More stories

  • in

    Marine heatwaves and the collapse of marginal North Atlantic kelp forests

    1.
    Frölicher, T. L. & Laufkötter, C. Emerging risks from marine heat waves. Nat. Commun.9, 650 (2018).
    ADS  PubMed  PubMed Central  Google Scholar 
    2.
    Coumou, D. & Rahmstorf, S. A decade of weather extremes. Nat. Clim. Change2, 491–496 (2012).
    ADS  Google Scholar 

    3.
    Gaines, S. D. & Denny, M. W. The largest, smallest, highest, lowest, longest, and shortest: Extremes in ecology. Ecology74, 1677–1692 (1993).
    Google Scholar 

    4.
    Hobday, A. J. et al. A hierarchical approach to defining marine heatwaves. Prog. Oceanogr.141, 227–238 (2016).
    ADS  Google Scholar 

    5.
    Smale, D. A. et al. Marine heatwaves threaten global biodiversity and the provision of ecosystem services. Nat. Clim. Change9, 306–312 (2019).
    ADS  Google Scholar 

    6.
    Harris, R. M. B. et al. Biological responses to the press and pulse of climate trends and extreme events. Nat. Clim. Change8, 579–587 (2018).
    ADS  Google Scholar 

    7.
    Oliver, E. C. J. et al. Longer and more frequent marine heatwaves over the past century. Nat. Commun.9, 1324 (2018).
    ADS  PubMed  PubMed Central  Google Scholar 

    8.
    Oliver, E. C. J. et al. The unprecedented 2015/16 Tasman Sea marine heatwave. Nat. Commun.8, 16101 (2017).
    ADS  PubMed  PubMed Central  Google Scholar 

    9.
    IPCC. The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. (Cambridge University Press, Cambridge, 2013).

    10.
    Jentsch, A. & Beierkuhnlein, C. External geophysics, climate and environment. C. R. Geosci.340 (2008).

    11.
    Wernberg, T., Smale, D. A. & Thomsen, M. S. A decade of climate change experiments on marine organisms: Procedures, patterns and problems. Glob. Change Biol.18, 1491–1498 (2012).
    ADS  Google Scholar 

    12.
    Kordas, R. L., Harley, C. D. G. & O’Connor, M. I. Community ecology in a warming world: The influence of temperature on interspecific interactions in marine systems. J. Exp. Mar. Biol. Ecol.400, 218–226 (2011).
    Google Scholar 

    13.
    Hobday, A. J. et al. Categorizing and naming marine heatwaves. Oceanography31, 162–173 (2018).
    Google Scholar 

    14.
    Wernberg, T. et al. An extreme climatic event alters marine ecosystem structure in a global biodiversity hotspot. Nat. Clim. Change3, 78–82 (2013).
    ADS  Google Scholar 

    15.
    Wernberg, T., Krumhansl, K. A., Filbee-Dexter, K. & Pedersen, M. F. In World Seas: An Environmental Evaluation, Vol III: Ecological Issues and Environmental Impacts (ed. Sheppard, C.) (Academic Press, Cambridge, 2019).

    16.
    Lüning, K., Yarish, C. & Kirkman, H. Seaweeds: Their Environment, Biogeography, and Ecophysiology (Wiley, Hoboken, 1990).
    Google Scholar 

    17.
    Assis, J., Araújo, M. B. & Serrão, E. A. Projected climate changes threaten ancient refugia of kelp forests in the North Atlantic. Glob. Change Biol.24, e55–e66 (2018).
    ADS  Google Scholar 

    18.
    Wilson, K. L., Skinner, M. A. & Lotze, H. K. Projected 21st-century distribution of canopy-forming seaweeds in the Northwest Atlantic with climate change. Divers. Distrib. 25, 582–602. (2019).
    Article  Google Scholar 

    19.
    Fernández, C. The retreat of large brown seaweeds on the north coast of Spain: The case of Saccorhiza polyschides. Eur. J. Phycol.46, 352–360 (2011).
    Google Scholar 

    20.
    Filbee-Dexter, K., Feehan, C. J. & Scheibling, R. E. Large-scale degradation of a kelp ecosystem in an ocean warming hotspot. Mar. Ecol. Prog. Ser.543, 141–152 (2016).
    ADS  CAS  Google Scholar 

    21.
    Wernberg, T. et al. Climate-driven regime shift of a temperate marine ecosystem. Science (80-).353, 169–172 (2016).
    ADS  CAS  Google Scholar 

    22.
    Rogers-Bennett, L. & Catton, C. A. Marine heat wave and multiple stressors tip bull kelp forest to sea urchin barrens. Sci. Rep.9, 1–9 (2019).
    CAS  Google Scholar 

    23.
    Arafeh-Dalmau, N. et al. Extreme marine heatwaves alter kelp forest community near its equatorward distribution limit. Front. Mar. Sci.6, 499 (2019).
    Google Scholar 

    24.
    Starko, S. et al. Environmental heterogeneity mediates scale-dependent declines in kelp diversity on intertidal rocky shores. PLoS ONE14, e0213191 (2019).
    CAS  PubMed  PubMed Central  Google Scholar 

    25.
    Cavanaugh, K. C., Reed, D. C., Bell, T. W., Castorani, M. C. N. & Beas-Luna, R. Spatial variability in the resistance and resilience of giant kelp in southern and Baja California to a multiyear heatwave. Front. Mar. Sci.6, 413 (2019).
    Google Scholar 

    26.
    Simonson, E., Scheibling, R. & Metaxas, A. Kelp in hot water: I. Warming seawater temperature induces weakening and loss of kelp tissue. Mar. Ecol. Prog. Ser.537, 89–104 (2015).
    ADS  CAS  Google Scholar 

    27.
    Nepper-Davidsen, J., Andersen, D. T. & Pedersen, M. F. Effects of simulated heat wave scenarios on Saccharina latissima: Prolonged exposure to sub-lethal temperatures may cause irreversible damage. Mar. Ecol. Prog. Ser. 630, 25–39 (2020).
    ADS  Google Scholar 

    28.
    Hollarsmith, J. A., Buschmann, A. H., Camus, C. & Grosholz, E. D. Varying reproductive success under ocean warming and acidification across giant kelp (Macrocystis pyrifera) populations. J. Exp. Mar. Biol. Ecol.522, 151247 (2020).
    Google Scholar 

    29.
    Straub, S. C. Effects of marine heatwaves on canopy forming seaweeds and marine forests (University of Western Australia, Perth, 2019).
    Google Scholar 

    30.
    Wernberg, T. et al. Genetic diversity and kelp forest vulnerability to climatic stress. Sci. Rep.8, 1851 (2018).
    ADS  PubMed  PubMed Central  Google Scholar 

    31.
    Bernhardt, J. R. & Leslie, H. M. Resilience to climate change in coastal marine ecosystems. Ann. Rev. Mar. Sci.5, 371–392 (2013).
    PubMed  Google Scholar 

    32.
    Filbee-Dexter, K. & Wernberg, T. Rise of Turfs: A new battlefront for globally declining kelp forests. Bioscience68, 64–76 (2018).
    Google Scholar 

    33.
    Krause-Jensen, D. & Duarte, C. M. Substantial role of macroalgae in marine carbon sequestration. Nat. Geosci.9, 737–742 (2016).
    ADS  CAS  Google Scholar 

    34.
    Norderhaug, K. M. & Christie, H. Secondary production in a Laminaria hyperborea kelp forest and variation according to wave exposure. Estuar. Coast. Shelf Sci.95, 135–144 (2011).
    ADS  Google Scholar 

    35.
    Bertocci, I., Araújo, R., Oliveira, P. & Sousa-Pinto, I. Potential effects of kelp species on local fisheries. J. Appl. Ecol.52, 1216–1226 (2015).
    Google Scholar 

    36.
    Wernberg, T. & Filbee-Dexter, K. Missing the marine forest for the trees. Mar. Ecol. Prog. Ser.612, 209–215 (2019).
    ADS  Google Scholar 

    37.
    Albretsen, J., Aure, J., Sætre, R. & Danielssen, D. S. Climatic variability in the Skagerrak and coastal waters of Norway. ICES J. Mar. Sci.69, 758–763 (2012).
    Google Scholar 

    38.
    Andersen, G. S., Steen, H., Christie, H., Fredriksen, S. & Emil Moy, F. Seasonal patterns of sporophyte growth, fertility, fouling, and mortality of Saccharina latissima in Skagerrak, Norway: Implications for Forest Recovery. J. Mar. Biol.2011, 690375 (2011).
    Google Scholar 

    39.
    Krumhansl, K. & Scheibling, R. Detrital production in Nova Scotian kelp beds: Patterns and processes. Mar. Ecol. Prog. Ser.421, 67–82 (2011).
    ADS  Google Scholar 

    40.
    Brady-Campbell, M. M., Campbell, D. B. & Harlin, M. M. Productivity of kelp (Laminaria spp.) near the southern limit in the Northwestern Atlantic Ocean. Mar. Ecol. Prog. Ser.18, 79–88 (1984).
    ADS  Google Scholar 

    41.
    Grace, S. P. Ecomorphology of the Temperate Scleractinian Astrangia poculata: Coral–Macroalgal Interactions in Narragansett Bay (University of Rhode Island, South Kingstown, 2004).
    Google Scholar 

    42.
    Moy, F. E. & Christie, H. Large-scale shift from sugar kelp (Saccharina latissima) to ephemeral algae along the south and west coast of Norway. Mar. Biol. Res.8, 309–321 (2012).
    Google Scholar 

    43.
    Lee, J.-A. & Brinkhuis, B. H. Reproductive phenology of Laminaria saccharina (L.) Lamour. (Phaeophyta) at the southern limit of its distribution in the northwestern Atlantic Ocean. J. Phycol.22, 276–285 (1986).
    Google Scholar 

    44.
    Feehan, C. J., Grace, S. P. & Narvaez, C. A. Ecological feedbacks stabilize a turf-dominated ecosystem at the southern extent of kelp forests in the Northwest Atlantic. Sci. Rep.9, 7078 (2019).
    ADS  PubMed  PubMed Central  Google Scholar 

    45.
    Sjøtun, K. Seasonal lamina growth in two age groups of Laminaria saccharina (L.) Lamour. in Western Norway. Bot. Mar.36, 433–442 (1993).
    Google Scholar 

    46.
    Martinez, E. A., Cardenas, L. & Pinto, R. Recovery and genetic diversity of the intertidal kelp Lessonia nigrescens (Phaeophyceae) 20 years after El Nino 1982/831. J. Phycol.39, 504–508 (2003).
    Google Scholar 

    47.
    Edwards, M. & Estes, J. Catastrophe, recovery and range limitation in NE Pacific kelp forests: A large-scale perspective. Mar. Ecol. Prog. Ser.320, 79–87 (2006).
    ADS  Google Scholar 

    48.
    Ummenhofer, C. C. & Meehl, G. A. Extreme weather and climate events with ecological relevance: A review. Philos. Trans. R. Soc. B Biol. Sci.372, 20160135 (2017).
    Google Scholar 

    49.
    Hobday, A. J. & Pecl, G. T. Identification of global marine hotspots: Sentinels for change and vanguards for adaptation action. Rev. Fish Biol. Fish.24, 415–425 (2014).
    Google Scholar 

    50.
    Sjøtun, K., Fredriksen, S., Lein, T. E., Rueness, J. & Sivertsen, K. Population studies of Laminaria hyperborea from its northern range of distribution in Norway. Hydrobiologia260–261, 215–221 (1993).
    Google Scholar 

    51.
    O’Brien, J. M. & Scheibling, R. E. Low recruitment, high tissue loss, and juvenile mortality limit recovery of kelp following large-scale defoliation. Mar. Biol.165, 171 (2018).
    Google Scholar 

    52.
    Borum, K., Pedersen, M. F., Krause-Jensen, D. & Christensen, N. Biomass, photosynthesis and growth of Laminaria saccharina in a high-arctic fjord, NE Greenland. Mar. Biol.141, 11–19 (2002).
    Google Scholar 

    53.
    Nielsen, M. M. et al. Growth dynamics of Saccharina latissima (Laminariales, Phaeophyceae) in Aarhus Bay, Denmark, and along the species’ distribution range. Mar. Biol.161, 2011–2022 (2014).
    CAS  Google Scholar 

    54.
    tom Dieck, I. Temperature tolerance and survival in darkness of kelp gametophytes (Laminariales, Phaeophyta): Ecological and biogeographical implications. Mar. Ecol. Prog. Ser.100, 253–264 (1993).
    ADS  Google Scholar 

    55.
    Bolton, J. J. & Lüning, K. Optimal growth and maximal survival temperatures of Atlantic Laminaria species (Phaeophyta) in culture. Mar. Biol.66, 89–94 (1982).
    Google Scholar 

    56.
    Andersen, G. S., Pedersen, M. F. & Nielsen, S. L. Temperature acclimation and heat tolerance of photosynthesis in Norwegian Saccharina latissima (Laminariales, Phaeophyceae). J. Phycol.49, 689–700 (2013).
    CAS  PubMed  Google Scholar 

    57.
    Jump, A. S. & Penuelas, J. Running to stand still: Adaptation and the response of plants to rapid climate change. Ecol. Lett.8, 1010–1020 (2005).
    Google Scholar 

    58.
    Niu, S. et al. Plant growth and mortality under climatic extremes: An overview. Environ. Exp. Bot.98, 13–19 (2014).
    Google Scholar 

    59.
    Bennett, S., Wernberg, T., Arackal Joy, B., de Bettignies, T. & Campbell, A. H. Central and rear-edge populations can be equally vulnerable to warming. Nat. Commun.6, 10280 (2015).
    ADS  CAS  PubMed  PubMed Central  Google Scholar 

    60.
    Gorman, D. & Connell, S. D. Recovering subtidal forests in human-dominated landscapes. J. Appl. Ecol.46, 1258–1265 (2009).
    Google Scholar 

    61.
    Burek, K., O’Brien, J. & Scheibling, R. Wasted effort: Recruitment and persistence of kelp on algal turf. Mar. Ecol. Prog. Ser.600, 3–19 (2018).
    ADS  Google Scholar 

    62.
    Norderhaug, K. M. et al. Effects of climate and eutrophication on the diversity of hard bottom communities on the Skagerrak coast 1990–2010. Mar. Ecol. Prog. Ser.530, 29–46 (2015).
    ADS  CAS  Google Scholar 

    63.
    Gorgula, S. & Connell, S. Expansive covers of turf-forming algae on human-dominated coast: The relative effects of increasing nutrient and sediment loads. Mar. Biol.145, 613–619 (2004).
    Google Scholar 

    64.
    Bennett, S., Duarte, C. M., Marbà, N. & Wernberg, T. Integrating within-species variation in thermal physiology into climate change ecology. Philos. Trans. R. Soc. B Biol. Sci.374, 20180550 (2019).
    Google Scholar 

    65.
    Lüning, K. Temperature tolerance and biogeography of seaweeds: The marine algal flora of Helgoland (North Sea) as an example. Helgoländer Meeresunters. 38, 305–317 (1984).
    Google Scholar 

    66.
    Lee, J. A. & Brinkhuis, B. H. Seasonal light and temperature interaction effects on development of Laminaria saccharina (Phaeophyta) gametophytes and juvenile sporophytes. J. Phycol.24, 181–191 (1988).
    Google Scholar 

    67.
    Pedersen, M. F. et al. Detrital carbon production and export in high latitude kelp forests. Oecologia192, 227–239 (2020).
    ADS  PubMed  Google Scholar 

    68.
    Schlegel, R. W. & Smit, A. J. heatwaveR: Detect Heatwaves and Cold-Spells. (2019).

    69.
    Wasko, C. & Sharma, A. Quantile regression for investigating scaling of extreme precipitation with temperature. Water Resour. Res.50, 3608–3614 (2014).
    ADS  Google Scholar 

    70.
    Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw.67, 1–48 (2015).
    Google Scholar 

    71.
    Schlegel, R. W. Marine Heatwave Tracker. https://doi.org/10.5281/zenodo.3787872 (2020).
    Article  Google Scholar  More

  • in

    Unique inducible filamentous motility identified in pathogenic Bacillus cereus group species

    Isolation of an environmental contaminant with preferential expansion on C. jejuni cell lawns
    We observed a contaminant colony that paradoxically grew preferentially on small, spot-plated lawns of C. jejuni cells on Mueller Hinton (MH) agar (1.5% w/v). The MH plate had previously been inoculated with C. jejuni cells spotted and incubated microaerobically at 38 °C overnight before being stored for several days aerobically at room temperature. Transfer of contaminant cells onto new, similarly prepared spot-plated lawns of C. jejuni resulted in the contaminant again growing preferentially atop the C. jejuni lawns, with minimal growth on the rich agar in between spots of C. jejuni lawns (Fig. 1a). The contaminant was isolated for further study and the strain named ML-A2C4.
    Fig. 1: Identification of the filamentous motile environmental isolate as Bacillus mobilis ML-A2C4.

    a ML-A2C4 filamentous growth on C. jejuni lawn spots (small circles). b ML-A2C4 growth on a control 1.5% agar MH plate (left) and on a MH plate spread with a full confluent C. jejuni lawn (center) after 48 h aerobic incubation at 30 °C. The red box shows a close-up view of the filaments at the growth edge (right). c Quantification of the visible growth diameter on control MH plates (black bars) and plates with C. jejuni lawns (red bars) over time (n = 5) with error bars indicating standard deviation (SD). Statistical analysis was performed for growth diameter on C. jejuni lawn plates versus control plates using the Student’s t test with Welch’s correction, and for 48 vs. 24 h using repeated measures one-way ANOVA, with ****p  More

  • in

    The first evidence for Late Pleistocene dogs in Italy

    1.
    Larson, G. et al. Rethinking dog domestication by integrating genetics, archaeology, and biogeography. Proc. Natl. Acad. Sci. U. S. A.109, 8878–8883 (2012).
    ADS  CAS  PubMed  PubMed Central  Google Scholar 
    2.
    Shannon, L. M. Genetic structure in village dogs reveals a Central Asian domestication origin. Proc. Natl. Acad. Sci. U. S. A.112, 13639–13644 (2015).
    ADS  CAS  PubMed  PubMed Central  Google Scholar 

    3.
    Skoglund, P., Ersmark, E., Palkopoulou, E. & Dalén, L. Ancient wolf genome reveals an early divergence of domestic dog ancestors and admixture into high-latitude breeds. Curr. Biol.25, 1–5 (2015).
    Google Scholar 

    4.
    Thalmann, O. et al. Complete mitochondrial genomes of ancient canids suggest a European origin of domestic dogs. Science342, 871–874 (2013).
    ADS  CAS  PubMed  Google Scholar 

    5.
    Frantz, L. A. et al. Genomic and archaeological evidence suggests a dual origin of domestic dogs. Science352, 1228–1231 (2016).
    ADS  CAS  PubMed  Google Scholar 

    6.
    Germonpré, M. et al. Fossil dogs and wolves from Palaeolithic sites in Belgium, the Ukraine and Russia: osteometry, ancient DNA and stable isotopes. J. Archaeol. Sci.36, 473–490 (2009).
    Google Scholar 

    7.
    Germonpré, M. et al. Palaeolithic dogs and the early domestication of the wolf: a reply to the comments of Crockford and Kuzmin (2012). J. Archaeol. Sci.40, 786–792 (2013).
    Google Scholar 

    8.
    Gremonpré, M. et al. Palaeolithic dogs and Pleistocene wolves revisited: a reply to Morey (2014). J. Archaeol. Sci.54, 210–216 (2015).
    Google Scholar 

    9.
    Germonpré, M. et al. Palaeolithic and prehistoric dogs and Pleistocene wolves from Yakutia: identification of isolated skulls. J. Archaeol. Sci.78, 1–19 (2017).
    Google Scholar 

    10.
    Crockford, S. J. & Kuzmin, Y. V. Comments on Germonpré et al. (2012) Journal of Archaeological Science 36, 2009 “Fossil dogs and wolves from Palaeolithic sites in Belgium, the Ukraine and Russia: osteometry, ancient DNA and stable isotopes”, and Germonpré, Lázki cková-Galetová, and Sablin, Journal of Archaeological Science 39, 2012 “Palaeolithic dog skulls at the Gravettian Predmostí site, the Czech Republic”. J. Archaeol. Sci.39, 2797–2801 (2012).
    Google Scholar 

    11.
    Morey, D. F. In search of Paleolithic dogs: a quest with mixed results. J. Archaeol. Sci.52, 300–307 (2014).
    CAS  Google Scholar 

    12.
    Botigué, L. R. et al. Ancient European dog genomes reveal continuity since the Early Neolithic. Nat. Commun.8, 16082 (2017).
    ADS  PubMed  PubMed Central  Google Scholar 

    13.
    Camarós, E., Münzel, S. C., Cueto, M., Rivals, F. & Conard, N. J. The evolution of Paleolithic hominin–carnivore interaction written in teeth: stories from the Swabian Jura (Germany). J. Archaeol. Sci.6, 798–809 (2016).
    Google Scholar 

    14.
    Ovodov, N. D. et al. A 33,000-year-old incipient dog from the Altai Mountains of Siberia: evidence of the earliest domestication disrupted by the Last Glacial Maximum. PLoS ONE6, e22821 (2011).
    ADS  CAS  PubMed  PubMed Central  Google Scholar 

    15.
    Sablin, M. & Khlopachev, G. The earliest Ice Age dogs: evidence from Eliseevichi. Curr. Anthropol.43, 795–799 (2002).
    Google Scholar 

    16.
    Boudadi-Maligne, M. & Escarguel, G. A biometric re-evaluation of recent claims for Early Upper Palaeolithic wolf domestication in Eurasia. J. Archaeol. Sci.45, 80–89 (2014).
    Google Scholar 

    17.
    Drake, A. G., Coquerelle, M. & Colombeau, G. 3D morphometric analysis of fossil canid skulls contradicts the suggested domestication of dogs during the late Paleolithic. Sci. Rep.5, 8299 (2015).
    ADS  CAS  PubMed  PubMed Central  Google Scholar 

    18.
    Morey, D. F. & Jeger, R. Paleolithic dogs: why sustained domestication then?. J. Archaeol. Sci.3, 420–428 (2015).
    Google Scholar 

    19.
    Napierala, H. & Uerpmann, H. P. A ‘new’ palaeolithic dog from central Europe. Intl. J. Osteoarchaeol.22, 127–137 (2012).
    Google Scholar 

    20.
    Perri, A. R. A wolf in dog’s clothing: initial dog domestication and Pleistocene wolf variation. J. Archaeol. Sci.68, 1–4 (2016).
    Google Scholar 

    21.
    Janssens, L. et al. A new look at an old dog: Bonn-Oberkassel reconsidered. J. Archaeol. Sci.92, 126–138 (2018).
    Google Scholar 

    22.
    Pionnier-Capitan, M. et al. New evidence for Upper Palaeolithic small domestic dogs in South-Western Europe. J. Archaeol. Sci.38, 2123–2140 (2011).
    Google Scholar 

    23.
    Boudadi-Maligne, M., Mallye, J. B., Langlais, M. & Barshay-Szdmit, C. Des restes de chiens magdaléniens à l’abri du Morin (Gironde, France) Implications socio-économiques d’une innovation zootechnique. Paleo23, 39–54 (2012).
    Google Scholar 

    24.
    Thalmann, O. & Perri, A. R. Paleogenomics 273–306 (Springer, Cham, 2018).
    Google Scholar 

    25.
    Mariotti Lippi, M., Foggi, B., Aranguren, B., Ronchitelli, A. & Revedin, A. Multistep food plant processing at Grotta Paglicci (Southern Italy) around 32,600 cal B.P.. Proc. Natl. Acad. Sci. U. S. A.112, 12075–12080 (2015).
    ADS  PubMed  PubMed Central  Google Scholar 

    26.
    Mezzena, F. & Palma di Cesnola, A. Industria acheulena “in situ” nei depositi esterni della Grotta Paglicci (Rignano Garganico – Foggia). Riv. Sci. Preist.26, 3–30 (1971).
    Google Scholar 

    27.
    Crezzini, J. et al. A spotted hyaena den in the Middle Palaeolithic of Grotta Paglicci (Gargano promontory, Apulia, Southern Italy). Archaeol. Anthropol. Sci.8, 227–240 (2016).
    Google Scholar 

    28.
    Palma di Cesnola, A. L’Aurignacien et le Gravettien ancien de la grotte Paglicci au Mont Gargano. L’Anthropologie110, 355–370 (2006).
    Google Scholar 

    29.
    PalmadiCesnola, A. Le Paléolithique supérieur en Italie (Jérôme Millon, Grenoble, 2001).
    Google Scholar 

    30.
    Berto, C., Boscato, P., Boschin, F., Luzi, E. & Ronchitelli, A. Paleoenvironmental and paleoclimatic context during the Upper Paleolithic (late Upper Pleistocene) in the Italian Peninsula. The small mammal record from Grotta Paglicci (Rignano Garganico, Foggia, Southern Italy). Quat. Sci. Rev.168, 30–41 (2017).
    ADS  Google Scholar 

    31.
    Boschin, F. et al. The palaeoecological meaning of macromammal remains from archaeological sites exemplified by the case study of Grotta Paglicci (Upper Palaeolithic, southern Italy). Quat. Res.90, 470–482 (2018).
    CAS  Google Scholar 

    32.
    Borgia, V., Boschin, F. & Ronchitelli, A. Bone and antler working at Grotta Paglicci (Rignano Garganico, Foggia, southern Italy). Quat. Int.403, 23–39 (2016).
    Google Scholar 

    33.
    Condemi, S. et al. I resti umani rinvenuti a Paglicci (Rignano Garganico – FG): nota preliminare. Annali dell’Uiversità di Ferrara, Museologia Scientifica e Naturalistica10(2), 233–238 (2014).
    Google Scholar 

    34.
    Arrighi, S., Borgia, V., d’Errico, F. & Ronchitelli, A. I ciottoli decorati di Paglicci: raffigurazioni e utilizzo. Riv. Sci. Preist.58, 39–58 (2008).
    Google Scholar 

    35.
    Arrighi, S., Borgia, V., d’Errico, F., Ricci, S. & Ronchitelli, A. Manifestazioni d’arte inedite e analisi tecnologica dell’arte mobiliare di Grotta Paglicci (Rignano Garganico – Foggia). Preist. Alpina46, 49–58 (2012).
    Google Scholar 

    36.
    Arrighi, S. et al. Grotta Paglicci (Rignano Garganico, Foggia): analisi sulle materie coloranti. Preist. Alpina46, 91–92 (2012).
    Google Scholar 

    37.
    Ronchitelli, A. et al. When technology joins symbolic behaviour: the gravettian burials at Grotta Paglicci (Rignano Garganico – Foggia – southern Italy). Quat. Int.359–360, 423–441 (2015).
    Google Scholar 

    38.
    Cassoli, P. F., Fiore, I. & Tagliacozzo, A. Butchering and exploitation of large mammals in the Epigravettian levels of Grotta Romanelli (Apulia, Italy). Anthropozoologica25–26, 309–318 (1997).
    Google Scholar 

    39.
    Sardella, R. et al. Grotta Romanelli (southern Italy, Apulia): legacies and issues in excavating a key site for the Pleistocene of the Mediterranean. Riv. Ital. Paleontol. Strat.124, 247–264 (2018).
    Google Scholar 

    40.
    Sardella, R. et al. Grotta Romanelli (Lecce, Southern Italy) between past and future: new studies and perspectives for an archaeo-geosite symbol of the Palaeolithic in Europe. Geoheritage11, 1413–1432 (2019).
    Google Scholar 

    41.
    Calcagnile, L. et al. New radiocarbon dating results from the Upper Paleolithic–Mesolithic levels in Grotta Romanelli (Apulia, southern Italy). Radiocarbon61, 1211–1220 (2019).
    CAS  Google Scholar 

    42.
    Cassoli, P.F., Gala, M. & Tagliacozzo, A. In Grotta Romanelli nel centenario della sua scoperta (1900–2000). Conference Proceedings (eds Fabbri, P.F., Ingravallo, E., Mangia, A.) 91–111 (Congedo Editore, Galatina, 2003).

    43.
    Tagliacozzo, A. Grotta Romanelli nel centenario della sua scoperta (1900–2000). Conference Proceedings (eds Fabbri, P.F., Ingravallo, E., Mangia, A.) 169–216 (Congedo Editore, Galatina, 2003).

    44.
    Boschin, F., Bernardini, F., Zanolli, C. & Tuniz, C. MicroCT imaging of red fox talus: a non-invasive approach to evaluate age at death. Archaeometry57, 194–211 (2015).
    CAS  Google Scholar 

    45.
    Boschin, F., Zanolli, C., Bernardini, F., Princivalle, F. & Tuniz, C. A Look from the inside: MicroCT analysis of burned bones. Ethnobiol. Lett.6, 41–49 (2015).
    Google Scholar 

    46.
    Geiger, M. et al. Unaltered sequence of dental, skeletal, and sexual maturity in domestic dogs compared to the wolf. Zool. Lett.2, 16 (2016).
    Google Scholar 

    47.
    Payne, S. & Bull, G. Components of variation in measurements of pig bones and teeth, and the use of measurements to distinguish wild from domestic pig remains. Archaeozoologia2, 27–66 (1988).
    Google Scholar 

    48.
    Zanolli, C. et al. Inner tooth morphology of Homo erectus from Zhoukoudian. New evidence from an old collection housed at Uppsala University, Sweden. J. Hum. Evol.116, 1–13 (2018).
    PubMed  Google Scholar 

    49.
    Zanolli, C. et al. Evidence for increased hominid diversity in the Early to Middle Pleistocene of Indonesia. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-019-0860-z (2019).
    Article  PubMed  Google Scholar 

    50.
    Maricic, T., Whitten, M. & Pääbo, S. Multiplexed DNA sequence capture of mitochondrial genomes using PCR products. PLoS ONE5, e14004 (2010).
    ADS  PubMed  PubMed Central  Google Scholar 

    51.
    Hefner, R. & Geffen, E. Group size and home range of the Arabian wolf (Canis lupus) in Southern Israel. J. Mammal.80, 611–619 (1999).
    Google Scholar 

    52.
    Gaubert, P. et al. Reviving the African Wolf Canis lupus lupaster in North and West Africa: a mitochondrial lineage ranging more than 6,000 km wide. PLoS ONE7, e42740 (2012).
    ADS  CAS  PubMed  PubMed Central  Google Scholar 

    53.
    Prothero, D. R. et al. Size and shape stasis in late Pleistocene mammals and birds from Rancho La Brea during the Last Glacial-Interglacial cycle. Quat. Sci. Rev.56, 1–10 (2012).
    ADS  Google Scholar 

    54.
    Payne, S. Paleolithic site of Douara Cave and Paleogeography of Palmyra Basin in Syria, part III: animal bones and further analysis of archeological materials 1–108 (University of Tokyo Press, Tokyo, 1983).
    Google Scholar 

    55.
    Mecozzi, B. & Lucenti, S. B. The Late Pleistocene Canis lupus (Canidae, Mammalia) from Avetrana (Apulia, Italy): reappraisal and new insights on the European glacial wolves, I. J. Geosci.137, 138–150 (2018).
    Google Scholar 

    56.
    Rustioni, M., Ferretti, M. P., Mazza, P., Pavia, M. & Varola, A. The vertebrate fauna from Cardamone (Apulia, southern Italy): an example of Mediterranean mammoth fauna. Deinsea9, 395–404 (2003).
    Google Scholar 

    57.
    Sardella, R. et al. The wolf from Grotta Romanelli (Apulia, Italy) and its implications in the evolutionary history of Canis lupus in the Late Pleistocene of Southern Italy. Quat. Int.328–329, 179–195 (2014).
    Google Scholar 

    58.
    Trut, L. N. The Genetics of the Dog 15–42 (CABI Publishing, New York, 2001).
    Google Scholar 

    59.
    Hare, B., Wobber, V. & Wrangham, R. The self-domestication hypothesis: evolution of bonobo psychology is due to selection against aggression. Anim. Behav.83, 573–585 (2012).
    Google Scholar 

    60.
    Lord, K. A., Larson, G., Coppinger, R. P. & Karlsson, E. The history of farm foxes undermines the animal domestication syndrome. Trends Ecol.35, 125–136 (2020).
    Google Scholar 

    61.
    Marshall-Pescini, S., Cafazzo, S., Virány, Z. & Range, F. Integrating social ecology in explanation of wolf-dog behavioural differences. Curr. Opin. Behav. Sci.16, 80–86 (2017).
    Google Scholar 

    62.
    Leonard, J. A., Vilà, C., Fox-Dobbs, K., Koch, P. L. & Wayne, R. K. Megafaunal extinctions and the disappearance of a specialized wolf ecomorph. Curr. Biol.17, 1146–1150 (2007).
    CAS  PubMed  Google Scholar 

    63.
    Hare, B., Brown, M., Williamson, C. & Tommasello, M. The domestication of social cognition in dogs. Science298, 1634–1636 (2002).
    ADS  CAS  PubMed  Google Scholar 

    64.
    Wobber, V. et al. Breed differences in domestic dogs’ (Canis familiaris) comprehension of human communicative signals. Interact. Stud.10, 206–224 (2009).
    Google Scholar 

    65.
    Riedel, A. I resti animali della grotta delle Ossa (Škocjan). Atti del Museo Civico di Storia Naturale di Trieste30, 125–208 (1977).
    Google Scholar 

    66.
    Detry, C. & Cardoso, J. L. On some remains of dog (Canis familiaris) from the Mesolithic shell-middens of Muge, Portugal. J. Archaeol. Sci.37, 2762–2774 (2010).
    Google Scholar 

    67.
    von den Driesch, A. A guide to measurement of animal bones from archaeological sites. Peabody Mus. Bull.1, 1–148 (1976).
    Google Scholar 

    68.
    Tuniz, C. et al. The ICTP-Elettra X-ray laboratory for cultural heritage and archaeology. Nucl. Instrum. Methods Phys. Res. A711, 106–110 (2013).
    ADS  CAS  Google Scholar 

    69.
    Fajardo, R. J., Ryan, T. M. & Kappelman, J. Assessing the accuracy of high resolution X-ray computed tomography of primate trabecular bone by comparisons with histological sections. Am. J. Phys. Anthropol.118, 1–10 (2002).
    PubMed  Google Scholar 

    70.
    Coleman, M. N. & Colbert, M. W. CT thresholding protocols for taking measurements on three-dimensional models. Am. J. Phys. Anthropol.133, 723–725 (2007).
    PubMed  Google Scholar 

    71.
    Bouxsein, M. et al. Guidelines for assessment of bone microstructure in rodents using micro-computed tomography. J. Bone Miner. Res.25, 1468–1486 (2010).
    PubMed  Google Scholar 

    72.
    Shipman, P., Foster, G. & Schoeninger, M. Burnt bones and teeth: an experimental study of color, morphology, crystal structure and shrinkage. J. Archaeol. Sci.11, 307–325 (1984).
    Google Scholar 

    73.
    Ghezzo, E. & Rook, L. Cuon alpinus (Pallas, 1811) (Mammalia, Carnivora) from Equi (Late Pleistocene, Massa-Carrara, Italy): anatomical analysis and palaeoethological contextualisation. Rend. Fis. Acc. Lincei25, 492–504 (2014).
    Google Scholar 

    74.
    Gunz, P. & Mitteroecker, P. Semilandmarks: a method for quantifying curves and surfaces. Hystrix24, 103–109 (2013).
    Google Scholar 

    75.
    Adams, D.C., Collyer, D.L., Kaliontzopoulou, A. & Sherratt, E. Geomorph: software for geometric morphometric analyses. R package version 3.0.5. https://cran.r-project.org/package=geomorph (2017).

    76.
    Schlager, S. Statistical Shape and Deformation Analysis 217–256 (Academic Press, London, 2017).
    Google Scholar 

    77.
    Mitteroecker, P. & Bookstein, F. L. Linear discrimination, ordination, and the visualization of selection gradients in modern morphometrics. Evol. Biol.38, 100–114 (2011).
    Google Scholar 

    78.
    Dray, S. & Dufour, A. B. The ade4 package: implementing the duality diagram for ecologists. J. Stat. Softw.22, 1–20 (2007).
    Google Scholar 

    79.
    Bookstein, F. L. Morphometric Tools for Landmark Data: Geometry and Biology (Cambridge University Press, Cambridge, 1991).
    Google Scholar 

    80.
    Dabney, J. et al. Complete mitochondrial genome sequence of a Middle Pleistocene cave bear reconstructed from ultrashort DNA fragments. Proc. Natl. Acad. Sci. U. S. A.110, 15758–15763 (2013).
    ADS  CAS  PubMed  PubMed Central  Google Scholar 

    81.
    Meyer, M. & Kircher, M. Illumina sequencing library preparation for highly multiplexed target capture and sequencing. Cold Spring Harb. Protoc. https://doi.org/10.1101/pdb.prot5448 (2010).
    Article  PubMed  Google Scholar 

    82.
    Peltzer, G. et al. EAGER: efficient ancient genome reconstruction. Genome Biol.17, 60 (2016).
    PubMed  PubMed Central  Google Scholar 

    83.
    Kim, K. S., Lee, S. E., Jeong, H. W. & Ha, J. H. The complete nucleotide sequence of the domestic dog (Canis familiaris) mitochondrial genome. Mol. Phylogenet. Evol.10, 210–220 (1998).
    CAS  PubMed  Google Scholar 

    84.
    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics25, 1754–1760 (2009).
    CAS  PubMed  PubMed Central  Google Scholar 

    85.
    Schubert, M. et al. Improving ancient DNA read mapping against modern reference genomes. BMC Genom.13, 178 (2012).
    CAS  Google Scholar 

    86.
    Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics25, 2078–2079 (2009).
    PubMed  PubMed Central  Google Scholar 

    87.
    Jonsson, H., Ginolhac, A., Schubert, M., Johnson, P. L. & Orlando, L. mapDamage2.0: fast approximate Bayesian estimates of ancient DNA damage parameters. Bioinformatics29, 1682–1684 (2013).
    CAS  PubMed  PubMed Central  Google Scholar 

    88.
    Loog, L. et al. Ancient DNA suggests modern wolves trace their origin to a Late Pleistocene expansion from Beringia. Mol Ecol.00, 1–15. https://doi.org/10.1111/mec.15329 (2019).
    Article  Google Scholar 

    89.
    Kumar, S., Stecher, G. & Tamura, K. MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evol.33, 1870–1874 (2016).
    CAS  PubMed  Google Scholar 

    90.
    Edgar, C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res.32, 1792–1797 (2004).
    CAS  PubMed  PubMed Central  Google Scholar 

    91.
    Bouckaert, R. et al. BEAST 2: a software platform for Bayesian evolutionary analysis. PLoS Comput. Biol.10, e1003537 (2014).
    PubMed  PubMed Central  Google Scholar 

    92.
    Rambaut, A., Suchard, M.A., Xie, D. & Drummond, A.J. Tracer v1.6. https://tree.bio.ed.ac.uk/software/tracer (2014)

    93.
    Bronk Ramsey, C. Bayesian analysis of radiocarbon dates. Radiocarbon51, 337–360 (2009).
    Google Scholar 

    94.
    Reimer, P. J. et al. IntCal13 and Marine13 radiocarbon age calibration curves 0–50,000 years cal BP. Radiocarbon55, 1869–1887 (2013).
    CAS  Google Scholar 

    95.
    Street, M., Napierala, H. & Janssens, L. The late Palaeolithic dog from Bonn-Oberkassel in context. Rheinische Ausgrabungen72, 253–274 (2015).
    Google Scholar 

    96.
    Bronk Ramsey, C., Higham, T., Bowles, A. & Hedges, R. Improvements to the pretreatment of bones at Oxford. Radiocarbon46(1), 155–163 (2004).
    Google Scholar 

    97.
    Fedi, M. E., Cartocci, A., Manetti, M., Taccetti, F. & Mandò, P. A. The 14C AMS facility at LABEC, Florence. Nucl. Instrum. Methods Phys. Res. B259, 18–22 (2007).
    ADS  CAS  Google Scholar 

    98.
    Boschin, F. Exploitation of carnivores, lagomorphs and rodents at Grotta Paglicci during the Epigravettian: the dawn of a new subsistence strategy?. J. Archaeol. Sci. Rep.26, 101871 (2019).
    Google Scholar  More

  • in

    A large-scale assessment of lakes reveals a pervasive signal of land use on bacterial communities

    1.
    Adrian R, O’Reilly CM, Zagarese H, Baines SB, Hessen DO, Keller W, et al. Lakes as sentinels of climate change. Limnol Oceanogr. 2009;54:2283–97.
    PubMed  PubMed Central  Google Scholar 
    2.
    Tranvik LJ, Downing JA, Cotner JB, Loiselle SA, Striegl RG, Ballatore TJ, et al. Lakes and reservoirs as regulators of carbon cycling and climate. Limnol Oceanogr. 2009;54:2298–314.
    Google Scholar 

    3.
    Arbuckle KE, Downing JA. The influence of watershed land use on lake N: P in a predominantly agricultural landscape. Limnol Oceanogr. 2001;46:970–5.
    Google Scholar 

    4.
    Taranu ZE, Gregory-Eaves I. Quantifying relationships among phosphorus, agriculture, and lake depth at an inter-regional scale. Ecosystems. 2008;11:715–25.
    Google Scholar 

    5.
    Heisler J, Glibert PM, Burkholder JM, Anderson DM, Cochlan W, Dennison WC, et al. Eutrophication and harmful algal blooms: a scientific consensus. Harmful Algae. 2008;8:3–13.
    PubMed  PubMed Central  Google Scholar 

    6.
    Scavia D, David Allan J, Arend KK, Bartell S, Beletsky D, Bosch NS, et al. Assessing and addressing the re-eutrophication of Lake Erie: Central basin hypoxia. J Gt Lakes Res. 2014;40:226–46.
    Google Scholar 

    7.
    Bastviken D, Cole J, Pace M, Tranvik L. Methane emissions from lakes: dependence of lake characteristics, two regional assessments, and a global estimate. Glob Biogeochem Cycles. 2004;18:1–12.
    Google Scholar 

    8.
    Novotny EV, Murphy D, Stefan HG. Increase of urban lake salinity by road deicing salt. Sci Total Environ. 2008;406:131–44.
    PubMed  Google Scholar 

    9.
    Dugan HA, Bartlett SL, Burke SM, Doubek JP, Krivak-Tetley FE, Skaff NK, et al. Salting our freshwater lakes. Proc Natl Acad Sci USA. 2017;114:4453–8.
    PubMed  Google Scholar 

    10.
    Hobbie SE, Finlay JC, Janke BD, Nidzgorski DA, Millet DB, Baker LA. Contrasting nitrogen and phosphorus budgets in urban watersheds and implications for managing urban water pollution. Proc Natl Acad Sci. 2017;114:4177–82.
    PubMed  Google Scholar 

    11.
    Shade A, Kent AD, Jones SE, Newton RJ, Triplett EW, McMahon KD. Interannual dynamics and phenology of bacterial communities in a eutrophic lake. Limnol Oceanogr. 2007;52:487–94.
    Google Scholar 

    12.
    Kara EL, Hanson PC, Hu YH, Winslow L, McMahon KD. A decade of seasonal dynamics and co-occurrences within freshwater bacterioplankton communities from eutrophic Lake Mendota, WI, USA. ISME J. 2013;7:680–4.
    PubMed  Google Scholar 

    13.
    Marmen S, Blank L, Al-Ashhab A, Malik A, Ganzert L, Lalzar M, et al. The role of land use types and water chemical properties in structuring the microbiome of a connected lake system. Front Microbiol. 2020;11:1–16.
    Google Scholar 

    14.
    Environment Canada Whole organism responses and intersex severity in rainbow darter (Etheostoma caeruleum) following exposures to municipal wastewater in the Grand River basin, ON, Canada. Part A, Municipal Water Use Rep. 2011;159:2011–301.
    Google Scholar 

    15.
    Huot Y, Brown CA, Potvin G, Antoniades D, Baulch HM, Beisner BE, et al. The NSERC Canadian Lake Pulse Network: a national assessment of lake health providing science for water management in a changing climate. Sci Total Environ. 2019;695:133668.
    PubMed  Google Scholar 

    16.
    Lu Y, Wang R, Zhang Y, Su H, Wang P, Jenkins A, et al. Ecosystem health towards sustainability. Ecosyst Heal Sustain. 2015;1:1–15.
    Google Scholar 

    17.
    Hering D, Borja A, Carvalho L, Feld CK. Assessment and recovery of European water bodies: Key messages from the WISER project. Hydrobiologia 2013;704:1–9.
    Google Scholar 

    18.
    U.S. Environmental Protection Agency. National Lake Assessment: a collaborative survey of the Nation’s Lakes. Washington, DC: EPA 841-R-09-001; 2009.

    19.
    Ecological Stratification Working Group. A national ecological framework for Canada. Urbana-Champaign, Illinois: Ecological Stratification Working Group; 1996.

    20.
    Glaz P, Gagné JP, Archambault P, Sirois P, Nozais C. Impact of forest harvesting on water quality and fluorescence characteristics of dissolved organic matter in eastern Canadian Boreal Shield lakes in summer. Biogeosciences. 2015;12:6999–7011.
    Google Scholar 

    21.
    Patton C, Kryskalla J. Methods of analysis by the U.S. Geological Survey National Water Quality Laboratory—evaluation of alakline digestion as an alternative to kjedahl digestion for determination of total and dissolved nitrogen and phosphorous. Denver, Colorado: Water-Resources Investigations Report 03; 2003.

    22.
    U.S. Environmental Protection Agency. Method 200.7: determination of metals and trace elements in water and wastes by inductively coupled plasma-atomic emission spectrometry. Cincinatti, Ohio: U.S. Environmental Protection Agency; 1994.

    23.
    U.S. Environmental Protection Agency. Method 300.1: determination of inorganic anions in drinking water by ion chromatography. Cincinatti, Ohio; 1997.

    24.
    Wu Y. Barcode Demultiplex for Illumina I1, R1, R2 fastq.gz files. 2014.

    25.
    Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17:10.
    Google Scholar 

    26.
    Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.
    PubMed  PubMed Central  Google Scholar 

    27.
    Rohwer RR, Hamilton JJ, Newton RJ, McMahon KD. TaxAss: leveraging a custom freshwater database achieves fine-scale taxonomic resolution. mSphere. 2018;3:1–14.
    Google Scholar 

    28.
    Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 2013;41:590–6.
    Google Scholar 

    29.
    McMurdie PJ, Holmes S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.
    PubMed  PubMed Central  Google Scholar 

    30.
    Price MN, Dehal PS, Arkin AP. FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS ONE. 2010;5:e9490.
    PubMed  PubMed Central  Google Scholar 

    31.
    Dray S, Dufour A-B. The ade4 Package: implementing the duality diagram for ecologists. J Stat Softw. 2007;22:1–20.
    Google Scholar 

    32.
    Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, Mcglinn D, et al. Vegan: community ecology package. 2016. https://cran.r-project.org; https://github.com/vegandevs/vegan.

    33.
    Hair J, Tatham R, Anderson R, Black W. Multivariate data analysis. 5th ed. London: Prentice-Hall; 1998.
    Google Scholar 

    34.
    R Development Core Team T. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2009.
    Google Scholar 

    35.
    Pinheiro J, Bates D, DebRoy S, Sarkar D, R Development Core Team T. nlme: linear and nonlinear mixed effect models. R package version. 3.1-141; 2019.

    36.
    Bates D, Mächler M, Bolker BM, Walker SC. Fitting linear mixed-effects models using lme4. J Stat Softw. 2015;67:1–51.
    Google Scholar 

    37.
    Rosseel Y. Lavaan: an R package for structural equation modeling. J Stat Softw. 2012;48:1–37.
    Google Scholar 

    38.
    Albanese D, Filosi M, Visintainer R, Riccadonna S, Jurman G, Furlanello C. Minerva and minepy: a C engine for the MINE suite and its R, Python and MATLAB wrappers. Bioinformatics. 2013;29:407–8.
    PubMed  Google Scholar 

    39.
    Kurtz ZD, Müller CL, Miraldi ER, Littman DR, Blaser MJ, Bonneau RA. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput Biol. 2015;11:e1004226.
    PubMed  PubMed Central  Google Scholar 

    40.
    Banerjee S, Walder F, Büchi L, Meyer M, Held AY, Gattinger A, et al. Agricultural intensification reduces microbial network complexity and the abundance of keystone taxa in roots. ISME J. 2019;13:1722–36.
    PubMed  PubMed Central  Google Scholar 

    41.
    Barberán A, Bates ST, Casamayor EO, Fierer N. Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J. 2012;6:343–51.
    PubMed  Google Scholar 

    42.
    Stegen JC, Lin X, Fredrickson JK, Chen X, Kennedy DW, Murray CJ, et al. Quantifying community assembly processes and identifying features that impose them. ISME J. 2013;7:2069–79.
    PubMed  PubMed Central  Google Scholar 

    43.
    Benlloch S, López-López A, Casamayor EO, Øvreås L, Goddard V, Daae FL, et al. Prokaryotic genetic diversity throughout the salinity gradient of a coastal solar saltern. Environ Microbiol. 2002;4:349–60.
    PubMed  Google Scholar 

    44.
    Abed RMM, Kohls K, De Beer D. Effect of salinity changes on the bacterial diversity, photosynthesis and oxygen consumption of cyanobacterial mats from an intertidal flat of the Arabian Gulf. Environ Microbiol. 2007;9:1384–92.
    PubMed  Google Scholar 

    45.
    Lozupone CA, Knight R. Global patterns in bacterial diversity. Proc Natl Acad Sci USA. 2007;104:11436–40.
    PubMed  Google Scholar 

    46.
    Wu QL, Zwart G, Schauer M, Kamst-Van Agterveld MP, Hahn MW. Bacterioplankton community composition along a salinity gradient of sixteen high-mountain lakes located on the Tibetan Plateau, China. Appl Environ Microbiol. 2006;72:5478–85.
    PubMed  PubMed Central  Google Scholar 

    47.
    Wang J, Yang D, Zhang Y, Shen J, van der Gast C, Hahn MW, et al. Do patterns of bacterial diversity along salinity gradients differ from those observed for macroorganisms? PLoS ONE. 2011;6:e27597.
    PubMed  PubMed Central  Google Scholar 

    48.
    Kelly VR, Lovett GM, Weathers KC, Findlay SEG, Strayer DL, Burns DJ, et al. Long-term sodium chloride retention in a rural watershed: legacy effects of road salt on streamwater concentration. Environ Sci Technol. 2008;42:410–5.
    PubMed  Google Scholar 

    49.
    Corsi SR, Graczyk DJ, Geis SW, Booth NL, Richards KD. A fresh look at road salt: aquatic toxicity and water-quality impacts on local, regional, and national scales. Environ Sci Technol. 2010;44:7376–82.
    PubMed  PubMed Central  Google Scholar 

    50.
    Levine SN, Schindler DW. Influence of nitrogen to phosphorus supply ratios and physicochemical conditions on cyanobacteria and phytoplankton species composition in the Experimental Lakes Area, Canada. Can J Fish Aquat Sci. 1999;56:451–66.
    Google Scholar 

    51.
    Stockner JG, Shortreed KS. Response of Anabaena and Synechococcus to manipulation of nitrogen: phosphorus ratios in a lake fertilization experiment. Limnol Oceanogr. 1988;33:1348–61.
    Google Scholar 

    52.
    Thad Scott J, McCarthys MJ. Nitrogen fixation may not balance the nitrogen pool in lakes over timescales relevant to eutrophication management. Limnol Oceanogr. 2010;55:1265–70.
    Google Scholar 

    53.
    Håkanson L, Blenckner T, Bryhn AC, Hellström SS. The influence of calcium on the chlorophyll-phosphorus relationship and lake Secchi depths. Hydrobiologia. 2005;537:111–23.
    Google Scholar 

    54.
    Eiler A, Heinrich F, Bertilsson S. Coherent dynamics and association networks among lake bacterioplankton taxa. ISME J. 2012;6:330–42.
    PubMed  Google Scholar 

    55.
    Peura S, Bertilsson S, Jones RI, Eiler A. Resistant microbial cooccurrence patterns inferred by network topology. Appl Environ Microbiol. 2015;81:2090–7.
    PubMed  PubMed Central  Google Scholar 

    56.
    Logares R, Tesson SVM, Canbäck B, Pontarp M, Hedlund K, Rengefors K. Contrasting prevalence of selection and drift in the community structuring of bacteria and microbial eukaryotes. Environ Microbiol. 2018;20:2231–40.
    PubMed  Google Scholar 

    57.
    Lindström ES, Kamst-Van Agterveld MP, Zwart G. Distribution of typical freshwater bacterial groups is associated with pH, temperature, and lake water retention time. Appl Environ Microbiol. 2005;71:8201–6.
    PubMed  PubMed Central  Google Scholar 

    58.
    Lauber CL, Hamady M, Knight R, Fierer N. Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl Environ Microbiol. 2009;75:5111–20.
    PubMed  PubMed Central  Google Scholar 

    59.
    Xiong J, Liu Y, Lin X, Zhang H, Zeng J, Hou J, et al. Geographic distance and pH drive bacterial distribution in alkaline lake sediments across Tibetan Plateau. Environ Microbiol. 2012;14:2457–66.
    PubMed  PubMed Central  Google Scholar 

    60.
    Findlay DL, Kasian SEM. Phytoplankton community responses to acidification of lake 223, experimental lakes area, northwestern Ontario. Water Air Soil Pollut. 1986;30:719–26.
    Google Scholar 

    61.
    Findlay DL, Kasian SEM. The effect of incremental pH recovery on the Lake 223 phytoplankton community. Can J Fish Aquat Sci. 1996;53:856–64.
    Google Scholar 

    62.
    Maberly SC. Diel, episodic and seasonal changes in pH and concentrations of inorganic carbon in a productive lake. Freshw Biol. 2008;35:579–98.
    Google Scholar 

    63.
    Tong Y, Lin G, Ke X, Liu F, Zhu G, Gao G, et al. Comparison of microbial community between two shallow freshwater lakes in middle Yangtze basin, East China. Chemosphere. 2005;60:85–92.
    PubMed  Google Scholar 

    64.
    Romina Schiaffino M, Unrein F, Gasol JM, Massana R, Balagué V, Izaguirre I. Bacterial community structure in a latitudinal gradient of lakes: the roles of spatial versus environmental factors. Freshw Biol. 2011;56:1973–91.
    Google Scholar 

    65.
    Zeng J, Yang L, Li J, Liang Y, Xiao L, Jiang L, et al. Vertical distribution of bacterial community structure in the sediments of two eutrophic lakes revealed by denaturing gradient gel electrophoresis (DGGE) and multivariate analysis techniques. World J Microbiol Biotechnol. 2009;25:225–33.
    Google Scholar 

    66.
    Canfield DE, Bachmann RW. Prediction of total phosphorus concentrations, chlorophyll a, and Secchi depths in natural and artificial lakes. Can J Fish Aquat Sci. 1981;38:414–23.
    Google Scholar 

    67.
    Meeuwig JJ, Peters RH. Circumventing phosphorus in lake management: a comparison of chlorophyll a predictions from land-use and phosphorus-loading models. Can J Fish Aquat Sci. 1996;53:1795–806.
    Google Scholar 

    68.
    Yang L, Lei K, Meng W, Fu G, Yan W. Temporal and spatial changes in nutrients and chlorophyll-α in a shallow lake, Lake Chaohu, China: an 11-year investigation. J Environ Sci (China). 2013;25:1117–23.
    Google Scholar 

    69.
    Kraemer SA, Soucy JPR, Kassen R. Antagonistic interactions of soil pseudomonads are structured in time. FEMS Microbiol Ecol. 2017;93:1–9.
    Google Scholar  More

  • in

    Why deforestation and extinctions make pandemics more likely

    NEWS
    07 August 2020

    Researchers are redoubling efforts to understand links between species loss and emerging diseases — and use that information to predict and stop future outbreaks.

    Jeff Tollefson

    Search for this author in:

    Controlling deforestation (shown here, in a tropical rainforest in the Congo Basin) could decrease the risk of future pandemics, experts say.Credit: Patrick Landmann/Science Photo Library

    As humans diminish biodiversity by cutting down forests and building more infrastructure, they’re increasing the risk of disease pandemics such as COVID-19. Many ecologists have long suspected this, but a new study helps to reveal why: while some species are going extinct, those that tend to survive and thrive — rats and bats, for instance — are more likely to host potentially dangerous pathogens that can make the jump to humans.
    The analysis of around 6,800 ecological communities on 6 continents adds to a growing body of evidence that connects trends in human development and biodiversity loss to disease outbreaks — but stops short of projecting where new disease outbreaks might occur.
    “We’ve been warning about this for decades,” says Kate Jones, an ecological modeller at University College London and an author on the study, published on 5 August in Nature1. “Nobody paid any attention.”
    Jones is one of a cadre of researchers that has long been delving into relationships among biodiversity, land use and emerging infectious diseases. Their work has mostly flown below the radar, but now, as the world reels from the COVID-19 pandemic, efforts to map risks in communities across the globe and to project where diseases are most likely to emerge are taking centre stage.

    Last week, the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) hosted an online workshop on the nexus between biodiversity loss and emerging diseases. The organization’s goal now is to produce an expert assessment of the science underlying that connection ahead of a United Nations summit in New York that’s planned for September, where governments are expected to make new commitments to preserve biodiversity.
    Others are calling for a more wide-ranging course of action. On 24 July, an interdisciplinary group of scientists, including virologists, economists and ecologists, published an essay in Science2, arguing that governments can help reduce the risk of future pandemics by controlling deforestation and curbing the wildlife trade, which involves the sale and consumption of wild — and often rare — animals that can host dangerous pathogens.
    Most efforts to prevent the spread of new diseases tend to focus on vaccine development, early diagnosis and containment, but that’s like treating the symptoms without addressing the underlying cause, says Peter Daszak, a zoologist at the non-governmental organization EcoHealth Alliance in New York, who chaired the IPBES workshop. He says COVID-19 has helped to clarify the need to investigate biodiversity’s role in pathogen transmission.
    The latest work by Jones’s team bolsters the case for action, Daszak says. “We’re looking for ways to shift behaviour that would directly benefit biodiversity and reduce health risks.”
    Concentrating risk
    Previous research has shown that outbreaks of diseases such as severe acute respiratory syndrome (SARS) and bird influenza that cross over from animals to humans have increased in the past few decades3,4. This phenomenon is likely to be the direct result of increased contact between humans, wildlife and livestock, as people move into undeveloped areas. These interactions happen more frequently on the frontier of human expansion because of changes to the natural landscape and increased encounters with animals. A study published in April by researchers at Stanford University in California found that deforestation and habitat fragmentation in Uganda increased direct encounters between primates and people, as primates ventured out of the forest to raid crops and people ventured in to collect wood5.
    But a key question over the past decade has been whether the decline in biodiversity that inevitably accompanies human expansion on the rural frontier increases the pool of pathogens that can make the jump from animals to humans. Work by Jones and others6 suggests that the answer in many cases is yes, because a loss in biodiversity usually results in a few species replacing many — and these species tend to be the ones hosting pathogens that can spread to humans.
    For their latest analysis, Jones and her team compiled more than 3.2 million records from several hundred ecological studies at sites around the world, ranging from native forests to cropland to cities. They found that the populations of species known to host diseases transmissible to humans — including 143 mammals such as bats, rodents and various primates — increased as the landscape changed from natural to urban, and as biodiversity generally decreased.

    The next step for Jones’s team is to examine the likelihood of disease transmission to the human population. The group has already made this type of evaluation for Ebola virus outbreaks in Africa, creating risk maps based on development trends, the presence of probable host species, and socio-economic factors that determine the pace at which a virus might spread once it enters the human population7. The group’s risk maps accurately captured where outbreaks occurred in the Democratic Republic of the Congo (DRC) in the past few years, suggesting that it is possible to understand and project risks on the basis of relationships between factors such as land use, ecology, climate and biodiversity.
    Some researchers urge caution when communicating that biodiversity hotspots are where outbreaks are likely to occur. “My worry, frankly, is that people are going to cut down the forests more if this is where they think the next pandemic is going to come from,” says Dan Nepstad, a tropical ecologist and founder of the Earth Innovation Institute based in San Francisco, California, a non-profit organization that campaigns for sustainable development. Efforts to preserve biodiversity will only work, he says, if they address the economic and cultural factors that drive deforestation and the rural poor’s dependency on hunting and trading wild animals.
    Ibrahima Socé Fall, an epidemiologist and head of the World Health Organization’s emergency operations in Africa, agrees that understanding the ecology — as well as the social and economic trends — of the rural frontier will be crucial to projecting the risk of future disease outbreaks. “Sustainable development is crucial,” he says. “If we continue to have this level of deforestation, disorganized mining and unplanned development, we are going to have more outbreaks.”
    Coordinating efforts
    One message that the IPBES’s upcoming report is likely to deliver is that scientists and policymakers need to treat the rural frontier more holistically, addressing issues of public health, the environment and sustainable development in tandem. In the wake of the COVID-19 pandemic, many scientists and conservationists have emphasized curbing the wildlife trade — an industry worth an estimated US$20 billion annually in China, where the first coronavirus infections appeared. China has temporarily suspended its trade. But Daszak says the industry is just one piece in a larger puzzle that involves hunting, livestock, land use and ecology.

    Wildlife markets like this one in Bali, Indonesia, sustain the livelihoods of many people. But they are also under scrutiny as hotspots for pathogen transmission.Credit: Amilia Roso/The Sydney Morning Herald via Getty

    “Ecologists should be working with infectious-disease researchers, public-health workers and medics to track environmental change, assess the risk of pathogens crossing over and reduce risky human activities,” he says.
    Daszak was an author of last month’s essay in Science, which argued that governments could substantially reduce the risk of future pandemics such as COVID-19 by investing in efforts to curb deforestation and the wildlife trade, as well as in efforts to monitor, prevent and control new virus outbreaks from wildlife and livestock. The team estimated that the cost of these actions would ring in at $22 billion to $33 billion annually, including $19.4 billion for ending trade in wild meat in China — a step that not all experts think is desirable or necessary — and up to $9.6 billion to help curb tropical deforestation. The total investment would be two orders of magnitude less than the $5.6-trillion price tag estimated for the COVID-19 pandemic, the team estimates.

    Fall says the key is to align efforts by government and international agencies focused on public health, animal health, the environment and sustainable development. The latest Ebola outbreak in the DRC, which began in 2018 and ended last month, had its roots not just in disease but also in deforestation, mining, political instability and the movement of people. The goal must be to focus resources on the riskiest areas and manage interactions between people and animals, both wild and domestic, Fall says.
    With the right collaboration between human health, animal health and environmental authorities, Fall says, “you have some mechanisms for early warnings”.

    doi: 10.1038/d41586-020-02341-1

    References

    1.
    Gibb, R. et al. Nature https://doi.org/10.1038/s41586-020-2562-8 (2020).

    2.
    Dobson, A. P. et al. Science 369, 379–381 (2020).

    3.
    Jones, K. E. et al. Nature 451, 990–993 (2008).

    4.
    Smith, K. F. et al. J. R. Soc. Interface 11, 20140950 (2014).

    5.
    Bloomfield, L. S. P., McIntosh, T. L. & Lambin, E. Landscape Ecol. 35, 985–1000 (2020).

    6.
    Faust, C. L. et al. Ecol. Lett. 21, 471–483 (2018).

    7.
    Redding, D. W. et al. Nature Commun. 10, 4531 (2019).

    Download references

    Latest on:

    An essential round-up of science news, opinion and analysis, delivered to your inbox every weekday.

    Related Articles More

  • in

    Satellites find penguins by following the poo

    A space-based sensor has detected new colonies of emperor penguins on Antarctic sea ice. Credit: Christopher Walton

    Ecology
    07 August 2020

    Images from space bolster the population count, but the birds remain vulnerable to climate change.

    From their vantage point high above Antarctica, sharp-eyed satellites have spotted eight previously unknown colonies of emperor penguins. The discovery boosts emperor penguin numbers by 5–10%.
    The iconic birds breed and raise their young on sea ice frozen to Antarctica’s shoreline. These habitats are threatened by climate change, so scientists have been working to get a complete census of emperor penguins (Aptenodytes forsteri) to assess how the bird’s populations might change.
    Peter Fretwell and Philip Trathan at the British Antarctic Survey in Cambridge, UK, used the European Space Agency’s Sentinel-2 satellites to search for dark smudges of guano-stained ice. They identified eight newfound penguin colonies located around the rim of the continent; one was on sea ice frozen around icebergs grounded far offshore. Using the images, the authors also pinpointed three colonies that had been reported in the 1960s and 1980s but not confirmed since.
    The findings bring the total number of emperor penguin colonies to 61. Many are in areas vulnerable to climate change. More

  • in

    Bacterial mock communities as standards for reproducible cytometric microbiome analysis

    1.
    Müller, S. & Nebe-von-Caron, G. Functional single-cell analyses: flow cytometry and cell sorting of microbial populations and communities. FEMS Microbiol. Rev. 34, 554–587 (2010).
    Article  Google Scholar 
    2.
    Günther, S. et al. Species-sorting and mass-transfer paradigms control managed natural metacommunities. Environ. Microbiol. 18, 4862–4877 (2016).
    Article  Google Scholar 

    3.
    Props, R., Monsieurs, P., Mysara, M., Clement, L. & Boon, N. Measuring the biodiversity of microbial communities by flow cytometry. Methods Ecol. Evol. 7, 1376–1385 (2016).
    Article  Google Scholar 

    4.
    Liu, Z. et al. Ecological stability properties of microbial communities assessed by flow cytometry. mSphere 3, e00564–17 (2018).
    CAS  PubMed  PubMed Central  Google Scholar 

    5.
    Liu, Z. et al. Neutral mechanisms and niche differentiation in steady-state insular microbial communities revealed by single cell analysis. Environ. Microbiol. 21, 164–181 (2019).
    CAS  Article  Google Scholar 

    6.
    De Vrieze, J., Boon, N. & Verstrate, W. Taking the technical microbiome into the next decade. Environ. Microbiol. 20, 1991–2000 (2018).
    Article  Google Scholar 

    7.
    Koch, C. et al. Cytometric fingerprinting for analyzing microbial intracommunity structure variation and identifying subcommunity function. Nat. Protoc. 8, 190–202 (2013).
    CAS  Article  Google Scholar 

    8.
    Mage, L. M. et al. Shape-based separation of synthetic microparticles. Nat. Mater. 18, 82–89 (2019).
    CAS  Article  Google Scholar 

    9.
    Müller, S. Modes of cytometric bacterial DNA pattern: a tool for pursuing growth. Cell Prolif. 40, 621–639 (2007).
    Article  Google Scholar 

    10.
    Ludwig, J., Höner zu Siederdissen, C., Liu, Z., Stadler, P. F. & Müller, S. flowEMMi: an automated model-based clustering tool for microbial cytometric data. BMC Bioinforma. 20, 643 (2019).
    CAS  Article  Google Scholar 

    11.
    Koch, C., Fetzer, I., Harms, H. & Müller, S. CHIC-an automated approach for the detection of dynamic variations in complex microbial communities. Cytom. A 83, 561–567 (2013).
    Article  Google Scholar 

    12.
    Liu, Z. & Müller, S. Bacterial community diversity dynamics highlight degrees of nestedness and turnover patterns. Cytom. Part A https://onlinelibrary.wiley.com/doi/10.1002/cyto.a.23965 (2020)

    13.
    Aghaeepour, N. et al. Critical assessment of automated flow cytometry data analysis techniques. Nat. Methods 10, 228–238 (2013).
    CAS  Article  Google Scholar 

    14.
    Peters, J. M. & Ansari, M. Q. Multiparameter flow cytometry in the diagnosis and management of acute leukemia. Arch. Pathol. Lab. Med. 135, 44–54 (2011).
    PubMed  Google Scholar 

    15.
    Bendall, S. C. et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687–696 (2011).
    CAS  Article  Google Scholar 

    16.
    Spitzer, H. M. & Nolan, G. P. Mass cytometry: single cells, many features. Cell 165, 780–791 (2016).
    CAS  Article  Google Scholar 

    17.
    Overmann, J., Abt, B. & Sikorski, J. Present and future of culturing bacteria. Annu. Rev. Microbiol. 8, 711–730 (2017).
    Article  Google Scholar 

    18.
    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).
    CAS  Article  Google Scholar 

    19.
    Roesch, L. F. et al. Pyrosequencing enumerates and contrasts soil microbial diversity. ISME J. 1, 283–290 (2007).
    CAS  Article  Google Scholar 

    20.
    Singer, E. et al. Next generation sequencing data of a defined microbial mock community. Sci. Data 3, 160081 (2016).
    Article  Google Scholar 

    21.
    Hallmaier-Wacker, L. K., Lueert, S., Roos, C. & Knauf, S. The impact of storage buffer, DNA extraction method, and polymerase on microbial analysis. Sci. Rep. 8, 6292 (2018).
    Article  Google Scholar 

    22.
    Hardwick, S. A. et al. Synthetic microbe communities provide internal reference standards for metagenome sequencing and analysis. Nat. Commun. 9, 3096 (2018).
    Article  Google Scholar 

    23.
    Hornung, B. V. H., Zwittink, R. D. & Kuijper, E. J. Issues and current standards of controls in microbiome research. FEMS Microbiol. Ecol. 95, fiz045 (2019).
    CAS  Article  Google Scholar 

    24.
    Sze, M. A. & Schloss, P. D. The impact of DNA polymerase and number of rounds of amplification in PCR on 16S rRNA gene sequence data. mSphere 4, e00163–19 (2019).
    CAS  Article  Google Scholar 

    25.
    Clingenpeel, S., Clum, A., Schwientel, P., Rinke, C. & Woyke, T. Reconstructing each cell’s genome within complex communities—dream or reality? Front. Microbiol. 8, 771 (2015).
    Google Scholar 

    26.
    Stepanauskas, R. et al. Improved genome recovery and intergrated cell-size analyses of individual uncultured microbial cells and viral particles. Nat. Commun. 8, 84 (2017).
    Article  Google Scholar 

    27.
    De Bruin, O. M. & Birnboim, H. C. A method for assessing efficiency of bacterial cell disruption and DNA release. BMC Microbiol. 16, 197 (2016).
    Article  Google Scholar 

    28.
    Mie, G. Beiträge zur optik trüber medien, speziell kolloidaler metallösungen. Ann. Phys. 25, 377–445 (1908).
    CAS  Article  Google Scholar 

    29.
    Woyke, T., Doud, D. F. R. & Schulz, F. The trajectory of microbial single-cell sequencing. Nat. Methods 14, 1045–1054 (2017).
    CAS  Article  Google Scholar 

    30.
    Jahn, M. et al. Subpopulation-proteomics in prokaryotic populations. Curr. Opin. Biotech. 24, 79–87 (2013).
    CAS  Article  Google Scholar 

    31.
    Schloss, P. D. et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).
    CAS  Article  Google Scholar 

    32.
    Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).
    CAS  Article  Google Scholar 

    33.
    Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).

    34.
    Lane, D. J. in Nucleic Acid Techniques in Bacterial Systematics (eds. Stackebrandt, E. & Goodfellow, M.) 115–175 (Wiley, 1991).

    35.
    Lambrecht, J. et al. Flow cytometric quantification, sorting and sequencing of methanogenic archaea based on F420 autofluorescence. Microb. Cell Fact. 16, 180 (2017).
    Article  Google Scholar 

    36.
    Besmer, M. D. et al. The feasibility of automated online flow cytometry for in-situ monitoring of microbial dynamics in aquatic ecosystems. Front. Microbiol 5, 265 (2014).
    Article  Google Scholar 

    37.
    Takahashi, S., Tomita, J., Nishioka, K., Hisada, T. & Nishijima, M. Development of a prokaryotic universal primer for simultaneous analysis of bacteria and archaea using next-generation sequencing. PLoS ONE 9, e105592 (2014).
    Article  Google Scholar 

    38.
    Herlemann, D. P. et al. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J. 5, 1571–1579 (2011).
    CAS  Article  Google Scholar  More

  • in

    Composition and activity of nitrifier communities in soil are unresponsive to elevated temperature and CO2, but strongly affected by drought

    1.
    Foley JA, DeFries R, Asner GP, Barford C, Bonan G, Carpenter SR, et al. Global consequences of land use. Science. 2005;309:570–4.
    CAS  PubMed  Google Scholar 
    2.
    Rockström J, Steffen W, Noone K, Persson Å, Chapin FS, Lambin EF, et al. A safe operating space for humanity. Nature. 2009;461:472–5.
    Google Scholar 

    3.
    Graham EB, Knelman JE, Schindlbacher A, Siciliano S, Breulmann M, Yannarell A, et al. Microbes as engines of ecosystem function: when does community structure enhance predictions of ecosystem processes? Front Microbiol. 2016;7:1–10.
    Google Scholar 

    4.
    Cavicchioli R, Ripple WJ, Timmis KN, Azam F, Bakken LR, Baylis M, et al. Scientists’ warning to humanity: microorganisms and climate change. Nat Rev Microbiol. 2019;17:569–86.
    CAS  PubMed  PubMed Central  Google Scholar 

    5.
    Hoegh-Guldberg O, Jacob D, Taylor M, Bindi M, Brown S, Camilloni I, et al. Impacts of 1.5 °C global warming on natural and human systems. In: Masson-Delmotte V, Zhai P, Pörtner HO, Roberts D, Skea J, Shukla PR, et al., editors. Geneva, Switzerland: World Meteorological Organization Technical Document; 2018.

    6.
    Dieleman WIJ, Vicca S, Tingey D, De Angelis P, Hagedorn F, Morgan JA, et al. Simple additive effects are rare: a quantitative review of plant biomass and soil process responses to combined manipulations of CO 2 and temperature. Glob Chang Biol. 2012;18:2681–93.
    PubMed  Google Scholar 

    7.
    Song J, Wan S, Piao S, Knapp AK, Classen AT, Vicca S, et al. A meta-analysis of 1,119 manipulative experiments on terrestrial carbon-cycling responses to global change. Nat Ecol Evol. 2019;3:1309–20.
    PubMed  Google Scholar 

    8.
    Kuypers MMM, Marchant HK, Kartal B. The microbial nitrogen-cycling network. Nat Rev Microbiol. 2018;16:263–76. Nature Publishing Group.
    CAS  PubMed  Google Scholar 

    9.
    Prosser JI, Nicol GW. Archaeal and bacterial ammonia-oxidisers in soil: the quest for niche specialisation and differentiation. Trends Microbiol. 2012;20:523–31.

    10.
    Martens-Habbena W, Berube PM, Urakawa H, De La Torre JR, Stahl DA. Ammonia oxidation kinetics determine niche separation of nitrifying Archaea and Bacteria. Nature. 2009;461:976–9.
    CAS  PubMed  Google Scholar 

    11.
    Fuchslueger L, Kastl EM, Bauer F, Kienzl S, Hasibeder R, Ladreiter-Knauss T, et al. Effects of drought on nitrogen turnover and abundances of ammonia-oxidizers in mountain grassland. Biogeosciences. 2014;11:6003–15.
    Google Scholar 

    12.
    Kits KD, Pjevac P, Daebeler A, Han P, Albertsen M, Romano S, et al. Kinetic analysis of a complete nitrifier reveals an oligotrophic lifestyle. Nature. 2017;549:269–72.
    CAS  PubMed  PubMed Central  Google Scholar 

    13.
    Di HJ, Cameron KC, Shen JP, Winefield CS, Ocallaghan M, Bowatte S, et al. Nitrification driven by bacteria and not archaea in nitrogen-rich grassland soils. Nat Geosci. 2009;2:621–4.
    CAS  Google Scholar 

    14.
    Jia Z, Conrad R. Bacteria rather than Archaea dominate microbial ammonia oxidation in an agricultural soil. Environ Microbiol. 2009;11:1658–71.
    CAS  PubMed  Google Scholar 

    15.
    Zhalnina K, Dörr de Quadros P, Camargo FAO, Triplett EW. Drivers of archaeal ammonia-oxidizing communities in soil. Front Microbiol. 2012;3:1–9.
    Google Scholar 

    16.
    Gruber-Dorninger C, Pester M, Kitzinger K, Savio DF, Loy A, Rattei T, et al. Functionally relevant diversity of closely related Nitrospira in activated sludge. ISME J. 2015;9:643–55.
    CAS  PubMed  Google Scholar 

    17.
    Daims H, Lebedeva EV, Pjevac P, Han P, Herbold C, Albertsen M, et al. Complete nitrification by Nitrospira bacteria. Nature. 2015;528:504–9.
    CAS  PubMed  PubMed Central  Google Scholar 

    18.
    van Kessel MAHJ, Kartal B, MSM Jetten, Albertsen M, Op den Camp HJM, Lücker S, et al. Complete nitrification by a single microorganism. Nature. 2015;528:555–9.
    PubMed  PubMed Central  Google Scholar 

    19.
    Poghosyan L, Koch H, Lavy A, Frank J, van Kessel MAHJ, Jetten MSM, et al. Metagenomic recovery of two distinct comammox Nitrospira from the terrestrial subsurface. Environ Microbiol. 2019;00:1–11.
    Google Scholar 

    20.
    Wang Z, Cao Y, Zhu-Barker X, Nicol GW, Wright AL, Jia Z, et al. Comammox Nitrospira clade B contributes to nitrification in soil. Soil Biol Biochem. 2019;135:392–5.
    CAS  Google Scholar 

    21.
    Dusenge ME, Duarte AG, Way DA. Plant carbon metabolism and climate change: elevated CO2 and temperature impacts on photosynthesis, photorespiration and respiration. N. Phytol. 2019;221:32–49. John Wiley & Sons, Ltd.
    CAS  Google Scholar 

    22.
    de Graaff MA, van Groenigen KJ, Six J, Hungate B, van Kessel C. Interactions between plant growth and soil nutrient cycling under elevated CO2: a meta-analysis. Glob Chang Biol. 2006;12:2077–91.
    Google Scholar 

    23.
    Kuzyakov Y, Horwath WR, Dorodnikov M, Blagodatskaya E. Review and synthesis of the effects of elevated atmospheric CO2 on soil processes: No changes in pools, but increased fluxes and accelerated cycles. Soil Biol Biochem. 2019;128:66–78.
    CAS  Google Scholar 

    24.
    Luo Y, Su B, Currie WS, Dukes J. Progressive nitrogen limitation of ecosystem responses to rising atmospheric carbon dioxide. Bioscience. 2004;54:731–9.

    25.
    Liang J, Qi X, Souza L, Luo Y. Processes regulating progressive nitrogen limitation under elevated carbon dioxide: a meta-analysis. Biogeosciences. 2016;13:2689–99.
    CAS  Google Scholar 

    26.
    He Z, Xu M, Deng Y, Kang S, Kellogg L, Wu L, et al. Metagenomic analysis reveals a marked divergence in the structure of belowground microbial communities at elevated CO2. Ecol Lett. 2010;13:564–75.
    PubMed  Google Scholar 

    27.
    Horz HP, Barbrook A, Field CB, Bohannan BJM. Ammonia-oxidizing bacteria respond to multifactorial global change. Proc Natl Acad Sci USA. 2004;101:15136–41.
    CAS  PubMed  Google Scholar 

    28.
    Bradford MA, Davies CA, Frey SD, Maddox TR, Melillo JM, Mohan JE, et al. Thermal adaptation of soil microbial respiration to elevated temperature. Ecol Lett. 2008;11:1316–27.
    PubMed  Google Scholar 

    29.
    Liu Q, Piao S, Janssens IA, Fu Y, Peng S, Lian X, et al. Extension of the growing season increases vegetation exposure to frost. Nat Commun. 2018;9:426.
    PubMed  PubMed Central  Google Scholar 

    30.
    Lax S, Abreu CI, Gore J. Higher temperatures generically favour slower-growing bacterial species in multispecies communities. Nat Ecol Evol. 2020;4:560–657.
    PubMed  Google Scholar 

    31.
    Tourna M, Freitag TE, Nicol GW, Prosser JI. Growth, activity and temperature responses of ammonia-oxidizing archaea and bacteria in soil microcosms. Environ Microbiol. 2008;10:1357–64.
    CAS  PubMed  Google Scholar 

    32.
    Fierer N, Carney KM, Horner-Devine MC, Megonigal JP. The biogeography of ammonia-oxidizing bacterial communities in soil. Micro Ecol. 2009;58:435–45.
    Google Scholar 

    33.
    Schimel JP. Life in dry soils: effects of drought on soil microbial communities and processes. Annu Rev Ecol Evol Syst. 2018;49:409–32.
    Google Scholar 

    34.
    Kuzyakov Y, Horwath WR, Dorodnikov M, Blagodatskaya E. Review and synthesis of the effects of elevated atmospheric CO2 on soil processes: No changes in pools, but increased fluxes and accelerated cycles. Soil Biol Biochem. 2019;128:66–78.

    35.
    Yue K, Peng Y, Fornara DA, Van Meerbeek K, Vesterdal L, Yang W, et al. Responses of nitrogen concentrations and pools to multiple environmental change drivers: a meta-analysis across terrestrial ecosystems. Glob Ecol Biogeogr. 2019;28:690–724.
    Google Scholar 

    36.
    Bai E, Li S, Xu W, Li W, Dai W, Jiang P. A meta-analysis of experimental warming effects on terrestrial nitrogen pools and dynamics. N. Phytol. 2013;199:431–40.
    CAS  Google Scholar 

    37.
    Piepho HP, Herndl M, Pötsch EM, Bahn M. Designing an experiment with quantitative treatment factors to study the effects of climate change. J Agron Crop Sci. 2017;203:584–92.
    CAS  Google Scholar 

    38.
    Vance ED, Brookes PC, Jenkinson DS. An extraction method for measuring soil microbial biomass C. Soil Biol Biochem. 1987;19:703–7.
    CAS  Google Scholar 

    39.
    Hood-Nowotny R, Umana NH-N, Inselbacher E, Oswald- Lachouani P, Wanek W. Alternative methods for measuring inorganic, organic, and total dissolved nitrogen in soil. Soil Sci Soc Am J. 2010;74:1018–27.
    CAS  Google Scholar 

    40.
    Wanek W, Mooshammer M, Blöchl A, Hanreich A, Richter A. Determination of gross rates of amino acid production and immobilization in decomposing leaf litter by a novel 15N isotope pool dilution technique. Soil Biol Biochem. 2010;42:1293–302.
    CAS  Google Scholar 

    41.
    Sørensen P, Jensen ES. Sequential diffusion of ammonium and nitrate from soil extracts to a polytetrafluoroethylene trap for 15N determination. Anal Chim Acta. 1991;252:201–3.
    Google Scholar 

    42.
    Lachouani P, Frank AH, Wanek W. A suite of sensitive chemical methods to determine the δ 15N of ammonium, nitrate and total dissolved N in soil extracts. Rapid Commun Mass Spectrom. 2010;24:3615–23.
    CAS  PubMed  Google Scholar 

    43.
    Angel R, Claus P, Conrad R. Methanogenic archaea are globally ubiquitous in aerated soils and become active under wet anoxic conditions. ISME J. 2012;6:847–62.
    CAS  PubMed  Google Scholar 

    44.
    Apprill A, McNally S, Parsons R, Weber L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat Micro Ecol. 2015;75:129–37.
    Google Scholar 

    45.
    Parada AE, Needham DM, Fuhrman JA. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol. 2016;18:1403–14.
    CAS  PubMed  Google Scholar 

    46.
    Herbold CW, Pelikan C, Kuzyk O, Hausmann B, Angel R, Berry D, et al. A flexible and economical barcoding approach for highly multiplexed amplicon sequencing of diverse target genes. Front Microbiol. 2015;6:1–8.
    Google Scholar 

    47.
    Purkhold U, Wagner M, Timmermann G, Pommerening-Röser A, Koops HP. 16S rRNA and amoA-based phylogeny of 12 novel betaproteobacterial ammonia-oxidizing isolates: extension of the dataset and proposal of a new lineage within the nitrosomonads. Int J Syst Evol Microbiol. 2003;53:1485–94.
    CAS  PubMed  Google Scholar 

    48.
    Alves RJE, Minh BQ, Urich T, Von Haeseler A, Schleper C. Unifying the global phylogeny and environmental distribution of ammonia-oxidising archaea based on amoA genes. Nat Commun. 2018;9:1–17.
    CAS  Google Scholar 

    49.
    Berger SA, Krompass D, Stamatakis A. Performance, accuracy, and web server for evolutionary placement of short sequence reads under maximum likelihood. Syst Biol. 2011;60:291–302.
    PubMed  PubMed Central  Google Scholar 

    50.
    Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3.
    CAS  PubMed  PubMed Central  Google Scholar 

    51.
    Aigle A, Prosser JI, Gubry-Rangin C. The application of high-throughput sequencing technology to analysis of amoA phylogeny and environmental niche specialisation of terrestrial bacterial ammonia-oxidisers. Environ Microbiome. 2019;14:3.
    Google Scholar 

    52.
    Pjevac P, Schauberger C, Poghosyan L, Herbold CW, van Kessel MAHJ, Daebeler A, et al. AmoA-targeted polymerase chain reaction primers for the specific detection and quantification of comammox Nitrospira in the environment. Front Microbiol. 2017;8:1–11.
    Google Scholar 

    53.
    Pruesse E, Peplies J, Glöckner FO. SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics. 2012;28:1823–9.
    CAS  PubMed  PubMed Central  Google Scholar 

    54.
    Trifinopoulos J, Nguyen L-T, von Haeseler A, Minh BQ. W-IQ-TREE: a fast online phylogenetic tool for maximum likelihood analysis. Nucleic Acids Res. 2016;44:W232–5.
    CAS  PubMed  PubMed Central  Google Scholar 

    55.
    Kalyaanamoorthy S, Minh BQ, Wong TKF, von Haeseler A, Jermiin LS. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods. 2017;14:587–9.
    CAS  PubMed  PubMed Central  Google Scholar 

    56.
    Letunic I, Bork P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 2019;47:W256–9.
    CAS  PubMed  PubMed Central  Google Scholar 

    57.
    Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.
    CAS  PubMed  PubMed Central  Google Scholar 

    58.
    Pester M, Maixner F, Berry D, Rattei T, Koch H, Lücker S, et al. NxrB encoding the beta subunit of nitrite oxidoreductase as functional and phylogenetic marker for nitrite-oxidizing Nitrospira. Environ Microbiol. 2014;16:3055–71.
    CAS  PubMed  Google Scholar 

    59.
    Sauder LA, Albertsen M, Engel K, Schwarz J, Nielsen PH, Wagner M, et al. Correction: cultivation and characterization of Candidatus nitrosocosmicus exaquare, an ammonia-oxidizing archaeon from a municipal wastewater treatment system. ISME J. 2020.

    60.
    Kozak M, Piepho HP. What’s normal anyway? Residual plots are more telling than significance tests when checking ANOVA assumptions. J Agron Crop Sci. 2018;204:86–98.
    Google Scholar 

    61.
    Langsrud Ø. ANOVA for unbalanced data: use type II instead of Type III sums of squares. Stat Comput. 2003;13:163–7.
    Google Scholar 

    62.
    McMurdie PJ, Holmes S. phyloseq: an R Package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.
    CAS  PubMed  PubMed Central  Google Scholar 

    63.
    Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: Community Ecology Package. R package version 2.5-6. https://CRAN.R-project.org/package=vegan.

    64.
    Stier AC, Geange SW, Hanson KM, Bolker BM. Predator density and timing of arrival affect reef fish community assembly. Ecology. 2013;94:1057–68.
    PubMed  Google Scholar 

    65.
    Anderson MJ. Permutational multivariate analysis of variance (PERMANOVA). Wiley StatsRef: Statistics Reference Online. Chichester, UK: John Wiley & Sons, Ltd; 2017. p 1–15.

    66.
    Fierer N, Schimel JP. A proposed mechanism for the pulse in carbon dioxide production commonly observed following the rapid rewetting of a dry soil. Soil Sci Soc Am J. 2010;67:798.
    Google Scholar 

    67.
    Lehtovirta-Morley LE. Ammonia oxidation: ecology, physiology, biochemistry and why they must all come together. FEMS Microbiol Lett. 2018;365:1–9.
    Google Scholar 

    68.
    Schimel JP, Schaeffer SM. Microbial control over carbon cycling in soil. Front Microbiol. 2012;3:1–11.
    Google Scholar 

    69.
    Fierer N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat Rev Microbiol. 2017;15:579–90.
    CAS  PubMed  Google Scholar 

    70.
    Larsen KS, Andresen LC, Beier C, Jonasson S, Albert KR, Ambus P, et al. Reduced N cycling in response to elevated CO2, warming, and drought in a Danish heathland: synthesizing results of the CLIMAITE project after two years of treatments. Glob Chang Biol. 2011;17:1884–99.
    Google Scholar 

    71.
    Brenzinger K, Kujala K, Horn MA, Moser G, Guillet C, Kammann C, et al. Soil conditions rather than long-term exposure to elevated CO2 affect soil microbial communities associated with N-cycling. Front Microbiol. 2017;8:1–14.
    Google Scholar 

    72.
    Rütting T, Hovenden MJ. Soil nitrogen cycle unresponsive to decadal long climate change in a Tasmanian grassland. Biogeochemistry. 2020;147:99–107.
    Google Scholar 

    73.
    Rustad LE, Campbell JL, Marion GM, Norby RJ, Mitchell MJ, Hartley AE, et al. A meta-analysis of the response of soil respiration, net nitrogen mineralization, and aboveground plant growth to experimental ecosystem warming. Oecologia. 2001;126:543–62.
    CAS  PubMed  Google Scholar 

    74.
    Fuchslueger L, Wild B, Mooshammer M, Takriti M, Kienzl S, Knoltsch A, et al. Microbial carbon and nitrogen cycling responses to drought and temperature in differently managed mountain grasslands. Soil Biol Biochem. 2019;135:144–53.
    CAS  Google Scholar 

    75.
    Coskun D, Britto DT, Shi W, Kronzucker HJ. Nitrogen transformations in modern agriculture and the role of biological nitrification inhibition. Nat Plants. 2017;3:17074.
    CAS  PubMed  Google Scholar 

    76.
    Subbarao GV, Yoshihashi T, Worthington M, Nakahara K, Ando Y, Sahrawat KL, et al. Suppression of soil nitrification by plants. Plant Sci. 2015;233:155–64.
    CAS  PubMed  Google Scholar 

    77.
    Canarini A, Dijkstra FA. Dry-rewetting cycles regulate wheat carbon rhizodeposition, stabilization and nitrogen cycling. Soil Biol Biochem. 2015;81:195–203.
    CAS  Google Scholar 

    78.
    Karlowsky S, Augusti A, Ingrisch J, Akanda MKU, Bahn M, Gleixner G. Drought-induced accumulation of root exudates supports post-drought recovery of microbes in mountain grassland. Front Plant Sci. 2018;871:1–16.
    Google Scholar 

    79.
    Manzoni S, Schimel JP, Barbara S. Results from a responses of soil microbial communities to water stress: results from a meta-analysis. Ecology. 2017;93:930–8.
    Google Scholar 

    80.
    Canarini A, Merchant A, Dijkstra FA. Drought effects on Helianthus annuus and Glycine max metabolites: from phloem to root exudates. Rhizosphere. 2016;2:85–97.
    Google Scholar 

    81.
    Hashem A, Kumar A, Al-Dbass AM, Alqarawi AA, Al-Arjani A-BF, Singh G, et al. Arbuscular mycorrhizal fungi and biochar improves drought tolerance in chickpea. Saudi J Biol Sci. 2019;26:614–24.
    CAS  PubMed  Google Scholar 

    82.
    Williams A, de Vries FT. Plant root exudation under drought: implications for ecosystem functioning. N. Phytol. 2020;225:1899–1905.
    Google Scholar 

    83.
    Subbarao GV, Rondon M, Ito O, Ishikawa T, Rao IM, Nakahara K, et al. Biological nitrification inhibition (BNI)—Is it a widespread phenomenon? Plant Soil. 2007;294:5–18.
    CAS  Google Scholar 

    84.
    Homyak PM, Allison SD, Huxman TE, Goulden ML, Treseder KK. Effects of drought manipulation on soil nitrogen cycling: a meta-analysis. J Geophys Res Biogeosci. 2017;122:3260–72.
    CAS  Google Scholar 

    85.
    Fuchslueger L, Bahn M, Fritz K, Hasibeder R, Richter A. Experimental drought reduces the transfer of recently fixed plant carbon to soil microbes and alters the bacterial community composition in a mountain meadow. N. Phytol. 2014;201:916–27.
    CAS  Google Scholar 

    86.
    Thion C, Prosser JI. Differential response of nonadapted ammonia-oxidising archaea and bacteria to drying-rewetting stress. FEMS Microbiol Ecol. 2014;90:380–9.
    CAS  PubMed  Google Scholar 

    87.
    Norton JM, Klotz MG, Stein LY, Arp DJ, Bottomley PJ, Chain PSG, et al. Complete genome sequence of Nitrosospira multiformis, an ammonia-oxidizing bacterium from the soil environment. Appl Environ Microbiol. 2008;74:3559–72.
    CAS  PubMed  PubMed Central  Google Scholar 

    88.
    Spang A, Poehlein A, Offre P, Zumbrägel S, Haider S, Rychlik N, et al. The genome of the ammonia-oxidizing CandidatusNitrososphaera gargensis: Insights into metabolic versatility and environmental adaptations. Environ Microbiol. 2012;14:3122–45.
    CAS  PubMed  Google Scholar 

    89.
    Kerou M, Offre P, Valledor L, Abby SS, Melcher M, Nagler M, et al. Proteomics and comparative genomics of Nitrososphaera viennensis reveal the core genome and adaptations of archaeal ammonia oxidizers. Proc Natl Acad Sci USA. 2016;113:E7937–46.
    CAS  PubMed  Google Scholar 

    90.
    Nicol GW, Hink L, Gubry-Rangin C, Prosser JI, Lehtovirta-Morley LE. Genome Sequence of “ Candidatus Nitrosocosmicus franklandus” C13, a terrestrial ammonia-oxidizing archaeon. Microbiol Resour Announc. 2019;8:1–3.
    Google Scholar 

    91.
    Sauder LA, Albertsen M, Engel K, Schwarz J, Nielsen PH, Wagner M, et al. Cultivation and characterization of Candidatus Nitrosocosmicus exaquare, an ammonia-oxidizing archaeon from a municipal wastewater treatment system. ISME J. 2017;11:1142–57.
    CAS  PubMed  PubMed Central  Google Scholar 

    92.
    Lehtovirta-Morley LE, Ge C, Ross J, Yao H, Nicol GW, Prosser JI. Characterisation of terrestrial acidophilic archaeal ammonia oxidisers and their inhibition and stimulation by organic compounds. FEMS Microbiol Ecol. 2014;89:542–52.
    CAS  PubMed  PubMed Central  Google Scholar 

    93.
    Stieglmeier M, Klingl A, Alves RJE, Rittmann SKMR, Melcher M, Leisch N, et al. Nitrososphaera viennensis gen. nov., sp. nov., an aerobic and mesophilic, ammonia-oxidizing archaeon from soil and a member of the archaeal phylum Thaumarchaeota. Int J Syst Evol Microbiol. 2014;64:2738–52.
    CAS  PubMed  PubMed Central  Google Scholar 

    94.
    Jung MY, Kim JG, Sinninghe Damsté JS, Rijpstra WIC, Madsen EL, Kim SJ, et al. A hydrophobic ammonia-oxidizing archaeon of the Nitrosocosmicus clade isolated from coal tar-contaminated sediment. Environ Microbiol Rep. 2016;8:983–92.
    CAS  PubMed  Google Scholar 

    95.
    Gwak JH, Jung MY, Hong H, Kim JG, Quan ZX, Reinfelder JR, et al. Archaeal nitrification is constrained by copper complexation with organic matter in municipal wastewater treatment plants. ISME J. 2020;14:335–46.
    CAS  PubMed  Google Scholar 

    96.
    Nowka B, Daims H, Spieck E. Comparison of oxidation kinetics of nitrite-oxidizing bacteria: nitrite availability as a key factor in niche differentiation. Appl Environ Microbiol. 2015;81:745–53.
    PubMed  PubMed Central  Google Scholar 

    97.
    Prosser JI. The ecology of nitrifying bacteria. In: Bothe H, Ferguson SJ, editors. Newton WEBT-B of the NC. Biology of the Nitrogen Cycle. Amsterdam: Elsevier; 2007. p 223–43.

    98.
    Norton JM, Stark JM. Regulation and measurement of nitrification in terrestrial systems. In: Klotz MGBT-M in E. Research on nitrification and related processes, Part A. 2011. Academic Press, United States, p 343–68.

    99.
    Koch H, Galushko A, Albertsen M, Schintlmeister A, Gruber-Dorninger C, Lücker S, et al. Growth of nitrite-oxidizing bacteria by aerobic hydrogen oxidation. Science. 2014;345:1052 LP–1054.
    Google Scholar 

    100.
    Koch H, Lücker S, Albertsen M, Kitzinger K, Herbold C, Spieck E, et al. Expanded metabolic versatility of ubiquitous nitrite-oxidizing bacteria from the genus Nitrospira. Proc Natl Acad Sci USA. 2015;112:11371–6.
    CAS  PubMed  Google Scholar 

    101.
    Daebeler A, Bodelier PLE, Yan Z, Hefting MM, Jia Z, Laanbroek HJ. Interactions between Thaumarchaea, Nitrospira and methanotrophs modulate autotrophic nitrification in volcanic grassland soil. ISME J. 2014;8:2397–410.
    CAS  PubMed  PubMed Central  Google Scholar 

    102.
    Kim DG, Vargas R, Bond-Lamberty B, Turetsky MR. Effects of soil rewetting and thawing on soil gas fluxes: a review of current literature and suggestions for future research. Biogeosciences. 2012;9:2459–83.
    CAS  Google Scholar 

    103.
    Wrage N, Velthof GL, Van Beusichem ML, Oenema O. Role of nitrifier denitrification in the production of nitrous oxide. Soil Biol Biochem. 2001;33:1723–32.
    CAS  Google Scholar 

    104.
    Stein LY. Surveying N2O-producing pathways in bacteria. In: Klotz MGBT-M in E. Research on nitrification and related processes, Part A. 2011. Academic Press, United States, pp 131–52.

    105.
    Kozlowski JA, Stieglmeier M, Schleper C, Klotz MG, Stein LY. Pathways and key intermediates required for obligate aerobic ammonia-dependent chemolithotrophy in bacteria and Thaumarchaeota. ISME J. 2016;10:1836–45.
    CAS  PubMed  PubMed Central  Google Scholar 

    106.
    Kits KD, Jung MY, Vierheilig J, Pjevac P, Sedlacek CJ, Liu S, et al. Low yield and abiotic origin of N2O formed by the complete nitrifier Nitrospira inopinata. Nat Commun. 2019;10:1–12.
    CAS  Google Scholar  More