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    French crop yield, area and production data for ten staple crops from 1900 to 2018 at county resolution

    Crop dataCrop area (in hectare, ha, for sown areas) and production (in kg) statistics on departmental level from 1900 until 1988 were collected from books of national agricultural statistics (‘Statistique agricole annuelle’ or ‘Annuaire de statistique agricole’) compiled by the French Ministry of Agriculture; detailed references are provided in the supplementary information. Numbers were manually digitized from photocopied versions of the original paper documents. Data from 1989 to 2018 were derived from digital statistics from the Agreste database (‘Statistique agricole annuelle’ compiled by the Service de la Statistique et de la Prospective (SSP), Secrétariat Général du Ministère de l’Agriculture, de l’Agroalimentaire et de la Forêt (MAAF), France); details are provided in the supplementary information. Yields were calculated from total production and sown area for each department to avoid apparently often incorrect yield values printed in the old statistics books. Yields are given in kilogram per hectare (kg/ha, for sown area) for dry mass with 10–16% moisture content, depending on the crop.Data are available for ten crops: soft wheat (spring and winter separately), durum wheat, maize, oats (spring and winter), rapeseed (spring and winter), barley (spring and winter), potatoes, sugarbeet, sunflower and wine. The split into spring and winter crops eventually results in 18 distinct crop-cultivar types. Time frames with available data and the correspondence between French and English names are provided in Table 1.Table 1 Data set description for yields on department level.Full size tableThe shapes of French departments have changed over time. We use the 96 mainland (Metropolitan France) departments in their current form and subsume historical values to modern departments as follows. Corsica was one single department until 1975 but then split into Corse-du-Sud and Haute-Corse. Data for Corsica until 1975 were split equally (area, production) or copied (yield) to both new departments. Seine and Seine-et-Oise were two departments until 1967, but then subdivided into seven new departments on 1 January 1968. To account for this, we consider the values of the seven new departments (Essonne, Hauts-de-Seine, Paris, Seine-Saint-Denis, Val-de-Marne, Val-d’Oise, Yvelines) only from 1968 on and unite the two old departments into one counter-factual (“Seine_SeineOise” in the data tables) until 1967.Multiple cropping per year within this set of crops is accounted for by separate area data, but is practically nonexistent in France6.Quality filtersSome yield values had to be considered as outliers, also after checking for digitizing errors. There were four criteria for defining an outlier. First, absolute yield values larger than a physiologically currently unreachable threshold were removed; threshold values were 15 t/ha for barley and durum wheat, 200 t/ha for sugarbeet and potatoes, 20 t/ha for maize, oats and wheat, 10 t/ha for rape and sunflower and 200 hl/ha for wine. These thresholds were chosen to eliminate visually obvious outliers likely due to mismatches between area and production records. The values are set slightly above current maximum attained yields, thus remaining permissive and removing only obvious errors in this first step. Additionally, all yield values for winter rape in 1944, spring rape in 1968 and spring barley in 1980 were removed due to wrongly reported values in the yearbooks. This first step removed in total 167 yield data points. Second, the top 1% of yield values across all departments per decade were removed. Third, values above or below the mean +/− four times the standard deviation of each crop-department time series (for yield, area and production separately) were removed. Fourth, and finally, a similar variance filter as in the third step was applied within each decade of a single time series, filtering values above or below decadal mean +/− two (for yield) or three (area, production) decadal standard deviations. The latter three filters removed, on average, 3.6% of the yield and 0.2% of the area or production data, respectively (Table 1). There were, as a median, 43 yield outliers per department (out of 1,260 data points on average), with a range of 4 (department Hauts de Seine) and 255 (Nord) and an interquartile range of 35–50 outliers. Outliers were masked as missing values to avoid introducing a bias from any correction. In the accompanying data sets we provide two version of the full data set, one without any corrections (“RAW”) and one where the filters described above have been applied (“FILTERED”).ValidationNationally aggregated area, production and yield data from our data set were validated with national data from 1961 to 2018 provided by the FAO (http://faostat3.fao.org/home/E). Area and production data for crops with separate spring and winter data were summed on department level to test agreement with area and production data digitized for the ‘total’ crop. More

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    Climate drives long-term change in Antarctic Silverfish along the western Antarctic Peninsula

    1.DeWitt, H. H. The character of the midwater fish fauna of the Ross Sea, Antarctica. Antarctic Ecol. 1, 305–314 (1970).
    Google Scholar 
    2.Guglielmo, L., Granata, A. & Greco, S. Distribution and abundance of postlarval and juvenile Pleuragramma antarcticum (Pisces, Nototheniidae) off Terra Nova Bay (Ross Sea, Antarctica). Polar Biol. 19, 37–51 (1997).
    Google Scholar 
    3.La Mesa, M. & Eastman, J. T. Antarctic silverfish: life strategies of a key species in the high-Antarctic ecosystem. Fish Fisheries 13, 241–266 (2012).
    Google Scholar 
    4.La Mesa, M., Eastman, J. T. & Vacchi, M. The role of notothenioid fish in the food web of the Ross Sea shelf waters: a review. Polar Biol. 27, 321–338 (2004).
    Google Scholar 
    5.Pinkerton, M. H., Bradford-Grieve, J. M. & Hanchet, S. M. A balanced model of the food web of the Ross Sea, Antarctica. CCAMLR Sci. 17, 1–31 (2010).
    Google Scholar 
    6.Caccavo, J. A. et al. Along-shelf connectivity and circumpolar gene flow in Antarctic silverfish (Pleuragramma antarctica). Sci. Rep. 8, 1–16 (2018).
    Google Scholar 
    7.Beers, J. M. & Jayasundara, N. Antarctic notothenioid fish: what are the future consequences of ‘losses’ and ‘gains’ acquired during long-term evolution at cold and stable temperatures? J. Exp. Biol. 218, 1834–1845 (2015).PubMed 

    Google Scholar 
    8.Bilyk, K. T. & DeVries, A. L. Heat tolerance and its plasticity in Antarctic fishes. Compar. Biochem. Physiol. A Mol. Integr. Physiol. 158, 382–390 (2011).
    Google Scholar 
    9.Sandersfeld, T., Davison, W., Lamare, M. D., Knust, R. & Richter, C. Elevated temperature causes metabolic trade-offs at the whole-organism level in the Antarctic fish Trematomus bernacchii. J. Exp. Biol. 218, 2373–2381 (2015).PubMed 

    Google Scholar 
    10.Cook, A. J. et al. Ocean forcing of glacier retreat in the western Antarctic Peninsula. Science 353, 283–286 (2016).CAS 
    PubMed 

    Google Scholar 
    11.Stammerjohn, S. E. & Scambos, T. A. Warming reaches the South Pole. Nat. Clim. Change 10, 710–711 (2020).
    Google Scholar 
    12.Henley, S. F. et al. Variability and change in the west Antarctic Peninsula marine system: research priorities and opportunities. Prog. Oceanogr. 173, 208–237 (2019).
    Google Scholar 
    13.Mintenbeck, K. & Torres, J. J. in The Antarctic silverfish: a keystone species in a changing ecosystem, 253–286 (Springer, 2017).14.Vacchi, M. et al. A nursery area for the Antarctic silverfish Pleuragramma antarcticum at Terra Nova Bay (Ross Sea): first estimate of distribution and abundance of eggs and larvae under the seasonal sea-ice. Polar Biol. 35, 1573–1585 (2012).
    Google Scholar 
    15.Vacchi, M., La Mesa, M., Dalu, M. & Macdonald, J. Early life stages in the life cycle of Antarctic silverfish, Pleuragramma antarcticum in Terra Nova Bay, Ross Sea. Antartic Sci. 16, 299–305 (2004).
    Google Scholar 
    16.Kellermann, A. K. Midwater fish ecology. Found. Ecol. Res. West Antarctic Peninsula 70, 231–256 (1996).
    Google Scholar 
    17.La Mesa, M., Riginella, E., Mazzoldi, C. & Ashford, J. Reproductive resilience of ice-dependent Antarctic silverfish in a rapidly changing system along the Western Antarctic Peninsula. Mar. Ecol. 36, 235–245 (2015).
    Google Scholar 
    18.Parker, M. L. et al. Assemblages of micronektonic fishes and invertebrates in a gradient of regional warming along the Western Antarctic Peninsula. J. Mar. Syst. 152, 18–41 (2015).
    Google Scholar 
    19.Ross, R. M. et al. Trends, cycles, interannual variability for three pelagic species west of the Antarctic Peninsula 1993–2008. Mar. Ecol. Prog. Ser. 515, 11–32 (2014).
    Google Scholar 
    20.Koubbi, P. et al. Spatial distribution and inter-annual variations in the size frequency distribution and abundances of Pleuragramma antarcticum larvae in the Dumont d’Urville Sea from 2004 to 2010. Polar Sci. 5, 225–238 (2011).
    Google Scholar 
    21.Davis, L. B., Hofmann, E. E., Klinck, J. M., Piñones, A. & Dinniman, M. S. Distributions of krill and Antarctic silverfish and correlations with environmental variables in the western Ross Sea, Antarctica. Mar. Ecol. Prog. Ser. 584, 45–65 (2017).CAS 

    Google Scholar 
    22.La Mesa, M. et al. Influence of environmental conditions on spatial distribution and abundance of early life stages of Antarctic silverfish, Pleuragramma antarcticum (Nototheniidae), in the Ross Sea. Antarctic Sci. 22, 243 (2010).
    Google Scholar 
    23.Raphael, M. N. et al. The Amundsen Sea low: variability, change, and impact on Antarctic climate. Bull. Am. Meteorol. Soc. 97, 111–121 (2016).
    Google Scholar 
    24.Fogt, R. L., Wovrosh, A. J., Langen, R. A. & Simmonds, I. The characteristic variability and connection to the underlying synoptic activity of the Amundsen-Bellingshausen Seas Low. J. Geophys. Res. Atmos. https://doi.org/10.1029/2011JD017337 (2012).25.Hosking, J. S., Orr, A., Marshall, G. J., Turner, J. & Phillips, T. The influence of the Amundsen–Bellingshausen Seas low on the climate of West Antarctica and its representation in coupled climate model simulations. J. Clim. 26, 6633–6648 (2013).
    Google Scholar 
    26.Hosking, J. S., Orr, A., Bracegirdle, T. J. & Turner, J. Future circulation changes off West Antarctica: sensitivity of the Amundsen Sea Low to projected anthropogenic forcing. Geophys. Res. Lett. 43, 367–376 (2016).
    Google Scholar 
    27.Hobbs, W. R. et al. A review of recent changes in Southern Ocean sea ice, their drivers and forcings. Glob. Planet. Change 143, 228–250 (2016).
    Google Scholar 
    28.Stammerjohn, S. E. et al. Seasonal sea ice changes in the Amundsen Sea, Antarctica, over the period of 1979–2014. Elementa Sci. Anthropocene 3, 000055 (2015).29.Holland, M. M., Landrum, L., Raphael, M. N. & Kwok, R. The regional, seasonal, and lagged influence of the Amundsen Sea Low on Antarctic sea ice. Geophys. Res. Lett. 45, 11–227 (2018).
    Google Scholar 
    30.Thoma, M., Jenkins, A., Holland, D. & Jacobs, S. Modelling circumpolar deep water intrusions on the Amundsen Sea continental shelf, Antarctica. Geophys. Res. Lett. https://doi.org/10.1029/2008GL034939 (2008).31.Dotto, T. S. et al. Control of the oceanic heat content of the Getz‐Dotson Trough, Antarctica, by the Amundsen Sea Low. J. Geophys. Res. Oceans 125, e2020JC016113 (2020).32.Holland, P. R., Bracegirdle, T. J., Dutrieux, P., Jenkins, A. & Steig, E. J. West Antarctic ice loss influenced by internal climate variability and anthropogenic forcing. Nat. Geosci. 12, 718–724 (2019).CAS 

    Google Scholar 
    33.Dinniman, M. S., Klinck, J. M. & Hofmann, E. E. Sensitivity of circumpolar deep water transport and Ice Shelf Basal Melt along the West Antarctic Peninsula to changes in the winds. J. Clim. 25, 4799–4816 (2012).
    Google Scholar 
    34.Dinniman, M. S., Klinck, J. M. & Smith, W. O. A model study of circumpolar deep water on the West Antarctic Peninsula and Ross Sea continental shelves. Deep Sea Res. II Top. Stud. Oceanogr. 58, 1508–1523 (2011).CAS 

    Google Scholar 
    35.Nakayama, Y., Menemenlis, D., Zhang, H., Schodlok, M. & Rignot, E. Origin of circumpolar deep water intruding onto the Amundsen and Bellingshausen Sea continental shelves. Nat. Commun. 9, 3403 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    36.Spence, P. et al. Rapid subsurface warming and circulation changes of Antarctic coastal waters by poleward shifting winds. Geophys. Res. Lett. 41, 4601–4610 (2014).
    Google Scholar 
    37.Greaves, B. L. et al. The Southern Annular Mode (SAM) influences phytoplankton communities in the seasonal ice zone of the Southern Ocean. Biogeosciences 17, 3815–3835 (2020).CAS 

    Google Scholar 
    38.Steinberg, D. K. et al. Long-term (1993–2013) changes in macrozooplankton off the Western Antarctic Peninsula. Deep Sea Res. I Oceanogr. Res. Papers 101, 54–70 (2015).
    Google Scholar 
    39.La, H. S. et al. Zooplankton and micronekton respond to climate fluctuations in the Amundsen Sea polynya, Antarctica. Sci. Rep. 9, 10087 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    40.Granata, A., Zagami, G., Vacchi, M. & Guglielmo, L. Summer and spring trophic niche of larval and juvenile Pleuragramma antarcticum in the Western Ross Sea, Antarctica. Polar Biol. 32, 369–382 (2009).
    Google Scholar 
    41.Bhaskaran, K., Gasparrini, A., Hajat, S., Smeeth, L. & Armstrong, B. Time series regression studies in environmental epidemiology. Int. J. Epidemiol. 42, 1187–1195 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    42.Ghigliotti, L. et al. Reproductive features of the Antarctic silverfish (Pleuragramma antarctica) from the western Ross Sea. Polar Biol. 40, 199–211 (2017).
    Google Scholar 
    43.Chapman, E. W., Hofmann, E. E., Patterson, D. L., Ribic, C. A. & Fraser, W. R. Marine and terrestrial factors affecting Adélie ­penguin Pygoscelis adeliae chick growth and recruitment off the western Antarctic Peninsula. Mar. Ecol. Prog. Ser. 436, 273–289 (2011).
    Google Scholar 
    44.Coggins, J. H. J. & McDonald, A. J. The influence of the Amundsen Sea Low on the winds in the Ross Sea and surroundings: Insights from a synoptic climatology. J. Geophys. Res. Atmos. 120, 2167–2189 (2015).
    Google Scholar 
    45.Assmann, K. M. et al. Variability of circumpolar deep water transport onto the Amundsen Sea Continental shelf through a shelf break trough. J. Geophys. Res. Oceans 118, 6603–6620 (2013).
    Google Scholar 
    46.Moffat, C., Owens, B. & Beardsley, R. C. On the characteristics of circumpolar deep water intrusions to the west Antarctic Peninsula Continental Shelf. J. Geophys. Res. Oceans https://doi.org/10.1029/2008JC004955 (2009).47.Dahlke, F. T., Wohlrab, S., Butzin, M. & Pörtner, H.-O. Thermal bottlenecks in the life cycle define climate vulnerability of fish. Science 369, 65–70 (2020).CAS 
    PubMed 

    Google Scholar 
    48.Regan, H. C., Holland, P. R., Meredith, M. P. & Pike, J. Sources, variability and fate of freshwater in the Bellingshausen Sea, Antarctica. Deep Sea Res I Oceanogr. Res. Pap. 133, 59–71 (2018).
    Google Scholar 
    49.Holland, P. R. et al. Modeled trends in Antarctic sea ice thickness. J. Clim. 27, 3784–3801 (2014).
    Google Scholar 
    50.Hoppmann, M. et al. Platelet ice, the Southern Ocean’s hidden ice: a review. Ann. Glaciol. 61, 341–368 (2020).
    Google Scholar 
    51.Arrigo, K. R. Sea ice ecosystems. Annu. Rev. Mar. Sci 6, 439–467 (2014).
    Google Scholar 
    52.Veazey, A. L., Jeffries, M. O. & Morris, K. Small-scale variability of physical properties and structural characteristics of Antarctic fast ice. Ann. Glaciol. 20, 61–66 (1994).
    Google Scholar 
    53.Garrison, D. L., Ackley, S. F. & Buck, K. R. A physical mechanism for establishing algal populations in frazil ice. Nature 306, 363–365 (1983).CAS 

    Google Scholar 
    54.Quetin, L. B. & Ross, R. M. in Smithsonian at the Poles: Contributions to International Polar Year Science (eds Krupnik, I., Lang, M. A. & Miller, S. E.) 285–298 (IPY, 2009).55.Meredith, M. P. & King, J. C. Rapid climate change in the ocean west of the Antarctic Peninsula during the second half of the 20th century. Geophys. Res. Lett. https://doi.org/10.1029/2005GL024042 (2005).56.Turner, J. et al. Absence of 21st century warming on Antarctic Peninsula consistent with natural variability. Nature 535, 411–415 (2016).CAS 
    PubMed 

    Google Scholar 
    57.Rintoul, S. R. et al. Choosing the future of Antarctica. Nature 558, 233–241 (2018).CAS 
    PubMed 

    Google Scholar 
    58.Turner, J., Phillips, T., Hosking, J. S., Marshall, G. J. & Orr, A. The Amundsen Sea low. Int. J. Climatol. 33, 1818–1829 (2013).
    Google Scholar 
    59.Ding, Q., Steig, E. J., Battisti, D. S. & Küttel, M. Winter warming in West Antarctica caused by central tropical Pacific warming. Nat. Geosci. 4, 398–403 (2011).CAS 

    Google Scholar 
    60.Moline, M. A., Claustre, H., Frazer, T. K., Schofield, O. & Vernet, M. Alteration of the food web along the Antarctic Peninsula in response to a regional warming trend. Glob. Change Biol. 10, 1973–1980 (2004).
    Google Scholar 
    61.Gleiber, M. Long-Term Change in Copepod Community Structure in the Western Antarctic Peninsula: Linkage to Climate and Implications for Carbon Cycling. Dissertations, Theses, and Masters Projects, College of William and Mary, Virginia Institute of Marine Science (2014).62.Wöhrmann, A. P., Hagen, W. & Kunzmann, A. Adaptations of the Antarctic silverfish Pleuragramma antarcticum(Pisces: Nototheniidae) to pelagic life in high-Antarctic waters. Mar. Ecol. Prog. Ser. 151, 205–218 (1997).
    Google Scholar 
    63.Venables, H. J., Clarke, A. & Meredith, M. P. Wintertime controls on summer stratification and productivity at the western Antarctic Peninsula. Limnol. Oceanogr. 58, 1035–1047 (2013).
    Google Scholar 
    64.Meredith, M. P. et al. Variability in the freshwater balance of northern Marguerite Bay, Antarctic Peninsula: results from δ18O. Deep Sea Res. II Top. Stud. Oceanogr. 55, 309–322 (2008).
    Google Scholar 
    65.Slosarczyk, W. Attempts at a quantitative estimate by trawl sampling of distribution of postlarval and juvenile notothenioids (Pisces, Perciformes) in relation to environmental conditions in the Antarctic Peninsula region during SIBEX 1983–84. Mem Natl Inst Polar Res Spec Issue. 40, 299–315 (1986).
    Google Scholar 
    66.Varsamos, S., Nebel, C. & Charmantier, G. Ontogeny of osmoregulation in postembryonic fish: a review. Compar. Biochem. Physiol. A Mol. Integr. Physiol. 141, 401–429 (2005).
    Google Scholar 
    67.Gille, S. T., McKee, D. C. & Martinson, D. G. Temporal changes in the Antarctic circumpolar current: implications for the Antarctic Continental Shelves. Oceanography 29, 96–105 (2016).
    Google Scholar 
    68.Thompson, D. W. J. et al. Signatures of the Antarctic ozone hole in Southern Hemisphere surface climate change. Nat. Geosci. 4, 741–749 (2011).CAS 

    Google Scholar 
    69.Allen, M. et al. Technical summary: global warming of 1.5 °C. An IPCC Special Report on the impacts of global warming of 1.5 °C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty. https://www.ipcc.ch/site/assets/uploads/sites/2/2018/12/SR15_TS_High_Res.pdf (2019).70.Screen, J. A., Bracegirdle, T. J. & Simmonds, I. Polar climate change as manifest in atmospheric circulation. Curr. Clim. Change Rep. 4, 383–395 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    71.Gao, M. et al. Historical fidelity and future change of Amundsen Sea Low under 1.5 °C–4 °C global warming in CMIP6. Atmos. Res. 255, 105533 (2021).
    Google Scholar 
    72.Emslie, S. D. & McDaniel, J. D. Adélie penguin diet and climate change during the middle to late Holocene in northern Marguerite Bay, Antarctic Peninsula. Polar Biol. 25, 222–229 (2002).
    Google Scholar 
    73.Fraser, W. R. & Trivelpiece, W. Z. Factors controlling the distribution of seabirds: winter-summer heterogeneity in the distribution of Adélie penguin populations. In Foundations for Ecological Research West of the Antarctic Peninsula 257–272 (American Geophysical Union, 1996).74.Cimino, M. A., Lynch, H. J., Saba, V. S. & Oliver, M. J. Projected asymmetric response of Adélie penguins to Antarctic climate change. Sci. Rep. 6, 28785 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    75.Ainley, D. G. et al. Post-fledging survival of Adélie penguins at multiple colonies: chicks raised on fish do well. Mar. Ecol. Prog. Ser. 601, 239–251 (2018).
    Google Scholar 
    76.Ruck, K. E., Steinberg, D. K. & Canuel, E. A. Regional differences in quality of krill and fish as prey along the Western Antarctic Peninsula. Mar. Ecol. Prog. Ser. 509, 39–55 (2014).CAS 

    Google Scholar 
    77.Ainley, D. G. et al. Decadal trends in abundance, size and condition of Antarctic toothfish in McMurdo Sound, Antarctica, 1972–2011. Fish Fisheries 14, 343–363 (2013).
    Google Scholar 
    78.Eastman, J. T. Pleuragramma antarcticum (Pisces, Nototheniidae) as food for other fishes in McMurdo Sound, Antarctica. Polar Biol. 4, 155–160 (1985).
    Google Scholar 
    79.Hanchet, S. et al. The Antarctic toothfish (Dissostichus mawsoni): biology, ecology, and life history in the Ross Sea region. Hydrobiologia 761, 397–414 (2015).
    Google Scholar 
    80.Pinkerton, M., Hanchet, S. & Bradford-Grieve, J. Finding the role of Antarctic toothfish in the Ross Sea ecosystem. Water Atmos. 15, 20–21 (2007).
    Google Scholar 
    81.Hanchet, S. M. & Rickard, G. J. A hypothetical life cycle for Antarctic toothfish (Dissostichus mawsoni) in the Ross Sea region. CCAMLR Sci. 15, 35–53 (2008).
    Google Scholar 
    82.Fuiman, L., Davis, R. & Williams, T. Behavior of midwater fishes under the Antarctic ice: observations by a predator. Mar. Biol. 140, 815–822 (2002).
    Google Scholar 
    83.Casaux, R., Baroni, A. & Ramón, A. The diet of the Weddell Seal Leptonychotes weddellii at the Danco Coast, Antarctic Peninsula. Polar Biol. 29, 257–262 (2006).
    Google Scholar 
    84.Ponganis, P. J. & Stockard, T. K. Short note: the Antarctic toothfish: how common a prey for Weddell seals? Antarctic Sci. 19, 441–442 (2007).
    Google Scholar 
    85.Rumolo, P. et al. The diet of Weddell seals (Leptonychotes weddellii) in Terra Nova Bay using stable isotope analysis. Eur. Zool. J. 87, 94–104 (2020).
    Google Scholar 
    86.Hubold, G. & Ekau, W. Feeding patterns of post-larval and juvenile notothenioids in the southern Weddell Sea (Antarctica). Polar Biol. 10, 255–260 (1990).87.Moreno, C., Rueda, T. & Asencio, G. The trophic niche of Pleuragramma antarcticum in the Bransfield Strait, Antarctica: quantitative comparison with other areas of the Southern Ocean. Ser. Cient. INACH 35, 101–117 (1986).88.Gleiber, M. R., Steinberg, D. K. & Schofield, O. M. E. Copepod summer grazing and fecal pellet production along the Western Antarctic Peninsula. J. Plankton Res. 38, 732–750 (2016).CAS 

    Google Scholar 
    89.Garzio, L., Steinberg, D., Erickson, M. & Ducklow, H. Microzooplankton grazing along the Western Antarctic Peninsula. Aquat. Microb. Ecol. 70, 215–232 (2013).
    Google Scholar 
    90.Hobbie, J. E. Scientific accomplishments of the Long Term Ecological Research Program: an introduction. Bioscience 53, 17–20 (2003).
    Google Scholar 
    91.Hughes, B. B. et al. Long-term studies contribute disproportionately to ecology and policy. Bioscience 67, 271–281 (2017).
    Google Scholar 
    92.Hilton, E. J., Watkins-Colwell, G. J. & Huber, S. K. The expanding role of natural history collections. Ichthyol. Herpetol. 109, 379–391 (2021).
    Google Scholar 
    93.Hoey, J. A. et al. Using multiple natural tags provides evidence for extensive larval dispersal across space and through time in summer flounder. Mol. Ecol. 29, 1421–1435 (2020).CAS 
    PubMed 

    Google Scholar 
    94.Houde, E. D. Emerging from Hjort’s shadow. J. Northw. Atl. Fish. Sci 41, 53–70 (2008).
    Google Scholar 
    95.Ducklow, H. W. et al. Marine pelagic ecosystems: the West Antarctic Peninsula. Philos. Trans. R. Soc. B Biol. Sci. 362, 67–94 (2007).
    Google Scholar 
    96.Smith, R. C. et al. The Palmer LTER: a long-term ecological research program at Palmer Station, Antarctica. Oceanography 8, 77–86 (1995).
    Google Scholar 
    97.Kellermann, A. K. Identification key and catalogue of larval Antarctic fishes. Ber. Polarforsch 1–138 (1990).98.Stammerjohn, S. E., Martinson, D. G., Smith, R. C. & Iannuzzi, R. A. Sea ice in the western Antarctic Peninsula region: spatio-temporal variability from ecological and climate change perspectives. Deep Sea Res. II Top. Stud. Oceanogr. 55, 2041–2058 (2008).
    Google Scholar 
    99.Hurrell, J. W. Decadal trends in the North Atlantic oscillation: regional temperatures and precipitation. Science 269, 676–679 (1995).CAS 
    PubMed 

    Google Scholar 
    100.Hosking, J. S. & National Center for Atmospheric Research Staff. (eds) The Climate Data Guide: Amundsen Sea Low indices. https://climatedataguide.ucar.edu/climate-data/amundsen-sea-low-indices (2020).101.O’Brien, R. M. A caution regarding rules of thumb for variance inflation factors. Qual. Quant. 41, 673–690 (2007).
    Google Scholar 
    102.Gareth, J., Daniela, W., Trevor, H. & Robert, T. An Introduction to Statistical Learning: With Applications in R (Spinger, 2013).103.Shono, H. Application of the Tweedie distribution to zero-catch data in CPUE analysis. Fisheries Res. 93, 154–162 (2008).
    Google Scholar 
    104.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).105.Denes, F. V., Silveira, L. F. & Beissinger, S. R. Estimating abundance of unmarked animal populations: accounting for imperfect detection and other sources of zero inflation. Methods Ecol. Evol. 6, 543–556 (2015).
    Google Scholar 
    106.Zuur, A. F. & Ieno, E. N. Beginner´s Guide to Zero-inflated Models with R (Highland Statistics Ltd., 2016).107.Barnett, A. G., Koper, N., Dobson, A. J., Schmiegelow, F. & Manseau, M. Using information criteria to select the correct variance–covariance structure for longitudinal data in ecology. Methods Ecol. Evol. 1, 15–24 (2010).
    Google Scholar 
    108.Clark, I. Statistics or geostatistics? Sampling error or nugget effect? J. Southern African Inst. Mining Metall. 110, 307–312 (2010).
    Google Scholar 
    109.Gschlößl, S. & Czado, C. Modelling count data with overdispersion and spatial effects. Stat. Papers 49, 531–552 (2008).
    Google Scholar 
    110.Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).
    Google Scholar 
    111.Aho, K., Derryberry, D. & Peterson, T. Model selection for ecologists: the worldviews of AIC and BIC. Ecology 95, 631–636 (2014).PubMed 

    Google Scholar 
    112.Lüdecke, D. ggeffects: Tidy data frames of marginal effects from regression models. JOSS 3, 772 (2018).
    Google Scholar 
    113.Francq, B. G., Lin, D. & Hoyer, W. Confidence, prediction, and tolerance in linear mixed models. Stat. Med. 38, 5603–5622 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    114.Spineli, L. M. & Pandis, N. Prediction interval in random-effects meta-analysis. Am. J. Orthod. Dentofacial Orthop. 157, 586–588 (2020).PubMed 

    Google Scholar 
    115.Comiso, J. C. Variability and trends in Antarctic surface temperatures from in situ and satellite infrared measurements. J. Clim. 13, 1674–1696 (2000).
    Google Scholar 
    116.Comiso, J. C. & Nishio, F. Trends in the sea ice cover using enhanced and compatible AMSR-E, SSM/I, and SMMR data. J. Geophys. Res. Oceans https://doi.org/10.1029/2007JC004257 (2008).117.Hersbach, H. et al. ERA5 monthly averaged data on single levels from 1979 to present. https://doi.org/10.24381/CDS.F17050D7 (2019).118.Reynolds, R. W., Rayner, N. A., Smith, T. M., Stokes, D. C. & Wang, W. An improved in situ and satellite SST analysis for climate. J. Clim. 15, 1609–1625 (2002).
    Google Scholar  More

  • in

    Environmental structure impacts microbial composition and secondary metabolism

    1.Martiny JBH, Bohannan BJM, Brown JH, Colwell RK, Fuhrman JA, Green JL, et al. Microbial biogeography: putting microorganisms on the map. Nat Rev Microbiol. 2006;4:102–12.CAS 
    PubMed 

    Google Scholar 
    2.Caswell H, Cohen JE. Disturbance, interspecific interaction and diversity in metapopulations. Biol J Linn Soc. 1991;42:193–218.
    Google Scholar 
    3.Tolker-Nielsen T, Molin S. Spatial organization of microbial biofilm communities. Microb Ecol. 2000;40:75–84.CAS 
    PubMed 

    Google Scholar 
    4.Yanni D, Márquez-Zacarías P, Yunker PJ, Ratcliff WC. Drivers of spatial structure in social microbial communities. Curr Biol. 2019;29:R545–50.CAS 
    PubMed 

    Google Scholar 
    5.Ho A, Angel R, Veraart AJ, Daebeler A, Jia Z, Kim SY, et al. Biotic interactions in microbial communities as modulators of biogeochemical processes: methanotrophy as a model system. Front Microbiol. 2016;7:1–11.
    Google Scholar 
    6.Falkowski PG, Fenchel T, Delong EF. The microbial engines that drive earth’s biogeochemical cycles. Science. 2008;320:1034–9.CAS 
    PubMed 

    Google Scholar 
    7.Overmann J, van Gemerden H. Microbial interactions involving sulfur bacteria: Implications for the ecology and evolution of bacterial communities. FEMS Microbiol Rev. 2000;24:591–9.CAS 
    PubMed 

    Google Scholar 
    8.García-Bayona L, Comstock LE. Bacterial antagonism in host-associated microbial communities. Science. 2018;361:1–11.
    Google Scholar 
    9.Coyte KZ, Schluter J, Foster KR. The ecology of the microbiome: Networks, competition, and stability. Science. 2015;350:663–6.CAS 
    PubMed 

    Google Scholar 
    10.Wang X, Li X, Ling J. Streptococcus gordonii LuxS/autoinducer-2 quorum-sensing system modulates the dual-species biofilm formation with Streptococcus mutans. J Basic Microbiol. 2017;57:605–16.CAS 
    PubMed 

    Google Scholar 
    11.Hotterbeekx A, Kumar-Singh S, Goossens H, Malhotra-Kumar S. In vivo and In vitro interactions between Pseudomonas aeruginosa and Staphylococcus spp. Front Cell Infect Microbiol. 2017;7:1–13.
    Google Scholar 
    12.Dal Co A, van Vliet S, Kiviet DJ, Schlegel S, Ackermann M. Short-range interactions govern the dynamics and functions of microbial communities. Nat Ecol Evol. 2020;4:366–75. https://doi.org/10.1038/s41559-019-1080-2.Article 
    PubMed 

    Google Scholar 
    13.Justice NB, Sczesnak A, Hazen TC, Arkin AP. Environmental selection, dispersal, and organism interactions shape community assembly in high-throughput enrichment culturing. Appl Environ Microbiol. 2017;83:1–16.
    Google Scholar 
    14.Hilker M. New synthesis: parallels between biodiversity and chemodiversity. J Chem Ecol. 2014;40:225–6.CAS 
    PubMed 

    Google Scholar 
    15.Raguso R, Agrawal A, Douglas A, Jander G, Kessler A, Poveda K, et al. The raison d’être of chemical ecology. Ecology. 2015;96:617–30.PubMed 

    Google Scholar 
    16.Tilman D. Competition and biodiversity in spatially structured habitats. Ecology. 1994;75:2–16.
    Google Scholar 
    17.Geyrhofer L, Brenner N. Coexistence and cooperation in structured habitats. BMC Ecol. 2020;20:1–15. https://doi.org/10.1186/s12898-020-00281-y.Article 

    Google Scholar 
    18.Wakano JY, Nowak MA, Hauert C. Spatial dynamics of ecological public goods. Proc Natl Acad Sci USA. 2009;106:7910–4.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    19.Nemergut DR, Schmidt SK, Fukami T, O’Neill SP, Bilinski TM, Stanish LF, et al. Patterns and processes of microbial community assembly. Microbiol Mol Biol Rev. 2013;77:342–56.PubMed 
    PubMed Central 

    Google Scholar 
    20.Lowery NV, Ursell T. Structured environments fundamentally alter dynamics and stability of ecological communities. Proc Natl Acad Sci USA. 2019;116:379–88.CAS 

    Google Scholar 
    21.Lee JZ, Craig Everroad R, Karaoz U, Detweiler AM, Pett-Ridge J, Weber PK, et al. Metagenomics reveals niche partitioning within the phototrophic zone of a microbial mat. PLoS ONE. 2018;13:1–19.
    Google Scholar 
    22.Quinn RA, Comstock W, Zhang T, Morton JT, da Silva R, Tran A, et al. Niche partitioning of a pathogenic microbiome driven by chemical gradients. Sci Adv. 2018;4:1–12.
    Google Scholar 
    23.Fenchel T, Finlay B. Oxygen and the spatial structure of microbial communities. Biol Rev. 2008;83:553–69.PubMed 

    Google Scholar 
    24.Esteban DJ, Hysa B, Bartow-McKenney C. Temporal and spatial distribution of the microbial community of winogradsky columns. PLoS ONE. 2015;10:1–21.
    Google Scholar 
    25.Azam F. Microbial control of oceanic carbon flux: The plot thickens. Science. 1998;280:694–6.CAS 

    Google Scholar 
    26.McNally L, Brown SP. Building the microbiome in health and disease: niche construction and social conflict in bacteria. Philos Trans R Soc B Biol Sci. 2015;370:1–8.
    Google Scholar 
    27.Schreiber F, Ackermann M. Environmental drivers of metabolic heterogeneity in clonal microbial populations. Curr Opin Biotechnol. 2020;62:202–11. https://doi.org/10.1016/j.copbio.2019.11.018.CAS 
    Article 
    PubMed 

    Google Scholar 
    28.Lopez D, Vlamakis H, Kolter R. Biofilms. Cold Spring Harbor Perspectives in Biology. 2010;2:1–11.
    Google Scholar 
    29.Picketts STA, Cadenasso ML. Landscape ecology: spatial heterogeneity in ecological systems. NCASI Techn Bull. 1999;2:420.
    Google Scholar 
    30.Chao L, Levin BR. Structured habitats and the evolution of anticompetitor toxins in bacteria. Proc Natl Acad Sci USA. 1981;78:6324–8.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    31.Rainey PB, Travisano M. Adaptive radiation in a heterogeneous environment. Nature. 1998;394:69–72.CAS 
    PubMed 

    Google Scholar 
    32.Cardinale BJ. Biodiversity improves water quality through niche partitioning. Nature. 2011;472:86–91.CAS 
    PubMed 

    Google Scholar 
    33.Loreau M, Naeem S, Inchausti P, Bengtsson J, Grime JP, Hector A, et al. Ecology: biodiversity and ecosystem functioning: current knowledge and future challenges. Science. 2001;294:804–8.CAS 
    PubMed 

    Google Scholar 
    34.Wellborn GA, Langerhans RB. Ecological opportunity and the adaptive diversification of lineages. Ecol Evol. 2015;5:176–95.PubMed 

    Google Scholar 
    35.Czárán TL, Hoekstra RF. Killer-sensitive coexistence in metapopulations of micro-organisms. Proc R Soc B Biol Sci. 2003;270:1373–8.
    Google Scholar 
    36.West SA, Griffin AS, Gardner A, Diggle SP. Social evolution theory for microorganisms. Nat Rev Microbiol. 2006;4:597–607.CAS 
    PubMed 

    Google Scholar 
    37.Wagner M, Loy A, Nogueira R, Purkhold U, Lee N, Daims H. Microbial community composition and function in wastewater treatment plants. Antonie Van Leeuwenhoek. 2002;81:665–80.CAS 
    PubMed 

    Google Scholar 
    38.Johnson DR, Lee TK, Park J, Fenner K, Helbling DE. The functional and taxonomic richness of wastewater treatment plant microbial communities are associated with each other and with ambient nitrogen and carbon availability. Environ Microbiol. 2015;17:4851–60.CAS 
    PubMed 

    Google Scholar 
    39.Liébana R, Arregui L, Santos A, Murciano A, Marquina D, Serrano S. Unravelling the interactions among microbial populations found in activated sludge during biofilm formation. FEMS Microbiol Ecol. 2016;92:1–13.
    Google Scholar 
    40.Reasoner DJ, Geldreich EE. A new medium for the enumeration and subculture of bacteria from potable water. Appl Environ Microbiol. 1985;49:1–7.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    41.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. 2015;18:1403–14.PubMed 

    Google Scholar 
    42.Junkins EN, Stevenson BS. Using plate-wash PCR and high-throughput sequencing to measure cultivated diversity for natural product discovery efforts. Front Microbiol. 2021;12:1–14.
    Google Scholar 
    43.Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17:10–12.
    Google Scholar 
    44.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.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    45.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 
    46.Yilmaz P, Parfrey LW, Yarza P, Gerken J, Pruesse E, Quast C, et al. The SILVA and “all-species Living Tree Project (LTP)” taxonomic frameworks. Nucleic Acids Res. 2014;42:643–8.
    Google Scholar 
    47.Davis NM, Proctor DM, Holmes SP, Relman DA, Callahan BJ. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome. 2018;6:1–14.
    Google Scholar 
    48.Wright ES. DECIPHER: Harnessing local sequence context to improve protein multiple sequence alignment. BMC Bioinformatics. 2015;16:1–14. https://doi.org/10.1186/s12859-015-0749-z.CAS 
    Article 

    Google Scholar 
    49.Wright ES. Using DECIPHER v2.0 to analyze big biological sequence data in R. R J. 2016;8:352–9.
    Google Scholar 
    50.Schliep KP. phangorn: phylogenetic analysis in R. Bioinformatics. 2011;27:592–3.CAS 
    PubMed 

    Google Scholar 
    51.McMurdie PJ, Holmes S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:1–11.
    Google Scholar 
    52.Willis A, Bunge J. Estimating diversity via frequency ratios. Biometrics. 2015;71:1042–9.PubMed 

    Google Scholar 
    53.Pielou EC. The measurement of diversity in different types of biological collections. J Theor Biol. 1966;13:131–44.
    Google Scholar 
    54.Levene H. Robust tests for equality of variances. In: Olkin I, editor. Contributions to probability and statistics: essays in honor of Harold Hotelling. Stanford University Press, Palo Alto, California, USA; 1960. p. 278–92.55.Fox J, Weisberg S. An R companion to applied regression. 3rd ed. Thousand Oaks, CA: Sage; 2019.56.Lozupone C, Knight R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol. 2005;71:8228–35.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    57.Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: community ecology package. R Package; 2019.58.Martin BD, Witten D, Willis AD. Modeling microbial abundances and dysbiosis with beta-binomial regression. Ann Appl Stat. 2020;14:94–115.PubMed 
    PubMed Central 

    Google Scholar 
    59.Chambers MC, MacLean B, Burke R, Amodei D, Ruderman DL, Neumann S, et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat Biotechnol. 2012;30:918–20.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    60.Pluskal T, Castillo S, Villar-Briones A, Orešič M. MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics. 2010;11:1–11.61.Myers OD, Sumner SJ, Li S, Barne S, Du X. One step forward for reducing false positive and false negative compound identifications from mass spectrometry metabolomics data: new algorithms for constructing extracted ion chromatograms and detecting chromatographic peaks. Anal Chem. 2017;89:8696–703.CAS 
    PubMed 

    Google Scholar 
    62.Wang M, Carver JJ, Phelan VV, Sanchez LM, Garg N;, Peng Y, et al. Sharing and community curation of mass spectrometry data with GNPS. Nat Biotechnol. 2017;34:828–37.
    Google Scholar 
    63.Nothias LF, Petras D, Schmid R, Dührkop K, Rainer J, Sarvepalli A, et al. Feature-based molecular networking in the GNPS analysis environment. Nat Methods. 2020;17:905–8. https://doi.org/10.1038/s41592-020-0933-6.CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    64.Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models. Genome Res. 2003;13:2498–504. http://ci.nii.ac.jp/naid/110001910481/.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    65.R Core Team. R: a language and environment for R Foundation for Statistical Computing. 2018. https://www.r-project.org/.66.Bates D, Mächler M, Bolker BM, Walker SC. Fitting linear mixed-effects models using lme4. J Stat Softw. 2015;67:1–48.
    Google Scholar 
    67.Djoumbou Feunang Y, Eisner R, Knox C, Chepelev L, Hastings J, Owen G, et al. ClassyFire: automated chemical classification with a comprehensive, computable taxonomy. J Cheminform. 2016;8:1–20.
    Google Scholar 
    68.O’Brien J, Wright GD. An ecological perspective of microbial secondary metabolism. Curr Opin Biotechnol. 2011;22:552–8. https://doi.org/10.1016/j.copbio.2011.03.010.CAS 
    Article 
    PubMed 

    Google Scholar 
    69.Thierbach S, Wienhold M, Fetzner S, Hennecke U. Synthesis and biological activity of methylated derivatives of the Pseudomonas metabolites HHQ, HQNO and PQS. Beilstein J Org Chem. 2019;15:187–93.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    70.Morales-Soto N, Dunham SJB, Baig NF, Ellis JF, Madukoma CS, Bohn PW, et al. Spatially dependent alkyl quinolone signaling responses to antibiotics in Pseudomonas aeruginosa swarms. J Biol Chem. 2018;293:9544–52.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    71.Heeb S, Fletcher MP, Chhabra SR, Diggle SP, Williams P, Cámara M. Quinolones: from antibiotics to autoinducers. FEMS Microbiol Rev. 2011;35:247–74.CAS 
    PubMed 

    Google Scholar 
    72.Grollman AP. Inhibitors of protein biosynthesis. II. Mode of action of anisomycin. J Biolog Chem. 1967;242:3226–33. https://doi.org/10.1016/S0021-9258(18)95953-3.CAS 
    Article 

    Google Scholar 
    73.Sobin BA, Tanner FW Jr. Anisomycin, a new anti-protozoan antibiotic. J Am Chem Soc. 1954;76:4053–4053.CAS 

    Google Scholar 
    74.Gross H, Stockwell VO, Henkels MD, Nowak-Thompson B, Loper JE, Gerwick WH. The genomisotopic approach: a systematic method to isolate products of orphan biosynthetic gene clusters. Chem Biol. 2007;14:53–63.CAS 
    PubMed 

    Google Scholar 
    75.Jang JY, Yang SY, Kim YC, Lee CW, Park MS, Kim JC, et al. Identification of orfamide A as an insecticidal metabolite produced by Pseudomonas protegens F6. J Agric Food Chem. 2013;61:6786–91.CAS 
    PubMed 

    Google Scholar 
    76.Ma Z, Geudens N, Kieu NP, Sinnaeve D, Ongena M, Martins JC, et al. Biosynthesis, chemical structure, and structure-activity relationship of orfamide lipopeptides produced by Pseudomonas protegens and related species. Front Microbiol. 2016;7:1–16.
    Google Scholar 
    77.Figueira V, Vaz-Moreira I, Silva M, Manaia CM. Diversity and antibiotic resistance of Aeromonas spp. in drinking and waste water treatment plants. Water Res. 2011;45:5599–611.CAS 
    PubMed 

    Google Scholar 
    78.Skwor T, Stringer S, Haggerty J, Johnson J, Duhr S, Johnson M, et al. Prevalence of potentially pathogenic antibiotic-resistant Aeromonas spp. in treated urban wastewater effluents versus recipient riverine populations: a 3-year comparative study. Appl Environ Microbiol. 2020;86:1–16.
    Google Scholar 
    79.Janda JM, Abbott SL. The genus Aeromonas: taxonomy, pathogenicity, and infection. Clin Microbiol Rev. 2010;23:35–73.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    80.Rema T, Lawrence JR, Dynes JJ, Hitchcock AP, Korber DR. Microscopic and spectroscopic analyses of chlorhexidine tolerance in Delftia acidovorans biofilms. Antimicrob Agents Chemother. 2014;58:5673–86.PubMed 
    PubMed Central 

    Google Scholar 
    81.Assanta MA, Roy D, Lemay MJ, Montpetit D. Attachment of Arcobacter butzleri, a new waterborne pathogen, to water distribution pipe surfaces. J Food Protect. 2002;65:1240–7.
    Google Scholar 
    82.Costerton JW, Stewart PS, Greenberg EP. Bacterial biofilms: a common cause of persistent infections. Science. 1999;284:1318–22.CAS 
    PubMed 

    Google Scholar 
    83.Harrison F, Paul J, Massey RC, Buckling A. Interspecific competition and siderophore-mediated cooperation in Pseudomonas aeruginosa. ISME J. 2008;2:49–55.PubMed 

    Google Scholar 
    84.Inglis RF, Roberts PG, Gardner A, Buckling A. Spite and the scale of competition in Pseudomonas aeruginosa. Am Nat. 2011;178:276–85.PubMed 

    Google Scholar 
    85.van der Meij A, Worsley SF, Hutchings MI, van Wezel GP. Chemical ecology of antibiotic production by Actinomycetes. FEMS Microbiol Rev. 2017;41:392–416.PubMed 

    Google Scholar 
    86.Traxler MF, Kolter R. Natural products in soil microbe interactions and evolution. Nat Prod Rep. 2015;32:956–70.CAS 
    PubMed 

    Google Scholar 
    87.Kinkel LL, Schlatter DC, Xiao K, Baines AD. Sympatric inhibition and niche differentiation suggest alternative coevolutionary trajectories among Streptomycetes. ISME J. 2014;8:249–56. https://doi.org/10.1038/ismej.2013.175. [Internet]Available fromCAS 
    Article 
    PubMed 

    Google Scholar 
    88.Pacala SW, Levin SA. Biologically generated spatial pattern and the coexistence of competing species. In: Tilman D, Kareiva P, editors. Spatial ecology: the role of space in population dynamics and interspecific interactions; Princeton University Press, Princeton, New Jersey, USA; 1997.89.Zhou J, Ning D. Stochastic community assembly: does it matter in microbial ecology? Microbiol Mol Biol Rev. 2017;81:1–32.
    Google Scholar 
    90.Haig SJ, Quince C, Davies RL, Dorea CC, Collinsa G. The relationship between microbial community evenness and function in slow sand filters. mBio. 2015;6:1–12.
    Google Scholar 
    91.Wittebolle L, Marzorati M, Clement L, Balloi A, Daffonchio D, Heylen K, et al. Initial community evenness favours functionality under selective stress. Nature. 2009;458:623–6.CAS 
    PubMed 

    Google Scholar 
    92.Davies J, Ryan KS. Introducing the parvome: bioactive compounds in the microbial world. ACS Chem Biol. 2012;7:252–9.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    93.Bassler BL, Losick R. Bacterially speaking. Cell. 2006;125:237–46.CAS 
    PubMed 

    Google Scholar 
    94.Venturi V. Regulation of quorum sensing in Pseudomonas. FEMS Microbiol Rev. 2006;30:274–91.CAS 
    PubMed 

    Google Scholar 
    95.Granato ET, Meiller-Legrand TA, Foster KR. The evolution and ecology of bacterial warfare. Curr Biol. 2019;29:R521–37. https://doi.org/10.1016/j.cub.2019.04.024.CAS 
    Article 
    PubMed 

    Google Scholar 
    96.Estrela S, Brown SP. Community interactions and spatial structure shape selection on antibiotic resistant lineages. PLoS Comput Biol. 2018;14:1–21.CAS 

    Google Scholar 
    97.Hibbing ME, Fuqua C, Parsek MR, Peterson SB. Bacterial competition: surviving and thriving in the microbial jungle. Nat Rev Microbiol. 2010;8:15–25.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    98.Garcia-Garcera M, Rocha EPC. Community diversity and habitat structure shape the repertoire of extracellular proteins in bacteria. Nat Commun. 2020;11:1–11. https://doi.org/10.1038/s41467-020-14572-x.CAS 
    Article 

    Google Scholar  More

  • in

    Global and regional health and food security under strict conservation scenarios

    1.Butchart, S. H. M. et al. Global biodiversity: indicators of recent declines. Science 328, 1164–1168 (2010).CAS 
    Article 

    Google Scholar 
    2.Buchanan, G. M., Butchart, S. H. M., Chandler, G. & Gregory, R. D. Assessment of national-level progress towards elements of the Aichi Biodiversity Targets. Ecol. Indic. 116, 106497 (2020).Article 

    Google Scholar 
    3.Butchart, S. H. M. et al. in Global Assessment Report of the Intergovernmental Science–Policy Platform on Biodiversity and Ecosystem Services (eds Berkes, F. & Brooks, T. M.) Ch. 3 (IPBES Secretariat, 2019); https://doi.org/10.5281/zenodo.38320534.Maxwell, S. L. et al. Area-based conservation in the twenty-first century. Nature 586, 217–227 (2020).CAS 
    Article 

    Google Scholar 
    5.Locke, H. Nature needs half: a necessary and hopeful new agenda for protected areas. Nat. N. S. W. 58, 7–17 (2014).
    Google Scholar 
    6.Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67, 534–545 (2017).Article 

    Google Scholar 
    7.Dinerstein, E. et al. A global deal for nature: guiding principles, milestones, and targets. Sci. Adv. 5, eaaw2869 (2019).CAS 
    Article 

    Google Scholar 
    8.Mehrabi, Z., Ellis, E. C. & Ramankutty, N. The challenge of feeding the world while conserving half the planet. Nat. Sustain. 1, 409–412 (2018).Article 

    Google Scholar 
    9.Kok, M. T. J. et al. Assessing ambitious nature conservation strategies within a 2 degree warmer and food-secure world. Preprint at bioRxiv https://doi.org/10.1101/2020.08.04.236489 (2020).10.Rosa, I. M. D. et al. Multiscale scenarios for nature futures. Nat. Ecol. Evol. 1, 1416–1419 (2017).Article 

    Google Scholar 
    11.Obermeister, N. Local knowledge, global ambitions: IPBES and the advent of multi-scale models and scenarios. Sustain. Sci. 14, 843–856 (2019).Article 

    Google Scholar 
    12.Pereira, L. M. et al. Developing multiscale and integrative nature–people scenarios using the Nature Futures Framework. People Nat. 2, 1172–1195 (2020).Article 

    Google Scholar 
    13.Rabin, S. S. et al. Impacts of future agricultural change on ecosystem service indicators. Earth Syst. Dynam. 11, 357–376 (2019).Article 

    Google Scholar 
    14.Springmann, M. et al. Global and regional health effects of future food production under climate change: a modelling study. Lancet 387, 1937–1946 (2016).Article 

    Google Scholar 
    15.Springmann, M. et al. Health and nutritional aspects of sustainable diet strategies and their association with environmental impacts: a global modelling analysis with country-level detail. Lancet Planet. Health 2, e451–e461 (2018).Article 

    Google Scholar 
    16.Dinerstein, E. et al. A “Global Safety Net” to reverse biodiversity loss and stabilize Earth’s climate. Sci. Adv. 6, eabb2824 (2020).Article 

    Google Scholar 
    17.Locke, H. et al. Three global conditions for biodiversity conservation and sustainable use: an implementation framework. Natl Sci. Rev. https://doi.org/10.1093/nsr/nwz136 (2019).18.Waldron, A. et al. Protecting 30% of the Planet for Nature: Costs, Benefits and Economic Implications (Campaign for Nature, 2020).19.Strassburg, B. B. N. et al. Global priority areas for ecosystem restoration. Nature 586, 724–729 (2020).CAS 
    Article 

    Google Scholar 
    20.O’Neill, B. C. et al. The roads ahead: narratives for Shared Socioeconomic Pathways describing world futures in the 21st century. Glob. Environ. Change 42, 169–180 (2015).21.Riahi, K. et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob. Environ. Change 42, 153–168 (2017).22.Tauli-Corpuz, V., Alcorn, J., Molnar, A., Healy, C. & Barrow, E. Cornered by PAs: adopting rights-based approaches to enable cost-effective conservation and climate action. World Dev. 130, 104923 (2020).Article 

    Google Scholar 
    23.Kashwan, P. V., Duffy, R., Massé, F., Asiyanbi, A. P. & Marijnen, E. From racialized neocolonial global conservation to an inclusive and regenerative conservation. Environ. Sci. Policy Sustain. Dev. 63, 4–19 (2021).Article 

    Google Scholar 
    24.The State of Food Security and Nutrition in the World 2017: Building Resilience for Peace and Food Security (FAO, 2017).25.Schleicher, J. et al. Protecting half of the planet could directly affect over one billion people. Nat. Sustain. 2, 1094–1096 (2019).Article 

    Google Scholar 
    26.Allan, J. R. et al. The minimum land area requiring conservation attention to safeguard biodiversity. Preprint at bioRxiv https://doi.org/10.1101/839977 (2021).27.Brockington, D. & Wilkie, D. Protected areas and poverty. Phil. Trans. R. Soc. B 370, 20140271 (2015).28.Protected Planet Report 2020 (UNEP–WCMC and IUCN, 2021).29.Naidoo, R. et al. Evaluating the impacts of protected areas on human well-being across the developing world. Sci. Adv. 5, eaav3006 (2019).CAS 
    Article 

    Google Scholar 
    30.Dutta, A., Allan, J., Shimray, G., General, S. & Pact, A. I. P. RE: “A ‘Global Safety Net’ to reverse biodiversity loss and stabilize Earth’s climate”. Sci. Adv. 6, eabb2824 (2020).Article 

    Google Scholar 
    31.Simmons, B. A., Nolte, C. & McGowan, J. Tough questions for the “30 × 30” conservation agenda. Front. Ecol. Environ. 19, 322–323 (2021).Article 

    Google Scholar 
    32.Jung, M. et al. Areas of global importance for conserving terrestrial biodiversity, carbon and water. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-021-01528-7 (2021).33.The IUCN Red List of Threatened Species Version 2019.2 (IUCN, 2019).34.The World Database of Key Biodiversity Areas (KBA Partnership, 2019); www.keybiodiversityareas.org35.Mogg, S., Fastre, C. & Visconti, P. Targeted expansion of protected areas to maximise the persistence of terrestrial mammals. Preprint at bioRxiv https://doi.org/10.1101/608992 (2019).36.Gurobi Optimizer Reference Manual (Gurobi Optimization, 2019).37.Hanson, J. O. et al. prioritizr: Systematic Conservation Prioritization in R. R package version 5.0.3 https://CRAN.R-project.org/package=prioritizr (2020).38.Hurtt, G., Chini, L., Frolking, S. & Sahajpal, R. Land-Use Harmonization (LUH2) (Global Ecology Laboratory, Univ. Maryland, 2017).39.Protected Planet: The World Database on Protected Areas (WDPA) (UNEP-WCMC and IUCN, accessed April 2019); www.protectedplanet.net40.Dellink, R., Chateau, J., Lanzi, E. & Magné, B. Long-term economic growth projections in the Shared Socioeconomic Pathways. Glob. Environ. Change 42, 200–214 (2017).41.Jones, B. & O’Neill, B. C. Spatially explicit global population scenarios consistent with the Shared Socioeconomic Pathways. Environ. Res. Lett. 11, 84003 (2016).42.van Vuuren, D. P. et al. The representative concentration pathways: an overview. Climatic Change 109, 5–31 (2011).Article 

    Google Scholar 
    43.Engström, K. et al. Assessing uncertainties in global cropland futures using a conditional probabilistic modelling framework. Earth Syst. Dynam. 7, 893–915 (2016).44.Alexander, P. et al. Drivers for global agricultural land use change: the nexus of diet, population, yield and bioenergy. Glob. Environ. Change 35, 138–147 (2015).Article 

    Google Scholar 
    45.Popp, A. et al. Land-use transition for bioenergy and climate stabilization: model comparison of drivers, impacts and interactions with other land use based mitigation options. Climatic Change 123, 495–509 (2014).Article 

    Google Scholar 
    46.GBD Results Tool (IHME, 2020); http://ghdx.healthdata.org/gbd-results-tool47.KC, S. & Lutz, W. The human core of the Shared Socioeconomic Pathways: population scenarios by age, sex and level of education for all countries to 2100. Glob. Environ. Change 42, 181–192 (2017). More

  • in

    Low tropical diversity during the adaptive radiation of early land plants

    1.Gaston, K. J. Global patterns of biodiversity. Nature 405, 220–227 (2000).CAS 
    Article 

    Google Scholar 
    2.Friis, E. M., Crane, P. R. & Pedersen, K. R. Early Flowers and Angiosperm Evolution (Cambridge Univ. Press, 2011).3.Blomenkemper, P. et al. A hidden cradle of plant evolution in Permian tropical lowlands. Science 362, 1414–1416 (2018).CAS 
    Article 

    Google Scholar 
    4.Kenrick, P. & Crane, P. R. The Origin and Early Diversification of Land Plants: A Cladistic Study (Smithsonian Institution Scholarly Press, 1997).5.Puttick, M. N. et al. The interrelationships of land plants and the nature of the nature of the ancestral embryophyte. Curr. Biol. 28, 733–745 (2018).CAS 
    Article 

    Google Scholar 
    6.Morris, J. L. et al. The timescale of early land plant evolution. Proc. Natl Acad. Sci. USA 115, 2274–2283 (2018).Article 

    Google Scholar 
    7.Wellman, C. H., Steemans, P. & Vecoli, M. in Early Palaeozoic Biogeography and Palaeogeography (eds Harper, D. & Servais, T.) Ch. 29 (Geological Society of London, 2014).8.Edwards, D. et al. Piecing together the eophytes—a new group of ancient plants containing cryptospores. New Phytol. 233, 1440–1455 (2021).Article 

    Google Scholar 
    9.Gray, J. The microfossil record of early land plants; advances in understanding of early terrestrialization, 1970–1984. Philos. Trans. R. Soc. Lond. B 309, 167–195 (1985).Article 

    Google Scholar 
    10.Wellman, C. H. Cryptospores from the type area for the Caradoc Series (Ordovician) in southern Britain. Palaeontology 55, 103–136 (1996).
    Google Scholar 
    11.Torsvik, T. H. & Cocks, L. R. M. Earth History and Palaeogeography (Cambridge Univ. Press, 2017).12.Harland, W. B. The Geology of Svalbard (Geological Society of London, 1997).13.Davies, N. S., Berry, C. M., Marshall, J. E. A., Wellman, C. H. & Lindemann, F.-J. The Devonian landscape factory: plant–sediment interactions in the Old Red Sandstone of Svalbard and the rise of vegetation as a biogeomorphic agent. J. Geol. Soc. Lond. https://doi.org/10.1144/jgs2020-225 (2021).14.Blieck, A., Goujet, D. & Janvier, P. The vertebrate stratigraphy of the Lower Devonian (Red Bay Group and Wood Bay Formation) of Spitsbergen. Mod. Geol. 11, 197–217 (1987).
    Google Scholar 
    15.Blom, H. & Goujet, D. Thelodont scales from the Lower Devonian Red Bay Group, Spitsbergen. Palaeontology 45, 795–820 (2002).Article 

    Google Scholar 
    16.Pernègre, V. N. & Blieck, A. A revised heterostrachan-cased ichthyostratigraphy of the Wood Bay Formation (Lower Devonian, Spitsbergen), and correlation with Russian Arctic archipelagos. Geodiversitas 38, 5–20 (2016).Article 

    Google Scholar 
    17.Wellman, C. H. & Richardson, J. B. Sporomorph assemblages from the ‘Lower Old Red Sandstone’ of Lorne Scotland. Spec. Pap. Palaeontol. 55, 41–101.18.Richardson J. B. Taxonomy and classification of some new Early Devonian cryptospores from England. Spec. Pap. Palaeontol. 55, 7–40 (1996).19.Steemans, P. Etude palynostratgraphique du Devonian Inferieur dans l’Ouest de l’Europe. Mém. Soc. Géol. Minér. Bretagne 27, 1–453 (1989).
    Google Scholar 
    20.Rodriguez, R. M. Palinologia de las Formaciones del Silurico Superior-Devonico Inferior de la Cordillera Cantabrica, Noroeste de España (Institución Fray Bernardino de Sahagún, de la Excelentísima Diputación provincial de León y del Servicio de Publicaciones de la Universidad de León, 1983).21.Richardson, J. B., Rodriguez, R. M. & Sutherland, S. J. E. Palynological zonation of Mid-Palaeozoic sequences from the Cantabrian Mountains, NW Spain: implications for inter-regional and interfacies correlation of the Ludfor/Pridoli and Silurian/Devonian boundaries, and plant dispersal patterns. Bull. Nat. Hist. Mus. Lond. 57, 115–162 (2001).
    Google Scholar 
    22.Rubinstein, C. & Steemans, P. Miospore assemblages from the Silurian–Devonian boundary, in borehole A1–61, Ghadames Basin, Libya. Rev. Palaeobot. Palynol. 118, 397–412 (2002).Article 

    Google Scholar 
    23.Spina, A. & Vecoli, M. Palynostratigraphy and vegetational change in the Siluro-Devonian of the Ghadamis basin, North Africa. Palaeogeog. Palaeoclimatol. Palaeoecol. 282, 1–18 (2009).Article 

    Google Scholar 
    24.Hao, S. G. & Gensel, P. G. in Plants Invade the Land (eds Gensel, P. G. & Edwards, D.) 103–119 (Columbia Univ. Press, 2001).25.Wellman, C. H. et al. Spore assemblages from the Lower Devonian Xujiachong Formation from Qujing, Yunnan, China. Palaeontology 55, 583–611 (2012).Article 

    Google Scholar 
    26.Hao, S. & Xue, J. The Early Devonian Posongchong Flora of Yunnan (Science Press, 2013).27.Edwards, D., & Li, C.-S. Further insights into the Lower Devonian terrestrial vegetation of Sichuan Province, China. Rev. Palaeobot. Palynol. 253, 37–48 (2018).Article 

    Google Scholar 
    28.Gao, L. Early Devonian spore and acritarchs from the Guijiatum Formation of Qujing, China. Bull. Inst. Geol. Chin. Acad. Sci. 9, 125–136 (1984).
    Google Scholar 
    29.Tian, J. et al. Late Silurian to early Devonian palynomorphs from Qujing, Yunnan, southwest China. Acta Geol. Sin. 85, 559–568 (2011).Article 

    Google Scholar 
    30.Høeg, O. A. The Downtonian and Dittonian flora of Spitsbergen. Skr. Svalbard Ishavet 83, 1–229 (1942).
    Google Scholar 
    31.Morris, J. L., Edwards, D. & Richardson, J. B. in Transformative Paleobotany (eds Krings, M. et al.) 49–67 (Academic Press, 2018).32.McSweeney, F. R., Shimeta, J. & Buckeridge, J. St. J. S. Two new genera of early Tracheophyta (Zosterophyllaceae) from the upper Silurian–Lower Devonian of Victoria, Australia. Alcheringa https://doi.org/10.1080/03115518.2020.1744725 (2020).33.Xue, J. H. et al. Silurian–Devonian terrestrial revolution in South China: taxonomy, diversity, and character evolution of vascular plants in a paleogeographically isolated low-latitude region. Earth Sci. Rev. 180, 92–125 (2018).Article 

    Google Scholar 
    34.Hao, S. G. et al. Zosterophyllum Penhallow around the Silurian–Devonian boundary of northeastern Yunnan, China. Int. J. Plant Sci. 168, 477–489 (2007).Article 

    Google Scholar 
    35.Hao, S. G. et al. Earliest rooting system and root: shoot ratio from a new Zosterophyllum plant. New Phytol. 185, 217–225 (2009).Article 

    Google Scholar 
    36.Xue, J.-Z. Two zosterophyll plants from the Lower Devonian (Lochkovian) Xitun Formation of northeastern Yunnan, China. Acta Geol. Sin. 83, 504–512 (2009).Article 

    Google Scholar 
    37.Xue, J.-Z. Lochkovian plants from the Xitun Formation of Yunnan, China and their palaeophytogeographical significance. Geol. Mag. 149, 333–344 (2012).Article 

    Google Scholar 
    38.Sun, Y. et al. Lethally high temperatures during the early Triassic greenhouse. Science 6105, 366–370 (2012).Article 

    Google Scholar 
    39.Meng, X. Y. & Gai, Z. K. Falxcornus, a new genus of Tridensaspidae (Galeaspida, stem-Gnathostomata) from the Lower Devonian in Qujing, Yunnan, China. Hist. Biol. https://doi.org/10.1080/08912963.2021.1952198 (2021).40.Traverse, A. Paleopalynology 2nd edn (Springer, 2007). More

  • in

    Vertical stratification of insect abundance and species richness in an Amazonian tropical forest

    1.Nakamura, A. et al. Forests and their canopies: Achievements and horizons in canopy science. Trends Ecol. Evol. 32, 438–451 (2017).PubMed 

    Google Scholar 
    2.Scheffers, B. R. et al. Microhabitats reduce animal’s exposure to climate extremes. Glob. Change Biol. 20, 495–503 (2014).ADS 

    Google Scholar 
    3.Lefsky, M. A. et al. Estimates of forest canopy height and aboveground biomass using ICESat. Geophys. Res. Lett. 32, L22S02 (2005).
    Google Scholar 
    4.Ellwood, M. D. F. & Foster, W. A. Doubling the estimate of invertebrate biomass in a rainforest canopy. Nature 429, 549–551 (2004).ADS 
    CAS 
    PubMed 

    Google Scholar 
    5.Dial, R. et al. Arthropod abundance, canopy structure, and microclimate in a Bornean lowland tropical rain forest. Biotropica 38, 643–652 (2006).
    Google Scholar 
    6.Valencia, R. et al. High tree alpha-diversity in Amazonian Ecuador. Biodivers. Conserv. 3, 21–28 (1994).
    Google Scholar 
    7.Stone, M. J. et al. Edge effects and beta diversity in ground and canopy beetle communities of fragmented subtropical forest. PLoS ONE 13, e0193369 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    8.Nadkarni, N. M. Diversity of species and interactions in the upper tree canopy of forest ecosystems. Am. Zool. 34, 70–78 (1994).
    Google Scholar 
    9.Stanton, D. E. et al. Rapid nitrogen fixation by canopy microbiome in tropical forest determined by both phosphorus and molybdenum. Ecology 100(9), e02795 (2019).PubMed 

    Google Scholar 
    10.Basset, Y. et al. (eds) Arthropods of Tropical Forests. Spatio-Temporal Dynamics and Resource Use in the Canopy (Cambridge University Press, 2003).
    Google Scholar 
    11.Schowalter, T. D. et al. Post-hurricane successional dynamics in abundance and diversity of canopy arthropods in a tropical rainforest. Environ. Entomol. 46, 11–20 (2017).CAS 
    PubMed 

    Google Scholar 
    12.Silva, R. R. & Brandão, C. R. F. Morphological patterns and community organization in leaf-litter ant assemblages. Ecol. Monogr. 80, 107–124 (2010).
    Google Scholar 
    13.McCaig, T., Sam, L., Nakamura, L. & Stork, N. E. Is insect vertical distribution in rainforests better explained by distance from the canopy top or distance from the ground?. Biodivers. Conserv. 29, 1081–1103 (2020).
    Google Scholar 
    14.Floren, A. & Linsenmair, K. E. The influence of anthropogenic disturbances on the structure of arboreal arthropod communities. Plant Ecol. 153, 153–167 (2001).
    Google Scholar 
    15.Adis, J. et al. Canopy fogging of an overstory tree—Recommendations for standardization. Ecotropica 4, 93–97 (1998).
    Google Scholar 
    16.Bar-Ness, Y. D. et al. Sampling forest canopy arthropod biodiversity with three novel minimal-cost trap designs. Aust. J. Entomol. 51, 12–21. https://doi.org/10.1111/j.1440-6055.2011.00836.x (2012).Article 

    Google Scholar 
    17.Erwin, T. L. Canopy arthropod biodiversity: A chronology of sampling techniques and results. Rev. Peru. Entomol. 2, 71–77 (1990).
    Google Scholar 
    18.Floren, A. Sampling arthropods from the canopy by insecticidal knockdown. In Manual on Field Recording Techniques and Protocols for All Taxa Biodiversity Inventories, Part 1 Vol. 8 (eds Eymann, J., Degref, J., Häuser, C. et al.) 158–172 (ABC Taxa, 2010).
    Google Scholar 
    19.Leather, S. R. (ed.) Insect Sampling in Forest Ecosystems (Blackwell Science, 2005).
    Google Scholar 
    20.Lowman, M., Moffett, M. & Rinker, H. B. A new technique for taxonomic and ecological sampling in rain forest canopies. Selbyana 14, 75–79 (1993).
    Google Scholar 
    21.Lowman, M. D., Kitching, R. L. & Carruthers, G. Arthropod sampling in Australian subtropical rain forest: How accurate are some of the more common techniques?. Selbyana 17, 36–42 (1996).
    Google Scholar 
    22.Lowman, M. D., Schowalter, T. D. & Franklin, J. F. Methods in Forest Canopy Research (University of California Press, 2012).
    Google Scholar 
    23.Majer, J. D. & Recher, H. F. Invertebrate communities on Western Australian eucalypts—A comparison of branch clipping and chemical knockdown procedures. Aust. J. Ecol. 13, 269–278. https://doi.org/10.1111/j.1442-9993.1988.tb00974.x (1988).Article 

    Google Scholar 
    24.Ozanne, C. M. P. Techniques and methods for sampling canopy insects. In Insect Sampling in forest ecosystems (ed. Leather, S. R.) 146–165 (Blackwell, 2005).
    Google Scholar 
    25.Paarmann, W. & Stork, N. E. Canopy fogging, a method of collecting living insects for investigation of life history strategies. J. Nat. Hist. 21, 563–566. https://doi.org/10.1080/00222938700770341 (1987).Article 

    Google Scholar 
    26.Parker, G. G., Smith, A. P. & Hogan, K. P. Access to the upper forest canopy with a large tower crane. Bioscience 42, 664–670. https://doi.org/10.2307/1312172 (1992).Article 

    Google Scholar 
    27.Skvarla, M. J., Larson, J. L., Fisher, J. R. & Dowling, A. P. G. A review of terrestrial and canopy malaise traps. Ann. Entomol. Soc. Am. 114(1), 27–47. https://doi.org/10.1093/aesa/saaa044 (2021).Article 

    Google Scholar 
    28.Stork, N. E. Australian tropical forest canopy crane: New tools for new frontiers. Aust. Ecol. 32, 4–9. https://doi.org/10.1111/j.1442-9993.2007.01740.x (2007).Article 

    Google Scholar 
    29.Basset, Y. et al. IBISCA-Panama, a large-scale study of arthropod beta-diversity and vertical stratification in a lowland rainforest: Rationale, study sites and field protocols. Bull. Inst. R. Sci. Nat. Belg. Entomol. 77, 39–69 (2007).
    Google Scholar 
    30.Basset, Y., Cizek, L. & Cuénoud, P. Arthropod diversity in a tropical forest. Science 338, 1481–1484. https://doi.org/10.1126/science.1226727 (2012).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    31.Kitching, R. L. et al. The biodiversity of arthropods from Australian rainforest canopies: General introduction, methods, sites and ordinal results. Aust. J. Ecol. 18, 181–191. https://doi.org/10.1111/j.1442-9993.1993.tb00442.x (1993).Article 

    Google Scholar 
    32.Lindo, Z. & Winchester, N. N. Oribatid mite communities and foliar litter decomposition in canopy suspended soils and forest floor habitats of western red cedar forests, Vancouver Island, Canada. Soil Biol. Biochem. 39, 2957–2966. https://doi.org/10.1016/j.soilbio.2007.06.009 (2007).CAS 
    Article 

    Google Scholar 
    33.Schowalter, T. D. Canopy arthropod communities in relation to forest age and alternative harvest practices in western Oregon. For. Ecol. Manage 78, 115–125 (1995).
    Google Scholar 
    34.Southwood, T. R. E., Moran, V. C. & Kennedy, C. E. J. The assessment of arboreal insect fauna: Comparisons of knockdown sampling and faunal lists. Ecol. Entomol. 7, 331–340. https://doi.org/10.1111/j.1365-2311.1982.tb00674.x (1982).Article 

    Google Scholar 
    35.Stork, N. E. Guild structure of arthropods from Bornean rain forest trees. Ecol. Entomol. 12, 69–80. https://doi.org/10.1111/j.1365-2311.1987.tb00986.x (1987).Article 

    Google Scholar 
    36.Stork, N. E. et al. (eds) Canopy Arthropods (Chapman & Hall, 1997).
    Google Scholar 
    37.DeVries, P. J. Stratification of fruit-feeding nymphalid butterflies in a Costa Rican rain forest. J. Res. Lepid. 26, 98–108 (1988).ADS 

    Google Scholar 
    38.Hill, C. J., Gillison, A. N. & Jones, R. E. The spatial distribution of rain forest butterflies at three sites in North Queensland, Australia. J. Trop. Ecol. 8, 37–46 (1992).
    Google Scholar 
    39.Medina, M. C., Robbins, R. K. & Lamas, G. Vertical stratification of flight by Ithomiinae butterflies (Lepidoptera: Nymphalidae) at Pakitza, Manu National Park, Peru. In Manu—The Biodiversity of Southeastern Peru (eds Wilson, D. E. & Sandoval, A.) 211–216 (Smithsonian Institution, 1996).
    Google Scholar 
    40.DeVries, P. J., Murray, D. & Lande, R. Species diversity in vertical, horizontal, and temporal dimensions of a fruitfeeding butterfly community in an Ecuadorian rainforest. Biol. J. Linn. Soc. 62, 343–364. https://doi.org/10.1111/j.1095-8312.1997.tb01630.x (1997).Article 

    Google Scholar 
    41.DeVries, P. J., Murray, D. & Lande, R. Species diversity in vertical, horizontal, and temporal dimensions of a fruit-feeding butterfly community in an Ecuadorian rain forest. Biol. J. Linn. Soc. 62, 343–364 (1997).
    Google Scholar 
    42.Beccaloni, G. W. Vertical stratification of ithomiine butterfly (Nymphalidae: Ithomiinae) mimicry complexes: The relationship between adult flight height and larval host-plant height. Biol. J. Linn. Soc. 62, 313–341 (1997).
    Google Scholar 
    43.Schulze, C. H., Linsenmair, K. E. & Fiedler, K. Understorey versus canopy: Patterns of vertical stratification and diversity among Lepidoptera in a Bornean Rain Forest. Plant Ecol. 153, 133–152. https://doi.org/10.1023/A:1017589711553 (2001).Article 

    Google Scholar 
    44.Fordyce, J. A. & DeVries, P. J. A tale of two communities: Eotropical butterfly assemblages show higher beta diversity in the canopy compared to the understory. Oecologia 181, 235–243. https://doi.org/10.1007/s00442-016-3562-0 (2016).ADS 
    Article 
    PubMed 

    Google Scholar 
    45.Santos, J. P., Iserhard, C. A., Carreira, J. Y. O. & Freitas, A. V. L. Monitoring fruit-feeding butterfly assemblages in two vertical strata in seasonal Atlantic Forest: Temporal species turnover is lower in the canopy. J. Trop. Ecol. 33(5), 345–355 (2017).
    Google Scholar 
    46.Lourido, G. M., Motta, C. S., Graça, M. B. & Rafael, J. A. Diversity patterns of hawkmoths (Lepidoptera: Sphingidae) in the canopy of an ombrophilous forest in Central Amazon, Brazil. Acta Amazon. 48, 117–125 (2018).
    Google Scholar 
    47.Araujo, P. F., Freitas, A. V. L., Gonçalves, G. A. S. & Ribeiro, D. B. Vertical stratification on a small scale: The distribution of fruit-feeding butterflies in a semi-deciduous Atlantic forest in Brazil. Stud. Neotrop. Fauna Environ. 56, 10–39 (2021).
    Google Scholar 
    48.Charles, E. & Basset, Y. Vertical stratification of leaf-beetle assemblages (Coleoptera: Chrysomelidae) in two forest types in Panama. J. Trop. Ecol. 21, 329–336. https://doi.org/10.1017/S0266467405002300 (2005).Article 

    Google Scholar 
    49.Grimbacher, P. S. & Stork, N. E. Vertical stratification of feeding guilds and body size in beetle assemblages from an Australian tropical rainforest. Aust. Ecol. 32, 77–85. https://doi.org/10.1111/j.1442-9993.2007.01735.x (2007).Article 

    Google Scholar 
    50.Floren, A. & Schmidl, J. (eds) Canopy Arthropod Research in Europe: Basic and Applied Studies from the High Frontier (Bioform Entomology & Equipment, 2008).
    Google Scholar 
    51.Stork, N. E. et al. Vertical stratification of beetles in tropical rainforests as sampled by light traps in North Queensland, Australia. Austral Ecol. 41(2), 168–178 (2015).
    Google Scholar 
    52.Tregidgo, D. J., Qie, L., Barlow, J., Sodhi, N. S. & Lee-Hong, L. S. Vertical stratification responses of an arboreal dung beetle species to tropical forest fragmentation in Malaysia. Biotropica 42, 521–552 (2010).
    Google Scholar 
    53.Davis, A. J., Sutton, S. L. & Brendell, M. J. D. Vertical distribution of beetles in a tropical rainforest in Sulawesi: The role of the canopy in contributing to Biodiversity. Sepilok Bull. 13 & 14, 59–83 (2011).
    Google Scholar 
    54.Heatwole, H. Changes in ant assemblages across an arctic treeline. Rev d’Entomol du Quebec 34, 10–22 (1989).
    Google Scholar 
    55.Roubik, D. W. Tropical pollinators in the canopy and understory: Field data and theory for stratum “preferences”. J. Ins. Behav. 6, 659–673. https://doi.org/10.1007/BF01201668 (1993).Article 

    Google Scholar 
    56.Longino, J. T. & Colwell, R. K. Biodiversity assessment using structured inventory: Capturing the ant fauna of a tropical rain forest. Ecol. Appl. 7, 1263–1277. https://doi.org/10.1890/1051-0761(1997)007[1263:BAUSIC]2.0.CO;2 (1997).Article 

    Google Scholar 
    57.Vance, A. C. C., Smith, S. M., Malcolm, J. R., Huber, J. & Bellocq, M. I. Differences between forest type and vertical strata in the diversity and composition of hymenopteran families and mymarid genera in Northeastern Temperate Forests. Environ. Entomol. 36, 1073–1083. https://doi.org/10.1603/0046-225X(2007)36[1073:DBFTAV]2.0.CO;2 (2007).CAS 
    Article 
    PubMed 

    Google Scholar 
    58.Hernández-Flores, J. et al. Effect of forest disturbance on ant (Hymenoptera: Formicidae) diversity in a Mexican tropical dry forest canopy. Insect Conserv. Diver. 14(3), 393–402. https://doi.org/10.1111/icad.12466 (2020).Article 

    Google Scholar 
    59.Roberts, H. R. Arboreal Orthoptera in the rain forest of Costa Rica collected with insecticide: A report on the grasshoppers (Acrididae) including new species. Proc. Acad. Nat. Sci. Phila. 125, 46–66 (1973).
    Google Scholar 
    60.Rodgers, D. J. & Kitching, R. L. Vertical stratification of rainforest collembolan (Collembola: Insecta) assemblages: Description of ecological patterns and hypotheses concerning their generation. Ecography 21, 392–400. https://doi.org/10.1111/j.1600-0587.1998.tb00404.x (1998).Article 

    Google Scholar 
    61.Krab, E. J., Oorsprong, H., Berg, M. P. & Cornelissen, J. H. C. Turning northern peatlands upside down: Disentangling microclimate and substrate quality effects on vertical distribution of Collembola. Funct. Ecol. 24, 1362–1369. https://doi.org/10.1111/j.1365-2435.2010.01754.x (2010).Article 

    Google Scholar 
    62.Coots, C., Lambdin, P., Grant, J., Rhea, R. & Mockford, E. Vertical stratification and co-occurrence patterns of the psocoptera community associated with Eastern Hemlock, Tsuga canadensis (L.) Carrière, in the Southern Appalachians. Forests 3, 127–136. https://doi.org/10.3390/f3010127 (2012).Article 

    Google Scholar 
    63.Wardhaugh, C. W. et al. Vertical stratification in the spatial distribution of the beech scale insect (Ultracoelostoma assimile) in Nothofagus tree canopies in New Zealand. Ecol. Entomol. 31, 185–195 (2006).
    Google Scholar 
    64.Brown, B. V. et al. Comprehensive inventory of true flies (Diptera) at a tropical site. Commun. Biol. 1, 1–8 (2018).ADS 

    Google Scholar 
    65.Borkent, A. et al. Remarkable fly (Diptera) diversity in a patch of Costa Rican cloud forest: Why inventory is a vital science. Zootaxa 4402, 53–90 (2018).PubMed 

    Google Scholar 
    66.Hebert, P. D. N. et al. Counting animal species with DNA barcodes: Canadian insects. Philos. Trans. R. Soc. Lond. Ser. B. 371, 20150333 (2016).
    Google Scholar 
    67.Basset, Y. et al. Arthropod distribution in a tropical rainforest: Tackling a four dimensional puzzle. PLoS ONE 10, e0144110 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    68.MacArthur, R. H. Population ecology of some warblers of northeastern coniferous forests. Ecology 39, 599–619 (1958).
    Google Scholar 
    69.Higuchi, N. et al. Governos locais amazônicos e as questões climáticas globais 103 (INPA/edição dos autores, 2009).
    Google Scholar 
    70.Brown, B. V. Malaise trap catches and the crisis in Neotropical dipterology. Am. Entomol. 51, 180–183 (2005).
    Google Scholar 
    71.Gressitt, J. L. & Gressitt, M. K. An improved Malaise trap. Pacific Insects 4, 87–90 (1962).
    Google Scholar 
    72.van Achterberg, K. Can Townes type Malaise traps be improved? Some recent developments. Entomologische Berichten 69, 129–135 (2009).
    Google Scholar 
    73.R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (Accessed 20 October 2021); https://www.R-project.org/.
    74.Konietschke, F. (2011). nparcomp: nparcomp-package. R package version 1.0-1. (Accessed 20 October 2021); http://CRAN.R-project.org/package=nparcomp75.Alboukadel Kassambara (2020). ggpubr: ‘ggplot2’ Based Publication Ready Plots. R package version 0.3.0. (Accessed 20 October 2021); https://CRAN.R-project.org/package=ggpubr76.Watson, J. E. M. et al. The exceptional value of intact forest ecosystems. Nat. Ecol. Evol. 2, 599–610 (2018).PubMed 

    Google Scholar 
    77.Gibson, L. et al. Primary forests are irreplaceable for sustaining tropical biodiversity. Nature 478, 378–381 (2011).ADS 
    CAS 
    PubMed 

    Google Scholar 
    78.Qin, Y. et al. Improved estimates of forest cover and loss in the Brazilian Amazon in 2000–2017. Nat. Sustain. 2, 764–772 (2019).
    Google Scholar 
    79.Gardner, T. A. et al. Predicting the uncertain future of tropical forest species in a data vacuum. Biotropica 39, 25–30 (2007).
    Google Scholar  More

  • in

    Cultivation and biogeochemical analyses reveal insights into methanogenesis in deep subseafloor sediment at a biogenic gas hydrate site

    1.Macdonald IR, Guinasso NL, Sassen R, Brooks JM, Lee L, Scott KT. Gas hydrate that breaches the sea-floor on the continental-slope of the Gulf-of-Mexico. Geology. 1994;22:699–702.CAS 

    Google Scholar 
    2.Kvenvolden KA. A review of the geochemistry of methane in natural gas hydrate. Org Geochem. 1995;23:997–1008.CAS 

    Google Scholar 
    3.Milkov AV. Molecular and stable isotope compositions of natural gas hydrates: a revised global dataset and basic interpretations in the context of geological settings. Org Geochem. 2005;36:681–702.CAS 

    Google Scholar 
    4.Cragg BA, Parkes RJ, Fry JC, Weightman AJ, Rochelle PA, Maxwell JR. Bacterial populations and processes in sediments containing gas hydrates (ODP Leg 146: Cascadia Margin). Earth Planet Sc Lett. 1996;139:497–507.CAS 

    Google Scholar 
    5.Yoshioka H, Maruyama A, Nakamura T, Higashi Y, Fuse H, Sakata S, et al. Activities and distribution of methanogenic and methane-oxidizing microbes in marine sediments from the Cascadia Margin. Geobiology. 2010;8:223–33.CAS 
    PubMed 

    Google Scholar 
    6.Yoshioka H, Sakata S, Cragg BA, Parkes RJ, Fujii T. Microbial methane production rates in gas hydrate-bearing sediments from the eastern Nankai Trough, off central Japan. Geochem J. 2009;43:315–21.CAS 

    Google Scholar 
    7.Heuer VB, Inagaki F, Morono Y, Kubo Y, Spivack AJ, Viehweger B, et al. Temperature limits to deep subseafloor life in the Nankai Trough subduction zone. Science. 2020;370:1230–4.CAS 
    PubMed 

    Google Scholar 
    8.Wellsbury P, Goodman K, Cragg BA, Parkes RJ. The geomicrobiology of deep marine sediments from Blake Ridge containing methane hydrate (sites 994, 995 and 997). Proc Ocean Drill Program Sci results. 2000;164:379–91.
    Google Scholar 
    9.Bidle KA, Kastner M, Bartlett DH. A phylogenetic analysis of microbial communities associated with methane hydrate containing marine fluids and sediments in the Cascadia margin (ODP site 892B). Fems Microbiol Lett. 1999;177:101–8.CAS 
    PubMed 

    Google Scholar 
    10.Reed DW, Fujita Y, Delwiche ME, Blackwelder DB, Sheridan PP, Uchida T, et al. Microbial communities from methane hydrate-bearing deep marine sediments in a forearc basin. Appl Environ Microbiol. 2002;68:3759–70.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    11.Briggs BR, Inagaki F, Morono Y, Futagami T, Huguet C, Rosell-Mele A, et al. Bacterial dominance in subseafloor sediments characterized by methane hydrates. FEMS Microbiol Ecol. 2012;81:88–98.CAS 
    PubMed 

    Google Scholar 
    12.Kendall MM, Boone DR. Cultivation of methanogens from shallow marine sediments at Hydrate Ridge, Oregon. Archaea. 2006;2:31–38.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    13.Fry JC, Parkes RJ, Cragg BA, Weightman AJ, Webster G. Prokaryotic biodiversity and activity in the deep subseafloor biosphere. FEMS Microbiol Ecol. 2008;66:181–96.CAS 
    PubMed 

    Google Scholar 
    14.Nunoura T, Takaki Y, Shimamura S, Kakuta J, Kazama H, Hirai M, et al. Variance and potential niche separation of microbial communities in subseafloor sediments off Shimokita Peninsula, Japan. Environ Microbiol. 2016;18:1889–906.CAS 
    PubMed 

    Google Scholar 
    15.Mikucki JA, Liu Y, Delwiche M, Colwell FS, Boone DR. Isolation of a methanogen from deep marine sediments that contain methane hydrates, and description of Methanoculleus submarinus sp. nov. Appl Environ Microbiol. 2003;69:3311–6.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    16.Weng C-Y, Chen S-C, Lai M-C, Wu S-Y, Lin S, Yang TF, et al. Methanoculleus taiwanensis sp. nov., a methanogen isolated from deep marine sediment at the deformation front area near Taiwan. Int J Syst Evol Micr. 2015;65:1044–9.CAS 

    Google Scholar 
    17.Kendall MM, Liu Y, Sieprawska-Lupa M, Stetter KO, Whitman WB, Boone DR. Methanococcus aeolicus sp. nov., a mesophilic, methanogenic archaeon from shallow and deep marine sediments. Int J Syst Evol Microbiol. 2006;56:1525–9.CAS 
    PubMed 

    Google Scholar 
    18.Strąpoć D, Ashby M, Wood L, Levinson R, Huizinga B. Significant contribution of methyl/methanol-utilising methanogenic pathway in a subsurface biogas environment. In: Skovhus T, Whitby C, editors. Applied microbiology and molecular biology in oilfield systems. Dordrecht: Springer; 2010. p. 211–6.19.Guo H, Liu R, Yu Z, Zhang H, Yun J, Li Y, et al. Pyrosequencing reveals the dominance of methylotrophic methanogenesis in a coal bed methane reservoir associated with Eastern Ordos Basin in China. Int J Coal Geol. 2012;93:56–61.CAS 

    Google Scholar 
    20.Katayama T, Yoshioka H, Muramoto Y, Usami J, Fujiwara K, Yoshida S, et al. Physicochemical impacts associated with natural gas development on methanogenesis in deep sand aquifers. ISME J. 2015;9:436–46.CAS 
    PubMed 

    Google Scholar 
    21.Yanagawa K, Tani A, Yamamoto N, Hachikubo A, Kano A, Matsumoto R, et al. Biogeochemical cycle of methanol in anoxic deep-sea sediments. Microbes Environ. 2016;31:190–3.PubMed 
    PubMed Central 

    Google Scholar 
    22.Colwell F, Matsumoto R, Reed D. A review of the gas hydrates, geology, and biology of the Nankai Trough. Chem Geol. 2004;205:391–404.CAS 

    Google Scholar 
    23.Uchida T, Waseda A, Namikawa T. Methane accumulation and high concentration of gas hydrate in marine and terrestrial sandy sediments. In: Collett T, Johnson A, Knapp C, Boswell R, editors. Natural gas hydrates: energy resource potential and associated geologic hazards. Tulsa: American Association of Petroleum Geologists Memoir 89; 2009. p. 401–13.24.Katayama T, Yoshioka H, Takahashi HA, Amo M, Fujii T, Sakata S. Changes in microbial communities associated with gas hydrates in subseafloor sediments from the Nankai Trough. FEMS Microbiol Ecol. 2016;92:fiw093.PubMed 

    Google Scholar 
    25.Oba M, Sakata S, Fujii T. Archaeal polar lipids in subseafloor sediments from the Nankai Trough: Implications for the distribution of methanogens in the deep marine subsurface. Org Geochem. 2015;78:153–60.CAS 

    Google Scholar 
    26.Noguchi S, Shimoda N, Takano O, Oikawa N, Inamori T, Saeki T, et al. 3-D internal architecture of methane hydrate-bearing turbidite channels in the eastern Nankai Trough, Japan. Mar Pet Geol. 2011;28:1817–28.
    Google Scholar 
    27.Fujii T, Suzuki K, Takayama T, Tamaki M, Komatsu Y, Konno Y, et al. Geological setting and characterization of a methane hydrate reservoir distributed at the first offshore production test site on the Daini-Atsumi Knoll in the eastern Nankai Trough, Japan. Mar Pet Geol. 2015;66:310–22.CAS 

    Google Scholar 
    28.Kanno T, Fukuhara M, Osawa O, Chee S, Takekoshi M, Wang X, et al. Estimation of geothermal gradient in marine gas-hydrate-bearing formation in the Eastern Nankai Trough. Beijing, China: Proceedings of the 8th International Conference on Gas Hydrates (ICGH8–2014); 2014.29.Kaneko M, Takano Y, Ogawa NO, Sato Y, Yoshida N, Ohkouchi N. Estimation of methanogenesis by quantification of coenzyme F430 in marine sediments. Geochem J. 2016;50:453–60.CAS 

    Google Scholar 
    30.Kaneko M, Takano Y, Chikaraishi Y, Ogawa NO, Asakawa S, Watanabe T, et al. Quantitative analysis of coenzyme F430 in environmental samples: a new diagnostic tool for methanogenesis and anaerobic methane oxidation. Anal Chem. 2014;86:3633–8.CAS 
    PubMed 

    Google Scholar 
    31.Katayama T, Kamagata Y Cultivation of Methanogens. Hydrocarbon and lipid microbiology protocols. In: McGenity T, Timmis K, Nogales B, editors. Springer protocols handbooks. Berlin, Heidelberg: Springer; 2016. p. 177–95.32.Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009;75:7537–41.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    33.Pruesse E, Quast C, Knittel K, Fuchs BM, Ludwig W, Peplies J, et al. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 2007;35:7188–96.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    34.Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S. MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Biol Evol. 2011;28:2731–9.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    35.Jobb G, von Haeseler A, Strimmer K. TREEFINDER: a powerful graphical analysis environment for molecular phylogenetics. BMC Evol Biol. 2004;4:18.PubMed 
    PubMed Central 

    Google Scholar 
    36.Whiticar MJ. Carbon and hydrogen isotope systematics of bacterial formation and oxidation of methane. Chem Geol. 1999;161:291–314.CAS 

    Google Scholar 
    37.Scheller S, Goenrich M, Thauer RK, Jaun B. Methyl-coenzyme M reductase from methanogenic archaea: Isotope effects on the formation and anaerobic oxidation of methane. J Am Chem Soc. 2013;135:14975–84.CAS 
    PubMed 

    Google Scholar 
    38.Diekert G, Konheiser U, Piechulla K, Thauer RK. Nickel requirement and factor F430 content of methanogenic bacteria. J Bacteriol. 1981;148:459–64.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    39.Mayr S, Latkoczy C, Krüger M, Günther D, Shima S, Thauer RK, et al. Structure of an F430 variant from archaea associated with anaerobic oxidation of methane. J Am Chem Soc. 2008;130:10758–67.CAS 
    PubMed 

    Google Scholar 
    40.House CH, Orphan VJ, Turk KA, Thomas B, Pernthaler A, Vrentas JM, et al. Extensive carbon isotopic heterogeneity among methane seep microbiota. Environ Microbiol. 2009;11:2207–15.CAS 
    PubMed 

    Google Scholar 
    41.Lloyd KG, Alperin MJ, Teske A. Environmental evidence for net methane production and oxidation in putative ANaerobic MEthanotrophic (ANME) archaea. Environ Microbiol. 2011;13:2548–64.CAS 
    PubMed 

    Google Scholar 
    42.Laso-Pérez R, Wegener G, Knittel K, Widdel F, Harding KJ, Krukenberg V, et al. Thermophilic archaea activate butane via alkyl-coenzyme M formation. Nature. 2016;539:396–401.PubMed 

    Google Scholar 
    43.Inagaki F, Nunoura T, Nakagawa S, Teske A, Lever M, Lauer A, et al. Biogeographical distribution and diversity of microbes in methane hydrate-bearing deep marine sediments on the Pacific Ocean Margin. Proc Natl Acad Sci USA. 2006;103:2815–20.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    44.Marchesi JR, Weightman AJ, Cragg BA, Parkes RJ, Fry JC. Methanogen and bacterial diversity and distribution in deep gas hydrate sediments from the Cascadia Margin as revealed by 16S rRNA molecular analysis. FEMS Microbiol Ecol. 2001;34:221–8.CAS 
    PubMed 

    Google Scholar 
    45.Nunoura T, Inagaki F, Delwiche ME, Colwell FS, Takai K. Subseafloor microbial communities in methane hydrate-bearing sediment at two distinct locations (ODP Leg 204) in the Cascadia Margin. Microbes Environ. 2008;23:317–25.PubMed 

    Google Scholar 
    46.Cord-Ruwisch R, Ollivier B. Interspecific hydrogen transfer during methanol degradation by Sporomusa acidovorans and hydrogenophilic anaerobes. Arch Microbiol. 1986;144:163–5.CAS 

    Google Scholar 
    47.Heijthuijsen JHFG, Hansen TA. Interspecies hydrogen transfer in co-cultures of methanol-utilizing acidogens and sulfate-reducing or methanogenic bacteria. FEMS Microbiol Ecol. 1986;2:57–64.
    Google Scholar 
    48.Eichler B, Schink B. Oxidation of primary aliphatic alcohols by Acetobacterium carbinolicum sp. nov., a homoacetogenic anaerobe. Arch Microbiol. 1984;140:147–52.CAS 

    Google Scholar 
    49.Parkes RJ, Cragg B, Roussel E, Webster G, Weightman A, Sass H. A review of prokaryotic populations and processes in sub-seafloor sediments, including biosphere: geosphere interactions. Mar Geol. 2014;352:409–25.CAS 

    Google Scholar 
    50.Imachi H, Aoi K, Tasumi E, Saito Y, Yamanaka Y, Saito Y, et al. Cultivation of methanogenic community from subseafloor sediments using a continuous-flow bioreactor. ISME J. 2011;5:1913–25.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    51.Newberry CJ, Webster G, Cragg BA, Parkes RJ, Weightman AJ, Fry JC. Diversity of prokaryotes and methanogenesis in deep subsurface sediments from the Nankai Trough, Ocean Drilling Program Leg 190. Environ Microbiol. 2004;6:274–87.PubMed 

    Google Scholar 
    52.Orsi WD, Edgcomb VP, Christman GD, Biddle JF. Gene expression in the deep biosphere. Nature 2013;499:205–8.CAS 
    PubMed 

    Google Scholar 
    53.Vigneron A, L’Haridon S, Godfroy A, Roussel EG, Cragg BA, Parkes RJ, et al. Evidence of active methanogen communities in shallow sediments of the sonora margin cold seeps. Appl Environ Microbiol. 2015;81:3451–9.CAS 
    PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Species delimitation and mitonuclear discordance within a species complex of biting midges

    1.De Queiroz, K. Species concepts and species delimitation. Syst. Biol. 56, 879–886. https://doi.org/10.1080/10635150701701083 (2007).Article 
    PubMed 

    Google Scholar 
    2.Coyne, J. A. & Orr, H. A. Speciation (Sinauer Associates Inc, 2004).
    Google Scholar 
    3.Endler, J. A. Gene flow and population differentiation: studies of clines suggest that differentiation along environmental gradients may be independent of gene flow. Science 179, 243–250 (1973).CAS 
    PubMed 
    ADS 

    Google Scholar 
    4.Mayr, E. Systematics and the Origin of Species, from the Viewpoint of a Zoologist (Harvard University Press, 1999).
    Google Scholar 
    5.Richardson, J. L., Urban, M. C., Bolnick, D. I. & Skelly, D. K. Microgeographic adaptation and the spatial scale of evolution. Trends Ecol. Evol. 29, 165–176 (2014).PubMed 

    Google Scholar 
    6.Nosil, P. Ernst Mayr and the integration of geographic and ecological factors in speciation. Biol. J. Lin. Soc. 95, 26–46 (2008).
    Google Scholar 
    7.Kisel, Y. & Barraclough, T. G. Speciation has a spatial scale that depends on levels of gene flow. Am. Nat. 175, 316–334 (2010).PubMed 

    Google Scholar 
    8.Leliaert, F. et al. DNA-based species delimitation in algae. Eur. J. Phycol. 49, 179–196 (2014).
    Google Scholar 
    9.Carstens, B. C., Pelletier, T. A., Reid, N. M. & Satler, J. D. How to fail at species delimitation. Mol. Ecol. 22, 4369–4383 (2013).PubMed 

    Google Scholar 
    10.Schlick-Steiner, B. C. et al. Integrative taxonomy: a multisource approach to exploring biodiversity. Annu. Rev. Entomol. 55, 421–438 (2010).CAS 
    PubMed 

    Google Scholar 
    11.Capblancq, T., Mavárez, J., Rioux, D. & Després, L. Speciation with gene flow: evidence from a complex of alpine butterflies (Coenonympha, Satyridae). Ecol. Evol. 9, 6444–6457 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    12.Pedraza-Marrón, C. d. R. et al. Genomics overrules mitochondrial DNA, siding with morphology on a controversial case of species delimitation. Proc. R. Soc. B 286, 20182924 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    13.Hinojosa, J. C. et al. A mirage of cryptic species: genomics uncover striking mitonuclear discordance in the butterfly Thymelicus sylvestris. Mol. Ecol. 28, 3857–3868 (2019).PubMed 

    Google Scholar 
    14.Nygren, A. et al. A mega-cryptic species complex hidden among one of the most common annelids in the North East Atlantic. PLoS ONE 13, e0198356 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    15.Thielsch, A., Knell, A., Mohammadyari, A., Petrusek, A. & Schwenk, K. Divergent clades or cryptic species? Mito-nuclear discordance in a Daphnia species complex. BMC Evol. Biol. 17, 1–9 (2017).
    Google Scholar 
    16.Eyer, P. A. & Hefetz, A. Cytonuclear incongruences hamper species delimitation in the socially polymorphic desert ants of the Cataglyphis albicans group in Israel. J. Evol. Biol. 31, 1828–1842 (2018).CAS 
    PubMed 

    Google Scholar 
    17.Borkent, A. Biology of Disease Vectors. 2nd edn, i–xxiii + 1–785 (Elsevier Academic Press, 2004).18.Mellor, P., Boorman, J. & Baylis, M. Culicoides biting midges: their role as arbovirus vectors. Annu. Rev. Entomol. 45, 307–340 (2000).CAS 
    PubMed 

    Google Scholar 
    19.Rushton, J. & Lyons, N. Economic impact of Bluetongue: a review of the effects on production. Veterinaria italiana 51, 401–406 (2015).PubMed 

    Google Scholar 
    20.Tabachnick, W. J. Culicoides vriipennis and Bluetongue-Virus eidemiology in the United States. Annu. Rev. Entomol. 41, 23–43. https://doi.org/10.1146/annurev.en.41.010196.000323 (1996).CAS 
    Article 
    PubMed 

    Google Scholar 
    21.Wirth, W. W. & Jones, R. H. The North American Subspecies of Culicoides variipennis (Diptera, Heleidae). U. S. Dep. Agric. Tech. Bull 1170, 1–35 (1957).
    Google Scholar 
    22.Holbrook, F. R. et al. Sympatry in the Culicoides variipennis Complex (Diptera: Ceratopogonidae): a Taxonomic Reassessment. J. Med. Entomol. 37, 65–76. https://doi.org/10.1603/0022-2585-37.1.65 (2000).CAS 
    Article 
    PubMed 

    Google Scholar 
    23.Hopken, M. W. Pathogen Vectors at the Wildlife-Livestock Interface: Molecular Approaches to Elucidating Culicoides (Diptera: Ceratopogonidae) Biology (University of Colorado, 2016).
    Google Scholar 
    24.Shults, P. A Study of the Taxonomy, Ecology, and Systematics of Culicoides Species (Diptera: Ceratopogonidae) Including those Associated with Deer Breeding Facilities in Southeast Texas (Texas A&M University, 2015).
    Google Scholar 
    25.Velten, R. K. & Mullens, B. A. Field morphological variation and laboratory hybridization of Culicoides variipennis sonorensis and C. v. occidentalis (Diptera:Ceratopogonidae) in southern California. J. Med. Entomol. 34, 277–284 (1997).CAS 
    PubMed 

    Google Scholar 
    26.Fontaine, M. C. et al. Extensive introgression in a malaria vector species complex revealed by phylogenomics. Science 347, 1258522 (2015).PubMed 

    Google Scholar 
    27.Bolnick, D. I. & Otto, S. P. The magnitude of local adaptation under genotype-dependent dispersal. Ecol. Evol. 3, 4722–4735 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    28.Slatkin, M. Isolation by distance in equilibrium and non-equilibrium populations. Evolution 47, 264–279 (1993).PubMed 

    Google Scholar 
    29.Pante, E. et al. Species are hypotheses: avoid connectivity assessments based on pillars of sand. Mol. Ecol. 24, 525–544 (2015).PubMed 

    Google Scholar 
    30.Jacquet, S. et al. Colonization of the Mediterranean basin by the vector biting midge species Culicoides imicola: an old story. Mol. Ecol. 24, 5707–5725. https://doi.org/10.1111/mec.13422 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    31.Onyango, M. G. et al. Genotyping of whole genome amplified reduced representation libraries reveals a cryptic population of Culicoides brevitarsis in the Northern Territory, Australia. BMC Genomics 17, 769. https://doi.org/10.1186/s12864-016-3124-1 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    32.Onyango, M. G. et al. Delineation of the population genetic structure of Culicoides imicola in East and South Africa. Parasit. Vectors 8, 660. https://doi.org/10.1186/s13071-015-1277-4 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    33.Mignotte, A. et al. High dispersal capacity of Culicoides obsoletus (Diptera: Ceratopogonidae), vector of bluetongue and Schmallenberg viruses, revealed by landscape genetic analyses. Parasit. Vectors 14, 1–14 (2021).
    Google Scholar 
    34.Sanders, C. J. & Carpenter, S. Assessment of an immunomarking technique for the study of dispersal of Culicoides biting midges. Infect. Genet. Evol. 28, 583–587 (2014).PubMed 

    Google Scholar 
    35.Kluiters, G., Swales, H. & Baylis, M. Local dispersal of palaearctic Culicoides biting midges estimated by mark-release-recapture. Parasit. Vectors 8, 86 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    36.Ducheyne, E. et al. Quantifying the wind dispersal of Culicoides species in Greece and Bulgaria. Geospat. Health 10, 177–189 (2007).
    Google Scholar 
    37.Purse, B. V. et al. Climate change and the recent emergence of bluetongue in Europe. Nat. Rev. Microbiol. 3, 171–181 (2005).CAS 
    PubMed 

    Google Scholar 
    38.Jacquet, S. et al. Range expansion of the Bluetongue vector, Culicoides imicola, in continental France likely due to rare wind-transport events. Sci. Rep. https://doi.org/10.1038/srep27247 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    39.Rundle, H. D. & Nosil, P. Ecological speciation. Ecol. Lett. 8, 336–352 (2005).
    Google Scholar 
    40.Wang, I. J. & Bradburd, G. S. Isolation by environment. Mol. Ecol. 23, 5649–5662 (2014).PubMed 

    Google Scholar 
    41.Shults, P. A Study of Culicoides Biting Midges in the Subgenus Monoculicoides: Population Genetics, Taxonomy, Systematics, and Control. Ph.D. thesis, Texas A&M University (2021).42.Jewiss-Gaines, A., Barelli, L. & Hunter, F. F. First records of Culicoides sonorensis (Diptera: Ceratopogonidae), a known vector of bluetongue virus, Southern Ontario. J. Med. Entomol. 54, 757–762. https://doi.org/10.1093/jme/tjw215 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    43.Chan, K. M. & Levin, S. A. Leaky prezygotic isolation and porous genomes: rapid introgression of maternally inherited DNA. Evolution 59, 720–729 (2005).CAS 
    PubMed 

    Google Scholar 
    44.Harrison, R. G. Hybrid zones: windows on evolutionary process. Oxf. Surv. Evol. Biol. 7, 69–128 (1990).
    Google Scholar 
    45.Harrison, R. G. Animal mitochondrial DNA as a genetic marker in population and evolutionary biology. Trends Ecol. Evol. 4, 6–11 (1989).CAS 
    PubMed 

    Google Scholar 
    46.Després, L. One, Two or More Species? Mitonuclear Discordance and Species Delimitation. Molecular ecology 28(17), 3845–3847 (2019).PubMed 

    Google Scholar 
    47.Janes, J. K. et al. The K= 2 conundrum. Mol. Ecol. 26, 3594–3602 (2017).PubMed 

    Google Scholar 
    48.De Meester, L., Vanoverbeke, J., Kilsdonk, L. J. & Urban, M. C. Evolving perspectives on monopolization and priority effects. Trends Ecol. Evol. 31, 136–146 (2016).PubMed 

    Google Scholar 
    49.Ballard, J. W. O., Chernoff, B. & James, A. C. Divergence of mitochondrial DNA is not corroborated by nuclear DNA, morphology, or behavior in Drosophila simulans. Evolution 56, 527–545 (2002).PubMed 

    Google Scholar 
    50.Behura, S., Sahu, S., Mohan, M. & Nair, S. Wolbachia in the Asian rice gall midge, Orseolia oryzae (Wood-Mason): Correlation between host mitotypes and infection status. Insect Mol. Biol. 10, 163–171 (2001).CAS 
    PubMed 

    Google Scholar 
    51.Covey, H. et al. Cryptic Wolbachia (Rickettsiales: Rickettsiaceae) detection and prevalence in Culicoides (Diptera: Ceratopogonidae) midge populations in the United States. J. Med. Entomol. 57, 1262–1269. https://doi.org/10.1093/jme/tjaa003 (2020).Article 
    PubMed 

    Google Scholar 
    52.Pagès, N., Muñoz-Muñoz, F., Verdún, M., Pujol, N. & Talavera, S. First detection of Wolbachia-infected Culicoides (Diptera: Ceratopogonidae) in Europe: Wolbachia and Cardinium infection across Culicoides communities revealed in Spain. Parasit. Vectors 10, 582. https://doi.org/10.1186/s13071-017-2486-9 (2017).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    53.Pilgrim, J. et al. Cardinium symbiosis as a potential confounder of mtDNA based phylogeographic inference in Culicoides imicola (Diptera: Ceratopogonidae), a vector of veterinary viruses. Parasit. Vectors 14, 100. https://doi.org/10.1186/s13071-020-04568-3 (2021).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    54.Hare, M. P. Prospects for nuclear gene phylogeography. Trends Ecol. Evol. 16, 700–706 (2001).
    Google Scholar 
    55.Onyango, M. G. et al. Assessment of population genetic structure in the arbovirus vector midge, Culicoides brevitarsis (Diptera: Ceratopogonidae), using multi-locus DNA microsatellites. Vet. Res. 46, 108. https://doi.org/10.1186/s13567-015-0250-8 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    56.Fonseca, D. M., Smith, J. L., Kim, H.-C. & Mogi, M. Population genetics of the mosquito Culex pipiens pallens reveals sex-linked asymmetric introgression by Culex quinquefasciatus. Infect. Genet. Evol. 9, 1197–1203 (2009).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    57.Goubert, C., Minard, G., Vieira, C. & Boulesteix, M. Population genetics of the Asian tiger mosquito Aedes albopictus, an invasive vector of human diseases. Heredity 117, 125–134 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    58.Lehmann, T. et al. Microgeographic structure of Anopheles gambiae in western Kenya based on mtDNA and microsatellite loci. Mol. Ecol. 6, 243–253 (1997).CAS 
    PubMed 

    Google Scholar 
    59.Chapuis, M.-P. & Estoup, A. Microsatellite null alleles and estimation of population differentiation. Mol. Biol. Evol. 24, 621–631. https://doi.org/10.1093/molbev/msl191 (2006).CAS 
    Article 
    PubMed 

    Google Scholar 
    60.Manni, M. et al. Molecular markers for analyses of intraspecific genetic diversity in the Asian Tiger mosquito, Aedes albopictus. Parasit. Vectors 8, 1–11 (2015).
    Google Scholar 
    61.Arntzen, J. W., Jehle, R., Bardakci, F., Burke, T. & Wallis, G. P. Asymmetric viability of reciprocal-cross hybrids between Crested and Marbled Newts (Triturus cristatus and T. marmoratus). Evolution 63, 1191–1202. https://doi.org/10.1111/j.1558-5646.2009.00611.x (2009).Article 
    PubMed 

    Google Scholar 
    62.Gibeaux, R. et al. Paternal chromosome loss and metabolic crisis contribute to hybrid inviability in Xenopus. Nature 553, 337. https://doi.org/10.1038/nature25188 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    63.Werren, J. H., Baldo, L. & Clark, M. E. Wolbachia: master manipulators of invertebrate biology. Nat. Rev. Microbiol. 6, 741 (2008).CAS 
    PubMed 

    Google Scholar 
    64.Servedio, M. R. & Kirkpatrick, M. The effects of gene flow on reinforcement. Evolution 51, 1764–1772. https://doi.org/10.1111/j.1558-5646.1997.tb05100.x (1997).Article 
    PubMed 

    Google Scholar 
    65.Howard, D. J. Reinforcement: origin, dynamics, and fate of an evolutionary hypothesis. Hybrid zones and the evolutionary process, 46–69 (1993).66.Yukilevich, R. Asymmetrical patterns of speciation uniquely support reinforcement in Drosophila. Evolution 66, 1430–1446. https://doi.org/10.1111/j.1558-5646.2011.01534.x (2012).Article 
    PubMed 

    Google Scholar 
    67.Downes, J. A. The Culicoides variipennis complex: a necessary re-alignment of nomenclature (Diptera: Ceratopogonidae). Can. Entomol. 110, 63–69 (1978).
    Google Scholar 
    68.Toews, D. P. & Brelsford, A. The biogeography of mitochondrial and nuclear discordance in animals. Mol. Ecol. 21, 3907–3930 (2012).CAS 
    PubMed 

    Google Scholar 
    69.Smith, H. & Mullens, B. A. Seasonal activity, size, and parity of Culicoides occidentalis (Diptera: Ceratopogonidae) in a coastal southern California salt marsh. J. Med. Entomol. 40, 352–355. https://doi.org/10.1603/0022-2585-40.3.352 (2003).Article 
    PubMed 

    Google Scholar 
    70.Linley, J. The effect of salinity on oviposition and egg hatching in Culicoides variipennis sonorensis (Diptera: Ceratopogonidae). J. Am. Mosq. Control Assoc. 2, 79–82 (1986).CAS 
    PubMed 

    Google Scholar 
    71.Gerry, A. C. & Mullens, B. A. Response of Male Culicoides variipennis sonorensis (Diptera: Ceratopogonidae) to carbon dioxide and observations of mating behavior on and near cattle. J. Med. Entomol. 35, 239–244. https://doi.org/10.1093/jmedent/35.3.239 (1998).CAS 
    Article 
    PubMed 

    Google Scholar 
    72.Nolan, D. V. et al. Rapid diagnostic PCR assays for members of the Culicoides obsoletus and Culicoides pulicaris species complexes, implicated vectors of bluetongue virus in Europe. Vet. Microbiol. 124, 82–94 (2007).CAS 
    PubMed 

    Google Scholar 
    73.Sebastiani, F. et al. Molecular differentiation of the Old World Culicoides imicola species complex (Diptera, Ceratopogonidae), inferred using random amplified polymorphic DNA markers. Mol. Ecol. 10, 1773–1786 (2001).CAS 
    PubMed 

    Google Scholar 
    74.Carlson, D. Identification of mosquitoes of Anopheles gambiae species complex A and B by analysis of cuticular components. Science 207, 1089–1091 (1980).CAS 
    PubMed 
    ADS 

    Google Scholar 
    75.Palacios, G. et al. Characterization of the Sandfly fever Naples species complex and description of a new Karimabad species complex (genus Phlebovirus, family Bunyaviridae). J. Gen. Virol. 95, 292 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    76.Rivas, G., Souza, N. & Peixoto, A. A. Analysis of the activity patterns of two sympatric sandfly siblings of the Lutzomyia longipalpis species complex from Brazil. Med. Vet. Entomol. 22, 288–290 (2008).CAS 
    PubMed 

    Google Scholar 
    77.Wilson, W. C. et al. Current status of bluetongue virus in the Americas. Bluetongue 10, 197–220 (2009).
    Google Scholar 
    78.Allen, S. E. et al. Epizootic Hemorrhagic Disease in White-Tailed Deer, Canada. Emerg. Infect. Dis. 25, 832–834. https://doi.org/10.3201/eid2504.180743 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    79.McGregor, B. L. et al. Field data implicating Culicoides stellifer and Culicoides venustus (Diptera: Ceratopogonidae) as vectors of epizootic hemorrhagic disease virus. Parasit. Vectors 12, 258. https://doi.org/10.1186/s13071-019-3514-8 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    80.Shults, P., Ho, A., Martin, E. M., McGregor, B. L. & Vargo, E. L. Genetic diversity of Culicoides stellifer (Diptera: Ceratopogonidae) in the Southeastern United States compared with sequences from Ontario, Canada. J. Med. Entomol. 57, 1324–1327. https://doi.org/10.1093/jme/tjaa025 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    81.Mallet, J. Hybridization as an invasion of the genome. Trends Ecol. Evol. 20, 229–237 (2005).PubMed 

    Google Scholar 
    82.Ciota, A. T., Chin, P. A. & Kramer, L. D. The effect of hybridization of Culex pipiens complex mosquitoes on transmission of West Nile virus. Parasit. Vectors 6, 1–4 (2013).
    Google Scholar 
    83.Meiswinkel, R., Gomulski, L., Delécolle, J., Goffredo, M. & Gasperi, G. The taxonomy of Culicoides vector complexes-unfinished business. Vet. Ital. 40, 151–159 (2004).CAS 
    PubMed 

    Google Scholar 
    84.Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics (Oxford, England) 32, 3047–3048. https://doi.org/10.1093/bioinformatics/btw354 (2016).CAS 
    Article 

    Google Scholar 
    85.Andrews, S. Babraham bioinformatics-FastQC a quality control tool for high throughput sequence data. https://www.bioinformatics.babraham.ac.uk/projects/fastqc (2010).86.Rochette, N. C., Rivera-Colón, A. G. & Catchen, J. M. Stacks 2: Analytical methods for paired-end sequencing improve RADseq-based population genomics. Mol. Ecol. 28, 4737–4754 (2019).CAS 
    PubMed 

    Google Scholar 
    87.Morales-Hojas, R. et al. The genome of the biting midge Culicoides sonorensis and gene expression analyses of vector competence for bluetongue virus. BMC Genomics 19, 624. https://doi.org/10.1186/s12864-018-5014-1 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    88.Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics (Oxford, England) 25, 1754–1760 (2009).CAS 

    Google Scholar 
    89.Pante, E. et al. Use of RAD sequencing for delimiting species. Heredity 114, 450–459 (2015).CAS 
    PubMed 

    Google Scholar 
    90.Benestan, L. M. et al. Conservation genomics of natural and managed populations: building a conceptual and practical framework. Mol. Ecol. 25, 2967–2977 (2016).PubMed 

    Google Scholar 
    91.Lischer, H. E. & Excoffier, L. PGDSpider: an automated data conversion tool for connecting population genetics and genomics programs. Bioinformatics (Oxford, England) 28, 298–299 (2012).CAS 

    Google Scholar 
    92.Pina-Martins, F., Silva, D. N., Fino, J. & Paulo, O. S. Structure_threader: An improved method for automation and parallelization of programs structure, fastStructure and MavericK on multicore CPU systems. Mol. Ecol. Resour. 17, e268–e274 (2017).CAS 
    PubMed 

    Google Scholar 
    93.Raj, A., Stephens, M. & Pritchard, J. K. Variational Inference of Population Structure in Large SNP Datasets. bioRxiv 10, 001073 (2013).
    Google Scholar 
    94.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.http://www.R-project.org/ (2013).95.Jombart, Thibaut, and Caitlin Collins. A tutorial for discriminant analysis of principal components (DAPC) using adegenet 2.0. 0. London: Imperial College London, MRC Centre for Outbreak Analysis and Modelling (2015).96.Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics (Oxford, England) 30, 1312–1313 (2014).CAS 

    Google Scholar 
    97.Leaché, A. D., Banbury, B. L., Felsenstein, J., De Oca, A.N.-M. & Stamatakis, A. Short tree, long tree, right tree, wrong tree: New acquisition bias corrections for inferring SNP phylogenies. Syst. Biol. 64, 1032–1047 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    98.Pattengale, N. D., Alipour, M., Bininda-Emonds, O. R., Moret, B. M. & Stamatakis, A. How many bootstrap replicates are necessary?. J. Comput. Biol. 17, 337–354 (2010).MathSciNet 
    CAS 
    PubMed 

    Google Scholar 
    99.Trifinopoulos, J., Nguyen, L.-T., von Haeseler, A. & Minh, B. Q. W-IQ-TREE: A fast online phylogenetic tool for maximum likelihood analysis. Nucleic Acids Res. 44, W232–W235 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    100.Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K., Von Haeseler, A. & Jermiin, L. S. ModelFinder: Fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    101.Nguyen, L.-T., Schmidt, H. A., Von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).CAS 
    PubMed 

    Google Scholar 
    102.Hoang, D. T., Chernomor, O., Von Haeseler, A., Minh, B. Q. & Vinh, L. S. UFBoot2: improving the ultrafast bootstrap approximation. Mol. Biol. Evol. 35, 518–522 (2018).CAS 
    PubMed 

    Google Scholar 
    103.Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: Assessing the performance of PhyML 30. Syst. Biol. 59, 307–321. https://doi.org/10.1093/sysbio/syq010 (2010).CAS 
    Article 
    PubMed 

    Google Scholar 
    104.Rousset, F. genepop’007: a complete re‐implementation of the genepop software for Windows and Linux. Molecular ecology resources 8(1), 103–106 (2008).
    Google Scholar 
    105.Rousset, F. Genetic differentiation between individuals. J Evol Biol 13, 58–62 (2000).
    Google Scholar 
    106.Loiselle, B. A., Sork, V. L., Nason, J. & Graham, C. Spatial genetic structure of a tropical understory shrub, Psychotria officinalis (Rubiaceae). Am. J. Bot. 82, 1420–1425 (1995).
    Google Scholar 
    107.Hardy, O. & Vekemans, X. SPAGeDi 1.5. A program for Spatial Pattern Analysis of Genetic Diversity. User’s manual http://ebe.ulb.ac.be/ebe/SPAGeDi_files/SPAGeDi_1.5_Manual.pdf. Université Libre de Bruxelles, Brussells, Belgium.[Google Scholar] (2015).108.Jay, F., Sjödin, P., Jakobsson, M. & Blum, M. G. Anisotropic isolation by distance: the main orientations of human genetic differentiation. Mol. Biol. Evol. 30, 513–525 (2013).CAS 
    PubMed 

    Google Scholar 
    109.Piry, S. et al. Mapping Averaged Pairwise Information (MAPI): a new exploratory tool to uncover spatial structure. Methods Ecol. Evol. 7, 1463–1475 (2016).
    Google Scholar 
    110.Kearse, M. et al. Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics (Oxford, England) 28, 1647–1649. https://doi.org/10.1093/bioinformatics/bts199 (2012).Article 

    Google Scholar 
    111.Hopken, M. W. Pathogen Vectors at The Wildlife-Livestock Interface: Molecular Approaches to Elucidating Culicoides (Diptera: Ceratopogonidae) Ph.D. thesis, Colorado State University (2016).112.Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    113.Bandelt, H. J., Forster, P. & Rohl, A. Median-joining networks for inferring intraspecific phylogenies. Mol. Biol. Evol. 16, 37–48. https://doi.org/10.1093/oxfordjournals.molbev.a026036 (1999).CAS 
    Article 
    PubMed 

    Google Scholar  More