcomb, V. P. & Wang, Y. Microbiomes in the challenger deep slope and bottom-axis sediments. Nature Communications 13, 1515 (2022).ADS 
    CAS 

    Google Scholar 
    Zhang, H. et al. Metagenome sequencing and 768 microbial genomes from cold seep in South China Sea. Scientific Data 9, 480 (2022).CAS 

    Google Scholar 
    Zhuang, J. L., Zhou, Y. Y., Liu, Y. D. & Li, W. Flocs are the main source of nitrous oxide in a high-rate anammox granular sludge reactor: insights from metagenomics and fed-batch experiments. Water Research 186, e116321 (2020).
    Google Scholar 
    Shiffman, M. E. et al. Gene and genome-centric analyses of koala and wombat fecal microbiomes point to metabolic specialization for eucalyptus digestion. PeerJ 5, 4075 (2017).
    Google Scholar 
    Murphy, S. M. C., Bautista, M. A., Cramm, M. A. & Hubert, C. R. J. Diesel and crude oil biodegradation by cold-adapted microbial communities in the Labrador Sea. Applied and Environmental Microbiology 87, e00800–21 (2021).ADS 
    CAS 

    Google Scholar 
    Suarez, C. et al. Metagenomic evidence of a novel family of anammox bacteria in a subsea environment. Environmental Microbiology 24, 2348–2360 (2022).CAS 

    Google Scholar 
    Dharamshi, J.E. et al. Genomic diversity and biosynthetic capabilities of sponge-associated chlamydiae. The ISME Journal (2022).Florian, P. O., Hugo, R. & Mathieu, A. Recovery of metagenome-assembled genomes from a human fecal sample with pacific biosciences high-fidelity sequencing. Microbiology Resource Announcements 11, e00250–22 (2022).
    Google Scholar 
    Bloom, S. M. et al. Cysteine dependence of Lactobacillus iners is a potential therapeutic target for vaginal microbiota modulation. Nature Microbiology 7, 434–450 (2022).CAS 

    Google Scholar 
    Aylward, F. O. et al. Diel cycling and long-term persistence of viruses in the ocean’s euphotic zone. Proceedings of the National Academy of Sciences 114, 11446–11451 (2017).ADS 
    CAS 

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

    Google Scholar 
    Bowers, R. M. et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nature biotechnology 35, 725 (2017).CAS 

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

    Google Scholar 
    Louca, S. The rates of global bacterial and archaeal dispersal. ISME Journal 16, 159–167 (2021).ADS 

    Google Scholar 
    Ondov, B. D. et al. Mash: fast genome and metagenome distance estimation using minhash. Genome Biology 17, 132 (2016).
    Google Scholar 
    Müllner, D. fastcluster: Fast hierarchical, agglomerative clustering routines for R and Python. Journal of Statistical Software 53, 1–18 (2013).
    Google Scholar 
    Kinene, T., Wainaina, J., Maina, S., Boykin, L.M. & Kliman, R.M. Methods for rooting trees, vol. 3, 489–493 (Academic Press, Oxford, 2016).Louca, S. & Doebeli, M. Efficient comparative phylogenetics on large trees. Bioinformatics 34, 1053–1055 (2018).CAS 

    Google Scholar 
    Rees, J. A. & Cranston, K. Automated assembly of a reference taxonomy for phylogenetic data synthesis. Biodiversity Data Journal 5, e12581 (2017).
    Google Scholar 
    Heck, K. et al. Evaluating methods for purifying cyanobacterial cultures by qPCR and high-throughput Illumina sequencing. Journal of Microbiological Methods 129, 55–60 (2016).CAS 

    Google Scholar 
    Cornet, L. et al. Consensus assessment of the contamination level of publicly available cyanobacterial genomes. PLOS ONE 13, e0200323 (2018).
    Google Scholar 
    Alneberg, J. et al. Genomes from uncultivated prokaryotes: a comparison of metagenome-assembled and single-amplified genomes. Microbiome 6, 173 (2018).
    Google Scholar 
    Eddy, S. R. Accelerated profile HMM searches. PLoS Computational Biology 7, e1002195 (2011).ADS 
    MathSciNet 
    CAS 

    Google Scholar 
    Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nature Methods 12, 59–60 (2014).
    Google Scholar 
    Pedregosa, F. et al. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011).MathSciNet 
    MATH 

    Google Scholar  More

  • in

    Benthic biota of Chilean fjords and channels in 25 years of cruises of the National Oceanographic Committee

    The data were recorded under the DarwinCore standard55,56 in a matrix named “Benthic biota of CIMAR-Fiordos and Southern Ice Field Cruises”58. The occurrence dataset contains direct basic information (description, scope [temporal, geographic and taxonomic], methodology, bibliography, contacts, data description, GBIF registration and citation), project details, metrics (taxonomy and occurrences classification), activity (citations and download events) and download options. The following data fields were occupied:Column 1: “occurrenceID” (single indicator of the biological record indicating the cruise and correlative record).Column 2: “basisOfRecord” (“PreservedSpecimen” for occurrence records with catalogue number of scientific collection, “MaterialCitation” for any literature record).Column 3: “institutionCode” (The acronym in use by the institution having custody of the sample or information referred to in the record).Column 4: “collectionCode” (The name of the cruise).Column 5: “catalogNumber” (The repository number in museums or correlative number).Column 6: “type” (All records entered as “text”).Column 7: “language” (Spanish, English or both).Column 8: “institutionID” (The identifier for the institution having custody of the sample or information referred to in the record).Column 9: “collectionID” (The identifier for the collection or dataset from which the record was derived).Column 10: “datasetID” (The code “CONA-benthic-biota-database” for entire database).Column 11: “recordedBy” (Author/s who recorded the original occurrence [publication source]).Column 12: “individualCount” (Number of individuals recorded).Column 13: “associatedReferences” (Publication source [report and/or paper/s] for each record).Column 14: “samplingProtocol” (The sampling gear for each record).Column 15: “eventDate” (The date-time or interval during which the record occurred).Column 16: “eventRemarks” (Comments or notes about the event).Column 17: “continent” (Location).Column 18: “country” (Location).Column 19: “countryCode” (The standard code for the country in which the location occurs).Column 20: “stateProvince” (Location, refers to the Administrative Region of Chile).Column 21: “county” (Location, refers to the Administrative Province of Chile).Column 22: “municipality” (Location, refers to the Administrative Commune of Chile).Column 23: “locality” (The specific name of the place).Column 24: “verbatimLocality” (The original textual description of the place).Column 25: “verbatimDepth” (The original description of the depth).Column 26: “minimumDepthInMeters” (The shallowest depth of a range of depths).Column 27: “maximumDepthInMeters” (The deepest depth of a range of depths).Column 28: “locationRemarks” (The name of the sample station of the cruise).Column 29: “verbatimLatitude” (The verbatim original latitude of the location).Column 30: “verbatimLongitude” (The verbatim original longitude of the location).Column 31: “verbatimCoordinateSystem” (The coordinate format for the “verbatimLatitude” and “verbatimLongitude” or the “verbatimCoordinates” of the location).Column 32: “verbatimSRS” (The spatial reference system [SRS] upon which coordinates given in “verbatimLatitude” and “verbatimLongitude” are based)Column 33: “decimalLatitude” (The geographic latitude in decimal degrees).Column 34: “decimalLongitude” (The geographic longitude in decimal degrees).Column 35: “geodeticDatum” (The spatial reference system [SRS] upon which the geographic coordinates given in “decimalLatitude” and “decimalLongitude” was based).Column 36: “coordinateUncertaintyInMeters” (The horizontal distance from the given “decimalLatitude” and “decimalLongitude” describing the smallest circle containing the whole of the location).Column 37: “georeferenceRemarks” (Notes about the spatial description determination).Column 38: “identifiedBy” (Responsible for recording the original occurrence [publication source]).Column 39: “dateIdentified” (The date-time or interval during which the identification occurred.)Column 40: “identificationQualifier” (A taxonomic determination [e.g., “sp.”, “cf.”]).Column 41: “scientificNameID” (An identifier for the nomenclatural details of a scientific name).Column 42: “scientificName” (The name of species or taxon of the occurrence record).Column 43: “kingdom” (The scientific name of the kingdom in which the taxon is classified).Column 44: “phylum” (The scientific name of the phylum or division in which the taxon is classified).Column 45: “class” (The scientific name of the class in which the taxon is classified).Column 46: “order” (The scientific name of the order in which the taxon is classified).Column 47: “family” (The scientific name of the family in which the taxon is classified).Column 48: “genus” (The scientific name of the genus in which the taxon is classified).Column 49: “subgenus” (The scientific name of the subgenus in which the taxon is classified).Column 50: “specificEpithet” (The name of the first or species epithet of the “scientificName”).Column 51: “infraspecificEpithet” (The name of the lowest or terminal infraspecific epithet of the “scientificName”).Column 52: “taxonRank” (The taxonomic rank of the most specific name in the “scientificName”).Column 53: “scientificNameAuthorship” (The authorship information for the “scientificName” formatted according to the conventions of the applicable nomenclatural Code).Column 54: “verbatimIdentification” (A string representing the taxonomic identification as it appeared in the original record).The information sources (see Fig. 2b) provided a total of 107 publications (22 cruise reports and 85 scientific papers; see Fig. 2c). Nineteen of the 22 cruise reports reviewed provided species occurrence records8,28,29,30,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46, one provided qualitative or descriptive data, with no recorded occurrences31, and two did not provide information on benthic biota (CIMAR-9 and −23 cruises). Of all the scientific papers reviewed, 74 provided records of species occurrences (Table 2), while 11 did not provide any record, as they were data without occurrences of geographically referenced species or with descriptive or qualitative information: Foraminifera59,60,61,62, Annelida63,64,65,66, Fishes67, Mollusca68 and Echinodermata69. The phyla with the highest number of publications were the following: Annelida (present in 18 reports and 21 papers), Mollusca (in 14 and 20), Arthropoda (in 10 and 18), Echinodermata (in 10 and 9), Chordata (in 10 and 9) and Foraminifera (in 4 and 10).Table 2 Publications with >100 occurrences, indicating the main recorded taxa.Full size tableThe information registry includes data on occurrences and number of individuals for 8,854 records (files in the database), representing 1,225 species (Fig. 3). The main taxa in terms of occurrence and number of species were Annelida (mainly Polychaeta), Foraminifera, Mollusca and Arthopoda (mainly Crustacea), together accumulating ~70% of total occurrences and ~73% of the total species (Fig. 3). The large number of recorded occurrences of Myzozoa (10%) should be highlighted, which, however, only represent about 32 species. Echinodermata represented ~8% of occurrences and 7% of species.Fig. 3Occurrences and total species by taxon, considering large taxonomic groups of the benthic biota recorded in the CIMAR 1 to 25 and CDHS-1995 cruises. The absolute values of occurrences and species are represented in parentheses.Full size imageThe cruises with the highest number of occurrences were CIMAR-2 (with 1,424), followed by CIMAR-8 (1,040) and CIMAR-16 (813) (Fig. 4). Three dominant taxonomic groups were recorded in most cruises, except for cruises CIMAR-1, CIMAR-4, CIMAR-17, CIMAR-18 and CIMAR-24 (Fig. 4). The cruises with the highest number of species recorded were CIMAR-2 (with 335), CIMAR-3 (328) and CIMAR-8 (323) (Fig. 5). Three or fewer dominant taxonomic groups were recorded only in the CIMAR-1, CIMAR-4, CIMAR-17, CIMAR-18 and CIMAR-24 cruises (Fig. 5).Fig. 4Total occurrences and percentages per dominant taxon recorded in each of the CIMAR 1 to 25 and CDHS-1995 cruises. The absolute values of occurrences per dominant taxon are represented in parentheses.Full size imageFig. 5Total species and percentages per dominant taxon recorded in each of the CIMAR 1 to 25 and CDHS-1995 cruises. The absolute values of species per dominant taxon are represented in parentheses.Full size imageThe latitudinal bands 42°S and 45°S are those with the highest number of occurrences (Fig. 6), while the 56°S and 46°S bands had the fewest. The highest number of species was recorded in the 52°S and 50°S latitudinal bands, while, as with the occurrences, the lowest values corresponded to the 56°S and 46°S latitudinal bands (Fig. 6).Fig. 6Occurrences and number of species recorded by latitudinal band from the CIMAR 1 to 25 and CDHS-1995 cruises. SEP: South-eastern Pacific.Full size image More

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    Quantifying the feeding behavior and trophic impact of a widespread oceanic ctenophore

    This study provides quantitative data for Ocyropsis spp. feeding mechanisms and in situ data for gut contents during both day and night to begin assessing their trophic role in oceanic waters. Previous studies qualitatively described the feeding pattern of Ocyropsis spp.15 whereby this animal uses a unique capture mechanism among lobate ctenophores: direct transfer from lobe to mouth and encounters involving the mouth actively grabbing copepod prey24. These previous observations are confirmed as Ocyropsis spp. is able to deploy its dexterous, prehensile mouth to effectively capture prey within the lobes (Figs. 2, 3) and quantitative assessments of predation are also provided. It should be noted that while Ocyropsis spp. are known to occasionally consume a wide variety of prey types and sizes15, this study focuses only on copepod prey because our field data showed recognizable prey in Ocyropsis spp. guts was almost exclusively copepods.For example, mean speed of the mouth is less than 6 mm s−1 during predation events on copepods. Thus, while it may look rapid to the human eye, this is far below the escape swimming speeds exhibited by many copepods which are capable of moving at speeds of up to 500 mm s−125,26. Our observations show that the mechanism of capture is thus not reliant on grabbing copepods from the water between the ctenophore lobes with the mouth, but rather aided by copepod contact with the ctenophore lobes. Copepods between the lobes swam only with a speed of 7.94 mm s−1 (S.D. 7.25), to which the average mouth speed (5.83 mm s−1 (S.D. 1.68)) is comparable (Table 1). This suggests that Ocyropsis is able to reduce copepod swimming activity either by trapping them against the lobes (lobes respond to contact by prey) and/or the use of some form of adhesion or chemical that acts to reduce copepod activity. This unusual form of predation using a prehensile mouth allows Ocyropsis to be highly effective predators without the use of prey capturing tentillae seen in other lobate species.The presence of multiple prey has the potential to disrupt a raptorial type feeder such as Ocyropsis spp. more so than other lobates, since they lack tentillae, which would allow them to capture multiple prey simultaneously. Instead Ocyropsis spp. transfer one prey at a time directly from lobe to mouth15,27. So how is this ctenophore able to maintain such a high overall capture rate? The answer appears to be that Ocyropsis will modulate the number of attempts with the prehensile mouth depending on the number of prey present. For example, we did not observe any captures on the first attempt with the mouth with multiple prey, but the animals made up to 8 attempts at capturing the nearest copepod. This is in contrast to single copepod encounters in which ctenophores captured copepods on the first attempt 61% of the time and rarely made over 2 attempts, never exceeding 3 attempts (Figs. 3a, 5a, Table 1). This demonstrates Ocyropsis spp. can adjust its behavior to maintain high overall capture success when presented with multiple simultaneous prey. It is also interesting to note that the resulting increase in handling time due to making more attempts during multiple prey encounters is still lower than the handling time for most other lobates dealing with single prey27,28. It is not clear how often Ocyropsis spp. need to deal with multiple copepods simultaneously in nature, as oceanic waters contain characteristically low ctenophore prey densities compared to coastal zones9,29, however prey can be highly patchy and it appears that the unique prey capture mechanism of Ocyropsis spp. is still able to operate effectively in high density patches by increasing the number of attempts before aborting the attack which could serve as a means to maintain similar ingestion rates to single prey encounters.Typically, the feeding sequence of a ctenophore involves capture of prey in sticky colloblast cells and retraction of tentillae and/or ciliary transport of prey to the mouth15,27,30. These feeding mechanisms result in a range of handling times ranging from 2.5 s for Bolinopsis. infundibulum28 to nearly 22 min for Pleurobrachia bachei27. Capture rates can also be quite high, with overall capture success rates up to 74% for Mnemiopsis leidyi2,3. We found Ocyropsis has a relatively fast mean handling time of 6.3 s when a single copepod was present between the lobes, but handling time increased by approximately 2.5-fold if multiple prey were present. Overall capture success rates were comparable to the highly effective coastal ctenophore, M. leidyi, with a 71% success rate with single prey present and 81% capture rates if multiple prey were present between the lobes. Thus, Ocyropsis spp. are able to capture prey with high efficiency despite the differences in feeding mechanics compared to coastal lobate ctenophores. Additionally, since encounter rates of planktivores are directly related to the time spent searching for prey and time spent handling prey27, the relatively short handling time of Ocyropsis spp. and their direct feeding mechanism may allow them to sample more water and encounter a larger proportion of the available prey population than other species.Diel patterns of prey consumptionMany planktivorous species exhibit higher gut fullness at night31,32, due to higher prey availability in surface waters as a result of a diel vertical migration33,34. In situ gut content images showed that Ocyropsis spp. had a significantly higher gut fullness at night (12.4%) compared to during the day (4.2%) (Fig. 7). Ocyropsis spp. also had higher numbers of prey per individual gut at night, although overall biomass was not significantly different between night and day (Fig. 7). This can be explained by differences in prey characteristics; prey observed in the gut during the day were significantly larger (Table 2). This may be due to an ability to feed more selectively during the day since overall prey densities are lower. It should also be considered that turbulence in surface waters is, on average, much lower at night compared to daytime35 and that even small amounts of turbulence can negatively impact ctenophore feeding36,37. Therefore, smaller prey may have a higher likelihood of evading detection of Ocyropsis during the day compared to night, especially since these animals are most frequently observed in the upper 15 m of oceanic waters.Kremer, et al.38 estimates that O. crystallina requires 252 prey items to sustain itself. On average, Ocyropsis spp. in this study consume over 500 prey d−1. This exceeds their metabolic demands and suggests the observed population, on the western edge of the Gulf Stream, are likely to be actively growing and reproducing. The time required to digest prey items averaged 44 min for Ocyropsis which is faster than many, but not all, gelatinous zooplankton39,40,41. Digestion times of other gelatinous taxa span a range of times from 15 min to over 7 h at 20 °C40 and are impacted by size and number of prey per gut as well as temperature39,42,43. Digestion observations were performed at an ambient temperature of 25 °C and thus, these numbers represent a conservative estimate because the temperature of the water from which the animals were collected was 26.7–27.4 °C. Ocyropsis spp. would likely experience an increase in digestion rate with increased temperature.Digestion time was not impacted by the number of prey in the gut or by ctenophore body length. This differs from trends seen in other gelatinous taxa, such as A. aurita, M. leidyi, and B. infundibulum, where increasing body size resulted in faster digestion time39,40 and where increasing number of prey in the gut leads to longer digestion times39,40,41. In this study however, ctenophores were offered only a few copepods to ingest, thus it is likely they were not fed enough prey to satiate and slow the digestion process. Also worth considering is that the metabolic rate of O. crystallina does not appear to be affected by body size38. Though metabolic rates were not measured, this aligns with our finding that body size had no significant effect on digestion time. Analysis of in situ gut contents showed a significant positive logarithmic relationship between ctenophore length and total prey biomass per gut (Fig. 8). Individuals smaller than 20 mm in this study typically had fewer than the average number of copepods per gut (19), and larger individuals were the main driver of this relationship. This suggests that small Ocyropsis ( More

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    The applicability of species sensitivity distributions to the development of generic doses for phytosanitary irradiation

    Pimentel, D., Zuniga, R. & Morrison, D. Update on the environmental and economic costs associated with alien-invasive species in the United States. Ecol. Econ. https://doi.org/10.1016/j.ecolecon.2004.10.002 (2005).Article 

    Google Scholar 
    Linders, T. E. W. et al. Direct and indirect effects of invasive species: Biodiversity loss is a major mechanism by which an invasive tree affects ecosystem functioning. J. Ecol. https://doi.org/10.1111/1365-2745.13268 (2019).Article 

    Google Scholar 
    Campbell, F. T. The science of risk assessment for phytosanitary regulation and the impact of changing trade regulations. Bioscience https://doi.org/10.1641/0006-3568(2001)051[0148:TSORAF]2.0.CO;2 (2001).Article 

    Google Scholar 
    Paini, D. R. et al. Global threat to agriculture from invasive species. Proc. Natl. Acad. Sci. U. S. A. https://doi.org/10.1073/pnas.1602205113 (2016).Article 

    Google Scholar 
    Westphal, M. I., Browne, M., MacKinnon, K. & Noble, I. The link between international trade and the global distribution of invasive alien species. Biol. Invasions https://doi.org/10.1007/s10530-007-9138-5 (2008).Article 

    Google Scholar 
    Hennessey, M. et al. Phytosanitary Treatments. In The Handbook of Plant Biosecurity (eds Gordh, G. & Mckirdy, S.) 269–308 (Springer, Dordrecht, 2014).
    Google Scholar 
    Melvin Couey, H. & Chew, V. Confidence limits and sample size in quarantine research. J. Econ. Entomol. 79, 887–890 (1986).
    Google Scholar 
    Schortemeyer, M. et al. Appropriateness of probit-9 in the development of quarantine treatments for timber and timber commodities. J. Econ. Entomol. 104, 717–731 (2011).CAS 

    Google Scholar 
    Haack, R. A., Uzunovic, A., Hoover, K. & Cook, J. A. Seeking alternatives to probit 9 when developing treatments for wood packaging materials under ISPM No. 15. EPPO Bull. 41, 39–45 (2011).
    Google Scholar 
    Liqudio, N. J., Griffin, R. L. & Vick, K. W. Quarantine security for commodities: current approaches and potential strategies. In Proceedings of Joint Workshops of the Agricultural Research Service and the Animal and Plant Health Inspection Service, June 5–9 and July 31 -August 5, 1995 56 (1997).Follett, P. A. Phytosanitary irradiation for fresh horticultural commodities: Generic treatments, current issues, and next steps. Stewart Postharvest Rev. 3, 1–7 (2014).MathSciNet 

    Google Scholar 
    Hallman, G. J. & Loaharanu, P. Generic ionizing radiation quarantine treatments against fruit flies (Diptera: Tephritidae) proposed. J. Econ. Entomol. 95, 893–901 (2002).
    Google Scholar 
    Follett, P. A. & Armstrong, J. W. Revised irradiation doses to control melon fly, mediterranean fruit fly, and oriental fruit fly (Diptera: Tephritidae) and a generic dose for tephritid fruit flies. J. Econ. Entomol. 97, 1254–1262 (2004).
    Google Scholar 
    Follett, P. A. & Snook, K. Irradiation for quarantine control of the invasive light brown apple moth (Lepidoptera: Tortricidae) and a generic dose for tortricid eggs and larvae. J. Econ. Entomol. 105, 1971–1978 (2013).
    Google Scholar 
    Hallman, G. J., Arthur, V., Blackburn, C. M. & Parker, A. G. The case for a generic phytosanitary irradiation dose of 250Gy for Lepidoptera eggs and larvae. Radiat. Phys. Chem. 89, 70–75 (2013).ADS 
    CAS 

    Google Scholar 
    Hallman, G. J. Generic phytosanitary irradiation dose of 300 Gy proposed for the Insecta excluding pupal and adult Lepidoptera. Florida Entomol. 99, 206–210 (2016).
    Google Scholar 
    IPPC. ISPM 28. Annex 39. Irradiation treatment for the genus Anastrepha. 1–6 (2021).IPPC. ISPM 28. Annex 7. Irradiation Treatment for fruit flies of the family Tephritidae (generic). 1–6 (2021).Posthuma, L., Suter, G. W. & Traas, T. P. Species sensitivity distributions in ecotoxicology. Species sensitivity distributions in ecotoxicology (CRC Press, 2002). https://doi.org/10.1201/9781420032314.Book 

    Google Scholar 
    Newman, M. C. et al. Applying species-sensitivity distributions in ecological risk assessment: Assumptions of distribution type and sufficient numbers of species. Environ. Toxicol. Chem. 19, 508–515 (2000).CAS 

    Google Scholar 
    van Straalen, N. M. & van Leeuwen, C. J. European history of species sensitivity distributions. In Species Sensitivity Distributions in Ecotoxicology 43–60 (CRC Press, 2001). Doi:https://doi.org/10.1201/9781420032314.ch3.ANZECC & ARMCANZ. Australian and New Zealand guidelines for fresh and marine water quality. aquatic ecosystems. Aust. New Zeal. Environ. Conserv. Counc. Agric. Resour. Manag. Counc. Aust. New Zeal. 1–103 (2000).Aldenberg, T. & Jaworska, J. S. Uncertainty of the hazardous concentration and fraction affected for normal species sensitivity distributions. Ecotoxicol. Environ. Saf. 46, 1–18 (2000).CAS 

    Google Scholar 
    Hallman, G. J. Generic phytosanitary irradiation treatment for “true weevils” (Coleoptera: Curculionidae) infesting fresh commodities. Florida Entomol. 99, 197–201 (2016).
    Google Scholar 
    Follett, P. A. Irradiation for quarantine control of coffee berry borer, hypothenemus hampei (coleoptera: Curculionidae: Scolytinae) in coffee and a proposed generic dose for snout beetles (coleoptera: Curculionoidea). J. Econ. Entomol. 111, 1633–1637 (2018).CAS 

    Google Scholar 
    Earle, N. W., Simmons, L. A. & Nilakhe, S. S. Laboratory studies of sterility and competitiveness of boll weevils irradiated in an atmosphere of nitrogen, carbon dioxide, or air. J. Econ. Entomol. 72, 687–691 (1979).
    Google Scholar 
    Follett, P. A., McQuate, G. T., Sylva, C. D. & Swedman, A. Sensitivity of the quarantine pest rough Sweetpotato weevil, Blosyrus asellus to postharvest irradiation treatment. Proc. Hawaiian Entomol. Soc. 48, 23–27 (2016).
    Google Scholar 
    Hallman, G. J. Ionizing irradiation quarantine treatment against plum curculio (Coleoptera: Curculionidae). J. Econ. Entomol. 96, 1399–1404 (2003).
    Google Scholar 
    Jacklin, S. W., Richardson, E. C. & Yonce, C. E. Substerilizing doses of gamma irradiation to produce population suppression in plum curculio1. J. Econ. Entomol. 63, 1053–1057 (1970).
    Google Scholar 
    Yoshida, T., Fukami, J. I., Fukunaga, K. & Matsuyama, A. Control of harmful insects in timbers by irradiation: doses required for sterilization and inhibition of emergence of the minute pine bark beetle, Cryphalus fulvus. Jpn. J. Appl. Entomol. Zool. 18, 52–58 (1974).
    Google Scholar 
    Follett, P. A. Irradiation as a methyl bromide alternative for postharvest control of Omphisa anastomosalis (Lepidoptera: Pyralidae) and euscepes postfasciatus and cylas formicarius elegantulus (Coleoptera: Curculionidae) in sweet potatoes. J. Econ. Entomol. 99, 32–37 (2006).
    Google Scholar 
    Gould, W. P. & Hallman, G. J. Irradiation disinfestation of diaprepes root weevil (Coleoptera: Curculionidae) and papaya fruit fly (Diptera: Tephritidae). Florida Entomol. 87, 391–392 (2004).
    Google Scholar 
    van Haandel, A. et al. Tolerance of Hylurgus ligniperda (F.) (Coleoptera: Scolytinae) and Arhopalus ferus (Mulsant) (Coleoptera: Cerambycidae) to ionising radiation: a comparison with existing generic radiation phytosanitary treatments. New Zeal. J. For. Sci. 47, 1–9 (2017).Burgess, E. E. & Bennett, S. E. Sterilization of the male alfalfa weevil (Hypera postica: Curculionidae) by X-Radiation. J. Econ. Entomol. 59, 268–270 (1966).
    Google Scholar 
    Wood, D. L. & Stark, R. W. The effects of gamma radiation on the biology and behavior of adult ips confusus (LeConte) (Coleoptera: Scolytidae). Can. Entomol. 98, 1–10 (1966).
    Google Scholar 
    Wang, X. et al. Effect of X-ray (9 MeV) irradiation on the development and propagation of Ips sexdentatus. Plant Quar. 25, 28–31 (2011).
    Google Scholar 
    Zhan, G. et al. Effect of irradiation on development and propagation of larch bark beetle (Coleoptera: Scolytoidea). J. Nucl. Agric. Sci. 25, 1200–1205 (2011).
    Google Scholar 
    Gerstle, C. & Sazo, L. Efecto de las radiaciones de Cesio 137 sobre la fertilidad de hembras de Naupactus xanthographus (Germar) (Coleoptera: Curculionidae). Cienc. e Investig. Agrar. 16, 69–73 (1989).
    Google Scholar 
    Manoto, E. C., Obra, G. B., Reyes, M. R. & Resilva, S. S. Irradiation as a quarantine treatment for ornamentals. IAEA-Tecdoc 1082, 81–91 (1999).
    Google Scholar 
    Duvenhage, A. J. & Johnson, S. A. The potential of irradiation as a postharvest disinfestation treatment against phlyctinus callosus (Coleoptera: Curculionidae). J. Econ. Entomol. 107, 154–160 (2014).CAS 

    Google Scholar 
    Jaynes, A. & Godwin, P. A. Sterilization of the white-pine weevil with gamma radiation. J. Econ. Entomol. 50, 393–395 (1957).CAS 

    Google Scholar 
    Aldryhim, Y. N. & Adam, E. E. Efficacy of gamma irradiation against Sitophilus granarius (L.) (Coleoptera: Curculionidae). J. Stored Prod. Res. 35, 225–232 (1999).
    Google Scholar 
    Follett, P. A. et al. Irradiation quarantine treatment for control of Sitophilus oryzae (Coleoptera: Curculionidae) in rice. J. Stored Prod. Res. 52, 63–67 (2013).
    Google Scholar 
    Hu, T., Chen, C. C. & Peng, W. K. Lethal effect of gamma irradiation on Sitophilus zeamais (Coleoptera: Curculionidae). Formos. Entomol. 23, 145–150 (2003).
    Google Scholar 
    Arthur, V. & Wiendl, F. M. Comportamento e competitividade sexual de adultos de Sphenophorus levis Vaurie, 1978 (col., Curculionidae), uma praga da cana-de-açucar, irradiados com radiações gama do cobaldo-60. Brazilian J. Agric. 68, 57–66 (1993).
    Google Scholar 
    Obra, G. B., Resilva, S. S., Follett, P. A. & Lorenzana, L. R. J. Large-scale confirmatory tests of a phytosanitary irradiation treatment against Sternochetus frigidus (Coleoptera: Curculionidae) in Philippine mango. J. Econ. Entomol. 107, 161–165 (2014).
    Google Scholar 
    Seo, S. T. et al. Mango weevil: Cobalt-60 γ-irradiation of packaged mangoes. J. Econ. Entomol. 67, 504–505 (1974).
    Google Scholar 
    Yoshida, T., Fukami, J. I., Fukunaga, K. & Matsuyama, A. Effects of gamma radiation on Xyleborus perforans (Wollaston) pupae and adults. J. Pestic. Sci. 2, 413–420 (1977).
    Google Scholar 
    Yoshida, T., Fukami, J. I., Fukunaga, K. & Matsuyama, A. Control of the harmful insects in timbers by irradiation: Doses required for kill, sterilization and inhibition of emergence in three species of ambrosia beetles (Xyleborini) in Japan. Jpn. J. Appl. Entomol. Zool. 19, 193–202 (1975).
    Google Scholar 
    Follett, P. A. & McQuate, G. T. Accelerated development of quarantine treatments for insects on poor hosts. J. Econ. Entomol. https://doi.org/10.1603/0022-0493-94.5.1005 (2001).Article 

    Google Scholar 
    Plazzi, F., Ferrucci, R. R. & Passamonti, M. Phylogenetic representativeness: A new method for evaluating taxon sampling in evolutionary studies. BMC Bioinform. 11, 1–15 (2010).
    Google Scholar 
    Moore, D. R. J., Priest, C. D., Galic, N., Brain, R. A. & Rodney, S. I. Correcting for phylogenetic autocorrelation in species sensitivity distributions. Integr. Environ. Assess. Manag. 16, (2020).Carr, G. J. & Belanger, S. E. SSDs revisited: Part I—A framework for sample size guidance on species sensitivity distribution analysis. Environ. Toxicol. Chem. 38, 1514–1525 (2019).CAS 

    Google Scholar 
    Wheeler, J. R., Grist, E. P. M., Leung, K. M. Y., Morritt, D. & Crane, M. Species sensitivity distributions: Data and model choice. Mar. Pollut. Bull. 45, 192–202 (2002).CAS 

    Google Scholar 
    Duboudin, C., Ciffroy, P. & Magaud, H. Acute-to-chronic species sensitivity distribution extrapolation. Environ. Toxicol. Chem. 23, 1774–1785 (2004).CAS 

    Google Scholar 
    Esteves, S. M. et al. Can we predict diatoms herbicide sensitivities with phylogeny? Influence of intraspecific and interspecific variability. Ecotoxicology 26, 1065–1077 (2017).CAS 

    Google Scholar 
    Hiki, K. & Iwasaki, Y. Can we reasonably predict chronic species sensitivity distributions from acute species sensitivity distributions?. Environ. Sci. Technol. 54, 13131–13136 (2020).ADS 
    CAS 

    Google Scholar 
    Baird, D. J. & Van den Brink, P. J. Using biological traits to predict species sensitivity to toxic substances. Ecotoxicol. Environ. Saf. 67, 296–301 (2007).CAS 

    Google Scholar 
    Guénard, G., von der Ohe, P. C., Walker, S. C., Lek, S. & Legendre, P. Using phylogenetic information and chemical properties to predict species tolerances to pesticides. Proc. R. Soc. B Biol. Sci. 281, 1–9 (2014).
    Google Scholar 
    Larras, F., Keck, F., Montuelle, B., Rimet, F. & Bouchez, A. Linking diatom sensitivity to herbicides to phylogeny: A step forward for biomonitoring?. Environ. Sci. Technol. 48, 1921–1930 (2014).ADS 
    CAS 

    Google Scholar 
    Hayashi, T. I. & Kashiwagi, N. A bayesian method for deriving species-sensitivity distributions: Selecting the best-fit tolerance distributions of taxonomic groups. Hum. Ecol. Risk Assess. 16, 251–263 (2010).CAS 

    Google Scholar 
    Xu, F. L. et al. Key issues for the development and application of the species sensitivity distribution (SSD) model for ecological risk assessment. Ecol. Indic. 54, 227–237 (2015).CAS 

    Google Scholar 
    Dowse, R., Tang, D., Palmer, C. G. & Kefford, B. J. Risk assessment using the species sensitivity distribution method: Data quality versus data quantity. Environ. Toxicol. Chem. 32, 1360–1369 (2013).CAS 

    Google Scholar 
    Dias, V. S. et al. Relative tolerance of three morphotypes of the anastrepha fraterculus complex (Diptera: Tephritidae) to cold phytosanitary Treatment. J. Econ. Entomol. 113, 1176–1182 (2020).CAS 

    Google Scholar 
    Myers, S. W., Cancio-Martinez, E., Hallman, G. J., Fontenot, E. A. & Vreysen, M. J. B. Relative tolerance of six Bactrocera (Diptera: Tephritidae) species to phytosanitary cold treatment. J. Econ. Entomol. 109, 2341–2347 (2016).
    Google Scholar 
    Gazit, Y., Akiva, R. & Gavriel, S. Cold tolerance of the Mediterranean fruit fly in date and mandarin. J. Econ. Entomol. 107, 1745–1750 (2014).
    Google Scholar 
    Zhao, J. et al. Gamma radiation as a phytosanitary treatment against larvae and pupae of Bactrocera dorsalis (Diptera: Tephritidae) in guava fruits. Food Control 72, 360–366 (2017).
    Google Scholar 
    Thorley, J. & Schwarz, C. ssdtools: An R package to fit Species sensitivity distributions. J. Open Sour. Softw. 3, 1–2 (2018).
    Google Scholar 
    Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoritic Approach 2nd edn. (Springer, 2002). https://doi.org/10.1007/978-0-387-22456-5_7.Book 
    MATH 

    Google Scholar 
    Mazucheli, J., Menezes, A. F. B. & Nadarajah, S. mle.tools: An R package for maximum likelihood bias correction. R. J. 9, 268–290 (2017).
    Google Scholar 
    Cox, D. R. & Snell, E. J. A general definition of residuals. J. R. Stat. Soc. Ser. B 30, 248–265 (1968).MathSciNet 
    MATH 

    Google Scholar 
    Follett, P. A. Irradiation as a quarantine treatment for mango seed weevil (Coleoptera: Curculionidae). Proc. Hawaii. Entomol. Soc. 35, 95–100 (2001).
    Google Scholar  More

  • in

    Global patterns of water storage in the rooting zones of vegetation

    Teuling, A. J., Seneviratne, S. I., Williams, C. & Troch, P. A. Observed timescales of evapotranspiration response to soil moisture. Geophys. Res. Lett. 33, L23403 (2006).Gao, H. et al. Climate controls how ecosystems size the root zone storage capacity at catchment scale. Geophys. Res. Lett. 41, 7916–7923 (2014).Article 

    Google Scholar 
    Milly, P. C. D. Climate, soil water storage, and the average annual water balance. Water Resour. Res. 30, 2143–2156 (1994).Article 

    Google Scholar 
    Hahm, W. J. et al. Low subsurface water storage capacity relative to annual rainfall decouples Mediterranean plant productivity and water use from rainfall variability. Geophys. Res. Lett. 46, 6544–6553 (2019).Article 

    Google Scholar 
    Seneviratne, S. I. et al. Investigating soil moisture–climate interactions in a changing climate: a review. Earth Sci. Rev. 99, 125–161 (2010).Article 

    Google Scholar 
    Thompson, S. E. et al. Comparative hydrology across AmeriFlux sites: the variable roles of climate, vegetation, and groundwater. Water Resour. Res. 47, W00J07 (2011).Fan, Y., Miguez-Macho, G., Jobbágy, E. G., Jackson, R. B. & Otero-Casal, C. Hydrologic regulation of plant rooting depth. Proc. Natl Acad. Sci. USA 114, 10572–10577 (2017).Article 

    Google Scholar 
    Hain, C. R., Crow, W. T., Anderson, M. C. & Tugrul Yilmaz, M. Diagnosing neglected soil moisture source–sink processes via a thermal infrared-based two-source energy balance model. J. Hydrometeorol. 16, 1070–1086 (2015).Article 

    Google Scholar 
    Rempe, D. M. & Dietrich, W. E. Direct observations of rock moisture, a hidden component of the hydrologic cycle. Proc. Natl Acad. Sci. USA 115, 2664–2669 (2018).Article 

    Google Scholar 
    Dawson, T. E., Jesse Hahm, W. & Crutchfield-Peters, K. Digging deeper: what the critical zone perspective adds to the study of plant ecophysiology. N. Phytol. 226, 666–671 (2020).Article 

    Google Scholar 
    McCormick, E. L. et al. Widespread woody plant use of water stored in bedrock. Nature 597, 225–229 (2021).Article 

    Google Scholar 
    Maxwell, R. M. & Condon, L. E. Connections between groundwater flow and transpiration partitioning. Science 353, 377–380 (2016).Article 

    Google Scholar 
    Schlemmer, L., Schär, C., Lüthi, D. & Strebel, L. A groundwater and runoff formulation for weather and climate models. J. Adv. Model. Earth Syst. 10, 1809–1832 (2018).Article 

    Google Scholar 
    Teuling, A. J. et al. Contrasting response of European forest and grassland energy exchange to heatwaves. Nat. Geosci. 3, 722–727 (2010).Article 

    Google Scholar 
    Koirala, S. et al. Global distribution of groundwater–vegetation spatial covariation. Geophys. Res. Lett. 44, 4134–4142 (2017).Article 

    Google Scholar 
    Esteban, E. J. L., Castilho, C. V., Melgaço, K. L. & Costa, F. R. C. The other side of droughts: wet extremes and topography as buffers of negative drought effects in an Amazonian forest. N. Phytol. 229, 1995–2006 (2021).Article 

    Google Scholar 
    Liu, Y., Konings, A. G., Kennedy, D. & Gentine, P. Global coordination in plant physiological and rooting strategies in response to water stress. Glob. Biogeochem. Cycles 35, e2020GB006758 (2021).Article 

    Google Scholar 
    Schenk, H. J. & Jackson, R. B. The global biogeography of roots. Ecol. Monogr. 72, 311–328 (2002).Article 

    Google Scholar 
    Canadell, J. et al. Maximum rooting depth of vegetation types at the global scale. Oecologia 108, 583–595 (1996).Article 

    Google Scholar 
    Weaver, J. E. & Darland, R. W. Soil–root relationships of certain native grasses in various soil types. Ecol. Monogr. 19, 303–338 (1949).Article 

    Google Scholar 
    Chitra-Tarak, R. et al. Hydraulically-vulnerable trees survive on deep-water access during droughts in a tropical forest. N. Phytol. 231, 1798–1813 (2021).Article 

    Google Scholar 
    Schenk, H. J. & Jackson, R. B. Mapping the global distribution of deep roots in relation to climate and soil characteristics. Geoderma 126, 129–140 (2005).Article 

    Google Scholar 
    Franklin, O. et al. Organizing principles for vegetation dynamics. Nat. Plants 6, 444–453 (2020).Article 

    Google Scholar 
    Kleidon, A. & Heimann, M. A method of determining rooting depth from a terrestrial biosphere model and its impacts on the global water and carbon cycle. Glob. Change Biol. 4, 275–286 (1998).Article 

    Google Scholar 
    Schymanski, S. J., Sivapalan, M., Roderick, M. L., Hutley, L. B. & Beringer, J. An optimality-based model of the dynamic feedbacks between natural vegetation and the water balance. Water Resour. Res. 45, W01412 (2009).Wang-Erlandsson, L. et al. Global root zone storage capacity from satellite-based evaporation. Hydrol. Earth Syst. Sci. 20, 1459–1481 (2016).Article 

    Google Scholar 
    Knapp, A. K. & Smith, M. D. Variation among biomes in temporal dynamics of aboveground primary production. Science 291, 481–484 (2001).Article 

    Google Scholar 
    Anderson, M. A two-source time-integrated model for estimating surface fluxes using thermal infrared remote sensing. Remote Sens. Environ. 60, 195–216 (1997).Article 

    Google Scholar 
    Hain, C. R. & Anderson, M. C. Estimating morning change in land surface temperature from MODIS day/night observations: applications for surface energy balance modeling. Geophys. Res. Lett. 44, 9723–9733 (2017).Article 

    Google Scholar 
    Tumber-Dávila, S. J., Schenk, H. J., Du, E. & Jackson, R. B. Plant sizes and shapes above- and belowground and their interactions with climate. New Phytol. https://nph.onlinelibrary.wiley.com/doi/abs/10.1111/nph.18031 (2022).Harmonized World Soil Database Version 1.0 (FAO, 2008).Wieder, W. Regridded Harmonized World Soil Database Version 1.2 (ORNL DAAC, 2014); https://doi.org/10.3334/ORNLDAAC/1247Balland, V., Pollacco, J. A. P. & Arp, P. A. Modeling soil hydraulic properties for a wide range of soil conditions. Ecol. Model. 219, 300–316 (2008).Article 

    Google Scholar 
    Agee, E. et al. Root lateral interactions drive water uptake patterns under water limitation. Adv. Water Resour. 151, 103896 (2021).Article 

    Google Scholar 
    Krakauer, N. Y., Li, H. & Fan, Y. Groundwater flow across spatial scales: importance for climate modeling. Environ. Res. Lett. 9, 034003 (2014).Article 

    Google Scholar 
    Stoy, P. C. et al. Reviews and syntheses: turning the challenges of partitioning ecosystem evaporation and transpiration into opportunities. Biogeosciences 16, 3747–3775 (2019).Article 

    Google Scholar 
    Jackson, R. B., Moore, L. A., Hoffmann, W. A., Pockman, W. T. & Linder, C. R. Ecosystem rooting depth determined with caves and DNA. Proc. Natl Acad. Sci. USA 96, 11387–11392 (1999).Article 

    Google Scholar 
    Pelletier, J. D. et al. A gridded global data set of soil, intact regolith, and sedimentary deposit thicknesses for regional and global land surface modeling. J. Adv. Model. Earth Syst. 8, 41–65 (2016).Article 

    Google Scholar 
    Parmesan, C. & Hanley, M. E. Plants and climate change: complexities and surprises. Ann. Bot. 116, 849–864 (2015).Article 

    Google Scholar 
    Pendergrass, A. G., Knutti, R., Lehner, F., Deser, C. & Sanderson, B. M. Precipitation variability increases in a warmer climate. Sci. Rep. 7, 17966 (2017).Siebert, S. et al. Development and validation of the global map of irrigation areas. Hydrol. Earth Syst. Sci. 9, 535–547 (2005).Article 

    Google Scholar 
    Friedl, M. A. et al. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sens. Environ. 114, 168–182 (2010).Article 

    Google Scholar 
    Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth. BioScience 51, 933–938 (2001).Article 

    Google Scholar 
    Mu, Q., Heinsch, F. A., Zhao, M. & Running, S. W. Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sens. Environ. 111, 519–536 (2007).Article 

    Google Scholar 
    Fisher, J. B. et al. ECOSTRESS: NASA’s next generation mission to measure evapotranspiration from the international space station. Water Resour. Res. 56, e2019WR026058 (2020).Article 

    Google Scholar 
    Davis, T. W. et al. Simple process-led algorithms for simulating habitats (SPLASH v.1.0): robust indices of radiation, evapotranspiration and plant-available moisture. Geosci. Model Dev. 10, 689–708 (2017).Article 

    Google Scholar 
    Weedon, G. P. et al. The WFDEI meteorological forcing data set: WATCH forcing data methodology applied to ERA-Interim reanalysis data. Water Resour. Res. 50, 7505–7514 (2014).Article 

    Google Scholar 
    Orth, R., Koster, R. D. & Seneviratne, S. I. Inferring soil moisture memory from streamflow observations using a simple water balance model. J. Hydrometeorol. 14, 1773–1790 (2013).Article 

    Google Scholar 
    Stocker, B. cwd v.1.0: R package for cumulative water deficit calculation. Zenodo https://doi.org/10.5281/zenodo.5359053 (2021).Zhang, Y. et al. Model-based analysis of the relationship between sun-induced chlorophyll fluorescence and gross primary production for remote sensing applications. Remote Sens. Environ. 187, 145–155 (2016).Article 

    Google Scholar 
    Duveiller, G. et al. A spatially downscaled sun-induced fluorescence global product for enhanced monitoring of vegetation productivity. Earth Syst. Sci. Data 12, 1101–1116 (2020).Article 

    Google Scholar 
    Joiner, J. et al. Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2. Atmos. Meas. Tech. 6, 2803–2823 (2013).Article 

    Google Scholar 
    Köhler, P., Guanter, L. & Joiner, J. A linear method for the retrieval of sun-induced chlorophyll fluorescence from GOME-2 and SCIAMACHY data. Atmos. Meas. Tech. 8, 2589–2608 (2015).Article 

    Google Scholar 
    Jiang, B. et al. Validation of the surface daytime net radiation product from version 4.0 GLASS product suite. IEEE Geosci. Remote Sens. Lett. 16, 509–513 (2019).Article 

    Google Scholar 
    Muggeo, V. M. R. Estimating regression models with unknown break-points. Stat. Med. 22, 3055–3071 (2003).Article 

    Google Scholar 
    Gilleland, E. & Katz, R. W. extRemes 2.0: an extreme value analysis package in R. J. Stat. Softw. 72, 1–39 (2016).Marthews, T. R., Dadson, S. J., Lehner, B., Abele, S. & Gedney, N. High-resolution global topographic index values for use in large-scale hydrological modelling. Hydrol. Earth Syst. Sci. 19, 91–104 (2015).Article 

    Google Scholar 
    Etopo1, Global 1 Arc-Minute Ocean Depth and Land Elevation from the US National Geophysical Data Center (NGDC) (National Geophysical Data Center, NESDIS, NOAA and US Department of Commerce, 2011); https://doi.org/10.5065/D69Z92Z5Beven, K. J. & Kirkby, M. J. A physically based, variable contributing area model of basin hydrology. Hydrol. Sci. J. 24, 43–69 (1979).Article 

    Google Scholar 
    Hansen, M. C., Townshend, J. R. G., DeFries, R. S. & Carroll, M. Estimation of tree cover using MODIS data at global, continental and regional/local scales. Int. J. Remote Sens. 26, 4359–4380 (2005).Article 

    Google Scholar 
    Stocker, B. D. Global rooting zone water storage capacity and rooting depth estimates. Zenodo https://doi.org/10.5281/zenodo.5515246 (2021).Stocker, B. stineb/mct: v3.0: re-submission to Nature Geoscience. Zenodo https://doi.org/10.5281/zenodo.6239187 (2022). More

  • in

    Intra-individual variation of hen movements is associated with later keel bone fractures in a quasi-commercial aviary

    Rufener, C. et al. Keel bone fractures are associated with individual mobility of laying hens in an aviary system. Appl. Anim. Behav. Sci. 217, 48–56 (2019).
    Google Scholar 
    Rentsch, A. K., Rufener, C. B., Spadavecchia, C., Stratmann, A. & Toscano, M. J. Laying hen’s mobility is impaired by keel bone fractures and does not improve with paracetamol treatment. Appl. Anim. Behav. Sci. 216, 19–25 (2019).
    Google Scholar 
    Rodriguez-Aurrekoetxea, A. & Estevez, I. Use of space and its impact on the welfare of laying hens in a commercial free-range system. Poult. Sci. 95, 2503–2513 (2016).CAS 

    Google Scholar 
    Fagan, W. F. et al. Spatial memory and animal movement. Ecol. Lett. 16, 1316–1329 (2013).
    Google Scholar 
    Campbell, D. L. M., Talk, A. C., Loh, Z. A., Dyall, T. R. & Lee, C. Spatial cognition and range use in free-range laying hens. Animals 8, 26 (2018).
    Google Scholar 
    de Jager, M., Weissing, F. J., Herman, P. M. J., Nolet, B. A. & van de Koppel, J. Lévy walks evolve through interaction between movement and environmental complexity. Science 1979(332), 1551–1553 (2011).
    Google Scholar 
    Krause, J., James, R. & Croft, D. P. Personality in the context of social networks. Philos. Trans. R. Soc. B Biol. Sci. 365, 4099–4106 (2010).CAS 

    Google Scholar 
    Ihwagi, F. W. et al. Poaching lowers elephant path tortuosity: Implications for conservation. J. Wildl. Manag. 83, 1022–1031 (2019).
    Google Scholar 
    Shaw, A. K. Causes and consequences of individual variation in animal movement. Mov. Ecol. 8, 1–12 (2020).
    Google Scholar 
    Matthews, S. G., Miller, A. L., Plötz, T. & Kyriazakis, I. Automated tracking to measure behavioural changes in pigs for health and welfare monitoring. Sci. Rep. 7, 1–12 (2017).CAS 

    Google Scholar 
    Berger-Tal, O. & Saltz, D. Using the movement patterns of reintroduced animals to improve reintroduction success. Curr. Zool. 60, 515–526 (2014).
    Google Scholar 
    Stuber, E. F., Carlson, B. S. & Jesmer, B. R. Spatial personalities: A meta-analysis of consistent individual differences in spatial behavior. Behav. Ecol. https://doi.org/10.1093/BEHECO/ARAB147 (2022).Article 

    Google Scholar 
    Sirovnik, J., Würbel, H. & Toscano, M. J. Feeder space affects access to the feeder, aggression, and feed conversion in laying hens in an aviary system. Appl. Anim. Behav. Sci. 198, 75–82 (2018).
    Google Scholar 
    Sirovnik, J., Voelkl, B., Keeling, L. J., Würbel, H. & Toscano, M. J. Breakdown of the ideal free distribution under conditions of severe and low competition. Behav. Ecol. Sociobiol. 75, 1–11 (2021).
    Google Scholar 
    Becot, L., Bedere, N., Burlot, T., Coton, J. & le Roy, P. Nest acceptance, clutch, and oviposition traits are promising selection criteria to improve egg production in cage-free system. PLoS ONE 16, e0251037 (2021).CAS 

    Google Scholar 
    Thompson, M. J., Evans, J. C., Parsons, S. & Morand-Ferron, J. Urbanization and individual differences in exploration and plasticity. Behav. Ecol. 29, 1415–1425 (2018).
    Google Scholar 
    Stamps, J. & Groothuis, T. G. G. The development of animal personality: Relevance, concepts and perspectives. Biol. Rev. 85, 301–325 (2010).
    Google Scholar 
    Salinas-Melgoza, A., Salinas-Melgoza, V. & Wright, T. F. Behavioral plasticity of a threatened parrot in human-modified landscapes. Biol. Conserv. 159, 303–312 (2013).
    Google Scholar 
    Stamps, J. A., Briffa, M. & Biro, P. A. Unpredictable animals: Individual differences in intraindividual variability (IIV). Anim. Behav. 83, 1325–1334 (2012).
    Google Scholar 
    Hertel, A. G., Royauté, R., Zedrosser, A. & Mueller, T. Biologging reveals individual variation in behavioural predictability in the wild. J. Anim. Ecol. 90, 723–737 (2021).
    Google Scholar 
    Biro, P. A. & Adriaenssens, B. Predictability as a personality trait: Consistent differences in intraindividual behavioral variation. Am. Nat. 182, 621–629 (2013).
    Google Scholar 
    Henriksen, R. et al. Intra-individual behavioural variability: A trait under genetic control. Int. J. Mol. Sci. 21, 8069 (2020).CAS 

    Google Scholar 
    Rufener, C. et al. Finding hens in a haystack: Consistency of movement patterns within and across individual laying hens maintained in large groups. Sci. Rep. 8, (2018).Campbell, D. L. M., Karcher, D. M. & Siegford, J. M. Location tracking of individual laying hens housed in aviaries with different litter substrates. Appl. Anim. Behav. 184, 74–79 (2016).
    Google Scholar 
    Weeks, C. A. & Nicol, C. J. Behavioural needs, priorities and preferences of laying hens. Worlds Poult. Sci. J. 62, 296–307 (2006).
    Google Scholar 
    Hartcher, K. M. & Jones, B. The welfare of layer hens in cage and cage-free housing systems. Worlds Poult. Sci. J. 73, 767–782 (2017).
    Google Scholar 
    Zeltner, E. & Hirt, H. Effect of artificial structuring on the use of laying hen runs in a free-range system. Br. Poult. Sci. 44, 533–537 (2010).
    Google Scholar 
    Stratmann, A. et al. Modification of aviary design reduces incidence of falls, collisions and keel bone damage in laying hens. Appl. Anim. Behav. Sci. 165, 112–123 (2015).
    Google Scholar 
    Vandekerchove, D., Herdt, P., Laevens, H. & Pasmans, F. Colibacillosis in caged layer hens: Characteristics of the disease and the aetiological agent. Avian Pathol. 33, 117–125 (2004).CAS 

    Google Scholar 
    Montalcini, C. M., Voelkl, B., Gómez, Y., Gantner, M. & Toscano, M. J. Evaluation of an active LF tracking system and data processing methods for livestock precision farming in the poultry sector. Sensors 22, 659 (2022).ADS 

    Google Scholar 
    Revelle, W. Procedures for psychological, psychometric, and personality research. (2021).Kaiser, H. F. The application of electronic computers to factor analysis. Educ. Psychol. Meas. 20, 141–151 (1960).
    Google Scholar 
    Rufener, C., Baur, S., Stratmann, A. & Toscano, M. J. A reliable method to assess keel bone fractures in laying hens from radiographs using a tagged visual analogue scale. Front. Vet. Sci. 5, 124 (2018).
    Google Scholar 
    Tauson, R., Kjaer, J., Maria, G. A., Cepero, R. & Holm, K.-E. The creation of a common scoring system for the integument and health of laying hens: Applied scoring of integument and health in laying hens. Final report Health from the Laywell project. https://www.laywel.eu/web/pdf/deliverables%2031-33%20health.pdf (2005).Hertel, A. G. et al. A guide for studying among-individual behavioral variation from movement data in the wild. Mov. Ecol. 8, (2020).Nakagawa, S. & Schielzeth, H. Repeatability for Gaussian and non-Gaussian data: A practical guide for biologists. Biol. Rev. 85, 935–956 (2010).
    Google Scholar 
    Dingemanse, N. J., Kazem, A. J. N., Réale, D. & Wright, J. Behavioural reaction norms: Animal personality meets individual plasticity. Trends Ecol. Evol. 25, 81–89 (2010).
    Google Scholar 
    Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J Stat Softw 67, (2015).Cleasby, I. R., Nakagawa, S. & Schielzeth, H. Quantifying the predictability of behaviour: Statistical approaches for the study of between-individual variation in the within-individual variance. Methods Ecol. Evol. 6, 27–37 (2015).
    Google Scholar 
    Bürkner, P.-C. brms: An R package for bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).
    Google Scholar 
    Vehtari, A., Gelman, A. & Gabry, J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27, 1413–1432 (2017).MathSciNet 
    MATH 

    Google Scholar 
    Gelman, A. & Rubin, D. B. Inference from iterative simulation using multiple sequences. Stat. Sci. 7, 457–472 (1992).MATH 

    Google Scholar 
    Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: The MCMCglmm R package. J. Stat. Softw. 33, 1–22 (2010).
    Google Scholar 
    Houslay, T. M. & Wilson, A. J. Avoiding the misuse of BLUP in behavioural ecology. Behav. Ecol. 28, 948–952 (2017).
    Google Scholar 
    Hertel, A. G., Niemelä, P. T., Dingemanse, N. J. & Mueller, T. Don’t poke the bear: Using tracking data to quantify behavioural syndromes in elusive wildlife. Anim. Behav. 147, 91–104 (2019).
    Google Scholar 
    Spiegel, O., Leu, S. T., Bull, C. M. & Sih, A. What’s your move? Movement as a link between personality and spatial dynamics in animal populations. Ecol. Lett. 20, 3–18 (2017).ADS 

    Google Scholar 
    Bell, A. M., Hankison, S. J. & Laskowski, K. L. The repeatability of behaviour: A meta-analysis. Anim. Behav. 77, 771–783 (2009).
    Google Scholar 
    Occhiuto, F., Vázquez-Diosdado, J. A., Carslake, C. & Kaler, J. Personality and predictability in farmed calves using movement and space-use behaviours quantified by ultra-wideband sensors. R. Soc. Open Sci. 9, (2022).Moinard, C. et al. Accuracy of laying hens in jumping upwards and downwards between perches in different light environments. Appl. Anim. Behav. Sci. 85, 77–92 (2004).
    Google Scholar 
    Baur, S., Rufener, C., Toscano, M. J. & Geissbühler, U. Radiographic evaluation of keel bone damage in laying hens—Morphologic and temporal observations in a longitudinal study. Front. Vet. Sci. 1, 129 (2020).
    Google Scholar 
    Cordiner, L. S. & Savory, C. J. Use of perches and nestboxes by laying hens in relation to social status, based on examination of consistency of ranking orders and frequency of interaction. Appl. Anim. Behav. Sci. 71, 305–317 (2001).
    Google Scholar 
    Rufener, C. & Makagon, M. M. Keel bone fractures in laying hens: A systematic review of prevalence across age, housing systems, and strains. J. Anim. Sci. 98, S36–S51 (2020).
    Google Scholar 
    Nasr, M. A. F., Nicol, C. J., Wilkins, L. & Murrell, J. C. The effects of two non-steroidal anti-inflammatory drugs on the mobility of laying hens with keel bone fractures. Vet. Anaesth. Analg. 42, 197–204 (2015).CAS 

    Google Scholar 
    Nasr, M., Murrell, J., Wilkins, L. J. & Nicol, C. J. The effect of keel fractures on egg-production parameters, mobility and behaviour in individual laying hens. Anim. Welf. 21, 127–135 (2012).CAS 

    Google Scholar 
    Koolhaas, J. M. & van Reenen, C. G. Animal behavior and well-being symposium: Interaction between coping style/personality, stress, and welfare: Relevance for domestic farm animals. J. Anim. Sci. 94, 2284–2296 (2016).CAS 

    Google Scholar 
    Coppens, C. M., de Boer, S. F. & Koolhaas, J. M. Coping styles and behavioural flexibility: Towards underlying mechanisms. Philos. Trans. R. Soc. B Biol. Sci. 365, 4021 (2010).
    Google Scholar 
    Koolhaas, J. M., de Boer, S. F., Coppens, C. M. & Buwalda, B. Neuroendocrinology of coping styles: Towards understanding the biology of individual variation. Front. Neuroendocrinol. 31, 307–321 (2010).CAS 

    Google Scholar 
    Finkemeier, M.-A., Langbein, J. & Puppe, B. Personality research in mammalian farm animals: Concepts, measures, and relationship to welfare. Front. Vet. Sci. 5, 131 (2018).
    Google Scholar 
    Martin, J. G. A., Pirotta, E., Petelle, M. B. & Blumstein, D. T. Genetic basis of between-individual and within-individual variance of docility. J. Evol. Biol. 30, 796–805 (2017).CAS 

    Google Scholar 
    Prentice, P. M., Houslay, T. M., Martin, J. G. A. & Wilson, A. J. Genetic variance for behavioural ‘predictability’ of stress response. J. Evol. Biol. 33, 642–652 (2020).
    Google Scholar  More

  • in

    Multilayer networks of plasmid genetic similarity reveal potential pathways of gene transmission

    WHO. Global antimicrobial resistance and use surveillance system (GLASS) report: 2021; https://apps.who.int/iris/bitstream/handle/10665/341666/9789240027336-eng.pdfVan Boeckel TP, Glennon EE, Chen D, Gilbert M, Robinson TP, Grenfell BT, et al. Reducing antimicrobial use in food animals. Science. 2017;357:1350–2. https://doi.org/10.1126/science.aao1495Article 
    CAS 

    Google Scholar 
    ONeill J. Antimicrobials in agriculture and the environment: reducing unnecessary use and waste. Rev Antimicrob Resistance; 2015:1–28.Van Boeckel TP, Brower C, Gilbert M, Grenfell BT, Levin SA, Robinson TP, et al. Global trends in antimicrobial use in food animals. Proc Natl Acad Sci USA. 2015;112:5649–54. https://doi.org/10.1073/pnas.1503141112Article 
    CAS 

    Google Scholar 
    Managaki S, Murata A, Takada H, Tuyen BC, Chiem NH. Distribution of macrolides, sulfonamides, and trimethoprim in tropical waters: ubiquitous occurrence of veterinary antibiotics in the Mekong Delta. Environ Sci Technol. 2007;41:8004–10. https://doi.org/10.1021/es0709021Article 
    CAS 

    Google Scholar 
    Woolhouse M, Ward M, van Bunnik B, Farrar J. Antimicrobial resistance in humans, livestock and the wider environment. Philos Trans R Soc Lond B Biol Sci. 2015;370:20140083 https://doi.org/10.1098/rstb.2014.0083Article 
    CAS 

    Google Scholar 
    Noyes NR, Yang X, Linke LM, Magnuson RJ, Cook SR, Zaheer R, et al. Characterization of the resistome in manure, soil and wastewater from dairy and beef production systems. Sci Rep. 2016;6:24645. https://doi.org/10.1038/srep24645Article 
    CAS 

    Google Scholar 
    Agga GE, Cook KL, Netthisinghe AMP, Gilfillen RA, Woosley PB, Sistani KR. Persistence of antibiotic resistance genes in beef cattle backgrounding environment over two years after cessation of operation. PLoS One. 2019;14:e0212510. https://doi.org/10.1371/journal.pone.0212510Article 
    CAS 

    Google Scholar 
    Hudson JA, Frewer LJ, Jones G, Brereton PA, Whittingham MJ, Stewart G. The agri-food chain and antimicrobial resistance: a review. Trends Food Sci Technol. 2017;69:131–47. https://doi.org/10.1016/j.tifs.2017.09.007Article 
    CAS 

    Google Scholar 
    Gillings MR. Lateral gene transfer, bacterial genome evolution, and the Anthropocene. Ann NY Acad Sci. 2017;1389:20–36. https://doi.org/10.1111/nyas.13213Article 

    Google Scholar 
    Rodríguez-Beltrán J, DelaFuente J, León-Sampedro R, MacLean RC, San Millán Á. Beyond horizontal gene transfer: the role of plasmids in bacterial evolution. Nat Rev Microbiol. 2021;6:347–59. https://doi.org/10.1038/s41579-020-00497-1Article 
    CAS 

    Google Scholar 
    Zhang T, Zhang XX, Ye L. Plasmid metagenome reveals high levels of antibiotic resistance genes and mobile genetic elements in activated sludge. PLoS One. 2011;6:e26041. https://doi.org/10.1371/journal.pone.0026041Article 
    CAS 

    Google Scholar 
    Li AD, Li LG, Zhang T. Exploring antibiotic resistance genes and metal resistance genes in plasmid metagenomes from wastewater treatment plants. Front Microbiol. 2015;6:1025. https://doi.org/10.3389/fmicb.2015.01025Article 

    Google Scholar 
    Bukowski M, Piwowarczyk R, Madry A, Zagorski-Przybylo R, Hydzik M, Wladyka B. Prevalence of antibiotic and heavy metal resistance determinants and virulence-related genetic elements in plasmids of Staphylococcus aureus. Front Microbiol. 2019;10:805. https://doi.org/10.3389/fmicb.2019.00805Article 

    Google Scholar 
    Ramírez-Díaz MI, Díaz-Magaña A, Meza-Carmen V, Johnstone L, Cervantes C, Rensing C. Nucleotide sequence of Pseudomonas aeruginosa conjugative plasmid pUM505 containing virulence and heavy-metal resistance genes. Plasmid. 2011;66:7–18. https://doi.org/10.1016/j.plasmid.2011.03.002Article 
    CAS 

    Google Scholar 
    Haenni M, Poirel L, Kieffer N, Châtre P, Saras E, Métayer V, et al. Co-occurrence of extended spectrum β lactamase and MCR-1 encoding genes on plasmids. Lancet Infect Dis. 2016;16:281–2. https://doi.org/10.1016/S1473-3099(16)00007-4Article 
    CAS 

    Google Scholar 
    Peter S, Bosio M, Gross C, Bezdan D, Gutierrez J, Oberhettinger P, et al. Tracking of antibiotic resistance transfer and rapid plasmid evolution in a hospital setting by nanopore sequencing. mSphere. 2020;5. https://doi.org/10.1128/mSphere.00525-20Halary S, Leigh JW, Cheaib B, Lopez P, Bapteste E. Network analyses structure genetic diversity in independent genetic worlds. Proc Natl Acad Sci USA. 2010;107:127–32. https://doi.org/10.1073/pnas.0908978107Article 

    Google Scholar 
    Bosi E, Fani R, Fondi M. The mosaicism of plasmids revealed by atypical genes detection and analysis. BMC Genom. 2011;12:403. https://doi.org/10.1186/1471-2164-12-403Article 
    CAS 

    Google Scholar 
    Pesesky MW, Tilley R, Beck DAC. Mosaic plasmids are abundant and unevenly distributed across prokaryotic taxa. Plasmid. 2019;102:10–18. https://doi.org/10.1016/j.plasmid.2019.02.003Article 
    CAS 

    Google Scholar 
    Casjens SR, Gilcrease EB, Vujadinovic M, Mongodin EF, Luft BJ, Schutzer SE, et al. Plasmid diversity and phylogenetic consistency in the Lyme disease agent Borrelia burgdorferi. BMC Genom. 2017;18:165. https://doi.org/10.1186/s12864-017-3553-5Article 
    CAS 

    Google Scholar 
    Madec JY, Haenni M. Antimicrobial resistance plasmid reservoir in food and food-producing animals. Plasmid. 2018;99:72–81. https://doi.org/10.1016/j.plasmid.2018.09.001Article 
    CAS 

    Google Scholar 
    Ceccarelli D, Kant A, van Essen-Zandbergen A, Dierikx C, Hordijk J, Wit B, et al. Diversity of plasmids and genes encoding resistance to extended spectrum cephalosporins in commensal escherichia coli from dutch livestock in 2007–2017. Front Microbiol. 2019;10. https://doi.org/10.3389/fmicb.2019.00076Auffret MD, Dewhurst RJ, Duthie CA, Rooke JA, John Wallace R, Freeman TC, et al. The rumen microbiome as a reservoir of antimicrobial resistance and pathogenicity genes is directly affected by diet in beef cattle. Microbiome. 2017;5:159. https://doi.org/10.1186/s40168-017-0378-zArticle 

    Google Scholar 
    Sabino YNV, Santana MF, Oyama LB, Santos FG, Moreira AJS, Huws SA, et al. Characterization of antibiotic resistance genes in the species of the rumen microbiota. Nat Commun. 2019;10:5252. https://doi.org/10.1038/s41467-019-13118-0Article 
    CAS 

    Google Scholar 
    Brown Kav A, Benhar I, Mizrahi I. Rumen plasmids. In: Gophna U, editor. Lateral gene transfer in evolution. New York, NY: Springer New York; 2013. p. 105–20.Mizrahi I, Wallace RJ, Moraïs S. The rumen microbiome: balancing food security and environmental impacts. Nat Rev Microbiol. 2021;19:553–66. https://doi.org/10.1038/s41579-021-00543-6Article 
    CAS 

    Google Scholar 
    Dionisio F, Zilhão R, Gama JA. Interactions between plasmids and other mobile genetic elements affect their transmission and persistence. Plasmid. 2019;102:29–36. https://doi.org/10.1016/j.plasmid.2019.01.003Article 
    CAS 

    Google Scholar 
    Brown Kav A, Sasson G, Jami E, Doron-Faigenboim A, Benhar I, Mizrahi I. Insights into the bovine rumen plasmidome. Proc Natl Acad Sci USA. 2012;109:5452–7. https://doi.org/10.1073/pnas.1116410109Article 

    Google Scholar 
    Kav AB, Rozov R, Bogumil D, Sørensen SJ, Hansen LH, Benhar I, et al. Unravelling plasmidome distribution and interaction with its hosting microbiome. Environ Microbiol. 2020;22:32–44. https://doi.org/10.1111/1462-2920.14813Article 

    Google Scholar 
    Jørgensen TS, Xu Z, Hansen MA, Sørensen SJ, Hansen LH. Hundreds of circular novel plasmids and DNA elements identified in a rat cecum metamobilome. PLoS One. 2014;9:e87924. https://doi.org/10.1371/journal.pone.0087924Article 
    CAS 

    Google Scholar 
    He Q, Pilosof S, Tiedje KE, Ruybal-Pesántez S, Artzy-Randrup Y, Baskerville EB, et al. Networks of genetic similarity reveal non-neutral processes shape strain structure in Plasmodium falciparum. Nat Commun. 2018;9:1817. https://doi.org/10.1038/s41467-018-04219-3Article 
    CAS 

    Google Scholar 
    Acman M, van Dorp L, Santini JM, Balloux F. Large-scale network analysis captures biological features of bacterial plasmids. Nat Commun. 2020;11:2452. https://doi.org/10.1038/s41467-020-16282-wArticle 
    CAS 

    Google Scholar 
    Redondo-Salvo S, Fernández-López R, Ruiz R, Vielva L, de Toro M, Rocha EPC, et al. Pathways for horizontal gene transfer in bacteria revealed by a global map of their plasmids. Nat Commun. 2020;11:3602. https://doi.org/10.1038/s41467-020-17278-2Article 
    CAS 

    Google Scholar 
    Savary P, Foltête JC, Moal H, Vuidel G, Garnier S. Analysing landscape effects on dispersal networks and gene flow with genetic graphs. Mol Ecol Resour. 2021;21:1167–85. https://doi.org/10.1111/1755-0998.13333Article 

    Google Scholar 
    Pilosof S, He Q, Tiedje KE, Ruybal-Pesántez S, Day KP, Pascual M. Competition for hosts modulates vast antigenic diversity to generate persistent strain structure in Plasmodium falciparum. PLoS Biol. 2019;17:e3000336. https://doi.org/10.1371/journal.pbio.3000336Article 
    CAS 

    Google Scholar 
    Brilli M, Mengoni A, Fondi M, Bazzicalupo M, Liò P, Fani R. Analysis of plasmid genes by phylogenetic profiling and visualization of homology relationships using Blast2Network. BMC Bioinform. 2008;9:551. https://doi.org/10.1186/1471-2105-9-551Article 
    CAS 

    Google Scholar 
    Fondi M, Karkman A, Tamminen MV, Bosi E, Virta M, Fani R, et al. “Every gene is everywhere but the environment selects”: global geolocalization of gene sharing in environmental samples through network analysis. Genome Biol Evol. 2016;8:1388–1400. https://doi.org/10.1093/gbe/evw077Article 

    Google Scholar 
    Tamminen M, Virta M, Fani R, Fondi M. Large-scale analysis of plasmid relationships through gene-sharing networks. Mol Biol Evol. 2012;29:1225–40. https://doi.org/10.1093/molbev/msr292Article 
    CAS 

    Google Scholar 
    Yamashita A, Sekizuka T, Kuroda M. Characterization of antimicrobial resistance dissemination across plasmid communities classified by network analysis. Pathogens. 2014;3:356–76. https://doi.org/10.3390/pathogens3020356Article 
    CAS 

    Google Scholar 
    Pastor-Satorras R, Castellano C, Van Mieghem P, Vespignani A. Epidemic processes in complex networks. Rev Mod Phys. 2015;87:925–79. https://doi.org/10.1103/RevModPhys.87.925Article 

    Google Scholar 
    Pilosof S, Morand S, Krasnov BR, Nunn CL. Potential parasite transmission in multi-host networks based on parasite sharing. PLoS One. 2015;10:e0117909 https://doi.org/10.1371/journal.pone.0117909Article 
    CAS 

    Google Scholar 
    VanderWaal KL, Atwill ER, Isbell LA, McCowan B.Linking social and pathogen transmission networks using microbial genetics in giraffe (Giraffa camelopardalis).J Anim Ecol.2014;83:406–14. https://doi.org/10.1111/1365-2656.12137Article 

    Google Scholar 
    Kauffman K, Werner CS, Titcomb G, Pender M, Rabezara JY, Herrera JP, et al. Comparing transmission potential networks based on social network surveys, close contacts and environmental overlap in rural Madagascar. J R Soc Interface. 2022;19:20210690. https://doi.org/10.1098/rsif.2021.0690Article 

    Google Scholar 
    Dallas TA, Han BA, Nunn CL, Park AW, Stephens PR, Drake JM. Host traits associated with species roles in parasite sharing networks. Oikos. 2019;128:23–32. https://doi.org/10.1111/oik.05602Article 

    Google Scholar 
    Matlock W, Chau KK, AbuOun M, Stubberfield E, Barker L, Kavanagh J, et al. Genomic network analysis of environmental and livestock F-type plasmid populations. ISME J. 2021;15:2322–35. https://doi.org/10.1038/s41396-021-00926-wArticle 
    CAS 

    Google Scholar 
    Pilosof S, Porter MA, Pascual M, Kéfi S. The multilayer nature of ecological networks. Nat Ecol Evol. 2017;1:0101. https://doi.org/10.1038/s41559-017-0101Article 

    Google Scholar 
    Paull SH, Song S, McClure KM, Sackett LC, Kilpatrick AM, Johnson PTJ. From superspreaders to disease hotspots: linking transmission across hosts and space. Front Ecol Environ. 2012;10:75–82. https://doi.org/10.1890/110111Article 

    Google Scholar 
    Hutchinson MC, Bramon Mora B, Pilosof S, Barner AK, Kéfi S, Thébault E, et al. Seeing the forest for the trees: putting multilayer networks to work for community ecology. Funct Ecol. 2019;33:206–17. https://doi.org/10.1111/1365-2435.13237Article 

    Google Scholar 
    Kivelä M, Arenas A, Barthelemy M, Gleeson JP, Moreno Y, Porter MA. Multilayer networks. J Complex Netw. 2014;2:203–71. https://doi.org/10.1093/comnet/cnu016Article 

    Google Scholar 
    Lloyd-Smith JO, Schreiber SJ, Kopp PE, Getz WM. Superspreading and the effect of individual variation on disease emergence. Nature. 2005;438:355–9. https://doi.org/10.1038/nature04153Article 
    CAS 

    Google Scholar 
    Fortuna MA, Popa-Lisseanu AG, Ibáñez C, Bascompte J. The roosting spatial network of a bird-predator bat. Ecology. 2009;90:934–44. https://doi.org/10.1890/08-0174.1Article 

    Google Scholar 
    Newman MEJ, Girvan M. Finding and evaluating community structure in networks. Phys Rev E Stat Nonlin Soft Matter Phys. 2004;69:026113. https://doi.org/10.1103/PhysRevE.69.026113Article 
    CAS 

    Google Scholar 
    Rosvall M, Bergstrom CT. Maps of random walks on complex networks reveal community structure. Proc Natl Acad Sci USA. 2008;105:1118–23. https://doi.org/10.1073/pnas.0706851105Article 

    Google Scholar 
    De Domenico M, Lancichinetti A, Arenas A, Rosvall M. Identifying modular flows on multilayer networks reveals highly overlapping organization in interconnected systems. Phys Rev X. 2015;5:011027. https://doi.org/10.1103/PhysRevX.5.011027Article 
    CAS 

    Google Scholar 
    Farage C, Edler D, Eklöf A, Rosvall M, Pilosof S. Identifying flow modules in ecological networks using Infomap. Methods Ecol Evol. 2021;12:778–86. https://doi.org/10.1111/2041-210x.13569Article 

    Google Scholar 
    Popa O, Hazkani-Covo E, Landan G, Martin W, Dagan T. Directed networks reveal genomic barriers and DNA repair bypasses to lateral gene transfer among prokaryotes. Genome Res. 2011;21:599–609. https://doi.org/10.1101/gr.115592.110Article 
    CAS 

    Google Scholar 
    Smillie C, Garcillán-Barcia MP, Francia MV, Rocha EPC, de la Cruz F. Mobility of plasmids. Microbiol Mol Biol Rev. 2010;74:434–52. https://doi.org/10.1128/MMBR.00020-10Article 
    CAS 

    Google Scholar 
    Garcillán-Barcia MP, Francia MV, de la Cruz F. The diversity of conjugative relaxases and its application in plasmid classification. FEMS Microbiol Rev. 2009;33:657–87. https://doi.org/10.1111/j.1574-6976.2009.00168.xArticle 
    CAS 

    Google Scholar 
    Coluzzi C, Guédon G, Devignes MD, Ambroset C, Loux V, Lacroix T, et al. A glimpse into the world of integrative and mobilizable elements in streptococci reveals an unexpected diversity and novel families of mobilization proteins. Front Microbiol. 2017;8:443. https://doi.org/10.3389/fmicb.2017.00443Article 

    Google Scholar 
    Moraïs S, Mizrahi I. Islands in the stream: from individual to communal fiber degradation in the rumen ecosystem. FEMS Microbiol Rev. 2019;43:362–79. https://doi.org/10.1093/femsre/fuz007Article 
    CAS 

    Google Scholar 
    León-Sampedro R, DelaFuente J, Díaz-Agero C, Crellen T, Musicha P, Rodríguez-Beltrán J, et al. Pervasive transmission of a carbapenem resistance plasmid in the gut microbiota of hospitalized patients. Nat Microbiol. 2021;6:606–16. https://doi.org/10.1038/s41564-021-00879-yArticle 
    CAS 

    Google Scholar 
    Rocha LEC, Singh V, Esch M, Lenaerts T, Liljeros F, Thorson A. Dynamic contact networks of patients and MRSA spread in hospitals. Sci Rep. 2020;10:9336. https://doi.org/10.1038/s41598-020-66270-9Article 
    CAS 

    Google Scholar 
    Lerner A, Adler A, Abu-Hanna J, Cohen Percia S, Kazma Matalon M, Carmeli Y. Spread of KPC-producing carbapenem-resistant Enterobacteriaceae: the importance of super-spreaders and rectal KPC concentration. Clin Microbiol Infect. 2015;21:470.e1–7. https://doi.org/10.1016/j.cmi.2014.12.015Article 
    CAS 

    Google Scholar 
    Stein RA, Katz DE. Escherichia coli, cattle and the propagation of disease. FEMS Microbiol Lett. 2017;364. https://doi.org/10.1093/femsle/fnx050.de Freslon I, Martínez-López B, Belkhiria J, Strappini A, Monti G. Use of social network analysis to improve the understanding of social behaviour in dairy cattle and its impact on disease transmission. Appl Anim Behav Sci. 2019;213:47–54. https://doi.org/10.1016/j.applanim.2019.01.006Article 

    Google Scholar 
    Rushmore J, Caillaud D, Hall RJ, Stumpf RM, Meyers LA, Altizer S. Network-based vaccination improves prospects for disease control in wild chimpanzees. J R Soc Interface. 2014;11:20140349. https://doi.org/10.1098/rsif.2014.0349Article 

    Google Scholar 
    Xue H, Cordero OX, Camas FM, Trimble W, Meyer F, Guglielmini J, et al. Eco-evolutionary dynamics of episomes among ecologically cohesive bacterial populations. MBio. 2015;6:e00552–15. https://doi.org/10.1128/mBio.00552-15Article 
    CAS 

    Google Scholar 
    Evans DR, Griffith MP, Sundermann AJ, Shutt KA, Saul MI, Mustapha MM, et al. Systematic detection of horizontal gene transfer across genera among multidrug-resistant bacteria in a single hospital. Elife. 2020;9. https://doi.org/10.7554/eLife.53886Abe R, Oyama F, Akeda Y, Nozaki M, Hatachi T, Okamoto Y, et al. Hospital-wide outbreaks of carbapenem-resistant Enterobacteriaceae horizontally spread through a clonal plasmid harbouring bla IMP-1 in children’s hospitals in Japan. J Antimicrob Chemother. 2021;76:3314–7.Article 
    CAS 

    Google Scholar 
    Bingen EH, Desjardins P, Arlet G, Bourgeois F, Mariani-Kurkdjian P, Lambert-Zechovsky NY, et al. Molecular epidemiology of plasmid spread among extended broad-spectrum beta-lactamase-producing Klebsiella pneumoniae isolates in a pediatric hospital. J Clin Microbiol. 1993;31:179–84. https://doi.org/10.1128/jcm.31.2.179-184.1993.Bai H, He LY, Wu DL, Gao FZ, Zhang M, Zou HY, et al. Spread of airborne antibiotic resistance from animal farms to the environment: dispersal pattern and exposure risk. Environ Int. 2022;158:106927 https://doi.org/10.1016/j.envint.2021.106927Article 
    CAS 

    Google Scholar 
    Boyland NK, Mlynski DT, James R, Brent LJN, Croft DP. The social network structure of a dynamic group of dairy cows: from individual to group level patterns. Appl Anim Behav Sci. 2016;174:1–10. https://doi.org/10.1016/j.applanim.2015.11.016Article 

    Google Scholar 
    Björk JR, Dasari M, Grieneisen L, Archie EA. Primate microbiomes over time: longitudinal answers to standing questions in microbiome research. Am J Primatol. 2019;81:e22970. https://doi.org/10.1002/ajp.22970Article 

    Google Scholar 
    Dib JR, Wagenknecht M, Farías ME, Meinhardt F. Strategies and approaches in plasmidome studies—uncovering plasmid diversity disregarding of linear elements? Front Microbiol. 2015;6. https://doi.org/10.3389/fmicb.2015.00463Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77. https://doi.org/10.1089/cmb.2012.0021Article 
    CAS 

    Google Scholar 
    Rozov R, Brown Kav A, Bogumil D, Shterzer N, Halperin E, Mizrahi I, et al. Recycler: an algorithm for detecting plasmids from de novo assembly graphs. Bioinformatics. 2017;33:475–82. https://doi.org/10.1093/bioinformatics/btw651Article 
    CAS 

    Google Scholar 
    Orlek A, Stoesser N, Anjum MF, Doumith M, Ellington MJ, Peto T, et al. Plasmid classification in an era of whole-genome sequencing: application in studies of antibiotic resistance epidemiology. Front Microbiol. 2017;8:182. https://doi.org/10.3389/fmicb.2017.00182Article 

    Google Scholar 
    Komsta L, Novomestky F. Moments, cumulants, skewness, kurtosis and related tests. R package version. 2015;14.Rosvall M, Axelsson D, Bergstrom CT. The map equation. Eur Phys J Spec Top. 2010;178:13–23. https://doi.org/10.1140/epjst/e2010-01179-1Article 

    Google Scholar 
    Bascompte J, Jordano P, Melián CJ, Olesen JM. The nested assembly of plant–animal mutualistic networks. Proc Natl Acad Sci USA. 2003;100:9383–7. https://doi.org/10.1073/pnas.1633576100Article 
    CAS 

    Google Scholar 
    Vázquez DP, Poulin R, Krasnov BR, Shenbrot GI. Species abundance and the distribution of specialization in host–parasite interaction networks. J Anim Ecol. 2005;74:946–55.Article 

    Google Scholar 
    Fortuna MA, Stouffer DB, Olesen JM, Jordano P, Mouillot D, Krasnov BR, et al. Nestedness versus modularity in ecological networks: two sides of the same coin? J Anim Ecol. 2010;79:811–7. https://doi.org/10.1111/j.1365-2656.2010.01688.xArticle 

    Google Scholar 
    Gillespie DT. Exact stochastic simulation of coupled chemical reactions. J Phys Chem. 1977;81:2340–61. https://doi.org/10.1021/j100540a008Article 
    CAS 

    Google Scholar 
    R Core Team. R: a language and environment for statistical computing; 2021. More