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    Distance sampling surveys reveal 17 million vertebrates directly killed by the 2020’s wildfires in the Pantanal, Brazil

    1.Chiang, F., Mazdiyasni, O. & AghaKouchak, A. Evidence of anthropogenic impacts on global drought frequency, duration, and intensity. Nat. Commun. 12, 2754 (2021).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    2.Spinoni, J., Naumann, G., Carrao, H., Barbosa, P. & Vogt, J. World drought frequency, duration, and severity for 1951–2010. Int. J. Climatol. 34, 2792–2804 (2014).
    Google Scholar 
    3.Duane, A., Castellnou, M. & Brotons, L. Towards a comprehensive look at global drivers of novel extreme wildfire events. Clim. Change 165(3), 1–21 (2021).
    Google Scholar 
    4.Krawchuk, M. A., Moritz, M. A., Parisien, M. A., Van Dorn, J. & Hayhoe, K. Global Pyrogeography: The current and future distribution of wildfire. PLoS ONE 4(4), e5102 (2009).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    5.Williams, A. P. et al. Observed impacts of anthropogenic climate change on wildfire in California. Earth’s Fut. 7, 892–910 (2019).ADS 

    Google Scholar 
    6.Garcia, L. C. et al. Record-breaking wildfires in the world’s largest continuous tropical wetland: Integrative Fire Management is urgently needed for both biodiversity and humans. J. Environ. Manag. 293, 112870 (2021).CAS 

    Google Scholar 
    7.Bowman, D. M. J. S. et al. Vegetation fires in the Anthropocene. Nat. Rev. Earth Environ. 1, 500–515 (2020).ADS 

    Google Scholar 
    8.Criado, M. G., Myers-Smith, I. H., Bjorkman, A. D., Lehmann, C. E. R. & Stevens, N. Woody plant encroachment intensifies under climate change across tundra and savanna biomes. Glob. Ecol. Biogeogr. 29(5), 925–943 (2020).
    Google Scholar 
    9.Mancini, L. D., Corona, P. & Salvati, L. Ranking the importance of Wildfires’ human drivers through a multi-model regression approach. Environ. Impact Assess. Rev. 72, 177–186 (2018).
    Google Scholar 
    10.Moreira, F. et al. Landscape – wildfire interactions in southern Europe: Implications for landscape management. J. Environ. Manag. 92(10), 2389–2402 (2011).
    Google Scholar 
    11.Clarke, H. et al. The proximal drivers of large fires: A pyrogeographic study. Front. Earth Sci. 8, 90 (2020).ADS 

    Google Scholar 
    12.Abram, N. J. et al. Connections of climate change and variability to large and extreme forest fires in southeast Australia. Commun. Earth Environ. 2, 1 (2021).ADS 

    Google Scholar 
    13.Daskin, J. H., Aires, F. & Staver, A. C. Determinants of tree cover in tropical floodplains. Proc. R. Soc. B. 286, 20191755 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    14.Kotze, D. C. The effects of fire on wetland structure and functioning. Afr. J. Aquat. Sci. 38(3), 237–247 (2013).
    Google Scholar 
    15.Tedim, F. et al. Defining Extreme Wildfire Events: difficulties, challenges, and impacts. Fire 1, 9 (2018).
    Google Scholar 
    16.Libonati, R. et al. Sistema ALARMES – Alerta de área queimada Pantanal, situação final de 2020 https://www.researchgate.net/publication/350103205_Nota_Tecnica_012021_LASA-UFRJ_Queimadas_Pantanal_2020?channel=doi&linkId=6051109d92851cd8ce483fb1&showFulltext=true (2021).17.Libonati, R., DaCamara, C. C., Peres, F. L., de Carvalho, L. A. S. & Garcia, L. C. Rescue Brazil’s burning Pantanal wetlands. Nature 588, 217–219 (2020).ADS 
    CAS 
    PubMed 

    Google Scholar 
    18.Marengo, J. A. et al. Extreme drought in the Brazilian Pantanal in 2019–2020: Characterization, causes and impacts. Front. Water 3, 639204 (2021).
    Google Scholar 
    19.Marengo, J. A., Alves, L. M. & Torres, R. R. Regional climate change scenarios in the Brazilian Pantanal watershed. Clim. Res. 68(2–3), 201–213 (2016).
    Google Scholar 
    20.Hardesty, J., Myers, R. & Fulks, W. Fire, ecosystems, and people: A preliminary assessment of fire as a global conservation issue. George Wright Forum 22, 78–87 (2005).
    Google Scholar 
    21.Bliege Bird, R., Bird, D. W., Codding, B. F., Parker, C. H. & Jones, J. H. The “fire stick farming” hypothesis: Australian Aboriginal foraging strategies, biodiversity, and anthropogenic fire mosaics. Proc. Natl. Acad. Sci. USA 105(39), 14796–14801 (2008).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    22.Beerling, D. J. & Osborne, C. P. The origin of the savanna biome. Glob. Chang. Biol. 12, 2023–2031 (2006).ADS 

    Google Scholar 
    23.Simon, M. F. et al. Recent assembly of the Cerrado, a neotropical plant diversity hotspot, by in situ evolution of adaptations to fire. Proc. Natl. Acad. Sci. USA 106, 20359–20364 (2009).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    24.Pott, A. & Pott, V. J. Features and conservation of the Brazilian Pantanal wetland. Wetl. Ecol. Manag. 12, 547–552 (2004).
    Google Scholar 
    25.Ferraz-Vicentini, K. R. & Salgado-Laboriau, M. L. Palynological analysis of a palm swamp in Central Brasil. J. South Am. Earth Sci. 9(3–4), 207–219 (1996).ADS 

    Google Scholar 
    26.Engstrom, R. T. First-order fire effects on animals: review and recommendations. Fire Ecol. 6(1), 115–130 (2010).
    Google Scholar 
    27.Whelan, R. J., Rodgerson, L., Dickman, C. R. & Sutherland, E. F. Critical life processes of plants and animals: Developing a process-based understanding of population changes in fireprone landscapes (Cambridge University Press, 2002).
    Google Scholar 
    28.van Eeden, L. M. et al. Impacts of the unprecedented 2019–2020 bushfires on Australian animals. https://www.wwf.org.au/ArticleDocuments/353/WWF_Impacts-of-the-unprecedented-2019-2020-bushfires-on-Australian-animals.pdf.aspx (2020).29.Pacheco, L. F., Quispe-Calle, L. C., Suárez-Guzmán, F. A., Ocampo, M. & Claure-Herrera, A. J. Muerte de mamíferos por los incendios de 2019 en la Chiquitania. Ecol. Boliv. 56(1), 4–16 (2021).
    Google Scholar 
    30.Berlinck, C. B. et al. The Pantanal is on fire and only a sustainable agenda can save the largest wetland in the world. Braz. J. Biol. 82, e244200 (2021).CAS 
    PubMed 

    Google Scholar 
    31.Andersen, A. N., Woinarski, J. C. Z. & Parr, C. L. Savanna burning for biodiversity: Fire management for faunal conservation in Australian tropical savannas. Austral Ecol. 37, 658–667 (2012).
    Google Scholar 
    32.Komarek, R. Fire and the changing wildlife habitat. Proc. Tall Timbers Fire Ecol. Conf. 2, 35–43 (1963).
    Google Scholar 
    33.Layme, V. M. G., Lima, A. P. & Magnusson, W. E. Effects of fire, food availability and vegetation on the distribution of the rodent Bolomys lasiurus in an Amazonian savanna. J. Trop. Ecol. 20, 183–187 (2004).
    Google Scholar 
    34.Roberts, S. L., van Wagtendonk, J. W., Miles, A. K., Kelt, D. A. & Lutz, J. A. Modeling the effects of fire severity and spatial complexity on small mammals in Yosemite National Park, California. Fire Ecol. 4(2), 83–104 (2008).
    Google Scholar 
    35.Smith, J. K. Wildland Fire in Ecosystems: Effects of Fire on Fauna (Rocky Mountain Research Station, Colorado, 2000).36.Woinarski, J. C. Z. & Legge, S. The impacts of fire on birds in Australia’s tropical savannas. Emu 113(4), 319–352 (2013).
    Google Scholar 
    37.Pires, A. S., Fernandez, F. A., de Freitas, D. & Feliciano, B. R. Influence of edge and fire-induced changes on spatial distribution of small mammals in Brazilian Atlantic Forest fragments. Stud. Neotrop. Fauna Environ. 40(1), 7–14 (2005).
    Google Scholar 
    38.Silveira, L. F., Rodrigues, H. G., Jácomo, A. T. A. & Diniz Filho, J. A. F. Impact of wildfires on the megafauna of Emas National Park, Central Brazil. Oryx 33, 108–114 (1999).39.Tomas, W. M. et al. Checklist of mammals from Mato Grosso do Sul, Brazil. Iheringia, Sér. zool. 107(Suppl), e2017155 (2017).40.Tomas, W. M. et al. Mammals in the Pantanal wetland, Brazil (Pensoft Publishers, 2010).
    Google Scholar 
    41.Burnham, K. P., Anderson, D. R. & Laake, J. L. Estimation of density from line transect sampling of biological populations. Ecol. Monogr. 72, 1–202 (1980).
    Google Scholar 
    42.Jolly, W. M. et al. Climate-induced variations in global wildfire danger from 1979 to 2013. Nat. Commun. 6, 7537 (2015).ADS 
    CAS 
    PubMed 

    Google Scholar 
    43.Thielen, D. Quo vadis Pantanal? Expected precipitation extremes and drought dynamics from changing sea surface temperature. PLoS ONE 15(1), e0227437 (2020).44.Ciemer, C. et al. An early-warning indicator for Amazon droughts exclusively based on tropical Atlantic Sea surface temperatures. Environ. Res. Lett. 15, 094087 (2020).45.Boers, N., Marwan, N., Barbosa, H. M. J. & Kurths, J. A deforestation-induced tipping point for the South American monsoon system. Sci. Rep. 7, 41489 (2017).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    46.Bergier, I. et al. Amazon rainforest modulation of water security in the Pantanal wetland. Sci. Total Environ. 619–620, 1116–1125 (2018).ADS 
    PubMed 

    Google Scholar 
    47.Hofmann, G. et al. The Brazilian Cerrado is becoming hotter and drier. Glob. Chang. Biol. 00, 1–14 (2021).
    Google Scholar 
    48.Tomas, W. M. et al. Sustainability Agenda for the Pantanal Wetland: perspectives on a collaborative interface for science, policy, and decision-making. Trop. Conserv. Sci. 12, 1–30 (2019).ADS 

    Google Scholar 
    49.Schulz, C. Physical, ecological and human dimensions of environmental change in Brazil’s Pantanal wetland: Synthesis and research agenda. Sci. Total Environ. 687, 1011–1027 (2019).ADS 
    CAS 
    PubMed 

    Google Scholar 
    50.Harris, M. B. et al. Safeguarding the Pantanal wetlands: Threats and conservation initiatives. Conserv. Biol. 19(3), 714–720 (2005).
    Google Scholar 
    51.Ely, P., Fantin-Cruz, I., Tritico, H. M., Girard, P. & Kaplan, D. Dam-induced hydrologic alterations in the rivers feeding the Pantanal. Front. Environ. Sci. 8, 256 (2020).
    Google Scholar 
    52.Roque, F. O. et al. Simulating land use changes, sediment yields, and pesticide use in the Upper Paraguay River Basin: Implications for conservation of the Pantanal wetland. Agric. Ecosyst. Environ. 314, 107405 (2021).53.Guerra, A. et al. Drivers and projections of vegetation loss in the Pantanal and surrounding ecosystems. Land Use Policy 91, 104388 (2020).54.Berlinck, C. N., Lima, L. H. A. & Carvalho Junior, E. A. R. Historical survey of research related to fire management and fauna conservation in the world and in Brazil. Biota Neotropica 21(3), e20201144 (2021).55.Estado de Mato Grosso do Sul. DECRETO Nº 15.654, de 15 de abril de 2021. Institui o Plano Estadual de Manejo Integrado do Fogo, e Dá Outras Providências. (Diário Oficial do Estado, Mato Grosso do Sul nº 10.477, 2021).56.Marino, E. et al. Forest fuel management for wildfire prevention in Spain: A quantitative SWOT analysis. Int. J. Wildland Fire 23, 373–384 (2014).
    Google Scholar 
    57.Finney, M. A. & Cohen, J. D. Expectation and Evaluation of Fuel Management Objectives (Rocky Mountain Research Station, Colorado, 2003).58.Amiro, B. D., Stocks, B. J., Alexander, M. E., Flannigan, M. D. & Wotton, B. M. Fire, climate change, carbon and fuel management in the Canadian boreal forest. Int. J. Wildland Fire 10(4), 405–413 (2001).
    Google Scholar 
    59.Rocca, M. E., Brown, P. M., MacDonald, L. H. & Carrico, C. M. Climate change impacts on fire regimes and key ecosystem services in Rocky Mountain forests. Forest Ecol. Manag. 327, 290–305 (2014).
    Google Scholar 
    60.Pott, V. J., Pott, A., Lima, L. C. P., Moreira, S. N. & Oliveira, A. K. M. Aquatic macrophyte diversity of the Pantanal wetland and upper basin. Braz. J. Biol. 71(1), 255–563 (2011).CAS 
    PubMed 

    Google Scholar 
    61.Britski, H. A., Silimon, K. Z. S. & Lopes, B. S. Peixes do Pantanal: Manual de Identificação (EMPRAPA, Brasília, 2007).62.Sousa, T. P. et al. Cytogenetic and molecular data Support the occurrence of three Gymnotus species (Gymnotiformes: Gymnotidae) used as live bait in Corumbá, Brazil: Implications for conservation and management of professional fishing. Zebrafish 14(2), 177–186 (2017).PubMed 

    Google Scholar 
    63.Piva, A., Caramaschi, U. & Albuquerque, N. R. A new species of Elachistocleis (Anura: Microhylidae) from the Brazilian Pantanal. Phyllomedusa 16(2), 143–154 (2017).
    Google Scholar 
    64.Strüssmann, C., Ribeiro, R. A. K., Ferreira, V. L., & Beda, A. D. F. Herpetofauna do Pantanal Brasileiro [Herpetofauna of the Brazilian Pantanal]. (Sociedade Brasileira de Herpetologia, Belo Horizonte, 2007).65.Ferreira, V. L. et al. Répteis do Mato Grosso do Sul [Reptiles from Mato Grosso do Sul]. Brazil. Iheringia Sér. Zool. 107(Suppl), e2017153 (2017).66.Nunes, A. P. Quantas espécies de aves ocorrem no Pantanal? [How many bird species do occur in the Pantanal?]. Atualidades Ornitológicas 160, 45–54 (2011).
    Google Scholar 
    67.Tubelis, D. P. & Tomas, W. M. Bird species of the Pantanal wetland, Brazil.. Ararajuba 11(1), 5–37 (2003).
    Google Scholar 
    68.Thomas, L. et al. Distance software: design and analysis of distance sampling surveys for estimating population size. J. Appl. Ecol. 47, 5–14 (2010).PubMed 

    Google Scholar  More

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    Statistical inference, scale and noise in comparative anthropology

    To the Editor — In an insightful Comment Bliege Bird and Codding1 highlight a number of important issues to consider in the analysis of cross-cultural anthropological data. However, a casual reader of the Comment could be forgiven for taking away the message that cross-cultural data in anthropology is inherently flawed, and so is of limited use. We want to emphasize that comparative analysis plays an essential role in all non-experimental sciences, including anthropology and archaeology. This is because when systems cannot be manipulated due to scales of time and space, or issues of logistics or ethics, the only way to evaluate alternative outcomes is by analysing the results of natural experiments. More

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    Drivers of language loss

    1.Nettle, D. Linguistic Diversity (Oxford Univ. Press, USA, 1999).2.Campbell, L. & Belew, A. Cataloguing the World’s Endangered Languages (Routledge, 2018).3.Bromham, L. et al. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-021-01604-y (2021).4.Amano, T. et al. Proc. R. Soc. B 281, 20141574 (2014).Article 

    Google Scholar 
    5.Austin, P. K. & Sallabank, J. The Cambridge Handbook of Endangered Languages (Cambridge Univ. Press, 2011).6.Kandler, A., Unger, R. & Steele, J. Phil. Trans. R. Soc. B 365, 3855–3864 (2010).Article 

    Google Scholar 
    7.Kik, A. et al. Proc. Natl Acad. Sci. USA 118, e2100096118 (2021).CAS 
    Article 

    Google Scholar 
    8.Lewis, M. P., Simons, G. F. & Fennig, C. D. Ethnologue: Languages of the World 17th edn (SIL International, 2013).9.Fischer, S. D. in The Routledge Handbook of Historical Linguistics (ed. Bowern, C. & Evans, B.) Ch. 20, 443–465 (CRC Press, Routledge, 2015).10.Hou, L. & Kusters, A. in The Routledge Handbook of Linguistic Ethnography (ed. Tusting, K.) Ch. 25 (CRC Press, Routledge, 2019).11.Turner, M. K. & McDonald, B. M. J. Iwenhe Tyerrtye: What it Means to be an Aboriginal Person (IAD Press, 2010).12.Hercus, L. A. & Sutton, P. This is What Happened: Historical Narratives by Aborigines (Australian Institute of Aboriginal Studies, 1986).13.Meek, B. A. Annu. Rev. Anthropol. 48, 95–115 (2019).Article 

    Google Scholar  More

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    Publisher Correction: Collective behaviour can stabilize ecosystems

    AffiliationsDepartment of Integrative Biology, Oregon State University, Corvallis, OR, USABenjamin D. Dalziel & Mark NovakDepartment of Mathematics, Oregon State University, Corvallis, OR, USABenjamin D. DalzielCollege of Earth, Ocean and Atmospheric Sciences, Oregon State University, Corvallis, OR, USAJames R. WatsonDepartment of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, USAStephen P. EllnerAuthorsBenjamin D. DalzielMark NovakJames R. WatsonStephen P. EllnerCorresponding authorCorrespondence to
    Benjamin D. Dalziel. More

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    DNA barcodes evidence the contact zone of eastern and western caddisfly lineages in the Western Carpathians

    1.Manel, S., Schwartz, M. K., Luikart, G. & Taberlet, P. Landscape genetics: Combining landscape ecology and population genetics. Trends Ecol. Evol. 18, 189–197. https://doi.org/10.1016/S0169-5347(03)00008-9 (2003).Article 

    Google Scholar 
    2.Storfer, A., Murphy, M. A., Spear, S. F., Holderegger, R. & Waits, L. P. Landscape genetics: Where are we now?. Mol. Ecol. 19, 3496–3514. https://doi.org/10.1111/j.1365-294X.2010.04691.x (2010).Article 
    PubMed 

    Google Scholar 
    3.Alp, M., Keller, I., Westram, A. M. & Robinson, C. T. How river structure and biological traits influence gene flow: A population genetic study of two stream invertebrates with differing dispersal abilities. Freshw. Biol. 57, 969–981. https://doi.org/10.1111/j.1365-2427.2012.02758.x (2012).Article 

    Google Scholar 
    4.Mamos, T., Wattier, R., Majda, A., Sket, B. & Grabowski, M. Morphological vs. molecular delineation of taxa across montane regions in Europe: The case study of Gammarus balcanicus Schäferna, 1922 (Crustacea: Amphipoda). J. Zool. Syst. Evol. Res. 52, 237–248. https://doi.org/10.1111/jzs.12062 (2014).Article 

    Google Scholar 
    5.Mamos, T., Wattier, R., Burzýnski, A. & Grabowski, M. The legacy of a vanished sea: A high level of diversification within a European freshwater amphipod species complex driven by 15 My of Paratethys regression. Mol. Ecol. 25, 795–810. https://doi.org/10.1111/mec.13499 (2016).Article 
    PubMed 

    Google Scholar 
    6.Grabowski, M., Mamos, T., Bacela-Spychalska, K., Rewicz, T. & Wattier, R. A. Neogene paleogeography provides context for understanding the origin and spatial distribution of cryptic diversity in a widespread balkan freshwater amphipod. PeerJ 5, e3016. https://doi.org/10.7717/peerj.3016 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    7.Copilaş-Ciocianu, D., Zimţa, A. A., Grabowski, M. & Petrusek, A. Survival in northern microrefugia in an endemic Carpathian gammarid (Crustacea: Amphipoda). Zool. Scr. 47, 357–372. https://doi.org/10.1111/zsc.12285 (2018).Article 

    Google Scholar 
    8.Copilaș-Ciocianu, D., Zimța, A. & Petrusek, A. Integrative taxonomy reveals a new Gammarus species (Crustacea, Amphipoda) surviving in a previously unknown southeast European glacial refugium. J. Zool. Syst. Evol. Res. 57, 272–297. https://doi.org/10.1111/jzs.12248 (2019).Article 

    Google Scholar 
    9.Wattier, R. et al. Continental-scale patterns of hyper-cryptic diversity within the freshwater model taxon Gammarus fossarum (Crustacea, Amphipoda). Sci. Rep. 10, 16536. https://doi.org/10.1111/j.1365-2699.2012.02793.x (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    10.Neumann, K. et al. Genetic spatial structure of European common hamsters (Cricetus cricetus)—A result of repeated range expansion and demographic bottlenecks. Mol. Ecol. 14, 1473–1483. https://doi.org/10.1111/j.1365-294X.2005.02519.x (2005).CAS 
    Article 
    PubMed 

    Google Scholar 
    11.Kotlík, P. et al. A northern glacial refugium for bank voles (Clethrionomys glareolus). PNAS 103, 14860–14864. https://doi.org/10.1073/pnas.0603237103 (2006).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    12.Theissinger, K. et al. Glacial survival and post-glacial recolonization of an arctic-alpine freshwater insect (Arcynopteryx dichroa, Plecoptera, Perlodidae) in Europe. J. Biogeogr. 40, 236–248. https://doi.org/10.1111/j.1365-2699.2012.02793.x (2012).Article 

    Google Scholar 
    13.Vörös, J., Mikulíček, P., Major, Á., Recuero, E. & Arntzen, J. W. Phylogeographic analysis reveals northern refugia for the riverine amphibian Triturus dobrogicus (Caudata: Salamandridae). Biol. J. Linn. Soc. 119, 974–991. https://doi.org/10.1111/bij.12866 (2016).Article 

    Google Scholar 
    14.Copilaș-Ciocianu, D., Rutová, T., Pařil, P. & Petrusek, A. Epigean gammarids survived millions of years of severe climatic fluctuations in high latitude refugia throughout the Western Carpathians. Mol. Phylogenet. Evol. 112, 218–229. https://doi.org/10.1016/j.ympev.2017.04.027 (2017).Article 

    Google Scholar 
    15.Juřičková, L. et al. Early postglacial recolonisation, refugial dynamics the origin of a major biodiversity hotspot. A case study from the Malá Fatra mountains, Western Carpathians, Slovakia. Holocene 28(4), 583–594. https://doi.org/10.1177/0959683617735592 (2017).ADS 
    Article 

    Google Scholar 
    16.Mamos, T., Jażdżewski, K., Čiamporová-Zaťovičová, Z., Čiampor, F. & Grabowski, M. Fuzzy species borders of glacial survivalists in the Carpathian biodiversity hotspot revealed using a multimarker approach. Sci. Rep. 11, 21629. https://doi.org/10.1038/s41598-021-00320-8 (2021).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    17.Pinceel, J., Jordaens, K., Pfenninger, M. & Backeljau, T. Rangewide phylogeography of a terrestrial slug in Europe: Evidence for Alpine refugia rapid colonization after the Pleistocene glaciations. Mol. Ecol. 14, 1133–1150. https://doi.org/10.1111/j.1365-294X.2005.02479.x (2005).CAS 
    Article 
    PubMed 

    Google Scholar 
    18.Magri, D. et al. A new scenario for the Quaternary history of European beech populations: Palaeobotanical evidence genetic consequences. New Phytol. 171, 199–221. https://doi.org/10.1111/j.1469-8137.2006.01740.x (2006).CAS 
    Article 
    PubMed 

    Google Scholar 
    19.Jamrichová, E., Potůčková, A. & Horsák, M. Landscape history, calcareous fen development historical events in the Slovak Eastern Carpathians. Veg. Hist. Archaeobot. 23, 497–513. https://doi.org/10.1007/s00334-013-0416-0 (2014).Article 

    Google Scholar 
    20.Jamrichová, E., Petr, L. & Jiménez-Alfaro, B. Pollen-inferred millennial changes in landscape patterns at a major biogeographical interface within Europe. J. Biogeogr. 44, 2386–2397 (2017).Article 

    Google Scholar 
    21.Wielstra, B., Babik, W. & Arntzen, J. W. The crested newt Triturus cristatus recolonized temperate Eurasia from an extra-Mediterranean glacial refugium. Biol. J. Linn. Soc. 114, 574–587. https://doi.org/10.1111/bij.12446 (2015).Article 

    Google Scholar 
    22.Mráz, P. & Ronikier, M. Biogeography of the Carpathians: Evolutionary spatial facets of biodiversity. Biol. J. Linn. Soc. 119, 528–559. https://doi.org/10.1111/bij.12918 (2016).Article 

    Google Scholar 
    23.Pauls, S. U., Lumbsch, H. A. T. & Haase, P. Phylogeography of the montane caddisfly Drusus discolor: Evidence for multiple refugia and periglacial survival. Mol. Ecol. 15(8), 2153–2169. https://doi.org/10.1111/j.1365-294X.2006.02916.x (2006).CAS 
    Article 
    PubMed 

    Google Scholar 
    24.Pauls, S. U., Theissinger, K., Ujvarosi, L., Bálint, M. & Haase, P. Patterns of population structure in two closely related, partially sympatric caddisflies in eastern Europe: Historic introgression, limited dispersal, and cryptic diversity. J. N. Am. Benthol. Soc. 28, 517–536. https://doi.org/10.1899/08-100.1 (2009).Article 

    Google Scholar 
    25.Lehrian, S., Pauls, S. U. & Haase, P. Contrasting patterns of population structure in the montane caddisflies Hydropsyche tenuis and Drusus discolor in the Central European highlands. Freshw. Biol. 54, 283–295. https://doi.org/10.1111/j.1365-2427.2008.02107.x (2009).Article 

    Google Scholar 
    26.Lande, R. & Shannon, S. The role of genetic variation in adaptation and population persistence in a changing environment. Evolution 216, 434–437 (1996).Article 

    Google Scholar 
    27.Frankham, R., Briscoe, D. A. & Ballou, J. D. Introduction to Conservation Genetics (Cambridge University Press, 2002).Book 

    Google Scholar 
    28.Robert, S. & Curtean-Bănăduc, A. Aspects concerning Târnava Mare and Târnava Mică rivers (Transylvania, Romania) caddisfly (Insecta, Trichoptera) larvae communities. Transylv. Rev. Syst. Ecol. Res. 2, 89–98 (2005).
    Google Scholar 
    29.Bálint, M., Ujvárosi, L., Dénes, A. L. & Octavian, P. European phylogeography of Rhyacophila tristis Pictet (Trichoptera: Rhyacophilidae): Preliminary results. Zoosymposia 5, 11–18. https://doi.org/10.11646/zoosymposia.5.1.1 (2011).Article 

    Google Scholar 
    30.Bielik, M. Geophysical features of the Slovak Western Carpathians. Geol. Q. 43, 251–262. https://doi.org/10.1016/j.quascirev.2008.08.019 (1999).Article 

    Google Scholar 
    31.Céréghino, R., Cugny, P. & Lavandier, P. Influence of intermittent hydropeaking on the longitudinal zonation patterns of benthic invertebrates in a mountain stream. Int. Rev. Hydrobiol. 87, 47–60. https://doi.org/10.1002/1522-2632(200201)87:1%3c47::AID-IROH47%3e3.0.CO;2-9 (2002).Article 

    Google Scholar 
    32.Sworobowicz, L., Mamos, T., Grabowski, M. & Wysocka, A. Lasting through the ice age: The role of the proglacial refugia in the maintenance of genetic diversity, population growth, and high dispersal rate in a widespread freshwater crustacean. Freshw. Biol. 65, 1028–1046. https://doi.org/10.1111/fwb.13487 (2020).CAS 
    Article 

    Google Scholar 
    33.Rudolph, K., Coleman, C. O., Mamos, T. & Grabowski, M. Description and post-glacial demography of Gammarus jazdzewskii sp. nov. (Crustacea: Amphipoda) from Central Europe. Syst. Biodivers. 16, 587–603. https://doi.org/10.1080/14772000.2018.1470118 (2018).Article 

    Google Scholar 
    34.Bozáňová, J., Čiamporová-Zaťovičová, Z., Čiampor, F. Jr., Mamos, T. & Grabowski, M. The tale of springs and streams: How different aquatic ecosystems impacted the mtDNA population structure of two riffle beetles in the Western Carpathians. PeerJ 8, e10039. https://doi.org/10.7717/peerj.10039 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    35.Jedlička, L., Kúdela, M., Szemes, T. & Celec, P. Population genetic structure of Simulium degrangei (Diptera: Simuliidae) from Western Carpathians. Biologia 67, 777–787. https://doi.org/10.2478/s11756-012-0057-2 (2012).Article 

    Google Scholar 
    36.Hughes, J. M., Bunn, S. E., Hurwood, D. A. & Cleary, C. Dispersal and recruitment of Tasiagma ciliata (Trichoptera: Tasmiidae) in rainforest streams, south-east Queensland, Australia. Freshw. Biol. 41, 1–10 (1998).
    Google Scholar 
    37.Finn, D. S., Theobald, D. M., Black, W. C. & Poff, N. L. Spatial population genetic structure and limited dispersal in a Rocky Mountain alpine stream insect. Mol. Ecol. 15, 3553–3566 (2006).CAS 
    Article 

    Google Scholar 
    38.Vuataz, L., Rutschmann, S., Monaghan, M. T. & Sartori, M. Molecular phylogeny and timing of diversification in Alpine Rhithrogena (Ephemeroptera: Heptageniidae). BMC Evol. Biol. 16, 194. https://doi.org/10.1186/s12862-016-0758-1 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    39.Schiffers, K., Bourne, E. C., Lavergne, S., Thuiller, W. & Travis, J. M. J. Limited evolutionary rescue of locally adapted populations facing climate change. Philos. Trans. R. Soc. B Biol. Sci. 368, 20120083. https://doi.org/10.1098/rstb.2012.0083 (2013).Article 

    Google Scholar 
    40.Spielman, D., Brook, B. & Frankham, R. Most species are not driven to extinction before genetic factors impact them. Proc. Natl. Acad. Sci. 101, 15261–15264 (2004).ADS 
    CAS 
    Article 

    Google Scholar 
    41.Frankham, R. Genetics and extinction. Biol. Conserv. 126, 131–140 (2005).Article 

    Google Scholar 
    42.Bunn, S. E. & Hughes, J. M. Dispersal and recruitment in streams: Evidence from genetic studies. J. N. Am. Benthol. Soc. 16, 338–346. https://doi.org/10.2307/1468022 (1997).Article 

    Google Scholar 
    43.Barron, E. & Pollard, D. High-resolution climate simulations of oxygen isotope stage 3 in Europe. Quat. Res. 28, 296–309. https://doi.org/10.1006/qres.2002.2374 (2002).Article 

    Google Scholar 
    44.Bennet, K. & Provan, J. What do we mean by “refugia”? Quat. Sci. Rev. 27, 2449–2455 (2008).ADS 
    Article 

    Google Scholar 
    45.Kondracki, J. Karpaty. Wydanie drugie i poprawione [The Carpathians. Ed. 2].—Wydawnictwa Szkolne i Pedagogiczne, Warszawa (1989).46.Grecula, P. (ed.). Geological evolution of the Western Carpathians. Monograph: Mineralia Slovaca (1997).47.Lukniš, M. The course of the last glaciation of the Western Carpathians in the relation to the Alps, to the glaciation of northern Europe, and to the division of the central European Wurm into periods. Geografický Časopis 16, 127–142 (1964).
    Google Scholar 
    48.Lindner, L., Dzierzek, J., Marciniak, B. & Nitychoruk, J. Outline of Quaternary glaciations in the Tatra Mts.: Their development, age and limits. Geol. Q. 47, 269–280 (2003).
    Google Scholar 
    49.Frost, S. Evaluation of kicking technique for sampling stream bottom fauna. Can. J. Zool. 49, 161–173. https://doi.org/10.1016/j.biocon.2005.05.002 (1971).Article 

    Google Scholar 
    50.Sedlák, E. Řád Chrostíci—Trichoptera. In Klíč vodních larev hmyzu (ed. Rozkošný, R.) 163–220 (ČSAV, 1980).
    Google Scholar 
    51.Waringer, J. & Graf, W. Atlas of Central European Trichoptera Larvae: Atlas der Mitteleuropäischen Köcherfliegenlarven (Erik Mauch, 2011).
    Google Scholar 
    52.Casquet, J., Thebaud, C. & Gillespie, R. G. Chelex without boiling, a rapid and easy technique to obtain stable amplifiable DNA from small amounts of ethanol-stored spiders. Mol. Ecol. Resour. 12(1), 136–141. https://doi.org/10.1111/j.1755-0998.2011.03073.x (2012).CAS 
    Article 
    PubMed 

    Google Scholar 
    53.Folmer, O., Black, M., Hoeh, W., Lutz, R. & Vrijenhoek, R. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol. Biotechnol. 3(5), 294–299 (1994).CAS 
    PubMed 

    Google Scholar 
    54.Bálint, M., Botoşaneanu, L., Ujvárosi, L. & Popescu, O. Taxonomic revision of Rhyacophila aquitanica (Trichoptera: Rhyacophilidae), based on molecular and morphological evidence and change of taxon status of Rhyacophila aquitanica ssp. carpathica to Rhyacophila carpathica stat. n. Zootaxa 2148, 39–48. https://doi.org/10.11646/zootaxa.2148.1.3 (2009).Article 

    Google Scholar 
    55.Simon, C. et al. Evolution, weighting and phylogenetic utility of mitochondrial gene sequences and a compilation of conserved polymerase chain reaction primers. Ann. Entomol. Soc. Am. 87, 651–701 (1994).CAS 
    Article 

    Google Scholar 
    56.Edgar, R. C. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797. https://doi.org/10.1093/nar/gkh340 (2004).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    57.Kumar, S., Stecher, G. & Tamura, K. MEGA7: Molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. 33, 1870–1874. https://doi.org/10.1093/molbev/msw054 (2016).CAS 
    Article 

    Google Scholar 
    58.Ratnasingham, S. & Hebert, P. D. N. The barcode of life data system. Mol. Ecol. Notes 7, 355–364. https://doi.org/10.1111/j.1471-8286.2007.01678.x (2007).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    59.Puillandre, N., Brouillet, S. & Achaz, G. ASAP: Assemble species by automatic partitioning. Mol. Ecol. Resour. 21(2), 609–620. https://doi.org/10.1111/1755-0998.13281 (2021).Article 
    PubMed 

    Google Scholar 
    60.Librado, P. & Rozas, J. DnaSP v5: A software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25(11), 1451–1452. https://doi.org/10.1093/bioinformatics/btp187 (2009).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    61.Leigh, J. W. & Bryant, D. POPART: Full-feature software for haplotype network construction. Methods Ecol. Evol. 6, 1110–1116. https://doi.org/10.1111/2041-210X.12410 (2015).Article 

    Google Scholar 
    62.Bouckaert, R. et al. BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 15(4), e1006650. https://doi.org/10.1371/journal.pcbi.1006650 (2019).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    63.Bouckaert, R. R. & Drummond, A. J. bModelTest: Bayesian phylogenetic site model averaging and model comparison. BMC Evol. Biol. 17(42), 1–11. https://doi.org/10.1186/s12862-017-0890-6 (2017).Article 

    Google Scholar 
    64.Brower, A. V. Z. Rapid morphological radiation and convergence among races of the butterfly Heliconius erato inferred from patterns of mitochondrial DNA evolution. PNAS 91(14), 6491–6495. https://doi.org/10.1073/pnas.91.14.6491 (1994).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    65.Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarisation in Bayesian phylogenetics using Tracer 1.7. Syst. Biol. 67(5), 901–904. https://doi.org/10.1093/sysbio/syy032 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    66.Miller, M. P. Alleles In Space (AIS): Computer software for the joint analysis of interindividual spatial and genetic information. J. Hered. 96, 722–724. https://doi.org/10.1093/jhered/esi119 (2005).CAS 
    Article 
    PubMed 

    Google Scholar 
    67.Mantel, N. The detection of disease clustering and a generalized regression approach. Cancer Res. 27, 209–220 (1967).CAS 
    PubMed 

    Google Scholar 
    68.Excoffier, L. & Lischer, H. E. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567. https://doi.org/10.1111/j.1755-0998.2010.02847.x (2010).Article 
    PubMed 

    Google Scholar 
    69.Tajima, F. The effect of change in population size on DNA polymorphism. Genetics 123(3), 597–601 (1989).CAS 
    Article 

    Google Scholar 
    70.Fu, Y. X. Statistical tests of neutrality of mutations against population growth, hitchhiking and background selection. Genetics 147(2), 915–925 (1997).CAS 
    Article 

    Google Scholar 
    71.Fu, Y. X. & Li, W. H. Statistical tests of neutrality of mutations. Genetics 14, 693–709 (1993).Article 

    Google Scholar  More

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    Sea-ice derived meltwater stratification slows the biological carbon pump: results from continuous observations

    Table S1 lists the data used in this paper, the instruments that it is based on, the data repositories, and in which figures the data are used.Global data setsBathymetryBathymetric data was taken from the International Bathymetric Chart of the Arctic Ocean (IBCAO 30 sec V3)64 available at https://www.ngdc.noaa.gov/mgg/bathymetry/arctic/grids/version3_0/.Sea ice concentrationWe use data derived from the Advanced Microwave Scanning Radiometer sensor AMSR-2 for the years 2013–18 processed in accordance with65 and downloaded from https://seaice.uni-bremen.de/sea-ice-concentration-amsr-eamsr2/66. At each grid point the sum of days during all April/May/June of 2013–2018 when the sea ice concentration at the grid point was >20% was divided by the total number of days with data in those months to obtain the percentage of days with ice concentration >20% (Fig. 1). For separate 7-day periods in April/May/June 2017 and 2018 the mean ice concentration over those 7 days was calculated and the 20% contour of this mean was plotted separately for each of those 7-day periods. For each mooring and each day, the ice concentration at the grid cell closest to the mooring was calculated (Fig. 4a and S1a), and if the ice concentration at the mooring was below 20%, the shortest distance to grid cells where the ice concentration exceeded 20% was calculated (Fig. 4a and S1a). If the ice concentration at the mooring exceeded 20%, the shortest distance to grid cells where the ice concentration was below 20% was calculated and the distance was defined as negative.Sea ice velocity and sea ice area exportIce area flux estimates in Fig. 2a are calculated using CERSAT (Center for Satellite Exploitation and Research, France) motion estimates together with CERSAT ice concentration information67. Fluxes are estimated along a zonal gate positioned at 82°N between 12°W and 20°E and a meridional gate at 20°E between 80.5°N and 82°N (Fig. 1) for the period 1994–2020 (January–May). The ice area flux at the gate is the integral of the product between the meridional and zonal ice drift and ice concentration. For a more detailed description we refer to ref. 68. Arctic-wide sea ice velocity anomalies (Fig. 2b, c) were computed from the OSI-405-c motion product provided by the Ocean and Sea Ice Satellite Application 635 Facility (OSISAF)69.Satellite chlorophyllSurface chlorophyll concentrations measured with the Sentinel 3 A OLCI (Ocean and Land Colour Instrument) were downloaded from https://earth.esa.int/web/sentinel/sentinel-data-access. The 8-day satellite data were averaged for the time series over grid points within boxes of 60 km by 60 km around the moorings.Atmospheric reanalysisERA-Interim reanalysis70 data at the surface on a 0.25° latitude by 0.25° longitude grid at 12 hourly resolution was downloaded from https://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/. Incoming shortwave radiation (ssr) and outgoing longwave radiation (str), sensible heat flux (sshf), and latent heat flux (slhf) were extracted and averaged to daily values.Physical numerical modelsFESOMIn this study, we used model data from the Finite-Element Sea ice-Ocean Model (FESOM) version 1.471. FESOM is a sea ice-ocean model that solves the hydrostatic primitive equations for the ocean and comprises a finite element sea ice component. It uses triangular surface meshes for spatial discretization, allowing for a refined mesh in regions of interest, while keeping a coarser mesh elsewhere. In the model configuration used here, a mesh resolution of nominally 1° was applied in the global oceans. The mesh was refined to 25 km north of 40°N, and to 4.5 km in the Nordic Seas and Arctic Ocean. In the wider Fram Strait (20°W-20°E/76°N-82°30′N), the mesh was further refined to 1 km. In this region, the simulation can be considered as eddy-resolving, as the local internal Rossby radius of deformation is about 2–6 km72,73. In the vertical, the model used 47 z-levels with a resolution of 10 m in the upper 100 m, and coarser resolution with depth (with a resolution of ~100 m at 800 m depth). For bottom topography, the RTopo-2 data set was used74. The model simulation covers the period 2010–2018 and has daily model output. It was forced with atmospheric reanalysis data from Era-Interim70, and was initialized with model fields from the simulation described in ref. 75. River runoff (except for Greenland) was taken from the JRA-55 data set76, and Greenland ice-sheet runoff was taken from ref. 77. Tides were not taken into account in this simulation. Here we studied the model data of 2016 to 2018 in Fram Strait for comparison with our observations.1-dimensional mixed layer depth modelThe PWP78 1-dimensional mixed layer model simulates the response of the ocean to surface fluxes. It ignores horizontal gradients and horizontal advection. This allows to judge whether certain surface flux conditions can on their own explain observed conditions. We ran the PWP model (as implemented for Matlab by http://www.po.gso.uri.edu/rafos/research/pwp/) with four different scenarios (Fig. S6: P17-M17, P17-M18, P18-M17, P18-M18) where: P17: An idealized initial profile based on the observed profiles (Fig. 3) representing the conditions in 2017: constant temperature of 2 °C in the vertical, linear salinity gradient from 30.5 at the surface to 35 at 50 m and another linear salinity gradient from 35 at 50 m to 35.1 at 200 m. P18: An idealized initial profile based on the observed profiles (Fig. 3) representing the conditions in 2018: Same as P17 except that the surface to 50 m salinity gradient is from 34.8 to 35. M17: A time series of the the meteorological forcing (10 m wind velocity, heat fluxes, and evaporation minus precipitation) from the ERA-Interim reanalysis (Fig. 4b) at the grid point closest to mooring HG-IV for the period 15-May-2017 to 01-Aug-2017. M18: Same as M17 but for the period 15-May-2018 to 01-Aug-2018. M17 and M18 are provided in Supplementary Data 1.Shipboard CTD dataShipboard CTD casts of a standard dual sensor Seabird 911+ CTD-rosette were occupied in spatial and temporal vicinity to the moored observations (Tab. S2) on three cruises: PS107 in 2017 (https://doi.org/10.1594/PANGAEA.894189), PS114 in 2018 (https://doi.org/10.1594/PANGAEA.898694) of RV Polarstern, and JR17005 in 2018 (https://doi.org/10.5285/84988765-5fc2-5bba-e053-6c86abc05d53) of RRS James Clark Ross. The data were processed according to standard routine79. Additionally, we use underway CTD data from an OceanScience underway CTD collected during PS107 in 2017 (https://doi.org/10.1594/PANGAEA.886146) and processed according to ref. 21.Mooring dataThe mooring data discussed in this paper is from two mooring clusters in the central and eastern Fram Strait (named “HG-IV” at ~79°N 4°20’E and “F4” at ~79°N 7°E) where moorings were located as close to each other as possible (the horizontal separation was equal to the water depth) in order to enable more measurements than could be fit physically onto a single mooring. Tab. S2/S3 list the deployment and recovery details of the moorings including the exact latitudes/longitudes as well as the individual instruments on the moorings. Note that all data shown in this paper from ~30 m depth and the temperature/salinity/oxygen data from ~55 m is from the HG-IV-S-* and F4-S-* moorings, while all other data is from the HG-IV-FEVI-* and F4-* moorings. The AZFP data is from F5-17 located roughly half way between the two clusters. All sensor based mooring raw data (except for the ASL AZFP data) is available at ref. 80.It is known that conversion factors for biogeochemical sensors (e.g., chlorophyll fluorescence) change over the seasons, depths, and regions81,82. In order to make as few assumptions as possible, we used the following approach: we could have determined the conversion factors from the instance when the ship was there with the CTD-rosette, but these conversion factors might not be appropriate for the majority of the time series. Hence, simply using the manufacturers’ calibrations, as we do here, introduces fewer uncertainties. Where we have different estimates of the same parameter, we present them together and demonstrate that they agree qualitatively and also mostly quantitatively (e.g., Fig. 5b). In particular the timing of events is robust.At some locations, the target variables were not measured the whole time or the measurements failed, hence we present what is available. The vertical location of the instruments (Fig. 4c and S1c) varied substantially (intermittently up to 200 m) as a result of mooring blow downs caused by strong intermittent ocean currents. Time series have not been corrected for this vertical motion, but data are not used during blow downs in order not to bias the time series interpretation by temporal changes introduced by instruments traversing through vertical property gradients.Physical sensor measurementsThe physical sensors (for pressure, temperature, conductivity, and oxygen) were pre-cruise manufacturer calibrated and processed similar to ref. 83; the processed data is also available at ref. 80.Mixed layer depth (MLD)Since there are no autonomous vertically profiling measurements available, we can only determine the minimum value of the mixed layer depth. At each hourly time step, the potential density difference (Δσ) between the uppermost (~30 m) temperature/salinity recorder and the underlying temperature/salinity recorders is calculated. The 0.5th percentile of each Δσ time series is added to the Δσ time series for the different deployments. This fixes slight offsets in the temperature and/or conductivity calibrations which result in too negative or too positive density differences. The minimum estimate of the mixed layer depth at hourly resolution is then determined as the depth of the deepest instrument where Δσ  0.05 kg m−3 for all depths at a time step, then the minimum mixed layer depth can only be determined as 0 for that time step. Daily values of the MLD were defined as the depth at which three hourly realizations of MLD were shallower within a 24 h time span and at which the remaining 21 MLD realizations were deeper. This biases the daily MLD estimate towards situations where phytoplankton is kept in the surface ocean rather than also being mixed down for some amount of time.Stratification estimated between 30 m and 55 mBased on the temperature and salinity time series observed at ~30 m and ~55 m, we estimate the buoyancy frequency as ({N}^{2}=frac{-g}{{rho }_{0}}frac{Delta rho }{Delta z}) where g is the acceleration due to gravity, Δσ is the potential density difference over the vertical distance of Δz = 25 m, and ρ0 is the average density. The contributions to stratification due to temperature (N2T) and salinity (N2S) are estimated as ({N}_{T}^{2}=g*alpha frac{Delta T}{Delta z}) and ({N}_{S}^{2}=-g*beta frac{Delta S}{Delta z}), respectively, where ΔT/ΔS are the temperature/salinity differences and α/β are the thermal expansion/haline contraction coefficients estimated from the average temperature/salinity at the two measurement depths.Apparent oxygen utilization (AOU)Oxygen concentration from the microcats was calculated using the pre-cruise manufacturer calibrations. AOU was calculated as the atmospherically equilibrated oxygen concentration (calculated from measured pressure, temperature, and salinity with sw_satO2 from the Seawater toolbox available at http://www.cmar.csiro.au/datacentre/ext_docs/seawater.htm) minus the measured oxygen concentration.LightPolar night/polar dayThe length of day (hours per 24 h that the sun is above the horizon) was calculated from the sunrise equation as implemented for Matlab by https://de.mathworks.com/matlabcentral/fileexchange/55509-sunrise-sunset.Photosynthetically available radiation (PAR)The WetLabs Eco PAR measured PAR for 5 (in 2016–2017) or 10 (in 2017–2018) individual measurements 1 s apart from each other before it slept for 1 h before repeating the measurement cycle. These 5 or 10 individual measurements are averaged linearly to obtain hourly values at ~30 m depth (Fig. 5a blue). Values below the detection limit are set to a constant of 10−1.32 μmol m−2 s−1. Hourly values are linearly averaged to daily values (Fig. 5a black). The incoming solar shortwave radiation varies as a function of season and latitude as well as cloud cover as represented in the ERA-Interim reanalysis (parameters ssr). Its unit of W m−2 is converted to PAR assuming a constant spectral distribution as 1 W m−2 = 2.1 μmol m−2 s−184. In order to compare the PAR measured at a depth of approximately 30 m to the surface values, we approximate a spectrally averaged diffuse attenuation coefficient for PAR in clear water using the values of85 as kd = 0.02 m−1 and apply it to calculate a constant exponential extinction applied to the reanalysis surface values (Fig. 5a yellow). The average PAR available (PARavailable) to phytoplankton being moved around in the clear water mixed layer of depth MLD was calculated as the depth averaged vertical integral of the clear water extinguished PAR at the surface (PARsurf from the shortwave radiation of ERA-Interim): ({{PAR}}_{{available}}=frac{1}{{MLD}}*{int }_{z=0}^{z={MLD}}{{PAR}}_{{surf}}*{e}^{-{k}_{d}z}{dz}) (Fig. 5a red).Chlorophyll concentration and optical backscatteringChlorophyll fluorescenceThe WetLabs ECO Triplet measures fluorescence at a “chlorophyll wavelength” and at a “CDOM wavelength” as well as optical scattering at 700 nm. The conversion from fluorescence to chlorophyll a concentration (in μg l−1) follows a manufacturer determined conversion determined for a mono-culture of phytoplankton (Thalassiosira weissflogii), which typically overestimates the chlorophyll concentration. Hence, we applied the community-established calibration bias of 2 for the WetLabs ECO-series fluorometer to these in situ fluorometric chlorophyll values81. This conversion factor may be different in ocean waters of Fram Strait, but it still gives reasonable agreement with independent estimates.Optical backscatteringThe EcoTriplet measured 8 individual measurements 1 s apart from each other before it slept for 1 h before repeating the measurement cycle. For the chlorophyll fluorescence, the individual measurements are averaged to hourly values. For the scattering, times when individual 1-second measurements exceed 0.002 m−1 sr−1 are indicative of strong optical backscattering not due to small particles in the water column, but rather to larger potentially aggregated particles. The times of strong backscattering are marked individually (Fig. 5b red).NutrientsNitrate (SUNA sensor)Prior to deployment (11 and 15 days for sensors deployed at HG-IV and F4, respectively), the reference spectrum of the sensors were updated as per manufacturer specifications. We first let the sensors cool down for 24 h at 0 °C in a temperature controlled laboratory. Next, the reference spectrum update was achieved by measuring Milli-Q water (i.e., no nitrate present). To verify if this update was successful, solutions with three different nitrate concentrations (3, 7, and 14 μmol l−1) were then measured, with the output being monitored live (expected to be within ±2 μmol l−1 of each concentration). A measuring time of 20 s yields stable results and was thus applied during the deployments with an interval of 6 h. Upon recovery, SUNA data were processed using the SeaBird UCI software package version 1.2.1. Here, temperature and salinity data were used to remove the spectrum of bromide and compensate for temperature dependent absorption using an algorithm developed by ref. 86. This step yields the spectrum of nitrate only, at a precision of ±0.3 μmol l−1. The sensor is characterized by a drift of 0.3 μmol l−1 per hour lamp time. Given the deployment settings, a total operational time of about 8 h was accumulated. Therefore, a linear drift correction of 2.4 μmol l−1 (365 days)−1 was applied. Up to this point, however, accuracy remains at 2 μmol l−1 as per manufacturer specifications. Therefore, an offset correction is then applied based on the in situ concentrations observed at the beginning of the deployment as well as with the RAS (see below) where available, with outliers excluded.Inorganic nutrients from Remote Access Samplers (RAS)McLane RAS were programmed to draw two 500 ml samples (1 h apart, starting at noon) approximately every other week. Samples within the RAS were collected in sterile plastic bags and fixed with 700 μl of 50% mercuric chloride solution. Upon recovery, two samples from a given sampling date were combined to yield a volume of 1 l, required for bacterial and phytoplankton genetic analyses (see below), and a 50-ml aliquot destined for the measurement of dissolved inorganic nutrients. Aliquots for nutrient analysis were collected in PE bottles, which were then stored frozen (−20 °C) until analysis on land. Analyses for inorganic nutrients were carried out using a QuAAtro Seal Analytical segmented continuous flow autoanalyser following standard colorimetric techniques. The accuracy of the analysis was evaluated through the measurement of KANSO LTD Japan Certified Reference Materials and corrections were applied accordingly. Finally, we evaluated pressure, temperature, and salinity data from the CTD (SBE37-SMP-ODO) attached to the RAS to determine whether the two samples taken one hour apart on a given date drew water from the same depth and with consistent properties.Carbonate system
    pCO
    2 and pH
    The calibration of SAMI pH and SAMI CO2 sensors was carried out by the manufacturer, approximately 2 months prior to deployment. The calibration certificates specify accuracy and precision of ±0.003/±0.001 pH units and ±3/ More

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    Towards the biogeography of prokaryotic genes

    1.Sunagawa, S. et al. Structure and function of the global ocean microbiome. Science 348, 1261359 (2015).PubMed 

    Google Scholar 
    2.Zou, Y. et al. 1,520 reference genomes from cultivated human gut bacteria enable functional microbiome analyses. Nat. Biotechnol. 37, 179–185 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    3.Mohammad, B. F. et al. Structure and function of the global topsoil microbiome. Nature 560 233–237 (2018).4.Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    5.Xiao, L. et al. A catalog of the mouse gut metagenome. Nat. Biotechnol. 33, 1103–1108 (2015).CAS 
    PubMed 

    Google Scholar 
    6.Coelho, L. P. et al. Similarity of the dog and human gut microbiomes in gene content and response to diet. Microbiome 6, 72 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    7.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.e20 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    8.Partridge, S. R., Kwong, S. M., Firth, N. & Jensen, S. O. Mobile genetic elements associated with antimicrobial resistance. Clin. Microbiol. Rev. 31, (2018).9.Mende, D. R. et al. ProGenomes2: An improved database for accurate and consistent habitat, taxonomic and functional annotations of prokaryotic genomes. Nucleic Acids Res. 48, D621–D625 (2020).CAS 
    PubMed 

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

    Google Scholar 
    11.Steinegger, M. & Söding, J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat. Biotechnol. 35, 1026–1028 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    12.Daniel H. et al. RefSeq: an update on prokaryotic genome annotation and curation. Nuc. Acids Res. 46, D851–D860 (2018).13.Mering, C. von et al. Quantitative phylogenetic assessment of microbial communities in diverse environments. Science 315, 1126–1130 (2007).ADS 

    Google Scholar 
    14.Richardson, E. J. et al. Gene exchange drives the ecological success of a multi-host bacterial pathogen. Nat. Ecol. Evol. 2, 1468–1478 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    15.Nielsen, H. B. et al. Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes. Nat. Biotechnol. 32, 822–828 (2014).CAS 
    PubMed 

    Google Scholar 
    16.Mende, D. R., Sunagawa, S., Zeller, G. & Bork, P. Accurate and universal delineation of prokaryotic species. Nat. Methods 10, 881–884 (2013).CAS 
    PubMed 

    Google Scholar 
    17.Huerta-Cepas, J. et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol. Biol. Evol. 34, 2115–2122 (2017).CAS 
    PubMed 
    PubMed Central 

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

    Google Scholar 
    19.Maistrenko, O. M. et al. Disentangling the impact of environmental and phylogenetic constraints on prokaryotic within-species diversity. ISME J. 14, 1247–1259 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    20.Baumdicker, F., Hess, W. R. & Pfaffelhuber, P. The diversity of a distributed genome in bacterial populations. Ann. Appl. Probab. 20, 1567–1606 (2010).MathSciNet 
    MATH 

    Google Scholar 
    21.Sela, I., Wolf, Y. I. & Koonin, E. V. Theory of prokaryotic genome evolution. Proc. Natl Acad. Sci. USA 113, 11399–11407 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    22.Dandekar, T., Snel, B., Huynen, M. & Bork, P. Conservation of gene order: a fingerprint of proteins that physically interact. Trends Biochem. Sci. 23, 324–328 (1998).CAS 
    PubMed 

    Google Scholar 
    23.Nei, M., Suzuki, Y. & Nozawa, M. The neutral theory of molecular evolution in the genomic era. Annu. Rev. Genomics Hum. Genet. 11, 265–289 (2010).CAS 
    PubMed 

    Google Scholar 
    24.Iranzo, J., Cuesta, J. A., Manrubia, S., Katsnelson, M. I. & Koonin, E. V. Disentangling the effects of selection and loss bias on gene dynamics. Proc. Natl Acad. Sci. USA 114, E5616–E5624 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    25.Wolf, Y. I., Makarova, K. S., Lobkovsky, A. E. & Koonin, E. V. Two fundamentally different classes of microbial genes. Nat. Microbiol. 2, 16208 (2016).CAS 
    PubMed 

    Google Scholar 
    26.Rasko, D. A. et al. The pangenome structure of Escherichia coli: comparative genomic analysis of E. coli commensal and pathogenic isolates. J. Bacteriol. 190, 6881–6893 (2008).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    27.Koskella, B., Hall, L. J. & Metcalf, C. J. E. The microbiome beyond the horizon of ecological and evolutionary theory. Nat. Ecol. Evol. 1, 1606–1615 (2017).PubMed 

    Google Scholar 
    28.Liu, R. et al. Gut microbiome and serum metabolome alterations in obesity and after weight-loss intervention. Nat. Med. 23, 859–868 (2017).CAS 
    PubMed 

    Google Scholar 
    29.Metcalf, J. L. et al. Microbial community assembly and metabolic function during mammalian corpse decomposition. Science 351, 158–162 (2015).ADS 
    PubMed 

    Google Scholar 
    30.Vincent, C. et al. Bloom and bust: intestinal microbiota dynamics in response to hospital exposures and Clostridium difficile colonization or infection. Microbiome 4, 12 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    31.Zeller, G. et al. Potential of fecal microbiota for early‐stage detection of colorectal cancer. Mol. Syst. Biol. 10, 766 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    32.Gibson, M. K. et al. Developmental dynamics of the preterm infant gut microbiota and antibiotic resistome. Nat. Microbiol. 1, 16024 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    33.Zhang, X. et al. The oral and gut microbiomes are perturbed in rheumatoid arthritis and partly normalized after treatment. Nat. Med. 21, 895–905 (2015).CAS 
    PubMed 

    Google Scholar 
    34.Brito, I. L. et al. Mobile genes in the human microbiome are structured from global to individual scales. Nature 535, 435–439 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    35.Vatanen, T. et al. Variation in microbiome LPS immunogenicity contributes to autoimmunity in humans. Cell 165, 842–853 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    36.Turnbaugh, P. J. et al. The human microbiome project. Nature 449, 804–810 (2007).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    37.Hannigan, G. D. et al. The human skin double-stranded DNA virome: topographical and temporal diversity, genetic enrichment, and dynamic associations with the host microbiome. MBio 6, e01578-15 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    38.Taft, D. H. et al. Intestinal microbiota of preterm infants differ over time and between hospitals. Microbiome 2, 36 (2014).PubMed 
    PubMed Central 

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

    Google Scholar 
    40.Wilhelm, R. C. et al. Biogeography and organic matter removal shape long-term effects of timber harvesting on forest soil microbial communities. ISME J. 11, 2552–2568 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    41.Xie, H. et al. Shotgun metagenomics of 250 adult twins reveals genetic and environmental impacts on the gut microbiome. Cell Syst. 3, 572–584.e3 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    42.The MetaSUB International Consortium. The metagenomics and metadesign of the subways and urban biomes (metasub) international consortium inaugural meeting report. Microbiome 4, 24 (2016).
    Google Scholar 
    43.Chatelier, E. L. et al. Richness of human gut microbiome correlates with metabolic markers. Nature 500, 541–546 (2013).PubMed 

    Google Scholar 
    44.Li, J. et al. Gut microbiota dysbiosis contributes to the development of hypertension. Microbiome 5, (2017).45.Pehrsson, E. C. et al. Interconnected microbiomes and resistomes in low-income human habitats. Nature 533, 212–216 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    46.Li, J. et al. An integrated catalog of reference genes in the human gut microbiome. Nat. Biotechnol. 32, 834–841 (2014).CAS 
    PubMed 

    Google Scholar 
    47.Feng, Q. et al. Gut microbiome development along the colorectal adenoma–carcinoma sequence. Nat. Commun. 6, 6528 (2015).ADS 
    CAS 
    PubMed 

    Google Scholar 
    48.Gu, Y. et al. Analyses of gut microbiota and plasma bile acids enable stratification of patients for antidiabetic treatment. Nat. Commun. 8, 1785 (2017).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    49.Karlsson, F. H. et al. Gut metagenome in european women with normal, impaired and diabetic glucose control. Nature 498, 99–103 (2013).ADS 
    CAS 
    PubMed 

    Google Scholar 
    50.Yu, J. et al. Metagenomic analysis of faecal microbiome as a tool towards targeted non-invasive biomarkers for colorectal cancer. Gut 66, 70–78 (2017).CAS 
    PubMed 

    Google Scholar 
    51.Youngster, I. et al. Fecal microbiota transplant for relapsing clostridium difficile infection using a frozen inoculum from unrelated donors: a randomized, open-label, controlled pilot study. Clin. Infect. Dis. 58, 1515–1522 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    52.Guittar, J., Shade, A. & Litchman, E. Trait-based community assembly and succession of the infant gut microbiome. Nat. Commun. 10, 512 (2019).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    53.Vogtmann, E. et al. Colorectal cancer and the human gut microbiome: reproducibility with whole-genome shotgun sequencing. PLoS ONE 11, e0155362 (2016).PubMed 
    PubMed Central 

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

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

    Google Scholar 
    56.Van Rossum, T. et al. Spatiotemporal dynamics of river viruses, bacteria and microeukaryotes. Preprint at https://doi.org/10.1101/259861 (2018).57.Feng, Q. et al. Integrated metabolomics and metagenomics analysis of plasma and urine identified microbial metabolites associated with coronary heart disease. Sci. Rep. 6, 22525 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    58.Oh, J., Byrd, A. L., Park, M., Kong, H. H. & Segre, J. A. Temporal stability of the human skin microbiome. Cell 165, 854–866 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    59.Xiao, L. et al. A reference gene catalogue of the pig gut microbiome. Nat. Microbiol. 1, 16161 (2016).CAS 
    PubMed 

    Google Scholar 
    60.R Core Team. R: a language and environment for statistical computing (R Foundation for Statistical Computing, 2014).61.Coelho, L. P. et al. NG-meta-profiler: Fast processing of metagenomes using ngless, a domain-specific language. Microbiome 7, 84 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    62.Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct De Bruijn graph. Bioinformatics 31, 1674–1676 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    63.Besemer, J. & Borodovsky, M. GeneMark: web software for gene finding in prokaryotes, eukaryotes and viruses. Nucleic Acids Res. 33, W451–W454 (2005).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    64.Coelho, L. P. Jug: Software for parallel reproducible computation in Python. J. Open Res. Softw. 5, 30 (2017).
    Google Scholar 
    65.Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using diamond. Nat. Methods 12, 59–60 (2015).CAS 
    PubMed 

    Google Scholar 
    66.Eberhardt, R. Y. et al. AntiFam: A tool to help identify spurious ORFs in protein annotation. Database 2012, bas003 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    67.Kang, D. et al. MetaBAT 2: An adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, e7359 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    68.Li, H. Aligning sequence reads, clone sequences and assembly contigs with bwa-mem. Preprint at https://arxiv.org/abs/1303.3997 (2013).69.Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    70.Bowers, R. M. et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat. Biotechnol. 35, 725–731 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    71.Zhou, W., Gay, N. & Oh, J. ReprDB and panDB: minimalist databases with maximal microbial representation. Microbiome 6, 15 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    72.Hingamp, P. et al. Exploring nucleo-cytoplasmic large DNA viruses in tara oceans microbial metagenomes. ISME J. 7, 1678–1695 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    73.Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).CAS 
    PubMed 

    Google Scholar 
    74.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 Res. 47, D309–D314 (2019).CAS 
    PubMed 

    Google Scholar 
    75.Eddy, S. R. Accelerated profile HMM searches. PLoS Comput. Biol. 7, e1002195 (2011).ADS 
    MathSciNet 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    76.Smyshlyaev, G., Barabas, O. & Bateman, A. Sequence analysis allows functional annotation of tyrosine recombinases in prokaryotic genomes. Mol. Syst. Biol. 17, e9880 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    77.Jia, B. et al. CARD 2017: Expansion and model-centric curation of the comprehensive antibiotic resistance database. Nucleic Acids Res. 45, D566–D573 (2017).CAS 
    PubMed 

    Google Scholar 
    78.Gibson, M. K., Forsberg, K. J. & Dantas, G. Improved annotation of antibiotic resistance determinants reveals microbial resistomes cluster by ecology. ISME J. 9, 207–216 (2015).CAS 
    PubMed 

    Google Scholar 
    79.Li, T., Fan, K., Wang, J. & Wang, W. Reduction of protein sequence complexity by residue grouping. Protein Eng. 16, 323–330 (2003).CAS 
    PubMed 

    Google Scholar 
    80.Zhao, M., Lee, W.-P., Garrison, E. P. & Marth, G. T. SSW library: an SIMD Smith–Waterman C/C++ library for use in genomic applications. PLoS ONE 8, e82138 (2013).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    81.Li, H. Minimap2: Pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2017).
    Google Scholar 
    82.Milanese, A. et al. Microbial abundance, activity and population genomic profiling with mOTUs2. Nat. Commun. 10, 1014 (2019).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    83.Salter, S. J. et al. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol. 12, 87 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    84.Kumar, R., Acharya, V., Singh, D. & Kumar, S. Strategies for high-altitude adaptation revealed from high-quality draft genome of non-violacein producing Janthinobacterium lividum ERGS5:01. Stand. Genomic Sci. 13, 11 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    85.Patijanasoontorn, B. et al. Hospital acquired Janthinobacterium lividum septicemia in srinagarind hospital. J. Med. Assoc. Thai. 75 Suppl 2, 6–10 (1992).PubMed 

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

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

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

    Google Scholar 
    89.Collins, R. E. & Higgs, P. G. Testing the infinitely many genes model for the evolution of the bacterial core genome and pangenome. Mol. Biol. Evol. 29, 3413–3425 (2012).CAS 
    PubMed 

    Google Scholar 
    90.Sievers, F. et al. Fast, scalable generation of high-quality protein multiple sequence alignments using clustal omega. Mol. Syst. Biol. 7, 539 (2011).PubMed 
    PubMed Central 

    Google Scholar 
    91.Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    92.Huerta-Cepas, J., Serra, F. & Bork, P. ETE 3: reconstruction, analysis, and visualization of phylogenomic data. Mol. Biol. Evol. 33, 1635–1638 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    93.Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    94.Suyama, M., Torrents, D. & Bork, P. PAL2NAL: robust conversion of protein sequence alignments into the corresponding codon alignments. Nucleic Acids Res. 34, W609–12 (2006).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    95.Murrell, B. et al. FUBAR: a fast, unconstrained Bayesian approximation for inferring selection. Mol. Biol. Evol. 30, 1196–1205 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    96.Smith, M. D. et al. Less is more: an adaptive branch-site random effects model for efficient detection of episodic diversifying selection. Mol. Biol. Evol. 32, 1342–1353 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    97.Washietl, S. et al. RNAcode: robust discrimination of coding and noncoding regions in comparative sequence data. RNA 17, 578–594 (2011).CAS 
    PubMed 
    PubMed Central 

    Google Scholar  More

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    Experimental evidence for recovery of mercury-contaminated fish populations

    Mercury additions to the study catchmentMETAALICUS was conducted on the Lake 658 catchment at the Experimental Lakes Area (ELA; now IISD-ELA), a remote area in the Precambrian Shield of northwestern Ontario, Canada (49° 43′ 95″ N, 93° 44′ 20″ W) set aside for whole-ecosystem research31. The Lake 658 catchment includes upland (41.2 ha), wetland (1.7 ha) and lake surface (8.4 ha) areas. Lake 658 is a double basin (13 m depth), circumneutral, headwater lake, with a fish community consisting of forage (yellow perch (P. flavescens) and blacknose shiner (Notropis heterolepis)), benthivorous (lake whitefish (C. clupeaformis) and white sucker (Catostomus commersonii)), and piscivorous (northern pike (E. lucius)) fishes. The lake is closed to fishing.Hg addition methods used in METAALICUS have been described in detail elsewhere19,32,33. In brief, three Hg spikes, each enriched with a different stable Hg isotope, were applied separately to the lake surface, upland and wetland areas. Upland and wetland spikes were applied once per year (when possible; Fig. 1a) by fixed-wing aircraft (Cessna 188 AGtruck). Mercury spikes (as HgNO3) were diluted in acidified water (pH 4) in a 500 l fiberglass tank and sprayed with a stainless-steel boom on upland (approximately 79.9% 200Hg) and wetland (approximately 90.1% 198Hg) areas. Spraying was completed during or immediately before a rain event, with wind speeds less than 15 km h−1 to minimize drift of spike Hg outside of target areas. Aerial spraying of upland and wetland areas left a 20-m buffer to the shoreline, which was sprayed by hand with a gas-powered pump and fire hose to within about 5 m of the lake32. Average net application rates of isotopically labelled Hg to the upland and wetland areas were 18.5 μg m−2 yr−1 and 17.8 μg m−2 yr−1, respectively.The average net application rate for lake spike Hg was 22.0 μg m−2 yr−1. For each lake addition, inorganic Hg enriched with approximately 89.7% 202Hg was added as HgNO3 from four 20-l carboys filled with acidified lake water (pH 4). Nine lake additions were conducted bi-weekly at dusk over an 18-week (wk) period during the open-water season of each year (2001–2007) by injecting at 70-cm depth into the propeller wash of trolling electric motors of two boats crisscrossing each basin of the lake32,33. It was previously demonstrated with 14C additions to an ELA lake that this approach evenly distributed spike added in the evening by the next morning34.We did not attempt to simulate Hg in rainfall for isotopic lake additions because it is impossible to simulate natural rainfall concentrations (about 10 ng l−1) in the 20-l carboys used for additions. Instead, our starting point for the experiment was to ensure that the spike was behaving as closely as possible to ambient surface water Hg very soon after it entered the lake. Several factors support this assertion. By the next morning each spike addition had increased epilimnetic Hg concentrations by only 1 ng l−1 202Hg. Average ambient concentrations were 2 ng l−1. Thus, while the Hg concentrations in the carboys were high (2.6 mg l−1), the receiving waters were soon at trace levels. Furthermore, we investigated if the additions altered the degree of bioavailability or photoreactivity of Hg(ii) in the receiving surface water. We examined the bioavailability of spike Hg(ii) as compared to ambient Hg in the lake itself using a genetically engineered bioreporter bacterium35. On seven occasions, epilimnetic samples were collected on the day before and within 12 h of spike additions. The spike was added to the lake as Hg(NO3)2, which is bioavailable to the bioreporter bacterium (detection limit = 0.1 ng Hg(ii) l−1), but we never saw bioavailable ambient or spike Hg(ii) in the lake, presumably because it was quickly bound to dissolved organic carbon (DOC). This indicates that, in terms of bioavailability, the spike Hg was behaving like ambient Hg soon after additions. Photoreactivity in the surface water was examined on seven occasions, by measuring the % of total Hg(ii) that was dissolved gaseous Hg for spike and ambient Hg, either 24 h or 48 h after the lake was spiked36. There was no significant difference (paired t-test, P > 0.05), demonstrating that by then the lake spike was behaving in the same way as ambient Hg during gaseous Hg production.Lake, food web and fish samplingWater samples were collected from May to October every four weeks at the deepest point of Lake 658. Water was pumped from six depths through acid-cleaned Teflon tubing into acid-cleaned Teflon or glass bottles. Water samples were filtered in-line using pre-ashed quartz fibre filters (Whatman GFQ, 0.7 µm). Subsequently, Hg species were measured in the filtered water samples (dissolved Hg and MeHg) and in particles collected on the quartz fibre filter (particulate Hg and MeHg).From 2001 to 2012, Lake 658 sediments were sampled at 4 fixed sites up to 5 times per year. Sampling frequency was highest in 2001, with monthly sampling from May to September, and declined over the course of the study. Fixed sites were located at depths of 0.5, 2, 3 and 7 m. A sediment survey of up to 12 additional sites was also conducted once or twice each year. Survey sites were selected to represent the full range of water depths in both basins. Cores were collected by hand by divers, or by subsampling sediments collected using a small box corer. Cores were capped and returned to the field station for processing within a few hours. For each site, three separate cores were sectioned and composited in zipper lock bags for a 0- to 2-cm depth sampling horizon, and then frozen at −20 °C.Bulk zooplankton and Chaoborus samples were collected from Lake 658 for MeHg analysis. Zooplankton were collected during the day from May to October (bi-weekly: 2001–2007; monthly: 2008–2015). A plankton net (150 μm, 0.5 m diameter) was towed vertically through the water column from 1 m above the lake bottom at the deepest point to the surface of the lake. Samples were frozen in plastic Whirl-Pak bags after removal of any Chaoborus using acid-washed tweezers. Dominant zooplankton taxa in Lake 658 included calanoid copepods (Diaptomus oregonensis) and Cladocera (Holopedium glacialis, Daphnia pulicaria and Daphnia mendotae). Chaoborus samples were collected monthly in the same manner at least 1 h after sunset. After collection, Chaoborus were picked from the sample using forceps and frozen in Whirl-Pak bags. Chaoborus were not separated by species for MeHg analyses, but both C. flavicans and C. punctipennis occur in the lake. Profundal chironomids were sampled at the deepest part of the lake using a standard Ekman grab sampler. Grab material was washed using water from a nearby lake and individual chironomids were picked by hand.All work with vertebrate animals was approved by Animal Care Committees (ACC) through the Canadian Council on Animal Care (Freshwater Institute ACC for Fisheries and Oceans Canada, 2001–2013; University of Manitoba ACC for IISD-ELA, 2014–2015). Licenses to Collect Fish for Scientific Purposes were granted annually by the Ontario Ministry of Natural Resources and Forestry. Prior to any Hg additions, a small-mesh fence was installed at the outlet of Lake 658 to the downstream lake to prevent movement of fish between lakes. Sampling for determination of MeHg concentrations (measured as total mercury (THg), see below) occurred each autumn (August–October; that is, the end of the growing season in north temperate lakes) for all fish species in Lake 658, and for northern pike and yellow perch in nearby reference Lake 240 (Extended Data Tables 2, 3). Fish collections occurred randomly throughout the lakes. Forage fish (YOY and 1+ yellow perch, and blacknose shiner) were captured using small mesh gillnets (6–10 mm) set for 90% of the Hg in muscle tissue from yellow perch in Lake 658 is MeHg40,41, here we report fish mercury data as MeHg.THg concentrations (ambient, lake spike, upland spike and wetland spike) in fish muscle samples were quantified by ICP-MS39. Samples were digested with HNO3/H2SO4 (7:3 v/v) and heated at 80 °C until brown NOx gases no longer formed. The THg in sample digests was reduced by SnCl2 to Hg0 which was then quantified by ICP-MS (Thermo-Finnigan Element2) using a continuous flow cold vapour generation technique41. To correct for procedural recoveries, all samples were spiked with 201HgCl2 prior to sample analysis. Samples of CRMs (DORM2 (2001–2011), DORM3 (2012–2013), DORM4 (2014–2015); National Research Council of Canada) were submitted to the same procedures; measured THg concentrations in the reference materials were not statistically different from certified values (P > 0.05). Detection limit for each of the spikes was 0.5% of ambient Hg.Calculations and statistical methodsAnalyses were completed with Statistica (6.1, Statsoft) and Sigmaplot (11.0, Systat Software). We present wet weight (w.w.) MeHg concentrations for all samples, except sediments which are dry weight (d.w.) concentrations. For zooplankton, Chaoborus, and profundal chironomids, d.w. MeHg concentrations were multiplied by a standard proportion (0.15) to yield w.w. concentrations for each sample42. The resulting w.w. concentrations were averaged over each open water season to determine annual means. For fish muscle biopsies, d.w. MeHg concentrations were multiplied by individual d.w. proportions to yield w.w. MeHg concentrations for each sample. To avoid any size-related biases, we calculated standardized annual MeHg concentrations (ambient and lake spike) for northern pike and lake whitefish by determining best-fit relationships between FL and MeHg concentrations for each year (quadratic polynomial, except for a linear fit for lake whitefish in 2004), and using the resulting regression equations to estimate MeHg concentrations at a standard FL43 (the mean FL of all fish sampled for each species: northern pike, 475 mm; lake whitefish, 530 mm). Square root transformation of raw northern pike data was required to satisfy assumptions of normality and homoscedasticity prior to standardization. The resulting data represent standardized concentrations of lake spike and ambient MeHg for each species each year.We used the ratio of lake spike and ambient Hg in each sample as a measure of the amount by which Hg concentrations were changed with the addition of isotopically enriched Hg:$${rm{P}}{rm{e}}{rm{r}}{rm{c}}{rm{e}}{rm{n}}{rm{t}},{rm{i}}{rm{n}}{rm{c}}{rm{r}}{rm{e}}{rm{a}}{rm{s}}{rm{e}}={[{rm{l}}{rm{a}}{rm{k}}{rm{e}}{rm{s}}{rm{p}}{rm{i}}{rm{k}}{rm{e}}{rm{H}}{rm{g}}]}_{i}/{[{rm{a}}{rm{m}}{rm{b}}{rm{i}}{rm{e}}{rm{n}}{rm{t}}{rm{H}}{rm{g}}]}_{i}times 100$$
    (1)
    where [lake spike Hg]i is the concentration of lake spike MeHg in sample i, and [ambient Hg]i is the concentration of ambient MeHg in sample i. For northern pike and lake whitefish, we calculated the mean annual relative increase from all individuals (not the size-standardized concentration data).Biomagnification factors (BMF) were calculated to describe differences in Hg concentrations between predator and prey5:$${rm{BMF}}={log }_{10}({[{rm{MeHg}}]}_{{rm{p}}{rm{r}}{rm{e}}{rm{d}}{rm{a}}{rm{t}}{rm{o}}{rm{r}}}/{[{rm{MeHg}}]}_{{rm{p}}{rm{r}}{rm{e}}{rm{y}}})$$
    (2)
    where [MeHg]predator is the mean (forage fish) or standardized (large-bodied fish) concentration of MeHg in the predator (ng g−1 w.w.) and [MeHg]prey is the mean concentration of MeHg in the prey (ng g−1 w.w.). MeHg concentration of prey items were averaged from samples collected throughout the open-water season immediately prior to autumn sampling of fish species to represent an integrated exposure for calculation of BMF. We used a dominant prey item to represent the diet of each fish species. For age 1+ yellow perch, northern pike, and lake whitefish, dominant prey items were zooplankton, forage fishes (YOY and 1+ yellow perch, and blacknose shiner) and Chaoborus, respectively.To assess loss of lake spike MeHg by northern pike during the recovery period (2008–2015), we calculated28 whole body burdens (in μg) of lake spike MeHg for the standardized population and for individuals that had been sampled in autumn 2007 (t0 is the final time spike Hg was added to the lake) and again in at least one subsequent year during annual autumn sampling (n = 16 fish, of which 1–9 individuals were recaptured annually from 2008–2015). This calculation of MeHg burden is a relative measure of whole fish Hg content because MeHg is higher in muscle tissue than in other tissue types28,40. For the standardized population data, we used best-fit relationships between FL (in mm) and body weight (in g; quadratic polynomial) to determine body weight at the standard FL. We multiplied this body weight by standard ambient and spike MeHg concentrations (in ng g−1 w.w.) in muscle tissue for each year to determine body burdens over time (in ng). For individual fish, we multiplied spike MeHg concentration (in ng g−1 w.w.) by body weight (in g) to yield individual body burdens (in ng). To account for differences among individuals and between individuals and the population, we normalized the data to examine the mean proportion of original (t0) lake spike MeHg burden present in northern pike each year of the recovery period (2008–2015).$${rm{change}},{rm{in}},{rm{burden}},{rm{from}},{t}_{0}={{rm{burden}}}_{{rm{tx}}}/{{rm{burden}}}_{{rm{t}}0}$$
    (3)
    We used a best fit regression (exponential decay, beginning in the second year of recovery) to estimate the half-life (50% of original burden) of lake spike MeHg for the population.Northern pike and lake whitefish ages were determined by cleithra and otoliths, respectively, if mortality had occurred, but most ages were quantified using fin rays collected from live fish44 (K. H. Mills, DFO or North/South Consultants). Northern pike of the sizes selected for biopsy sampling had a median age of 3 years (range: 2–12 years; n = 305); the median age of lake whitefish was 17 years (range: 3–38 years; n = 86).Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this paper. More